Merge pull request #5034 from freqtrade/new_release

New release 2021.5

.devcontainer/Dockerfile

@@ -1,20 +1,21 @@
 FROM freqtradeorg/freqtrade:develop
 
+USER root
 # Install dependencies
 COPY requirements-dev.txt /freqtrade/
+
 RUN apt-get update \
-    && apt-get -y install git mercurial sudo vim \
+    && apt-get -y install git mercurial sudo vim build-essential \
     && apt-get clean \
-    && pip install autopep8 -r docs/requirements-docs.txt -r requirements-dev.txt --no-cache-dir \
-    && useradd -u 1000 -U -m ftuser \
     && mkdir -p /home/ftuser/.vscode-server /home/ftuser/.vscode-server-insiders /home/ftuser/commandhistory \
     && echo "export PROMPT_COMMAND='history -a'" >> /home/ftuser/.bashrc \
     && echo "export HISTFILE=~/commandhistory/.bash_history" >> /home/ftuser/.bashrc \
-    && mv /root/.local /home/ftuser/.local/ \
     && chown ftuser:ftuser -R /home/ftuser/.local/ \
     && chown ftuser: -R /home/ftuser/
 
 USER ftuser
 
+RUN pip install --user autopep8 -r docs/requirements-docs.txt -r requirements-dev.txt --no-cache-dir
+
 # Empty the ENTRYPOINT to allow all commands
 ENTRYPOINT []

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,6 @@
+---
+blank_issues_enabled: false
+contact_links:
+  - name: Discord Server
+    url: https://discord.gg/MA9v74M
+    about: Ask a question or get community support from our Discord server

.github/workflows/ci.yml

@@ -301,7 +301,7 @@ jobs:
     runs-on: ubuntu-20.04
     steps:
     - name: Cleanup previous runs on this branch
-      uses: rokroskar/workflow-run-cleanup-action@v0.3.2
+      uses: rokroskar/workflow-run-cleanup-action@v0.3.3
       if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/stable' && github.repository == 'freqtrade/freqtrade'"
       env:
         GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.9.4-slim-buster as base
+FROM python:3.9.5-slim-buster as base
 
 # Setup env
 ENV LANG C.UTF-8

README.md

@@ -1,4 +1,4 @@
-# ![freqtrade](docs/assets/freqtrade_poweredby.svg)
+# ![freqtrade](https://raw.githubusercontent.com/freqtrade/freqtrade/develop/docs/assets/freqtrade_poweredby.svg)
 
 [![Freqtrade CI](https://github.com/freqtrade/freqtrade/workflows/Freqtrade%20CI/badge.svg)](https://github.com/freqtrade/freqtrade/actions/)
 [![Coverage Status](https://coveralls.io/repos/github/freqtrade/freqtrade/badge.svg?branch=develop&service=github)](https://coveralls.io/github/freqtrade/freqtrade?branch=develop)
@@ -154,7 +154,7 @@ You can also join our [Slack channel](https://join.slack.com/t/highfrequencybot/
 If you discover a bug in the bot, please
 [search our issue tracker](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
 first. If it hasn't been reported, please
-[create a new issue](https://github.com/freqtrade/freqtrade/issues/new) and
+[create a new issue](https://github.com/freqtrade/freqtrade/issues/new/choose) and
 ensure you follow the template guide so that our team can assist you as
 quickly as possible.
 
@@ -163,7 +163,7 @@ quickly as possible.
 Have you a great idea to improve the bot you want to share? Please,
 first search if this feature was not [already discussed](https://github.com/freqtrade/freqtrade/labels/enhancement).
 If it hasn't been requested, please
-[create a new request](https://github.com/freqtrade/freqtrade/issues/new)
+[create a new request](https://github.com/freqtrade/freqtrade/issues/new/choose)
 and ensure you follow the template guide so that it does not get lost
 in the bug reports.
 

build_helpers/install_ta-lib.sh

@@ -8,10 +8,13 @@ if [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
   tar zxvf ta-lib-0.4.0-src.tar.gz
   cd ta-lib \
   && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
+  && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD' -o config.guess \
+  && curl 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD' -o config.sub \
   && ./configure --prefix=${INSTALL_LOC}/ \
-  && make \
+  && make -j$(nproc) \
   && which sudo && sudo make install || make install \
   && cd ..
 else
   echo "TA-lib already installed, skipping installation"
 fi
+#  && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \

build_helpers/install_windows.ps1

@@ -1,16 +1,15 @@
 # Downloads don't work automatically, since the URL is regenerated via javascript.
 # Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
-# Invoke-WebRequest -Uri "https://download.lfd.uci.edu/pythonlibs/xxxxxxx/TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl" -OutFile "TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl"
 
 python -m pip install --upgrade pip
 
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 
 if ($pyv -eq '3.7') {
-    pip install build_helpers\TA_Lib-0.4.19-cp37-cp37m-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.20-cp37-cp37m-win_amd64.whl
 }
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.19-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.20-cp38-cp38-win_amd64.whl
 }
 
 pip install -r requirements-dev.txt

config_full.json.example

@@ -23,7 +23,8 @@
     "stoploss": -0.10,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 30,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "price_side": "bid",

docker/Dockerfile.aarch64

@@ -0,0 +1,58 @@
+FROM --platform=linux/arm64/v8 python:3.9.4-slim-buster as base
+
+# Setup env
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONFAULTHANDLER 1
+ENV PATH=/home/ftuser/.local/bin:$PATH
+ENV FT_APP_ENV="docker"
+
+# Prepare environment
+RUN mkdir /freqtrade \
+  && apt-get update \
+  && apt-get -y install libatlas3-base curl sqlite3 libhdf5-serial-dev sudo \
+  && apt-get clean \
+  && useradd -u 1000 -G sudo -U -m ftuser \
+  && chown ftuser:ftuser /freqtrade \
+  # Allow sudoers
+  && echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers
+
+WORKDIR /freqtrade
+
+# Install dependencies
+FROM base as python-deps
+RUN apt-get update \
+  && apt-get -y install curl build-essential libssl-dev git libffi-dev libgfortran5 pkg-config cmake gcc \
+  && apt-get clean \
+  && pip install --upgrade pip
+
+# Install TA-lib
+COPY build_helpers/* /tmp/
+RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+# Install dependencies
+COPY --chown=ftuser:ftuser requirements.txt requirements-hyperopt.txt /freqtrade/
+USER ftuser
+RUN  pip install --user --no-cache-dir numpy \
+  && pip install --user --no-cache-dir -r requirements-hyperopt.txt
+
+# Copy dependencies to runtime-image
+FROM base as runtime-image
+COPY --from=python-deps /usr/local/lib /usr/local/lib
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+COPY --from=python-deps --chown=ftuser:ftuser /home/ftuser/.local /home/ftuser/.local
+
+USER ftuser
+# Install and execute
+COPY --chown=ftuser:ftuser . /freqtrade/
+
+RUN pip install -e . --user --no-cache-dir \
+  && mkdir /freqtrade/user_data/ \
+  && freqtrade install-ui
+
+ENTRYPOINT ["freqtrade"]
+# Default to trade mode
+CMD [ "trade" ]

docs/backtesting.md

@@ -237,29 +237,29 @@ The most important in the backtesting is to understand the result.
 A backtesting result will look like that:
 
 ```
-========================================================= BACKTESTING REPORT ========================================================
+========================================================= BACKTESTING REPORT ==========================================================
-| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration |  Wins Draws Loss   Win%  |
-|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:-------------|-------------------------:|
-| ADA/BTC  |     35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00        |    14 |      0 |      21 |
+| ADA/BTC  |     35 |          -0.11 |          -3.88 |      -0.00019428 |          -1.94 | 4:35:00      |    14     0    21   40.0 |
-| ARK/BTC  |     11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00        |     3 |      0 |       8 |
+| ARK/BTC  |     11 |          -0.41 |          -4.52 |      -0.00022647 |          -2.26 | 2:03:00      |     3     0     8   27.3 |
-| BTS/BTC  |     32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00        |    18 |      0 |      14 |
+| BTS/BTC  |     32 |           0.31 |           9.78 |       0.00048938 |           4.89 | 5:05:00      |    18     0    14   56.2 |
-| DASH/BTC |     13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00        |     6 |      0 |       7 |
+| DASH/BTC |     13 |          -0.08 |          -1.07 |      -0.00005343 |          -0.53 | 4:39:00      |     6     0     7   46.2 |
-| ENG/BTC  |     18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00        |     8 |      0 |      10 |
+| ENG/BTC  |     18 |           1.36 |          24.54 |       0.00122807 |          12.27 | 2:50:00      |     8     0    10   44.4 |
-| EOS/BTC  |     36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00        |    16 |      0 |      20 |
+| EOS/BTC  |     36 |           0.08 |           3.06 |       0.00015304 |           1.53 | 3:34:00      |    16     0    20   44.4 |
-| ETC/BTC  |     26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00        |    11 |      0 |      15 |
+| ETC/BTC  |     26 |           0.37 |           9.51 |       0.00047576 |           4.75 | 6:14:00      |    11     0    15   42.3 |
-| ETH/BTC  |     33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00        |    16 |      0 |      17 |
+| ETH/BTC  |     33 |           0.30 |           9.96 |       0.00049856 |           4.98 | 7:31:00      |    16     0    17   48.5 |
-| IOTA/BTC |     32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00        |    14 |      0 |      18 |
+| IOTA/BTC |     32 |           0.03 |           1.09 |       0.00005444 |           0.54 | 3:12:00      |    14     0    18   43.8 |
-| LSK/BTC  |     15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00        |     6 |      0 |       9 |
+| LSK/BTC  |     15 |           1.75 |          26.26 |       0.00131413 |          13.13 | 2:58:00      |     6     0     9   40.0 |
-| LTC/BTC  |     32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00        |    11 |      0 |      21 |
+| LTC/BTC  |     32 |          -0.04 |          -1.38 |      -0.00006886 |          -0.69 | 4:49:00      |    11     0    21   34.4 |
-| NANO/BTC |     17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00        |    10 |      0 |       7 |
+| NANO/BTC |     17 |           1.26 |          21.39 |       0.00107058 |          10.70 | 1:55:00      |    10     0     7   58.5 |
-| NEO/BTC  |     23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00        |    10 |      0 |      13 |
+| NEO/BTC  |     23 |           0.82 |          18.97 |       0.00094936 |           9.48 | 2:59:00      |    10     0    13   43.5 |
-| REQ/BTC  |      9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00        |     4 |      0 |       5 |
+| REQ/BTC  |      9 |           1.17 |          10.54 |       0.00052734 |           5.27 | 3:47:00      |     4     0     5   44.4 |
-| XLM/BTC  |     16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00        |     7 |      0 |       9 |
+| XLM/BTC  |     16 |           1.22 |          19.54 |       0.00097800 |           9.77 | 3:15:00      |     7     0     9   43.8 |
-| XMR/BTC  |     23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00        |    12 |      0 |      11 |
+| XMR/BTC  |     23 |          -0.18 |          -4.13 |      -0.00020696 |          -2.07 | 5:30:00      |    12     0    11   52.2 |
-| XRP/BTC  |     35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00        |    12 |      0 |      23 |
+| XRP/BTC  |     35 |           0.66 |          22.96 |       0.00114897 |          11.48 | 3:49:00      |    12     0    23   34.3 |
-| ZEC/BTC  |     22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00        |     7 |      0 |      15 |
+| ZEC/BTC  |     22 |          -0.46 |         -10.18 |      -0.00050971 |          -5.09 | 2:22:00      |     7     0    15   31.8 |
-| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |     243 |
+| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00      |   186     0   243   43.4 |
-========================================================= SELL REASON STATS =========================================================
+========================================================= SELL REASON STATS ==========================================================
 | Sell Reason        |   Sells |  Wins |  Draws |  Losses |
 |:-------------------|--------:|------:|-------:|--------:|
 | trailing_stop_loss |     205 |   150 |      0 |      55 |
@@ -267,11 +267,11 @@ A backtesting result will look like that:
 | sell_signal        |      56 |    36 |      0 |      20 |
 | force_sell         |       2 |     0 |      0 |       2 |
 ====================================================== LEFT OPEN TRADES REPORT ======================================================
-| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws |  Losses |
+| Pair     |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Win Draw Loss Win% |
-|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|--------:|
+|:---------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|--------------------:|
-| ADA/BTC  |      1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |     1 |      0 |       0 |
+| ADA/BTC  |      1 |           0.89 |           0.89 |       0.00004434 |           0.44 | 6:00:00        |    1    0    0  100 |
-| LTC/BTC  |      1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |     1 |      0 |       0 |
+| LTC/BTC  |      1 |           0.68 |           0.68 |       0.00003421 |           0.34 | 2:00:00        |    1    0    0  100 |
-| TOTAL    |      2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |     2 |      0 |       0 |
+| TOTAL    |      2 |           0.78 |           1.57 |       0.00007855 |           0.78 | 4:00:00        |    2    0    0  100 |
 =============== SUMMARY METRICS ===============
 | Metric                | Value               |
 |-----------------------+---------------------|
@@ -297,6 +297,8 @@ A backtesting result will look like that:
 | Days win/draw/lose    | 12 / 82 / 25        |
 | Avg. Duration Winners | 4:23:00             |
 | Avg. Duration Loser   | 6:55:00             |
+| Zero Duration Trades  | 4.6% (20)           |
+| Rejected Buy signals  | 3089                |
 |                       |                     |
 | Min balance           | 0.00945123 BTC      |
 | Max balance           | 0.01846651 BTC      |
@@ -318,7 +320,7 @@ The last line will give you the overall performance of your strategy,
 here:
 
 ```
-| TOTAL    |         429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |      186 |    243 |
+| TOTAL    |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00      |   186     0   243   43.4 |
 ```
 
 The bot has made `429` trades for an average duration of `4:12:00`, with a performance of `76.20%` (profit), that means it has
@@ -384,6 +386,8 @@ It contains some useful key metrics about performance of your strategy on backte
 | Days win/draw/lose    | 12 / 82 / 25        |
 | Avg. Duration Winners | 4:23:00             |
 | Avg. Duration Loser   | 6:55:00             |
+| Zero Duration Trades  | 4.6% (20)           |
+| Rejected Buy signals  | 3089                |
 |                       |                     |
 | Min balance           | 0.00945123 BTC      |
 | Max balance           | 0.01846651 BTC      |
@@ -413,6 +417,8 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Best day` / `Worst day`: Best and worst day based on daily profit.
 - `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
 - `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
+- `Zero Duration Trades`: A number of trades that completed within same candle as they opened and had `trailing_stop_loss` sell reason. A significant amount of such trades may indicate that strategy is exploiting trailing stoploss behavior in backtesting and produces unrealistic results.
+- `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
 - `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
 - `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
 - `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
@@ -472,11 +478,11 @@ There will be an additional table comparing win/losses of the different strategi
 Detailed output for all strategies one after the other will be available, so make sure to scroll up to see the details per strategy.
 
 ```
-=========================================================== STRATEGY SUMMARY ===========================================================
+=========================================================== STRATEGY SUMMARY =========================================================================
-| Strategy    |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws | Losses |
+| Strategy    |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % | Avg Duration   |  Wins |  Draws | Losses | Drawdown % |
-|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|
+|:------------|-------:|---------------:|---------------:|-----------------:|---------------:|:---------------|------:|-------:|-------:|-----------:|
-| Strategy1   |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |    243 |
+| Strategy1   |    429 |           0.36 |         152.41 |       0.00762792 |          76.20 | 4:12:00        |   186 |      0 |    243 |       45.2 |
-| Strategy2   |   1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |   662 |      0 |    825 |
+| Strategy2   |   1487 |          -0.13 |        -197.58 |      -0.00988917 |         -98.79 | 4:43:00        |   662 |      0 |    825 |     241.68 |
 ```
 
 ## Next step

docs/configuration.md

@@ -68,8 +68,9 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `trailing_stop_positive_offset` | Offset on when to apply `trailing_stop_positive`. Percentage value which should be positive. More details in the [stoploss documentation](stoploss.md#trailing-stop-loss-only-once-the-trade-has-reached-a-certain-offset). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `0.0` (no offset).* <br> **Datatype:** Float
 | `trailing_only_offset_is_reached` | Only apply trailing stoploss when the offset is reached. [stoploss documentation](stoploss.md). [Strategy Override](#parameters-in-the-strategy). <br>*Defaults to `false`.*  <br> **Datatype:** Boolean
 | `fee` | Fee used during backtesting / dry-runs. Should normally not be configured, which has freqtrade fall back to the exchange default fee. Set as ratio (e.g. 0.001 = 0.1%). Fee is applied twice for each trade, once when buying, once when selling. <br> **Datatype:** Float (as ratio)
-| `unfilledtimeout.buy` | **Required.** How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `unfilledtimeout.buy` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
-| `unfilledtimeout.sell` | **Required.** How long (in minutes) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `unfilledtimeout.sell` | **Required.** How long (in minutes or seconds) the bot will wait for an unfilled sell order to complete, after which the order will be cancelled and repeated at current (new) price, as long as there is a signal. [Strategy Override](#parameters-in-the-strategy).<br> **Datatype:** Integer
+| `unfilledtimeout.unit` | Unit to use in unfilledtimeout setting. Note: If you set unfilledtimeout.unit to "seconds", "internals.process_throttle_secs" must be inferior or equal to timeout [Strategy Override](#parameters-in-the-strategy). <br> *Defaults to `minutes`.* <br> **Datatype:** String
 | `bid_strategy.price_side` | Select the side of the spread the bot should look at to get the buy rate. [More information below](#buy-price-side).<br> *Defaults to `bid`.* <br> **Datatype:** String (either `ask` or `bid`).
 | `bid_strategy.ask_last_balance` | **Required.** Interpolate the bidding price. More information [below](#buy-price-without-orderbook-enabled).
 | `bid_strategy.use_order_book` | Enable buying using the rates in [Order Book Bids](#buy-price-with-orderbook-enabled). <br> **Datatype:** Boolean

docs/docker_quickstart.md

@@ -10,7 +10,7 @@ Start by downloading and installing Docker CE for your platform:
 * [Windows](https://docs.docker.com/docker-for-windows/install/)
 * [Linux](https://docs.docker.com/install/)
 
-To simplify running freqtrade, please install [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the below [docker quick start guide](#docker-quick-start).
+To simplify running freqtrade, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the below [docker quick start guide](#docker-quick-start).
 
 ## Freqtrade with docker-compose
 
@@ -48,6 +48,8 @@ Create a new directory and place the [docker-compose file](https://raw.githubuse
     # Download the docker-compose file from the repository
     curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
 
+    # Edit the compose file to use an image named `*_pi` (stable_pi or develop_pi)
+
     # Pull the freqtrade image
     docker-compose pull
 
@@ -65,6 +67,40 @@ Create a new directory and place the [docker-compose file](https://raw.githubuse
         # image: freqtradeorg/freqtrade:develop_pi
         ```
 
+=== "ARM 64 Systenms (Mac M1, Raspberry Pi 4, Jetson Nano)"
+    In case of a Mac M1, make sure that your docker installation is running in native mode
+    Arm64 images are not yet provided via Docker Hub and need to be build locally first.
+    Depending on the device, this may take a few minutes (Apple M1) or multiple hours (Raspberry Pi)
+
+    ``` bash
+    # Clone Freqtrade repository
+    git clone  https://github.com/freqtrade/freqtrade.git
+    cd freqtrade
+    # Optionally switch to the stable version
+    git checkout stable   
+
+    # Modify your docker-compose file to enable building and change the image name 
+    # (see the Note Box below for necessary changes)
+
+    # Build image
+    docker-compose build
+    
+    # Create user directory structure
+    docker-compose run --rm freqtrade create-userdir --userdir user_data
+
+    # Create configuration - Requires answering interactive questions
+    docker-compose run --rm freqtrade new-config --config user_data/config.json
+    ```
+
+    !!! Note "Change your docker Image"
+        You have to change the docker image in the docker-compose file for your arm64 build to work properly.
+        ``` yml
+        image: freqtradeorg/freqtrade:custom_arm64
+        build:
+          context: .
+          dockerfile: "./docker/Dockerfile.aarch64"
+        ```
+
 The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
 The last 2 steps in the snippet create the directory with `user_data`, as well as (interactively) the default configuration based on your selections.
 

docs/hyperopt.md

@@ -249,15 +249,16 @@ We continue to define hyperoptable parameters:
 
 ```python
 class MyAwesomeStrategy(IStrategy):
-    buy_adx = IntParameter(20, 40, default=30, space="buy")
+    buy_adx = DecimalParameter(20, 40, decimals=1, default=30.1, space="buy")
     buy_rsi = IntParameter(20, 40, default=30, space="buy")
-    buy_adx_enabled = CategoricalParameter([True, False], space="buy")
+    buy_adx_enabled = CategoricalParameter([True, False], default=True, space="buy")
-    buy_rsi_enabled = CategoricalParameter([True, False], space="buy")
+    buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
-    buy_trigger = CategoricalParameter(['bb_lower', 'macd_cross_signal'], space="buy")
+    buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
 ```
 
-Above definition says: I have five parameters I want to randomly combine to find the best combination.
+The above definition says: I have five parameters I want to randomly combine to find the best combination.  
-Two of them are integer values (`buy_adx` and `buy_rsi`) and I want you test in the range of values 20 to 40.  
+`buy_rsi` is an integer parameter, which will be tested between 20 and 40. This space has a size of 20.  
+`buy_adx` is a decimal parameter, which will be evaluated between 20 and 40 with 1 decimal place (so values are 20.1, 20.2, ...). This space has a size of 200.  
 Then we have three category variables. First two are either `True` or `False`.
 We use these to either enable or disable the ADX and RSI guards.
 The last one we call `trigger` and use it to decide which buy trigger we want to use.

docs/includes/pairlists.md

@@ -112,6 +112,7 @@ The `PriceFilter` allows filtering of pairs by price. Currently the following pr
 
 * `min_price`
 * `max_price`
+* `max_value`
 * `low_price_ratio`
 
 The `min_price` setting removes pairs where the price is below the specified price. This is useful if you wish to avoid trading very low-priced pairs.
@@ -120,6 +121,11 @@ This option is disabled by default, and will only apply if set to > 0.
 The `max_price` setting removes pairs where the price is above the specified price. This is useful if you wish to trade only low-priced pairs.
 This option is disabled by default, and will only apply if set to > 0.
 
+The `max_value` setting removes pairs where the minimum value change is above a specified value.
+This is useful when an exchange has unbalanced limits. For example, if step-size = 1 (so you can only buy 1, or 2, or 3, but not 1.1 Coins) - and the price is pretty high (like 20$) as the coin has risen sharply since the last limit adaption.
+As a result of the above, you can only buy for 20$, or 40$ - but not for 25$.
+On exchanges that deduct fees from the receiving currency (e.g. FTX) - this can result in high value coins / amounts that are unsellable as the amount is slightly below the limit.
+
 The `low_price_ratio` setting removes pairs where a raise of 1 price unit (pip) is above the `low_price_ratio` ratio.
 This option is disabled by default, and will only apply if set to > 0.
 
@@ -193,7 +199,7 @@ If the volatility over the last 10 days is not in the range of 0.05-0.50, remove
 
 ### Full example of Pairlist Handlers
 
-The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value.
+The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#pricefilter), filtering all assets where 1 price unit is > 1%. Then the [`SpreadFilter`](#spreadfilter) and [`VolatilityFilter`](#volatilityfilter) is applied and pairs are finally shuffled with the random seed set to some predefined value.
 
 ```json
 "exchange": {
@@ -204,7 +210,7 @@ The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets,
     {
         "method": "VolumePairList",
         "number_assets": 20,
-        "sort_key": "quoteVolume",
+        "sort_key": "quoteVolume"
     },
     {"method": "AgeFilter", "min_days_listed": 10},
     {"method": "PrecisionFilter"},

docs/installation.md

@@ -60,7 +60,7 @@ OS Specific steps are listed first, the [Common](#common) section below is neces
     sudo apt-get update
 
     # install packages
-    sudo apt install -y python3-pip python3-venv python3-pandas python3-pip git
+    sudo apt install -y python3-pip python3-venv python3-pandas git
     ```
 
 === "RaspberryPi/Raspbian"
@@ -269,7 +269,7 @@ git clone https://github.com/freqtrade/freqtrade.git
 cd freqtrade      
 ```
 
-#### Freqtrade instal: Conda Environment
+#### Freqtrade install: Conda Environment
 
 Prepare conda-freqtrade environment, using file `environment.yml`, which exist in main freqtrade directory
 

docs/requirements-docs.txt

@@ -1,3 +1,3 @@
-mkdocs-material==7.1.3
+mkdocs-material==7.1.5
 mdx_truly_sane_lists==1.2
-pymdown-extensions==8.1.1
+pymdown-extensions==8.2

docs/rest-api.md

@@ -71,7 +71,10 @@ If you run your bot using docker, you'll need to have the bot listen to incoming
     "api_server": {
         "enabled": true,
         "listen_ip_address": "0.0.0.0",
-        "listen_port": 8080
+        "listen_port": 8080,
+        "username": "Freqtrader",
+        "password": "SuperSecret1!",
+        //...
     },
 ```
 
@@ -106,7 +109,10 @@ By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be use
     "api_server": {
         "enabled": true,
         "listen_ip_address": "0.0.0.0",
-        "listen_port": 8080
+        "listen_port": 8080,
+        "username": "Freqtrader",
+        "password": "SuperSecret1!",
+        //...
     }
 }
 ```

docs/sql_cheatsheet.md

@@ -19,7 +19,7 @@ The freqtrade docker image does contain sqlite3, so you can edit the database wi
 
 ``` bash
 docker-compose exec freqtrade /bin/bash
-sqlite3 <databasefile>.sqlite
+sqlite3 <database-file>.sqlite
 ```
 
 ## Open the DB
@@ -99,3 +99,32 @@ DELETE FROM trades WHERE id = 31;
 
 !!! Warning
     This will remove this trade from the database. Please make sure you got the correct id and **NEVER** run this query without the `where` clause.
+
+## Use a different database system
+
+!!! Warning
+    By using one of the below database systems, you acknowledge that you know how to manage such a system. Freqtrade will not provide any support with setup or maintenance (or backups) of the below database systems.
+
+### PostgreSQL
+
+Freqtrade supports PostgreSQL by using SQLAlchemy, which supports multiple different database systems.
+
+Installation:
+`pip install psycopg2`
+
+Usage:
+`... --db-url postgresql+psycopg2://<username>:<password>@localhost:5432/<database>`
+
+Freqtrade will automatically create the tables necessary upon startup.
+
+If you're running different instances of Freqtrade, you must either setup one database per Instance or use different users / schemas for your connections.
+
+### MariaDB / MySQL
+
+Freqtrade supports MariaDB by using SQLAlchemy, which supports multiple different database systems.
+
+Installation:
+`pip install pymysql`
+
+Usage:
+`... --db-url mysql+pymysql://<username>:<password>@localhost:3306/<database>`

docs/strategy-advanced.md

@@ -40,34 +40,79 @@ class AwesomeStrategy(IStrategy):
 !!! Note
     If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
 
-***
+## Dataframe access
 
-### Storing custom information using DatetimeIndex from `dataframe`
+You may access dataframe in various strategy functions by querying it from dataprovider.
 
-Imagine you need to store an indicator like `ATR` or `RSI` into `custom_info`. To use this in a meaningful way, you will not only need the raw data of the indicator, but probably also need to keep the right timestamps.
+``` python
+from freqtrade.exchange import timeframe_to_prev_date
 
-```python
-import talib.abstract as ta
 class AwesomeStrategy(IStrategy):
-    # Create custom dictionary
+    def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
-    custom_info = {}
+                           rate: float, time_in_force: str, sell_reason: str,
+                           current_time: 'datetime', **kwargs) -> bool:
+        # Obtain pair dataframe.
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
 
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # Obtain last available candle. Do not use current_time to look up latest candle, because 
-        # using "ATR" here as example
+        # current_time points to curret incomplete candle whose data is not available.
-        dataframe['atr'] = ta.ATR(dataframe)
+        last_candle = dataframe.iloc[-1].squeeze()
-        if self.dp.runmode.value in ('backtest', 'hyperopt'):
+        # <...>
-          # add indicator mapped to correct DatetimeIndex to custom_info
+
-          self.custom_info[metadata['pair']] = dataframe[['date', 'atr']].set_index('date')
+        # In dry/live runs trade open date will not match candle open date therefore it must be 
-        return dataframe
+        # rounded.
+        trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
+        # Look up trade candle.
+        trade_candle = dataframe.loc[dataframe['date'] == trade_date]
+        # trade_candle may be empty for trades that just opened as it is still incomplete.
+        if not trade_candle.empty:
+            trade_candle = trade_candle.squeeze()
+            # <...>
 ```
 
-!!! Warning
+!!! Warning "Using .iloc[-1]"
-    The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
+    You can use `.iloc[-1]` here because `get_analyzed_dataframe()` only returns candles that backtesting is allowed to see.
+    This will not work in `populate_*` methods, so make sure to not use `.iloc[]` in that area.
+    Also, this will only work starting with version 2021.5.
+
+***
+
+## Custom sell signal
+
+It is possible to define custom sell signals, indicating that specified position should be sold. This is very useful when we need to customize sell conditions for each individual trade, or if you need the trade profit to take the sell decision. 
+
+For example you could implement a 1:2 risk-reward ROI with `custom_sell()`.
+
+Using custom_sell() signals in place of stoplosses though *is not recommended*. It is a inferior method to using `custom_stoploss()` in this regard - which also allows you to keep the stoploss on exchange.
 
 !!! Note
-    If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
+    Returning a `string` or `True` from this method is equal to setting sell signal on a candle at specified time. This method is not called when sell signal is set already, or if sell signals are disabled (`use_sell_signal=False` or `sell_profit_only=True` while profit is below `sell_profit_offset`). `string` max length is 64 characters. Exceeding this limit will cause the message to be truncated to 64 characters.
 
-See `custom_stoploss` examples below on how to access the saved dataframe columns
+An example of how we can use different indicators depending on the current profit and also sell trades that were open longer than one day:
+
+``` python
+class AwesomeStrategy(IStrategy):
+    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+                    current_profit: float, **kwargs):
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
+        last_candle = dataframe.iloc[-1].squeeze()
+
+        # Above 20% profit, sell when rsi < 80
+        if current_profit > 0.2:
+            if last_candle['rsi'] < 80:
+                return 'rsi_below_80'
+
+        # Between 2% and 10%, sell if EMA-long above EMA-short
+        if 0.02 < current_profit < 0.1:
+            if last_candle['emalong'] > last_candle['emashort']:
+                return 'ema_long_below_80'
+
+        # Sell any positions at a loss if they are held for more than one day.
+        if current_profit < 0.0 and (current_time - trade.open_date_utc).days >= 1:
+            return 'unclog'
+```
+
+See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
 
 ## Custom stoploss
 
@@ -110,7 +155,7 @@ class AwesomeStrategy(IStrategy):
         :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
         :param current_profit: Current profit (as ratio), calculated based on current_rate.
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
-        :return float: New stoploss value, relative to the currentrate
+        :return float: New stoploss value, relative to the current rate
         """
         return -0.04
 ```
@@ -222,7 +267,6 @@ Instead of continuously trailing behind the current price, this example sets fix
 * Once profit is > 25% - set stoploss to 15% above open price.
 * Once profit is > 40% - set stoploss to 25% above open price.
 
-
 ``` python
 from datetime import datetime
 from freqtrade.persistence import Trade
@@ -248,63 +292,46 @@ class AwesomeStrategy(IStrategy):
         # return maximum stoploss value, keeping current stoploss price unchanged
         return 1
 ```
+
 #### Custom stoploss using an indicator from dataframe example
 
-Imagine you want to use `custom_stoploss()` to use a trailing indicator like e.g. "ATR"
+Absolute stoploss value may be derived from indicators stored in dataframe. Example uses parabolic SAR below the price as stoploss.
-
-See: "Storing custom information using DatetimeIndex from `dataframe`" example above) on how to store the indicator into `custom_info`
-
-!!! Warning
-    only use .iat[-1] in live mode, not in backtesting/hyperopt
-    otherwise you will look into the future
-    see [Common mistakes when developing strategies](strategy-customization.md#common-mistakes-when-developing-strategies) for more info.
 
 ``` python
-from freqtrade.persistence import Trade
-from freqtrade.state import RunMode
-
 class AwesomeStrategy(IStrategy):
 
-    # ... populate_* methods
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # <...>
+        dataframe['sar'] = ta.SAR(dataframe)
 
     use_custom_stoploss = True
 
     def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                         current_rate: float, current_profit: float, **kwargs) -> float:
 
-        result = 1
+        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
-        if self.custom_info and pair in self.custom_info and trade:
+        last_candle = dataframe.iloc[-1].squeeze()
-            # using current_time directly (like below) will only work in backtesting.
-            # so check "runmode" to make sure that it's only used in backtesting/hyperopt
-            if self.dp and self.dp.runmode.value in ('backtest', 'hyperopt'):
-              relative_sl = self.custom_info[pair].loc[current_time]['atr']
-            # in live / dry-run, it'll be really the current time
-            else:
-              # but we can just use the last entry from an already analyzed dataframe instead
-              dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
-                                                                       timeframe=self.timeframe)
-              # WARNING
-              # only use .iat[-1] in live mode, not in backtesting/hyperopt
-              # otherwise you will look into the future
-              # see: https://www.freqtrade.io/en/latest/strategy-customization/#common-mistakes-when-developing-strategies
-              relative_sl = dataframe['atr'].iat[-1]
 
-            if (relative_sl is not None):
+        # Use parabolic sar as absolute stoploss price
-                # new stoploss relative to current_rate
+        stoploss_price = last_candle['sar']
-                new_stoploss = (current_rate-relative_sl)/current_rate
-                # turn into relative negative offset required by `custom_stoploss` return implementation
-                result = new_stoploss - 1
 
-        return result
+        # Convert absolute price to percentage relative to current_rate
+        if stoploss_price < current_rate:
+            return (stoploss_price / current_rate) - 1
+
+        # return maximum stoploss value, keeping current stoploss price unchanged
+        return 1
 ```
 
+See [Dataframe access](#dataframe-access) for more information about dataframe use in strategy callbacks.
+
 ---
 
 ## Custom order timeout rules
 
 Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the `unfilledtimeout` section.
 
-However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if a order did time out or not.
+However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if an order did time out or not.
 
 !!! Note
     Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
@@ -530,7 +557,7 @@ Both attributes and methods may be overridden, altering behavior of the original
 
 ## Embedding Strategies
 
-Freqtrade provides you with with an easy way to embed the strategy into your configuration file.
+Freqtrade provides you with an easy way to embed the strategy into your configuration file.
 This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field,
 in your chosen config file.
 

docs/strategy-customization.md

@@ -159,7 +159,7 @@ Edit the method `populate_buy_trend()` in your strategy file to update your buy
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
-This will method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action".
+This method will also define a new column, `"buy"`, which needs to contain 1 for buys, and 0 for "no action".
 
 Sample from `user_data/strategies/sample_strategy.py`:
 
@@ -193,7 +193,7 @@ Please note that the sell-signal is only used if `use_sell_signal` is set to tru
 
 It's important to always return the dataframe without removing/modifying the columns `"open", "high", "low", "close", "volume"`, otherwise these fields would contain something unexpected.
 
-This will method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action".
+This method will also define a new column, `"sell"`, which needs to contain 1 for sells, and 0 for "no action".
 
 Sample from `user_data/strategies/sample_strategy.py`:
 
@@ -422,10 +422,6 @@ if self.dp:
     Returns an empty dataframe if the requested pair was not cached.
     This should not happen when using whitelisted pairs.
 
-
-!!! Warning "Warning about backtesting"
-    This method will return an empty dataframe during backtesting.
-
 ### *orderbook(pair, maximum)*
 
 ``` python
@@ -633,7 +629,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
         def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                             current_rate: float, current_profit: float, **kwargs) -> float:
 
-            # once the profit has risin above 10%, keep the stoploss at 7% above the open price
+            # once the profit has risen above 10%, keep the stoploss at 7% above the open price
             if current_profit > 0.10:
                 return stoploss_from_open(0.07, current_profit)
 

docs/telegram-usage.md

@@ -250,10 +250,14 @@ Return a summary of your profit/loss and performance.
 
