Merge branch 'develop' into pr/imxuwang/3799

This commit is contained in:
Matthias 2020-10-22 07:55:48 +02:00
commit 2f91f87ad3
174 changed files with 4148 additions and 1648 deletions

18
.devcontainer/Dockerfile Normal file
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@ -0,0 +1,18 @@
FROM freqtradeorg/freqtrade:develop
# Install dependencies
COPY requirements-dev.txt /freqtrade/
RUN apt-get update \
&& apt-get -y install git sudo vim \
&& 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 \
&& chown ftuser: -R /home/ftuser/
USER ftuser
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

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@ -0,0 +1,44 @@
{
"name": "freqtrade Develop",
"dockerComposeFile": [
"docker-compose.yml"
],
"service": "ft_vscode",
"workspaceFolder": "/freqtrade/",
"settings": {
"terminal.integrated.shell.linux": "/bin/bash",
"editor.insertSpaces": true,
"files.trimTrailingWhitespace": true,
"[markdown]": {
"files.trimTrailingWhitespace": false,
},
"python.pythonPath": "/usr/local/bin/python",
},
// Add the IDs of extensions you want installed when the container is created.
"extensions": [
"ms-python.python",
"ms-python.vscode-pylance",
"davidanson.vscode-markdownlint",
"ms-azuretools.vscode-docker",
],
// Use 'forwardPorts' to make a list of ports inside the container available locally.
// "forwardPorts": [],
// Uncomment the next line if you want start specific services in your Docker Compose config.
// "runServices": [],
// Uncomment the next line if you want to keep your containers running after VS Code shuts down.
// "shutdownAction": "none",
// Uncomment the next line to run commands after the container is created - for example installing curl.
// "postCreateCommand": "sudo apt-get update && apt-get install -y git",
// Uncomment to connect as a non-root user if you've added one. See https://aka.ms/vscode-remote/containers/non-root.
"remoteUser": "ftuser"
}

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@ -0,0 +1,24 @@
---
version: '3'
services:
ft_vscode:
build:
context: ..
dockerfile: ".devcontainer/Dockerfile"
volumes:
# Allow git usage within container
- "/home/${USER}/.ssh:/home/ftuser/.ssh:ro"
- "/home/${USER}/.gitconfig:/home/ftuser/.gitconfig:ro"
- ..:/freqtrade:cached
# Persist bash-history
- freqtrade-vscode-server:/home/ftuser/.vscode-server
- freqtrade-bashhistory:/home/ftuser/commandhistory
# Expose API port
ports:
- "127.0.0.1:8080:8080"
command: /bin/sh -c "while sleep 1000; do :; done"
volumes:
freqtrade-vscode-server:
freqtrade-bashhistory:

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@ -13,3 +13,4 @@ CONTRIBUTING.md
MANIFEST.in
README.md
freqtrade.service
user_data

View File

@ -4,8 +4,8 @@ on:
push:
branches:
- master
- stable
- develop
- github_actions_tests
tags:
release:
types: [published]
@ -19,14 +19,14 @@ jobs:
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ ubuntu-18.04, macos-latest ]
os: [ ubuntu-18.04, ubuntu-20.04, macos-latest ]
python-version: [3.7, 3.8]
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
@ -70,7 +70,7 @@ jobs:
pytest --random-order --cov=freqtrade --cov-config=.coveragerc
- name: Coveralls
if: (startsWith(matrix.os, 'ubuntu') && matrix.python-version == '3.8')
if: (startsWith(matrix.os, 'ubuntu-20') && matrix.python-version == '3.8')
env:
# Coveralls token. Not used as secret due to github not providing secrets to forked repositories
COVERALLS_REPO_TOKEN: 6D1m0xupS3FgutfuGao8keFf9Hc0FpIXu
@ -88,12 +88,16 @@ jobs:
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
flake8
- name: Sort imports (isort)
run: |
isort --check .
- name: Mypy
run: |
mypy freqtrade scripts
@ -121,7 +125,7 @@ jobs:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
@ -150,7 +154,7 @@ jobs:
run: |
cp config.json.example config.json
freqtrade create-userdir --userdir user_data
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --print-all
freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --print-all
- name: Flake8
run: |
@ -172,7 +176,7 @@ jobs:
url: ${{ secrets.SLACK_WEBHOOK }}
docs_check:
runs-on: ubuntu-latest
runs-on: ubuntu-20.04
steps:
- uses: actions/checkout@v2
@ -180,6 +184,17 @@ jobs:
run: |
./tests/test_docs.sh
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.8
- name: Documentation build
run: |
pip install -r docs/requirements-docs.txt
pip install mkdocs
mkdocs build
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
@ -190,18 +205,18 @@ jobs:
url: ${{ secrets.SLACK_WEBHOOK }}
cleanup-prior-runs:
runs-on: ubuntu-latest
runs-on: ubuntu-20.04
steps:
- name: Cleanup previous runs on this branch
uses: rokroskar/workflow-run-cleanup-action@v0.2.2
if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/master' && github.repository == 'freqtrade/freqtrade'"
if: "!startsWith(github.ref, 'refs/tags/') && github.ref != 'refs/heads/stable' && github.repository == 'freqtrade/freqtrade'"
env:
GITHUB_TOKEN: "${{ secrets.GITHUB_TOKEN }}"
# Notify on slack only once - when CI completes (and after deploy) in case it's successfull
notify-complete:
needs: [ build, build_windows, docs_check ]
runs-on: ubuntu-latest
runs-on: ubuntu-20.04
steps:
- name: Slack Notification
uses: homoluctus/slatify@v1.8.0
@ -214,13 +229,13 @@ jobs:
deploy:
needs: [ build, build_windows, docs_check ]
runs-on: ubuntu-18.04
runs-on: ubuntu-20.04
if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
uses: actions/setup-python@v2
with:
python-version: 3.8
@ -236,7 +251,7 @@ jobs:
- name: Publish to PyPI (Test)
uses: pypa/gh-action-pypi-publish@master
if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
if: (github.event_name == 'release')
with:
user: __token__
password: ${{ secrets.pypi_test_password }}
@ -244,7 +259,7 @@ jobs:
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@master
if: (steps.extract_branch.outputs.branch == 'master' || github.event_name == 'release')
if: (github.event_name == 'release')
with:
user: __token__
password: ${{ secrets.pypi_password }}

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@ -2,7 +2,7 @@ name: Update Docker Hub Description
on:
push:
branches:
- master
- stable
jobs:
dockerHubDescription:

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@ -33,7 +33,7 @@ jobs:
- script:
- cp config.json.example config.json
- freqtrade create-userdir --userdir user_data
- freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt
- freqtrade hyperopt --datadir tests/testdata -e 5 --strategy SampleStrategy --hyperopt SampleHyperOpt --hyperopt-loss SharpeHyperOptLossDaily
name: hyperopt
- script: flake8
name: flake8

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@ -8,8 +8,9 @@ Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/
Few pointers for contributions:
- Create your PR against the `develop` branch, not `master`.
- New features need to contain unit tests and must be PEP8 conformant (max-line-length = 100).
- Create your PR against the `develop` branch, not `stable`.
- New features need to contain unit tests, must conform to PEP8 (max-line-length = 100) and should be documented with the introduction PR.
- PR's can be declared as `[WIP]` - which signify Work in Progress Pull Requests (which are not finished).
If you are unsure, discuss the feature on our [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE)
or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
@ -18,7 +19,7 @@ or in a [issue](https://github.com/freqtrade/freqtrade/issues) before a PR.
Best start by reading the [documentation](https://www.freqtrade.io/) to get a feel for what is possible with the bot, or head straight to the [Developer-documentation](https://www.freqtrade.io/en/latest/developer/) (WIP) which should help you getting started.
## Before sending the PR:
## Before sending the PR
### 1. Run unit tests
@ -64,6 +65,14 @@ Guide for installing them is [here](http://flake8.pycqa.org/en/latest/user/using
mypy freqtrade
```
### 4. Ensure all imports are correct
#### Run isort
``` bash
isort .
```
## (Core)-Committer Guide
### Process: Pull Requests
@ -114,6 +123,6 @@ Contributors may be given commit privileges. Preference will be given to those w
1. Access to resources for cross-platform development and testing.
1. Time to devote to the project regularly.
Being a Committer does not grant write permission on `develop` or `master` for security reasons (Users trust Freqtrade with their Exchange API keys).
Being a Committer does not grant write permission on `develop` or `stable` for security reasons (Users trust Freqtrade with their Exchange API keys).
After being Committer for some time, a Committer may be named Core Committer and given full repository access.

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@ -1,4 +1,4 @@
FROM python:3.8.5-slim-buster
FROM python:3.8.6-slim-buster
RUN apt-get update \
&& apt-get -y install curl build-essential libssl-dev sqlite3 \
@ -22,7 +22,8 @@ RUN pip install numpy --no-cache-dir \
# Install and execute
COPY . /freqtrade/
RUN pip install -e . --no-cache-dir
RUN pip install -e . --no-cache-dir \
&& mkdir /freqtrade/user_data/
ENTRYPOINT ["freqtrade"]
# Default to trade mode
CMD [ "trade" ]

View File

@ -55,9 +55,8 @@ Please find the complete documentation on our [website](https://www.freqtrade.io
Freqtrade provides a Linux/macOS script to install all dependencies and help you to configure the bot.
```bash
git clone git@github.com:freqtrade/freqtrade.git
git clone -b develop https://github.com/freqtrade/freqtrade.git
cd freqtrade
git checkout develop
./setup.sh --install
```
@ -111,35 +110,38 @@ optional arguments:
Telegram is not mandatory. However, this is a great way to control your bot. More details and the full command list on our [documentation](https://www.freqtrade.io/en/latest/telegram-usage/)
- `/start`: Starts the trader
- `/stop`: Stops the trader
- `/status [table]`: Lists all open trades
- `/count`: Displays number of open trades
- `/start`: Starts the trader.
- `/stop`: Stops the trader.
- `/stopbuy`: Stop entering new trades.
- `/status [table]`: Lists all open trades.
- `/profit`: Lists cumulative profit from all finished trades
- `/forcesell <trade_id>|all`: Instantly sells the given trade (Ignoring `minimum_roi`).
- `/performance`: Show performance of each finished trade grouped by pair
- `/balance`: Show account balance per currency
- `/daily <n>`: Shows profit or loss per day, over the last n days
- `/help`: Show help message
- `/version`: Show version
- `/balance`: Show account balance per currency.
- `/daily <n>`: Shows profit or loss per day, over the last n days.
- `/help`: Show help message.
- `/version`: Show version.
## Development branches
The project is currently setup in two main branches:
- `develop` - This branch has often new features, but might also cause breaking changes.
- `master` - This branch contains the latest stable release. The bot 'should' be stable on this branch, and is generally well tested.
- `develop` - This branch has often new features, but might also contain breaking changes. We try hard to keep this branch as stable as possible.
- `stable` - This branch contains the latest stable release. This branch is generally well tested.
- `feat/*` - These are feature branches, which are being worked on heavily. Please don't use these unless you want to test a specific feature.
## Support
### Help / Slack
### Help / Slack / Discord
For any questions not covered by the documentation or for further
information about the bot, we encourage you to join our slack channel.
For any questions not covered by the documentation or for further information about the bot, we encourage you to join our slack channel.
- [Click here to join Slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE).
Alternatively, check out the newly created [discord server](https://discord.gg/MA9v74M).
*Note*: Since the discord server is relatively new, answers to questions might be slightly delayed as currently the user base quite small.
### [Bugs / Issues](https://github.com/freqtrade/freqtrade/issues?q=is%3Aissue)
If you discover a bug in the bot, please
@ -166,18 +168,18 @@ Please read our
[Contributing document](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
to understand the requirements before sending your pull-requests.
Coding is not a neccessity to contribute - maybe start with improving our documentation?
Coding is not a necessity to contribute - maybe start with improving our documentation?
Issues labeled [good first issue](https://github.com/freqtrade/freqtrade/labels/good%20first%20issue) can be good first contributions, and will help get you familiar with the codebase.
**Note** before starting any major new feature work, *please open an issue describing what you are planning to do* or talk to us on [Slack](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE). This will ensure that interested parties can give valuable feedback on the feature, and let others know that you are working on it.
**Important:** Always create your PR against the `develop` branch, not `master`.
**Important:** Always create your PR against the `develop` branch, not `stable`.
## Requirements
### Uptodate clock
### Up-to-date clock
The clock must be accurate, syncronized to a NTP server very frequently to avoid problems with communication to the exchanges.
The clock must be accurate, synchronized to a NTP server very frequently to avoid problems with communication to the exchanges.
### Min hardware required

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@ -7,10 +7,10 @@ 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-0.4.18-cp37-cp37m-win_amd64.whl
pip install build_helpers\TA_Lib-0.4.19-cp37-cp37m-win_amd64.whl
}
if ($pyv -eq '3.8') {
pip install build_helpers\TA_Lib-0.4.18-cp38-cp38-win_amd64.whl
pip install build_helpers\TA_Lib-0.4.19-cp38-cp38-win_amd64.whl
}
pip install -r requirements-dev.txt

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@ -2,6 +2,7 @@
# Replace / with _ to create a valid tag
TAG=$(echo "${BRANCH_NAME}" | sed -e "s/\//_/g")
TAG_PLOT=${TAG}_plot
echo "Running for ${TAG}"
# Add commit and commit_message to docker container
@ -16,6 +17,12 @@ else
docker pull ${IMAGE_NAME}:${TAG}
docker build --cache-from ${IMAGE_NAME}:${TAG} -t freqtrade:${TAG} .
fi
# Tag image for upload and next build step
docker tag freqtrade:$TAG ${IMAGE_NAME}:$TAG
docker build --cache-from freqtrade:${TAG} --build-arg sourceimage=${TAG} -t freqtrade:${TAG_PLOT} -f docker/Dockerfile.plot .
docker tag freqtrade:$TAG_PLOT ${IMAGE_NAME}:$TAG_PLOT
if [ $? -ne 0 ]; then
echo "failed building image"
@ -30,8 +37,6 @@ if [ $? -ne 0 ]; then
return 1
fi
# Tag image for upload
docker tag freqtrade:$TAG ${IMAGE_NAME}:$TAG
if [ $? -ne 0 ]; then
echo "failed tagging image"
return 1

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@ -5,15 +5,15 @@
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"timeframe": "5m",
"dry_run": false,
"dry_run": true,
"cancel_open_orders_on_exit": false,
"unfilledtimeout": {
"buy": 10,
"sell": 30
},
"bid_strategy": {
"ask_last_balance": 0.0,
"use_order_book": false,
"ask_last_balance": 0.0,
"order_book_top": 1,
"check_depth_of_market": {
"enabled": false,

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@ -7,7 +7,7 @@
"amount_reserve_percent": 0.05,
"amend_last_stake_amount": false,
"last_stake_amount_min_ratio": 0.5,
"dry_run": false,
"dry_run": true,
"cancel_open_orders_on_exit": false,
"timeframe": "5m",
"trailing_stop": false,
@ -116,7 +116,16 @@
"telegram": {
"enabled": true,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
"chat_id": "your_telegram_chat_id",
"notification_settings": {
"status": "on",
"warning": "on",
"startup": "on",
"buy": "on",
"sell": "on",
"buy_cancel": "on",
"sell_cancel": "on"
}
},
"api_server": {
"enabled": false,

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@ -27,12 +27,11 @@
"use_sell_signal": true,
"sell_profit_only": false,
"ignore_roi_if_buy_signal": false
},
"exchange": {
"name": "kraken",
"key": "",
"secret": "",
"key": "your_exchange_key",
"secret": "your_exchange_key",
"ccxt_config": {"enableRateLimit": true},
"ccxt_async_config": {
"enableRateLimit": true,

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@ -1,20 +0,0 @@
---
version: '3'
services:
freqtrade_develop:
build:
context: .
dockerfile: "./Dockerfile.develop"
volumes:
- ".:/freqtrade"
entrypoint:
- "freqtrade"
freqtrade_bash:
build:
context: .
dockerfile: "./Dockerfile.develop"
volumes:
- ".:/freqtrade"
entrypoint:
- "/bin/bash"

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@ -2,8 +2,10 @@
version: '3'
services:
freqtrade:
image: freqtradeorg/freqtrade:master
image: freqtradeorg/freqtrade:stable
# image: freqtradeorg/freqtrade:develop
# Use plotting image
# image: freqtradeorg/freqtrade:develop_plot
# Build step - only needed when additional dependencies are needed
# build:
# context: .

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@ -2,6 +2,7 @@ FROM freqtradeorg/freqtrade:develop
# Install dependencies
COPY requirements-dev.txt /freqtrade/
RUN pip install numpy --no-cache-dir \
&& pip install -r requirements-dev.txt --no-cache-dir

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@ -0,0 +1,7 @@
FROM freqtradeorg/freqtrade:develop_plot
RUN pip install jupyterlab --no-cache-dir
# Empty the ENTRYPOINT to allow all commands
ENTRYPOINT []

7
docker/Dockerfile.plot Normal file
View File

@ -0,0 +1,7 @@
ARG sourceimage=develop
FROM freqtradeorg/freqtrade:${sourceimage}
# Install dependencies
COPY requirements-plot.txt /freqtrade/
RUN pip install -r requirements-plot.txt --no-cache-dir

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@ -0,0 +1,16 @@
---
version: '3'
services:
ft_jupyterlab:
build:
context: ..
dockerfile: docker/Dockerfile.jupyter
restart: unless-stopped
container_name: freqtrade
ports:
- "127.0.0.1:8888:8888"
volumes:
- "./user_data:/freqtrade/user_data"
# Default command used when running `docker compose up`
command: >
jupyter lab --port=8888 --ip 0.0.0.0 --allow-root

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@ -27,9 +27,9 @@ class MyAwesomeHyperOpt2(MyAwesomeHyperOpt):
and then quickly switch between hyperopt classes, running optimization process with hyperopt class you need in each particular case:
```
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt ...
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
or
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 ...
$ freqtrade hyperopt --hyperopt MyAwesomeHyperOpt2 --hyperopt-loss SharpeHyperOptLossDaily --strategy MyAwesomeStrategy ...
```
## Creating and using a custom loss function

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@ -303,7 +303,7 @@ usage: freqtrade hyperopt [-h] [-v] [--logfile FILE] [-V] [-c PATH] [-d PATH]
[--spaces {all,buy,sell,roi,stoploss,trailing,default} [{all,buy,sell,roi,stoploss,trailing,default} ...]]
[--dmmp] [--print-all] [--no-color] [--print-json]
[-j JOBS] [--random-state INT] [--min-trades INT]
[--continue] [--hyperopt-loss NAME]
[--hyperopt-loss NAME]
optional arguments:
-h, --help show this help message and exit
@ -349,18 +349,14 @@ optional arguments:
reproducible hyperopt results.
--min-trades INT Set minimal desired number of trades for evaluations
in the hyperopt optimization path (default: 1).
--continue Continue hyperopt from previous runs. By default,
temporary files will be removed and hyperopt will
start from scratch.
--hyperopt-loss NAME Specify the class name of the hyperopt loss function
class (IHyperOptLoss). Different functions can
generate completely different results, since the
target for optimization is different. Built-in
Hyperopt-loss-functions are: DefaultHyperOptLoss,
Hyperopt-loss-functions are: ShortTradeDurHyperOptLoss,
OnlyProfitHyperOptLoss, SharpeHyperOptLoss,
SharpeHyperOptLossDaily, SortinoHyperOptLoss,
SortinoHyperOptLossDaily.(default:
`DefaultHyperOptLoss`).
SortinoHyperOptLossDaily.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).

View File

@ -375,7 +375,7 @@ Freqtrade is based on [CCXT library](https://github.com/ccxt/ccxt) that supports
exchange markets and trading APIs. The complete up-to-date list can be found in the
[CCXT repo homepage](https://github.com/ccxt/ccxt/tree/master/python).
However, the bot was tested by the development team with only Bittrex, Binance and Kraken,
so the these are the only officially supported exhanges:
so the these are the only officially supported exchanges:
- [Bittrex](https://bittrex.com/): "bittrex"
- [Binance](https://www.binance.com/): "binance"
@ -574,144 +574,7 @@ Assuming both buy and sell are using market orders, a configuration similar to t
```
Obviously, if only one side is using limit orders, different pricing combinations can be used.
## Pairlists and Pairlist Handlers
Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) Pairlist Handler).
Additionaly, [`AgeFilter`](#agefilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter) and [`SpreadFilter`](#spreadfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You should always configure either `StaticPairList` or `VolumePairList` as the starting Pairlist Handler.
Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
### Available Pairlist Handlers
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
* [`VolumePairList`](#volume-pair-list)
* [`AgeFilter`](#agefilter)
* [`PrecisionFilter`](#precisionfilter)
* [`PriceFilter`](#pricefilter)
* [`ShuffleFilter`](#shufflefilter)
* [`SpreadFilter`](#spreadfilter)
!!! Tip "Testing pairlists"
Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility subcommand to test your configuration quickly.
#### Static Pair List
By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration.
It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
```json
"pairlists": [
{"method": "StaticPairList"}
],
```
#### Volume Pair List
`VolumePairList` employs sorting/filtering of pairs by their trading volume. It selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
When used on the leading position of the chain of Pairlist Handlers, it does not consider `pair_whitelist` configuration setting, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
`VolumePairList` is based on the ticker data from exchange, as reported by the ccxt library:
* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
```json
"pairlists": [{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"refresh_period": 1800,
}],
```
#### AgeFilter
Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`).
When pairs are first listed on an exchange they can suffer huge price drops and volatility
in the first few days while the pair goes through its price-discovery period. Bots can often
be caught out buying before the pair has finished dropping in price.
This filter allows freqtrade to ignore pairs until they have been listed for at least `min_days_listed` days.
#### PrecisionFilter
Filters low-value coins which would not allow setting stoplosses.
#### PriceFilter
The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
* `min_price`
* `max_price`
* `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.
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 `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.
For `PriceFiler` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
Calculation example:
Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 - one price step above would be 0.00000012, which is ~9% higher than the previous price value. You may filter out this pair by using PriceFilter with `low_price_ratio` set to 0.09 (9%) or with `min_price` set to 0.00000011, correspondingly.
!!! Warning "Low priced pairs"
Low priced pairs with high "1 pip movements" are dangerous since they are often illiquid and it may also be impossible to place the desired stoploss, which can often result in high losses since price needs to be rounded to the next tradable price - so instead of having a stoploss of -5%, you could end up with a stoploss of -9% simply due to price rounding.
#### ShuffleFilter
Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
!!! Tip
You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order.
#### SpreadFilter
Removes pairs that have a difference between asks and bids above the specified ratio, `max_spread_ratio` (defaults to `0.005`).
Example:
If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027, the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` and this pair will be filtered out.
### Full example of Pairlist Handlers
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies both [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 priceunit is > 1%. Then the `SpreadFilter` is applied and pairs are finally shuffled with the random seed set to some predefined value.
```json
"exchange": {
"pair_whitelist": [],
"pair_blacklist": ["BNB/BTC"]
},
"pairlists": [
{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
},
{"method": "AgeFilter", "min_days_listed": 10},
{"method": "PrecisionFilter"},
{"method": "PriceFilter", "low_price_ratio": 0.01},
{"method": "SpreadFilter", "max_spread_ratio": 0.005},
{"method": "ShuffleFilter", "seed": 42}
],
```
--8<-- "includes/pairlists.md"
## Switch to Dry-run mode

