Merge branch 'release/0.12.0'

.dockerignore

@@ -0,0 +1,6 @@
+.git
+.gitignore
+Dockerfile
+.dockerignore
+config.json*
+*.sqlite

Dockerfile

@@ -1,20 +1,23 @@
-FROM    python:3.6.2
-
-RUN apt-get update
-RUN apt-get -y install build-essential
+FROM python:3.6.2
 
 # Install TA-lib
-RUN  wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
-RUN tar zxvf ta-lib-0.4.0-src.tar.gz
-RUN cd ta-lib && ./configure && make && make install
+RUN apt-get update && apt-get -y install build-essential && apt-get clean
+RUN curl -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz | \
+  tar xzvf - && \
+  cd ta-lib && \
+  ./configure && make && make install && \
+  cd .. && rm -rf ta-lib
 ENV LD_LIBRARY_PATH /usr/local/lib
 
 # Prepare environment
 RUN mkdir /freqtrade
-COPY . /freqtrade/
 WORKDIR /freqtrade
 
-# Install dependencies and execute
+# Install dependencies
+COPY requirements.txt /freqtrade/
 RUN pip install -r requirements.txt
+
+# Install and execute
+COPY . /freqtrade/
 RUN pip install -e .
 CMD ["freqtrade"]

README.md

@@ -30,15 +30,14 @@ in minutes and the value is the minimum ROI in percent.
 See the example below:
 ```
 "minimal_roi": {
-    "2880": 0.005, # Sell after 48 hours if there is at least 0.5% profit
-    "1440": 0.01,  # Sell after 24 hours if there is at least 1% profit
-    "720":  0.02,  # Sell after 12 hours if there is at least 2% profit
-    "360":  0.02,  # Sell after 6 hours if there is at least 2% profit
-    "0":    0.025  # Sell immediately if there is at least 2.5% profit
+    "50": 0.0,    # Sell after 30 minutes if the profit is not negative
+    "40": 0.01,   # Sell after 25 minutes if there is at least 1% profit
+    "30": 0.02,   # Sell after 15 minutes if there is at least 2% profit
+    "0":  0.045  # Sell immediately if there is at least 4.5% profit
 },
 ```
 
-`stoploss` is loss in percentage that should trigger a sale. 
+`stoploss` is loss in percentage that should trigger a sale.
 For example value `-0.10` will cause immediate sell if the
 profit dips below -10% for a given trade. This parameter is optional.
 
@@ -47,7 +46,9 @@ Possible values are `running` or `stopped`. (default=`running`)
 If the value is `stopped` the bot has to be started with `/start` first.
 
 `ask_last_balance` sets the bidding price. Value `0.0` will use `ask` price, `1.0` will
-use the `last` price and values between those interpolate between ask and last price. Using `ask` price will guarantee quick success in bid, but bot will also end up paying more then would probably have been necessary.
+use the `last` price and values between those interpolate between ask and last
+price. Using `ask` price will guarantee quick success in bid, but bot will also
+end up paying more then would probably have been necessary.
 
 The other values should be self-explanatory,
 if not feel free to raise a github issue.
@@ -84,16 +85,57 @@ $ pytest
 This will by default skip the slow running backtest set. To run backtest set:
 
 ```
-$ BACKTEST=true pytest
+$ BACKTEST=true pytest -s freqtrade/tests/test_backtesting.py
 ```
 
 #### Docker
+
+Building the image:
+
 ```
 $ cd freqtrade
 $ docker build -t freqtrade .
-$ docker run --rm -it freqtrade
 ```
 