 > **BITTREX:** Selling BTC/LTC with limit `0.01650000 (profit: ~-4.07%, -0.00008168)`
 
-### /forcebuy <pair>
+### /forcebuy <pair> [rate]
 
 > **BITTREX:** Buying ETH/BTC with limit `0.03400000` (`1.000000 ETH`, `225.290 USD`)
 
+Omitting the pair will open a query asking for the pair to buy (based on the current whitelist).
+
+![Telegram force-buy screenshot](assets/telegram_forcebuy.png)
+
 Note that for this to work, `forcebuy_enable` needs to be set to true.
 
 [More details](configuration.md#understand-forcebuy_enable)
@@ -262,11 +266,11 @@ Note that for this to work, `forcebuy_enable` needs to be set to true.
 
 Return the performance of each crypto-currency the bot has sold.
 > Performance:  
-> 1. `RCN/BTC 57.77%`  
+> 1. `RCN/BTC 0.003 BTC (57.77%) (1)`  
-> 2. `PAY/BTC 56.91%`  
+> 2. `PAY/BTC 0.0012 BTC (56.91%) (1)`  
-> 3. `VIB/BTC 47.07%`  
+> 3. `VIB/BTC 0.0011 BTC (47.07%) (1)`  
-> 4. `SALT/BTC 30.24%`  
+> 4. `SALT/BTC 0.0010 BTC (30.24%) (1)`  
-> 5. `STORJ/BTC 27.24%`  
+> 5. `STORJ/BTC 0.0009 BTC (27.24%) (1)`  
 > ...  
 
 ### /balance

docs/windows_installation.md

@@ -1,3 +1,5 @@
+# Windows installation
+
 We **strongly** recommend that Windows users use [Docker](docker_quickstart.md) as this will work much easier and smoother (also more secure).
 
 If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
@@ -21,7 +23,7 @@ git clone https://github.com/freqtrade/freqtrade.git
 
 Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
 
-As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.19‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version)
+As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib‑0.4.20‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version).
 
 Freqtrade provides these dependencies for the latest 2 Python versions (3.7 and 3.8) and for 64bit Windows.
 Other versions must be downloaded from the above link.

freqtrade/__init__.py

@@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2021.4'
+__version__ = '2021.5'
 
 if __version__ == 'develop':
 

freqtrade/commands/hyperopt_commands.py

@@ -7,6 +7,7 @@ from colorama import init as colorama_init
 from freqtrade.configuration import setup_utils_configuration
 from freqtrade.data.btanalysis import get_latest_hyperopt_file
 from freqtrade.exceptions import OperationalException
+from freqtrade.optimize.optimize_reports import show_backtest_result
 from freqtrade.state import RunMode
 
 
@@ -125,6 +126,12 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 
     if epochs:
         val = epochs[n]
+
+        metrics = val['results_metrics']
+        if 'strategy_name' in metrics:
+            show_backtest_result(metrics['strategy_name'], metrics,
+                                 metrics['stake_currency'])
+
         HyperoptTools.print_epoch_details(val, total_epochs, print_json, no_header,
                                           header_str="Epoch details")
 
@@ -132,11 +139,13 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
 def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
     """
     Filter our items from the list of hyperopt results
+    TODO: after 2021.5 remove all "legacy" mode queries.
     """
     if filteroptions['only_best']:
         epochs = [x for x in epochs if x['is_best']]
     if filteroptions['only_profitable']:
-        epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
+        epochs = [x for x in epochs if x['results_metrics'].get(
+            'profit', x['results_metrics'].get('profit_total', 0)) > 0]
 
     epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
 
@@ -153,34 +162,55 @@ def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
     return epochs
 
 
+def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
+    """
+    Filter epochs with trade-counts > trades
+    """
+    return [
+        x for x in epochs
+        if x['results_metrics'].get(
+            'trade_count', x['results_metrics'].get('total_trades', 0)
+        ) > trade_count
+    ]
+
+
 def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
 
     if filteroptions['filter_min_trades'] > 0:
-        epochs = [
+        epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
-            x for x in epochs
+
-            if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
-        ]
     if filteroptions['filter_max_trades'] > 0:
         epochs = [
             x for x in epochs
-            if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
+            if x['results_metrics'].get(
+                'trade_count', x['results_metrics'].get('total_trades')
+                ) < filteroptions['filter_max_trades']
         ]
     return epochs
 
 
 def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
 
+    def get_duration_value(x):
+        # Duration in minutes ...
+        if 'duration' in x['results_metrics']:
+            return x['results_metrics']['duration']
+        else:
+            # New mode
+            avg = x['results_metrics']['holding_avg']
+            return avg.total_seconds() // 60
+
     if filteroptions['filter_min_avg_time'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
+            if get_duration_value(x) > filteroptions['filter_min_avg_time']
         ]
     if filteroptions['filter_max_avg_time'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
+            if get_duration_value(x) < filteroptions['filter_max_avg_time']
         ]
 
     return epochs
@@ -189,28 +219,36 @@ def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
 def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
 
     if filteroptions['filter_min_avg_profit'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit']
+            if x['results_metrics'].get(
+                'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
+            ) > filteroptions['filter_min_avg_profit']
         ]
     if filteroptions['filter_max_avg_profit'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit']
+            if x['results_metrics'].get(
+                'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
+                ) < filteroptions['filter_max_avg_profit']
         ]
     if filteroptions['filter_min_total_profit'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
+            if x['results_metrics'].get(
+                'profit', x['results_metrics'].get('profit_total_abs', 0)
+                ) > filteroptions['filter_min_total_profit']
         ]
     if filteroptions['filter_max_total_profit'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
         epochs = [
             x for x in epochs
-            if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
+            if x['results_metrics'].get(
+                'profit', x['results_metrics'].get('profit_total_abs', 0)
+                ) < filteroptions['filter_max_total_profit']
         ]
     return epochs
 
@@ -218,11 +256,11 @@ def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
 def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
 
     if filteroptions['filter_min_objective'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
 
         epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
     if filteroptions['filter_max_objective'] is not None:
-        epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
+        epochs = _hyperopt_filter_epochs_trade(epochs, 0)
 
         epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
 

freqtrade/configuration/timerange.py

@@ -3,6 +3,7 @@ This module contains the argument manager class
 """
 import logging
 import re
+from datetime import datetime
 from typing import Optional
 
 import arrow
@@ -43,7 +44,7 @@ class TimeRange:
             self.startts = self.startts - seconds
 
     def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
-                                  min_date: arrow.Arrow) -> None:
+                                  min_date: datetime) -> None:
         """
         Adjust startts by <startup_candles> candles.
         Applies only if no startup-candles have been available.
@@ -54,11 +55,11 @@ class TimeRange:
         :return: None (Modifies the object in place)
         """
         if (not self.starttype or (startup_candles
-                                   and min_date.int_timestamp >= self.startts)):
+                                   and min_date.timestamp() >= self.startts)):
             # If no startts was defined, or backtest-data starts at the defined backtest-date
             logger.warning("Moving start-date by %s candles to account for startup time.",
                            startup_candles)
-            self.startts = (min_date.int_timestamp + timeframe_secs * startup_candles)
+            self.startts = int(min_date.timestamp() + timeframe_secs * startup_candles)
             self.starttype = 'date'
 
     @staticmethod

freqtrade/constants.py

@@ -11,6 +11,7 @@ DEFAULT_EXCHANGE = 'bittrex'
 PROCESS_THROTTLE_SECS = 5  # sec
 HYPEROPT_EPOCH = 100  # epochs
 RETRY_TIMEOUT = 30  # sec
+TIMEOUT_UNITS = ['minutes', 'seconds']
 DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
 DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
 UNLIMITED_STAKE_AMOUNT = 'unlimited'
@@ -137,7 +138,8 @@ CONF_SCHEMA = {
             'type': 'object',
             'properties': {
                 'buy': {'type': 'number', 'minimum': 1},
-                'sell': {'type': 'number', 'minimum': 1}
+                'sell': {'type': 'number', 'minimum': 1},
+                'unit': {'type': 'string', 'enum': TIMEOUT_UNITS, 'default': 'minutes'}
             }
         },
         'bid_strategy': {

freqtrade/data/btanalysis.py

@@ -156,6 +156,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
 
         data = data['strategy'][strategy]['trades']
         df = pd.DataFrame(data)
+        if not df.empty:
             df['open_date'] = pd.to_datetime(df['open_date'],
                                              utc=True,
                                              infer_datetime_format=True
@@ -167,7 +168,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
     else:
         # old format - only with lists.
         df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
-
+        if not df.empty:
             df['open_date'] = pd.to_datetime(df['open_date'],
                                              unit='s',
                                              utc=True,
@@ -180,6 +181,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
                                               )
             # Create compatibility with new format
             df['profit_abs'] = df['close_rate'] - df['open_rate']
+    if not df.empty:
         if 'profit_ratio' not in df.columns:
             df['profit_ratio'] = df['profit_percent']
         df = df.sort_values("open_date").reset_index(drop=True)

freqtrade/data/converter.py

@@ -145,6 +145,27 @@ def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
     return df
 
 
+def trim_dataframes(preprocessed: Dict[str, DataFrame], timerange,
+                    startup_candles: int) -> Dict[str, DataFrame]:
+    """
+    Trim startup period from analyzed dataframes
+    :param preprocessed: Dict of pair: dataframe
+    :param timerange: timerange (use start and end date if available)
+    :param startup_candles: Startup-candles that should be removed
+    :return: Dict of trimmed dataframes
+    """
+    processed: Dict[str, DataFrame] = {}
+
+    for pair, df in preprocessed.items():
+        trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles)
+        if not trimed_df.empty:
+            processed[pair] = trimed_df
+        else:
+            logger.warning(f'{pair} has no data left after adjusting for startup candles, '
+                           f'skipping.')
+    return processed
+
+
 def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
     """
     TODO: This should get a dedicated test

freqtrade/data/dataprovider.py

@@ -19,14 +19,25 @@ from freqtrade.state import RunMode
 
 logger = logging.getLogger(__name__)
 
+NO_EXCHANGE_EXCEPTION = 'Exchange is not available to DataProvider.'
+MAX_DATAFRAME_CANDLES = 1000
+
 
 class DataProvider:
 
-    def __init__(self, config: dict, exchange: Exchange, pairlists=None) -> None:
+    def __init__(self, config: dict, exchange: Optional[Exchange], pairlists=None) -> None:
         self._config = config
         self._exchange = exchange
         self._pairlists = pairlists
         self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
+        self.__slice_index: Optional[int] = None
+
+    def _set_dataframe_max_index(self, limit_index: int):
+        """
+        Limit analyzed dataframe to max specified index.
+        :param limit_index: dataframe index.
+        """
+        self.__slice_index = limit_index
 
     def _set_cached_df(self, pair: str, timeframe: str, dataframe: DataFrame) -> None:
         """
@@ -45,40 +56,6 @@ class DataProvider:
         """
         self._pairlists = pairlists
 
-    def refresh(self,
-                pairlist: ListPairsWithTimeframes,
-                helping_pairs: ListPairsWithTimeframes = None) -> None:
-        """
-        Refresh data, called with each cycle
-        """
-        if helping_pairs:
-            self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
-        else:
-            self._exchange.refresh_latest_ohlcv(pairlist)
-
-    @property
-    def available_pairs(self) -> ListPairsWithTimeframes:
-        """
-        Return a list of tuples containing (pair, timeframe) for which data is currently cached.
-        Should be whitelist + open trades.
-        """
-        return list(self._exchange._klines.keys())
-
-    def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
-        """
-        Get candle (OHLCV) data for the given pair as DataFrame
-        Please use the `available_pairs` method to verify which pairs are currently cached.
-        :param pair: pair to get the data for
-        :param timeframe: Timeframe to get data for
-        :param copy: copy dataframe before returning if True.
-                     Use False only for read-only operations (where the dataframe is not modified)
-        """
-        if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
-            return self._exchange.klines((pair, timeframe or self._config['timeframe']),
-                                         copy=copy)
-        else:
-            return DataFrame()
-
     def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
         """
         Get stored historical candle (OHLCV) data
@@ -111,47 +88,27 @@ class DataProvider:
 
     def get_analyzed_dataframe(self, pair: str, timeframe: str) -> Tuple[DataFrame, datetime]:
         """
+        Retrieve the analyzed dataframe. Returns the full dataframe in trade mode (live / dry),
+        and the last 1000 candles (up to the time evaluated at this moment) in all other modes.
         :param pair: pair to get the data for
         :param timeframe: timeframe to get data for
         :return: Tuple of (Analyzed Dataframe, lastrefreshed) for the requested pair / timeframe
             combination.
             Returns empty dataframe and Epoch 0 (1970-01-01) if no dataframe was cached.
         """
-        if (pair, timeframe) in self.__cached_pairs:
+        pair_key = (pair, timeframe)
-            return self.__cached_pairs[(pair, timeframe)]
+        if pair_key in self.__cached_pairs:
+            if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+                df, date = self.__cached_pairs[pair_key]
+            else:
+                df, date = self.__cached_pairs[pair_key]
+                if self.__slice_index is not None:
+                    max_index = self.__slice_index
+                    df = df.iloc[max(0, max_index - MAX_DATAFRAME_CANDLES):max_index]
+            return df, date
         else:
-
             return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
 
-    def market(self, pair: str) -> Optional[Dict[str, Any]]:
-        """
-        Return market data for the pair
-        :param pair: Pair to get the data for
-        :return: Market data dict from ccxt or None if market info is not available for the pair
-        """
-        return self._exchange.markets.get(pair)
-
-    def ticker(self, pair: str):
-        """
-        Return last ticker data from exchange
-        :param pair: Pair to get the data for
-        :return: Ticker dict from exchange or empty dict if ticker is not available for the pair
-        """
-        try:
-            return self._exchange.fetch_ticker(pair)
-        except ExchangeError:
-            return {}
-
-    def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
-        """
-        Fetch latest l2 orderbook data
-        Warning: Does a network request - so use with common sense.
-        :param pair: pair to get the data for
-        :param maximum: Maximum number of orderbook entries to query
-        :return: dict including bids/asks with a total of `maximum` entries.
-        """
-        return self._exchange.fetch_l2_order_book(pair, maximum)
-
     @property
     def runmode(self) -> RunMode:
         """
@@ -173,3 +130,86 @@ class DataProvider:
             return self._pairlists.whitelist.copy()
         else:
             raise OperationalException("Dataprovider was not initialized with a pairlist provider.")
+
+    def clear_cache(self):
+        """
+        Clear pair dataframe cache.
+        """
+        self.__cached_pairs = {}
+
+    # Exchange functions
+
+    def refresh(self,
+                pairlist: ListPairsWithTimeframes,
+                helping_pairs: ListPairsWithTimeframes = None) -> None:
+        """
+        Refresh data, called with each cycle
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        if helping_pairs:
+            self._exchange.refresh_latest_ohlcv(pairlist + helping_pairs)
+        else:
+            self._exchange.refresh_latest_ohlcv(pairlist)
+
+    @property
+    def available_pairs(self) -> ListPairsWithTimeframes:
+        """
+        Return a list of tuples containing (pair, timeframe) for which data is currently cached.
+        Should be whitelist + open trades.
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        return list(self._exchange._klines.keys())
+
+    def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
+        """
+        Get candle (OHLCV) data for the given pair as DataFrame
+        Please use the `available_pairs` method to verify which pairs are currently cached.
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param copy: copy dataframe before returning if True.
+                     Use False only for read-only operations (where the dataframe is not modified)
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
+            return self._exchange.klines((pair, timeframe or self._config['timeframe']),
+                                         copy=copy)
+        else:
+            return DataFrame()
+
+    def market(self, pair: str) -> Optional[Dict[str, Any]]:
+        """
+        Return market data for the pair
+        :param pair: Pair to get the data for
+        :return: Market data dict from ccxt or None if market info is not available for the pair
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        return self._exchange.markets.get(pair)
+
+    def ticker(self, pair: str):
+        """
+        Return last ticker data from exchange
+        :param pair: Pair to get the data for
+        :return: Ticker dict from exchange or empty dict if ticker is not available for the pair
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        try:
+            return self._exchange.fetch_ticker(pair)
+        except ExchangeError:
+            return {}
+
+    def orderbook(self, pair: str, maximum: int) -> Dict[str, List]:
+        """
+        Fetch latest l2 orderbook data
+        Warning: Does a network request - so use with common sense.
+        :param pair: pair to get the data for
+        :param maximum: Maximum number of orderbook entries to query
+        :return: dict including bids/asks with a total of `maximum` entries.
+        """
+        if self._exchange is None:
+            raise OperationalException(NO_EXCHANGE_EXCEPTION)
+        return self._exchange.fetch_l2_order_book(pair, maximum)

freqtrade/data/history/history_utils.py

@@ -265,9 +265,13 @@ def _download_trades_history(exchange: Exchange,
     """
     try:
 
-        since = timerange.startts * 1000 if \
+        until = None
-            (timerange and timerange.starttype == 'date') else int(arrow.utcnow().shift(
+        if (timerange and timerange.starttype == 'date'):
-                days=-new_pairs_days).float_timestamp) * 1000
+            since = timerange.startts * 1000
+            if timerange.stoptype == 'date':
+                until = timerange.stopts * 1000
+        else:
+            since = int(arrow.utcnow().shift(days=-new_pairs_days).float_timestamp) * 1000
 
         trades = data_handler.trades_load(pair)
 
@@ -295,6 +299,7 @@ def _download_trades_history(exchange: Exchange,
         # Default since_ms to 30 days if nothing is given
         new_trades = exchange.get_historic_trades(pair=pair,
                                                   since=since,
+                                                  until=until,
                                                   from_id=from_id,
                                                   )
         trades.extend(new_trades[1])
@@ -367,7 +372,7 @@ def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
                 logger.exception(f'Could not convert {pair} to OHLCV.')
 
 
-def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
+def get_timerange(data: Dict[str, DataFrame]) -> Tuple[datetime, datetime]:
     """
     Get the maximum common timerange for the given backtest data.
 
@@ -375,7 +380,7 @@ def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
     :return: tuple containing min_date, max_date
     """
     timeranges = [
-        (arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
+        (frame['date'].min().to_pydatetime(), frame['date'].max().to_pydatetime())
         for frame in data.values()
     ]
     return (min(timeranges, key=operator.itemgetter(0))[0],

freqtrade/edge/edge_positioning.py

@@ -1,6 +1,8 @@
 # pragma pylint: disable=W0603
 """ Edge positioning package """
 import logging
+from collections import defaultdict
+from copy import deepcopy
 from typing import Any, Dict, List, NamedTuple
 
 import arrow
@@ -12,8 +14,10 @@ from freqtrade.configuration import TimeRange
 from freqtrade.constants import DATETIME_PRINT_FORMAT, UNLIMITED_STAKE_AMOUNT
 from freqtrade.data.history import get_timerange, load_data, refresh_data
 from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.exchange import timeframe_to_seconds
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
-from freqtrade.strategy.interface import SellType
+from freqtrade.state import RunMode
+from freqtrade.strategy.interface import IStrategy, SellType
 
 
 logger = logging.getLogger(__name__)
@@ -45,7 +49,7 @@ class Edge:
 
         self.config = config
         self.exchange = exchange
-        self.strategy = strategy
+        self.strategy: IStrategy = strategy
 
         self.edge_config = self.config.get('edge', {})
         self._cached_pairs: Dict[str, Any] = {}  # Keeps a list of pairs
@@ -102,12 +106,31 @@ class Edge:
         logger.info('Using local backtesting data (using whitelist in given config) ...')
 
         if self._refresh_pairs:
+            timerange_startup = deepcopy(self._timerange)
+            timerange_startup.subtract_start(timeframe_to_seconds(
+                self.strategy.timeframe) * self.strategy.startup_candle_count)
             refresh_data(
                 datadir=self.config['datadir'],
                 pairs=pairs,
                 exchange=self.exchange,
                 timeframe=self.strategy.timeframe,
-                timerange=self._timerange,
+                timerange=timerange_startup,
+                data_format=self.config.get('dataformat_ohlcv', 'json'),
+            )
+            # Download informative pairs too
+            res = defaultdict(list)
+            for p, t in self.strategy.informative_pairs():
+                res[t].append(p)
+            for timeframe, inf_pairs in res.items():
+                timerange_startup = deepcopy(self._timerange)
+                timerange_startup.subtract_start(timeframe_to_seconds(
+                    timeframe) * self.strategy.startup_candle_count)
+                refresh_data(
+                    datadir=self.config['datadir'],
+                    pairs=inf_pairs,
+                    exchange=self.exchange,
+                    timeframe=timeframe,
+                    timerange=timerange_startup,
                     data_format=self.config.get('dataformat_ohlcv', 'json'),
                 )
 
@@ -125,8 +148,11 @@ class Edge:
             self._cached_pairs = {}
             logger.critical("No data found. Edge is stopped ...")
             return False
-
+        # Fake run-mode to Edge
+        prior_rm = self.config['runmode']
+        self.config['runmode'] = RunMode.EDGE
         preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
+        self.config['runmode'] = prior_rm
 
         # Print timeframe
         min_date, max_date = get_timerange(preprocessed)
@@ -183,7 +209,7 @@ class Edge:
         if pair in self._cached_pairs:
             return self._cached_pairs[pair].stoploss
         else:
-            logger.warning('tried to access stoploss of a non-existing pair, '
+            logger.warning(f'Tried to access stoploss of non-existing pair {pair}, '
                            'strategy stoploss is returned instead.')
             return self.strategy.stoploss
 
@@ -214,7 +240,7 @@ class Edge:
 
         return self._final_pairs
 
-    def accepted_pairs(self) -> list:
+    def accepted_pairs(self) -> List[Dict[str, Any]]:
         """
         return a list of accepted pairs along with their winrate, expectancy and stoploss
         """

freqtrade/exchange/__init__.py

@@ -7,6 +7,7 @@ from freqtrade.exchange.bibox import Bibox
 from freqtrade.exchange.binance import Binance
 from freqtrade.exchange.bittrex import Bittrex
 from freqtrade.exchange.bybit import Bybit
+from freqtrade.exchange.coinbasepro import Coinbasepro
 from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
                                          is_exchange_known_ccxt, is_exchange_officially_supported,
                                          market_is_active, timeframe_to_minutes, timeframe_to_msecs,
@@ -14,5 +15,6 @@ from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
                                          timeframe_to_seconds, validate_exchange,
                                          validate_exchanges)
 from freqtrade.exchange.ftx import Ftx
+from freqtrade.exchange.hitbtc import Hitbtc
 from freqtrade.exchange.kraken import Kraken
 from freqtrade.exchange.kucoin import Kucoin

freqtrade/exchange/bybit.py

@@ -18,7 +18,6 @@ class Bybit(Exchange):
     may still not work as expected.
     """
 
-    # fetchCurrencies API point requires authentication for Bybit,
     _ft_has: Dict = {
         "ohlcv_candle_limit": 200,
     }

freqtrade/exchange/coinbasepro.py

@@ -0,0 +1,23 @@
+""" CoinbasePro exchange subclass """
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Coinbasepro(Exchange):
+    """
+    CoinbasePro exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 300,
+    }

freqtrade/exchange/exchange.py

@@ -59,6 +59,7 @@ class Exchange:
     _ft_has_default: Dict = {
         "stoploss_on_exchange": False,
         "order_time_in_force": ["gtc"],
+        "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
         "ohlcv_partial_candle": True,
         "trades_pagination": "time",  # Possible are "time" or "id"
@@ -465,7 +466,7 @@ class Exchange:
     def amount_to_precision(self, pair: str, amount: float) -> float:
         '''
         Returns the amount to buy or sell to a precision the Exchange accepts
-        Reimplementation of ccxt internal methods - ensuring we can test the result is correct
+        Re-implementation of ccxt internal methods - ensuring we can test the result is correct
         based on our definitions.
         '''
         if self.markets[pair]['precision']['amount']:
@@ -479,7 +480,7 @@ class Exchange:
     def price_to_precision(self, pair: str, price: float) -> float:
         '''
         Returns the price rounded up to the precision the Exchange accepts.
-        Partial Reimplementation of ccxt internal method decimal_to_precision(),
+        Partial Re-implementation of ccxt internal method decimal_to_precision(),
         which does not support rounding up
         TODO: If ccxt supports ROUND_UP for decimal_to_precision(), we could remove this and
         align with amount_to_precision().
@@ -862,10 +863,11 @@ class Exchange:
                 "Fetching pair %s, interval %s, since %s %s...",
                 pair, timeframe, since_ms, s
             )
-
+            params = self._ft_has.get('ohlcv_params', {})
             data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
                                                      since=since_ms,
-                                                     limit=self.ohlcv_candle_limit(timeframe))
+                                                     limit=self.ohlcv_candle_limit(timeframe),
+                                                     params=params)
 
             # Some exchanges sort OHLCV in ASC order and others in DESC.
             # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
@@ -1118,6 +1120,27 @@ class Exchange:
 
         return order
 
+    def cancel_stoploss_order_with_result(self, order_id: str, pair: str, amount: float) -> Dict:
+        """
+        Cancel stoploss order returning a result.
+        Creates a fake result if cancel order returns a non-usable result
+        and fetch_order does not work (certain exchanges don't return cancelled orders)
+        :param order_id: stoploss-order-id to cancel
+        :param pair: Pair corresponding to order_id
+        :param amount: Amount to use for fake response
+        :return: Result from either cancel_order if usable, or fetch_order
+        """
+        corder = self.cancel_stoploss_order(order_id, pair)
+        if self.is_cancel_order_result_suitable(corder):
+            return corder
+        try:
+            order = self.fetch_stoploss_order(order_id, pair)
+        except InvalidOrderException:
+            logger.warning(f"Could not fetch cancelled stoploss order {order_id}.")
+            order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
+
+        return order
+
     @retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
     def fetch_order(self, order_id: str, pair: str) -> Dict:
         if self._config['dry_run']:
@@ -1237,6 +1260,9 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
+    def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
+        return order['id']
+
     @retrier
     def get_fee(self, symbol: str, type: str = '', side: str = '', amount: float = 1,
                 price: float = 1, taker_or_maker: str = 'maker') -> float:

freqtrade/exchange/ftx.py

@@ -8,6 +8,7 @@ from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, Invali
                                   OperationalException, TemporaryError)
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT, retrier
+from freqtrade.misc import safe_value_fallback2
 
 
 logger = logging.getLogger(__name__)
@@ -135,3 +136,8 @@ class Ftx(Exchange):
                 f'Could not cancel order due to {e.__class__.__name__}. Message: {e}') from e
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
+
+    def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
+        if order['type'] == 'stop':
+            return safe_value_fallback2(order['info'], order, 'orderId', 'id')
+        return order['id']

freqtrade/exchange/hitbtc.py

@@ -0,0 +1,23 @@
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Hitbtc(Exchange):
+    """
+    Hitbtc exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+
+    Please note that this exchange is not included in the list of exchanges
+    officially supported by the Freqtrade development team. So some features
+    may still not work as expected.
+    """
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 1000,
+        "ohlcv_params": {"sort": "DESC"}
+    }

freqtrade/exchange/kraken.py

@@ -53,6 +53,8 @@ class Kraken(Exchange):
                            # x["side"], x["amount"],
                            ) for x in orders]
             for bal in balances:
+                if not isinstance(balances[bal], dict):
+                    continue
                 balances[bal]['used'] = sum(order[1] for order in order_list if order[0] == bal)
                 balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used']
 

freqtrade/freqtradebot.py

@@ -28,7 +28,7 @@ from freqtrade.plugins.protectionmanager import ProtectionManager
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
 from freqtrade.rpc import RPCManager, RPCMessageType
 from freqtrade.state import State
-from freqtrade.strategy.interface import IStrategy, SellType
+from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
 from freqtrade.wallets import Wallets
 
@@ -267,7 +267,7 @@ class FreqtradeBot(LoggingMixin):
     def update_closed_trades_without_assigned_fees(self):
         """
         Update closed trades without close fees assigned.
-        Only acts when Orders are in the database, otherwise the last orderid is unknown.
+        Only acts when Orders are in the database, otherwise the last order-id is unknown.
         """
         if self.config['dry_run']:
             # Updating open orders in dry-run does not make sense and will fail.
@@ -552,7 +552,7 @@ class FreqtradeBot(LoggingMixin):
 
         if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
                 pair=pair, order_type=order_type, amount=amount, rate=buy_limit_requested,
-                time_in_force=time_in_force):
+                time_in_force=time_in_force, current_time=datetime.now(timezone.utc)):
             logger.info(f"User requested abortion of buying {pair}")
             return False
         amount = self.exchange.amount_to_precision(pair, amount)
@@ -601,6 +601,7 @@ class FreqtradeBot(LoggingMixin):
             pair=pair,
             stake_amount=stake_amount,
             amount=amount,
+            is_open=True,
             amount_requested=amount_requested,
             fee_open=fee,
             fee_close=fee,
@@ -630,7 +631,7 @@ class FreqtradeBot(LoggingMixin):
 
     def _notify_buy(self, trade: Trade, order_type: str) -> None:
         """
-        Sends rpc notification when a buy occured.
+        Sends rpc notification when a buy occurred.
         """
         msg = {
             'trade_id': trade.id,
@@ -652,7 +653,7 @@ class FreqtradeBot(LoggingMixin):
 
     def _notify_buy_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
         """
-        Sends rpc notification when a buy cancel occured.
+        Sends rpc notification when a buy cancel occurred.
         """
         current_rate = self.get_buy_rate(trade.pair, False)
 
@@ -713,7 +714,7 @@ class FreqtradeBot(LoggingMixin):
             except DependencyException as exception:
                 logger.warning('Unable to sell trade %s: %s', trade.pair, exception)
 
-        # Updating wallets if any trade occured
+        # Updating wallets if any trade occurred
         if trades_closed:
             self.wallets.update()
 
@@ -850,7 +851,8 @@ class FreqtradeBot(LoggingMixin):
             trade.stoploss_order_id = None
             logger.error(f'Unable to place a stoploss order on exchange. {e}')
             logger.warning('Selling the trade forcefully')
-            self.execute_sell(trade, trade.stop_loss, sell_reason=SellType.EMERGENCY_SELL)
+            self.execute_sell(trade, trade.stop_loss, sell_reason=SellCheckTuple(
+                sell_type=SellType.EMERGENCY_SELL))
 
         except ExchangeError:
             trade.stoploss_order_id = None
@@ -931,14 +933,15 @@ class FreqtradeBot(LoggingMixin):
         :return: None
         """
         if self.exchange.stoploss_adjust(trade.stop_loss, order):
-            # we check if the update is neccesary
+            # we check if the update is necessary
             update_beat = self.strategy.order_types.get('stoploss_on_exchange_interval', 60)
             if (datetime.utcnow() - trade.stoploss_last_update).total_seconds() >= update_beat:
                 # cancelling the current stoploss on exchange first
                 logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
                             f"(orderid:{order['id']}) in order to add another one ...")
                 try:
-                    co = self.exchange.cancel_stoploss_order(order['id'], trade.pair)
+                    co = self.exchange.cancel_stoploss_order_with_result(order['id'], trade.pair,
+                                                                         trade.amount)
                     trade.update_order(co)
                 except InvalidOrderException:
                     logger.exception(f"Could not cancel stoploss order {order['id']} "
@@ -961,7 +964,7 @@ class FreqtradeBot(LoggingMixin):
 
         if should_sell.sell_flag:
             logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
-            self.execute_sell(trade, sell_rate, should_sell.sell_type)
+            self.execute_sell(trade, sell_rate, should_sell)
             return True
         return False
 
@@ -972,15 +975,16 @@ class FreqtradeBot(LoggingMixin):
         timeout = self.config.get('unfilledtimeout', {}).get(side)
         ordertime = arrow.get(order['datetime']).datetime
         if timeout is not None:
-            timeout_threshold = arrow.utcnow().shift(minutes=-timeout).datetime
+            timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
-
+            timeout_kwargs = {timeout_unit: -timeout}
+            timeout_threshold = arrow.utcnow().shift(**timeout_kwargs).datetime
             return (order['status'] == 'open' and order['side'] == side
                     and ordertime < timeout_threshold)
         return False
 
     def check_handle_timedout(self) -> None:
         """
-        Check if any orders are timed out and cancel if neccessary
+        Check if any orders are timed out and cancel if necessary
         :param timeoutvalue: Number of minutes until order is considered timed out
         :return: None
         """
@@ -1042,6 +1046,16 @@ class FreqtradeBot(LoggingMixin):
 