View File

@ -1,12 +1,22 @@
# Analyzing bot data with Jupyter notebooks
You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/`.
You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
## Pro tips
## Quick start with docker
Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command: `docker-compose -f docker/docker-compose-jupyter.yml up`
This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
Please use the link that's printed in the console after startup for simplified login.
For more information, Please visit the [Data analysis with Docker](docker_quickstart.md#data-analayis-using-docker-compose) section.
### Pro tips
* See [jupyter.org](https://jupyter.org/documentation) for usage instructions.
* Don't forget to start a Jupyter notebook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
* Copy the example notebook before use so your changes don't get clobbered with the next freqtrade update.
* Copy the example notebook before use so your changes don't get overwritten with the next freqtrade update.
### Using virtual environment with system-wide Jupyter installation
@ -28,10 +38,8 @@ ipython kernel install --user --name=freqtrade
!!! Note
This section is provided for completeness, the Freqtrade Team won't provide full support for problems with this setup and will recommend to install Jupyter in the virtual environment directly, as that is the easiest way to get jupyter notebooks up and running. For help with this setup please refer to the [Project Jupyter](https://jupyter.org/) [documentation](https://jupyter.org/documentation) or [help channels](https://jupyter.org/community).
## Fine print
Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
!!! Warning
Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
## Recommended workflow

View File

@ -8,9 +8,11 @@ If no additional parameter is specified, freqtrade will download data for `"1m"`
Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
Otherwise `--exchange` becomes mandatory.
You can use a relative timerange (`--days 20`) or an absolute starting point (`--timerange 20200101`). For incremental downloads, the relative approach should be used.
!!! Tip "Tip: Updating existing data"
If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
Be careful though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.
### Usage
@ -18,9 +20,9 @@ Otherwise `--exchange` becomes mandatory.
usage: freqtrade download-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH]
[-p PAIRS [PAIRS ...]] [--pairs-file FILE]
[--days INT] [--dl-trades]
[--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
[--days INT] [--timerange TIMERANGE]
[--dl-trades] [--exchange EXCHANGE]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
[--erase]
[--data-format-ohlcv {json,jsongz,hdf5}]
[--data-format-trades {json,jsongz,hdf5}]
@ -32,12 +34,14 @@ optional arguments:
separated.
--pairs-file FILE File containing a list of pairs to download.
--days INT Download data for given number of days.
--timerange TIMERANGE
Specify what timerange of data to use.
--dl-trades Download trades instead of OHLCV data. The bot will
resample trades to the desired timeframe as specified
as --timeframes/-t.
--exchange EXCHANGE Exchange name (default: `bittrex`). Only valid if no
config is provided.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
--erase Clean all existing data for the selected
@ -67,6 +71,11 @@ Common arguments:
```
!!! Note "Startup period"
`download-data` is a strategy-independent command. The idea is to download a big chunk of data once, and then iteratively increase the amount of data stored.
For that reason, `download-data` does not care about the "startup-period" defined in a strategy. It's up to the user to download additional days if the backtest should start at a specific point in time (while respecting startup period).
### Data format
Freqtrade currently supports 3 data-formats for both OHLCV and trades data:
@ -100,7 +109,7 @@ usage: freqtrade convert-data [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-p PAIRS [PAIRS ...]] --format-from
{json,jsongz,hdf5} --format-to
{json,jsongz,hdf5} [--erase]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]]
[-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]]
optional arguments:
-h, --help show this help message and exit
@ -113,7 +122,7 @@ optional arguments:
Destination format for data conversion.
--erase Clean all existing data for the selected
exchange/pairs/timeframes.
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w} ...]
-t {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...], --timeframes {1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} [{1m,3m,5m,15m,30m,1h,2h,4h,6h,8h,12h,1d,3d,1w,2w,1M,1y} ...]
Specify which tickers to download. Space-separated
list. Default: `1m 5m`.
@ -286,6 +295,7 @@ This will download historical candle (OHLCV) data for all the currency pairs you
- To change the exchange used to download the historical data from, please use a different configuration file (you'll probably need to adjust rate limits etc.)
- To use `pairs.json` from some other directory, use `--pairs-file some_other_dir/pairs.json`.
- To download historical candle (OHLCV) data for only 10 days, use `--days 10` (defaults to 30 days).
- To download historical candle (OHLCV) data from a fixed starting point, use `--timerange 20200101-` - which will download all data from January 1st, 2020. Eventually set end dates are ignored.
- Use `--timeframes` to specify what timeframe download the historical candle (OHLCV) data for. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute data.
- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.

View File

@ -21,11 +21,24 @@ This will spin up a local server (usually on port 8000) so you can see if everyt
## Developer setup
To configure a development environment, best use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
Alternatively (if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -e .[all]`.
To configure a development environment, you can either use the provided [DevContainer](#devcontainer-setup), or use the `setup.sh` script and answer "y" when asked "Do you want to install dependencies for dev [y/N]? ".
Alternatively (e.g. if your system is not supported by the setup.sh script), follow the manual installation process and run `pip3 install -e .[all]`.
This will install all required tools for development, including `pytest`, `flake8`, `mypy`, and `coveralls`.
### Devcontainer setup
The fastest and easiest way to get started is to use [VSCode](https://code.visualstudio.com/) with the Remote container extension.
This gives developers the ability to start the bot with all required dependencies *without* needing to install any freqtrade specific dependencies on your local machine.
#### Devcontainer dependencies
* [VSCode](https://code.visualstudio.com/)
* [docker](https://docs.docker.com/install/)
* [Remote container extension documentation](https://code.visualstudio.com/docs/remote)
For more information about the [Remote container extension](https://code.visualstudio.com/docs/remote), best consult the documentation.
### Tests
New code should be covered by basic unittests. Depending on the complexity of the feature, Reviewers may request more in-depth unittests.
@ -50,51 +63,6 @@ def test_method_to_test(caplog):
```
### Local docker usage
The fastest and easiest way to start up is to use docker-compose.develop which gives developers the ability to start the bot up with all the required dependencies, *without* needing to install any freqtrade specific dependencies on your local machine.
#### Install
* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
* [docker](https://docs.docker.com/install/)
* [docker-compose](https://docs.docker.com/compose/install/)
#### Starting the bot
##### Use the develop dockerfile
``` bash
rm docker-compose.yml && mv docker-compose.develop.yml docker-compose.yml
```
#### Docker Compose
##### Starting
``` bash
docker-compose up
```
![Docker compose up](https://user-images.githubusercontent.com/419355/65456322-47f63a80-de06-11e9-90c6-3c74d1bad0b8.png)
##### Rebuilding
``` bash
docker-compose build
```
##### Executing (effectively SSH into the container)
The `exec` command requires that the container already be running, if you want to start it
that can be effected by `docker-compose up` or `docker-compose run freqtrade_develop`
``` bash
docker-compose exec freqtrade_develop /bin/bash
```
![image](https://user-images.githubusercontent.com/419355/65456522-ba671a80-de06-11e9-9598-df9ca0d8dcac.png)
## ErrorHandling
Freqtrade Exceptions all inherit from `FreqtradeException`.
@ -120,13 +88,15 @@ Below is an outline of exception inheritance hierarchy:
| +---+ InvalidOrderException
| |
| +---+ RetryableOrderError
| |
| +---+ InsufficientFundsError
|
+---+ StrategyError
```
## Modules
### Dynamic Pairlist
### Pairlists
You have a great idea for a new pair selection algorithm you would like to try out? Great.
Hopefully you also want to contribute this back upstream.
@ -261,13 +231,14 @@ jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to markdown freqtrade
This documents some decisions taken for the CI Pipeline.
* CI runs on all OS variants, Linux (ubuntu), macOS and Windows.
* Docker images are build for the branches `master` and `develop`.
* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:master_pi` and `develop_pi`.
* Docker images are build for the branches `stable` and `develop`.
* Docker images containing Plot dependencies are also available as `stable_plot` and `develop_plot`.
* Raspberry PI Docker images are postfixed with `_pi` - so tags will be `:stable_pi` and `develop_pi`.
* Docker images contain a file, `/freqtrade/freqtrade_commit` containing the commit this image is based of.
* Full docker image rebuilds are run once a week via schedule.
* Deployments run on ubuntu.
* ta-lib binaries are contained in the build_helpers directory to avoid fails related to external unavailability.
* All tests must pass for a PR to be merged to `master` or `develop`.
* All tests must pass for a PR to be merged to `stable` or `develop`.
## Creating a release
@ -284,19 +255,19 @@ git checkout -b new_release <commitid>
Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
* Merge the release branch (master) into this branch.
* Merge the release branch (stable) into this branch.
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
* Commit this part
* push that branch to the remote and create a PR against the master branch
* push that branch to the remote and create a PR against the stable branch
### Create changelog from git commits
!!! Note
Make sure that the master branch is up-to-date!
Make sure that the `stable` branch is up-to-date!
``` bash
# Needs to be done before merging / pulling that branch.
git log --oneline --no-decorate --no-merges master..new_release
git log --oneline --no-decorate --no-merges stable..new_release
```
To keep the release-log short, best wrap the full git changelog into a collapsible details section.
@ -312,11 +283,11 @@ To keep the release-log short, best wrap the full git changelog into a collapsib
### Create github release / tag
Once the PR against master is merged (best right after merging):
Once the PR against stable is merged (best right after merging):
* Use the button "Draft a new release" in the Github UI (subsection releases).
* Use the version-number specified as tag.
* Use "master" as reference (this step comes after the above PR is merged).
* Use "stable" as reference (this step comes after the above PR is merged).
* Use the above changelog as release comment (as codeblock)
## Releases

View File

@ -10,9 +10,9 @@ Pull the image from docker hub.
Branches / tags available can be checked out on [Dockerhub tags page](https://hub.docker.com/r/freqtradeorg/freqtrade/tags/).
```bash
docker pull freqtradeorg/freqtrade:master
docker pull freqtradeorg/freqtrade:stable
# Optionally tag the repository so the run-commands remain shorter
docker tag freqtradeorg/freqtrade:master freqtrade
docker tag freqtradeorg/freqtrade:stable freqtrade
```
To update the image, simply run the above commands again and restart your running container.
@ -20,7 +20,7 @@ To update the image, simply run the above commands again and restart your runnin
Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
!!! Note "Docker image update frequency"
The official docker images with tags `master`, `develop` and `latest` are automatically rebuild once a week to keep the base image uptodate.
The official docker images with tags `stable`, `develop` and `latest` are automatically rebuild once a week to keep the base image up-to-date.
In addition to that, every merge to `develop` will trigger a rebuild for `develop` and `latest`.
### Prepare the configuration files
@ -70,16 +70,16 @@ cp -n config.json.example config.json
Best start by pulling the official docker image from dockerhub as explained [here](#download-the-official-docker-image) to speed up building.
To add additional libraries to your docker image, best check out [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) which adds the [technical](https://github.com/freqtrade/technical) module to the image.
To add additional libraries to your docker image, best check out [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.technical) which adds the [technical](https://github.com/freqtrade/technical) module to the image.
```bash
docker build -t freqtrade -f Dockerfile.technical .
docker build -t freqtrade -f docker/Dockerfile.technical .
```
If you are developing using Docker, use `Dockerfile.develop` to build a dev Docker image, which will also set up develop dependencies:
If you are developing using Docker, use `docker/Dockerfile.develop` to build a dev Docker image, which will also set up develop dependencies:
```bash
docker build -f Dockerfile.develop -t freqtrade-dev .
docker build -f docker/Dockerfile.develop -t freqtrade-dev .
```
!!! Warning "Include your config file manually"

View File

@ -29,7 +29,7 @@ Create a new directory and place the [docker-compose file](https://github.com/fr
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/master/docker-compose.yml -o docker-compose.yml
curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
@ -46,7 +46,7 @@ Create a new directory and place the [docker-compose file](https://github.com/fr
mkdir ft_userdata
cd ft_userdata/
# Download the docker-compose file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/master/docker-compose.yml -o docker-compose.yml
curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
# Pull the freqtrade image
docker-compose pull
@ -61,7 +61,7 @@ Create a new directory and place the [docker-compose file](https://github.com/fr
!!! Note "Change your docker Image"
You have to change the docker image in the docker-compose file for your Raspberry build to work properly.
``` yml
image: freqtradeorg/freqtrade:master_pi
image: freqtradeorg/freqtrade:stable_pi
# image: freqtradeorg/freqtrade:develop_pi
```
@ -148,7 +148,7 @@ Head over to the [Backtesting Documentation](backtesting.md) to learn more.
### Additional dependencies with docker-compose
If your strategy requires dependencies not included in the default image (like [technical](https://github.com/freqtrade/technical)) - it will be necessary to build the image on your host.
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/Dockerfile.technical) for an example).
For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [docker/Dockerfile.technical](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.technical) for an example).
You'll then also need to modify the `docker-compose.yml` file and uncomment the build step, as well as rename the image to avoid naming collisions.
@ -160,3 +160,32 @@ You'll then also need to modify the `docker-compose.yml` file and uncomment the
```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
## Plotting with docker-compose
Commands `freqtrade plot-profit` and `freqtrade plot-dataframe` ([Documentation](plotting.md)) are available by changing the image to `*_plot` in your docker-compose.yml file.
You can then use these commands as follows:
``` bash
docker-compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
```
The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
## Data analayis using docker compose
Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command:
``` bash
docker-compose --rm -f docker/docker-compose-jupyter.yml up
```
This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
Please use the link that's printed in the console after startup for simplified login.
Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) uptodate.
``` bash
docker-compose -f docker/docker-compose-jupyter.yml build --no-cache
```