+For security reasons, your configuration file will not be included in the
+image, you will need to bind mount it. It is also advised to bind mount
+a SQLite database file (see second example) to keep it between updates.
+
+You can run a one-off container that is immediately deleted upon exiting with
+the following command (config.json must be in the current working directory):
+
+```
+$ docker run --rm -v `pwd`/config.json:/freqtrade/config.json -it freqtrade
+```
+
+To run a restartable instance in the background (feel free to place your
+configuration and database files wherever it feels comfortable on your
+filesystem):
+
+```
+$ cd ~/.freq
+$ touch tradesv2.sqlite
+$ docker run -d \
+  --name freqtrade \
+  -v ~/.freq/config.json:/freqtrade/config.json \
+  -v ~/.freq/tradesv2.sqlite:/freqtrade/tradesv2.sqlite \
+  freqtrade
+```
+If you are using `dry_run=True` you need to bind `tradesv2.dry_run.sqlite` instead of `tradesv2.sqlite`.
+
+You can then use the following commands to monitor and manage your container:
+
+```
+$ docker logs freqtrade
+$ docker logs -f freqtrade
+$ docker restart freqtrade
+$ docker stop freqtrade
+$ docker start freqtrade
+```
+
+You do not need to rebuild the image for configuration
+changes, it will suffice to edit `config.json` and restart the container.
+
 #### Contributing
 
 Feel like our bot is missing a feature? We welcome your pull requests! Few pointers for contributions:

config.json.example

@@ -4,10 +4,10 @@
     "stake_amount": 0.05,
     "dry_run": false,
     "minimal_roi": {
-        "60": 0.0,
-        "40": 0.01,
-        "20": 0.02,
-        "0": 0.03
+        "50":  0.0,
+        "40":  0.01,
+        "30":  0.02,
+        "0":  0.045
     },
     "stoploss": -0.40,
     "bid_strategy": {

freqtrade/__init__.py

@@ -1,3 +1,3 @@
-__version__ = '0.11.0'
+__version__ = '0.12.0'
 
 from . import main

freqtrade/analyze.py

@@ -6,7 +6,8 @@ import arrow
 import talib.abstract as ta
 from pandas import DataFrame
 
-from freqtrade.exchange import get_ticker_history
+from freqtrade import exchange
+from freqtrade.exchange import Bittrex, get_ticker_history
 
 logging.basicConfig(level=logging.DEBUG,
                     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
@@ -23,23 +24,23 @@ def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame
         .drop('BV', 1) \
         .rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'}) \
         .sort_values('date')
-    return df[df['date'].map(arrow.get) > minimum_date]
+    return df
 
 
 def populate_indicators(dataframe: DataFrame) -> DataFrame:
     """
     Adds several different TA indicators to the given DataFrame
     """
-    dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
+    dataframe['sar'] = ta.SAR(dataframe)
     dataframe['adx'] = ta.ADX(dataframe)
     stoch = ta.STOCHF(dataframe)
     dataframe['fastd'] = stoch['fastd']
     dataframe['fastk'] = stoch['fastk']
     dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
-    dataframe['cci'] = ta.CCI(dataframe, timeperiod=5)
-    dataframe['sma'] = ta.SMA(dataframe, timeperiod=100)
-    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=4)
+    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
     dataframe['mfi'] = ta.MFI(dataframe)
+    dataframe['cci'] = ta.CCI(dataframe)
     return dataframe
 
 
@@ -49,14 +50,12 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
     :param dataframe: DataFrame
     :return: DataFrame with buy column
     """
-
     dataframe.loc[
         (dataframe['close'] < dataframe['sma']) &
-        (dataframe['cci'] < -100) &
         (dataframe['tema'] <= dataframe['blower']) &
-        (dataframe['mfi'] < 30) &
-        (dataframe['fastd'] < 20) &
-        (dataframe['adx'] > 20),
+        (dataframe['mfi'] < 25) &
+        (dataframe['fastd'] < 25) &
+        (dataframe['adx'] > 30),
         'buy'] = 1
     dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
 
@@ -119,20 +118,26 @@ def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
     import matplotlib.pyplot as plt
 
     # Two subplots sharing x axis
-    fig, (ax1, ax2) = plt.subplots(2, sharex=True)
+    fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
     fig.suptitle(pair, fontsize=14, fontweight='bold')
-    ax1.plot(dataframe.index.values, dataframe['sar'], 'g_', label='pSAR')
     ax1.plot(dataframe.index.values, dataframe['close'], label='close')
     # ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
     ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
+    ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
+    ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
     ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
     ax1.legend()
 