         # Cancelled orders may have the status of 'canceled' or 'closed'
         if order['status'] not in ('cancelled', 'canceled', 'closed'):
+            filled_val = order.get('filled', 0.0) or 0.0
+            filled_stake = filled_val * trade.open_rate
+            minstake = self.exchange.get_min_pair_stake_amount(
+                trade.pair, trade.open_rate, self.strategy.stoploss)
+
+            if filled_val > 0 and filled_stake < minstake:
+                logger.warning(
+                    f"Order {trade.open_order_id} for {trade.pair} not cancelled, "
+                    f"as the filled amount of {filled_val} would result in an unsellable trade.")
+                return False
             corder = self.exchange.cancel_order_with_result(trade.open_order_id, trade.pair,
                                                             trade.amount)
             # Avoid race condition where the order could not be cancelled coz its already filled.
@@ -1150,16 +1164,16 @@ class FreqtradeBot(LoggingMixin):
             raise DependencyException(
                 f"Not enough amount to sell. Trade-amount: {amount}, Wallet: {wallet_amount}")
 
-    def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> bool:
+    def execute_sell(self, trade: Trade, limit: float, sell_reason: SellCheckTuple) -> bool:
         """
         Executes a limit sell for the given trade and limit
         :param trade: Trade instance
         :param limit: limit rate for the sell order
-        :param sellreason: Reason the sell was triggered
+        :param sell_reason: Reason the sell was triggered
         :return: True if it succeeds (supported) False (not supported)
         """
         sell_type = 'sell'
-        if sell_reason in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
+        if sell_reason.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
             sell_type = 'stoploss'
 
         # if stoploss is on exchange and we are on dry_run mode,
@@ -1171,15 +1185,17 @@ class FreqtradeBot(LoggingMixin):
         # First cancelling stoploss on exchange ...
         if self.strategy.order_types.get('stoploss_on_exchange') and trade.stoploss_order_id:
             try:
-                self.exchange.cancel_stoploss_order(trade.stoploss_order_id, trade.pair)
+                co = self.exchange.cancel_stoploss_order_with_result(trade.stoploss_order_id,
+                                                                     trade.pair, trade.amount)
+                trade.update_order(co)
             except InvalidOrderException:
                 logger.exception(f"Could not cancel stoploss order {trade.stoploss_order_id}")
 
         order_type = self.strategy.order_types[sell_type]
-        if sell_reason == SellType.EMERGENCY_SELL:
+        if sell_reason.sell_type == SellType.EMERGENCY_SELL:
             # Emergency sells (default to market!)
             order_type = self.strategy.order_types.get("emergencysell", "market")
-        if sell_reason == SellType.FORCE_SELL:
+        if sell_reason.sell_type == SellType.FORCE_SELL:
             # Force sells (default to the sell_type defined in the strategy,
             # but we allow this value to be changed)
             order_type = self.strategy.order_types.get("forcesell", order_type)
@@ -1189,8 +1205,8 @@ class FreqtradeBot(LoggingMixin):
 
         if not strategy_safe_wrapper(self.strategy.confirm_trade_exit, default_retval=True)(
                 pair=trade.pair, trade=trade, order_type=order_type, amount=amount, rate=limit,
-                time_in_force=time_in_force,
+                time_in_force=time_in_force, sell_reason=sell_reason.sell_reason,
-                sell_reason=sell_reason.value):
+                current_time=datetime.now(timezone.utc)):
             logger.info(f"User requested abortion of selling {trade.pair}")
             return False
 
@@ -1213,13 +1229,13 @@ class FreqtradeBot(LoggingMixin):
         trade.open_order_id = order['id']
         trade.sell_order_status = ''
         trade.close_rate_requested = limit
-        trade.sell_reason = sell_reason.value
+        trade.sell_reason = sell_reason.sell_reason
         # In case of market sell orders the order can be closed immediately
         if order.get('status', 'unknown') == 'closed':
             self.update_trade_state(trade, trade.open_order_id, order)
         Trade.query.session.flush()
 
-        # Lock pair for one candle to prevent immediate rebuys
+        # Lock pair for one candle to prevent immediate re-buys
         self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc),
                                 reason='Auto lock')
 
@@ -1229,7 +1245,7 @@ class FreqtradeBot(LoggingMixin):
 
     def _notify_sell(self, trade: Trade, order_type: str, fill: bool = False) -> None:
         """
-        Sends rpc notification when a sell occured.
+        Sends rpc notification when a sell occurred.
         """
         profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
         profit_trade = trade.calc_profit(rate=profit_rate)
@@ -1270,7 +1286,7 @@ class FreqtradeBot(LoggingMixin):
 
     def _notify_sell_cancel(self, trade: Trade, order_type: str, reason: str) -> None:
         """
-        Sends rpc notification when a sell cancel occured.
+        Sends rpc notification when a sell cancel occurred.
         """
         if trade.sell_order_status == reason:
             return
@@ -1323,7 +1339,7 @@ class FreqtradeBot(LoggingMixin):
         Handles closing both buy and sell orders.
         :param trade: Trade object of the trade we're analyzing
         :param order_id: Order-id of the order we're analyzing
-        :param action_order: Already aquired order object
+        :param action_order: Already acquired order object
         :return: True if order has been cancelled without being filled partially, False otherwise
         """
         if not order_id:
@@ -1393,7 +1409,7 @@ class FreqtradeBot(LoggingMixin):
     def get_real_amount(self, trade: Trade, order: Dict) -> float:
         """
         Detect and update trade fee.
-        Calls trade.update_fee() uppon correct detection.
+        Calls trade.update_fee() upon correct detection.
         Returns modified amount if the fee was taken from the destination currency.
         Necessary for exchanges which charge fees in base currency (e.g. binance)
         :return: identical (or new) amount for the trade
@@ -1426,8 +1442,8 @@ class FreqtradeBot(LoggingMixin):
         """
         fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
         """
-        trades = self.exchange.get_trades_for_order(order['id'], trade.pair,
+        trades = self.exchange.get_trades_for_order(self.exchange.get_order_id_conditional(order),
-                                                    trade.open_date)
+                                                    trade.pair, trade.open_date)
 
         if len(trades) == 0:
             logger.info("Applying fee on amount for %s failed: myTrade-Dict empty found", trade)

freqtrade/misc.py

@@ -6,7 +6,7 @@ import logging
 import re
 from datetime import datetime
 from pathlib import Path
-from typing import Any
+from typing import Any, Iterator, List
 from typing.io import IO
 
 import rapidjson
@@ -202,3 +202,14 @@ def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
         return render_template(templatefile, arguments)
     except TemplateNotFound:
         return render_template(templatefallbackfile, arguments)
+
+
+def chunks(lst: List[Any], n: int) -> Iterator[List[Any]]:
+    """
+    Split lst into chunks of the size n.
+    :param lst: list to split into chunks
+    :param n: number of max elements per chunk
+    :return: None
+    """
+    for chunk in range(0, len(lst), n):
+        yield (lst[chunk:chunk + n])

freqtrade/optimize/backtesting.py

@@ -15,7 +15,7 @@ from freqtrade.configuration import TimeRange, remove_credentials, validate_conf
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.data import history
 from freqtrade.data.btanalysis import trade_list_to_dataframe
-from freqtrade.data.converter import trim_dataframe
+from freqtrade.data.converter import trim_dataframes
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
@@ -63,9 +63,7 @@ class Backtesting:
         self.all_results: Dict[str, Dict] = {}
 
         self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
-
+        self.dataprovider = DataProvider(self.config, None)
-        dataprovider = DataProvider(self.config, self.exchange)
-        IStrategy.dp = dataprovider
 
         if self.config.get('strategy_list', None):
             for strat in list(self.config['strategy_list']):
@@ -96,7 +94,7 @@ class Backtesting:
                 "PrecisionFilter not allowed for backtesting multiple strategies."
             )
 
-        dataprovider.add_pairlisthandler(self.pairlists)
+        self.dataprovider.add_pairlisthandler(self.pairlists)
         self.pairlists.refresh_pairlist()
 
         if len(self.pairlists.whitelist) == 0:
@@ -112,15 +110,11 @@ class Backtesting:
         PairLocks.timeframe = self.config['timeframe']
         PairLocks.use_db = False
         PairLocks.reset_locks()
-        if self.config.get('enable_protections', False):
-            self.protections = ProtectionManager(self.config)
 
         self.wallets = Wallets(self.config, self.exchange, log=False)
 
         # Get maximum required startup period
         self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
-        # Load one (first) strategy
-        self._set_strategy(self.strategylist[0])
 
     def __del__(self):
         LoggingMixin.show_output = True
@@ -132,10 +126,17 @@ class Backtesting:
         Load strategy into backtesting
         """
         self.strategy: IStrategy = strategy
+        strategy.dp = self.dataprovider
         # Set stoploss_on_exchange to false for backtesting,
         # since a "perfect" stoploss-sell is assumed anyway
         # And the regular "stoploss" function would not apply to that case
         self.strategy.order_types['stoploss_on_exchange'] = False
+        if self.config.get('enable_protections', False):
+            conf = self.config
+            if hasattr(strategy, 'protections'):
+                conf = deepcopy(conf)
+                conf['protections'] = strategy.protections
+            self.protections = ProtectionManager(conf)
 
     def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
         """
@@ -159,7 +160,7 @@ class Backtesting:
 
         logger.info(f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
                     f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
-                    f'({(max_date - min_date).days} days)..')
+                    f'({(max_date - min_date).days} days).')
 
         # Adjust startts forward if not enough data is available
         timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
@@ -176,6 +177,8 @@ class Backtesting:
         Trade.use_db = False
         PairLocks.reset_locks()
         Trade.reset_trades()
+        self.rejected_trades = 0
+        self.dataprovider.clear_cache()
 
     def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
         """
@@ -189,8 +192,9 @@ class Backtesting:
         data: Dict = {}
         # Create dict with data
         for pair, pair_data in processed.items():
-            pair_data.loc[:, 'buy'] = 0  # cleanup from previous run
+            if not pair_data.empty:
-            pair_data.loc[:, 'sell'] = 0  # cleanup from previous run
+                pair_data.loc[:, 'buy'] = 0  # cleanup if buy_signal is exist
+                pair_data.loc[:, 'sell'] = 0  # cleanup if sell_signal is exist
 
             df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
@@ -214,6 +218,12 @@ class Backtesting:
         """
         # Special handling if high or low hit STOP_LOSS or ROI
         if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
+            if trade.stop_loss > sell_row[HIGH_IDX]:
+                # our stoploss was already higher than candle high,
+                # possibly due to a cancelled trade exit.
+                # sell at open price.
+                return sell_row[OPEN_IDX]
+
             # Set close_rate to stoploss
             return trade.stop_loss
         elif sell.sell_type == (SellType.ROI):
@@ -250,12 +260,13 @@ class Backtesting:
     def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
 
         sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX],  # type: ignore
-                                         sell_row[DATE_IDX], sell_row[BUY_IDX], sell_row[SELL_IDX],
+                                         sell_row[DATE_IDX].to_pydatetime(), sell_row[BUY_IDX],
+                                         sell_row[SELL_IDX],
                                          low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
 
         if sell.sell_flag:
-            trade.close_date = sell_row[DATE_IDX]
+            trade.close_date = sell_row[DATE_IDX].to_pydatetime()
-            trade.sell_reason = sell.sell_type.value
+            trade.sell_reason = sell.sell_reason
             trade_dur = int((trade.close_date_utc - trade.open_date_utc).total_seconds() // 60)
             closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
 
@@ -265,7 +276,8 @@ class Backtesting:
                     pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
                     rate=closerate,
                     time_in_force=time_in_force,
-                    sell_reason=sell.sell_type.value):
+                    sell_reason=sell.sell_reason,
+                    current_time=sell_row[DATE_IDX].to_pydatetime()):
                 return None
 
             trade.close(closerate, show_msg=False)
@@ -285,7 +297,7 @@ class Backtesting:
         # Confirm trade entry:
         if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
                 pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
-                time_in_force=time_in_force):
+                time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
             return None
 
         if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
@@ -293,7 +305,7 @@ class Backtesting:
             trade = LocalTrade(
                 pair=pair,
                 open_rate=row[OPEN_IDX],
-                open_date=row[DATE_IDX],
+                open_date=row[DATE_IDX].to_pydatetime(),
                 stake_amount=stake_amount,
                 amount=round(stake_amount / row[OPEN_IDX], 8),
                 fee_open=self.fee,
@@ -315,7 +327,7 @@ class Backtesting:
                 for trade in open_trades[pair]:
                     sell_row = data[pair][-1]
 
-                    trade.close_date = sell_row[DATE_IDX]
+                    trade.close_date = sell_row[DATE_IDX].to_pydatetime()
                     trade.sell_reason = SellType.FORCE_SELL.value
                     trade.close(sell_row[OPEN_IDX], show_msg=False)
                     LocalTrade.close_bt_trade(trade)
@@ -325,10 +337,18 @@ class Backtesting:
                     trades.append(trade1)
         return trades
 
+    def trade_slot_available(self, max_open_trades: int, open_trade_count: int) -> bool:
+        # Always allow trades when max_open_trades is enabled.
+        if max_open_trades <= 0 or open_trade_count < max_open_trades:
+            return True
+        # Rejected trade
+        self.rejected_trades += 1
+        return False
+
     def backtest(self, processed: Dict,
                  start_date: datetime, end_date: datetime,
                  max_open_trades: int = 0, position_stacking: bool = False,
-                 enable_protections: bool = False) -> DataFrame:
+                 enable_protections: bool = False) -> Dict[str, Any]:
         """
         Implement backtesting functionality
 
@@ -347,6 +367,10 @@ class Backtesting:
         trades: List[LocalTrade] = []
         self.prepare_backtest(enable_protections)
 
+        # Update dataprovider cache
+        for pair, dataframe in processed.items():
+            self.dataprovider._set_cached_df(pair, self.timeframe, dataframe)
+
         # Use dict of lists with data for performance
         # (looping lists is a lot faster than pandas DataFrames)
         data: Dict = self._get_ohlcv_as_lists(processed)
@@ -363,8 +387,9 @@ class Backtesting:
             open_trade_count_start = open_trade_count
 
             for i, pair in enumerate(data):
+                row_index = indexes[pair]
                 try:
-                    row = data[pair][indexes[pair]]
+                    row = data[pair][row_index]
                 except IndexError:
                     # missing Data for one pair at the end.
                     # Warnings for this are shown during data loading
@@ -373,16 +398,22 @@ class Backtesting:
                 # Waits until the time-counter reaches the start of the data for this pair.
                 if row[DATE_IDX] > tmp:
                     continue
-                indexes[pair] += 1
+
+                row_index += 1
+                self.dataprovider._set_dataframe_max_index(row_index)
+                indexes[pair] = row_index
 
                 # without positionstacking, we can only have one open trade per pair.
                 # max_open_trades must be respected
                 # don't open on the last row
-                if ((position_stacking or len(open_trades[pair]) == 0)
+                if (
-                        and (max_open_trades <= 0 or open_trade_count_start < max_open_trades)
+                    (position_stacking or len(open_trades[pair]) == 0)
+                    and self.trade_slot_available(max_open_trades, open_trade_count_start)
                     and tmp != end_date
-                        and row[BUY_IDX] == 1 and row[SELL_IDX] != 1
+                    and row[BUY_IDX] == 1
-                        and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
+                    and row[SELL_IDX] != 1
+                    and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
+                ):
                     trade = self._enter_trade(pair, row)
                     if trade:
                         # TODO: hacky workaround to avoid opening > max_open_trades
@@ -415,7 +446,14 @@ class Backtesting:
         trades += self.handle_left_open(open_trades, data=data)
         self.wallets.update()
 
-        return trade_list_to_dataframe(trades)
+        results = trade_list_to_dataframe(trades)
+        return {
+            'results': results,
+            'config': self.strategy.config,
+            'locks': PairLocks.get_all_locks(),
+            'rejected_signals': self.rejected_trades,
+            'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
+        }
 
     def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
         logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
@@ -437,32 +475,32 @@ class Backtesting:
         preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
 
         # Trim startup period from analyzed dataframe
-        for pair, df in preprocessed.items():
+        preprocessed = trim_dataframes(preprocessed, timerange, self.required_startup)
-            preprocessed[pair] = trim_dataframe(df, timerange,
-                                                startup_candles=self.required_startup)
-        min_date, max_date = history.get_timerange(preprocessed)
 
+        if not preprocessed:
+            raise OperationalException(
+                "No data left after adjusting for startup candles.")
+
+        min_date, max_date = history.get_timerange(preprocessed)
         logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
                     f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
-                    f'({(max_date - min_date).days} days)..')
+                    f'({(max_date - min_date).days} days).')
         # Execute backtest and store results
         results = self.backtest(
             processed=preprocessed,
-            start_date=min_date.datetime,
+            start_date=min_date,
-            end_date=max_date.datetime,
+            end_date=max_date,
             max_open_trades=max_open_trades,
             position_stacking=self.config.get('position_stacking', False),
             enable_protections=self.config.get('enable_protections', False),
         )
         backtest_end_time = datetime.now(timezone.utc)
-        self.all_results[self.strategy.get_strategy_name()] = {
+        results.update({
-            'results': results,
-            'config': self.strategy.config,
-            'locks': PairLocks.get_all_locks(),
-            'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
             'backtest_start_time': int(backtest_start_time.timestamp()),
             'backtest_end_time': int(backtest_end_time.timestamp()),
-        }
+        })
+        self.all_results[self.strategy.get_strategy_name()] = results
+
         return min_date, max_date
 
     def start(self) -> None:

freqtrade/optimize/hyperopt.py

@@ -4,24 +4,24 @@
 This module contains the hyperopt logic
 """
 
-import locale
 import logging
 import random
 import warnings
-from datetime import datetime
+from datetime import datetime, timezone
 from math import ceil
-from operator import itemgetter
 from pathlib import Path
 from typing import Any, Dict, List, Optional
 
+import numpy as np
 import progressbar
+import rapidjson
 from colorama import Fore, Style
 from colorama import init as colorama_init
 from joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
 from pandas import DataFrame
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
-from freqtrade.data.converter import trim_dataframe
+from freqtrade.data.converter import trim_dataframes
 from freqtrade.data.history import get_timerange
 from freqtrade.misc import file_dump_json, plural
 from freqtrade.optimize.backtesting import Backtesting
@@ -30,8 +30,8 @@ from freqtrade.optimize.hyperopt_auto import HyperOptAuto
 from freqtrade.optimize.hyperopt_interface import IHyperOpt  # noqa: F401
 from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss  # noqa: F401
 from freqtrade.optimize.hyperopt_tools import HyperoptTools
+from freqtrade.optimize.optimize_reports import generate_strategy_stats
 from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
-from freqtrade.strategy import IStrategy
 
 
 # Suppress scikit-learn FutureWarnings from skopt
@@ -65,6 +65,13 @@ class Hyperopt:
     custom_hyperopt: IHyperOpt
 
     def __init__(self, config: Dict[str, Any]) -> None:
+        self.buy_space: List[Dimension] = []
+        self.sell_space: List[Dimension] = []
+        self.roi_space: List[Dimension] = []
+        self.stoploss_space: List[Dimension] = []
+        self.trailing_space: List[Dimension] = []
+        self.dimensions: List[Dimension] = []
+
         self.config = config
 
         self.backtesting = Backtesting(self.config)
@@ -73,15 +80,15 @@ class Hyperopt:
             self.custom_hyperopt = HyperOptAuto(self.config)
         else:
             self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
+        self.backtesting._set_strategy(self.backtesting.strategylist[0])
         self.custom_hyperopt.strategy = self.backtesting.strategy
 
         self.custom_hyperoptloss = HyperOptLossResolver.load_hyperoptloss(self.config)
         self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
         time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
         strategy = str(self.config['strategy'])
-        self.results_file = (self.config['user_data_dir'] /
+        self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
-                             'hyperopt_results' /
+                                   f'strategy_{strategy}_{time_now}.fthypt')
-                             f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
         self.data_pickle_file = (self.config['user_data_dir'] /
                                  'hyperopt_results' / 'hyperopt_tickerdata.pkl')
         self.total_epochs = config.get('epochs', 0)
@@ -91,9 +98,7 @@ class Hyperopt:
         self.clean_hyperopt()
 
         self.num_epochs_saved = 0
-
+        self.current_best_epoch: Optional[Dict[str, Any]] = None
-        # Previous evaluations
-        self.epochs: List = []
 
         # Populate functions here (hasattr is slow so should not be run during "regular" operations)
         if hasattr(self.custom_hyperopt, 'populate_indicators'):
@@ -114,7 +119,7 @@ class Hyperopt:
             self.max_open_trades = 0
         self.position_stacking = self.config.get('position_stacking', False)
 
-        if self.has_space('sell'):
+        if HyperoptTools.has_space(self.config, 'sell'):
             # Make sure use_sell_signal is enabled
             if 'ask_strategy' not in self.config:
                 self.config['ask_strategy'] = {}
@@ -140,9 +145,7 @@ class Hyperopt:
                 logger.info(f"Removing `{p}`.")
                 p.unlink()
 
-    def _get_params_dict(self, raw_params: List[Any]) -> Dict:
+    def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
-
-        dimensions: List[Dimension] = self.dimensions
 
         # Ensure the number of dimensions match
         # the number of parameters in the list.
@@ -153,15 +156,24 @@ class Hyperopt:
         # and the values are taken from the list of parameters.
         return {d.name: v for d, v in zip(dimensions, raw_params)}
 
-    def _save_results(self) -> None:
+    def _save_result(self, epoch: Dict) -> None:
         """
         Save hyperopt results to file
+        Store one line per epoch.
+        While not a valid json object - this allows appending easily.
+        :param epoch: result dictionary for this epoch.
         """
-        num_epochs = len(self.epochs)
+        def default_parser(x):
-        if num_epochs > self.num_epochs_saved:
+            if isinstance(x, np.integer):
-            logger.debug(f"Saving {num_epochs} {plural(num_epochs, 'epoch')}.")
+                return int(x)
-            dump(self.epochs, self.results_file)
+            return str(x)
-            self.num_epochs_saved = num_epochs
+
+        with self.results_file.open('a') as f:
+            rapidjson.dump(epoch, f, default=default_parser,
+                           number_mode=rapidjson.NM_NATIVE | rapidjson.NM_NAN)
+            f.write("\n")
+
+        self.num_epochs_saved += 1
         logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
                      f"saved to '{self.results_file}'.")
         # Store hyperopt filename
@@ -175,18 +187,16 @@ class Hyperopt:
         """
         result: Dict = {}
 
-        if self.has_space('buy'):
+        if HyperoptTools.has_space(self.config, 'buy'):
-            result['buy'] = {p.name: params.get(p.name)
+            result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
-                             for p in self.hyperopt_space('buy')}
+        if HyperoptTools.has_space(self.config, 'sell'):
-        if self.has_space('sell'):
+            result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
-            result['sell'] = {p.name: params.get(p.name)
+        if HyperoptTools.has_space(self.config, 'roi'):
-                              for p in self.hyperopt_space('sell')}
+            result['roi'] = {str(k): v for k, v in
-        if self.has_space('roi'):
+                             self.custom_hyperopt.generate_roi_table(params).items()}
-            result['roi'] = self.custom_hyperopt.generate_roi_table(params)
+        if HyperoptTools.has_space(self.config, 'stoploss'):
-        if self.has_space('stoploss'):
+            result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
-            result['stoploss'] = {p.name: params.get(p.name)
+        if HyperoptTools.has_space(self.config, 'trailing'):
-                                  for p in self.hyperopt_space('stoploss')}
-        if self.has_space('trailing'):
             result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
 
         return result
@@ -208,71 +218,58 @@ class Hyperopt:
             )
             self.hyperopt_table_header = 2
 
-    def has_space(self, space: str) -> bool:
+    def init_spaces(self):
         """
-        Tell if the space value is contained in the configuration
+        Assign the dimensions in the hyperoptimization space.
         """
-        # The 'trailing' space is not included in the 'default' set of spaces
-        if space == 'trailing':
-            return any(s in self.config['spaces'] for s in [space, 'all'])
-        else:
-            return any(s in self.config['spaces'] for s in [space, 'all', 'default'])
 
-    def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
+        if HyperoptTools.has_space(self.config, 'buy'):
-        """
-        Return the dimensions in the hyperoptimization space.
-        :param space: Defines hyperspace to return dimensions for.
-        If None, then the self.has_space() will be used to return dimensions
-        for all hyperspaces used.
-        """
-        spaces: List[Dimension] = []
-
-        if space == 'buy' or (space is None and self.has_space('buy')):
             logger.debug("Hyperopt has 'buy' space")
-            spaces += self.custom_hyperopt.indicator_space()
+            self.buy_space = self.custom_hyperopt.indicator_space()
 
-        if space == 'sell' or (space is None and self.has_space('sell')):
+        if HyperoptTools.has_space(self.config, 'sell'):
             logger.debug("Hyperopt has 'sell' space")
-            spaces += self.custom_hyperopt.sell_indicator_space()
+            self.sell_space = self.custom_hyperopt.sell_indicator_space()
 
-        if space == 'roi' or (space is None and self.has_space('roi')):
+        if HyperoptTools.has_space(self.config, 'roi'):
             logger.debug("Hyperopt has 'roi' space")
-            spaces += self.custom_hyperopt.roi_space()
+            self.roi_space = self.custom_hyperopt.roi_space()
 
-        if space == 'stoploss' or (space is None and self.has_space('stoploss')):
+        if HyperoptTools.has_space(self.config, 'stoploss'):
             logger.debug("Hyperopt has 'stoploss' space")
-            spaces += self.custom_hyperopt.stoploss_space()
+            self.stoploss_space = self.custom_hyperopt.stoploss_space()
 
-        if space == 'trailing' or (space is None and self.has_space('trailing')):
+        if HyperoptTools.has_space(self.config, 'trailing'):
             logger.debug("Hyperopt has 'trailing' space")
-            spaces += self.custom_hyperopt.trailing_space()
+            self.trailing_space = self.custom_hyperopt.trailing_space()
-
+        self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
-        return spaces
+                           self.stoploss_space + self.trailing_space)
 
     def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
         """
         Used Optimize function. Called once per epoch to optimize whatever is configured.
         Keep this function as optimized as possible!
         """
-        params_dict = self._get_params_dict(raw_params)
+        backtest_start_time = datetime.now(timezone.utc)
-        params_details = self._get_params_details(params_dict)
+        params_dict = self._get_params_dict(self.dimensions, raw_params)
 
-        if self.has_space('roi'):
+        # Apply parameters
+        if HyperoptTools.has_space(self.config, 'roi'):
             self.backtesting.strategy.minimal_roi = (  # type: ignore
                 self.custom_hyperopt.generate_roi_table(params_dict))
 
-        if self.has_space('buy'):
+        if HyperoptTools.has_space(self.config, 'buy'):
             self.backtesting.strategy.advise_buy = (  # type: ignore
                 self.custom_hyperopt.buy_strategy_generator(params_dict))
 
-        if self.has_space('sell'):
+        if HyperoptTools.has_space(self.config, 'sell'):
             self.backtesting.strategy.advise_sell = (  # type: ignore
                 self.custom_hyperopt.sell_strategy_generator(params_dict))
 
-        if self.has_space('stoploss'):
+        if HyperoptTools.has_space(self.config, 'stoploss'):
             self.backtesting.strategy.stoploss = params_dict['stoploss']
 
-        if self.has_space('trailing'):
+        if HyperoptTools.has_space(self.config, 'trailing'):
             d = self.custom_hyperopt.generate_trailing_params(params_dict)
             self.backtesting.strategy.trailing_stop = d['trailing_stop']
             self.backtesting.strategy.trailing_stop_positive = d['trailing_stop_positive']
@@ -281,30 +278,42 @@ class Hyperopt:
             self.backtesting.strategy.trailing_only_offset_is_reached = \
                 d['trailing_only_offset_is_reached']
 
-        processed = load(self.data_pickle_file)
+        with self.data_pickle_file.open('rb') as f:
-
+            processed = load(f, mmap_mode='r')
-        min_date, max_date = get_timerange(processed)
+        bt_results = self.backtesting.backtest(
-
-        backtesting_results = self.backtesting.backtest(
             processed=processed,
-            start_date=min_date.datetime,
+            start_date=self.min_date,
-            end_date=max_date.datetime,
+            end_date=self.max_date,
             max_open_trades=self.max_open_trades,
             position_stacking=self.position_stacking,
             enable_protections=self.config.get('enable_protections', False),
-
         )
-        return self._get_results_dict(backtesting_results, min_date, max_date,
+        backtest_end_time = datetime.now(timezone.utc)
-                                      params_dict, params_details,
+        bt_results.update({
+            'backtest_start_time': int(backtest_start_time.timestamp()),
+            'backtest_end_time': int(backtest_end_time.timestamp()),
+        })
+
+        return self._get_results_dict(bt_results, self.min_date, self.max_date,
+                                      params_dict,
                                       processed=processed)
 
     def _get_results_dict(self, backtesting_results, min_date, max_date,
-                          params_dict, params_details, processed: Dict[str, DataFrame]):
+                          params_dict, processed: Dict[str, DataFrame]
-        results_metrics = self._calculate_results_metrics(backtesting_results)
+                          ) -> Dict[str, Any]:
-        results_explanation = self._format_results_explanation_string(results_metrics)
+        params_details = self._get_params_details(params_dict)
 
-        trade_count = results_metrics['trade_count']
+        strat_stats = generate_strategy_stats(
-        total_profit = results_metrics['total_profit']
+            processed, self.backtesting.strategy.get_strategy_name(),
+            backtesting_results, min_date, max_date, market_change=0
+        )
+        results_explanation = HyperoptTools.format_results_explanation_string(
+            strat_stats, self.config['stake_currency'])
+
+        not_optimized = self.backtesting.strategy.get_params_dict()
+
+        trade_count = strat_stats['total_trades']
+        total_profit = strat_stats['profit_total']
 
         # If this evaluation contains too short amount of trades to be
         # interesting -- consider it as 'bad' (assigned max. loss value)
@@ -312,50 +321,20 @@ class Hyperopt:
         # path. We do not want to optimize 'hodl' strategies.
         loss: float = MAX_LOSS
         if trade_count >= self.config['hyperopt_min_trades']:
-            loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
+            loss = self.calculate_loss(results=backtesting_results['results'],
-                                       min_date=min_date.datetime, max_date=max_date.datetime,
+                                       trade_count=trade_count,
+                                       min_date=min_date, max_date=max_date,
                                        config=self.config, processed=processed)
         return {
             'loss': loss,
             'params_dict': params_dict,
             'params_details': params_details,
-            'results_metrics': results_metrics,
+            'params_not_optimized': not_optimized,
+            'results_metrics': strat_stats,
             'results_explanation': results_explanation,
             'total_profit': total_profit,
         }
 
-    def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
-        wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0])
-        draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0])
-        losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0])
-        return {
-            'trade_count': len(backtesting_results.index),
-            'wins': wins,
-            'draws': draws,
-            'losses': losses,
-            'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
-            'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0,
-            'median_profit': backtesting_results['profit_ratio'].median() * 100.0,
-            'total_profit': backtesting_results['profit_abs'].sum(),
-            'profit': backtesting_results['profit_ratio'].sum() * 100.0,
-            'duration': backtesting_results['trade_duration'].mean(),
-        }
-
-    def _format_results_explanation_string(self, results_metrics: Dict) -> str:
-        """
-        Return the formatted results explanation in a string
-        """
-        stake_cur = self.config['stake_currency']
-        return (f"{results_metrics['trade_count']:6d} trades. "
-                f"{results_metrics['wins']}/{results_metrics['draws']}"
-                f"/{results_metrics['losses']} Wins/Draws/Losses. "
-                f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
-                f"Median profit {results_metrics['median_profit']: 6.2f}%. "
-                f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
-                f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
-                f"Avg duration {results_metrics['duration']:5.1f} min."
-                ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
-
     def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
         return Optimizer(
             dimensions,
@@ -374,25 +353,31 @@ class Hyperopt:
     def _set_random_state(self, random_state: Optional[int]) -> int:
         return random_state or random.randint(1, 2**16 - 1)
 
+    def prepare_hyperopt_data(self) -> None:
+        data, timerange = self.backtesting.load_bt_data()
+        logger.info("Dataload complete. Calculating indicators")
+
+        preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
+
+        # Trim startup period from analyzed dataframe
+        processed = trim_dataframes(preprocessed, timerange, self.backtesting.required_startup)
+
+        self.min_date, self.max_date = get_timerange(processed)
+
+        logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
+                    f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
+                    f'({(self.max_date - self.min_date).days} days)..')
+
+        dump(processed, self.data_pickle_file)
+
     def start(self) -> None:
         self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
         logger.info(f"Using optimizer random state: {self.random_state}")
         self.hyperopt_table_header = -1
-        data, timerange = self.backtesting.load_bt_data()
+        # Initialize spaces ...
-        logger.info("Dataload complete. Calculating indicators")
+        self.init_spaces()
-        preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
 
-        # Trim startup period from analyzed dataframe
+        self.prepare_hyperopt_data()
-        for pair, df in preprocessed.items():
-            preprocessed[pair] = trim_dataframe(df, timerange,
-                                                startup_candles=self.backtesting.required_startup)
-        min_date, max_date = get_timerange(preprocessed)
-
-        logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
-                    f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
-                    f'({(max_date - min_date).days} days)..')
-
-        dump(preprocessed, self.data_pickle_file)
 
         # We don't need exchange instance anymore while running hyperopt
         self.backtesting.exchange.close()
@@ -400,15 +385,12 @@ class Hyperopt:
         self.backtesting.exchange._api_async = None  # type: ignore
         # self.backtesting.exchange = None  # type: ignore
         self.backtesting.pairlists = None  # type: ignore
-        self.backtesting.strategy.dp = None  # type: ignore
-        IStrategy.dp = None  # type: ignore
 
         cpus = cpu_count()
         logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
         config_jobs = self.config.get('hyperopt_jobs', -1)
         logger.info(f'Number of parallel jobs set as: {config_jobs}')
 
-        self.dimensions: List[Dimension] = self.hyperopt_space()
         self.opt = self.get_optimizer(self.dimensions, config_jobs)
 
         if self.print_colorized:
@@ -474,25 +456,21 @@ class Hyperopt:
 
                             if is_best:
                                 self.current_best_loss = val['loss']
-                            self.epochs.append(val)
+                                self.current_best_epoch = val
 
-                            # Save results after each best epoch and every 100 epochs
+                            self._save_result(val)
-                            if is_best or current % 100 == 0:
-                                self._save_results()
 
                             pbar.update(current)
 
         except KeyboardInterrupt:
             print('User interrupted..')
 