View File

@ -82,20 +82,34 @@ Risk Reward Ratio ($R$) is a formula used to measure the expected gains of a giv
$$ R = \frac{\text{potential_profit}}{\text{potential_loss}} $$
???+ Example "Worked example of $R$ calculation"
Let's say that you think that the price of *stonecoin* today is $10.0. You believe that, because they will start mining stonecoin, it will go up to $15.0 tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to $0 tomorrow. You are planning to invest $100.<br>
Your potential profit is calculated as:<br>
Let's say that you think that the price of *stonecoin* today is $10.0. You believe that, because they will start mining stonecoin, it will go up to $15.0 tomorrow. There is the risk that the stone is too hard, and the GPUs can't mine it, so the price might go to $0 tomorrow. You are planning to invest $100, which will give you 10 shares (100 / 10).
Your potential profit is calculated as:
$\begin{aligned}
\text{potential_profit} &= (\text{potential_price} - \text{cost_per_unit}) * \frac{\text{investment}}{\text{cost_per_unit}} \\
&= (15 - 10) * \frac{100}{15}\\
&= 33.33
\end{aligned}$<br>
Since the price might go to $0, the $100 dolars invested could turn into 0. We can compute the Risk Reward Ratio as follows:<br>
\text{potential_profit} &= (\text{potential_price} - \text{entry_price}) * \frac{\text{investment}}{\text{entry_price}} \\
&= (15 - 10) * (100 / 10) \\
&= 50
\end{aligned}$
Since the price might go to $0, the $100 dollars invested could turn into 0.
We do however use a stoploss of 15% - so in the worst case, we'll sell 15% below entry price (or at 8.5$).
$\begin{aligned}
\text{potential_loss} &= (\text{entry_price} - \text{stoploss}) * \frac{\text{investment}}{\text{entry_price}} \\
&= (10 - 8.5) * (100 / 10)\\
&= 15
\end{aligned}$
We can compute the Risk Reward Ratio as follows:
$\begin{aligned}
R &= \frac{\text{potential_profit}}{\text{potential_loss}}\\
&= \frac{33.33}{100}\\
&= 0.333...
&= \frac{50}{15}\\
&= 3.33
\end{aligned}$<br>
What it effectivelly means is that the strategy have the potential to make $0.33 for each $1 invested.
What it effectively means is that the strategy have the potential to make 3.33$ for each $1 invested.
On a long horizon, that is, on many trades, we can calculate the risk reward by dividing the strategy' average profit on winning trades by the strategy' average loss on losing trades. We can calculate the average profit, $\mu_{win}$, as follows:

View File

@ -89,7 +89,7 @@ Same fix should be done in the configuration file, if order types are defined in
### How do I search the bot logs for something?
By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility subcommands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility sub-commands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
* In unix shells, this normally can be done as simple as:
```shell
@ -114,7 +114,7 @@ and then grep it as:
```shell
$ cat /path/to/mylogfile.log | grep 'something'
```
or even on the fly, as the bot works and the logfile grows:
or even on the fly, as the bot works and the log file grows:
```shell
$ tail -f /path/to/mylogfile.log | grep 'something'
```
@ -137,23 +137,17 @@ compute.
Since hyperopt uses Bayesian search, running for too many epochs may not produce greater results.
It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epocs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
It's therefore recommended to run between 500-1000 epochs over and over until you hit at least 10.000 epochs in total (or are satisfied with the result). You can best judge by looking at the results - if the bot keeps discovering better strategies, it's best to keep on going.
```bash
freqtrade hyperopt -e 1000
```
or if you want intermediate result to see
```bash
for i in {1..100}; do freqtrade hyperopt -e 1000; done
freqtrade hyperopt --hyperop SampleHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy SampleStrategy -e 1000
```
### Why does it take a long time to run hyperopt?
* Discovering a great strategy with Hyperopt takes time. Study www.freqtrade.io, the Freqtrade Documentation page, join the Freqtrade [Slack community](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) - or the Freqtrade [discord community](https://discord.gg/X89cVG). While you patiently wait for the most advanced, free crypto bot in the world, to hand you a possible golden strategy specially designed just for you.
* If you wonder why it can take from 20 minutes to days to do 1000 epocs here are some answers:
* If you wonder why it can take from 20 minutes to days to do 1000 epochs here are some answers:
This answer was written during the release 0.15.1, when we had:
@ -167,7 +161,7 @@ already 8\*10^9\*10 evaluations. A roughly total of 80 billion evals.
Did you run 100 000 evals? Congrats, you've done roughly 1 / 100 000 th
of the search space, assuming that the bot never tests the same parameters more than once.
* The time it takes to run 1000 hyperopt epocs depends on things like: The available cpu, harddisk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades.
* The time it takes to run 1000 hyperopt epochs depends on things like: The available cpu, hard-disk, ram, timeframe, timerange, indicator settings, indicator count, amount of coins that hyperopt test strategies on and the resulting trade count - which can be 650 trades in a year or 10.0000 trades depending if the strategy aims for big profits by trading rarely or for many low profit trades.
Example: 4% profit 650 times vs 0,3% profit a trade 10.000 times in a year. If we assume you set the --timerange to 365 days.
@ -180,7 +174,7 @@ Example:
The Edge module is mostly a result of brainstorming of [@mishaker](https://github.com/mishaker) and [@creslinux](https://github.com/creslinux) freqtrade team members.
You can find further info on expectancy, winrate, risk management and position size in the following sources:
You can find further info on expectancy, win rate, risk management and position size in the following sources:
- https://www.tradeciety.com/ultimate-math-guide-for-traders/
- http://www.vantharp.com/tharp-concepts/expectancy.asp

View File

@ -37,12 +37,20 @@ pip install -r requirements-hyperopt.txt
Before we start digging into Hyperopt, we recommend you to take a look at
the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt.py).
Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar.
The simplest way to get started is to use `freqtrade new-hyperopt --hyperopt AwesomeHyperopt`.
This will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
!!! Tip "About this page"
For this page, we will be using a fictional strategy called `AwesomeStrategy` - which will be optimized using the `AwesomeHyperopt` class.
### Checklist on all tasks / possibilities in hyperopt
The simplest way to get started is to use the following, command, which will create a new hyperopt file from a template, which will be located under `user_data/hyperopts/AwesomeHyperopt.py`.
``` bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
### Hyperopt checklist
Checklist on all tasks / possibilities in hyperopt
Depending on the space you want to optimize, only some of the below are required:
@ -54,17 +62,15 @@ Depending on the space you want to optimize, only some of the below are required
!!! Note
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
Optional - can also be loaded from a strategy:
Optional in hyperopt - can also be loaded from a strategy (recommended):
* copy `populate_indicators` from your strategy - otherwise default-strategy will be used
* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
!!! Note
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
!!! Note
You always have to provide a strategy to Hyperopt, even if your custom Hyperopt class contains all methods.
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
Rarely you may also need to override:
@ -80,17 +86,20 @@ Rarely you may also need to override:
# Have a working strategy at hand.
freqtrade new-hyperopt --hyperopt EmptyHyperopt
freqtrade hyperopt --hyperopt EmptyHyperopt --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
freqtrade hyperopt --hyperopt EmptyHyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss trailing --strategy MyWorkingStrategy --config config.json -e 100
```
### 1. Install a Custom Hyperopt File
### Create a Custom Hyperopt File
Put your hyperopt file into the directory `user_data/hyperopts`.
Let assume you want a hyperopt file `AwesomeHyperopt.py`:
Let assume you want a hyperopt file `awesome_hyperopt.py`:
Copy the file `user_data/hyperopts/sample_hyperopt.py` into `user_data/hyperopts/awesome_hyperopt.py`
``` bash
freqtrade new-hyperopt --hyperopt AwesomeHyperopt
```
### 2. Configure your Guards and Triggers
This command will create a new hyperopt file from a template, allowing you to get started quickly.
### Configure your Guards and Triggers
There are two places you need to change in your hyperopt file to add a new buy hyperopt for testing:
@ -102,14 +111,16 @@ There you have two different types of indicators: 1. `guards` and 2. `triggers`.
1. Guards are conditions like "never buy if ADX < 10", or never buy if current price is over EMA10.
2. Triggers are ones that actually trigger buy in specific moment, like "buy when EMA5 crosses over EMA10" or "buy when close price touches lower Bollinger band".
Hyperoptimization will, for each eval round, pick one trigger and possibly
multiple guards. The constructed strategy will be something like
"*buy exactly when close price touches lower Bollinger band, BUT only if
!!! Hint "Guards and Triggers"
Technically, there is no difference between Guards and Triggers.
However, this guide will make this distinction to make it clear that signals should not be "sticking".
Sticking signals are signals that are active for multiple candles. This can lead into buying a signal late (right before the signal disappears - which means that the chance of success is a lot lower than right at the beginning).
Hyper-optimization will, for each epoch round, pick one trigger and possibly
multiple guards. The constructed strategy will be something like "*buy exactly when close price touches lower Bollinger band, BUT only if
ADX > 10*".
If you have updated the buy strategy, i.e. changed the contents of
`populate_buy_trend()` method, you have to update the `guards` and
`triggers` your hyperopt must use correspondingly.
If you have updated the buy strategy, i.e. changed the contents of `populate_buy_trend()` method, you have to update the `guards` and `triggers` your hyperopt must use correspondingly.
#### Sell optimization
@ -126,7 +137,7 @@ To avoid naming collisions in the search-space, please prefix all sell-spaces wi
The Strategy class exposes the timeframe value as the `self.timeframe` attribute.
The same value is available as class-attribute `HyperoptName.timeframe`.
In the case of the linked sample-value this would be `SampleHyperOpt.timeframe`.
In the case of the linked sample-value this would be `AwesomeHyperopt.timeframe`.
## Solving a Mystery
@ -192,27 +203,25 @@ So let's write the buy strategy using these values:
return populate_buy_trend
```
Hyperopting will now call this `populate_buy_trend` as many times you ask it (`epochs`)
with different value combinations. It will then use the given historical data and make
buys based on the buy signals generated with the above function and based on the results
it will end with telling you which parameter combination produced the best profits.
Hyperopt will now call `populate_buy_trend()` many times (`epochs`) with different value combinations.
It will use the given historical data and make buys based on the buy signals generated with the above function.
Based on the results, hyperopt will tell you which parameter combination produced the best results (based on the configured [loss function](#loss-functions)).
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
add it to the `populate_indicators()` method in your custom hyperopt file.
!!! Note
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
add it to the `populate_indicators()` method in your strategy or hyperopt file.
## Loss-functions
Each hyperparameter tuning requires a target. This is usually defined as a loss function (sometimes also called objective function), which should decrease for more desirable results, and increase for bad results.
By default, Freqtrade uses a loss function, which has been with freqtrade since the beginning and optimizes mostly for short trade duration and avoiding losses.
A different loss function can be specified by using the `--hyperopt-loss <Class-name>` argument.
A loss function must be specified via the `--hyperopt-loss <Class-name>` argument (or optionally via the configuration under the `"hyperopt_loss"` key).
This class should be in its own file within the `user_data/hyperopts/` directory.
Currently, the following loss functions are builtin:
* `DefaultHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function)
* `ShortTradeDurHyperOptLoss` (default legacy Freqtrade hyperoptimization loss function) - Mostly for short trade duration and avoiding losses.
* `OnlyProfitHyperOptLoss` (which takes only amount of profit into consideration)
* `SharpeHyperOptLoss` (optimizes Sharpe Ratio calculated on trade returns relative to standard deviation)
* `SharpeHyperOptLossDaily` (optimizes Sharpe Ratio calculated on **daily** trade returns relative to standard deviation)
@ -229,21 +238,20 @@ Because hyperopt tries a lot of combinations to find the best parameters it will
We strongly recommend to use `screen` or `tmux` to prevent any connection loss.
```bash
freqtrade hyperopt --config config.json --hyperopt <hyperoptname> -e 5000 --spaces all
freqtrade hyperopt --config config.json --hyperopt <hyperoptname> --hyperopt-loss <hyperoptlossname> --strategy <strategyname> -e 500 --spaces all
```
Use `<hyperoptname>` as the name of the custom hyperopt used.
The `-e` option will set how many evaluations hyperopt will do. We recommend
running at least several thousand evaluations.
The `-e` option will set how many evaluations hyperopt will do. Since hyperopt uses Bayesian search, running too many epochs at once may not produce greater results. Experience has shown that best results are usually not improving much after 500-1000 epochs.
Doing multiple runs (executions) with a few 1000 epochs and different random state will most likely produce different results.
The `--spaces all` option determines that all possible parameters should be optimized. Possibilities are listed below.
!!! Note
By default, hyperopt will erase previous results and start from scratch. Continuation can be archived by using `--continue`.
!!! Warning
When switching parameters or changing configuration options, make sure to not use the argument `--continue` so temporary results can be removed.
Hyperopt will store hyperopt results with the timestamp of the hyperopt start time.
Reading commands (`hyperopt-list`, `hyperopt-show`) can use `--hyperopt-filename <filename>` to read and display older hyperopt results.
You can find a list of filenames with `ls -l user_data/hyperopt_results/`.
### Execute Hyperopt with different historical data source
@ -251,13 +259,13 @@ If you would like to hyperopt parameters using an alternate historical data set
you have on-disk, use the `--datadir PATH` option. By default, hyperopt
uses data from directory `user_data/data`.
### Running Hyperopt with Smaller Testset
### Running Hyperopt with a smaller test-set
Use the `--timerange` argument to change how much of the testset you want to use.
Use the `--timerange` argument to change how much of the test-set you want to use.
For example, to use one month of data, pass the following parameter to the hyperopt call:
```bash
freqtrade hyperopt --timerange 20180401-20180501
freqtrade hyperopt --hyperopt <hyperoptname> --strategy <strategyname> --timerange 20180401-20180501
```
### Running Hyperopt using methods from a strategy
@ -265,16 +273,15 @@ freqtrade hyperopt --timerange 20180401-20180501
Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
```bash
freqtrade hyperopt --strategy SampleStrategy --hyperopt SampleHyperopt
freqtrade hyperopt --hyperopt AwesomeHyperopt --hyperopt-loss SharpeHyperOptLossDaily --strategy AwesomeStrategy
```
### Running Hyperopt with Smaller Search Space
Use the `--spaces` option to limit the search space used by hyperopt.
Letting Hyperopt optimize everything is a huuuuge search space. Often it
might make more sense to start by just searching for initial buy algorithm.
Or maybe you just want to optimize your stoploss or roi table for that awesome
new buy strategy you have.
Letting Hyperopt optimize everything is a huuuuge search space.
Often it might make more sense to start by just searching for initial buy algorithm.
Or maybe you just want to optimize your stoploss or roi table for that awesome new buy strategy you have.
Legal values are:
@ -318,7 +325,7 @@ The initial state for generation of these random values (random state) is contro
If you have not set this value explicitly in the command line options, Hyperopt seeds the random state with some random value for you. The random state value for each Hyperopt run is shown in the log, so you can copy and paste it into the `--random-state` command line option to repeat the set of the initial random epochs used.
If you have not changed anything in the command line options, configuration, timerange, Strategy and Hyperopt classes, historical data and the Loss Function -- you should obtain same hyperoptimization results with same random state value used.
If you have not changed anything in the command line options, configuration, timerange, Strategy and Hyperopt classes, historical data and the Loss Function -- you should obtain same hyper-optimization results with same random state value used.
## Understand the Hyperopt Result
@ -371,7 +378,7 @@ By default, hyperopt prints colorized results -- epochs with positive profit are
You can use the `--print-all` command line option if you would like to see all results in the hyperopt output, not only the best ones. When `--print-all` is used, current best results are also colorized by default -- they are printed in bold (bright) style. This can also be switched off with the `--no-color` command line option.
!!! Note "Windows and color output"
Windows does not support color-output nativly, therefore it is automatically disabled. To have color-output for hyperopt running under windows, please consider using WSL.
Windows does not support color-output natively, therefore it is automatically disabled. To have color-output for hyperopt running under windows, please consider using WSL.
### Understand Hyperopt ROI results
@ -419,7 +426,9 @@ These ranges should be sufficient in most cases. The minutes in the steps (ROI d
If you have the `generate_roi_table()` and `roi_space()` methods in your custom hyperopt file, remove them in order to utilize these adaptive ROI tables and the ROI hyperoptimization space generated by Freqtrade by default.
Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps). A sample for these methods can be found in [user_data/hyperopts/sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
Override the `roi_space()` method if you need components of the ROI tables to vary in other ranges. Override the `generate_roi_table()` and `roi_space()` methods and implement your own custom approach for generation of the ROI tables during hyperoptimization if you need a different structure of the ROI tables or other amount of rows (steps).
A sample for these methods can be found in [sample_hyperopt_advanced.py](https://github.com/freqtrade/freqtrade/blob/develop/freqtrade/templates/sample_hyperopt_advanced.py).
### Understand Hyperopt Stoploss results
@ -441,7 +450,7 @@ Stoploss: -0.27996
In order to use this best stoploss value found by Hyperopt in backtesting and for live trades/dry-run, copy-paste it as the value of the `stoploss` attribute of your custom strategy:
```
``` python
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.27996
@ -475,7 +484,7 @@ Trailing stop:
In order to use these best trailing stop parameters found by Hyperopt in backtesting and for live trades/dry-run, copy-paste them as the values of the corresponding attributes of your custom strategy:
```
``` python
# Trailing stop
# These attributes will be overridden if the config file contains corresponding values.
trailing_stop = True
@ -494,10 +503,14 @@ Override the `trailing_space()` method and define the desired range in it if you
## Show details of Hyperopt results
After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` subcommands. The usage of these subcommands is described in the [Utils](utils.md#list-hyperopt-results) chapter.
After you run Hyperopt for the desired amount of epochs, you can later list all results for analysis, select only best or profitable once, and show the details for any of the epochs previously evaluated. This can be done with the `hyperopt-list` and `hyperopt-show` sub-commands. The usage of these sub-commands is described in the [Utils](utils.md#list-hyperopt-results) chapter.
## Validate backtesting results
Once the optimized strategy has been implemented into your strategy, you should backtest this strategy to make sure everything is working as expected.
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same set of arguments `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
To achieve same results (number of trades, their durations, profit, etc.) than during Hyperopt, please use same configuration and parameters (timerange, timeframe, ...) used for hyperopt `--dmmp`/`--disable-max-market-positions` and `--eps`/`--enable-position-stacking` for Backtesting.
Should results don't match, please double-check to make sure you transferred all conditions correctly.
Pay special care to the stoploss (and trailing stoploss) parameters, as these are often set in configuration files, which override changes to the strategy.
You should also carefully review the log of your backtest to ensure that there were no parameters inadvertently set by the configuration (like `stoploss` or `trailing_stop`).