-    # ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
+    ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
     ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
     # ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
     ax2.legend()
 
+    ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
+    ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
+    ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
+    ax3.legend()
+
     # Fine-tune figure; make subplots close to each other and hide x ticks for
     # all but bottom plot.
     fig.subplots_adjust(hspace=0)
@@ -143,6 +148,7 @@ def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
 if __name__ == '__main__':
     # Install PYQT5==5.9 manually if you want to test this helper function
     while True:
+        exchange.EXCHANGE = Bittrex({'key': '', 'secret': ''})
         test_pair = 'BTC_ETH'
         # for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
         #     get_buy_signal(pair)

freqtrade/exchange/bittrex.py

@@ -26,10 +26,6 @@ class Bittrex(Exchange):
     # Sleep time to avoid rate limits, used in the main loop
     SLEEP_TIME: float = 25
 
-    @property
-    def name(self) -> str:
-        return self.__class__.__name__
-
     @property
     def sleep_time(self) -> float:
         return self.SLEEP_TIME
@@ -40,13 +36,6 @@ class Bittrex(Exchange):
         _EXCHANGE_CONF.update(config)
         _API = _Bittrex(api_key=_EXCHANGE_CONF['key'], api_secret=_EXCHANGE_CONF['secret'])
 
-        # Check if all pairs are available
-        markets = self.get_markets()
-        exchange_name = self.name
-        for pair in _EXCHANGE_CONF['pair_whitelist']:
-            if pair not in markets:
-                raise RuntimeError('Pair {} is not available at {}'.format(pair, exchange_name))
-
     def buy(self, pair: str, rate: float, amount: float) -> str:
         data = _API.buy_limit(pair.replace('_', '-'), amount, rate)
         if not data['success']:

freqtrade/rpc/telegram.py

@@ -45,6 +45,7 @@ def init(config: dict) -> None:
         CommandHandler('stop', _stop),
         CommandHandler('forcesell', _forcesell),
         CommandHandler('performance', _performance),
+        CommandHandler('help', _help),
     ]
     for handle in handles:
         _updater.dispatcher.add_handler(handle)
@@ -301,6 +302,27 @@ def _performance(bot: Bot, update: Update) -> None:
     send_msg(message, parse_mode=ParseMode.HTML)
 
 
+@authorized_only
+def _help(bot: Bot, update: Update) -> None:
+    """
+    Handler for /help.
+    Show commands of the bot
+    :param bot: telegram bot
+    :param update: message update
+    :return: None
+    """
+    message = """
+*/start:* `Starts the trader`
+*/stop:* `Stops the trader`
+*/status:* `Lists all open trades`
+*/profit:* `Lists cumulative profit from all finished trades`
+*/forcesell <trade_id>:* `Instantly sells the given trade, regardless of profit`
+*/performance:* `Show performance of each finished trade grouped by pair`
+*/help:* `This help message`
+    """
+    send_msg(message, bot=bot)
+
+
 def send_msg(msg: str, bot: Bot = None, parse_mode: ParseMode = ParseMode.MARKDOWN) -> None:
     """
     Send given markdown message

freqtrade/tests/test_backtesting.py

@@ -18,7 +18,7 @@ def print_results(results):
         len(results.index),
         results.profit.mean() * 100.0,
         results.profit.sum(),
-        results.duration.mean()*5
+        results.duration.mean() * 5
     ))
 