-        self._save_results()
         logger.info(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
                     f"saved to '{self.results_file}'.")
 
-        if self.epochs:
+        if self.current_best_epoch:
-            sorted_epochs = sorted(self.epochs, key=itemgetter('loss'))
+            HyperoptTools.print_epoch_details(self.current_best_epoch, self.total_epochs,
-            best_epoch = sorted_epochs[0]
+                                              self.print_json)
-            HyperoptTools.print_epoch_details(best_epoch, self.total_epochs, self.print_json)
         else:
             # This is printed when Ctrl+C is pressed quickly, before first epochs have
             # a chance to be evaluated.

freqtrade/optimize/hyperopt_tools.py

@@ -1,19 +1,18 @@
 
 import io
+import locale
 import logging
 from collections import OrderedDict
 from pathlib import Path
-from pprint import pformat
+from typing import Any, Dict, List
-from typing import Dict, List
 
 import rapidjson
 import tabulate
 from colorama import Fore, Style
-from joblib import load
 from pandas import isna, json_normalize
 
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import round_dict
+from freqtrade.misc import round_coin_value, round_dict
 
 
 logger = logging.getLogger(__name__)
@@ -21,13 +20,38 @@ logger = logging.getLogger(__name__)
 
 class HyperoptTools():
 
+    @staticmethod
+    def has_space(config: Dict[str, Any], space: str) -> bool:
+        """
+        Tell if the space value is contained in the configuration
+        """
+        # The 'trailing' space is not included in the 'default' set of spaces
+        if space == 'trailing':
+            return any(s in config['spaces'] for s in [space, 'all'])
+        else:
+            return any(s in config['spaces'] for s in [space, 'all', 'default'])
+
+    @staticmethod
+    def _read_results_pickle(results_file: Path) -> List:
+        """
+        Read hyperopt results from pickle file
+        LEGACY method - new files are written as json and cannot be read with this method.
+        """
+        from joblib import load
+
+        logger.info(f"Reading pickled epochs from '{results_file}'")
+        data = load(results_file)
+        return data
+
     @staticmethod
     def _read_results(results_file: Path) -> List:
         """
         Read hyperopt results from file
         """
-        logger.info("Reading epochs from '%s'", results_file)
+        import rapidjson
-        data = load(results_file)
+        logger.info(f"Reading epochs from '{results_file}'")
+        with results_file.open('r') as f:
+            data = [rapidjson.loads(line) for line in f]
         return data
 
     @staticmethod
@@ -37,6 +61,9 @@ class HyperoptTools():
         """
         epochs: List = []
         if results_file.is_file() and results_file.stat().st_size > 0:
+            if results_file.suffix == '.pickle':
+                epochs = HyperoptTools._read_results_pickle(results_file)
+            else:
                 epochs = HyperoptTools._read_results(results_file)
             # Detection of some old format, without 'is_best' field saved
             if epochs[0].get('is_best') is None:
@@ -53,6 +80,7 @@ class HyperoptTools():
         Display details of the hyperopt result
         """
         params = results.get('params_details', {})
+        non_optimized = results.get('params_not_optimized', {})
 
         # Default header string
         if header_str is None:
@@ -69,8 +97,10 @@ class HyperoptTools():
             print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
 
         else:
-            HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:")
+            HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
-            HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:")
+                                               non_optimized)
+            HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
+                                               non_optimized)
             HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
             HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
             HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
@@ -96,12 +126,12 @@ class HyperoptTools():
                 result_dict.update(space_params)
 
     @staticmethod
-    def _params_pretty_print(params, space: str, header: str) -> None:
+    def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
-        if space in params:
+        if space in params or space in non_optimized:
             space_params = HyperoptTools._space_params(params, space, 5)
-            params_result = f"\n# {header}\n"
+            result = f"\n# {header}\n"
             if space == 'stoploss':
-                params_result += f"stoploss = {space_params.get('stoploss')}"
+                result += f"stoploss = {space_params.get('stoploss')}"
             elif space == 'roi':
                 # TODO: get rid of OrderedDict when support for python 3.6 will be
                 # dropped (dicts keep the order as the language feature)
@@ -110,28 +140,64 @@ class HyperoptTools():
                         (str(k), v) for k, v in space_params.items()
                     ),
                     default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
-                params_result += f"minimal_roi = {minimal_roi_result}"
+                result += f"minimal_roi = {minimal_roi_result}"
             elif space == 'trailing':
 
                 for k, v in space_params.items():
-                    params_result += f'{k} = {v}\n'
+                    result += f'{k} = {v}\n'
 
             else:
-                params_result += f"{space}_params = {pformat(space_params, indent=4)}"
+                no_params = HyperoptTools._space_params(non_optimized, space, 5)
-                params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
 
-            params_result = params_result.replace("\n", "\n    ")
+                result += f"{space}_params = {HyperoptTools._pprint(space_params, no_params)}"
-            print(params_result)
+
+            result = result.replace("\n", "\n    ")
+            print(result)
 
     @staticmethod
     def _space_params(params, space: str, r: int = None) -> Dict:
-        d = params[space]
+        d = params.get(space)
+        if d:
             # Round floats to `r` digits after the decimal point if requested
             return round_dict(d, r) if r else d
+        return {}
+
+    @staticmethod
+    def _pprint(params, non_optimized, indent: int = 4):
+        """
+        Pretty-print hyperopt results (based on 2 dicts - with add. comment)
+        """
+        p = params.copy()
+        p.update(non_optimized)
+        result = '{\n'
+
+        for k, param in p.items():
+            result += " " * indent + f'"{k}": '
+            result += f'"{param}",' if isinstance(param, str) else f'{param},'
+            if k in non_optimized:
+                result += "  # value loaded from strategy"
+            result += "\n"
+        result += '}'
+        return result
 
     @staticmethod
     def is_best_loss(results, current_best_loss: float) -> bool:
-        return results['loss'] < current_best_loss
+        return bool(results['loss'] < current_best_loss)
+
+    @staticmethod
+    def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
+        """
+        Return the formatted results explanation in a string
+        """
+        return (f"{results_metrics['total_trades']:6d} trades. "
+                f"{results_metrics['wins']}/{results_metrics['draws']}"
+                f"/{results_metrics['losses']} Wins/Draws/Losses. "
+                f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
+                f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
+                f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
+                f"({results_metrics['profit_total'] * 100: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
+                f"Avg duration {results_metrics['holding_avg']} min."
+                ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
 
     @staticmethod
     def _format_explanation_string(results, total_epochs) -> str:
@@ -156,12 +222,27 @@ class HyperoptTools():
         if 'results_metrics.winsdrawslosses' not in trials.columns:
             # Ensure compatibility with older versions of hyperopt results
             trials['results_metrics.winsdrawslosses'] = 'N/A'
+        legacy_mode = True
 
+        if 'results_metrics.total_trades' in trials:
+            legacy_mode = False
+            # New mode, using backtest result for metrics
+            trials['results_metrics.winsdrawslosses'] = trials.apply(
+                lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
+                          f"{x['results_metrics.losses']:>4}", axis=1)
+            trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
+                             'results_metrics.winsdrawslosses',
+                             'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
+                             'results_metrics.profit_total', 'results_metrics.holding_avg',
+                             'loss', 'is_initial_point', 'is_best']]
+        else:
+            # Legacy mode
             trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
                              'results_metrics.winsdrawslosses',
                              'results_metrics.avg_profit', 'results_metrics.total_profit',
                              'results_metrics.profit', 'results_metrics.duration',
                              'loss', 'is_initial_point', 'is_best']]
+
         trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
                           'Total profit', 'Profit', 'Avg duration', 'Objective',
                           'is_initial_point', 'is_best']
@@ -171,26 +252,28 @@ class HyperoptTools():
         trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Trades'] = trials['Trades'].astype(str)
-
+        perc_multi = 1 if legacy_mode else 100
         trials['Epoch'] = trials['Epoch'].apply(
             lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
         )
         trials['Avg profit'] = trials['Avg profit'].apply(
-            lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Avg duration'] = trials['Avg duration'].apply(
-            lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
+                      if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Objective'] = trials['Objective'].apply(
-            lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
+            lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
         )
 
+        stake_currency = config['stake_currency']
         trials['Profit'] = trials.apply(
-            lambda x: '{:,.8f} {} {}'.format(
+            lambda x: '{} {}'.format(
-                x['Total profit'], config['stake_currency'],
+                round_coin_value(x['Total profit'], stake_currency),
-                '({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
+                '({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
-            ).rjust(25+len(config['stake_currency']))
+            ).rjust(25+len(stake_currency))
-            if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
+            if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
             axis=1
         )
         trials = trials.drop(columns=['Total profit'])
@@ -251,6 +334,16 @@ class HyperoptTools():
         trials['Best'] = ''
         trials['Stake currency'] = config['stake_currency']
 
+        if 'results_metrics.total_trades' in trials:
+            base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
+                            'results_metrics.profit_mean', 'results_metrics.profit_median',
+                            'results_metrics.profit_total',
+                            'Stake currency',
+                            'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
+                            'loss', 'is_initial_point', 'is_best']
+            perc_multi = 100
+        else:
+            perc_multi = 1
             base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
                             'results_metrics.avg_profit', 'results_metrics.median_profit',
                             'results_metrics.total_profit',
@@ -272,21 +365,24 @@ class HyperoptTools():
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Epoch'] = trials['Epoch'].astype(str)
         trials['Trades'] = trials['Trades'].astype(str)
+        trials['Median profit'] = trials['Median profit'] * perc_multi
 
         trials['Total profit'] = trials['Total profit'].apply(
-            lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
+            lambda x: f'{x:,.8f}' if x != 0.0 else ""
         )
         trials['Profit'] = trials['Profit'].apply(
-            lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
+            lambda x: f'{x:,.2f}' if not isna(x) else ""
         )
         trials['Avg profit'] = trials['Avg profit'].apply(
-            lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
+            lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
         )
+        if perc_multi == 1:
             trials['Avg duration'] = trials['Avg duration'].apply(
-            lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
+                lambda x: f'{x:,.1f} m' if isinstance(
+                    x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
             )
         trials['Objective'] = trials['Objective'].apply(
-            lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
+            lambda x: f'{x:,.5f}' if x != 100000 else ""
         )
 
         trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])

freqtrade/optimize/optimize_reports.py

@@ -3,7 +3,6 @@ from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Any, Dict, List, Union
 
-from arrow import Arrow
 from numpy import int64
 from pandas import DataFrame
 from tabulate import tabulate
@@ -44,7 +43,7 @@ def _get_line_floatfmt(stake_currency: str) -> List[str]:
     Generate floatformat (goes in line with _generate_result_line())
     """
     return ['s', 'd', '.2f', '.2f', f'.{decimals_per_coin(stake_currency)}f',
-            '.2f', 'd', 'd', 'd', 'd']
+            '.2f', 'd', 's', 's']
 
 
 def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
@@ -53,7 +52,17 @@ def _get_line_header(first_column: str, stake_currency: str) -> List[str]:
     """
     return [first_column, 'Buys', 'Avg Profit %', 'Cum Profit %',
             f'Tot Profit {stake_currency}', 'Tot Profit %', 'Avg Duration',
-            'Wins', 'Draws', 'Losses']
+            'Win  Draw  Loss  Win%']
+
+
+def _generate_wins_draws_losses(wins, draws, losses):
+    if wins > 0 and losses == 0:
+        wl_ratio = '100'
+    elif wins == 0:
+        wl_ratio = '0'
+    else:
+        wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
+    return f'{wins:>4}  {draws:>4}  {losses:>4}  {wl_ratio:>4}'
 
 
 def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
@@ -153,7 +162,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
     return tabular_data
 
 
-def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
+def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
     """
     Generate summary per strategy
     :param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
@@ -165,6 +174,17 @@ def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
         tabular_data.append(_generate_result_line(
             results['results'], results['config']['dry_run_wallet'], strategy)
             )
+        try:
+            max_drawdown_per, _, _, _, _ = calculate_max_drawdown(results['results'],
+                                                                  value_col='profit_ratio')
+            max_drawdown_abs, _, _, _, _ = calculate_max_drawdown(results['results'],
+                                                                  value_col='profit_abs')
+        except ValueError:
+            max_drawdown_per = 0
+            max_drawdown_abs = 0
+        tabular_data[-1]['max_drawdown_per'] = round(max_drawdown_per * 100, 2)
+        tabular_data[-1]['max_drawdown_abs'] = \
+            round_coin_value(max_drawdown_abs, results['config']['stake_currency'], False)
     return tabular_data
 
 
@@ -194,7 +214,40 @@ def generate_edge_table(results: dict) -> str:
                     floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")  # type: ignore
 
 
+def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
+    """ Generate overall trade statistics """
+    if len(results) == 0:
+        return {
+            'wins': 0,
+            'losses': 0,
+            'draws': 0,
+            'holding_avg': timedelta(),
+            'winner_holding_avg': timedelta(),
+            'loser_holding_avg': timedelta(),
+        }
+
+    winning_trades = results.loc[results['profit_ratio'] > 0]
+    draw_trades = results.loc[results['profit_ratio'] == 0]
+    losing_trades = results.loc[results['profit_ratio'] < 0]
+    zero_duration_trades = len(results.loc[(results['trade_duration'] == 0) &
+                                           (results['sell_reason'] == 'trailing_stop_loss')])
+
+    return {
+        'wins': len(winning_trades),
+        'losses': len(losing_trades),
+        'draws': len(draw_trades),
+        'holding_avg': (timedelta(minutes=round(results['trade_duration'].mean()))
+                        if not results.empty else timedelta()),
+        'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
+                               if not winning_trades.empty else timedelta()),
+        'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
+                              if not losing_trades.empty else timedelta()),
+        'zero_duration_trades': zero_duration_trades,
+    }
+
+
 def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
+    """ Generate daily statistics """
     if len(results) == 0:
         return {
             'backtest_best_day': 0,
@@ -204,8 +257,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
             'winning_days': 0,
             'draw_days': 0,
             'losing_days': 0,
-            'winner_holding_avg': timedelta(),
-            'loser_holding_avg': timedelta(),
         }
     daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
     daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
@@ -217,9 +268,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
     draw_days = sum(daily_profit == 0)
     losing_days = sum(daily_profit < 0)
 
-    winning_trades = results.loc[results['profit_ratio'] > 0]
-    losing_trades = results.loc[results['profit_ratio'] < 0]
-
     return {
         'backtest_best_day': best_rel,
         'backtest_worst_day': worst_rel,
@@ -228,33 +276,28 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
         'winning_days': winning_days,
         'draw_days': draw_days,
         'losing_days': losing_days,
-        'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
-                               if not winning_trades.empty else timedelta()),
-        'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
-                              if not losing_trades.empty else timedelta()),
     }
 
 
-def generate_backtest_stats(btdata: Dict[str, DataFrame],
+def generate_strategy_stats(btdata: Dict[str, DataFrame],
-                            all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
+                            strategy: str,
-                            min_date: Arrow, max_date: Arrow
+                            content: Dict[str, Any],
+                            min_date: datetime, max_date: datetime,
+                            market_change: float
                             ) -> Dict[str, Any]:
     """
     :param btdata: Backtest data
-    :param all_results: backtest result - dictionary in the form:
+    :param strategy: Strategy name
-                     { Strategy: {'results: results, 'config: config}}.
+    :param content: Backtest result data in the format:
+                    {'results: results, 'config: config}}.
     :param min_date: Backtest start date
     :param max_date: Backtest end date
-    :return:
+    :param market_change: float indicating the market change
-    Dictionary containing results per strategy and a stratgy summary.
+    :return: Dictionary containing results per strategy and a stratgy summary.
     """
-    result: Dict[str, Any] = {'strategy': {}}
-    market_change = calculate_market_change(btdata, 'close')
-
-    for strategy, content in all_results.items():
     results: Dict[str, DataFrame] = content['results']
     if not isinstance(results, DataFrame):
-            continue
+        return {}
     config = content['config']
     max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
     starting_balance = config['dry_run_wallet']
@@ -270,6 +313,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
                                               results=results.loc[results['is_open']],
                                               skip_nan=True)
     daily_stats = generate_daily_stats(results)
+    trade_stats = generate_trading_stats(results)
     best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
                     key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
     worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
@@ -290,12 +334,13 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
         'total_volume': float(results['stake_amount'].sum()),
         'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
         'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
+        'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
         'profit_total': results['profit_abs'].sum() / starting_balance,
         'profit_total_abs': results['profit_abs'].sum(),
-            'backtest_start': min_date.datetime,
+        'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
-            'backtest_start_ts': min_date.int_timestamp * 1000,
+        'backtest_start_ts': int(min_date.timestamp() * 1000),
-            'backtest_end': max_date.datetime,
+        'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
-            'backtest_end_ts': max_date.int_timestamp * 1000,
+        'backtest_end_ts': int(max_date.timestamp() * 1000),
         'backtest_days': backtest_days,
 
         'backtest_run_start_ts': content['backtest_start_time'],
@@ -310,6 +355,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
         'starting_balance': starting_balance,
         'dry_run_wallet': starting_balance,
         'final_balance': content['final_balance'],
+        'rejected_signals': content['rejected_signals'],
         'max_open_trades': max_open_trades,
         'max_open_trades_setting': (config['max_open_trades']
                                     if config['max_open_trades'] != float('inf') else -1),
@@ -330,8 +376,8 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
         'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
         'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
         **daily_stats,
+        **trade_stats
     }
-        result['strategy'][strategy] = strat_stats
 
     try:
         max_drawdown, _, _, _, _ = calculate_max_drawdown(
@@ -341,9 +387,9 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
         strat_stats.update({
             'max_drawdown': max_drawdown,
             'max_drawdown_abs': drawdown_abs,
-                'drawdown_start': drawdown_start,
+            'drawdown_start': drawdown_start.strftime(DATETIME_PRINT_FORMAT),
             'drawdown_start_ts': drawdown_start.timestamp() * 1000,
-                'drawdown_end': drawdown_end,
+            'drawdown_end': drawdown_end.strftime(DATETIME_PRINT_FORMAT),
             'drawdown_end_ts': drawdown_end.timestamp() * 1000,
 
             'max_drawdown_low': low_val,
@@ -370,7 +416,30 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
             'csum_max': 0
         })
 
-    strategy_results = generate_strategy_metrics(all_results=all_results)
+    return strat_stats
+
+
+def generate_backtest_stats(btdata: Dict[str, DataFrame],
+                            all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
+                            min_date: datetime, max_date: datetime
+                            ) -> Dict[str, Any]:
+    """
+    :param btdata: Backtest data
+    :param all_results: backtest result - dictionary in the form:
+                     { Strategy: {'results: results, 'config: config}}.
+    :param min_date: Backtest start date
+    :param max_date: Backtest end date
+    :return: Dictionary containing results per strategy and a stratgy summary.
+    """
+    result: Dict[str, Any] = {'strategy': {}}
+    market_change = calculate_market_change(btdata, 'close')
+
+    for strategy, content in all_results.items():
+        strat_stats = generate_strategy_stats(btdata, strategy, content,
+                                              min_date, max_date, market_change=market_change)
+        result['strategy'][strategy] = strat_stats
+
+    strategy_results = generate_strategy_comparison(all_results=all_results)
 
     result['strategy_comparison'] = strategy_results
 
@@ -393,7 +462,8 @@ def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: st
     floatfmt = _get_line_floatfmt(stake_currency)
     output = [[
         t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
-        t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
+        t['profit_total_pct'], t['duration_avg'],
+        _generate_wins_draws_losses(t['wins'], t['draws'], t['losses'])
     ] for t in pair_results]
     # Ignore type as floatfmt does allow tuples but mypy does not know that
     return tabulate(output, headers=headers,
@@ -410,9 +480,7 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
     headers = [
         'Sell Reason',
         'Sells',
-        'Wins',
+        'Win  Draws  Loss  Win%',
-        'Draws',
-        'Losses',
         'Avg Profit %',
         'Cum Profit %',
         f'Tot Profit {stake_currency}',
@@ -420,7 +488,8 @@ def text_table_sell_reason(sell_reason_stats: List[Dict[str, Any]], stake_curren
     ]
 
     output = [[
-        t['sell_reason'], t['trades'], t['wins'], t['draws'], t['losses'],
+        t['sell_reason'], t['trades'],
+        _generate_wins_draws_losses(t['wins'], t['draws'], t['losses']),
         t['profit_mean_pct'], t['profit_sum_pct'],
         round_coin_value(t['profit_total_abs'], stake_currency, False),
         t['profit_total_pct'],
@@ -438,11 +507,22 @@ def text_table_strategy(strategy_results, stake_currency: str) -> str:
     """
     floatfmt = _get_line_floatfmt(stake_currency)
     headers = _get_line_header('Strategy', stake_currency)
+    # _get_line_header() is also used for per-pair summary. Per-pair drawdown is mostly useless
+    # therefore we slip this column in only for strategy summary here.
+    headers.append('Drawdown')
+
+    # Align drawdown string on the center two space separator.
+    drawdown = [f'{t["max_drawdown_per"]:.2f}' for t in strategy_results]
+    dd_pad_abs = max([len(t['max_drawdown_abs']) for t in strategy_results])
+    dd_pad_per = max([len(dd) for dd in drawdown])
+    drawdown = [f'{t["max_drawdown_abs"]:>{dd_pad_abs}} {stake_currency}  {dd:>{dd_pad_per}}%'
+                for t, dd in zip(strategy_results, drawdown)]
 
     output = [[
         t['key'], t['trades'], t['profit_mean_pct'], t['profit_sum_pct'], t['profit_total_abs'],
-        t['profit_total_pct'], t['duration_avg'], t['wins'], t['draws'], t['losses']
+        t['profit_total_pct'], t['duration_avg'],
-    ] for t in strategy_results]
+        _generate_wins_draws_losses(t['wins'], t['draws'], t['losses']), drawdown]
+        for t, drawdown in zip(strategy_results, drawdown)]
     # Ignore type as floatfmt does allow tuples but mypy does not know that
     return tabulate(output, headers=headers,
                     floatfmt=floatfmt, tablefmt="orgtbl", stralign="right")
@@ -452,9 +532,21 @@ def text_table_add_metrics(strat_results: Dict) -> str:
     if len(strat_results['trades']) > 0:
         best_trade = max(strat_results['trades'], key=lambda x: x['profit_ratio'])
         worst_trade = min(strat_results['trades'], key=lambda x: x['profit_ratio'])
+
+        # Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
+        # command stores these results and newer version of freqtrade must be able to handle old
+        # results with missing new fields.
+        zero_duration_trades = '--'
+
+        if 'zero_duration_trades' in strat_results:
+            zero_duration_trades_per = \
+                100.0 / strat_results['total_trades'] * strat_results['zero_duration_trades']
+            zero_duration_trades = f'{zero_duration_trades_per:.2f}% ' \
+                                   f'({strat_results["zero_duration_trades"]})'
+
         metrics = [
-            ('Backtesting from', strat_results['backtest_start'].strftime(DATETIME_PRINT_FORMAT)),
+            ('Backtesting from', strat_results['backtest_start']),
-            ('Backtesting to', strat_results['backtest_end'].strftime(DATETIME_PRINT_FORMAT)),
+            ('Backtesting to', strat_results['backtest_end']),
             ('Max open trades', strat_results['max_open_trades']),
             ('', ''),  # Empty line to improve readability
             ('Total trades', strat_results['total_trades']),
@@ -464,13 +556,12 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                                strat_results['stake_currency'])),
             ('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
                                                   strat_results['stake_currency'])),
-            ('Total profit %', f"{round(strat_results['profit_total'] * 100, 2)}%"),
+            ('Total profit %', f"{round(strat_results['profit_total'] * 100, 2):}%"),
             ('Trades per day', strat_results['trades_per_day']),
             ('Avg. stake amount', round_coin_value(strat_results['avg_stake_amount'],
                                                    strat_results['stake_currency'])),
             ('Total trade volume', round_coin_value(strat_results['total_volume'],
                                                     strat_results['stake_currency'])),
-
             ('', ''),  # Empty line to improve readability
             ('Best Pair', f"{strat_results['best_pair']['key']} "
                           f"{round(strat_results['best_pair']['profit_sum_pct'], 2)}%"),
@@ -488,6 +579,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                 f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
             ('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
             ('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
+            ('Zero Duration Trades', zero_duration_trades),
+            ('Rejected Buy signals', strat_results.get('rejected_signals', 'N/A')),
             ('', ''),  # Empty line to improve readability
 
             ('Min balance', round_coin_value(strat_results['csum_min'],
@@ -502,8 +595,8 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                                strat_results['stake_currency'])),
             ('Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
                                               strat_results['stake_currency'])),
-            ('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
+            ('Drawdown Start', strat_results['drawdown_start']),
-            ('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
+            ('Drawdown End', strat_results['drawdown_end']),
             ('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),
         ]
 
@@ -522,11 +615,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
         return message
 
 
-def show_backtest_results(config: Dict, backtest_stats: Dict):
+def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str):
-    stake_currency = config['stake_currency']
+    """
-
+    Print results for one strategy
-    for strategy, results in backtest_stats['strategy'].items():
+    """
-
     # Print results
     print(f"Result for strategy {strategy}")
     table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
@@ -554,6 +646,13 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
         print('=' * len(table.splitlines()[0]))
     print()
 
+
+def show_backtest_results(config: Dict, backtest_stats: Dict):
+    stake_currency = config['stake_currency']
+
+    for strategy, results in backtest_stats['strategy'].items():
+        show_backtest_result(strategy, results, stake_currency)
+
     if len(backtest_stats['strategy']) > 1:
         # Print Strategy summary table
 

freqtrade/persistence/migrations.py

@@ -123,6 +123,27 @@ def migrate_open_orders_to_trades(engine):
         """)
 
 
+def migrate_orders_table(decl_base, inspector, engine, table_back_name: str, cols: List):
+    # Schema migration necessary
+    engine.execute(f"alter table orders rename to {table_back_name}")
+    # drop indexes on backup table
+    for index in inspector.get_indexes(table_back_name):
+        engine.execute(f"drop index {index['name']}")
+
+    # let SQLAlchemy create the schema as required
+    decl_base.metadata.create_all(engine)
+
+    engine.execute(f"""
+        insert into orders ( id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
+        symbol, order_type, side, price, amount, filled, average, remaining, cost, order_date,
+        order_filled_date, order_update_date)
+        select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
+        symbol, order_type, side, price, amount, filled, null average, remaining, cost, order_date,
+        order_filled_date, order_update_date
+        from {table_back_name}
+        """)
+
+
 def check_migrate(engine, decl_base, previous_tables) -> None:
     """
     Checks if migration is necessary and migrates if necessary
@@ -145,6 +166,11 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
         logger.info('Moving open orders to Orders table.')
         migrate_open_orders_to_trades(engine)
     else:
-        pass
+        cols_order = inspector.get_columns('orders')
+
+        if not has_column(cols_order, 'average'):
+            tabs = get_table_names_for_table(inspector, 'orders')
             # Empty for now - as there is only one iteration of the orders table so far.
-        # table_back_name = get_backup_name(tabs, 'orders_bak')
+            table_back_name = get_backup_name(tabs, 'orders_bak')
+
+            migrate_orders_table(decl_base, inspector, engine, table_back_name, cols)

freqtrade/persistence/models.py

@@ -112,16 +112,17 @@ class Order(_DECL_BASE):
 
     trade = relationship("Trade", back_populates="orders")
 
-    ft_order_side = Column(String, nullable=False)
+    ft_order_side = Column(String(25), nullable=False)
-    ft_pair = Column(String, nullable=False)
+    ft_pair = Column(String(25), nullable=False)
     ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
 
-    order_id = Column(String, nullable=False, index=True)
+    order_id = Column(String(255), nullable=False, index=True)
-    status = Column(String, nullable=True)
+    status = Column(String(255), nullable=True)
-    symbol = Column(String, nullable=True)
+    symbol = Column(String(25), nullable=True)
-    order_type = Column(String, nullable=True)
+    order_type = Column(String(50), nullable=True)
-    side = Column(String, nullable=True)
+    side = Column(String(25), nullable=True)
     price = Column(Float, nullable=True)
+    average = Column(Float, nullable=True)
     amount = Column(Float, nullable=True)
     filled = Column(Float, nullable=True)
     remaining = Column(Float, nullable=True)
@@ -150,6 +151,7 @@ class Order(_DECL_BASE):
         self.price = order.get('price', self.price)
         self.amount = order.get('amount', self.amount)
         self.filled = order.get('filled', self.filled)
+        self.average = order.get('average', self.average)
         self.remaining = order.get('remaining', self.remaining)
         self.cost = order.get('cost', self.cost)
         if 'timestamp' in order and order['timestamp'] is not None:
@@ -567,23 +569,6 @@ class LocalTrade():
         else:
             return None
 
-    @staticmethod
-    def get_trades(trade_filter=None) -> Query:
-        """
-        Helper function to query Trades using filters.
-        :param trade_filter: Optional filter to apply to trades
-                             Can be either a Filter object, or a List of filters
-                             e.g. `(trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True),])`
-                             e.g. `(trade_filter=Trade.id == trade_id)`
-        :return: unsorted query object
-        """
-        if trade_filter is not None:
-            if not isinstance(trade_filter, list):
-                trade_filter = [trade_filter]
-            return Trade.query.filter(*trade_filter)
-        else:
-            return Trade.query
-
     @staticmethod
     def get_trades_proxy(*, pair: str = None, is_open: bool = None,
                          open_date: datetime = None, close_date: datetime = None,
@@ -636,83 +621,7 @@ class LocalTrade():
         """
         Query trades from persistence layer
         """
-        return Trade.get_trades(Trade.is_open.is_(True)).all()
+        return Trade.get_trades_proxy(is_open=True)
-
-    @staticmethod
-    def get_open_order_trades():
-        """
-        Returns all open trades
-        """
-        return Trade.get_trades(Trade.open_order_id.isnot(None)).all()
-
-    @staticmethod
-    def get_open_trades_without_assigned_fees():
-        """
-        Returns all open trades which don't have open fees set correctly
-        """
-        return Trade.get_trades([Trade.fee_open_currency.is_(None),
-                                 Trade.orders.any(),
-                                 Trade.is_open.is_(True),
-                                 ]).all()
-
-    @staticmethod
-    def get_sold_trades_without_assigned_fees():
-        """
-        Returns all closed trades which don't have fees set correctly
-        """
-        return Trade.get_trades([Trade.fee_close_currency.is_(None),
-                                 Trade.orders.any(),
-                                 Trade.is_open.is_(False),
-                                 ]).all()
-
-    @staticmethod
-    def total_open_trades_stakes() -> float:
-        """
-        Calculates total invested amount in open trades
-        in stake currency
-        """
-        if Trade.use_db:
-            total_open_stake_amount = Trade.query.with_entities(
-                func.sum(Trade.stake_amount)).filter(Trade.is_open.is_(True)).scalar()
-        else:
-            total_open_stake_amount = sum(
-                t.stake_amount for t in Trade.get_trades_proxy(is_open=True))
-        return total_open_stake_amount or 0
-
-    @staticmethod
-    def get_overall_performance() -> List[Dict[str, Any]]:
-        """
-        Returns List of dicts containing all Trades, including profit and trade count
-        """
-        pair_rates = Trade.query.with_entities(
-            Trade.pair,
-            func.sum(Trade.close_profit).label('profit_sum'),
-            func.count(Trade.pair).label('count')
-        ).filter(Trade.is_open.is_(False))\
-            .group_by(Trade.pair) \
-            .order_by(desc('profit_sum')) \
-            .all()
-        return [
-            {
-                'pair': pair,
-                'profit': rate,
-                'count': count
-            }
-            for pair, rate, count in pair_rates
-        ]
-
-    @staticmethod
-    def get_best_pair():
-        """
-        Get best pair with closed trade.
-        :returns: Tuple containing (pair, profit_sum)
-        """
-        best_pair = Trade.query.with_entities(
-            Trade.pair, func.sum(Trade.close_profit).label('profit_sum')
-        ).filter(Trade.is_open.is_(False)) \
-            .group_by(Trade.pair) \
-            .order_by(desc('profit_sum')).first()
-        return best_pair
 
     @staticmethod
     def stoploss_reinitialization(desired_stoploss):
@@ -749,15 +658,15 @@ class Trade(_DECL_BASE, LocalTrade):
 
     orders = relationship("Order", order_by="Order.id", cascade="all, delete-orphan")
 
-    exchange = Column(String, nullable=False)
+    exchange = Column(String(25), nullable=False)
-    pair = Column(String, nullable=False, index=True)
+    pair = Column(String(25), nullable=False, index=True)
     is_open = Column(Boolean, nullable=False, default=True, index=True)
     fee_open = Column(Float, nullable=False, default=0.0)
     fee_open_cost = Column(Float, nullable=True)
-    fee_open_currency = Column(String, nullable=True)
+    fee_open_currency = Column(String(25), nullable=True)
     fee_close = Column(Float, nullable=False, default=0.0)
     fee_close_cost = Column(Float, nullable=True)
-    fee_close_currency = Column(String, nullable=True)
+    fee_close_currency = Column(String(25), nullable=True)
     open_rate = Column(Float)
     open_rate_requested = Column(Float)
     # open_trade_value - calculated via _calc_open_trade_value
@@ -771,7 +680,7 @@ class Trade(_DECL_BASE, LocalTrade):
     amount_requested = Column(Float)
     open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
     close_date = Column(DateTime)
-    open_order_id = Column(String)
+    open_order_id = Column(String(255))
     # absolute value of the stop loss
     stop_loss = Column(Float, nullable=True, default=0.0)
     # percentage value of the stop loss
@@ -781,16 +690,16 @@ class Trade(_DECL_BASE, LocalTrade):
     # percentage value of the initial stop loss
     initial_stop_loss_pct = Column(Float, nullable=True)
     # stoploss order id which is on exchange
-    stoploss_order_id = Column(String, nullable=True, index=True)
+    stoploss_order_id = Column(String(255), nullable=True, index=True)
     # last update time of the stoploss order on exchange
     stoploss_last_update = Column(DateTime, nullable=True)
     # absolute value of the highest reached price
     max_rate = Column(Float, nullable=True, default=0.0)
     # Lowest price reached
     min_rate = Column(Float, nullable=True)
-    sell_reason = Column(String, nullable=True)
+    sell_reason = Column(String(100), nullable=True)
-    sell_order_status = Column(String, nullable=True)
+    sell_order_status = Column(String(100), nullable=True)
-    strategy = Column(String, nullable=True)
+    strategy = Column(String(100), nullable=True)
     timeframe = Column(Integer, nullable=True)
 
     def __init__(self, **kwargs):
@@ -810,7 +719,7 @@ class Trade(_DECL_BASE, LocalTrade):
                          open_date: datetime = None, close_date: datetime = None,
                          ) -> List['LocalTrade']:
         """
-        Helper function to query Trades.
+        Helper function to query Trades.j
         Returns a List of trades, filtered on the parameters given.
         In live mode, converts the filter to a database query and returns all rows
         In Backtest mode, uses filters on Trade.trades to get the result.
@@ -835,6 +744,109 @@ class Trade(_DECL_BASE, LocalTrade):
                 close_date=close_date
             )
 