137
docs/includes/pairlists.md Normal file
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@ -0,0 +1,137 @@
## Pairlists and Pairlist Handlers
Pairlist Handlers define the list of pairs (pairlist) that the bot should trade. They are configured in the `pairlists` section of the configuration settings.
In your configuration, you can use Static Pairlist (defined by the [`StaticPairList`](#static-pair-list) Pairlist Handler) and Dynamic Pairlist (defined by the [`VolumePairList`](#volume-pair-list) Pairlist Handler).
Additionally, [`AgeFilter`](#agefilter), [`PrecisionFilter`](#precisionfilter), [`PriceFilter`](#pricefilter), [`ShuffleFilter`](#shufflefilter) and [`SpreadFilter`](#spreadfilter) act as Pairlist Filters, removing certain pairs and/or moving their positions in the pairlist.
If multiple Pairlist Handlers are used, they are chained and a combination of all Pairlist Handlers forms the resulting pairlist the bot uses for trading and backtesting. Pairlist Handlers are executed in the sequence they are configured. You should always configure either `StaticPairList` or `VolumePairList` as the starting Pairlist Handler.
Inactive markets are always removed from the resulting pairlist. Explicitly blacklisted pairs (those in the `pair_blacklist` configuration setting) are also always removed from the resulting pairlist.
### Available Pairlist Handlers
* [`StaticPairList`](#static-pair-list) (default, if not configured differently)
* [`VolumePairList`](#volume-pair-list)
* [`AgeFilter`](#agefilter)
* [`PrecisionFilter`](#precisionfilter)
* [`PriceFilter`](#pricefilter)
* [`ShuffleFilter`](#shufflefilter)
* [`SpreadFilter`](#spreadfilter)
!!! Tip "Testing pairlists"
Pairlist configurations can be quite tricky to get right. Best use the [`test-pairlist`](utils.md#test-pairlist) utility sub-command to test your configuration quickly.
#### Static Pair List
By default, the `StaticPairList` method is used, which uses a statically defined pair whitelist from the configuration.
It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklist`.
```json
"pairlists": [
{"method": "StaticPairList"}
],
```
#### Volume Pair List
`VolumePairList` employs sorting/filtering of pairs by their trading volume. It selects `number_assets` top pairs with sorting based on the `sort_key` (which can only be `quoteVolume`).
When used in the chain of Pairlist Handlers in a non-leading position (after StaticPairList and other Pairlist Filters), `VolumePairList` considers outputs of previous Pairlist Handlers, adding its sorting/selection of the pairs by the trading volume.
When used on the leading position of the chain of Pairlist Handlers, it does not consider `pair_whitelist` configuration setting, but selects the top assets from all available markets (with matching stake-currency) on the exchange.
The `refresh_period` setting allows to define the period (in seconds), at which the pairlist will be refreshed. Defaults to 1800s (30 minutes).
`VolumePairList` is based on the ticker data from exchange, as reported by the ccxt library:
* The `quoteVolume` is the amount of quote (stake) currency traded (bought or sold) in last 24 hours.
```json
"pairlists": [{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
"refresh_period": 1800,
}],
```
#### AgeFilter
Removes pairs that have been listed on the exchange for less than `min_days_listed` days (defaults to `10`).
When pairs are first listed on an exchange they can suffer huge price drops and volatility
in the first few days while the pair goes through its price-discovery period. Bots can often
be caught out buying before the pair has finished dropping in price.
This filter allows freqtrade to ignore pairs until they have been listed for at least `min_days_listed` days.
#### PrecisionFilter
Filters low-value coins which would not allow setting stoplosses.
#### PriceFilter
The `PriceFilter` allows filtering of pairs by price. Currently the following price filters are supported:
* `min_price`
* `max_price`
* `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.
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 `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.
For `PriceFiler` at least one of its `min_price`, `max_price` or `low_price_ratio` settings must be applied.
Calculation example:
Min price precision for SHITCOIN/BTC is 8 decimals. If its price is 0.00000011 - one price step above would be 0.00000012, which is ~9% higher than the previous price value. You may filter out this pair by using PriceFilter with `low_price_ratio` set to 0.09 (9%) or with `min_price` set to 0.00000011, correspondingly.
!!! Warning "Low priced pairs"
Low priced pairs with high "1 pip movements" are dangerous since they are often illiquid and it may also be impossible to place the desired stoploss, which can often result in high losses since price needs to be rounded to the next tradable price - so instead of having a stoploss of -5%, you could end up with a stoploss of -9% simply due to price rounding.
#### ShuffleFilter
Shuffles (randomizes) pairs in the pairlist. It can be used for preventing the bot from trading some of the pairs more frequently then others when you want all pairs be treated with the same priority.
!!! Tip
You may set the `seed` value for this Pairlist to obtain reproducible results, which can be useful for repeated backtesting sessions. If `seed` is not set, the pairs are shuffled in the non-repeatable random order.
#### SpreadFilter
Removes pairs that have a difference between asks and bids above the specified ratio, `max_spread_ratio` (defaults to `0.005`).
Example:
If `DOGE/BTC` maximum bid is 0.00000026 and minimum ask is 0.00000027, the ratio is calculated as: `1 - bid/ask ~= 0.037` which is `> 0.005` and this pair will be filtered out.
### Full example of Pairlist Handlers
The below example blacklists `BNB/BTC`, uses `VolumePairList` with `20` assets, sorting pairs by `quoteVolume` and applies both [`PrecisionFilter`](#precisionfilter) and [`PriceFilter`](#price-filter), filtering all assets where 1 price unit is > 1%. Then the `SpreadFilter` is applied and pairs are finally shuffled with the random seed set to some predefined value.
```json
"exchange": {
"pair_whitelist": [],
"pair_blacklist": ["BNB/BTC"]
},
"pairlists": [
{
"method": "VolumePairList",
"number_assets": 20,
"sort_key": "quoteVolume",
},
{"method": "AgeFilter", "min_days_listed": 10},
{"method": "PrecisionFilter"},
{"method": "PriceFilter", "low_price_ratio": 0.01},
{"method": "SpreadFilter", "max_spread_ratio": 0.005},
{"method": "ShuffleFilter", "seed": 42}
],
```

View File

@ -8,7 +8,7 @@
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade/freqtrade/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork freqtrade/freqtrade on GitHub">Fork</a>
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/master.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
<a class="github-button" href="https://github.com/freqtrade/freqtrade/archive/stable.zip" data-icon="octicon-cloud-download" data-size="large" aria-label="Download freqtrade/freqtrade on GitHub">Download</a>
<!-- Place this tag where you want the button to render. -->
<a class="github-button" href="https://github.com/freqtrade" data-size="large" aria-label="Follow @freqtrade on GitHub">Follow @freqtrade</a>
@ -59,11 +59,17 @@ Alternatively
## Support
### Help / Slack
### Help / Slack / Discord
For any questions not covered by the documentation or for further information about the bot, we encourage you to join our passionate Slack community.
Click [here](https://join.slack.com/t/highfrequencybot/shared_invite/enQtNjU5ODcwNjI1MDU3LTU1MTgxMjkzNmYxNWE1MDEzYzQ3YmU4N2MwZjUyNjJjODRkMDVkNjg4YTAyZGYzYzlhOTZiMTE4ZjQ4YzM0OGE) to join the Freqtrade Slack channel.
Alternatively, check out the newly created [discord server](https://discord.gg/MA9v74M).
!!! Note
Since the discord server is relatively new, answers to questions might be slightly delayed as currently the user base quite small.
## Ready to try?
Begin by reading our installation guide [for docker](docker.md), or for [installation without docker](installation.md).
Begin by reading our installation guide [for docker](docker.md) (recommended), or for [installation without docker](installation.md).

View File

@ -31,7 +31,7 @@ Freqtrade provides the Linux/MacOS Easy Installation script to install all depen
The easiest way to install and run Freqtrade is to clone the bot Github repository and then run the Easy Installation script, if it's available for your platform.
!!! Note "Version considerations"
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `master` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
When cloning the repository the default working branch has the name `develop`. This branch contains all last features (can be considered as relatively stable, thanks to automated tests). The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
!!! Note
Python3.6 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.
@ -41,11 +41,11 @@ This can be achieved with the following commands:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
# git checkout master # Optional, see (1)
# git checkout stable # Optional, see (1)
./setup.sh --install
```
(1) This command switches the cloned repository to the use of the `master` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout master`/`git checkout develop` commands.
(1) This command switches the cloned repository to the use of the `stable` branch. It's not needed if you wish to stay on the `develop` branch. You may later switch between branches at any time with the `git checkout stable`/`git checkout develop` commands.
## Easy Installation Script (Linux/MacOS)
@ -56,7 +56,7 @@ $ ./setup.sh
usage:
-i,--install Install freqtrade from scratch
-u,--update Command git pull to update.
-r,--reset Hard reset your develop/master branch.
-r,--reset Hard reset your develop/stable branch.
-c,--config Easy config generator (Will override your existing file).
```
@ -76,7 +76,7 @@ This option will pull the last version of your current branch and update your vi
** --reset **
This option will hard reset your branch (only if you are on either `master` or `develop`) and recreate your virtualenv.
This option will hard reset your branch (only if you are on either `stable` or `develop`) and recreate your virtualenv.
** --config **
@ -174,7 +174,7 @@ Clone the git repository:
```bash
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
git checkout master
git checkout stable
```
#### 4. Install python dependencies

View File

@ -1,2 +1,3 @@
mkdocs-material==5.5.12
mkdocs-material==6.1.0
mdx_truly_sane_lists==1.2
pymdown-extensions==8.0.1

View File

@ -104,32 +104,42 @@ By default, the script assumes `127.0.0.1` (localhost) and port `8080` to be use
python3 scripts/rest_client.py --config rest_config.json <command> [optional parameters]
```
## Available commands
## Available endpoints
| Command | Description |
|----------|-------------|
| `ping` | Simple command testing the API Readiness - requires no authentication.
| `start` | Starts the trader
| `stop` | Stops the trader
| `start` | Starts the trader.
| `stop` | Stops the trader.
| `stopbuy` | Stops the trader from opening new trades. Gracefully closes open trades according to their rules.
| `reload_config` | Reloads the configuration file
| `reload_config` | Reloads the configuration file.
| `trades` | List last trades.
| `delete_trade <trade_id>` | Remove trade from the database. Tries to close open orders. Requires manual handling of this trade on the exchange.
| `show_config` | Shows part of the current configuration with relevant settings to operation
| `logs` | Shows last log messages
| `status` | Lists all open trades
| `count` | Displays number of trades used and available
| `profit` | Display a summary of your profit/loss from close trades and some stats about your performance
| `show_config` | Shows part of the current configuration with relevant settings to operation.
| `logs` | Shows last log messages.
| `status` | Lists all open trades.
| `count` | Displays number of trades used and available.
| `locks` | Displays currently locked pairs.
| `profit` | Display a summary of your profit/loss from close trades and some stats about your performance.
| `forcesell <trade_id>` | Instantly sells the given trade (Ignoring `minimum_roi`).
| `forcesell all` | Instantly sells all open trades (Ignoring `minimum_roi`).
| `forcebuy <pair> [rate]` | Instantly buys the given pair. Rate is optional. (`forcebuy_enable` must be set to True)
| `performance` | Show performance of each finished trade grouped by pair
| `balance` | Show account balance per currency
| `daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7)
| `whitelist` | Show the current whitelist
| `performance` | Show performance of each finished trade grouped by pair.
| `balance` | Show account balance per currency.
| `daily <n>` | Shows profit or loss per day, over the last n days (n defaults to 7).
| `whitelist` | Show the current whitelist.
| `blacklist [pair]` | Show the current blacklist, or adds a pair to the blacklist.
| `edge` | Show validated pairs by Edge if it is enabled.
| `version` | Show version
| `pair_candles` | Returns dataframe for a pair / timeframe combination while the bot is running. **Alpha**
| `pair_history` | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy. **Alpha**
| `plot_config` | Get plot config from the strategy (or nothing if not configured). **Alpha**
| `strategies` | List strategies in strategy directory. **Alpha**
| `strategy <strategy>` | Get specific Strategy content. **Alpha**
| `available_pairs` | List available backtest data. **Alpha**
| `version` | Show version.
!!! Warning "Alpha status"
Endpoints labeled with *Alpha status* above may change at any time without notice.
Possible commands can be listed from the rest-client script using the `help` command.
@ -140,6 +150,12 @@ python3 scripts/rest_client.py help
``` output
Possible commands:
available_pairs
Return available pair (backtest data) based on timeframe / stake_currency selection
:param timeframe: Only pairs with this timeframe available.
:param stake_currency: Only pairs that include this timeframe
balance
Get the account balance.
@ -179,9 +195,27 @@ logs
:param limit: Limits log messages to the last <limit> logs. No limit to get all the trades.
pair_candles
Return live dataframe for <pair><timeframe>.
:param pair: Pair to get data for
:param timeframe: Only pairs with this timeframe available.
:param limit: Limit result to the last n candles.
pair_history
Return historic, analyzed dataframe
:param pair: Pair to get data for
:param timeframe: Only pairs with this timeframe available.
:param strategy: Strategy to analyze and get values for
:param timerange: Timerange to get data for (same format than --timerange endpoints)
performance
Return the performance of the different coins.
plot_config
Return plot configuration if the strategy defines one.
profit
Return the profit summary.
@ -204,6 +238,14 @@ stop
stopbuy
Stop buying (but handle sells gracefully). Use `reload_config` to reset.
strategies
Lists available strategies
strategy
Get strategy details
:param strategy: Strategy class name
trades
Return trades history.
@ -215,7 +257,6 @@ version
whitelist
Show the current whitelist.
```
## Advanced API usage using JWT tokens

View File

@ -43,52 +43,6 @@ sqlite3
.schema <table_name>
```
### Trade table structure
```sql
CREATE TABLE trades(
id INTEGER NOT NULL,
exchange VARCHAR NOT NULL,
pair VARCHAR NOT NULL,
is_open BOOLEAN NOT NULL,
fee_open FLOAT NOT NULL,
fee_open_cost FLOAT,
fee_open_currency VARCHAR,
fee_close FLOAT NOT NULL,
fee_close_cost FLOAT,
fee_close_currency VARCHAR,
open_rate FLOAT,
open_rate_requested FLOAT,
open_trade_price FLOAT,
close_rate FLOAT,
close_rate_requested FLOAT,
close_profit FLOAT,
close_profit_abs FLOAT,
stake_amount FLOAT NOT NULL,
amount FLOAT,
open_date DATETIME NOT NULL,
close_date DATETIME,
open_order_id VARCHAR,
stop_loss FLOAT,
stop_loss_pct FLOAT,
initial_stop_loss FLOAT,
initial_stop_loss_pct FLOAT,
stoploss_order_id VARCHAR,
stoploss_last_update DATETIME,
max_rate FLOAT,
min_rate FLOAT,
sell_reason VARCHAR,
strategy VARCHAR,
timeframe INTEGER,
PRIMARY KEY (id),
CHECK (is_open IN (0, 1))
);
CREATE INDEX ix_trades_stoploss_order_id ON trades (stoploss_order_id);
CREATE INDEX ix_trades_pair ON trades (pair);
CREATE INDEX ix_trades_is_open ON trades (is_open);
```
## Get all trades in the table
```sql
@ -128,23 +82,12 @@ SET is_open=0,
WHERE id=31;
```
## Manually insert a new trade
```sql
INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
VALUES ('binance', 'ETH/BTC', 1, 0.0025, 0.0025, <open_rate>, <stake_amount>, <amount>, '<datetime>')
```
### Insert trade example
```sql
INSERT INTO trades (exchange, pair, is_open, fee_open, fee_close, open_rate, stake_amount, amount, open_date)
VALUES ('binance', 'ETH/BTC', 1, 0.0025, 0.0025, 0.00258580, 0.002, 0.7715262081, '2020-06-28 12:44:24.000000')
```
## Remove trade from the database
Maybe you'd like to remove a trade from the database, because something went wrong.
!!! Tip "Use RPC Methods to delete trades"
Consider using `/delete <tradeid>` via telegram or rest API. That's the recommended way to deleting trades.
If you'd still like to remove a trade from the database directly, you can use the below query.
```sql
DELETE FROM trades WHERE id = <tradeid>;

View File

@ -312,12 +312,17 @@ The name of the variable can be chosen at will, but should be prefixed with `cus
class Awesomestrategy(IStrategy):
# Create custom dictionary
cust_info = {}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Check if the entry already exists
if not metadata["pair"] in self._cust_info:
# Create empty entry for this pair
self._cust_info[metadata["pair"]] = {}
if "crosstime" in self.cust_info[metadata["pair"]:
self.cust_info[metadata["pair"]["crosstime"] += 1
self.cust_info[metadata["pair"]]["crosstime"] += 1
else:
self.cust_info[metadata["pair"]["crosstime"] = 1
self.cust_info[metadata["pair"]]["crosstime"] = 1
```
!!! Warning
@ -688,15 +693,15 @@ Locked pairs will show the message `Pair <pair> is currently locked.`.
Sometimes it may be desired to lock a pair after certain events happen (e.g. multiple losing trades in a row).
Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until)`.
`until` must be a datetime object in the future, after which trading will be reenabled for that pair.
Freqtrade has an easy method to do this from within the strategy, by calling `self.lock_pair(pair, until, [reason])`.
`until` must be a datetime object in the future, after which trading will be re-enabled for that pair, while `reason` is an optional string detailing why the pair was locked.
Locks can also be lifted manually, by calling `self.unlock_pair(pair)`.
To verify if a pair is currently locked, use `self.is_pair_locked(pair)`.
!!! Note
Locked pairs are not persisted, so a restart of the bot, or calling `/reload_config` will reset locked pairs.
Locked pairs will always be rounded up to the next candle. So assuming a `5m` timeframe, a lock with `until` set to 10:18 will lock the pair until the candle from 10:15-10:20 will be finished.
!!! Warning
Locking pairs is not functioning during backtesting.