 @pytest.fixture
@@ -30,10 +30,10 @@ def pairs():
 def conf():
     return {
         "minimal_roi": {
-            "60":  0.0,
+            "50":  0.0,
             "40":  0.01,
-            "20":  0.02,
-            "0":  0.03
+            "30":  0.02,
+            "0":  0.045
         },
         "stoploss": -0.40
     }

freqtrade/tests/test_hyperopt.py

@@ -0,0 +1,166 @@
+# pragma pylint: disable=missing-docstring
+import json
+import logging
+import os
+from functools import reduce
+
+import pytest
+import arrow
+from pandas import DataFrame
+
+from hyperopt import fmin, tpe, hp
+
+from freqtrade.analyze import analyze_ticker
+from freqtrade.main import should_sell
+from freqtrade.persistence import Trade
+
+logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
+
+def print_results(results):
+    print('Made {} buys. Average profit {:.2f}%. Total profit was {:.3f}. Average duration {:.1f} mins.'.format(
+        len(results.index),
+        results.profit.mean() * 100.0,
+        results.profit.sum(),
+        results.duration.mean() * 5
+    ))
+
+@pytest.fixture
+def pairs():
+    return ['btc-neo', 'btc-eth', 'btc-omg', 'btc-edg', 'btc-pay',
+            'btc-pivx', 'btc-qtum', 'btc-mtl', 'btc-etc', 'btc-ltc']
+
+@pytest.fixture
+def conf():
+    return {
+        "minimal_roi": {
+            "40":  0.0,
+            "30":  0.01,
+            "20":  0.02,
+            "0":  0.04
+        },
+        "stoploss": -0.05
+    }
+
+
+def backtest(conf, pairs, mocker, buy_strategy):
+    trades = []
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    for pair in pairs:
+        with open('freqtrade/tests/testdata/'+pair+'.json') as data_file:
+            data = json.load(data_file)
+
+            mocker.patch('freqtrade.analyze.get_ticker_history', return_value=data)
+            mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
+            mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
+            ticker = analyze_ticker(pair)
+            # for each buy point
+            for index, row in ticker[ticker.buy == 1].iterrows():
+                trade = Trade(
+                    open_rate=row['close'],
+                    open_date=arrow.get(row['date']).datetime,
+                    amount=1,
+                )
+                # calculate win/lose forwards from buy point
+                for index2, row2 in ticker[index:].iterrows():
+                    if should_sell(trade, row2['close'], arrow.get(row2['date']).datetime):
+                        current_profit = (row2['close'] - trade.open_rate) / trade.open_rate
+
+                        trades.append((pair, current_profit, index2 - index))
+                        break
+
+    labels = ['currency', 'profit', 'duration']
+    results = DataFrame.from_records(trades, columns=labels)
+
+    print_results(results)
+
+    # set the value below to suit your number concurrent trades so its realistic to 20days of data
+    TARGET_TRADES = 1200
+    if results.profit.sum() == 0 or results.profit.mean() == 0:
+        return 49999999999 # avoid division by zero, return huge value to discard result
+    return abs(len(results.index) - 1200.1) / (results.profit.sum() ** 2) * results.duration.mean() # the smaller the better
+
+def buy_strategy_generator(params):
+    print(params)
+    def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+        conditions = []
+        # GUARDS AND TRENDS
+        if params['below_sma']['enabled']:
+            conditions.append(dataframe['close'] < dataframe['sma'])
+        if params['over_sma']['enabled']:
+            conditions.append(dataframe['close'] > dataframe['sma'])
+        if params['mfi']['enabled']:
+            conditions.append(dataframe['mfi'] < params['mfi']['value'])
+        if params['fastd']['enabled']:
+            conditions.append(dataframe['fastd'] < params['fastd']['value'])
+        if params['adx']['enabled']:
+            conditions.append(dataframe['adx'] > params['adx']['value'])
+        if params['cci']['enabled']:
+            conditions.append(dataframe['cci'] < params['cci']['value'])
+        if params['over_sar']['enabled']:
+            conditions.append(dataframe['close'] > dataframe['sar'])
+        if params['uptrend_sma']['enabled']:
+            prevsma = dataframe['sma'].shift(1)
+            conditions.append(dataframe['sma'] > prevsma)
+
+        prev_fastd = dataframe['fastd'].shift(1)
+        # TRIGGERS
+        triggers = {
+            'lower_bb': dataframe['tema'] <= dataframe['blower'],
+            'faststoch10': (dataframe['fastd'] >= 10) & (prev_fastd < 10),
+        }
+        conditions.append(triggers.get(params['trigger']['type']))
+
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions),
+            'buy'] = 1
+        dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
+
+        return dataframe
+    return populate_buy_trend
+
+@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
+def test_hyperopt(conf, pairs, mocker):
+
+    def optimizer(params):
+        return backtest(conf, pairs, mocker, buy_strategy_generator(params))
+
+    space = {
+        'mfi': hp.choice('mfi', [
+            {'enabled': False},
+            {'enabled': True, 'value': hp.uniform('mfi-value', 2, 40)}
+        ]),
+        'fastd': hp.choice('fastd', [
+            {'enabled': False},
+            {'enabled': True, 'value': hp.uniform('fastd-value', 2, 40)}
+        ]),
+        'adx': hp.choice('adx', [
+            {'enabled': False},
+            {'enabled': True, 'value': hp.uniform('adx-value', 2, 40)}
+        ]),
+        'cci': hp.choice('cci', [
+            {'enabled': False},
+            {'enabled': True, 'value': hp.uniform('cci-value', -200, -100)}
+        ]),
+        'below_sma': hp.choice('below_sma', [
+            {'enabled': False},
+            {'enabled': True}
+        ]),
+        'over_sma': hp.choice('over_sma', [
+            {'enabled': False},
+            {'enabled': True}
+        ]),
+        'over_sar': hp.choice('over_sar', [
+            {'enabled': False},
+            {'enabled': True}
+        ]),
+        'uptrend_sma': hp.choice('uptrend_sma', [
+            {'enabled': False},
+            {'enabled': True}
+        ]),
+        'trigger': hp.choice('trigger', [
+            {'type': 'lower_bb'},
+            {'type': 'faststoch10'}
+        ]),
+    }
+
+    print('Best parameters {}'.format(fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40)))