+    @staticmethod
+    def get_trades(trade_filter=None) -> Query:
+        """
+        Helper function to query Trades using filters.
+        NOTE: Not supported in Backtesting.
+        :param trade_filter: Optional filter to apply to trades
+                             Can be either a Filter object, or a List of filters
+                             e.g. `(trade_filter=[Trade.id == trade_id, Trade.is_open.is_(True),])`
+                             e.g. `(trade_filter=Trade.id == trade_id)`
+        :return: unsorted query object
+        """
+        if not Trade.use_db:
+            raise NotImplementedError('`Trade.get_trades()` not supported in backtesting mode.')
+        if trade_filter is not None:
+            if not isinstance(trade_filter, list):
+                trade_filter = [trade_filter]
+            return Trade.query.filter(*trade_filter)
+        else:
+            return Trade.query
+
+    @staticmethod
+    def get_open_order_trades():
+        """
+        Returns all open trades
+        NOTE: Not supported in Backtesting.
+        """
+        return Trade.get_trades(Trade.open_order_id.isnot(None)).all()
+
+    @staticmethod
+    def get_open_trades_without_assigned_fees():
+        """
+        Returns all open trades which don't have open fees set correctly
+        NOTE: Not supported in Backtesting.
+        """
+        return Trade.get_trades([Trade.fee_open_currency.is_(None),
+                                 Trade.orders.any(),
+                                 Trade.is_open.is_(True),
+                                 ]).all()
+
+    @staticmethod
+    def get_sold_trades_without_assigned_fees():
+        """
+        Returns all closed trades which don't have fees set correctly
+        NOTE: Not supported in Backtesting.
+        """
+        return Trade.get_trades([Trade.fee_close_currency.is_(None),
+                                 Trade.orders.any(),
+                                 Trade.is_open.is_(False),
+                                 ]).all()
+
+    @staticmethod
+    def total_open_trades_stakes() -> float:
+        """
+        Calculates total invested amount in open trades
+        in stake currency
+        """
+        if Trade.use_db:
+            total_open_stake_amount = Trade.query.with_entities(
+                func.sum(Trade.stake_amount)).filter(Trade.is_open.is_(True)).scalar()
+        else:
+            total_open_stake_amount = sum(
+                t.stake_amount for t in LocalTrade.get_trades_proxy(is_open=True))
+        return total_open_stake_amount or 0
+
+    @staticmethod
+    def get_overall_performance() -> List[Dict[str, Any]]:
+        """
+        Returns List of dicts containing all Trades, including profit and trade count
+        NOTE: Not supported in Backtesting.
+        """
+        pair_rates = Trade.query.with_entities(
+            Trade.pair,
+            func.sum(Trade.close_profit).label('profit_sum'),
+            func.sum(Trade.close_profit_abs).label('profit_sum_abs'),
+            func.count(Trade.pair).label('count')
+        ).filter(Trade.is_open.is_(False))\
+            .group_by(Trade.pair) \
+            .order_by(desc('profit_sum_abs')) \
+            .all()
+        return [
+            {
+                'pair': pair,
+                'profit': profit,
+                'profit_abs': profit_abs,
+                'count': count
+            }
+            for pair, profit, profit_abs, count in pair_rates
+        ]
+
+    @staticmethod
+    def get_best_pair():
+        """
+        Get best pair with closed trade.
+        NOTE: Not supported in Backtesting.
+        :returns: Tuple containing (pair, profit_sum)
+        """
+        best_pair = Trade.query.with_entities(
+            Trade.pair, func.sum(Trade.close_profit).label('profit_sum')
+        ).filter(Trade.is_open.is_(False)) \
+            .group_by(Trade.pair) \
+            .order_by(desc('profit_sum')).first()
+        return best_pair
+
 
 class PairLock(_DECL_BASE):
     """
@@ -844,8 +856,8 @@ class PairLock(_DECL_BASE):
 
     id = Column(Integer, primary_key=True)
 
-    pair = Column(String, nullable=False, index=True)
+    pair = Column(String(25), nullable=False, index=True)
-    reason = Column(String, nullable=True)
+    reason = Column(String(255), nullable=True)
     # Time the pair was locked (start time)
     lock_time = Column(DateTime, nullable=False)
     # Time until the pair is locked (end time)

freqtrade/plot/plotting.py

@@ -77,6 +77,7 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
         )
     except ValueError as e:
         raise OperationalException(e) from e
+    if not trades.empty:
         trades = trim_dataframe(trades, timerange, 'open_date')
 
     return {"ohlcv": data,
@@ -540,8 +541,11 @@ def load_and_plot_trades(config: Dict[str, Any]):
 
         df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
         df_analyzed = trim_dataframe(df_analyzed, timerange)
+        if not trades.empty:
             trades_pair = trades.loc[trades['pair'] == pair]
             trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
+        else:
+            trades_pair = trades
 
         fig = generate_candlestick_graph(
             pair=pair,

freqtrade/plugins/pairlist/AgeFilter.py

@@ -71,14 +71,14 @@ class AgeFilter(IPairList):
                 daily_candles = candles[(p, '1d')] if (p, '1d') in candles else None
                 if not self._validate_pair_loc(p, daily_candles):
                     pairlist.remove(p)
-        logger.info(f"Validated {len(pairlist)} pairs.")
+        self.log_once(f"Validated {len(pairlist)} pairs.", logger.info)
         return pairlist
 
     def _validate_pair_loc(self, pair: str, daily_candles: Optional[DataFrame]) -> bool:
         """
         Validate age for the ticker
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
         # Check symbol in cache
@@ -86,7 +86,7 @@ class AgeFilter(IPairList):
             return True
 
         if daily_candles is not None:
-            if len(daily_candles) > self._min_days_listed:
+            if len(daily_candles) >= self._min_days_listed:
                 # We have fetched at least the minimum required number of daily candles
                 # Add to cache, store the time we last checked this symbol
                 self._symbolsChecked[pair] = int(arrow.utcnow().float_timestamp) * 1000

freqtrade/plugins/pairlist/IPairList.py

@@ -7,7 +7,7 @@ from copy import deepcopy
 from typing import Any, Dict, List
 
 from freqtrade.exceptions import OperationalException
-from freqtrade.exchange import market_is_active
+from freqtrade.exchange import Exchange, market_is_active
 from freqtrade.mixins import LoggingMixin
 
 
@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
 
 class IPairList(LoggingMixin, ABC):
 
-    def __init__(self, exchange, pairlistmanager,
+    def __init__(self, exchange: Exchange, pairlistmanager,
                  config: Dict[str, Any], pairlistconfig: Dict[str, Any],
                  pairlist_pos: int) -> None:
         """
@@ -28,7 +28,7 @@ class IPairList(LoggingMixin, ABC):
         """
         self._enabled = True
 
-        self._exchange = exchange
+        self._exchange: Exchange = exchange
         self._pairlistmanager = pairlistmanager
         self._config = config
         self._pairlistconfig = pairlistconfig
@@ -68,7 +68,7 @@ class IPairList(LoggingMixin, ABC):
         filter_pairlist() method.
 
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
         raise NotImplementedError()

freqtrade/plugins/pairlist/PerformanceFilter.py

@@ -39,7 +39,12 @@ class PerformanceFilter(IPairList):
         :return: new allowlist
         """
         # Get the trading performance for pairs from database
+        try:
             performance = pd.DataFrame(Trade.get_overall_performance())
+        except AttributeError:
+            # Performancefilter does not work in backtesting.
+            self.log_once("PerformanceFilter is not available in this mode.", logger.warning)
+            return pairlist
 
         # Skip performance-based sorting if no performance data is available
         if len(performance) == 0:

freqtrade/plugins/pairlist/PrecisionFilter.py

@@ -48,7 +48,7 @@ class PrecisionFilter(IPairList):
         Check if pair has enough room to add a stoploss to avoid "unsellable" buys of very
         low value pairs.
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
         stop_price = ticker['ask'] * self._stoploss

freqtrade/plugins/pairlist/PriceFilter.py

@@ -27,9 +27,13 @@ class PriceFilter(IPairList):
         self._max_price = pairlistconfig.get('max_price', 0)
         if self._max_price < 0:
             raise OperationalException("PriceFilter requires max_price to be >= 0")
+        self._max_value = pairlistconfig.get('max_value', 0)
+        if self._max_value < 0:
+            raise OperationalException("PriceFilter requires max_value to be >= 0")
         self._enabled = ((self._low_price_ratio > 0) or
                          (self._min_price > 0) or
-                         (self._max_price > 0))
+                         (self._max_price > 0) or
+                         (self._max_value > 0))
 
     @property
     def needstickers(self) -> bool:
@@ -51,6 +55,8 @@ class PriceFilter(IPairList):
             active_price_filters.append(f"below {self._min_price:.8f}")
         if self._max_price != 0:
             active_price_filters.append(f"above {self._max_price:.8f}")
+        if self._max_value != 0:
+            active_price_filters.append(f"Value above {self._max_value:.8f}")
 
         if len(active_price_filters):
             return f"{self.name} - Filtering pairs priced {' or '.join(active_price_filters)}."
@@ -61,7 +67,7 @@ class PriceFilter(IPairList):
         """
         Check if if one price-step (pip) is > than a certain barrier.
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
         if ticker.get('last', None) is None or ticker.get('last') == 0:
@@ -79,6 +85,32 @@ class PriceFilter(IPairList):
                               f"because 1 unit is {changeperc * 100:.3f}%", logger.info)
                 return False
 
+        # Perform low_amount check
+        if self._max_value != 0:
+            price = ticker['last']
+            market = self._exchange.markets[pair]
+            limits = market['limits']
+            if ('amount' in limits and 'min' in limits['amount']
+                    and limits['amount']['min'] is not None):
+                min_amount = limits['amount']['min']
+                min_precision = market['precision']['amount']
+
+                min_value = min_amount * price
+                if self._exchange.precisionMode == 4:
+                    # tick size
+                    next_value = (min_amount + min_precision) * price
+                else:
+                    # Decimal places
+                    min_precision = pow(0.1, min_precision)
+                    next_value = (min_amount + min_precision) * price
+                diff = next_value - min_value
+
+                if diff > self._max_value:
+                    self.log_once(f"Removed {pair} from whitelist, "
+                                  f"because min value change of {diff} > {self._max_value}.",
+                                  logger.info)
+                    return False
+
         # Perform min_price check.
         if self._min_price != 0:
             if ticker['last'] < self._min_price:
@@ -89,7 +121,7 @@ class PriceFilter(IPairList):
         # Perform max_price check.
         if self._max_price != 0:
             if ticker['last'] > self._max_price:
-                self.log_once(f"Removed {ticker['symbol']} from whitelist, "
+                self.log_once(f"Removed {pair} from whitelist, "
                               f"because last price > {self._max_price:.8f}", logger.info)
                 return False
 

freqtrade/plugins/pairlist/SpreadFilter.py

@@ -40,7 +40,7 @@ class SpreadFilter(IPairList):
         """
         Validate spread for the ticker
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
         if 'bid' in ticker and 'ask' in ticker and ticker['ask']:

freqtrade/plugins/pairlist/VolatilityFilter.py

@@ -90,7 +90,7 @@ class VolatilityFilter(IPairList):
         """
         Validate trading range
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
         # Check symbol in cache

freqtrade/plugins/pairlist/rangestabilityfilter.py

@@ -83,7 +83,7 @@ class RangeStabilityFilter(IPairList):
         """
         Validate trading range
         :param pair: Pair that's currently validated
-        :param ticker: ticker dict as returned from ccxt.load_markets()
+        :param ticker: ticker dict as returned from ccxt.fetch_tickers()
         :return: True if the pair can stay, false if it should be removed
         """
         # Check symbol in cache

freqtrade/rpc/api_server/api_schemas.py

@@ -57,6 +57,7 @@ class Count(BaseModel):
 class PerformanceEntry(BaseModel):
     pair: str
     profit: float
+    profit_abs: float
     count: int
 
 
@@ -268,7 +269,7 @@ class DeleteTrade(BaseModel):
 
 class PlotConfig_(BaseModel):
     main_plot: Dict[str, Any]
-    subplots: Optional[Dict[str, Any]]
+    subplots: Dict[str, Any]
 
 
 class PlotConfig(BaseModel):

freqtrade/rpc/fiat_convert.py

@@ -3,11 +3,13 @@ Module that define classes to convert Crypto-currency to FIAT
 e.g BTC to USD
 """
 
+import datetime
 import logging
 from typing import Dict
 
 from cachetools.ttl import TTLCache
 from pycoingecko import CoinGeckoAPI
+from requests.exceptions import RequestException
 
 from freqtrade.constants import SUPPORTED_FIAT
 
@@ -25,6 +27,7 @@ class CryptoToFiatConverter:
     _coingekko: CoinGeckoAPI = None
 
     _cryptomap: Dict = {}
+    _backoff: float = 0.0
 
     def __new__(cls):
         """
@@ -47,8 +50,21 @@ class CryptoToFiatConverter:
     def _load_cryptomap(self) -> None:
         try:
             coinlistings = self._coingekko.get_coins_list()
-            # Create mapping table from synbol to coingekko_id
+            # Create mapping table from symbol to coingekko_id
             self._cryptomap = {x['symbol']: x['id'] for x in coinlistings}
+        except RequestException as request_exception:
+            if "429" in str(request_exception):
+                logger.warning(
+                    "Too many requests for Coingecko API, backing off and trying again later.")
+                # Set backoff timestamp to 60 seconds in the future
+                self._backoff = datetime.datetime.now().timestamp() + 60
+                return
+            # If the request is not a 429 error we want to raise the normal error
+            logger.error(
+                "Could not load FIAT Cryptocurrency map for the following problem: {}".format(
+                  request_exception
+                )
+            )
         except (Exception) as exception:
             logger.error(
                 f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
@@ -127,6 +143,15 @@ class CryptoToFiatConverter:
         if crypto_symbol == fiat_symbol:
             return 1.0
 
+        if self._cryptomap == {}:
+            if self._backoff <= datetime.datetime.now().timestamp():
+                self._load_cryptomap()
+                # return 0.0 if we still dont have data to check, no reason to proceed
+                if self._cryptomap == {}:
+                    return 0.0
+            else:
+                return 0.0
+
         if crypto_symbol not in self._cryptomap:
             # return 0 for unsupported stake currencies (fiat-convert should not break the bot)
             logger.warning("unsupported crypto-symbol %s - returning 0.0", crypto_symbol)

freqtrade/rpc/rpc.py

@@ -24,7 +24,7 @@ from freqtrade.persistence.models import PairLock
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
 from freqtrade.state import State
-from freqtrade.strategy.interface import SellType
+from freqtrade.strategy.interface import SellCheckTuple, SellType
 
 
 logger = logging.getLogger(__name__)
@@ -178,7 +178,7 @@ class RPC:
                     current_rate = trade.close_rate
                 current_profit = trade.calc_profit_ratio(current_rate)
                 current_profit_abs = trade.calc_profit(current_rate)
-
+                current_profit_fiat: Optional[float] = None
                 # Calculate fiat profit
                 if self._fiat_converter:
                     current_profit_fiat = self._fiat_converter.convert_amount(
@@ -220,12 +220,13 @@ class RPC:
             return results
 
     def _rpc_status_table(self, stake_currency: str,
-                          fiat_display_currency: str) -> Tuple[List, List]:
+                          fiat_display_currency: str) -> Tuple[List, List, float]:
         trades = Trade.get_open_trades()
         if not trades:
             raise RPCException('no active trade')
         else:
             trades_list = []
+            fiat_profit_sum = NAN
             for trade in trades:
                 # calculate profit and send message to user
                 try:
@@ -243,6 +244,8 @@ class RPC:
                     )
                     if fiat_profit and not isnan(fiat_profit):
                         profit_str += f" ({fiat_profit:.2f})"
+                        fiat_profit_sum = fiat_profit if isnan(fiat_profit_sum) \
+                            else fiat_profit_sum + fiat_profit
                 trades_list.append([
                     trade.id,
                     trade.pair + ('*' if (trade.open_order_id is not None
@@ -256,7 +259,7 @@ class RPC:
                 profitcol += " (" + fiat_display_currency + ")"
 
             columns = ['ID', 'Pair', 'Since', profitcol]
-            return trades_list, columns
+            return trades_list, columns, fiat_profit_sum
 
     def _rpc_daily_profit(
             self, timescale: int,
@@ -554,7 +557,8 @@ class RPC:
             if not fully_canceled:
                 # Get current rate and execute sell
                 current_rate = self._freqtrade.get_sell_rate(trade.pair, False)
-                self._freqtrade.execute_sell(trade, current_rate, SellType.FORCE_SELL)
+                sell_reason = SellCheckTuple(sell_type=SellType.FORCE_SELL)
+                self._freqtrade.execute_sell(trade, current_rate, sell_reason)
         # ---- EOF def _exec_forcesell ----
 
         if self._freqtrade.state != State.RUNNING:
@@ -844,5 +848,7 @@ class RPC:
                                               df_analyzed, arrow.Arrow.utcnow().datetime)
 
     def _rpc_plot_config(self) -> Dict[str, Any]:
-
+        if (self._freqtrade.strategy.plot_config and
+                'subplots' not in self._freqtrade.strategy.plot_config):
+            self._freqtrade.strategy.plot_config['subplots'] = {}
         return self._freqtrade.strategy.plot_config

freqtrade/rpc/telegram.py

@@ -8,19 +8,21 @@ import logging
 from datetime import timedelta
 from html import escape
 from itertools import chain
-from typing import Any, Callable, Dict, List, Union
+from math import isnan
+from typing import Any, Callable, Dict, List, Optional, Union, cast
 
 import arrow
 from tabulate import tabulate
-from telegram import KeyboardButton, ParseMode, ReplyKeyboardMarkup, Update
+from telegram import (InlineKeyboardButton, InlineKeyboardMarkup, KeyboardButton, ParseMode,
+                      ReplyKeyboardMarkup, Update)
 from telegram.error import NetworkError, TelegramError
-from telegram.ext import CallbackContext, CommandHandler, Updater
+from telegram.ext import CallbackContext, CallbackQueryHandler, CommandHandler, Updater
 from telegram.utils.helpers import escape_markdown
 
 from freqtrade.__init__ import __version__
 from freqtrade.constants import DUST_PER_COIN
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import round_coin_value
+from freqtrade.misc import chunks, round_coin_value
 from freqtrade.rpc import RPC, RPCException, RPCHandler, RPCMessageType
 
 
@@ -87,7 +89,7 @@ class Telegram(RPCHandler):
         Validates the keyboard configuration from telegram config
         section.
         """
-        self._keyboard: List[List[Union[str, KeyboardButton]]] = [
+        self._keyboard: List[List[Union[str, KeyboardButton, InlineKeyboardButton]]] = [
             ['/daily', '/profit', '/balance'],
             ['/status', '/status table', '/performance'],
             ['/count', '/start', '/stop', '/help']
@@ -169,6 +171,11 @@ class Telegram(RPCHandler):
             [h.command for h in handles]
         )
 
+        self._current_callback_query_handler: Optional[CallbackQueryHandler] = None
+        self._callback_query_handlers = {
+            'forcebuy': CallbackQueryHandler(self._forcebuy_inline)
+        }
+
     def cleanup(self) -> None:
         """
         Stops all running telegram threads.
@@ -354,19 +361,31 @@ class Telegram(RPCHandler):
         :return: None
         """
         try:
-            statlist, head = self._rpc._rpc_status_table(
+            fiat_currency = self._config.get('fiat_display_currency', '')
-                self._config['stake_currency'], self._config.get('fiat_display_currency', ''))
+            statlist, head, fiat_profit_sum = self._rpc._rpc_status_table(
+                self._config['stake_currency'], fiat_currency)
 
+            show_total = not isnan(fiat_profit_sum) and len(statlist) > 1
             max_trades_per_msg = 50
             """
             Calculate the number of messages of 50 trades per message
             0.99 is used to make sure that there are no extra (empty) messages
             As an example with 50 trades, there will be int(50/50 + 0.99) = 1 message
             """
-            for i in range(0, max(int(len(statlist) / max_trades_per_msg + 0.99), 1)):
+            messages_count = max(int(len(statlist) / max_trades_per_msg + 0.99), 1)
-                message = tabulate(statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg],
+            for i in range(0, messages_count):
+                trades = statlist[i * max_trades_per_msg:(i + 1) * max_trades_per_msg]
+                if show_total and i == messages_count - 1:
+                    # append total line
+                    trades.append(["Total", "", "", f"{fiat_profit_sum:.2f} {fiat_currency}"])
+
+                message = tabulate(trades,
                                    headers=head,
                                    tablefmt='simple')
+                if show_total and i == messages_count - 1:
+                    # insert separators line between Total
+                    lines = message.split("\n")
+                    message = "\n".join(lines[:-1] + [lines[1]] + [lines[-1]])
                 self._send_msg(f"<pre>{message}</pre>", parse_mode=ParseMode.HTML)
         except RPCException as e:
             self._send_msg(str(e))
@@ -624,6 +643,25 @@ class Telegram(RPCHandler):
         except RPCException as e:
             self._send_msg(str(e))
 
+    def _forcebuy_action(self, pair, price=None):
+        try:
+            self._rpc._rpc_forcebuy(pair, price)
+        except RPCException as e:
+            self._send_msg(str(e))
+
+    def _forcebuy_inline(self, update: Update, _: CallbackContext) -> None:
+        if update.callback_query:
+            query = update.callback_query
+            pair = query.data
+            query.answer()
+            query.edit_message_text(text=f"Force Buying: {pair}")
+            self._forcebuy_action(pair)
+
+    @staticmethod
+    def _layout_inline_keyboard(buttons: List[InlineKeyboardButton],
+                                cols=3) -> List[List[InlineKeyboardButton]]:
+        return [buttons[i:i + cols] for i in range(0, len(buttons), cols)]
+
     @authorized_only
     def _forcebuy(self, update: Update, context: CallbackContext) -> None:
         """
@@ -636,10 +674,13 @@ class Telegram(RPCHandler):
         if context.args:
             pair = context.args[0]
             price = float(context.args[1]) if len(context.args) > 1 else None
-            try:
+            self._forcebuy_action(pair, price)
-                self._rpc._rpc_forcebuy(pair, price)
+        else:
-            except RPCException as e:
+            whitelist = self._rpc._rpc_whitelist()['whitelist']
-                self._send_msg(str(e))
+            pairs = [InlineKeyboardButton(pair, callback_data=pair) for pair in whitelist]
+            self._send_inline_msg("Which pair?",
+                                  keyboard=self._layout_inline_keyboard(pairs),
+                                  callback_query_handler='forcebuy')
 
     @authorized_only
     def _trades(self, update: Update, context: CallbackContext) -> None:
@@ -711,7 +752,10 @@ class Telegram(RPCHandler):
             trades = self._rpc._rpc_performance()
             output = "<b>Performance:</b>\n"
             for i, trade in enumerate(trades):
-                stat_line = (f"{i+1}.\t <code>{trade['pair']}\t{trade['profit']:.2f}% "
+                stat_line = (
+                    f"{i+1}.\t <code>{trade['pair']}\t"
+                    f"{round_coin_value(trade['profit_abs'], self._config['stake_currency'])} "
+                    f"({trade['profit']:.2f}%) "
                     f"({trade['count']})</code>\n")
 
                 if len(output + stat_line) >= MAX_TELEGRAM_MESSAGE_LENGTH:
@@ -750,12 +794,16 @@ class Telegram(RPCHandler):
         Handler for /locks.
         Returns the currently active locks
         """
-        locks = self._rpc._rpc_locks()
+        rpc_locks = self._rpc._rpc_locks()
+        if not rpc_locks['locks']:
+            self._send_msg('No active locks.', parse_mode=ParseMode.HTML)
+
+        for locks in chunks(rpc_locks['locks'], 25):
             message = tabulate([[
                 lock['id'],
                 lock['pair'],
                 lock['lock_end_time'],
-            lock['reason']] for lock in locks['locks']],
+                lock['reason']] for lock in locks],
                 headers=['ID', 'Pair', 'Until', 'Reason'],
                 tablefmt='simple')
             message = f"<pre>{escape(message)}</pre>"
@@ -860,9 +908,17 @@ class Telegram(RPCHandler):
         """
         try:
             edge_pairs = self._rpc._rpc_edge()
-            edge_pairs_tab = tabulate(edge_pairs, headers='keys', tablefmt='simple')
+            if not edge_pairs:
-            message = f'<b>Edge only validated following pairs:</b>\n<pre>{edge_pairs_tab}</pre>'
+                message = '<b>Edge only validated following pairs:</b>'
                 self._send_msg(message, parse_mode=ParseMode.HTML)
+
+            for chunk in chunks(edge_pairs, 25):
+                edge_pairs_tab = tabulate(chunk, headers='keys', tablefmt='simple')
+                message = (f'<b>Edge only validated following pairs:</b>\n'
+                           f'<pre>{edge_pairs_tab}</pre>')
+
+                self._send_msg(message, parse_mode=ParseMode.HTML)
+
         except RPCException as e:
             self._send_msg(str(e))
 
@@ -959,8 +1015,9 @@ class Telegram(RPCHandler):
             f"*Current state:* `{val['state']}`"
         )
 
-    def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN,
+    def _send_inline_msg(self, msg: str, callback_query_handler,
-                  disable_notification: bool = False) -> None:
+                         parse_mode: str = ParseMode.MARKDOWN, disable_notification: bool = False,
+                         keyboard: List[List[InlineKeyboardButton]] = None, ) -> None:
         """
         Send given markdown message
         :param msg: message
@@ -968,7 +1025,29 @@ class Telegram(RPCHandler):
         :param parse_mode: telegram parse mode
         :return: None
         """
-        reply_markup = ReplyKeyboardMarkup(self._keyboard, resize_keyboard=True)
+        if self._current_callback_query_handler:
+            self._updater.dispatcher.remove_handler(self._current_callback_query_handler)
+        self._current_callback_query_handler = self._callback_query_handlers[callback_query_handler]
+        self._updater.dispatcher.add_handler(self._current_callback_query_handler)
+
+        self._send_msg(msg, parse_mode, disable_notification,
+                       cast(List[List[Union[str, KeyboardButton, InlineKeyboardButton]]], keyboard),
+                       reply_markup=InlineKeyboardMarkup)
+
+    def _send_msg(self, msg: str, parse_mode: str = ParseMode.MARKDOWN,
+                  disable_notification: bool = False,
+                  keyboard: List[List[Union[str, KeyboardButton, InlineKeyboardButton]]] = None,
+                  reply_markup=ReplyKeyboardMarkup) -> None:
+        """
+        Send given markdown message
+        :param msg: message
+        :param bot: alternative bot
+        :param parse_mode: telegram parse mode
+        :return: None
+        """
+        if keyboard is None:
+            keyboard = self._keyboard
+        reply_markup = reply_markup(keyboard, resize_keyboard=True)
         try:
             try:
                 self._updater.bot.send_message(

freqtrade/strategy/hyper.py

@@ -5,7 +5,9 @@ This module defines a base class for auto-hyperoptable strategies.
 import logging
 from abc import ABC, abstractmethod
 from contextlib import suppress
-from typing import Any, Dict, Iterator, Optional, Sequence, Tuple, Union
+from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
+
+from freqtrade.optimize.hyperopt_tools import HyperoptTools
 
 
 with suppress(ImportError):
@@ -26,7 +28,8 @@ class BaseParameter(ABC):
     category: Optional[str]
     default: Any
     value: Any
-    hyperopt: bool = False
+    in_space: bool = False
+    name: str
 
     def __init__(self, *, default: Any, space: Optional[str] = None,
                  optimize: bool = True, load: bool = True, **kwargs):
@@ -131,7 +134,7 @@ class IntParameter(NumericParameter):
         Returns a List with 1 item (`value`) in "non-hyperopt" mode, to avoid
         calculating 100ds of indicators.
         """
-        if self.hyperopt:
+        if self.in_space and self.optimize:
             # Scikit-optimize ranges are "inclusive", while python's "range" is exclusive
             return range(self.low, self.high + 1)
         else:
@@ -247,6 +250,10 @@ class HyperStrategyMixin(object):
         """
         Initialize hyperoptable strategy mixin.
         """
+        self.config = config
+        self.ft_buy_params: List[BaseParameter] = []
+        self.ft_sell_params: List[BaseParameter] = []
+
         self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
 
     def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
@@ -257,15 +264,26 @@ class HyperStrategyMixin(object):
         """
         if category not in ('buy', 'sell', None):
             raise OperationalException('Category must be one of: "buy", "sell", None.')
+
+        if category is None:
+            params = self.ft_buy_params + self.ft_sell_params
+        else:
+            params = getattr(self, f"ft_{category}_params")
+
+        for par in params:
+            yield par.name, par
+
+    def _detect_parameters(self, category: str) -> Iterator[Tuple[str, BaseParameter]]:
+        """ Detect all parameters for 'category' """
         for attr_name in dir(self):
             if not attr_name.startswith('__'):  # Ignore internals, not strictly necessary.
                 attr = getattr(self, attr_name)
                 if issubclass(attr.__class__, BaseParameter):
-                    if (category and attr_name.startswith(category + '_')
+                    if (attr_name.startswith(category + '_')
                             and attr.category is not None and attr.category != category):
                         raise OperationalException(
                             f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
-                    if (category is None or category == attr.category or
+                    if (category == attr.category or
                             (attr_name.startswith(category + '_') and attr.category is None)):
                         yield attr_name, attr
 
@@ -283,9 +301,16 @@ class HyperStrategyMixin(object):
         """
         if not params:
             logger.info(f"No params for {space} found, using default values.")
+        param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
+
+        for attr_name, attr in self._detect_parameters(space):
+            attr.name = attr_name
+            attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
+            if not attr.category:
+                attr.category = space
+
+            param_container.append(attr)
 
-        for attr_name, attr in self.enumerate_parameters():
-            attr.hyperopt = hyperopt
             if params and attr_name in params:
                 if attr.load:
                     attr.value = params[attr_name]
@@ -295,3 +320,16 @@ class HyperStrategyMixin(object):
                                    f'Default value "{attr.value}" used.')
             else:
                 logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}')
+
+    def get_params_dict(self):
+        """
+        Returns list of Parameters that are not part of the current optimize job
+        """
+        params = {
+            'buy': {},
+            'sell': {}
+        }
+        for name, p in self.enumerate_parameters():
+            if not p.optimize or not p.in_space:
+                params[p.category][name] = p.value
+        return params

freqtrade/strategy/interface.py

@@ -7,7 +7,7 @@ import warnings
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta, timezone
 from enum import Enum
-from typing import Dict, List, NamedTuple, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 import arrow
 from pandas import DataFrame
@@ -24,6 +24,7 @@ from freqtrade.wallets import Wallets
 
 
 logger = logging.getLogger(__name__)
+CUSTOM_SELL_MAX_LENGTH = 64
 
 
 class SignalType(Enum):
@@ -45,6 +46,7 @@ class SellType(Enum):
     SELL_SIGNAL = "sell_signal"
     FORCE_SELL = "force_sell"
     EMERGENCY_SELL = "emergency_sell"
+    CUSTOM_SELL = "custom_sell"
     NONE = ""
 
     def __str__(self):
@@ -52,12 +54,20 @@ class SellType(Enum):
         return self.value
 
 
-class SellCheckTuple(NamedTuple):
+class SellCheckTuple(object):
     """
     NamedTuple for Sell type + reason
     """
-    sell_flag: bool
     sell_type: SellType
+    sell_reason: str = ''
+
+    def __init__(self, sell_type: SellType, sell_reason: str = ''):
+        self.sell_type = sell_type
+        self.sell_reason = sell_reason or sell_type.value
+
+    @property
+    def sell_flag(self):
+        return self.sell_type != SellType.NONE
 
 
 class IStrategy(ABC, HyperStrategyMixin):
@@ -151,6 +161,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param metadata: Additional information, like the currently traded pair
         :return: a Dataframe with all mandatory indicators for the strategies
         """
+        return dataframe
 
     @abstractmethod
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -160,6 +171,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param metadata: Additional information, like the currently traded pair
         :return: DataFrame with buy column
         """
+        return dataframe
 
     @abstractmethod
     def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -169,6 +181,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param metadata: Additional information, like the currently traded pair
         :return: DataFrame with sell column
         """
+        return dataframe
 
     def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
         """
@@ -216,7 +229,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         pass
 
     def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
-                            time_in_force: str, **kwargs) -> bool:
+                            time_in_force: str, current_time: datetime, **kwargs) -> bool:
         """
         Called right before placing a buy order.
         Timing for this function is critical, so avoid doing heavy computations or
@@ -231,6 +244,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param amount: Amount in target (quote) currency that's going to be traded.
         :param rate: Rate that's going to be used when using limit orders
         :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+        :param current_time: datetime object, containing the current datetime
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return bool: When True is returned, then the buy-order is placed on the exchange.
             False aborts the process
@@ -238,7 +252,8 @@ class IStrategy(ABC, HyperStrategyMixin):
         return True
 
     def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
-                           rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
+                           rate: float, time_in_force: str, sell_reason: str,
+                           current_time: datetime, **kwargs) -> bool:
         """
         Called right before placing a regular sell order.
         Timing for this function is critical, so avoid doing heavy computations or
@@ -257,6 +272,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param sell_reason: Sell reason.
             Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
                            'sell_signal', 'force_sell', 'emergency_sell']
+        :param current_time: datetime object, containing the current datetime
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return bool: When True is returned, then the sell-order is placed on the exchange.
             False aborts the process
@@ -285,6 +301,30 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         return self.stoploss
 