View File

@ -41,6 +41,34 @@ Talk to the [userinfobot](https://telegram.me/userinfobot)
Get your "Id", you will use it for the config parameter `chat_id`.
## Control telegram noise
Freqtrade provides means to control the verbosity of your telegram bot.
Each setting has the following possible values:
* `on` - Messages will be sent, and user will be notified.
* `silent` - Message will be sent, Notification will be without sound / vibration.
* `off` - Skip sending a message-type all together.
Example configuration showing the different settings:
``` json
"telegram": {
"enabled": true,
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id",
"notification_settings": {
"status": "silent",
"warning": "on",
"startup": "off",
"buy": "silent",
"sell": "on",
"buy_cancel": "silent",
"sell_cancel": "on"
}
},
```
## Telegram commands
Per default, the Telegram bot shows predefined commands. Some commands

View File

@ -423,7 +423,7 @@ freqtrade test-pairlist --config config.json --quote USDT BTC
## List Hyperopt results
You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` subcommand.
You can list the hyperoptimization epochs the Hyperopt module evaluated previously with the `hyperopt-list` sub-command.
```
usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
@ -432,10 +432,11 @@ usage: freqtrade hyperopt-list [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[--max-trades INT] [--min-avg-time FLOAT]
[--max-avg-time FLOAT] [--min-avg-profit FLOAT]
[--max-avg-profit FLOAT]
[--min-total-profit FLOAT] [--max-total-profit FLOAT]
[--min-total-profit FLOAT]
[--max-total-profit FLOAT]
[--min-objective FLOAT] [--max-objective FLOAT]
[--no-color] [--print-json] [--no-details]
[--export-csv FILE]
[--hyperopt-filename PATH] [--export-csv FILE]
optional arguments:
-h, --help show this help message and exit
@ -443,24 +444,27 @@ optional arguments:
--profitable Select only profitable epochs.
--min-trades INT Select epochs with more than INT trades.
--max-trades INT Select epochs with less than INT trades.
--min-avg-time FLOAT Select epochs on above average time.
--max-avg-time FLOAT Select epochs on under average time.
--min-avg-time FLOAT Select epochs above average time.
--max-avg-time FLOAT Select epochs below average time.
--min-avg-profit FLOAT
Select epochs on above average profit.
Select epochs above average profit.
--max-avg-profit FLOAT
Select epochs on below average profit.
Select epochs below average profit.
--min-total-profit FLOAT
Select epochs on above total profit.
Select epochs above total profit.
--max-total-profit FLOAT
Select epochs on below total profit.
Select epochs below total profit.
--min-objective FLOAT
Select epochs on above objective (- is added by default).
Select epochs above objective.
--max-objective FLOAT
Select epochs on below objective (- is added by default).
Select epochs below objective.
--no-color Disable colorization of hyperopt results. May be
useful if you are redirecting output to a file.
--print-json Print best result detailization in JSON format.
--print-json Print output in JSON format.
--no-details Do not print best epoch details.
--hyperopt-filename FILENAME
Hyperopt result filename.Example: `--hyperopt-
filename=hyperopt_results_2020-09-27_16-20-48.pickle`
--export-csv FILE Export to CSV-File. This will disable table print.
Example: --export-csv hyperopt.csv
@ -481,6 +485,10 @@ Common arguments:
Path to userdata directory.
```
!!! Note
`hyperopt-list` will automatically use the latest available hyperopt results file.
You can override this using the `--hyperopt-filename` argument, and specify another, available filename (without path!).
### Examples
List all results, print details of the best result at the end:
@ -501,17 +509,41 @@ You can show the details of any hyperoptimization epoch previously evaluated by
usage: freqtrade hyperopt-show [-h] [-v] [--logfile FILE] [-V] [-c PATH]
[-d PATH] [--userdir PATH] [--best]
[--profitable] [-n INT] [--print-json]
[--no-header]
[--hyperopt-filename PATH] [--no-header]
optional arguments:
-h, --help show this help message and exit
--best Select only best epochs.
--profitable Select only profitable epochs.
-n INT, --index INT Specify the index of the epoch to print details for.
--print-json Print best result detailization in JSON format.
--print-json Print output in JSON format.
--hyperopt-filename FILENAME
Hyperopt result filename.Example: `--hyperopt-
filename=hyperopt_results_2020-09-27_16-20-48.pickle`
--no-header Do not print epoch details header.
Common arguments:
-v, --verbose Verbose mode (-vv for more, -vvv to get all messages).
--logfile FILE Log to the file specified. Special values are:
'syslog', 'journald'. See the documentation for more
details.
-V, --version show program's version number and exit
-c PATH, --config PATH
Specify configuration file (default:
`userdir/config.json` or `config.json` whichever
exists). Multiple --config options may be used. Can be
set to `-` to read config from stdin.
-d PATH, --datadir PATH
Path to directory with historical backtesting data.
--userdir PATH, --user-data-dir PATH
Path to userdata directory.
```
!!! Note
`hyperopt-show` will automatically use the latest available hyperopt results file.
You can override this using the `--hyperopt-filename` argument, and specify another, available filename (without path!).
### Examples
Print details for the epoch 168 (the number of the epoch is shown by the `hyperopt-list` subcommand or by Hyperopt itself during hyperoptimization run):

View File

@ -21,7 +21,7 @@ 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_Lib0.4.18cp38cp38win_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 precompiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which needs to be downloaded and installed using `pip install TA_Lib0.4.19cp38cp38win_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.
@ -32,7 +32,7 @@ python -m venv .env
.env\Scripts\activate.ps1
# optionally install ta-lib from wheel
# Eventually adjust the below filename to match the downloaded wheel
pip install build_helpes/TA_Lib0.4.18cp38cp38win_amd64.whl
pip install build_helpes/TA_Lib0.4.19cp38cp38win_amd64.whl
pip install -r requirements.txt
pip install -e .
freqtrade

View File

@ -8,5 +8,6 @@ To launch Freqtrade as a module
from freqtrade import main
if __name__ == '__main__':
main.main()

View File

@ -8,23 +8,15 @@ Note: Be careful with file-scoped imports in these subfiles.
"""
from freqtrade.commands.arguments import Arguments
from freqtrade.commands.build_config_commands import start_new_config
from freqtrade.commands.data_commands import (start_convert_data,
start_download_data,
from freqtrade.commands.data_commands import (start_convert_data, start_download_data,
start_list_data)
from freqtrade.commands.deploy_commands import (start_create_userdir,
start_new_hyperopt,
from freqtrade.commands.deploy_commands import (start_create_userdir, start_new_hyperopt,
start_new_strategy)
from freqtrade.commands.hyperopt_commands import (start_hyperopt_list,
start_hyperopt_show)
from freqtrade.commands.list_commands import (start_list_exchanges,
start_list_hyperopts,
start_list_markets,
start_list_strategies,
start_list_timeframes,
start_show_trades)
from freqtrade.commands.optimize_commands import (start_backtesting,
start_edge, start_hyperopt)
from freqtrade.commands.hyperopt_commands import start_hyperopt_list, start_hyperopt_show
from freqtrade.commands.list_commands import (start_list_exchanges, start_list_hyperopts,
start_list_markets, start_list_strategies,
start_list_timeframes, start_show_trades)
from freqtrade.commands.optimize_commands import start_backtesting, start_edge, start_hyperopt
from freqtrade.commands.pairlist_commands import start_test_pairlist
from freqtrade.commands.plot_commands import (start_plot_dataframe,
start_plot_profit)
from freqtrade.commands.plot_commands import start_plot_dataframe, start_plot_profit
from freqtrade.commands.trade_commands import start_trading

View File

@ -9,6 +9,7 @@ from typing import Any, Dict, List, Optional
from freqtrade.commands.cli_options import AVAILABLE_CLI_OPTIONS
from freqtrade.constants import DEFAULT_CONFIG
ARGS_COMMON = ["verbosity", "logfile", "version", "config", "datadir", "user_data_dir"]
ARGS_STRATEGY = ["strategy", "strategy_path"]
@ -26,7 +27,7 @@ ARGS_HYPEROPT = ARGS_COMMON_OPTIMIZE + ["hyperopt", "hyperopt_path",
"use_max_market_positions", "print_all",
"print_colorized", "print_json", "hyperopt_jobs",
"hyperopt_random_state", "hyperopt_min_trades",
"hyperopt_continue", "hyperopt_loss"]
"hyperopt_loss"]
ARGS_EDGE = ARGS_COMMON_OPTIMIZE + ["stoploss_range"]
@ -56,7 +57,7 @@ ARGS_CONVERT_DATA_OHLCV = ARGS_CONVERT_DATA + ["timeframes"]
ARGS_LIST_DATA = ["exchange", "dataformat_ohlcv", "pairs"]
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "download_trades", "exchange",
ARGS_DOWNLOAD_DATA = ["pairs", "pairs_file", "days", "timerange", "download_trades", "exchange",
"timeframes", "erase", "dataformat_ohlcv", "dataformat_trades"]
ARGS_PLOT_DATAFRAME = ["pairs", "indicators1", "indicators2", "plot_limit",
@ -75,10 +76,10 @@ ARGS_HYPEROPT_LIST = ["hyperopt_list_best", "hyperopt_list_profitable",
"hyperopt_list_min_total_profit", "hyperopt_list_max_total_profit",
"hyperopt_list_min_objective", "hyperopt_list_max_objective",
"print_colorized", "print_json", "hyperopt_list_no_details",
"export_csv"]
"hyperoptexportfilename", "export_csv"]
ARGS_HYPEROPT_SHOW = ["hyperopt_list_best", "hyperopt_list_profitable", "hyperopt_show_index",
"print_json", "hyperopt_show_no_header"]
"print_json", "hyperoptexportfilename", "hyperopt_show_no_header"]
NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list-timeframes",
"list-markets", "list-pairs", "list-strategies", "list-data",
@ -161,16 +162,14 @@ class Arguments:
self.parser = argparse.ArgumentParser(description='Free, open source crypto trading bot')
self._build_args(optionlist=['version'], parser=self.parser)
from freqtrade.commands import (start_create_userdir, start_convert_data,
start_download_data, start_list_data,
start_hyperopt_list, start_hyperopt_show,
from freqtrade.commands import (start_backtesting, start_convert_data, start_create_userdir,
start_download_data, start_edge, start_hyperopt,
start_hyperopt_list, start_hyperopt_show, start_list_data,
start_list_exchanges, start_list_hyperopts,
start_list_markets, start_list_strategies,
start_list_timeframes, start_new_config,
start_new_hyperopt, start_new_strategy,
start_plot_dataframe, start_plot_profit, start_show_trades,
start_backtesting, start_hyperopt, start_edge,
start_test_pairlist, start_trading)
start_list_timeframes, start_new_config, start_new_hyperopt,
start_new_strategy, start_plot_dataframe, start_plot_profit,
start_show_trades, start_test_pairlist, start_trading)
subparsers = self.parser.add_subparsers(dest='command',
# Use custom message when no subhandler is added

View File

@ -1,13 +1,15 @@
import logging
from pathlib import Path
from typing import Any, Dict
from typing import Any, Dict, List
from questionary import Separator, prompt
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
from freqtrade.exchange import available_exchanges, MAP_EXCHANGE_CHILDCLASS
from freqtrade.misc import render_template
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, available_exchanges
from freqtrade.misc import render_template
logger = logging.getLogger(__name__)
@ -46,7 +48,7 @@ def ask_user_config() -> Dict[str, Any]:
Interactive questions built using https://github.com/tmbo/questionary
:returns: Dict with keys to put into template
"""
questions = [
questions: List[Dict[str, Any]] = [
{
"type": "confirm",
"name": "dry_run",

View File

@ -252,23 +252,20 @@ AVAILABLE_CLI_OPTIONS = {
metavar='INT',
default=1,
),
"hyperopt_continue": Arg(
"--continue",
help="Continue hyperopt from previous runs. "
"By default, temporary files will be removed and hyperopt will start from scratch.",
default=False,
action='store_true',
),
"hyperopt_loss": Arg(
'--hyperopt-loss',
help='Specify the class name of the hyperopt loss function class (IHyperOptLoss). '
'Different functions can generate completely different results, '
'since the target for optimization is different. Built-in Hyperopt-loss-functions are: '
'DefaultHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, SharpeHyperOptLossDaily, '
'SortinoHyperOptLoss, SortinoHyperOptLossDaily.'
'(default: `%(default)s`).',
'ShortTradeDurHyperOptLoss, OnlyProfitHyperOptLoss, SharpeHyperOptLoss, '
'SharpeHyperOptLossDaily, SortinoHyperOptLoss, SortinoHyperOptLossDaily.',
metavar='NAME',
default=constants.DEFAULT_HYPEROPT_LOSS,
),
"hyperoptexportfilename": Arg(
'--hyperopt-filename',
help='Hyperopt result filename.'
'Example: `--hyperopt-filename=hyperopt_results_2020-09-27_16-20-48.pickle`',
metavar='FILENAME',
),
# List exchanges
"print_one_column": Arg(
@ -375,7 +372,7 @@ AVAILABLE_CLI_OPTIONS = {
help='Specify which tickers to download. Space-separated list. '
'Default: `1m 5m`.',
choices=['1m', '3m', '5m', '15m', '30m', '1h', '2h', '4h',
'6h', '8h', '12h', '1d', '3d', '1w'],
'6h', '8h', '12h', '1d', '3d', '1w', '2w', '1M', '1y'],
default=['1m', '5m'],
nargs='+',
),

View File

@ -6,16 +6,15 @@ from typing import Any, Dict, List
import arrow
from freqtrade.configuration import TimeRange, setup_utils_configuration
from freqtrade.data.converter import (convert_ohlcv_format,
convert_trades_format)
from freqtrade.data.history import (convert_trades_to_ohlcv,
refresh_backtest_ohlcv_data,
from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
from freqtrade.data.history import (convert_trades_to_ohlcv, refresh_backtest_ohlcv_data,
refresh_backtest_trades_data)
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@ -25,11 +24,17 @@ def start_download_data(args: Dict[str, Any]) -> None:
"""
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
if 'days' in config and 'timerange' in config:
raise OperationalException("--days and --timerange are mutually exclusive. "
"You can only specify one or the other.")
timerange = TimeRange()
if 'days' in config:
time_since = arrow.utcnow().shift(days=-config['days']).strftime("%Y%m%d")
timerange = TimeRange.parse_timerange(f'{time_since}-')
if 'timerange' in config:
timerange = timerange.parse_timerange(config['timerange'])
if 'pairs' not in config:
raise OperationalException(
"Downloading data requires a list of pairs. "
@ -99,8 +104,9 @@ def start_list_data(args: Dict[str, Any]) -> None:
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
from freqtrade.data.history.idatahandler import get_datahandler
from tabulate import tabulate
from freqtrade.data.history.idatahandler import get_datahandler
dhc = get_datahandler(config['datadir'], config['dataformat_ohlcv'])
paircombs = dhc.ohlcv_get_available_data(config['datadir'])

View File

@ -4,13 +4,13 @@ from pathlib import Path
from typing import Any, Dict
from freqtrade.configuration import setup_utils_configuration
from freqtrade.configuration.directory_operations import (copy_sample_files,
create_userdata_dir)
from freqtrade.configuration.directory_operations import copy_sample_files, create_userdata_dir
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.misc import render_template, render_template_with_fallback
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)

View File

@ -5,9 +5,11 @@ from typing import Any, Dict, List
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.state import RunMode
logger = logging.getLogger(__name__)
@ -40,8 +42,9 @@ def start_hyperopt_list(args: Dict[str, Any]) -> None:
'filter_max_objective': config.get('hyperopt_list_max_objective', None),
}
results_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
results_file = get_latest_hyperopt_file(
config['user_data_dir'] / 'hyperopt_results',
config.get('hyperoptexportfilename'))
# Previous evaluations
epochs = Hyperopt.load_previous_results(results_file)
@ -80,8 +83,10 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
print_json = config.get('print_json', False)
no_header = config.get('hyperopt_show_no_header', False)
results_file = (config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
results_file = get_latest_hyperopt_file(
config['user_data_dir'] / 'hyperopt_results',
config.get('hyperoptexportfilename'))
n = config.get('hyperopt_show_index', -1)
filteroptions = {

View File

@ -5,20 +5,20 @@ from collections import OrderedDict
from pathlib import Path
from typing import Any, Dict, List
from colorama import init as colorama_init
from colorama import Fore, Style
import rapidjson
from colorama import Fore, Style
from colorama import init as colorama_init
from tabulate import tabulate
from freqtrade.configuration import setup_utils_configuration
from freqtrade.constants import USERPATH_HYPEROPTS, USERPATH_STRATEGIES
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, ccxt_exchanges,
market_is_active)
from freqtrade.exchange import available_exchanges, ccxt_exchanges, market_is_active
from freqtrade.misc import plural
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@ -203,15 +203,16 @@ def start_show_trades(args: Dict[str, Any]) -> None:
"""
Show trades
"""
from freqtrade.persistence import init, Trade
import json
from freqtrade.persistence import Trade, init_db
config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
if 'db_url' not in config:
raise OperationalException("--db-url is required for this command.")
logger.info(f'Using DB: "{config["db_url"]}"')
init(config['db_url'], clean_open_orders=False)
init_db(config['db_url'], clean_open_orders=False)
tfilter = []
if config.get('trade_ids'):

View File

@ -6,6 +6,7 @@ from freqtrade.configuration import setup_utils_configuration
from freqtrade.exceptions import DependencyException, OperationalException
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)
@ -58,6 +59,7 @@ def start_hyperopt(args: Dict[str, Any]) -> None:
# Import here to avoid loading hyperopt module when it's not used
try:
from filelock import FileLock, Timeout
from freqtrade.optimize.hyperopt import Hyperopt
except ImportError as e:
raise OperationalException(
@ -98,6 +100,7 @@ def start_edge(args: Dict[str, Any]) -> None:
:return: None
"""
from freqtrade.optimize.edge_cli import EdgeCli
# Initialize configuration
config = setup_optimize_configuration(args, RunMode.EDGE)
logger.info('Starting freqtrade in Edge mode')

View File

@ -7,6 +7,7 @@ from freqtrade.configuration import setup_utils_configuration
from freqtrade.resolvers import ExchangeResolver
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)

View File

@ -1,5 +1,4 @@
import logging
from typing import Any, Dict

View File

@ -1,7 +1,7 @@
# flake8: noqa: F401
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.configuration.check_exchange import check_exchange, remove_credentials
from freqtrade.configuration.timerange import TimeRange
from freqtrade.configuration.configuration import Configuration
from freqtrade.configuration.config_setup import setup_utils_configuration
from freqtrade.configuration.config_validation import validate_config_consistency
from freqtrade.configuration.configuration import Configuration
from freqtrade.configuration.timerange import TimeRange

View File

@ -2,11 +2,11 @@ import logging
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import (available_exchanges, get_exchange_bad_reason,