freqtrade/tests/testdata/download_backtest_data.py

@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+
+"""This script generate json data from bittrex"""
+
+from urllib.request import urlopen
+
+CURRENCIES = ["ok", "neo", "dash", "etc", "eth", "snt"]
+
+for cur in CURRENCIES:
+    url1 = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks?marketName=BTC-'
+    url = url1+cur+'&tickInterval=fiveMin'
+    x = urlopen(url)
+    json_data = x.read()
+    json_str = str(json_data, 'utf-8')
+    with open('btc-'+cur+'.json', 'w') as file:
+        file.write(json_str)

requirements.txt

@@ -1,6 +1,6 @@
 -e git+https://github.com/ericsomdahl/python-bittrex.git@d7033d0#egg=python-bittrex
-SQLAlchemy==1.1.13
-python-telegram-bot==8.0
+SQLAlchemy==1.1.14
+python-telegram-bot==8.1.1
 arrow==0.10.0
 requests==2.18.4
 urllib3==1.22
@@ -11,10 +11,13 @@ scipy==0.19.1
 jsonschema==2.6.0
 numpy==1.13.3
 TA-Lib==0.4.10
-pytest==3.2.2
+pytest==3.2.3
 pytest-mock==1.6.3
 pytest-cov==2.5.1
+hyperopt==0.1
+# do not upgrade networkx before this is fixed https://github.com/hyperopt/hyperopt/issues/325
+networkx==1.11
 
 # Required for plotting data
-#matplotlib==2.0.2
+#matplotlib==2.1.0
 #PYQT5==5.9

setup.cfg

@@ -1,5 +0,0 @@
-[aliases]
-test=pytest
-
-[tool:pytest]
-addopts = --cov=freqtrade --cov-config=.coveragerc freqtrade/tests/

setup.py

@@ -17,7 +17,7 @@ setup(name='freqtrade',
       install_requires=[
           'python-bittrex==0.1.3',
           'SQLAlchemy==1.1.13',
-          'python-telegram-bot==8.0',
+          'python-telegram-bot==8.1.1',
           'arrow==0.10.0',
           'requests==2.18.4',
           'urllib3==1.22',