+    def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
+                    current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
+        """
+        Custom sell signal logic indicating that specified position should be sold. Returning a
+        string or True from this method is equal to setting sell signal on a candle at specified
+        time. This method is not called when sell signal is set.
+
+        This method should be overridden to create sell signals that depend on trade parameters. For
+        example you could implement a stoploss relative to candle when trade was opened, or a custom
+        1:2 risk-reward ROI.
+
+        Custom sell reason max length is 64. Exceeding this limit will raise OperationalException.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return: To execute sell, return a string with custom sell reason or True. Otherwise return
+        None or False.
+        """
+        return None
+
     def informative_pairs(self) -> ListPairsWithTimeframes:
         """
         Define additional, informative pair/interval combinations to be cached from the exchange.
@@ -531,12 +571,33 @@ class IStrategy(ABC, HyperStrategyMixin):
                        and self.min_roi_reached(trade=trade, current_profit=current_profit,
                                                 current_time=date))
 
+        sell_signal = SellType.NONE
+        custom_reason = ''
+        # use provided rate in backtesting, not high/low.
+        current_rate = rate
+        current_profit = trade.calc_profit_ratio(current_rate)
+
         if (ask_strategy.get('sell_profit_only', False)
                 and current_profit <= ask_strategy.get('sell_profit_offset', 0)):
             # sell_profit_only and profit doesn't reach the offset - ignore sell signal
-            sell_signal = False
+            pass
+        elif ask_strategy.get('use_sell_signal', True) and not buy:
+            if sell:
+                sell_signal = SellType.SELL_SIGNAL
             else:
-            sell_signal = sell and not buy and ask_strategy.get('use_sell_signal', True)
+                custom_reason = strategy_safe_wrapper(self.custom_sell, default_retval=False)(
+                    pair=trade.pair, trade=trade, current_time=date, current_rate=current_rate,
+                    current_profit=current_profit)
+                if custom_reason:
+                    sell_signal = SellType.CUSTOM_SELL
+                    if isinstance(custom_reason, str):
+                        if len(custom_reason) > CUSTOM_SELL_MAX_LENGTH:
+                            logger.warning(f'Custom sell reason returned from custom_sell is too '
+                                           f'long and was trimmed to {CUSTOM_SELL_MAX_LENGTH} '
+                                           f'characters.')
+                            custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
+                    else:
+                        custom_reason = None
             # TODO: return here if sell-signal should be favored over ROI
 
         # Start evaluations
@@ -545,24 +606,23 @@ class IStrategy(ABC, HyperStrategyMixin):
         # Sell-signal
         # Stoploss
         if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
-            logger.debug(f"{trade.pair} - Required profit reached. sell_flag=True, "
+            logger.debug(f"{trade.pair} - Required profit reached. sell_type=SellType.ROI")
-                         f"sell_type=SellType.ROI")
+            return SellCheckTuple(sell_type=SellType.ROI)
-            return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
 
-        if sell_signal:
+        if sell_signal != SellType.NONE:
-            logger.debug(f"{trade.pair} - Sell signal received. sell_flag=True, "
+            logger.debug(f"{trade.pair} - Sell signal received. "
-                         f"sell_type=SellType.SELL_SIGNAL")
+                         f"sell_type=SellType.{sell_signal.name}" +
-            return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
+                         (f", custom_reason={custom_reason}" if custom_reason else ""))
+            return SellCheckTuple(sell_type=sell_signal, sell_reason=custom_reason)
 
         if stoplossflag.sell_flag:
 
-            logger.debug(f"{trade.pair} - Stoploss hit. sell_flag=True, "
+            logger.debug(f"{trade.pair} - Stoploss hit. sell_type={stoplossflag.sell_type}")
-                         f"sell_type={stoplossflag.sell_type}")
             return stoplossflag
 
         # This one is noisy, commented out...
-        # logger.debug(f"{trade.pair} - No sell signal. sell_flag=False")
+        # logger.debug(f"{trade.pair} - No sell signal.")
-        return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
+        return SellCheckTuple(sell_type=SellType.NONE)
 
     def stop_loss_reached(self, current_rate: float, trade: Trade,
                           current_time: datetime, current_profit: float,
@@ -626,9 +686,9 @@ class IStrategy(ABC, HyperStrategyMixin):
                 logger.debug(f"{trade.pair} - Trailing stop saved "
                              f"{trade.stop_loss - trade.initial_stop_loss:.6f}")
 
-            return SellCheckTuple(sell_flag=True, sell_type=sell_type)
+            return SellCheckTuple(sell_type=sell_type)
 
-        return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
+        return SellCheckTuple(sell_type=SellType.NONE)
 
     def min_roi_reached_entry(self, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:
         """

freqtrade/templates/base_config.json.j2

@@ -9,7 +9,8 @@
     "cancel_open_orders_on_exit": false,
     "unfilledtimeout": {
         "buy": 10,
-        "sell": 30
+        "sell": 30,
+        "unit": "minutes"
     },
     "bid_strategy": {
         "price_side": "bid",

freqtrade/templates/subtemplates/strategy_methods_advanced.j2

@@ -14,8 +14,9 @@ def bot_loop_start(self, **kwargs) -> None:
 
 use_custom_stoploss = True
 
-def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime',
-                    current_profit: float, **kwargs) -> float:
+                    current_rate: float, current_profit: float, dataframe: DataFrame,
+                    **kwargs) -> float:
     """
     Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
     e.g. returning -0.05 would create a stoploss 5% below current_rate.
@@ -31,13 +32,14 @@ def custom_stoploss(self, pair: str, trade: 'Trade', current_time: 'datetime', c
     :param current_time: datetime object, containing the current datetime
     :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
     :param current_profit: Current profit (as ratio), calculated based on current_rate.
+    :param dataframe: Analyzed dataframe for this pair. Can contain future data in backtesting.
     :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
     :return float: New stoploss value, relative to the currentrate
     """
     return self.stoploss
 
 def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
-                        time_in_force: str, **kwargs) -> bool:
+                        time_in_force: str, current_time: 'datetime', **kwargs) -> bool:
     """
     Called right before placing a buy order.
     Timing for this function is critical, so avoid doing heavy computations or
@@ -52,6 +54,7 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
     :param amount: Amount in target (quote) currency that's going to be traded.
     :param rate: Rate that's going to be used when using limit orders
     :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
+    :param current_time: datetime object, containing the current datetime
     :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
     :return bool: When True is returned, then the buy-order is placed on the exchange.
         False aborts the process
@@ -59,7 +62,8 @@ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: f
     return True
 
 def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
-                       rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
+                       rate: float, time_in_force: str, sell_reason: str,
+                       current_time: 'datetime', **kwargs) -> bool:
     """
     Called right before placing a regular sell order.
     Timing for this function is critical, so avoid doing heavy computations or
@@ -78,6 +82,7 @@ def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount:
     :param sell_reason: Sell reason.
         Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
                         'sell_signal', 'force_sell', 'emergency_sell']
+    :param current_time: datetime object, containing the current datetime
     :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
     :return bool: When True is returned, then the sell-order is placed on the exchange.
         False aborts the process

requirements-dev.txt

@@ -4,14 +4,14 @@
 -r requirements-hyperopt.txt
 
 coveralls==3.0.1
-flake8==3.9.1
+flake8==3.9.2
 flake8-type-annotations==0.1.0
-flake8-tidy-imports==4.2.1
+flake8-tidy-imports==4.3.0
 mypy==0.812
-pytest==6.2.3
+pytest==6.2.4
 pytest-asyncio==0.15.1
-pytest-cov==2.11.1
+pytest-cov==2.12.0
-pytest-mock==3.6.0
+pytest-mock==3.6.1
 pytest-random-order==1.0.4
 isort==5.8.0
 

requirements-hyperopt.txt

@@ -3,7 +3,7 @@
 
 # Required for hyperopt
 scipy==1.6.3
-scikit-learn==0.24.1
+scikit-learn==0.24.2
 scikit-optimize==0.8.1
 filelock==3.0.12
 joblib==1.0.1

requirements.txt

@@ -1,23 +1,23 @@
-numpy==1.20.2
+numpy==1.20.3
 pandas==1.2.4
 
-ccxt==1.48.76
+ccxt==1.50.48
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==3.4.7
 aiohttp==3.7.4.post0
-SQLAlchemy==1.4.11
+SQLAlchemy==1.4.15
-python-telegram-bot==13.4.1
+python-telegram-bot==13.5
-arrow==1.0.3
+arrow==1.1.0
-cachetools==4.2.1
+cachetools==4.2.2
 requests==2.25.1
 urllib3==1.26.4
 wrapt==1.12.1
 jsonschema==3.2.0
-TA-Lib==0.4.19
+TA-Lib==0.4.20
-technical==1.2.2
+technical==1.3.0
 tabulate==0.8.9
 pycoingecko==2.0.0
-jinja2==2.11.3
+jinja2==3.0.1
 tables==3.6.1
 blosc==1.10.2
 
@@ -31,10 +31,10 @@ python-rapidjson==1.0
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.63.0
+fastapi==0.65.1
 uvicorn==0.13.4
-pyjwt==2.0.1
+pyjwt==2.1.0
-aiofiles==0.6.0
+aiofiles==0.7.0
 
 # Support for colorized terminal output
 colorama==0.4.4

scripts/rest_client.py

@@ -396,7 +396,7 @@ def main(args):
         sys.exit()
 
     config = load_config(args['config'])
-    url = config.get('api_server', {}).get('server_url', '127.0.0.1')
+    url = config.get('api_server', {}).get('listen_ip_address', '127.0.0.1')
     port = config.get('api_server', {}).get('listen_port', '8080')
     username = config.get('api_server', {}).get('username')
     password = config.get('api_server', {}).get('password')

tests/commands/test_commands.py

@@ -918,10 +918,12 @@ def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
                     captured.out)
 
 
-def test_hyperopt_list(mocker, capsys, caplog, hyperopt_results):
+def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results,
+                       saved_hyperopt_results_legacy):
+    for _ in (saved_hyperopt_results, saved_hyperopt_results_legacy):
         mocker.patch(
             'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
-        MagicMock(return_value=hyperopt_results)
+            MagicMock(return_value=saved_hyperopt_results_legacy)
         )
 
         args = [
@@ -1150,10 +1152,10 @@ def test_hyperopt_list(mocker, capsys, caplog, hyperopt_results):
         f.unlink()
 
 
-def test_hyperopt_show(mocker, capsys, hyperopt_results):
+def test_hyperopt_show(mocker, capsys, saved_hyperopt_results):
     mocker.patch(
         'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
-        MagicMock(return_value=hyperopt_results)
+        MagicMock(return_value=saved_hyperopt_results)
     )
 
     args = [

tests/conftest.py

@@ -3,7 +3,7 @@ import json
 import logging
 import re
 from copy import deepcopy
-from datetime import datetime
+from datetime import datetime, timedelta
 from functools import reduce
 from pathlib import Path
 from unittest.mock import MagicMock, Mock, PropertyMock
@@ -21,6 +21,7 @@ from freqtrade.exchange import Exchange
 from freqtrade.freqtradebot import FreqtradeBot
 from freqtrade.persistence import LocalTrade, Trade, init_db
 from freqtrade.resolvers import ExchangeResolver
+from freqtrade.state import RunMode
 from freqtrade.worker import Worker
 from tests.conftest_trades import (mock_trade_1, mock_trade_2, mock_trade_3, mock_trade_4,
                                    mock_trade_5, mock_trade_6)
@@ -1677,6 +1678,7 @@ def buy_order_fee():
 @pytest.fixture(scope="function")
 def edge_conf(default_conf):
     conf = deepcopy(default_conf)
+    conf['runmode'] = RunMode.DRY_RUN
     conf['max_open_trades'] = -1
     conf['tradable_balance_ratio'] = 0.5
     conf['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
@@ -1778,7 +1780,7 @@ def open_trade():
 
 
 @pytest.fixture
-def hyperopt_results():
+def saved_hyperopt_results_legacy():
     return [
         {
             'loss': 0.4366182531161519,
@@ -1907,3 +1909,136 @@ def hyperopt_results():
             'is_best': False
             }
     ]
+
+
+@pytest.fixture
+def saved_hyperopt_results():
+    return [
+        {
+            'loss': 0.4366182531161519,
+            'params_dict': {
+                'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}},  # noqa: E501
+            'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'holding_avg': timedelta(minutes=3930.0)},  # noqa: E501
+            'results_explanation': '     2 trades. Avg profit  -1.25%. Total profit -0.00125625 BTC (  -2.51Σ%). Avg duration 3930.0 min.',  # noqa: E501
+            'total_profit': -0.00125625,
+            'current_epoch': 1,
+            'is_initial_point': True,
+            'is_best': True
+        }, {
+            'loss': 20.0,
+            'params_dict': {
+                'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 334, 'roi_t2': 683, 'roi_t3': 140, 'roi_p1': 0.06403981740598495, 'roi_p2': 0.055519840060645045, 'roi_p3': 0.3253712811342459, 'stoploss': -0.338070047333259},  # noqa: E501
+            'params_details': {
+                'buy': {'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'},  # noqa: E501
+                'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'},  # noqa: E501
+                'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0},  # noqa: E501
+                'stoploss': {'stoploss': -0.338070047333259}},
+            'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'holding_avg': timedelta(minutes=1200.0)},  # noqa: E501
+            'results_explanation': '     1 trades. Avg profit   0.12%. Total profit  0.00006185 BTC (   0.12Σ%). Avg duration 1200.0 min.',  # noqa: E501
+            'total_profit': 6.185e-05,
+            'current_epoch': 2,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 14.241196856510731,
+            'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}},  # noqa: E501
+            'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)},  # noqa: E501
+            'results_explanation': '   621 trades. Avg profit  -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.',  # noqa: E501
+            'total_profit': -0.13639474,
+            'current_epoch': 3,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 100000,
+            'params_dict': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1402, 'roi_t2': 676, 'roi_t3': 215, 'roi_p1': 0.06264755784937427, 'roi_p2': 0.14258587851894644, 'roi_p3': 0.20671291201040828, 'stoploss': -0.11818343570194478},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}},  # noqa: E501
+            'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()},  # noqa: E501
+            'results_explanation': '     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.',  # noqa: E501
+            'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_best': False
+        }, {
+            'loss': 0.22195522184191518,
+            'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014},   # noqa: E501
+            'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}},  # noqa: E501
+            'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)},  # noqa: E501
+            'results_explanation': '    14 trades. Avg profit  -0.35%. Total profit -0.00248014 BTC (  -4.96Σ%). Avg duration 3402.9 min.',  # noqa: E501
+            'total_profit': -0.002480140000000001,
+            'current_epoch': 5,
+            'is_initial_point': True,
+            'is_best': True
+        }, {
+            'loss': 0.545315889154162,
+            'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}},  # noqa: E501
+            'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'holding_avg': timedelta(minutes=636.9230769230769)},  # noqa: E501
+            'results_explanation': '    39 trades. Avg profit  -0.21%. Total profit -0.00417730 BTC (  -8.35Σ%). Avg duration 636.9 min.',  # noqa: E501
+            'total_profit': -0.0041773,
+            'current_epoch': 6,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 4.713497421432944,
+            'params_dict': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 771, 'roi_t2': 620, 'roi_t3': 145, 'roi_p1': 0.0586919200378493, 'roi_p2': 0.04984118697312542, 'roi_p3': 0.37521058680247044, 'stoploss': -0.14613268022709905},  # noqa: E501
+            'params_details': {
+                'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0},  # noqa: E501
+                'stoploss': {'stoploss': -0.14613268022709905}},  # noqa: E501
+            'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'holding_avg': timedelta(minutes=3140.377358490566)},  # noqa: E501
+            'results_explanation': '   318 trades. Avg profit  -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.',  # noqa: E501
+            'total_profit': -0.06339929,
+            'current_epoch': 7,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 20.0,  # noqa: E501
+            'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}},  # noqa: E501
+            'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'holding_avg': timedelta(minutes=5340.0)},  # noqa: E501
+            'results_explanation': '     1 trades. Avg profit   0.00%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration 5340.0 min.',  # noqa: E501
+            'total_profit': 0.0,
+            'current_epoch': 8,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 2.4731817780991223,
+            'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}},  # noqa: E501
+            'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'holding_avg': timedelta(minutes=6505.676855895196)},  # noqa: E501
+            'results_explanation': '   229 trades. Avg profit  -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.',  # noqa: E501
+            'total_profit': -0.044050070000000004,  # noqa: E501
+            'current_epoch': 9,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': -0.2604606005845212,  # noqa: E501
+            'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}},  # noqa: E501
+            'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'holding_avg': timedelta(minutes=2850.0)},  # noqa: E501
+            'results_explanation': '     4 trades. Avg profit   0.11%. Total profit  0.00021629 BTC (   0.43Σ%). Avg duration 2850.0 min.',  # noqa: E501
+            'total_profit': 0.00021629,
+            'current_epoch': 10,
+            'is_initial_point': True,
+            'is_best': True
+        }, {
+            'loss': 4.876465945994304,  # noqa: E501
+            'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}},  # noqa: E501
+            # New Hyperopt mode!
+            'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'holding_avg': timedelta(minutes=4282.5641025641025)},  # noqa: E501
+            'results_explanation': '   117 trades. Avg profit  -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.',  # noqa: E501
+            'total_profit': -0.07436117,
+            'current_epoch': 11,
+            'is_initial_point': True,
+            'is_best': False
+        }, {
+            'loss': 100000,
+            'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806},  # noqa: E501
+            'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}},  # noqa: E501
+            'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'holding_avg': timedelta()},  # noqa: E501
+            'results_explanation': '     0 trades. Avg profit    nan%. Total profit  0.00000000 BTC (   0.00Σ%). Avg duration   nan min.',  # noqa: E501
+            'total_profit': 0,
+            'current_epoch': 12,
+            'is_initial_point': True,
+            'is_best': False
+            }
+    ]

tests/data/test_dataprovider.py

@@ -246,3 +246,46 @@ def test_get_analyzed_dataframe(mocker, default_conf, ohlcv_history):
     assert dataframe.empty
     assert isinstance(time, datetime)
     assert time == datetime(1970, 1, 1, tzinfo=timezone.utc)
+
+    # Test backtest mode
+    default_conf["runmode"] = RunMode.BACKTEST
+    dp._set_dataframe_max_index(1)
+    dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
+
+    assert len(dataframe) == 1
+
+    dp._set_dataframe_max_index(2)
+    dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
+    assert len(dataframe) == 2
+
+    dp._set_dataframe_max_index(3)
+    dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
+    assert len(dataframe) == 3
+
+    dp._set_dataframe_max_index(500)
+    dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
+    assert len(dataframe) == len(ohlcv_history)
+
+
+def test_no_exchange_mode(default_conf):
+    dp = DataProvider(default_conf, None)
+
+    message = "Exchange is not available to DataProvider."
+
+    with pytest.raises(OperationalException, match=message):
+        dp.refresh([()])
+
+    with pytest.raises(OperationalException, match=message):
+        dp.ohlcv('XRP/USDT', '5m')
+
+    with pytest.raises(OperationalException, match=message):
+        dp.market('XRP/USDT')
+
+    with pytest.raises(OperationalException, match=message):
+        dp.ticker('XRP/USDT')
+
+    with pytest.raises(OperationalException, match=message):
+        dp.orderbook('XRP/USDT', 20)
+
+    with pytest.raises(OperationalException, match=message):
+        dp.available_pairs()

tests/edge/test_edge.py

@@ -344,6 +344,8 @@ def test_edge_process_no_trades(mocker, edge_conf, caplog):
 
 def test_edge_process_no_pairs(mocker, edge_conf, caplog):
     edge_conf['exchange']['pair_whitelist'] = []
+    mocker.patch('freqtrade.freqtradebot.validate_config_consistency')
+
     freqtrade = get_patched_freqtradebot(mocker, edge_conf)
     fee_mock = mocker.patch('freqtrade.exchange.Exchange.get_fee', return_value=0.001)
     mocker.patch('freqtrade.edge.edge_positioning.refresh_data')

tests/exchange/test_exchange.py

@@ -2084,6 +2084,46 @@ def test_cancel_stoploss_order(default_conf, mocker, exchange_name):
                            order_id='_', pair='TKN/BTC')
 
 
+@pytest.mark.parametrize("exchange_name", EXCHANGES)
+def test_cancel_stoploss_order_with_result(default_conf, mocker, exchange_name):
+    default_conf['dry_run'] = False
+    mocker.patch('freqtrade.exchange.Exchange.fetch_stoploss_order', return_value={'for': 123})
+    mocker.patch('freqtrade.exchange.Ftx.fetch_stoploss_order', return_value={'for': 123})
+    exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
+
+    mocker.patch('freqtrade.exchange.Exchange.cancel_stoploss_order',
+                 return_value={'fee': {}, 'status': 'canceled', 'amount': 1234})
+    mocker.patch('freqtrade.exchange.Ftx.cancel_stoploss_order',
+                 return_value={'fee': {}, 'status': 'canceled', 'amount': 1234})
+    co = exchange.cancel_stoploss_order_with_result(order_id='_', pair='TKN/BTC', amount=555)
+    assert co == {'fee': {}, 'status': 'canceled', 'amount': 1234}
+
+    mocker.patch('freqtrade.exchange.Exchange.cancel_stoploss_order',
+                 return_value='canceled')
+    mocker.patch('freqtrade.exchange.Ftx.cancel_stoploss_order',
+                 return_value='canceled')
+    # Fall back to fetch_stoploss_order
+    co = exchange.cancel_stoploss_order_with_result(order_id='_', pair='TKN/BTC', amount=555)
+    assert co == {'for': 123}
+
+    mocker.patch('freqtrade.exchange.Exchange.fetch_stoploss_order',
+                 side_effect=InvalidOrderException(""))
+    mocker.patch('freqtrade.exchange.Ftx.fetch_stoploss_order',
+                 side_effect=InvalidOrderException(""))
+
+    co = exchange.cancel_stoploss_order_with_result(order_id='_', pair='TKN/BTC', amount=555)
+    assert co['amount'] == 555
+    assert co == {'fee': {}, 'status': 'canceled', 'amount': 555, 'info': {}}
+
+    with pytest.raises(InvalidOrderException):
+        mocker.patch('freqtrade.exchange.Exchange.cancel_stoploss_order',
+                     side_effect=InvalidOrderException("Did not find order"))
+        mocker.patch('freqtrade.exchange.Ftx.cancel_stoploss_order',
+                     side_effect=InvalidOrderException("Did not find order"))
+        exchange = get_patched_exchange(mocker, default_conf, id=exchange_name)
+        exchange.cancel_stoploss_order_with_result(order_id='_', pair='TKN/BTC', amount=123)
+
+
 @pytest.mark.parametrize("exchange_name", EXCHANGES)
 def test_fetch_order(default_conf, mocker, exchange_name):
     default_conf['dry_run'] = True

tests/exchange/test_ftx.py

@@ -157,3 +157,26 @@ def test_fetch_stoploss_order(default_conf, mocker):
                            'fetch_stoploss_order', 'fetch_orders',
                            retries=API_FETCH_ORDER_RETRY_COUNT + 1,
                            order_id='_', pair='TKN/BTC')
+
+
+def test_get_order_id(mocker, default_conf):
+    exchange = get_patched_exchange(mocker, default_conf, id='ftx')
+    order = {
+        'type': STOPLOSS_ORDERTYPE,
+        'price': 1500,
+        'id': '1111',
+        'info': {
+            'orderId': '1234'
+        }
+    }
+    assert exchange.get_order_id_conditional(order) == '1234'
+
+    order = {
+        'type': 'limit',
+        'price': 1500,
+        'id': '1111',
+        'info': {
+            'orderId': '1234'
+        }
+    }
+    assert exchange.get_order_id_conditional(order) == '1111'

tests/exchange/test_kraken.py

@@ -90,6 +90,7 @@ def test_get_balances_prod(default_conf, mocker):
         '3ST': balance_item.copy(),
         '4ST': balance_item.copy(),
         'EUR': balance_item.copy(),
+        'timestamp': 123123
     })
     kraken_open_orders = [{'symbol': '1ST/EUR',
                            'type': 'limit',
@@ -138,7 +139,7 @@ def test_get_balances_prod(default_conf, mocker):
     default_conf['dry_run'] = False
     exchange = get_patched_exchange(mocker, default_conf, api_mock, id="kraken")
     balances = exchange.get_balances()
-    assert len(balances) == 5
+    assert len(balances) == 6
 
     assert balances['1ST']['free'] == 9.0
     assert balances['1ST']['total'] == 10.0

tests/optimize/test_backtest_detail.py

@@ -185,7 +185,7 @@ tc11 = BTContainer(data=[
     [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
     [1, 5000, 5050, 4950, 5100, 6172, 0, 0],
     [2, 5100, 5251, 5100, 5100, 6172, 0, 0],
-    [3, 4850, 5050, 4650, 4750, 6172, 0, 0],
+    [3, 5000, 5150, 4650, 4750, 6172, 0, 0],
     [4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
     stop_loss=-0.10, roi={"0": 0.10}, profit_perc=0.019, trailing_stop=True,
     trailing_only_offset_is_reached=True, trailing_stop_positive_offset=0.05,
@@ -440,6 +440,23 @@ tc27 = BTContainer(data=[
     trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=4)]
 )
 
+# Test 28: trailing_stop should raise so candle 3 causes a stoploss
+# Same case than tc11 - but candle 3 "gaps down" - the stoploss will be above the candle,
+# therefore "open" will be used
+# stop-loss: 10%, ROI: 10% (should not apply), stoploss adjusted candle 2
+tc28 = BTContainer(data=[
+    # D   O     H     L     C    V    B  S
+    [0, 5000, 5050, 4950, 5000, 6172, 1, 0],
+    [1, 5000, 5050, 4950, 5100, 6172, 0, 0],
+    [2, 5100, 5251, 5100, 5100, 6172, 0, 0],
+    [3, 4850, 5050, 4650, 4750, 6172, 0, 0],
+    [4, 4750, 4950, 4350, 4750, 6172, 0, 0]],
+    stop_loss=-0.10, roi={"0": 0.10}, profit_perc=-0.03, trailing_stop=True,
+    trailing_only_offset_is_reached=True, trailing_stop_positive_offset=0.05,
+    trailing_stop_positive=0.03,
+    trades=[BTrade(sell_reason=SellType.TRAILING_STOP_LOSS, open_tick=1, close_tick=3)]
+)
+
 TESTS = [
     tc0,
     tc1,
@@ -469,6 +486,7 @@ TESTS = [
     tc25,
     tc26,
     tc27,
+    tc28,
 ]
 
 
@@ -493,6 +511,7 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
     patch_exchange(mocker)
     frame = _build_backtest_dataframe(data.data)
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     backtesting.strategy.advise_buy = lambda a, m: frame
     backtesting.strategy.advise_sell = lambda a, m: frame
     caplog.set_level(logging.DEBUG)
@@ -501,13 +520,14 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
     # Dummy data as we mock the analyze functions
     data_processed = {pair: frame.copy()}
     min_date, max_date = get_timerange({pair: frame})
-    results = backtesting.backtest(
+    result = backtesting.backtest(
         processed=data_processed,
         start_date=min_date,
         end_date=max_date,
         max_open_trades=10,
     )
 
+    results = result['results']
     assert len(results) == len(data.trades)
     assert round(results["profit_ratio"].sum(), 3) == round(data.profit_perc, 3)
 

tests/optimize/test_backtesting.py

@@ -83,6 +83,7 @@ def simple_backtest(config, contour, mocker, testdatadir) -> None:
     patch_exchange(mocker)
     config['timeframe'] = '1m'
     backtesting = Backtesting(config)
+    backtesting._set_strategy(backtesting.strategylist[0])
 
     data = load_data_test(contour, testdatadir)
     processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
@@ -106,6 +107,7 @@ def _make_backtest_conf(mocker, datadir, conf=None, pair='UNITTEST/BTC'):
     data = trim_dictlist(data, -201)
     patch_exchange(mocker)
     backtesting = Backtesting(conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
     min_date, max_date = get_timerange(processed)
     return {
@@ -285,6 +287,7 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
     patch_exchange(mocker)
     get_fee = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5))
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     assert backtesting.config == default_conf
     assert backtesting.timeframe == '5m'
     assert callable(backtesting.strategy.ohlcvdata_to_dataframe)
@@ -315,11 +318,13 @@ def test_data_with_fee(default_conf, mocker, testdatadir) -> None:
 
     fee_mock = mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.5))
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     assert backtesting.fee == 0.1234
     assert fee_mock.call_count == 0
 
     default_conf['fee'] = 0.0
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     assert backtesting.fee == 0.0
     assert fee_mock.call_count == 0
 
@@ -330,6 +335,7 @@ def test_data_to_dataframe_bt(default_conf, mocker, testdatadir) -> None:
     data = history.load_data(testdatadir, '1m', ['UNITTEST/BTC'], timerange=timerange,
                              fill_up_missing=True)
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
     assert len(processed['UNITTEST/BTC']) == 102
 
@@ -361,12 +367,13 @@ def test_backtesting_start(default_conf, mocker, testdatadir, caplog) -> None:
     default_conf['timerange'] = '-1510694220'
 
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     backtesting.strategy.bot_loop_start = MagicMock()
     backtesting.start()
     # check the logs, that will contain the backtest result
     exists = [
         'Backtesting with data from 2017-11-14 21:17:00 '
-        'up to 2017-11-14 22:59:00 (0 days)..'
+        'up to 2017-11-14 22:59:00 (0 days).'
     ]
     for line in exists:
         assert log_has(line, caplog)
@@ -393,6 +400,7 @@ def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) ->
     default_conf['timerange'] = '20180101-20180102'
 
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     with pytest.raises(OperationalException, match='No data found. Terminating.'):
         backtesting.start()
 
@@ -465,6 +473,7 @@ def test_backtest__enter_trade(default_conf, fee, mocker) -> None:
     default_conf['stake_amount'] = 'unlimited'
     default_conf['max_open_trades'] = 2
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     pair = 'UNITTEST/BTC'
     row = [
         pd.Timestamp(year=2020, month=1, day=1, hour=5, minute=0),
@@ -508,19 +517,21 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
     mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
     patch_exchange(mocker)
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     pair = 'UNITTEST/BTC'
     timerange = TimeRange('date', None, 1517227800, 0)
     data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
                              timerange=timerange)
     processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
     min_date, max_date = get_timerange(processed)
-    results = backtesting.backtest(
+    result = backtesting.backtest(
         processed=processed,
         start_date=min_date,
         end_date=max_date,
         max_open_trades=10,
         position_stacking=False,
     )
+    results = result['results']
     assert not results.empty
     assert len(results) == 2
 
@@ -569,6 +580,7 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
     mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
     patch_exchange(mocker)
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
 
     # Run a backtesting for an exiting 1min timeframe
     timerange = TimeRange.parse_timerange('1510688220-1510700340')
@@ -583,13 +595,14 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
         max_open_trades=1,
         position_stacking=False,
     )
-    assert not results.empty
+    assert not results['results'].empty
-    assert len(results) == 1
+    assert len(results['results']) == 1
 
 
 def test_processed(default_conf, mocker, testdatadir) -> None:
     patch_exchange(mocker)
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
 
     dict_of_tickerrows = load_data_test('raise', testdatadir)
     dataframes = backtesting.strategy.ohlcvdata_to_dataframe(dict_of_tickerrows)
@@ -623,7 +636,7 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
     # While buy-signals are unrealistic, running backtesting
     # over and over again should not cause different results
     for [contour, numres] in tests:
-        assert len(simple_backtest(default_conf, contour, mocker, testdatadir)) == numres
+        assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == numres
 
 
 @pytest.mark.parametrize('protections,contour,expected', [
@@ -648,7 +661,7 @@ def test_backtest_pricecontours(default_conf, fee, mocker, testdatadir,
     mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
     # While buy-signals are unrealistic, running backtesting
     # over and over again should not cause different results
-    assert len(simple_backtest(default_conf, contour, mocker, testdatadir)) == expected
+    assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == expected
 
 
 def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
@@ -660,10 +673,11 @@ def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
 
     backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     backtesting.strategy.advise_buy = fun  # Override
     backtesting.strategy.advise_sell = fun  # Override
-    results = backtesting.backtest(**backtest_conf)
+    result = backtesting.backtest(**backtest_conf)
-    assert results.empty
+    assert result['results'].empty
 
 
 def test_backtest_only_sell(mocker, default_conf, testdatadir):
@@ -675,10 +689,11 @@ def test_backtest_only_sell(mocker, default_conf, testdatadir):
 
     backtest_conf = _make_backtest_conf(mocker, conf=default_conf, datadir=testdatadir)
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     backtesting.strategy.advise_buy = fun  # Override
     backtesting.strategy.advise_sell = fun  # Override
-    results = backtesting.backtest(**backtest_conf)
+    result = backtesting.backtest(**backtest_conf)
-    assert results.empty
+    assert result['results'].empty
 
 
 def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
@@ -688,12 +703,14 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
                                         pair='UNITTEST/BTC', datadir=testdatadir)
     default_conf['timeframe'] = '1m'
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     backtesting.strategy.advise_buy = _trend_alternate  # Override
     backtesting.strategy.advise_sell = _trend_alternate  # Override
-    results = backtesting.backtest(**backtest_conf)
+    result = backtesting.backtest(**backtest_conf)
     # 200 candles in backtest data
     # won't buy on first (shifted by 1)
     # 100 buys signals
+    results = result['results']
     assert len(results) == 100
     # One trade was force-closed at the end
     assert len(results.loc[results['is_open']]) == 0
@@ -729,6 +746,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
     default_conf['timeframe'] = '5m'
 
     backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
     backtesting.strategy.advise_buy = _trend_alternate_hold  # Override
     backtesting.strategy.advise_sell = _trend_alternate_hold  # Override
 
@@ -745,9 +763,9 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
     results = backtesting.backtest(**backtest_conf)
 