is_exchange_bad, is_exchange_known_ccxt,
is_exchange_officially_supported)
from freqtrade.exchange import (available_exchanges, get_exchange_bad_reason, is_exchange_bad,
is_exchange_known_ccxt, is_exchange_officially_supported)
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)

View File

@ -1,10 +1,12 @@
import logging
from typing import Any, Dict
from freqtrade.state import RunMode
from .check_exchange import remove_credentials
from .config_validation import validate_config_consistency
from .configuration import Configuration
from .check_exchange import remove_credentials
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)

View File

@ -9,6 +9,7 @@ from freqtrade import constants
from freqtrade.exceptions import OperationalException
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)

View File

@ -10,14 +10,14 @@ from typing import Any, Callable, Dict, List, Optional
from freqtrade import constants
from freqtrade.configuration.check_exchange import check_exchange
from freqtrade.configuration.deprecated_settings import process_temporary_deprecated_settings
from freqtrade.configuration.directory_operations import (create_datadir,
create_userdata_dir)
from freqtrade.configuration.directory_operations import create_datadir, create_userdata_dir
from freqtrade.configuration.load_config import load_config_file
from freqtrade.exceptions import OperationalException
from freqtrade.loggers import setup_logging
from freqtrade.misc import deep_merge_dicts, json_load
from freqtrade.state import NON_UTIL_MODES, TRADING_MODES, RunMode
logger = logging.getLogger(__name__)
@ -263,6 +263,9 @@ class Configuration:
self._args_to_config(config, argname='hyperopt_path',
logstring='Using additional Hyperopt lookup path: {}')
self._args_to_config(config, argname='hyperoptexportfilename',
logstring='Using hyperopt file: {}')
self._args_to_config(config, argname='epochs',
logstring='Parameter --epochs detected ... '
'Will run Hyperopt with for {} epochs ...'
@ -295,9 +298,6 @@ class Configuration:
self._args_to_config(config, argname='hyperopt_min_trades',
logstring='Parameter --min-trades detected: {}')
self._args_to_config(config, argname='hyperopt_continue',
logstring='Hyperopt continue: {}')
self._args_to_config(config, argname='hyperopt_loss',
logstring='Using Hyperopt loss class name: {}')

View File

@ -3,8 +3,9 @@ import shutil
from pathlib import Path
from typing import Any, Dict, Optional
from freqtrade.exceptions import OperationalException
from freqtrade.constants import USER_DATA_FILES
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)

View File

@ -11,6 +11,7 @@ import rapidjson
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)

View File

@ -11,7 +11,6 @@ DEFAULT_EXCHANGE = 'bittrex'
PROCESS_THROTTLE_SECS = 5 # sec
HYPEROPT_EPOCH = 100 # epochs
RETRY_TIMEOUT = 30 # sec
DEFAULT_HYPEROPT_LOSS = 'DefaultHyperOptLoss'
DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
DEFAULT_DB_DRYRUN_URL = 'sqlite:///tradesv3.dryrun.sqlite'
UNLIMITED_STAKE_AMOUNT = 'unlimited'
@ -39,6 +38,8 @@ USERPATH_HYPEROPTS = 'hyperopts'
USERPATH_STRATEGIES = 'strategies'
USERPATH_NOTEBOOKS = 'notebooks'
TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
# Soure files with destination directories within user-directory
USER_DATA_FILES = {
'sample_strategy.py': USERPATH_STRATEGIES,
@ -201,6 +202,18 @@ CONF_SCHEMA = {
'enabled': {'type': 'boolean'},
'token': {'type': 'string'},
'chat_id': {'type': 'string'},
'notification_settings': {
'type': 'object',
'properties': {
'status': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'warning': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'startup': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'buy_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS},
'sell_cancel': {'type': 'string', 'enum': TELEGRAM_SETTING_OPTIONS}
}
}
},
'required': ['enabled', 'token', 'chat_id']
},

View File

@ -2,17 +2,17 @@
Helpers when analyzing backtest data
"""
import logging
from datetime import timezone
from pathlib import Path
from typing import Dict, Union, Tuple, Any, Optional
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import pandas as pd
from datetime import timezone
from freqtrade import persistence
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.misc import json_load
from freqtrade.persistence import Trade
from freqtrade.persistence import Trade, init_db
logger = logging.getLogger(__name__)
@ -21,10 +21,11 @@ BT_DATA_COLUMNS = ["pair", "profit_percent", "open_date", "close_date", "index",
"open_rate", "close_rate", "open_at_end", "sell_reason"]
def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
def get_latest_optimize_filename(directory: Union[Path, str], variant: str) -> str:
"""
Get latest backtest export based on '.last_result.json'.
:param directory: Directory to search for last result
:param variant: 'backtest' or 'hyperopt' - the method to return
:return: string containing the filename of the latest backtest result
:raises: ValueError in the following cases:
* Directory does not exist
@ -44,10 +45,57 @@ def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
with filename.open() as file:
data = json_load(file)
if 'latest_backtest' not in data:
if f'latest_{variant}' not in data:
raise ValueError(f"Invalid '{LAST_BT_RESULT_FN}' format.")
return data['latest_backtest']
return data[f'latest_{variant}']
def get_latest_backtest_filename(directory: Union[Path, str]) -> str:
"""
Get latest backtest export based on '.last_result.json'.
:param directory: Directory to search for last result
:return: string containing the filename of the latest backtest result
:raises: ValueError in the following cases:
* Directory does not exist
* `directory/.last_result.json` does not exist
* `directory/.last_result.json` has the wrong content
"""
return get_latest_optimize_filename(directory, 'backtest')
def get_latest_hyperopt_filename(directory: Union[Path, str]) -> str:
"""
Get latest hyperopt export based on '.last_result.json'.
:param directory: Directory to search for last result
:return: string containing the filename of the latest hyperopt result
:raises: ValueError in the following cases:
* Directory does not exist
* `directory/.last_result.json` does not exist
* `directory/.last_result.json` has the wrong content
"""
try:
return get_latest_optimize_filename(directory, 'hyperopt')
except ValueError:
# Return default (legacy) pickle filename
return 'hyperopt_results.pickle'
def get_latest_hyperopt_file(directory: Union[Path, str], predef_filename: str = None) -> Path:
"""
Get latest hyperopt export based on '.last_result.json'.
:param directory: Directory to search for last result
:return: string containing the filename of the latest hyperopt result
:raises: ValueError in the following cases:
* Directory does not exist
* `directory/.last_result.json` does not exist
* `directory/.last_result.json` has the wrong content
"""
if isinstance(directory, str):
directory = Path(directory)
if predef_filename:
return directory / predef_filename
return directory / get_latest_hyperopt_filename(directory)
def load_backtest_stats(filename: Union[Path, str]) -> Dict[str, Any]:
@ -169,7 +217,7 @@ def load_trades_from_db(db_url: str, strategy: Optional[str] = None) -> pd.DataF
Can also serve as protection to load the correct result.
:return: Dataframe containing Trades
"""
persistence.init(db_url, clean_open_orders=False)
init_db(db_url, clean_open_orders=False)
columns = ["pair", "open_date", "close_date", "profit", "profit_percent",
"open_rate", "close_rate", "amount", "trade_duration", "sell_reason",

View File

@ -10,8 +10,8 @@ from typing import Any, Dict, List
import pandas as pd
from pandas import DataFrame, to_datetime
from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
DEFAULT_TRADES_COLUMNS)
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
logger = logging.getLogger(__name__)

View File

@ -17,6 +17,7 @@ from freqtrade.exceptions import ExchangeError, OperationalException
from freqtrade.exchange import Exchange
from freqtrade.state import RunMode
logger = logging.getLogger(__name__)

View File

@ -5,10 +5,8 @@ Includes:
* load data for a pair (or a list of pairs) from disk
* download data from exchange and store to disk
"""
from .history_utils import (convert_trades_to_ohlcv, # noqa: F401
get_timerange, load_data, load_pair_history,
refresh_backtest_ohlcv_data,
refresh_backtest_trades_data, refresh_data,
# flake8: noqa: F401
from .history_utils import (convert_trades_to_ohlcv, get_timerange, load_data, load_pair_history,
refresh_backtest_ohlcv_data, refresh_backtest_trades_data, refresh_data,
validate_backtest_data)
from .idatahandler import get_datahandler # noqa: F401
from .idatahandler import get_datahandler

View File

@ -7,12 +7,12 @@ import pandas as pd
from freqtrade import misc
from freqtrade.configuration import TimeRange
from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
DEFAULT_TRADES_COLUMNS,
from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS,
ListPairsWithTimeframes)
from .idatahandler import IDataHandler, TradeList
logger = logging.getLogger(__name__)

View File

@ -9,15 +9,14 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS
from freqtrade.data.converter import (clean_ohlcv_dataframe,
ohlcv_to_dataframe,
trades_remove_duplicates,
trades_to_ohlcv)
from freqtrade.data.converter import (clean_ohlcv_dataframe, ohlcv_to_dataframe,
trades_remove_duplicates, trades_to_ohlcv)
from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import Exchange
from freqtrade.misc import format_ms_time
logger = logging.getLogger(__name__)
@ -136,7 +135,6 @@ def _load_cached_data_for_updating(pair: str, timeframe: str, timerange: Optiona
start = None
if timerange:
if timerange.starttype == 'date':
# TODO: convert to date for conversion
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
# Intentionally don't pass timerange in - since we need to load the full dataset.

View File

@ -14,10 +14,10 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.converter import (clean_ohlcv_dataframe,
trades_remove_duplicates, trim_dataframe)
from freqtrade.data.converter import clean_ohlcv_dataframe, trades_remove_duplicates, trim_dataframe
from freqtrade.exchange import timeframe_to_seconds
logger = logging.getLogger(__name__)
# Type for trades list

View File

@ -8,12 +8,12 @@ from pandas import DataFrame, read_json, to_datetime
from freqtrade import misc
from freqtrade.configuration import TimeRange
from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS,
ListPairsWithTimeframes)
from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, ListPairsWithTimeframes
from freqtrade.data.converter import trades_dict_to_list
from .idatahandler import IDataHandler, TradeList
logger = logging.getLogger(__name__)

View File

@ -9,11 +9,12 @@ import utils_find_1st as utf1st
from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, DATETIME_PRINT_FORMAT
from freqtrade.exceptions import OperationalException
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.strategy.interface import SellType
logger = logging.getLogger(__name__)
@ -309,8 +310,10 @@ class Edge:
# Calculating number of losing trades, average win and average loss
df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
df['average_win'] = df['profit_sum'] / df['nb_win_trades']
df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
df['average_win'] = np.where(df['nb_win_trades'] == 0, 0.0,
df['profit_sum'] / df['nb_win_trades'])
df['average_loss'] = np.where(df['nb_loss_trades'] == 0, 0.0,
df['loss_sum'] / df['nb_loss_trades'])
# Win rate = number of profitable trades / number of trades
df['winrate'] = df['nb_win_trades'] / df['nb_trades']

View File

@ -51,6 +51,13 @@ class RetryableOrderError(InvalidOrderException):
"""
class InsufficientFundsError(InvalidOrderException):
"""
This error is used when there are not enough funds available on the exchange
to create an order.
"""
class TemporaryError(ExchangeError):
"""
Temporary network or exchange related error.

View File

@ -1,19 +1,16 @@
# flake8: noqa: F401
# isort: off
from freqtrade.exchange.common import MAP_EXCHANGE_CHILDCLASS
from freqtrade.exchange.exchange import Exchange
from freqtrade.exchange.exchange import (get_exchange_bad_reason,
is_exchange_bad,
is_exchange_known_ccxt,
is_exchange_officially_supported,
ccxt_exchanges,
available_exchanges)
from freqtrade.exchange.exchange import (timeframe_to_seconds,
timeframe_to_minutes,
timeframe_to_msecs,
timeframe_to_next_date,
timeframe_to_prev_date)
from freqtrade.exchange.exchange import (market_is_active)
from freqtrade.exchange.kraken import Kraken
from freqtrade.exchange.binance import Binance
# isort: on
from freqtrade.exchange.bibox import Bibox
from freqtrade.exchange.binance import Binance
from freqtrade.exchange.bittrex import Bittrex
from freqtrade.exchange.exchange import (available_exchanges, ccxt_exchanges,
get_exchange_bad_reason, is_exchange_bad,
is_exchange_known_ccxt, is_exchange_officially_supported,
market_is_active, timeframe_to_minutes, timeframe_to_msecs,
timeframe_to_next_date, timeframe_to_prev_date,
timeframe_to_seconds)
from freqtrade.exchange.ftx import Ftx
from freqtrade.exchange.kraken import Kraken

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@ -4,6 +4,7 @@ from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)

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@ -4,12 +4,12 @@ from typing import Dict
import ccxt
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
logger = logging.getLogger(__name__)
@ -20,20 +20,9 @@ class Binance(Exchange):
"order_time_in_force": ['gtc', 'fok', 'ioc'],
"trades_pagination": "id",
"trades_pagination_arg": "fromId",
"l2_limit_range": [5, 10, 20, 50, 100, 500, 1000],
}
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
"""
get order book level 2 from exchange
20180619: binance support limits but only on specific range
"""
limit_range = [5, 10, 20, 50, 100, 500, 1000]
# get next-higher step in the limit_range list
limit = min(list(filter(lambda x: limit <= x, limit_range)))
return super().fetch_l2_order_book(pair, limit)
def stoploss_adjust(self, stop_loss: float, order: Dict) -> bool:
"""
Verify stop_loss against stoploss-order value (limit or price)
@ -80,7 +69,7 @@ class Binance(Exchange):
'stop price: %s. limit: %s', pair, stop_price, rate)
return order
except ccxt.InsufficientFunds as e:
raise ExchangeError(
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to sell amount {amount} at rate {rate}. '
f'Message: {e}') from e

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@ -0,0 +1,23 @@
""" Bittrex exchange subclass """
import logging
from typing import Dict
from freqtrade.exchange import Exchange
logger = logging.getLogger(__name__)
class Bittrex(Exchange):
"""
Bittrex 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 = {
"l2_limit_range": [1, 25, 500],
}

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@ -3,13 +3,17 @@ import logging
import time
from functools import wraps
from freqtrade.exceptions import (DDosProtection, RetryableOrderError,
TemporaryError)
from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
logger = logging.getLogger(__name__)
# Maximum default retry count.
# Functions are always called RETRY_COUNT + 1 times (for the original call)
API_RETRY_COUNT = 4
API_FETCH_ORDER_RETRY_COUNT = 5
BAD_EXCHANGES = {
"bitmex": "Various reasons.",
"bitstamp": "Does not provide history. "

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@ -8,24 +8,25 @@ import logging
from copy import deepcopy
from datetime import datetime, timezone
from math import ceil
from random import randint
from typing import Any, Dict, List, Optional, Tuple
import arrow
import ccxt
import ccxt.async_support as ccxt_async
from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE,
TRUNCATE, decimal_to_precision)
from ccxt.base.decimal_to_precision import (ROUND_DOWN, ROUND_UP, TICK_SIZE, TRUNCATE,
decimal_to_precision)
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.converter import ohlcv_to_dataframe, trades_dict_to_list
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
RetryableOrderError, TemporaryError)
from freqtrade.exchange.common import BAD_EXCHANGES, retrier, retrier_async
from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
InvalidOrderException, OperationalException, RetryableOrderError,
TemporaryError)
from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, BAD_EXCHANGES, retrier,
retrier_async)
from freqtrade.misc import deep_merge_dicts, safe_value_fallback2
CcxtModuleType = Any
@ -52,7 +53,7 @@ class Exchange:
"ohlcv_partial_candle": True,
"trades_pagination": "time", # Possible are "time" or "id"
"trades_pagination_arg": "since",
"l2_limit_range": None,
}
_ft_has: Dict = {}
@ -487,11 +488,11 @@ class Exchange:
def dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
rate: float, params: Dict = {}) -> Dict[str, Any]:
order_id = f'dry_run_{side}_{randint(0, 10**6)}'
order_id = f'dry_run_{side}_{datetime.now().timestamp()}'
_amount = self.amount_to_precision(pair, amount)
dry_order = {
"id": order_id,
'pair': pair,
'id': order_id,
'symbol': pair,
'price': rate,
'average': rate,
'amount': _amount,
@ -500,6 +501,7 @@ class Exchange:
'side': side,
'remaining': _amount,
'datetime': arrow.utcnow().isoformat(),
'timestamp': int(arrow.utcnow().timestamp * 1000),
'status': "closed" if ordertype == "market" else "open",
'fee': None,
'info': {}
@ -538,7 +540,7 @@ class Exchange:
amount, rate_for_order, params)
except ccxt.InsufficientFunds as e:
raise ExchangeError(
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} {side} order on market {pair}. '
f'Tried to {side} amount {amount} at rate {rate}.'
f'Message: {e}') from e
@ -1027,7 +1029,7 @@ class Exchange:
return order
@retrier(retries=5)
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
try:
@ -1056,6 +1058,27 @@ class Exchange:
# Assign method to fetch_stoploss_order to allow easy overriding in other classes
fetch_stoploss_order = fetch_order
def fetch_order_or_stoploss_order(self, order_id: str, pair: str,
stoploss_order: bool = False) -> Dict:
"""
Simple wrapper calling either fetch_order or fetch_stoploss_order depending on
the stoploss_order parameter
:param stoploss_order: If true, uses fetch_stoploss_order, otherwise fetch_order.
"""
if stoploss_order:
return self.fetch_stoploss_order(order_id, pair)
return self.fetch_order(order_id, pair)
@staticmethod
def get_next_limit_in_list(limit: int, limit_range: Optional[List[int]]):
"""
Get next greater value in the list.
Used by fetch_l2_order_book if the api only supports a limited range
"""
if not limit_range:
return limit
return min([x for x in limit_range if limit <= x] + [max(limit_range)])
@retrier
def fetch_l2_order_book(self, pair: str, limit: int = 100) -> dict:
"""
@ -1064,9 +1087,10 @@ class Exchange:
Returns a dict in the format
{'asks': [price, volume], 'bids': [price, volume]}
"""
limit1 = self.get_next_limit_in_list(limit, self._ft_has['l2_limit_range'])
try:
return self._api.fetch_l2_order_book(pair, limit)
return self._api.fetch_l2_order_book(pair, limit1)
except ccxt.NotSupported as e:
raise OperationalException(
f'Exchange {self._api.name} does not support fetching order book.'

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@ -4,11 +4,11 @@ from typing import Any, Dict
import ccxt
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
from freqtrade.exchange.common import API_FETCH_ORDER_RETRY_COUNT, retrier
logger = logging.getLogger(__name__)
@ -71,7 +71,7 @@ class Ftx(Exchange):
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise ExchangeError(
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e
@ -88,7 +88,7 @@ class Ftx(Exchange):
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier(retries=5)
@retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
def fetch_stoploss_order(self, order_id: str, pair: str) -> Dict:
if self._config['dry_run']:
try:

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@ -4,12 +4,12 @@ from typing import Any, Dict
import ccxt
from freqtrade.exceptions import (DDosProtection, ExchangeError,
InvalidOrderException, OperationalException,
TemporaryError)
from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, InvalidOrderException,
OperationalException, TemporaryError)
from freqtrade.exchange import Exchange
from freqtrade.exchange.common import retrier
logger = logging.getLogger(__name__)
@ -98,7 +98,7 @@ class Kraken(Exchange):
'stop price: %s.', pair, stop_price)
return order
except ccxt.InsufficientFunds as e:
raise ExchangeError(
raise InsufficientFundsError(
f'Insufficient funds to create {ordertype} sell order on market {pair}. '
f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
f'Message: {e}') from e

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@ -12,17 +12,17 @@ from typing import Any, Dict, List, Optional
import arrow
from cachetools import TTLCache
from freqtrade import __version__, constants, persistence
from freqtrade import __version__, constants
from freqtrade.configuration import validate_config_consistency
from freqtrade.data.converter import order_book_to_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.edge import Edge
from freqtrade.exceptions import (DependencyException, ExchangeError,
from freqtrade.exceptions import (DependencyException, ExchangeError, InsufficientFundsError,
InvalidOrderException, PricingError)
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date
from freqtrade.misc import safe_value_fallback, safe_value_fallback2
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.persistence import Order, Trade, cleanup_db, init_db
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.state import State
@ -30,6 +30,7 @@ from freqtrade.strategy.interface import IStrategy, SellType
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
@ -57,8 +58,8 @@ class FreqtradeBot:
# Cache values for 1800 to avoid frequent polling of the exchange for prices
# Caching only applies to RPC methods, so prices for open trades are still
# refreshed once every iteration.
self._sell_rate_cache = TTLCache(maxsize=100, ttl=1800)
self._buy_rate_cache = TTLCache(maxsize=100, ttl=1800)
self._sell_rate_cache: TTLCache = TTLCache(maxsize=100, ttl=1800)
self._buy_rate_cache: TTLCache = TTLCache(maxsize=100, ttl=1800)
self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
@ -67,7 +68,7 @@ class FreqtradeBot:
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
persistence.init(self.config.get('db_url', None), clean_open_orders=self.config['dry_run'])
init_db(self.config.get('db_url', None), clean_open_orders=self.config['dry_run'])
self.wallets = Wallets(self.config, self.exchange)
@ -122,7 +123,7 @@ class FreqtradeBot:
self.check_for_open_trades()
self.rpc.cleanup()
persistence.cleanup()
cleanup_db()
def startup(self) -> None:
"""
@ -134,6 +135,10 @@ class FreqtradeBot:
# Adjust stoploss if it was changed
Trade.stoploss_reinitialization(self.strategy.stoploss)
# Only update open orders on startup
# This will update the database after the initial migration
self.update_open_orders()
def process(self) -> None:
"""
Queries the persistence layer for open trades and handles them,
@ -144,6 +149,8 @@ class FreqtradeBot:
# Check whether markets have to be reloaded and reload them when it's needed
self.exchange.reload_markets()
self.update_closed_trades_without_assigned_fees()
# Query trades from persistence layer
trades = Trade.get_open_trades()
@ -227,6 +234,104 @@ class FreqtradeBot:
open_trades = len(Trade.get_open_trades())
return max(0, self.config['max_open_trades'] - open_trades)
def update_open_orders(self):
"""
Updates open orders based on order list kept in the database.
Mainly updates the state of orders - but may also close trades
"""
orders = Order.get_open_orders()
logger.info(f"Updating {len(orders)} open orders.")
for order in orders:
try:
fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
order.ft_order_side == 'stoploss')
self.update_trade_state(order.trade, order.order_id, fo)
except ExchangeError as e:
logger.warning(f"Error updating Order {order.order_id} due to {e}")
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.
"""
trades: List[Trade] = Trade.get_sold_trades_without_assigned_fees()
for trade in trades:
if not trade.is_open and not trade.fee_updated('sell'):
# Get sell fee
order = trade.select_order('sell', False)
if order:
logger.info(f"Updating sell-fee on trade {trade} for order {order.order_id}.")
self.update_trade_state(trade, order.order_id,
stoploss_order=order.ft_order_side == 'stoploss')
trades: List[Trade] = Trade.get_open_trades_without_assigned_fees()
for trade in trades:
if trade.is_open and not trade.fee_updated('buy'):
order = trade.select_order('buy', False)
if order:
logger.info(f"Updating buy-fee on trade {trade} for order {order.order_id}.")
self.update_trade_state(trade, order.order_id)
def handle_insufficient_funds(self, trade: Trade):
"""
Determine if we ever opened a sell order for this trade.
If not, try update buy fees - otherwise "refind" the open order we obviously lost.
"""
sell_order = trade.select_order('sell', None)
if sell_order:
self.refind_lost_order(trade)
else:
self.reupdate_buy_order_fees(trade)
def reupdate_buy_order_fees(self, trade: Trade):
"""
Get buy order from database, and try to reupdate.
Handles trades where the initial fee-update did not work.
"""
logger.info(f"Trying to reupdate buy fees for {trade}")
order = trade.select_order('buy', False)
if order:
logger.info(f"Updating buy-fee on trade {trade} for order {order.order_id}.")
self.update_trade_state(trade, order.order_id)
def refind_lost_order(self, trade):
"""
Try refinding a lost trade.
Only used when InsufficientFunds appears on sell orders (stoploss or sell).
Tries to walk the stored orders and sell them off eventually.
"""
logger.info(f"Trying to refind lost order for {trade}")
for order in trade.orders:
logger.info(f"Trying to refind {order}")
fo = None
if not order.ft_is_open:
logger.debug(f"Order {order} is no longer open.")
continue
if order.ft_order_side == 'buy':
# Skip buy side - this is handled by reupdate_buy_order_fees
continue
try:
fo = self.exchange.fetch_order_or_stoploss_order(order.order_id, order.ft_pair,
order.ft_order_side == 'stoploss')
if order.ft_order_side == 'stoploss':
if fo and fo['status'] == 'open':
# Assume this as the open stoploss order
trade.stoploss_order_id = order.order_id
elif order.ft_order_side == 'sell':
if fo and fo['status'] == 'open':
# Assume this as the open order
trade.open_order_id = order.order_id
if fo:
logger.info(f"Found {order} for trade {trade}.jj")
self.update_trade_state(trade, order.order_id, fo,
stoploss_order=order.ft_order_side == 'stoploss')
except ExchangeError:
logger.warning(f"Error updating {order.order_id}.")
#
# BUY / enter positions / open trades logic and methods
#
@ -528,6 +633,7 @@ class FreqtradeBot:
order = self.exchange.buy(pair=pair, ordertype=order_type,
amount=amount, rate=buy_limit_requested,
time_in_force=time_in_force)
order_obj = Order.parse_from_ccxt_object(order, pair, 'buy')
order_id = order['id']
order_status = order.get('status', None)
@ -556,7 +662,6 @@ class FreqtradeBot:
stake_amount = order['cost']
amount = safe_value_fallback(order, 'filled', 'amount')
buy_limit_filled_price = safe_value_fallback(order, 'average', 'price')
order_id = None
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
@ -581,10 +686,11 @@ class FreqtradeBot:
strategy=self.strategy.get_strategy_name(),
timeframe=timeframe_to_minutes(self.config['timeframe'])
)
trade.orders.append(order_obj)
# Update fees if order is closed
if order_status == 'closed':
self.update_trade_state(trade, order)
self.update_trade_state(trade, order_id, order)
Trade.session.add(trade)
Trade.session.flush()
@ -783,8 +889,16 @@ class FreqtradeBot:
stoploss_order = self.exchange.stoploss(pair=trade.pair, amount=trade.amount,
stop_price=stop_price,
order_types=self.strategy.order_types)
order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
trade.orders.append(order_obj)
trade.stoploss_order_id = str(stoploss_order['id'])
return True
except InsufficientFundsError as e:
logger.warning(f"Unable to place stoploss order {e}.")
# Try to figure out what went wrong
self.handle_insufficient_funds(trade)
except InvalidOrderException as e:
trade.stoploss_order_id = None
logger.error(f'Unable to place a stoploss order on exchange. {e}')
@ -814,13 +928,17 @@ class FreqtradeBot:
except InvalidOrderException as exception:
logger.warning('Unable to fetch stoploss order: %s', exception)
if stoploss_order:
trade.update_order(stoploss_order)
# We check if stoploss order is fulfilled
if stoploss_order and stoploss_order['status'] in ('closed', 'triggered'):
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
self.update_trade_state(trade, stoploss_order, sl_order=True)
self.update_trade_state(trade, trade.stoploss_order_id, stoploss_order,
stoploss_order=True)
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair,
timeframe_to_next_date(self.config['timeframe']))
self.strategy.lock_pair(trade.pair, timeframe_to_next_date(self.config['timeframe']),
reason='Auto lock')
self._notify_sell(trade, "stoploss")
return True
@ -869,10 +987,11 @@ class FreqtradeBot:
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('Trailing stoploss: cancelling current stoploss on exchange (id:{%s}) '
'in order to add another one ...', order['id'])
logger.info(f"Cancelling current stoploss on exchange for pair {trade.pair} "
f"(orderid:{order['id']}) in order to add another one ...")
try:
self.exchange.cancel_stoploss_order(order['id'], trade.pair)
co = self.exchange.cancel_stoploss_order(order['id'], trade.pair)
trade.update_order(co)
except InvalidOrderException:
logger.exception(f"Could not cancel stoploss order {order['id']} "
f"for pair {trade.pair}")
@ -927,7 +1046,7 @@ class FreqtradeBot:
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
fully_cancelled = self.update_trade_state(trade, order)
fully_cancelled = self.update_trade_state(trade, trade.open_order_id, order)
if (order['side'] == 'buy' and (order['status'] == 'open' or fully_cancelled) and (
fully_cancelled
@ -995,8 +1114,7 @@ class FreqtradeBot:
if isclose(filled_amount, 0.0, abs_tol=constants.MATH_CLOSE_PREC):
logger.info('Buy order fully cancelled. Removing %s from database.', trade)
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
Trade.session.flush()
trade.delete()
was_trade_fully_canceled = True
reason += f", {constants.CANCEL_REASON['FULLY_CANCELLED']}"
else:
@ -1007,7 +1125,7 @@ class FreqtradeBot:
# we need to fall back to the values from order if corder does not contain these keys.
trade.amount = filled_amount
trade.stake_amount = trade.amount * trade.open_rate
self.update_trade_state(trade, corder, trade.amount)
self.update_trade_state(trade, trade.open_order_id, corder)
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
@ -1121,23 +1239,33 @@ class FreqtradeBot:
logger.info(f"User requested abortion of selling {trade.pair}")
return False
try:
# Execute sell and update trade record
order = self.exchange.sell(pair=str(trade.pair),
order = self.exchange.sell(pair=trade.pair,
ordertype=order_type,
amount=amount, rate=limit,
time_in_force=time_in_force
)
except InsufficientFundsError as e:
logger.warning(f"Unable to place order {e}.")
# Try to figure out what went wrong
self.handle_insufficient_funds(trade)
return False
order_obj = Order.parse_from_ccxt_object(order, trade.pair, 'sell')
trade.orders.append(order_obj)
trade.open_order_id = order['id']
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
# In case of market sell orders the order can be closed immediately
if order.get('status', 'unknown') == 'closed':
self.update_trade_state(trade, order)
self.update_trade_state(trade, trade.open_order_id, order)
Trade.session.flush()
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(trade.pair, timeframe_to_next_date(self.config['timeframe']))
self.strategy.lock_pair(trade.pair, timeframe_to_next_date(self.config['timeframe']),
reason='Auto lock')
self._notify_sell(trade, order_type)
@ -1230,30 +1358,35 @@ class FreqtradeBot:
# Common update trade state methods
#
def update_trade_state(self, trade: Trade, action_order: dict = None,
order_amount: float = None, sl_order: bool = False) -> bool:
def update_trade_state(self, trade: Trade, order_id: str, action_order: Dict[str, Any] = None,
stoploss_order: bool = False) -> bool:
"""
Checks trades with open orders and updates the amount if necessary
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
:return: True if order has been cancelled without being filled partially, False otherwise
"""
# Get order details for actual price per unit
if trade.open_order_id:
order_id = trade.open_order_id
elif trade.stoploss_order_id and sl_order:
order_id = trade.stoploss_order_id
else:
if not order_id:
logger.warning(f'Orderid for trade {trade} is empty.')
return False
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.fetch_order(order_id, trade.pair)
order = action_order or self.exchange.fetch_order_or_stoploss_order(order_id,
trade.pair,
stoploss_order)
except InvalidOrderException as exception:
logger.warning('Unable to fetch order %s: %s', order_id, exception)
return False
trade.update_order(order)
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order, order_amount)
new_amount = self.get_real_amount(trade, order)
if not isclose(safe_value_fallback(order, 'filled', 'amount'), new_amount,
abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
@ -1291,7 +1424,7 @@ class FreqtradeBot:
return real_amount
return amount
def get_real_amount(self, trade: Trade, order: Dict, order_amount: float = None) -> float:
def get_real_amount(self, trade: Trade, order: Dict) -> float:
"""
Detect and update trade fee.
Calls trade.update_fee() uppon correct detection.
@ -1300,7 +1433,6 @@ class FreqtradeBot:
:return: identical (or new) amount for the trade
"""
# Init variables
if order_amount is None:
order_amount = safe_value_fallback(order, 'filled', 'amount')
# Only run for closed orders
if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
@ -1325,7 +1457,7 @@ class FreqtradeBot:
"""
fee-detection fallback to Trades. Parses result of fetch_my_trades to get correct fee.
"""
trades = self.exchange.get_trades_for_order(trade.open_order_id, trade.pair,
trades = self.exchange.get_trades_for_order(order['id'], trade.pair,
trade.open_date)
if len(trades) == 0:

View File

@ -1,12 +1,12 @@
import logging
import sys
from logging import Formatter
from logging.handlers import (BufferingHandler, RotatingFileHandler,
SysLogHandler)
from logging.handlers import BufferingHandler, RotatingFileHandler, SysLogHandler
from typing import Any, Dict
from freqtrade.exceptions import OperationalException
logger = logging.getLogger(__name__)
LOGFORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'

View File

@ -7,6 +7,7 @@ import logging
import sys
from typing import Any, List
# check min. python version
if sys.version_info < (3, 6):
sys.exit("Freqtrade requires Python version >= 3.6")

View File

@ -12,6 +12,7 @@ from typing.io import IO
import numpy as np
import rapidjson
logger = logging.getLogger(__name__)
@ -41,7 +42,7 @@ def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
return dates.dt.to_pydatetime()
def file_dump_json(filename: Path, data: Any, is_zip: bool = False) -> None:
def file_dump_json(filename: Path, data: Any, is_zip: bool = False, log: bool = True) -> None:
"""
Dump JSON data into a file
:param filename: file to create
@ -52,11 +53,13 @@ def file_dump_json(filename: Path, data: Any, is_zip: bool = False) -> None:
if is_zip:
if filename.suffix != '.gz':
filename = filename.with_suffix('.gz')
if log:
logger.info(f'dumping json to "{filename}"')
with gzip.open(filename, 'w') as fp:
rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
with gzip.open(filename, 'w') as fpz:
rapidjson.dump(data, fpz, default=str, number_mode=rapidjson.NM_NATIVE)
else:
if log:
logger.info(f'dumping json to "{filename}"')
with open(filename, 'w') as fp:
rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)

View File

@ -4,31 +4,39 @@
This module contains the backtesting logic
"""
import logging
from collections import defaultdict
from copy import deepcopy
from datetime import datetime, timedelta
from typing import Any, Dict, List, NamedTuple, Optional, Tuple
import arrow
from pandas import DataFrame
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.optimize.optimize_reports import (generate_backtest_stats,
show_backtest_results,
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
store_backtest_stats)
from freqtrade.pairlist.pairlistmanager import PairListManager
from freqtrade.persistence import Trade
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
from freqtrade.strategy.interface import IStrategy, SellCheckTuple, SellType
logger = logging.getLogger(__name__)
# Indexes for backtest tuples
DATE_IDX = 0
BUY_IDX = 1
OPEN_IDX = 2
CLOSE_IDX = 3
SELL_IDX = 4
LOW_IDX = 5
HIGH_IDX = 6
class BacktestResult(NamedTuple):
"""
@ -116,7 +124,7 @@ class Backtesting:
"""
Load strategy into backtesting
"""
self.strategy = strategy
self.strategy: IStrategy = strategy
# 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
@ -148,12 +156,14 @@ class Backtesting:
return data, timerange
def _get_ohlcv_as_lists(self, processed: Dict) -> Dict[str, DataFrame]:
def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
"""
Helper function to convert a processed dataframes into lists for performance reasons.
Used by backtest() - so keep this optimized for performance.
"""
# Every change to this headers list must evaluate further usages of the resulting tuple
# and eventually change the constants for indexes at the top
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
data: Dict = {}
# Create dict with data
@ -173,10 +183,10 @@ class Backtesting:
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = [x for x in df_analyzed.itertuples()]
data[pair] = [x for x in df_analyzed.itertuples(index=False, name=None)]
return data
def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
def _get_close_rate(self, sell_row: Tuple, trade: Trade, sell: SellCheckTuple,
trade_dur: int) -> float:
"""
Get close rate for backtesting result
@ -187,12 +197,12 @@ class Backtesting:
return trade.stop_loss
elif sell.sell_type == (SellType.ROI):
roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
if roi is not None:
if roi is not None and roi_entry is not None:
if roi == -1 and roi_entry % self.timeframe_min == 0:
# When forceselling with ROI=-1, the roi time will always be equal to trade_dur.
# If that entry is a multiple of the timeframe (so on candle open)
# - we'll use open instead of close
return sell_row.open
return sell_row[OPEN_IDX]
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
close_rate = - (trade.open_rate * roi + trade.open_rate *
@ -200,57 +210,38 @@ class Backtesting:
if (trade_dur > 0 and trade_dur == roi_entry
and roi_entry % self.timeframe_min == 0
and sell_row.open > close_rate):
and sell_row[OPEN_IDX] > close_rate):
# new ROI entry came into effect.
# use Open rate if open_rate > calculated sell rate
return sell_row.open
return sell_row[OPEN_IDX]
# Use the maximum between close_rate and low as we
# cannot sell outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return max(close_rate, sell_row.low)
return max(close_rate, sell_row[LOW_IDX])
else:
# This should not be reached...
return sell_row.open
return sell_row[OPEN_IDX]
else:
return sell_row.open
return sell_row[OPEN_IDX]
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ohlcv: List, trade_count_lock: Dict,
stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
def _get_sell_trade_entry(self, trade: Trade, sell_row: Tuple) -> Optional[BacktestResult]:
trade = Trade(
pair=pair,
open_rate=buy_row.open,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=round(stake_amount / buy_row.open, 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
)
logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
# calculate win/lose forwards from buy point
for sell_row in partial_ohlcv:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, sell_row.buy,
sell_row.sell, low=sell_row.low, high=sell_row.high)
sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX], sell_row[DATE_IDX],
sell_row[BUY_IDX], sell_row[SELL_IDX],
low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
trade_dur = int((sell_row[DATE_IDX] - trade.open_date).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
return BacktestResult(pair=pair,
return BacktestResult(pair=trade.pair,
profit_percent=trade.calc_profit_ratio(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
open_date=buy_row.date,
open_rate=buy_row.open,
open_date=trade.open_date,
open_rate=trade.open_rate,
open_fee=self.fee,
close_date=sell_row.date,
close_date=sell_row[DATE_IDX],
close_rate=closerate,
close_fee=self.fee,
amount=trade.amount,
@ -258,33 +249,40 @@ class Backtesting:
open_at_end=False,
sell_reason=sell.sell_type
)
if partial_ohlcv:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ohlcv[-1]
bt_res = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
open_date=buy_row.date,
open_rate=buy_row.open,
return None
def handle_left_open(self, open_trades: Dict[str, List[Trade]],
data: Dict[str, List[Tuple]]) -> List[BacktestResult]:
"""
Handling of left open trades at the end of backtesting
"""
trades = []
for pair in open_trades.keys():
if len(open_trades[pair]) > 0:
for trade in open_trades[pair]:
sell_row = data[pair][-1]
trade_entry = BacktestResult(pair=trade.pair,
profit_percent=trade.calc_profit_ratio(
rate=sell_row[OPEN_IDX]),
profit_abs=trade.calc_profit(sell_row[OPEN_IDX]),
open_date=trade.open_date,
open_rate=trade.open_rate,
open_fee=self.fee,
close_date=sell_row.date,
close_rate=sell_row.open,
close_date=sell_row[DATE_IDX],
close_rate=sell_row[OPEN_IDX],
close_fee=self.fee,
amount=trade.amount,
trade_duration=int((
sell_row.date - buy_row.date).total_seconds() // 60),
sell_row[DATE_IDX] - trade.open_date
).total_seconds() // 60),
open_at_end=True,
sell_reason=SellType.FORCE_SELL
)
logger.debug(f"{pair} - Force selling still open trade, "
f"profit percent: {bt_res.profit_percent}, "
f"profit abs: {bt_res.profit_abs}")
return bt_res
return None
trades.append(trade_entry)
return trades
def backtest(self, processed: Dict, stake_amount: float,
start_date: arrow.Arrow, end_date: arrow.Arrow,
start_date: datetime, end_date: datetime,
max_open_trades: int = 0, position_stacking: bool = False) -> DataFrame:
"""
Implement backtesting functionality
@ -306,19 +304,21 @@ class Backtesting:
f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
)
trades = []
trade_count_lock: Dict = {}
# 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)
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List] = defaultdict(list)
open_trade_count = 0
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
while tmp <= end_date:
open_trade_count_start = open_trade_count
for i, pair in enumerate(data):
if pair not in indexes:
@ -332,42 +332,52 @@ class Backtesting:
continue
# Waits until the time-counter reaches the start of the data for this pair.
if row.date > tmp.datetime:
if row[DATE_IDX] > tmp:
continue
indexes[pair] += 1
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if (not position_stacking and pair in lock_pair_until
and row.date <= lock_pair_until[pair]):
# without positionstacking, we can only have one open trade per pair.
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
if not trade_count_lock.get(row.date, 0) < max_open_trades:
continue
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
# max_open_trades must be respected
# don't open on the last row
if ((position_stacking or len(open_trades[pair]) == 0)
and max_open_trades > 0 and open_trade_count_start < max_open_trades
and tmp != end_date
and row[BUY_IDX] == 1 and row[SELL_IDX] != 1):
# Enter trade
trade = Trade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX],
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
)
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
open_trades[pair].append(trade)
for trade in open_trades[pair]:
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(pair, row, data[pair][indexes[pair]-1:],
trade_count_lock, stake_amount,
max_open_trades)
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occured
if trade_entry:
logger.debug(f"{pair} - Locking pair till "
f"close_date={trade_entry.close_date}")
lock_pair_until[pair] = trade_entry.close_date
# logger.debug(f"{pair} - Backtesting sell {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
lock_pair_until[pair] = end_date.datetime
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
@ -380,12 +390,6 @@ class Backtesting:
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
position_stacking = self.config.get('position_stacking', False)
data, timerange = self.load_bt_data()
@ -395,6 +399,15 @@ class Backtesting:
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
self._set_strategy(strat)
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
@ -407,17 +420,21 @@ class Backtesting:
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
results = self.backtest(
processed=preprocessed,
stake_amount=self.config['stake_amount'],
start_date=min_date,
end_date=max_date,
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=max_open_trades,
position_stacking=position_stacking,
)
all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
}
stats = generate_backtest_stats(data, all_results, min_date=min_date, max_date=max_date)
stats = generate_backtest_stats(self.config, data, all_results,
min_date=min_date, max_date=max_date)
if self.config.get('export', False):
store_backtest_stats(self.config['exportfilename'], stats)

View File

@ -1,5 +1,5 @@
"""
DefaultHyperOptLoss
ShortTradeDurHyperOptLoss
This module defines the default HyperoptLoss class which is being used for
Hyperoptimization.
"""
@ -26,7 +26,7 @@ EXPECTED_MAX_PROFIT = 3.0
MAX_ACCEPTED_TRADE_DURATION = 300
class DefaultHyperOptLoss(IHyperOptLoss):
class ShortTradeDurHyperOptLoss(IHyperOptLoss):
"""
Defines the default loss function for hyperopt
"""
@ -50,3 +50,7 @@ class DefaultHyperOptLoss(IHyperOptLoss):
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss
return result
# Create an alias for This to allow the legacy Method to work as well.
DefaultHyperOptLoss = ShortTradeDurHyperOptLoss

View File

@ -7,12 +7,12 @@ import logging
from typing import Any, Dict
from freqtrade import constants
from freqtrade.configuration import (TimeRange, remove_credentials,
validate_config_consistency)
from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
from freqtrade.edge import Edge
from freqtrade.optimize.optimize_reports import generate_edge_table
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
logger = logging.getLogger(__name__)

View File

@ -10,6 +10,7 @@ import logging
import random
import warnings
from collections import OrderedDict
from datetime import datetime
from math import ceil
from operator import itemgetter
from pathlib import Path
@ -21,24 +22,22 @@ import rapidjson
import tabulate
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 joblib import Parallel, cpu_count, delayed, dump, load, wrap_non_picklable_objects
from pandas import DataFrame, isna, json_normalize
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural, round_dict
from freqtrade.misc import file_dump_json, plural, round_dict
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import \
IHyperOptLoss # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import (HyperOptLossResolver,
HyperOptResolver)
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
from freqtrade.strategy import IStrategy
# Suppress scikit-learn FutureWarnings from skopt
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
@ -77,19 +76,16 @@ class Hyperopt:
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")
self.results_file = (self.config['user_data_dir'] /
'hyperopt_results' / 'hyperopt_results.pickle')
'hyperopt_results' / f'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)
self.current_best_loss = 100
if not self.config.get('hyperopt_continue'):
self.clean_hyperopt()
else:
logger.info("Continuing on previous hyperopt results.")
self.num_epochs_saved = 0
@ -98,14 +94,14 @@ class Hyperopt:
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
self.backtesting.strategy.advise_indicators = \
self.custom_hyperopt.populate_indicators # type: ignore
self.backtesting.strategy.advise_indicators = ( # type: ignore
self.custom_hyperopt.populate_indicators) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
self.backtesting.strategy.advise_buy = \
self.custom_hyperopt.populate_buy_trend # type: ignore
self.backtesting.strategy.advise_buy = ( # type: ignore
self.custom_hyperopt.populate_buy_trend) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
self.backtesting.strategy.advise_sell = \
self.custom_hyperopt.populate_sell_trend # type: ignore
self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.populate_sell_trend) # type: ignore
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
@ -165,6 +161,10 @@ class Hyperopt:
self.num_epochs_saved = num_epochs
logger.debug(f"{self.num_epochs_saved} {plural(self.num_epochs_saved, 'epoch')} "
f"saved to '{self.results_file}'.")
# Store hyperopt filename
latest_filename = Path.joinpath(self.results_file.parent, LAST_BT_RESULT_FN)
file_dump_json(latest_filename, {'latest_hyperopt': str(self.results_file.name)},
log=False)
@staticmethod
def _read_results(results_file: Path) -> List:
@ -262,6 +262,11 @@ class Hyperopt:
),
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
params_result += f"minimal_roi = {minimal_roi_result}"
elif space == 'trailing':
for k, v in space_params.items():
params_result += f'{k} = {v}\n'
else:
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
@ -324,8 +329,9 @@ class Hyperopt:
'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', 'W/D/L', 'Avg profit', 'Total profit',
'Profit', 'Avg duration', 'Objective', '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']
trials['is_profit'] = False
trials.loc[trials['is_initial_point'], 'Best'] = '* '
trials.loc[trials['is_best'], 'Best'] = 'Best'
@ -502,16 +508,16 @@ class Hyperopt:
params_details = self._get_params_details(params_dict)
if self.has_space('roi'):
self.backtesting.strategy.minimal_roi = \
self.custom_hyperopt.generate_roi_table(params_dict)
self.backtesting.strategy.minimal_roi = ( # type: ignore
self.custom_hyperopt.generate_roi_table(params_dict))
if self.has_space('buy'):
self.backtesting.strategy.advise_buy = \
self.custom_hyperopt.buy_strategy_generator(params_dict)
self.backtesting.strategy.advise_buy = ( # type: ignore
self.custom_hyperopt.buy_strategy_generator(params_dict))
if self.has_space('sell'):
self.backtesting.strategy.advise_sell = \
self.custom_hyperopt.sell_strategy_generator(params_dict)
self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.sell_strategy_generator(params_dict))
if self.has_space('stoploss'):
self.backtesting.strategy.stoploss = params_dict['stoploss']
@ -532,8 +538,8 @@ class Hyperopt:
backtesting_results = self.backtesting.backtest(
processed=processed,
stake_amount=self.config['stake_amount'],
start_date=min_date,
end_date=max_date,
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=self.max_open_trades,
position_stacking=self.position_stacking,
)
@ -574,7 +580,7 @@ class Hyperopt:
'wins': wins,
'draws': draws,
'losses': losses,
'winsdrawslosses': f"{wins}/{draws}/{losses}",
'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
'avg_profit': backtesting_results.profit_percent.mean() * 100.0,
'median_profit': backtesting_results.profit_percent.median() * 100.0,
'total_profit': backtesting_results.profit_abs.sum(),
@ -656,8 +662,6 @@ class Hyperopt:
self.backtesting.strategy.dp = None # type: ignore
IStrategy.dp = None # type: ignore
self.epochs = self.load_previous_results(self.results_file)
cpus = cpu_count()
logger.info(f"Found {cpus} CPU cores. Let's make them scream!")
config_jobs = self.config.get('hyperopt_jobs', -1)

View File

@ -13,6 +13,7 @@ from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
logger = logging.getLogger(__name__)

View File

@ -6,8 +6,8 @@ Hyperoptimization.
"""
from datetime import datetime
from pandas import DataFrame
import numpy as np
from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss

View File

@ -6,8 +6,8 @@ Hyperoptimization.
"""
from datetime import datetime
from pandas import DataFrame
import numpy as np
from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss

View File

@ -1,17 +1,18 @@
import logging
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, List
from typing import Any, Dict, List, Union
from arrow import Arrow
from pandas import DataFrame
from numpy import int64
from pandas import DataFrame
from tabulate import tabulate
from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import calculate_max_drawdown, calculate_market_change
from freqtrade.data.btanalysis import calculate_market_change, calculate_max_drawdown
from freqtrade.misc import file_dump_json
logger = logging.getLogger(__name__)
@ -122,7 +123,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
profit_mean = result['profit_percent'].mean()
profit_sum = result["profit_percent"].sum()
profit_percent_tot = round(result['profit_percent'].sum() * 100.0 / max_open_trades, 2)
profit_percent_tot = result['profit_percent'].sum() / max_open_trades
tabular_data.append(
{
@ -136,25 +137,25 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
'profit_sum': profit_sum,
'profit_sum_pct': round(profit_sum * 100, 2),
'profit_total_abs': result['profit_abs'].sum(),
'profit_total_pct': profit_percent_tot,
'profit_total': profit_percent_tot,
'profit_total_pct': round(profit_percent_tot * 100, 2),
}
)
return tabular_data
def generate_strategy_metrics(stake_currency: str, max_open_trades: int,
all_results: Dict) -> List[Dict]:
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
"""
Generate summary per strategy
:param stake_currency: stake-currency - used to correctly name headers
:param max_open_trades: Maximum allowed open trades used for backtest
:param all_results: Dict of <Strategyname: BacktestResult> containing results for all strategies
:return: List of Dicts containing the metrics per Strategy
"""
tabular_data = []
for strategy, results in all_results.items():
tabular_data.append(_generate_result_line(results, max_open_trades, strategy))
tabular_data.append(_generate_result_line(
results['results'], results['config']['max_open_trades'], strategy)
)
return tabular_data
@ -218,25 +219,29 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
}
def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
all_results: Dict[str, DataFrame],
def generate_backtest_stats(btdata: Dict[str, DataFrame],
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
min_date: Arrow, max_date: Arrow
) -> Dict[str, Any]:
"""
:param config: Configuration object used for backtest
:param btdata: Backtest data
:param all_results: backtest result - dictionary with { Strategy: results}.
: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.
"""
stake_currency = config['stake_currency']
max_open_trades = config['max_open_trades']
result: Dict[str, Any] = {'strategy': {}}
market_change = calculate_market_change(btdata, 'close')
for strategy, results in all_results.items():
for strategy, content in all_results.items():
results: Dict[str, DataFrame] = content['results']
if not isinstance(results, DataFrame):
continue
config = content['config']
max_open_trades = config['max_open_trades']
stake_currency = config['stake_currency']
pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
max_open_trades=max_open_trades,
@ -276,6 +281,16 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
'max_open_trades': (config['max_open_trades']
if config['max_open_trades'] != float('inf') else -1),
'timeframe': config['timeframe'],
# Parameters relevant for backtesting
'stoploss': config['stoploss'],
'trailing_stop': config.get('trailing_stop', False),
'trailing_stop_positive': config.get('trailing_stop_positive'),
'trailing_stop_positive_offset': config.get('trailing_stop_positive_offset', 0.0),
'trailing_only_offset_is_reached': config.get('trailing_only_offset_is_reached', False),
'minimal_roi': config['minimal_roi'],
'use_sell_signal': config['ask_strategy']['use_sell_signal'],
'sell_profit_only': config['ask_strategy']['sell_profit_only'],
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
**daily_stats,
}
result['strategy'][strategy] = strat_stats
@ -299,9 +314,7 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
'drawdown_end_ts': 0,
})
strategy_results = generate_strategy_metrics(stake_currency=stake_currency,
max_open_trades=max_open_trades,
all_results=all_results)
strategy_results = generate_strategy_metrics(all_results=all_results)
result['strategy_comparison'] = strategy_results

View File

@ -2,9 +2,10 @@
Minimum age (days listed) pair list filter
"""
import logging
import arrow
from typing import Any, Dict
import arrow
from freqtrade.exceptions import OperationalException
from freqtrade.misc import plural
from freqtrade.pairlist.IPairList import IPairList

View File

@ -36,7 +36,7 @@ class IPairList(ABC):
self._pairlist_pos = pairlist_pos
self.refresh_period = self._pairlistconfig.get('refresh_period', 1800)
self._last_refresh = 0
self._log_cache = TTLCache(maxsize=1024, ttl=self.refresh_period)
self._log_cache: TTLCache = TTLCache(maxsize=1024, ttl=self.refresh_period)
@property
def name(self) -> str:

View File

@ -4,8 +4,9 @@ Precision pair list filter
import logging
from typing import Any, Dict
from freqtrade.pairlist.IPairList import IPairList
from freqtrade.exceptions import OperationalException
from freqtrade.pairlist.IPairList import IPairList
logger = logging.getLogger(__name__)

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