     # Make sure we have parallel trades
-    assert len(evaluate_result_multi(results, '5m', 2)) > 0
+    assert len(evaluate_result_multi(results['results'], '5m', 2)) > 0
     # make sure we don't have trades with more than configured max_open_trades
-    assert len(evaluate_result_multi(results, '5m', 3)) == 0
+    assert len(evaluate_result_multi(results['results'], '5m', 3)) == 0
 
     backtest_conf = {
         'processed': processed,
@@ -757,7 +775,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
         'position_stacking': False,
     }
     results = backtesting.backtest(**backtest_conf)
-    assert len(evaluate_result_multi(results, '5m', 1)) == 0
+    assert len(evaluate_result_multi(results['results'], '5m', 1)) == 0
 
 
 def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
@@ -789,9 +807,9 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
         'Parameter --timerange detected: 1510694220-1510700340 ...',
         f'Using data directory: {testdatadir} ...',
         'Loading data from 2017-11-14 20:57:00 '
-        'up to 2017-11-14 22:58:00 (0 days)..',
+        'up to 2017-11-14 22:58:00 (0 days).',
         'Backtesting with data from 2017-11-14 21:17:00 '
-        'up to 2017-11-14 22:58:00 (0 days)..',
+        'up to 2017-11-14 22:58:00 (0 days).',
         'Parameter --enable-position-stacking detected ...'
     ]
 
@@ -802,8 +820,20 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
 @pytest.mark.filterwarnings("ignore:deprecated")
 def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
 
+    default_conf['ask_strategy'].update({
+        "use_sell_signal": True,
+        "sell_profit_only": False,
+        "sell_profit_offset": 0.0,
+        "ignore_roi_if_buy_signal": False,
+    })
     patch_exchange(mocker)
-    backtestmock = MagicMock(return_value=pd.DataFrame(columns=BT_DATA_COLUMNS))
+    backtestmock = MagicMock(return_value={
+        'results': pd.DataFrame(columns=BT_DATA_COLUMNS),
+        'config': default_conf,
+        'locks': [],
+        'rejected_signals': 20,
+        'final_balance': 1000,
+        })
     mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                  PropertyMock(return_value=['UNITTEST/BTC']))
     mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
@@ -817,7 +847,7 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
                           text_table_strategy=strattable_mock,
                           generate_pair_metrics=MagicMock(),
                           generate_sell_reason_stats=sell_reason_mock,
-                          generate_strategy_metrics=strat_summary,
+                          generate_strategy_comparison=strat_summary,
                           generate_daily_stats=MagicMock(),
                           )
     patched_configuration_load_config_file(mocker, default_conf)
@@ -851,9 +881,9 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
         'Parameter --timerange detected: 1510694220-1510700340 ...',
         f'Using data directory: {testdatadir} ...',
         'Loading data from 2017-11-14 20:57:00 '
-        'up to 2017-11-14 22:58:00 (0 days)..',
+        'up to 2017-11-14 22:58:00 (0 days).',
         'Backtesting with data from 2017-11-14 21:17:00 '
-        'up to 2017-11-14 22:58:00 (0 days)..',
+        'up to 2017-11-14 22:58:00 (0 days).',
         'Parameter --enable-position-stacking detected ...',
         'Running backtesting for Strategy DefaultStrategy',
         'Running backtesting for Strategy TestStrategyLegacy',
@@ -865,10 +895,14 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
 
 @pytest.mark.filterwarnings("ignore:deprecated")
 def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdatadir, capsys):
-
+    default_conf['ask_strategy'].update({
+        "use_sell_signal": True,
+        "sell_profit_only": False,
+        "sell_profit_offset": 0.0,
+        "ignore_roi_if_buy_signal": False,
+    })
     patch_exchange(mocker)
-    backtestmock = MagicMock(side_effect=[
+    result1 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
-        pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
                             'profit_ratio': [0.0, 0.0],
                             'profit_abs': [0.0, 0.0],
                             'open_date': pd.to_datetime(['2018-01-29 18:40:00',
@@ -882,8 +916,8 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
                             'open_rate': [0.104445, 0.10302485],
                             'close_rate': [0.104969, 0.103541],
                             'sell_reason': [SellType.ROI, SellType.ROI]
-                      }),
+                            })
-        pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
+    result2 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
                             'profit_ratio': [0.03, 0.01, 0.1],
                             'profit_abs': [0.01, 0.02, 0.2],
                             'open_date': pd.to_datetime(['2018-01-29 18:40:00',
@@ -899,7 +933,22 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
                             'open_rate': [0.104445, 0.10302485, 0.122541],
                             'close_rate': [0.104969, 0.103541, 0.123541],
                             'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
-                      }),
+                            })
+    backtestmock = MagicMock(side_effect=[
+        {
+            'results': result1,
+            'config': default_conf,
+            'locks': [],
+            'rejected_signals': 20,
+            'final_balance': 1000,
+        },
+        {
+            'results': result2,
+            'config': default_conf,
+            'locks': [],
+            'rejected_signals': 20,
+            'final_balance': 1000,
+        }
     ])
     mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
                  PropertyMock(return_value=['UNITTEST/BTC']))
@@ -930,9 +979,9 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
         'Parameter --timerange detected: 1510694220-1510700340 ...',
         f'Using data directory: {testdatadir} ...',
         'Loading data from 2017-11-14 20:57:00 '
-        'up to 2017-11-14 22:58:00 (0 days)..',
+        'up to 2017-11-14 22:58:00 (0 days).',
         'Backtesting with data from 2017-11-14 21:17:00 '
-        'up to 2017-11-14 22:58:00 (0 days)..',
+        'up to 2017-11-14 22:58:00 (0 days).',
         'Parameter --enable-position-stacking detected ...',
         'Running backtesting for Strategy DefaultStrategy',
         'Running backtesting for Strategy TestStrategyLegacy',

tests/optimize/test_hyperopt.py

@@ -5,7 +5,7 @@ import re
 from datetime import datetime
 from pathlib import Path
 from typing import Dict, List
-from unittest.mock import MagicMock
+from unittest.mock import ANY, MagicMock
 
 import pandas as pd
 import pytest
@@ -18,10 +18,12 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.optimize.hyperopt import Hyperopt
 from freqtrade.optimize.hyperopt_auto import HyperOptAuto
 from freqtrade.optimize.hyperopt_tools import HyperoptTools
+from freqtrade.optimize.optimize_reports import generate_strategy_stats
 from freqtrade.optimize.space import SKDecimal
 from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
 from freqtrade.state import RunMode
 from freqtrade.strategy.hyper import IntParameter
+from freqtrade.strategy.interface import SellType
 from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
                             patched_configuration_load_config_file)
 
@@ -29,23 +31,7 @@ from .hyperopts.default_hyperopt import DefaultHyperOpt
 
 
 # Functions for recurrent object patching
-def create_results(mocker, hyperopt, testdatadir) -> List[Dict]:
+def create_results() -> List[Dict]:
-    """
-    When creating results, mock the hyperopt so that *by default*
-      - we don't create any pickle'd files in the filesystem
-      - we might have a pickle'd file so make sure that we return
-        false when looking for it
-    """
-    hyperopt.results_file = testdatadir / 'optimize/ut_results.pickle'
-
-    mocker.patch.object(Path, "is_file", MagicMock(return_value=False))
-    stat_mock = MagicMock()
-    stat_mock.st_size = 1
-    mocker.patch.object(Path, "stat", MagicMock(return_value=stat_mock))
-
-    mocker.patch.object(Path, "unlink", MagicMock(return_value=True))
-    mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
-    mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
 
     return [{'loss': 1, 'result': 'foo', 'params': {}, 'is_best': True}]
 
@@ -319,54 +305,49 @@ def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
     assert caplog.record_tuples == []
 
 
-def test_save_results_saves_epochs(mocker, hyperopt, testdatadir, caplog) -> None:
+def test_save_results_saves_epochs(mocker, hyperopt, tmpdir, caplog) -> None:
-    epochs = create_results(mocker, hyperopt, testdatadir)
+    # Test writing to temp dir and reading again
-    mock_dump = mocker.patch('freqtrade.optimize.hyperopt.dump', return_value=None)
+    epochs = create_results()
-    mock_dump_json = mocker.patch('freqtrade.optimize.hyperopt.file_dump_json', return_value=None)
+    hyperopt.results_file = Path(tmpdir / 'ut_results.fthypt')
-    results_file = testdatadir / 'optimize' / 'ut_results.pickle'
 
     caplog.set_level(logging.DEBUG)
 
-    hyperopt.epochs = epochs
+    for epoch in epochs:
-    hyperopt._save_results()
+        hyperopt._save_result(epoch)
-    assert log_has(f"1 epoch saved to '{results_file}'.", caplog)
+    assert log_has(f"1 epoch saved to '{hyperopt.results_file}'.", caplog)
-    mock_dump.assert_called_once()
-    mock_dump_json.assert_called_once()
 
-    hyperopt.epochs = epochs + epochs
+    hyperopt._save_result(epochs[0])
-    hyperopt._save_results()
+    assert log_has(f"2 epochs saved to '{hyperopt.results_file}'.", caplog)
-    assert log_has(f"2 epochs saved to '{results_file}'.", caplog)
+
+    hyperopt_epochs = HyperoptTools.load_previous_results(hyperopt.results_file)
+    assert len(hyperopt_epochs) == 2
 
 
-def test_read_results_returns_epochs(mocker, hyperopt, testdatadir, caplog) -> None:
+def test_load_previous_results(testdatadir, caplog) -> None:
-    epochs = create_results(mocker, hyperopt, testdatadir)
-    mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
-    results_file = testdatadir / 'optimize' / 'ut_results.pickle'
-    hyperopt_epochs = HyperoptTools._read_results(results_file)
-    assert log_has(f"Reading epochs from '{results_file}'", caplog)
-    assert hyperopt_epochs == epochs
-    mock_load.assert_called_once()
 
-
+    results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
-def test_load_previous_results(mocker, hyperopt, testdatadir, caplog) -> None:
-    epochs = create_results(mocker, hyperopt, testdatadir)
-    mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
-    mocker.patch.object(Path, 'is_file', MagicMock(return_value=True))
-    statmock = MagicMock()
-    statmock.st_size = 5
-    # mocker.patch.object(Path, 'stat', MagicMock(return_value=statmock))
-
-    results_file = testdatadir / 'optimize' / 'ut_results.pickle'
 
     hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
 
-    assert hyperopt_epochs == epochs
+    assert len(hyperopt_epochs) == 5
-    mock_load.assert_called_once()
+    assert log_has_re(r"Reading pickled epochs from .*", caplog)
 
-    del epochs[0]['is_best']
+    caplog.clear()
-    mock_load = mocker.patch('freqtrade.optimize.hyperopt_tools.load', return_value=epochs)
 
-    with pytest.raises(OperationalException):
+    # Modern version
+    results_file = testdatadir / 'strategy_SampleStrategy.fthypt'
+
+    hyperopt_epochs = HyperoptTools.load_previous_results(results_file)
+
+    assert len(hyperopt_epochs) == 5
+    assert log_has_re(r"Reading epochs from .*", caplog)
+
+
+def test_load_previous_results2(mocker, testdatadir, caplog) -> None:
+    mocker.patch('freqtrade.optimize.hyperopt_tools.HyperoptTools._read_results_pickle',
+                 return_value=[{'asdf': '222'}])
+    results_file = testdatadir / 'hyperopt_results_SampleStrategy.pickle'
+    with pytest.raises(OperationalException, match=r"The file .* incompatible.*"):
         HyperoptTools.load_previous_results(results_file)
 
 
@@ -384,7 +365,8 @@ def test_roi_table_generation(hyperopt) -> None:
 
 
 def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
-    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
+    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
+    dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
     mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
 
     mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
@@ -423,9 +405,9 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
 
     out, err = capsys.readouterr()
     assert 'Best result:\n\n*    1/1: foo result Objective: 1.00000\n' in out
-    assert dumper.called
+    # Should be called for historical candle data
-    # Should be called twice, once for historical candle data, once to save evaluations
+    assert dumper.call_count == 1
-    assert dumper.call_count == 2
+    assert dumper2.call_count == 1
     assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
     assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
     assert hasattr(hyperopt, "max_open_trades")
@@ -433,18 +415,42 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
     assert hasattr(hyperopt, "position_stacking")
 
 
-def test_format_results(hyperopt):
+def test_hyperopt_format_results(hyperopt):
-    # Test with BTC as stake_currency
+
-    trades = [
+    bt_result = {
-        ('ETH/BTC', 2, 2, 123),
+        'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
-        ('LTC/BTC', 1, 1, 123),
+                                          "UNITTEST/BTC", "UNITTEST/BTC"],
-        ('XPR/BTC', -1, -2, -246)
+                                 "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
-    ]
+                                 "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
-    labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration']
+                                 "open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
-    df = pd.DataFrame.from_records(trades, columns=labels)
+                                               Arrow(2017, 11, 14, 21, 36, 00).datetime,
-    results_metrics = hyperopt._calculate_results_metrics(df)
+                                               Arrow(2017, 11, 14, 22, 12, 00).datetime,
-    results_explanation = hyperopt._format_results_explanation_string(results_metrics)
+                                               Arrow(2017, 11, 14, 22, 44, 00).datetime],
-    total_profit = results_metrics['total_profit']
+                                 "close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 10, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 43, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 58, 00).datetime],
+                                 "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
+                                 "close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
+                                 "trade_duration": [123, 34, 31, 14],
+                                 "is_open": [False, False, False, True],
+                                 "stake_amount": [0.01, 0.01, 0.01, 0.01],
+                                 "sell_reason": [SellType.ROI, SellType.STOP_LOSS,
+                                                 SellType.ROI, SellType.FORCE_SELL]
+                                 }),
+        'config': hyperopt.config,
+        'locks': [],
+        'final_balance': 0.02,
+        'rejected_signals': 2,
+        'backtest_start_time': 1619718665,
+        'backtest_end_time': 1619718665,
+        }
+    results_metrics = generate_strategy_stats({'XRP/BTC': None}, '', bt_result,
+                                              Arrow(2017, 11, 14, 19, 32, 00),
+                                              Arrow(2017, 12, 14, 19, 32, 00), market_change=0)
+
+    results_explanation = HyperoptTools.format_results_explanation_string(results_metrics, 'BTC')
+    total_profit = results_metrics['profit_total_abs']
 
     results = {
         'loss': 0.0,
@@ -458,21 +464,9 @@ def test_format_results(hyperopt):
     }
 
     result = HyperoptTools._format_explanation_string(results, 1)
-    assert result.find(' 66.67%')
+    assert ' 0.71%' in result
-    assert result.find('Total profit 1.00000000 BTC')
+    assert 'Total profit  0.00003100 BTC' in result
-    assert result.find('2.0000Σ %')
+    assert '0:50:00 min' in result
-
-    # Test with EUR as stake_currency
-    trades = [
-        ('ETH/EUR', 2, 2, 123),
-        ('LTC/EUR', 1, 1, 123),
-        ('XPR/EUR', -1, -2, -246)
-    ]
-    df = pd.DataFrame.from_records(trades, columns=labels)
-    results_metrics = hyperopt._calculate_results_metrics(df)
-    results['total_profit'] = results_metrics['total_profit']
-    result = HyperoptTools._format_explanation_string(results, 1)
-    assert result.find('Total profit 1.00000000 EUR')
 
 
 @pytest.mark.parametrize("spaces, expected_results", [
@@ -503,10 +497,10 @@ def test_format_results(hyperopt):
     (['default', 'buy'],
      {'buy': True, 'sell': True, 'roi': True, 'stoploss': True, 'trailing': False}),
 ])
-def test_has_space(hyperopt, spaces, expected_results):
+def test_has_space(hyperopt_conf, spaces, expected_results):
     for s in ['buy', 'sell', 'roi', 'stoploss', 'trailing']:
-        hyperopt.config.update({'spaces': spaces})
+        hyperopt_conf.update({'spaces': spaces})
-        assert hyperopt.has_space(s) == expected_results[s]
+        assert HyperoptTools.has_space(hyperopt_conf, s) == expected_results[s]
 
 
 def test_populate_indicators(hyperopt, testdatadir) -> None:
@@ -577,22 +571,39 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
                           'hyperopt_min_trades': 1,
                           })
 
-    trades = [
+    backtest_result = {
-        ('TRX/BTC', 0.023117, 0.000233, 100)
+        'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
-    ]
+                                          "UNITTEST/BTC", "UNITTEST/BTC"],
-    labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration']
+                                 "profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
-    backtest_result = pd.DataFrame.from_records(trades, columns=labels)
+                                 "profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
+                                 "open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
+                                               Arrow(2017, 11, 14, 21, 36, 00).datetime,
+                                               Arrow(2017, 11, 14, 22, 12, 00).datetime,
+                                               Arrow(2017, 11, 14, 22, 44, 00).datetime],
+                                 "close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 10, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 43, 00).datetime,
+                                                Arrow(2017, 11, 14, 22, 58, 00).datetime],
+                                 "open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
+                                 "close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
+                                 "trade_duration": [123, 34, 31, 14],
+                                 "is_open": [False, False, False, True],
+                                 "stake_amount": [0.01, 0.01, 0.01, 0.01],
+                                 "sell_reason": [SellType.ROI, SellType.STOP_LOSS,
+                                                 SellType.ROI, SellType.FORCE_SELL]
+                                 }),
+        'config': hyperopt_conf,
+        'locks': [],
+        'rejected_signals': 20,
+        'final_balance': 1000,
+    }
 
-    mocker.patch(
+    mocker.patch('freqtrade.optimize.hyperopt.Backtesting.backtest', return_value=backtest_result)
-        'freqtrade.optimize.hyperopt.Backtesting.backtest',
+    mocker.patch('freqtrade.optimize.hyperopt.get_timerange',
-        MagicMock(return_value=backtest_result)
+                 return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
-    )
-    mocker.patch(
-        'freqtrade.optimize.hyperopt.get_timerange',
-        MagicMock(return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
-    )
     patch_exchange(mocker)
-    mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())
+    mocker.patch.object(Path, 'open')
+    mocker.patch('freqtrade.optimize.hyperopt.load', return_value={'XRP/BTC': None})
 
     optimizer_param = {
         'adx-value': 0,
@@ -626,11 +637,11 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
         'trailing_only_offset_is_reached': False,
     }
     response_expected = {
-        'loss': 1.9840569076926293,
+        'loss': 1.9147239021396234,
-        'results_explanation': ('     1 trades. 1/0/0 Wins/Draws/Losses. '
+        'results_explanation': ('     4 trades. 4/0/0 Wins/Draws/Losses. '
-                                'Avg profit   2.31%. Median profit   2.31%. Total profit  '
+                                'Avg profit   0.77%. Median profit   0.71%. Total profit  '
-                                '0.00023300 BTC (   2.31\N{GREEK CAPITAL LETTER SIGMA}%). '
+                                '0.00003100 BTC (   0.00\N{GREEK CAPITAL LETTER SIGMA}%). '
-                                'Avg duration 100.0 min.'
+                                'Avg duration 0:50:00 min.'
                                 ).encode(locale.getpreferredencoding(), 'replace').decode('utf-8'),
         'params_details': {'buy': {'adx-enabled': False,
                                    'adx-value': 0,
@@ -641,10 +652,10 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
                                    'rsi-enabled': False,
                                    'rsi-value': 0,
                                    'trigger': 'macd_cross_signal'},
-                           'roi': {0: 0.12000000000000001,
+                           'roi': {"0": 0.12000000000000001,
-                                   20.0: 0.02,
+                                   "20.0": 0.02,
-                                   50.0: 0.01,
+                                   "50.0": 0.01,
-                                   110.0: 0},
+                                   "110.0": 0},
                            'sell': {'sell-adx-enabled': False,
                                     'sell-adx-value': 0,
                                     'sell-fastd-enabled': True,
@@ -660,21 +671,16 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
                                         'trailing_stop_positive': 0.02,
                                         'trailing_stop_positive_offset': 0.07}},
         'params_dict': optimizer_param,
-        'results_metrics': {'avg_profit': 2.3117,
+        'params_not_optimized': {'buy': {}, 'sell': {}},
-                            'draws': 0,
+        'results_metrics': ANY,
-                            'duration': 100.0,
+        'total_profit': 3.1e-08
-                            'losses': 0,
-                            'winsdrawslosses': '   1    0    0',
-                            'median_profit': 2.3117,
-                            'profit': 2.3117,
-                            'total_profit': 0.000233,
-                            'trade_count': 1,
-                            'wins': 1},
-        'total_profit': 0.00023300
     }
 
     hyperopt = Hyperopt(hyperopt_conf)
-    hyperopt.dimensions = hyperopt.hyperopt_space()
+    hyperopt.min_date = Arrow(2017, 12, 10)
+    hyperopt.max_date = Arrow(2017, 12, 13)
+    hyperopt.init_spaces()
+    hyperopt.dimensions = hyperopt.dimensions
     generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
     assert generate_optimizer_value == response_expected
 
@@ -691,7 +697,8 @@ def test_clean_hyperopt(mocker, hyperopt_conf, caplog):
 
 
 def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
-    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
+    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
+    dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
     mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
 
     mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
@@ -742,13 +749,14 @@ def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
         ':{},"stoploss":null,"trailing_stop":null}'
     )
     assert result_str in out  # noqa: E501
-    assert dumper.called
+    # Should be called for historical candle data
-    # Should be called twice, once for historical candle data, once to save evaluations
+    assert dumper.call_count == 1
-    assert dumper.call_count == 2
+    assert dumper2.call_count == 1
 
 
 def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
-    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
+    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
+    dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
     mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
     mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
                  MagicMock(return_value=(MagicMock(), None)))
@@ -790,13 +798,14 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
 
     out, err = capsys.readouterr()
     assert '{"params":{"mfi-value":null,"sell-mfi-value":null},"minimal_roi":{},"stoploss":null}' in out  # noqa: E501
-    assert dumper.called
+    # Should be called for historical candle data
-    # Should be called twice, once for historical candle data, once to save evaluations
+    assert dumper.call_count == 1
-    assert dumper.call_count == 2
+    assert dumper2.call_count == 1
 
 
 def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
-    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
+    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
+    dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
     mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
     mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
                  MagicMock(return_value=(MagicMock(), None)))
@@ -837,13 +846,14 @@ def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
 
     out, err = capsys.readouterr()
     assert '{"minimal_roi":{},"stoploss":null}' in out
-    assert dumper.called
+
-    # Should be called twice, once for historical candle data, once to save evaluations
+    assert dumper.call_count == 1
-    assert dumper.call_count == 2
+    assert dumper2.call_count == 1
 
 
 def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
-    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
+    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
+    dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
     mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
     mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
                  MagicMock(return_value=(MagicMock(), None)))
@@ -885,9 +895,9 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
 
     out, err = capsys.readouterr()
     assert 'Best result:\n\n*    1/1: foo result Objective: 1.00000\n' in out
-    assert dumper.called
+    assert dumper.call_count == 1
-    # Should be called twice, once for historical candle data, once to save evaluations
+    assert dumper2.call_count == 1
-    assert dumper.call_count == 2
+
     assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
     assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
     assert hasattr(hyperopt, "max_open_trades")
@@ -923,7 +933,8 @@ def test_simplified_interface_all_failed(mocker, hyperopt_conf) -> None:
 
 
 def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
-    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
+    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
+    dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
     mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
     mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
                  MagicMock(return_value=(MagicMock(), None)))
@@ -966,8 +977,8 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
     out, err = capsys.readouterr()
     assert 'Best result:\n\n*    1/1: foo result Objective: 1.00000\n' in out
     assert dumper.called
-    # Should be called twice, once for historical candle data, once to save evaluations
+    assert dumper.call_count == 1
-    assert dumper.call_count == 2
+    assert dumper2.call_count == 1
     assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
     assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
     assert hasattr(hyperopt, "max_open_trades")
@@ -976,7 +987,8 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
 
 
 def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
-    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
+    dumper = mocker.patch('freqtrade.optimize.hyperopt.dump')
+    dumper2 = mocker.patch('freqtrade.optimize.hyperopt.Hyperopt._save_result')
     mocker.patch('freqtrade.optimize.hyperopt.file_dump_json')
     mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
                  MagicMock(return_value=(MagicMock(), None)))
@@ -1019,8 +1031,8 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
     out, err = capsys.readouterr()
     assert 'Best result:\n\n*    1/1: foo result Objective: 1.00000\n' in out
     assert dumper.called
-    # Should be called twice, once for historical candle data, once to save evaluations
+    assert dumper.call_count == 1
-    assert dumper.call_count == 2
+    assert dumper2.call_count == 1
     assert hasattr(hyperopt.backtesting.strategy, "advise_sell")
     assert hasattr(hyperopt.backtesting.strategy, "advise_buy")
     assert hasattr(hyperopt, "max_open_trades")
@@ -1108,7 +1120,7 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmpdir, fee) -> None:
     assert isinstance(hyperopt.custom_hyperopt, HyperOptAuto)
     assert isinstance(hyperopt.backtesting.strategy.buy_rsi, IntParameter)
 
-    assert hyperopt.backtesting.strategy.buy_rsi.hyperopt is True
+    assert hyperopt.backtesting.strategy.buy_rsi.in_space is True
     assert hyperopt.backtesting.strategy.buy_rsi.value == 35
     buy_rsi_range = hyperopt.backtesting.strategy.buy_rsi.range
     assert isinstance(buy_rsi_range, range)
@@ -1133,3 +1145,17 @@ def test_SKDecimal():
     assert space.transform([2.0]) == [200]
     assert space.transform([1.0]) == [100]
     assert space.transform([1.5, 1.6]) == [150, 160]
+
+
+def test___pprint():
+    params = {'buy_std': 1.2, 'buy_rsi': 31, 'buy_enable': True, 'buy_what': 'asdf'}
+    non_params = {'buy_notoptimied': 55}
+
+    x = HyperoptTools._pprint(params, non_params)
+    assert x == """{
+    "buy_std": 1.2,
+    "buy_rsi": 31,
+    "buy_enable": True,
+    "buy_what": "asdf",
+    "buy_notoptimied": 55,  # value loaded from strategy
+}"""

tests/optimize/test_optimize_reports.py

@@ -1,5 +1,6 @@
+import datetime
 import re
-from datetime import datetime, timedelta, timezone
+from datetime import timedelta
 from pathlib import Path
 
 import pandas as pd
@@ -7,14 +8,15 @@ import pytest
 from arrow import Arrow
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import LAST_BT_RESULT_FN
+from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
 from freqtrade.data import history
 from freqtrade.data.btanalysis import get_latest_backtest_filename, load_backtest_data
 from freqtrade.edge import PairInfo
 from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_daily_stats,
                                                  generate_edge_table, generate_pair_metrics,
                                                  generate_sell_reason_stats,
-                                                 generate_strategy_metrics, store_backtest_stats,
+                                                 generate_strategy_comparison,
+                                                 generate_trading_stats, store_backtest_stats,
                                                  text_table_bt_results, text_table_sell_reason,
                                                  text_table_strategy)
 from freqtrade.resolvers.strategy_resolver import StrategyResolver
@@ -26,25 +28,22 @@ def test_text_table_bt_results():
 
     results = pd.DataFrame(
         {
-            'pair': ['ETH/BTC', 'ETH/BTC'],
+            'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
-            'profit_ratio': [0.1, 0.2],
+            'profit_ratio': [0.1, 0.2, -0.05],
-            'profit_abs': [0.2, 0.4],
+            'profit_abs': [0.2, 0.4, -0.1],
-            'trade_duration': [10, 30],
+            'trade_duration': [10, 30, 20],
-            'wins': [2, 0],
-            'draws': [0, 0],
-            'losses': [0, 0]
         }
     )
 
     result_str = (
-        '|    Pair |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |'
+        '|    Pair |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % |'
-        '   Tot Profit % |   Avg Duration |   Wins |   Draws |   Losses |\n'
+        '   Avg Duration |   Win  Draw  Loss  Win% |\n'
-        '|---------+--------+----------------+----------------+------------------+'
+        '|---------+--------+----------------+----------------+------------------+----------------+'
-        '----------------+----------------+--------+---------+----------|\n'
+        '----------------+-------------------------|\n'
-        '| ETH/BTC |      2 |          15.00 |          30.00 |       0.60000000 |'
+        '| ETH/BTC |      3 |           8.33 |          25.00 |       0.50000000 |          12.50 |'
-        '          15.00 |        0:20:00 |      2 |       0 |        0 |\n'
+        '        0:20:00 |     2     0     1  66.7 |\n'
-        '|   TOTAL |      2 |          15.00 |          30.00 |       0.60000000 |'
+        '|   TOTAL |      3 |           8.33 |          25.00 |       0.50000000 |          12.50 |'
-        '          15.00 |        0:20:00 |      2 |       0 |        0 |'
+        '        0:20:00 |     2     0     1  66.7 |'
     )
 
     pair_results = generate_pair_metrics(data={'ETH/BTC': {}}, stake_currency='BTC',
@@ -80,6 +79,7 @@ def test_generate_backtest_stats(default_conf, testdatadir):
         'config': default_conf,
         'locks': [],
         'final_balance': 1000.02,
+        'rejected_signals': 20,
         'backtest_start_time': Arrow.utcnow().int_timestamp,
         'backtest_end_time': Arrow.utcnow().int_timestamp,
         }
@@ -96,8 +96,8 @@ def test_generate_backtest_stats(default_conf, testdatadir):
     assert 'DefStrat' in stats['strategy']
     assert 'strategy_comparison' in stats
     strat_stats = stats['strategy']['DefStrat']
-    assert strat_stats['backtest_start'] == min_date.datetime
+    assert strat_stats['backtest_start'] == min_date.strftime(DATETIME_PRINT_FORMAT)
-    assert strat_stats['backtest_end'] == max_date.datetime
+    assert strat_stats['backtest_end'] == max_date.strftime(DATETIME_PRINT_FORMAT)
     assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
     # Above sample had no loosing trade
     assert strat_stats['max_drawdown'] == 0.0
@@ -127,6 +127,7 @@ def test_generate_backtest_stats(default_conf, testdatadir):
         'config': default_conf,
         'locks': [],
         'final_balance': 1000.02,
+        'rejected_signals': 20,
         'backtest_start_time': Arrow.utcnow().int_timestamp,
         'backtest_end_time': Arrow.utcnow().int_timestamp,
         }
@@ -140,8 +141,8 @@ def test_generate_backtest_stats(default_conf, testdatadir):
     strat_stats = stats['strategy']['DefStrat']
 
     assert strat_stats['max_drawdown'] == 0.013803
-    assert strat_stats['drawdown_start'] == datetime(2017, 11, 14, 22, 10, tzinfo=timezone.utc)
+    assert strat_stats['drawdown_start'] == '2017-11-14 22:10:00'
-    assert strat_stats['drawdown_end'] == datetime(2017, 11, 14, 22, 43, tzinfo=timezone.utc)
+    assert strat_stats['drawdown_end'] == '2017-11-14 22:43:00'
     assert strat_stats['drawdown_end_ts'] == 1510699380000
     assert strat_stats['drawdown_start_ts'] == 1510697400000
     assert strat_stats['pairlist'] == ['UNITTEST/BTC']
@@ -226,8 +227,6 @@ def test_generate_daily_stats(testdatadir):
     assert res['winning_days'] == 14
     assert res['draw_days'] == 4
     assert res['losing_days'] == 3
-    assert res['winner_holding_avg'] == timedelta(seconds=1440)
-    assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
 
     # Select empty dataframe!
     res = generate_daily_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
@@ -238,6 +237,23 @@ def test_generate_daily_stats(testdatadir):
     assert res['losing_days'] == 0
 
 
+def test_generate_trading_stats(testdatadir):
+    filename = testdatadir / "backtest-result_new.json"
+    bt_data = load_backtest_data(filename)
+    res = generate_trading_stats(bt_data)
+    assert isinstance(res, dict)
+    assert res['winner_holding_avg'] == timedelta(seconds=1440)
+    assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
+    assert 'wins' in res
+    assert 'losses' in res
+    assert 'draws' in res
+
+    # Select empty dataframe!
+    res = generate_trading_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
+    assert res['wins'] == 0
+    assert res['losses'] == 0
+
+
 def test_text_table_sell_reason():
 
     results = pd.DataFrame(
@@ -254,14 +270,14 @@ def test_text_table_sell_reason():
     )
 
     result_str = (
-        '|   Sell Reason |   Sells |   Wins |   Draws |   Losses |'
+        '|   Sell Reason |   Sells |   Win  Draws  Loss  Win% |   Avg Profit % |   Cum Profit % |'
-        '   Avg Profit % |   Cum Profit % |   Tot Profit BTC |   Tot Profit % |\n'
+        '   Tot Profit BTC |   Tot Profit % |\n'
-        '|---------------+---------+--------+---------+----------+'
+        '|---------------+---------+--------------------------+----------------+----------------+'
-        '----------------+----------------+------------------+----------------|\n'
+        '------------------+----------------|\n'
-        '|           roi |       2 |      2 |       0 |        0 |'
+        '|           roi |       2 |      2     0     0   100 |             15 |             30 |'
-        '             15 |             30 |              0.6 |             15 |\n'
+        '              0.6 |             15 |\n'
-        '|     stop_loss |       1 |      0 |       0 |        1 |'
+        '|     stop_loss |       1 |      0     0     1     0 |            -10 |            -10 |'
-        '            -10 |            -10 |             -0.2 |             -5 |'
+        '             -0.2 |             -5 |'
     )
 
     sell_reason_stats = generate_sell_reason_stats(max_open_trades=2,
@@ -309,9 +325,12 @@ def test_text_table_strategy(default_conf):
     default_conf['max_open_trades'] = 2
     default_conf['dry_run_wallet'] = 3
     results = {}
+    date = datetime.datetime(year=2020, month=1, day=1, hour=12, minute=30)
+    delta = datetime.timedelta(days=1)
     results['TestStrategy1'] = {'results': pd.DataFrame(
         {
             'pair': ['ETH/BTC', 'ETH/BTC', 'ETH/BTC'],
+            'close_date': [date, date + delta, date + delta * 2],
             'profit_ratio': [0.1, 0.2, 0.3],
             'profit_abs': [0.2, 0.4, 0.5],
             'trade_duration': [10, 30, 10],
@@ -324,6 +343,7 @@ def test_text_table_strategy(default_conf):
     results['TestStrategy2'] = {'results': pd.DataFrame(
         {
             'pair': ['LTC/BTC', 'LTC/BTC', 'LTC/BTC'],
+            'close_date': [date, date + delta, date + delta * 2],
             'profit_ratio': [0.4, 0.2, 0.3],
             'profit_abs': [0.4, 0.4, 0.5],
             'trade_duration': [15, 30, 15],
@@ -335,18 +355,17 @@ def test_text_table_strategy(default_conf):
     ), 'config': default_conf}
 
     result_str = (
-        '|      Strategy |   Buys |   Avg Profit % |   Cum Profit % |   Tot'
+        '|      Strategy |   Buys |   Avg Profit % |   Cum Profit % |   Tot Profit BTC |'
-        ' Profit BTC |   Tot Profit % |   Avg Duration |   Wins |   Draws |   Losses |\n'
+        '   Tot Profit % |   Avg Duration |   Win  Draw  Loss  Win% |              Drawdown |\n'
         '|---------------+--------+----------------+----------------+------------------+'
-        '----------------+----------------+--------+---------+----------|\n'
+        '----------------+----------------+-------------------------+-----------------------|\n'
         '| TestStrategy1 |      3 |          20.00 |          60.00 |       1.10000000 |'
-        '          36.67 |        0:17:00 |      3 |       0 |        0 |\n'
+        '          36.67 |        0:17:00 |     3     0     0   100 | 0.00000000 BTC  0.00% |\n'
         '| TestStrategy2 |      3 |          30.00 |          90.00 |       1.30000000 |'
-        '          43.33 |        0:20:00 |      3 |       0 |        0 |'
+        '          43.33 |        0:20:00 |     3     0     0   100 | 0.00000000 BTC  0.00% |'
     )
 
-    strategy_results = generate_strategy_metrics(all_results=results)
+    strategy_results = generate_strategy_comparison(all_results=results)
-
     assert text_table_strategy(strategy_results, 'BTC') == result_str
 
 

tests/plugins/test_pairlist.py

@@ -7,10 +7,11 @@ import pytest
 
 from freqtrade.constants import AVAILABLE_PAIRLISTS
 from freqtrade.exceptions import OperationalException
+from freqtrade.persistence import Trade
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.plugins.pairlistmanager import PairListManager
 from freqtrade.resolvers import PairListResolver
-from tests.conftest import get_patched_freqtradebot, log_has, log_has_re
+from tests.conftest import get_patched_exchange, get_patched_freqtradebot, log_has, log_has_re
 
 
 @pytest.fixture(scope="function")
@@ -406,6 +407,9 @@ def test_VolumePairList_refresh_empty(mocker, markets_empty, whitelist_conf):
     ([{"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
       {"method": "PriceFilter", "low_price_ratio": 0.02}],
         "USDT", ['ETH/USDT', 'NANO/USDT']),
+    ([{"method": "VolumePairList", "number_assets": 20, "sort_key": "quoteVolume"},
+      {"method": "PriceFilter", "max_value": 0.000001}],
+        "USDT", ['NANO/USDT']),
     ([{"method": "StaticPairList"},
       {"method": "RangeStabilityFilter", "lookback_days": 10,
        "min_rate_of_change": 0.01, "refresh_period": 1440}],
@@ -488,6 +492,8 @@ def test_VolumePairList_whitelist_gen(mocker, whitelist_conf, shitcoinmarkets, t
                                    r'because last price < .*%$', caplog) or
                         log_has_re(r'^Removed .* from whitelist, '
                                    r'because last price > .*%$', caplog) or
+                        log_has_re(r'^Removed .* from whitelist, '
+                                   r'because min value change of .*', caplog) or
                         log_has_re(r"^Removed .* from whitelist, because ticker\['last'\] "
                                    r"is empty.*", caplog))
             if pairlist['method'] == 'VolumePairList':
@@ -512,6 +518,18 @@ def test_PrecisionFilter_error(mocker, whitelist_conf) -> None:
         PairListManager(MagicMock, whitelist_conf)
 
 
+def test_PerformanceFilter_error(mocker, whitelist_conf, caplog) -> None:
+    whitelist_conf['pairlists'] = [{"method": "StaticPairList"}, {"method": "PerformanceFilter"}]
+    if hasattr(Trade, 'query'):
+        del Trade.query
+    mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
+    exchange = get_patched_exchange(mocker, whitelist_conf)
+    pm = PairListManager(exchange, whitelist_conf)
+    pm.refresh_pairlist()
+
+    assert log_has("PerformanceFilter is not available in this mode.", caplog)
+
+
 def test_gen_pair_whitelist_not_supported(mocker, default_conf, tickers) -> None:
     default_conf['pairlists'] = [{'method': 'VolumePairList', 'number_assets': 10}]
 
@@ -787,6 +805,10 @@ def test_spreadfilter_invalid_data(mocker, default_conf, markets, tickers, caplo
      "[{'PriceFilter': 'PriceFilter - Filtering pairs priced below 0.00002000.'}]",
      None
      ),
+    ({"method": "PriceFilter", "max_value": 0.00002000},
+     "[{'PriceFilter': 'PriceFilter - Filtering pairs priced Value above 0.00002000.'}]",
+     None
+     ),
     ({"method": "PriceFilter"},
      "[{'PriceFilter': 'PriceFilter - No price filters configured.'}]",
      None
@@ -803,6 +825,10 @@ def test_spreadfilter_invalid_data(mocker, default_conf, markets, tickers, caplo
      None,
      "PriceFilter requires max_price to be >= 0"
      ),  # OperationalException expected
+    ({"method": "PriceFilter", "max_value": -1.00010000},
+     None,
+     "PriceFilter requires max_value to be >= 0"
+     ),  # OperationalException expected
     ({"method": "RangeStabilityFilter", "lookback_days": 10, "min_rate_of_change": 0.01},
      "[{'RangeStabilityFilter': 'RangeStabilityFilter - Filtering pairs with rate of change below "
      "0.01 over the last days.'}]",

tests/rpc/test_fiat_convert.py

@@ -1,6 +1,7 @@
 # pragma pylint: disable=missing-docstring, too-many-arguments, too-many-ancestors,
 # pragma pylint: disable=protected-access, C0103
 
+import datetime
 from unittest.mock import MagicMock
 
 import pytest
@@ -21,6 +22,12 @@ def test_fiat_convert_is_supported(mocker):
 def test_fiat_convert_find_price(mocker):
     fiat_convert = CryptoToFiatConverter()
 
+    fiat_convert._cryptomap = {}
+    fiat_convert._backoff = 0
+    mocker.patch('freqtrade.rpc.fiat_convert.CryptoToFiatConverter._load_cryptomap',
+                 return_value=None)
+    assert fiat_convert.get_price(crypto_symbol='BTC', fiat_symbol='EUR') == 0.0
+
     with pytest.raises(ValueError, match=r'The fiat ABC is not supported.'):
         fiat_convert._find_price(crypto_symbol='BTC', fiat_symbol='ABC')
 
@@ -115,6 +122,28 @@ def test_fiat_convert_without_network(mocker):
     CryptoToFiatConverter._coingekko = cmc_temp
 
 
+def test_fiat_too_many_requests_response(mocker, caplog):
+    # Because CryptoToFiatConverter is a Singleton we reset the listings
+    req_exception = "429 Too Many Requests"
+    listmock = MagicMock(return_value="{}", side_effect=RequestException(req_exception))
+    mocker.patch.multiple(
+        'freqtrade.rpc.fiat_convert.CoinGeckoAPI',
+        get_coins_list=listmock,
+    )
+    # with pytest.raises(RequestEsxception):
+    fiat_convert = CryptoToFiatConverter()
+    fiat_convert._cryptomap = {}
+    fiat_convert._load_cryptomap()
+
+    length_cryptomap = len(fiat_convert._cryptomap)
+    assert length_cryptomap == 0
+    assert fiat_convert._backoff > datetime.datetime.now().timestamp()
+    assert log_has(
+              'Too many requests for Coingecko API, backing off and trying again later.',
+              caplog
+            )
+
+
 def test_fiat_invalid_response(mocker, caplog):
     # Because CryptoToFiatConverter is a Singleton we reset the listings
     listmock = MagicMock(return_value="{'novalidjson':DEADBEEFf}")

tests/rpc/test_rpc.py

@@ -199,28 +199,31 @@ def test_rpc_status_table(default_conf, ticker, fee, mocker) -> None:
 
     freqtradebot.enter_positions()
 
-    result, headers = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
+    result, headers, fiat_profit_sum = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
     assert "Since" in headers
     assert "Pair" in headers
     assert 'instantly' == result[0][2]
     assert 'ETH/BTC' in result[0][1]
     assert '-0.41%' == result[0][3]
+    assert isnan(fiat_profit_sum)
     # Test with fiatconvert
 
     rpc._fiat_converter = CryptoToFiatConverter()
-    result, headers = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
+    result, headers, fiat_profit_sum = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
     assert "Since" in headers
     assert "Pair" in headers
     assert 'instantly' == result[0][2]
     assert 'ETH/BTC' in result[0][1]
     assert '-0.41% (-0.06)' == result[0][3]
+    assert '-0.06' == f'{fiat_profit_sum:.2f}'
 
     mocker.patch('freqtrade.freqtradebot.FreqtradeBot.get_sell_rate',
                  MagicMock(side_effect=ExchangeError("Pair 'ETH/BTC' not available")))
-    result, headers = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
+    result, headers, fiat_profit_sum = rpc._rpc_status_table(default_conf['stake_currency'], 'USD')
     assert 'instantly' == result[0][2]
     assert 'ETH/BTC' in result[0][1]
     assert 'nan%' == result[0][3]
+    assert isnan(fiat_profit_sum)
 
 
 def test_rpc_daily_profit(default_conf, update, ticker, fee,

tests/rpc/test_rpc_apiserver.py

@@ -710,7 +710,7 @@ def test_api_stats(botclient, mocker, ticker, fee, markets,):
     assert 'draws' in rc.json()['durations']
 
 
-def test_api_performance(botclient, mocker, ticker, fee):
+def test_api_performance(botclient, fee):
     ftbot, client = botclient
     patch_get_signal(ftbot, (True, False))
 
@@ -728,6 +728,7 @@ def test_api_performance(botclient, mocker, ticker, fee):
 
     )
     trade.close_profit = trade.calc_profit_ratio()
+    trade.close_profit_abs = trade.calc_profit()
     Trade.query.session.add(trade)
 
     trade = Trade(
@@ -743,14 +744,16 @@ def test_api_performance(botclient, mocker, ticker, fee):
         close_rate=0.391
     )
     trade.close_profit = trade.calc_profit_ratio()
+    trade.close_profit_abs = trade.calc_profit()
+
     Trade.query.session.add(trade)
     Trade.query.session.flush()
 
     rc = client_get(client, f"{BASE_URI}/performance")
     assert_response(rc)
     assert len(rc.json()) == 2
-    assert rc.json() == [{'count': 1, 'pair': 'LTC/ETH', 'profit': 7.61},
+    assert rc.json() == [{'count': 1, 'pair': 'LTC/ETH', 'profit': 7.61, 'profit_abs': 0.01872279},
-                         {'count': 1, 'pair': 'XRP/ETH', 'profit': -5.57}]
+                         {'count': 1, 'pair': 'XRP/ETH', 'profit': -5.57, 'profit_abs': -0.1150375}]
 
 
 def test_api_status(botclient, mocker, ticker, fee, markets):
@@ -1142,6 +1145,14 @@ def test_api_plot_config(botclient):
     assert_response(rc)
     assert rc.json() == ftbot.strategy.plot_config
     assert isinstance(rc.json()['main_plot'], dict)
+    assert isinstance(rc.json()['subplots'], dict)
+
+    ftbot.strategy.plot_config = {'main_plot': {'sma': {}}}
+    rc = client_get(client, f"{BASE_URI}/plot_config")
+    assert_response(rc)
+
+    assert isinstance(rc.json()['main_plot'], dict)
+    assert isinstance(rc.json()['subplots'], dict)
 
 
 def test_api_strategies(botclient):

tests/rpc/test_rpc_telegram.py

@@ -4,6 +4,7 @@
 
 import re
 from datetime import datetime
+from functools import reduce
 from random import choice, randint
 from string import ascii_uppercase
 from unittest.mock import ANY, MagicMock
@@ -54,6 +55,14 @@ class DummyCls(Telegram):
         raise Exception('test')
 
 
+def get_telegram_testobject_with_inline(mocker, default_conf, mock=True, ftbot=None):
+    inline_msg_mock = MagicMock()
+    telegram, ftbot, msg_mock = get_telegram_testobject(mocker, default_conf)
+    mocker.patch('freqtrade.rpc.telegram.Telegram._send_inline_msg', inline_msg_mock)
+
+    return telegram, ftbot, msg_mock, inline_msg_mock
+
+
 def get_telegram_testobject(mocker, default_conf, mock=True, ftbot=None):
     msg_mock = MagicMock()
     if mock:
@@ -902,6 +911,33 @@ def test_forcebuy_handle_exception(default_conf, update, mocker) -> None:
     assert msg_mock.call_args_list[0][0][0] == 'Forcebuy not enabled.'
 
 
+def test_forcebuy_no_pair(default_conf, update, mocker) -> None:
+    mocker.patch('freqtrade.rpc.rpc.CryptoToFiatConverter._find_price', return_value=15000.0)
+
+    fbuy_mock = MagicMock(return_value=None)
+    mocker.patch('freqtrade.rpc.RPC._rpc_forcebuy', fbuy_mock)
+
+    telegram, freqtradebot, _, inline_msg_mock = get_telegram_testobject_with_inline(mocker,
+                                                                                     default_conf)
+    patch_get_signal(freqtradebot, (True, False))
+
+    context = MagicMock()
+    context.args = []
+    telegram._forcebuy(update=update, context=context)
+
+    assert fbuy_mock.call_count == 0
+    assert inline_msg_mock.call_count == 1
+    assert inline_msg_mock.call_args_list[0][0][0] == 'Which pair?'
+    assert inline_msg_mock.call_args_list[0][1]['callback_query_handler'] == 'forcebuy'
+    keyboard = inline_msg_mock.call_args_list[0][1]['keyboard']
+    assert reduce(lambda acc, x: acc + len(x), keyboard, 0) == 4
+    update = MagicMock()
+    update.callback_query = MagicMock()
+    update.callback_query.data = 'XRP/USDT'
+    telegram._forcebuy_inline(update, None)
+    assert fbuy_mock.call_count == 1
+
+
 def test_performance_handle(default_conf, update, ticker, fee,
                             limit_buy_order, limit_sell_order, mocker) -> None:
 
@@ -929,7 +965,7 @@ def test_performance_handle(default_conf, update, ticker, fee,
     telegram._performance(update=update, context=MagicMock())
     assert msg_mock.call_count == 1
     assert 'Performance' in msg_mock.call_args_list[0][0][0]
-    assert '<code>ETH/BTC\t6.20% (1)</code>' in msg_mock.call_args_list[0][0][0]
+    assert '<code>ETH/BTC\t0.00006217 BTC (6.20%) (1)</code>' in msg_mock.call_args_list[0][0][0]
 
 
 def test_count_handle(default_conf, update, ticker, fee, mocker) -> None:
@@ -969,6 +1005,11 @@ def test_telegram_lock_handle(default_conf, update, ticker, fee, mocker) -> None
     )
     telegram, freqtradebot, msg_mock = get_telegram_testobject(mocker, default_conf)
     patch_get_signal(freqtradebot, (True, False))
+    telegram._locks(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert 'No active locks.' in msg_mock.call_args_list[0][0][0]
+
+    msg_mock.reset_mock()
 
     PairLocks.lock_pair('ETH/BTC', arrow.utcnow().shift(minutes=4).datetime, 'randreason')
     PairLocks.lock_pair('XRP/BTC', arrow.utcnow().shift(minutes=20).datetime, 'deadbeef')
@@ -1102,6 +1143,15 @@ def test_edge_enabled(edge_conf, update, mocker) -> None:
     assert '<b>Edge only validated following pairs:</b>\n<pre>' in msg_mock.call_args_list[0][0][0]
     assert 'Pair      Winrate    Expectancy    Stoploss' in msg_mock.call_args_list[0][0][0]
 
+    msg_mock.reset_mock()
+
+    mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
+        return_value={}))
+    telegram._edge(update=update, context=MagicMock())
+    assert msg_mock.call_count == 1
+    assert '<b>Edge only validated following pairs:</b>' in msg_mock.call_args_list[0][0][0]
+    assert 'Winrate' not in msg_mock.call_args_list[0][0][0]
+
 
 def test_telegram_trades(mocker, update, default_conf, fee):
 

tests/strategy/test_default_strategy.py

@@ -36,9 +36,11 @@ def test_default_strategy(result, fee):
     )
 
     assert strategy.confirm_trade_entry(pair='ETH/BTC', order_type='limit', amount=0.1,
-                                        rate=20000, time_in_force='gtc') is True
+                                        rate=20000, time_in_force='gtc',
+                                        current_time=datetime.utcnow()) is True
     assert strategy.confirm_trade_exit(pair='ETH/BTC', trade=trade, order_type='limit', amount=0.1,
-                                       rate=20000, time_in_force='gtc', sell_reason='roi') is True
+                                       rate=20000, time_in_force='gtc', sell_reason='roi',
+                                       current_time=datetime.utcnow()) is True
 
     assert strategy.custom_stoploss(pair='ETH/BTC', trade=trade, current_time=datetime.now(),
                                     current_rate=20_000, current_profit=0.05) == strategy.stoploss

tests/strategy/test_interface.py

@@ -382,6 +382,50 @@ def test_stop_loss_reached(default_conf, fee, profit, adjusted, expected, traili
     strategy.custom_stoploss = original_stopvalue
 
 
+def test_custom_sell(default_conf, fee, caplog) -> None:
+
+    default_conf.update({'strategy': 'DefaultStrategy'})
+
+    strategy = StrategyResolver.load_strategy(default_conf)
+    trade = Trade(
+        pair='ETH/BTC',
+        stake_amount=0.01,
+        amount=1,
+        open_date=arrow.utcnow().shift(hours=-1).datetime,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        exchange='binance',
+        open_rate=1,
+    )
+
+    now = arrow.utcnow().datetime
+    res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
+
+    assert res.sell_flag is False
+    assert res.sell_type == SellType.NONE
+
+    strategy.custom_sell = MagicMock(return_value=True)
+    res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
+    assert res.sell_flag is True
+    assert res.sell_type == SellType.CUSTOM_SELL
+    assert res.sell_reason == 'custom_sell'
+
+    strategy.custom_sell = MagicMock(return_value='hello world')
+
+    res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
+    assert res.sell_type == SellType.CUSTOM_SELL
+    assert res.sell_flag is True
+    assert res.sell_reason == 'hello world'
+
+    caplog.clear()
+    strategy.custom_sell = MagicMock(return_value='h' * 100)
+    res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
+    assert res.sell_type == SellType.CUSTOM_SELL
+    assert res.sell_flag is True
+    assert res.sell_reason == 'h' * 64
+    assert log_has_re('Custom sell reason returned from custom_sell is too long.*', caplog)
+
+
 def test_analyze_ticker_default(ohlcv_history, mocker, caplog) -> None:
     caplog.set_level(logging.DEBUG)
     ind_mock = MagicMock(side_effect=lambda x, meta: x)
@@ -592,7 +636,7 @@ def test_hyperopt_parameters():
     assert len(list(intpar.range)) == 1
     # Range contains ONLY the default / value.
     assert list(intpar.range) == [intpar.value]
-    intpar.hyperopt = True
+    intpar.in_space = True
 
     assert len(list(intpar.range)) == 6
     assert list(intpar.range) == [0, 1, 2, 3, 4, 5]
@@ -627,4 +671,4 @@ def test_auto_hyperopt_interface(default_conf):
     strategy.sell_rsi = IntParameter([0, 10], default=5, space='buy')
 
     with pytest.raises(OperationalException, match=r"Inconclusive parameter.*"):
-        [x for x in strategy.enumerate_parameters('sell')]
+        [x for x in strategy._detect_parameters('sell')]

tests/test_freqtradebot.py

@@ -1371,7 +1371,8 @@ def test_handle_stoploss_on_exchange_trailing_error(mocker, default_conf, fee, c
     }
     mocker.patch('freqtrade.exchange.Binance.cancel_stoploss_order',
                  side_effect=InvalidOrderException())
-    mocker.patch('freqtrade.exchange.Binance.fetch_stoploss_order', stoploss_order_hanging)
+    mocker.patch('freqtrade.exchange.Binance.fetch_stoploss_order',
+                 return_value=stoploss_order_hanging)
     freqtrade.handle_trailing_stoploss_on_exchange(trade, stoploss_order_hanging)
     assert log_has_re(r"Could not cancel stoploss order abcd for pair ETH/BTC.*", caplog)
 
@@ -1959,7 +1960,7 @@ def test_handle_trade_roi(default_conf, ticker, limit_buy_order_open,
     # if ROI is reached we must sell
     patch_get_signal(freqtrade, value=(False, True))
     assert freqtrade.handle_trade(trade)
-    assert log_has("ETH/BTC - Required profit reached. sell_flag=True, sell_type=SellType.ROI",
+    assert log_has("ETH/BTC - Required profit reached. sell_type=SellType.ROI",
                    caplog)
 
 
@@ -1988,7 +1989,7 @@ def test_handle_trade_use_sell_signal(
 
     patch_get_signal(freqtrade, value=(False, True))
     assert freqtrade.handle_trade(trade)
-    assert log_has("ETH/BTC - Sell signal received. sell_flag=True, sell_type=SellType.SELL_SIGNAL",
+    assert log_has("ETH/BTC - Sell signal received. sell_type=SellType.SELL_SIGNAL",
                    caplog)
 
 
@@ -2430,13 +2431,22 @@ def test_handle_cancel_buy(mocker, caplog, default_conf, limit_buy_order) -> Non
     freqtrade._notify_buy_cancel = MagicMock()
 
     trade = MagicMock()
-    trade.pair = 'LTC/ETH'
+    trade.pair = 'LTC/USDT'
+    trade.open_rate = 200
     limit_buy_order['filled'] = 0.0
     limit_buy_order['status'] = 'open'
     reason = CANCEL_REASON['TIMEOUT']
     assert freqtrade.handle_cancel_buy(trade, limit_buy_order, reason)
     assert cancel_order_mock.call_count == 1
 
+    cancel_order_mock.reset_mock()
+    caplog.clear()
+    limit_buy_order['filled'] = 0.01
+    assert not freqtrade.handle_cancel_buy(trade, limit_buy_order, reason)
+    assert cancel_order_mock.call_count == 0
+    assert log_has_re("Order .* for .* not cancelled, as the filled amount.* unsellable.*", caplog)
+
+    caplog.clear()
     cancel_order_mock.reset_mock()
     limit_buy_order['filled'] = 2
     assert not freqtrade.handle_cancel_buy(trade, limit_buy_order, reason)
@@ -2491,7 +2501,8 @@ def test_handle_cancel_buy_corder_empty(mocker, default_conf, limit_buy_order,
     freqtrade._notify_buy_cancel = MagicMock()
 
     trade = MagicMock()
-    trade.pair = 'LTC/ETH'
+    trade.pair = 'LTC/USDT'
+    trade.open_rate = 200
     limit_buy_order['filled'] = 0.0
     limit_buy_order['status'] = 'open'
     reason = CANCEL_REASON['TIMEOUT']
@@ -2592,14 +2603,16 @@ def test_execute_sell_up(default_conf, ticker, fee, ticker_sell_up, mocker) -> N
         fetch_ticker=ticker_sell_up
     )
     # Prevented sell ...
-    freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'], sell_reason=SellType.ROI)
+    freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'],
+                           sell_reason=SellCheckTuple(sell_type=SellType.ROI))
     assert rpc_mock.call_count == 0
     assert freqtrade.strategy.confirm_trade_exit.call_count == 1
 
     # Repatch with true
     freqtrade.strategy.confirm_trade_exit = MagicMock(return_value=True)
 
-    freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'], sell_reason=SellType.ROI)
+    freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'],
+                           sell_reason=SellCheckTuple(sell_type=SellType.ROI))
     assert freqtrade.strategy.confirm_trade_exit.call_count == 1
 
     assert rpc_mock.call_count == 1
@@ -2651,7 +2664,7 @@ def test_execute_sell_down(default_conf, ticker, fee, ticker_sell_down, mocker)
     )
 
     freqtrade.execute_sell(trade=trade, limit=ticker_sell_down()['bid'],
-                           sell_reason=SellType.STOP_LOSS)
+                           sell_reason=SellCheckTuple(sell_type=SellType.STOP_LOSS))
 
     assert rpc_mock.call_count == 2
     last_msg = rpc_mock.call_args_list[-1][0][0]
@@ -2708,7 +2721,7 @@ def test_execute_sell_down_stoploss_on_exchange_dry_run(default_conf, ticker, fe
 
     trade.stop_loss = 0.00001099 * 0.99
     freqtrade.execute_sell(trade=trade, limit=ticker_sell_down()['bid'],
-                           sell_reason=SellType.STOP_LOSS)
+                           sell_reason=SellCheckTuple(sell_type=SellType.STOP_LOSS))
 
     assert rpc_mock.call_count == 2
     last_msg = rpc_mock.call_args_list[-1][0][0]
@@ -2760,7 +2773,7 @@ def test_execute_sell_sloe_cancel_exception(mocker, default_conf, ticker, fee, c
     trade.stoploss_order_id = "abcd"
 
     freqtrade.execute_sell(trade=trade, limit=1234,
-                           sell_reason=SellType.STOP_LOSS)
+                           sell_reason=SellCheckTuple(sell_type=SellType.STOP_LOSS))
     assert sellmock.call_count == 1
     assert log_has('Could not cancel stoploss order abcd', caplog)
 
@@ -2810,7 +2823,7 @@ def test_execute_sell_with_stoploss_on_exchange(default_conf, ticker, fee, ticke
     )
 
     freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'],
-                           sell_reason=SellType.SELL_SIGNAL)
+                           sell_reason=SellCheckTuple(sell_type=SellType.STOP_LOSS))
 
     trade = Trade.query.first()
     assert trade
@@ -2915,7 +2928,8 @@ def test_execute_sell_market_order(default_conf, ticker, fee,
     )
     freqtrade.config['order_types']['sell'] = 'market'
 
-    freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'], sell_reason=SellType.ROI)
+    freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'],
+                           sell_reason=SellCheckTuple(sell_type=SellType.ROI))
 
     assert not trade.is_open
     assert trade.close_profit == 0.0620716
@@ -2969,8 +2983,9 @@ def test_execute_sell_insufficient_funds_error(default_conf, ticker, fee,
         fetch_ticker=ticker_sell_up
     )
 
+    sell_reason = SellCheckTuple(sell_type=SellType.ROI)
     assert not freqtrade.execute_sell(trade=trade, limit=ticker_sell_up()['bid'],
-                                      sell_reason=SellType.ROI)
+                                      sell_reason=sell_reason)
     assert mock_insuf.call_count == 1
 
 
@@ -3063,7 +3078,7 @@ def test_sell_profit_only_enable_loss(default_conf, limit_buy_order, limit_buy_o
     freqtrade = FreqtradeBot(default_conf)
     patch_get_signal(freqtrade)
     freqtrade.strategy.stop_loss_reached = MagicMock(return_value=SellCheckTuple(
-        sell_flag=False, sell_type=SellType.NONE))
+        sell_type=SellType.NONE))
     freqtrade.enter_positions()
 
     trade = Trade.query.first()
@@ -3212,7 +3227,7 @@ def test_locked_pairs(default_conf, ticker, fee, ticker_sell_down, mocker, caplo
     )
 
     freqtrade.execute_sell(trade=trade, limit=ticker_sell_down()['bid'],
-                           sell_reason=SellType.STOP_LOSS)
+                           sell_reason=SellCheckTuple(sell_type=SellType.STOP_LOSS))
     trade.close(ticker_sell_down()['bid'])
     assert freqtrade.strategy.is_pair_locked(trade.pair)
 

tests/test_integration.py

@@ -51,8 +51,8 @@ def test_may_execute_sell_stoploss_on_exchange_multi(default_conf, ticker, fee,
         side_effect=[stoploss_order_closed, stoploss_order_open, stoploss_order_open])
     # Sell 3rd trade (not called for the first trade)
     should_sell_mock = MagicMock(side_effect=[
-        SellCheckTuple(sell_flag=False, sell_type=SellType.NONE),
+        SellCheckTuple(sell_type=SellType.NONE),
-        SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)]
+        SellCheckTuple(sell_type=SellType.SELL_SIGNAL)]
     )
     cancel_order_mock = MagicMock()
     mocker.patch('freqtrade.exchange.Binance.stoploss', stoploss)
@@ -63,7 +63,7 @@ def test_may_execute_sell_stoploss_on_exchange_multi(default_conf, ticker, fee,
         amount_to_precision=lambda s, x, y: y,
         price_to_precision=lambda s, x, y: y,
         fetch_stoploss_order=stoploss_order_mock,
-        cancel_stoploss_order=cancel_order_mock,
+        cancel_stoploss_order_with_result=cancel_order_mock,
     )
 
     mocker.patch.multiple(
@@ -156,11 +156,11 @@ def test_forcebuy_last_unlimited(default_conf, ticker, fee, limit_buy_order, moc
         _notify_sell=MagicMock(),
     )
     should_sell_mock = MagicMock(side_effect=[
-        SellCheckTuple(sell_flag=False, sell_type=SellType.NONE),
+        SellCheckTuple(sell_type=SellType.NONE),
-        SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL),
+        SellCheckTuple(sell_type=SellType.SELL_SIGNAL),
-        SellCheckTuple(sell_flag=False, sell_type=SellType.NONE),
+        SellCheckTuple(sell_type=SellType.NONE),
-        SellCheckTuple(sell_flag=False, sell_type=SellType.NONE),
+        SellCheckTuple(sell_type=SellType.NONE),
-        SellCheckTuple(sell_flag=None, sell_type=SellType.NONE)]
+        SellCheckTuple(sell_type=SellType.NONE)]
     )
     mocker.patch("freqtrade.strategy.interface.IStrategy.should_sell", should_sell_mock)
 

tests/test_persistence.py

@@ -7,7 +7,7 @@ from unittest.mock import MagicMock
 
 import arrow
 import pytest
-from sqlalchemy import create_engine
+from sqlalchemy import create_engine, inspect
 
 from freqtrade import constants
 from freqtrade.exceptions import DependencyException, OperationalException
@@ -627,6 +627,63 @@ def test_migrate_new(mocker, default_conf, fee, caplog):
     assert orders[1].order_id == 'stop_order_id222'
     assert orders[1].ft_order_side == 'stoploss'
 
+    caplog.clear()
+    # Drop latest column
+    engine.execute("alter table orders rename to orders_bak")
+    inspector = inspect(engine)
+
+    for index in inspector.get_indexes('orders_bak'):
+        engine.execute(f"drop index {index['name']}")
+    # Recreate table
+    engine.execute("""
+        CREATE TABLE orders (
+            id INTEGER NOT NULL,
+            ft_trade_id INTEGER,
+            ft_order_side VARCHAR NOT NULL,
+            ft_pair VARCHAR NOT NULL,
+            ft_is_open BOOLEAN NOT NULL,
+            order_id VARCHAR NOT NULL,
+            status VARCHAR,
+            symbol VARCHAR,
+            order_type VARCHAR,
+            side VARCHAR,
+            price FLOAT,
+            amount FLOAT,
+            filled FLOAT,
+            remaining FLOAT,
+            cost FLOAT,
+            order_date DATETIME,
+            order_filled_date DATETIME,
+            order_update_date DATETIME,
+            PRIMARY KEY (id),
+            CONSTRAINT _order_pair_order_id UNIQUE (ft_pair, order_id),
+            FOREIGN KEY(ft_trade_id) REFERENCES trades (id)
+        )
+        """)
+
+    engine.execute("""
+    insert into orders ( id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
+        symbol, order_type, side, price, amount, filled, remaining, cost, order_date,
+        order_filled_date, order_update_date)
+        select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id, status,
+        symbol, order_type, side, price, amount, filled, remaining, cost, order_date,
+        order_filled_date, order_update_date
+        from orders_bak
+    """)
+
+    # Run init to test migration
+    init_db(default_conf['db_url'], default_conf['dry_run'])
+
+    assert log_has("trying orders_bak1", caplog)
+
+    orders = Order.query.all()
+    assert len(orders) == 2
+    assert orders[0].order_id == 'buy_order'
+    assert orders[0].ft_order_side == 'buy'
+
+    assert orders[1].order_id == 'stop_order_id222'
+    assert orders[1].ft_order_side == 'stoploss'
+
 
 def test_migrate_mid_state(mocker, default_conf, fee, caplog):
     """
@@ -774,11 +831,16 @@ def test_adjust_min_max_rates(fee):
 
 
 @pytest.mark.usefixtures("init_persistence")
-def test_get_open(fee):
+@pytest.mark.parametrize('use_db', [True, False])
+def test_get_open(fee, use_db):
+    Trade.use_db = use_db
+    Trade.reset_trades()
 
-    create_mock_trades(fee)
+    create_mock_trades(fee, use_db)
     assert len(Trade.get_open_trades()) == 4
 
+    Trade.use_db = True
+
 
 @pytest.mark.usefixtures("init_persistence")
 def test_to_json(default_conf, fee):
@@ -1083,6 +1145,13 @@ def test_get_trades_proxy(fee, use_db):
     Trade.use_db = True
 
 
+def test_get_trades_backtest():
+    Trade.use_db = False
+    with pytest.raises(NotImplementedError, match=r"`Trade.get_trades\(\)` not .*"):
+        Trade.get_trades([])
+    Trade.use_db = True
+
+
 @pytest.mark.usefixtures("init_persistence")
 def test_get_overall_performance(fee):
 
@@ -1216,11 +1285,24 @@ def test_Trade_object_idem():
     trade = vars(Trade)
     localtrade = vars(LocalTrade)
 
+    excludes = (
+        'delete',
+        'session',
+        'query',
+        'open_date',
+        'get_best_pair',
+        'get_overall_performance',
+        'total_open_trades_stakes',
+        'get_sold_trades_without_assigned_fees',
+        'get_open_trades_without_assigned_fees',
+        'get_open_order_trades',
+        'get_trades',
+        )
+
     # Parent (LocalTrade) should have the same attributes
     for item in trade:
         # Exclude private attributes and open_date (as it's not assigned a default)
-        if (not item.startswith('_')
+        if (not item.startswith('_') and item not in excludes):
-                and item not in ('delete', 'session', 'query', 'open_date')):
             assert item in localtrade
 
     # Fails if only a column is added without corresponding parent field