Merge branch 'release/0.13.0'

Reduce memory use in backtesting

.coveragerc

@@ -0,0 +1,2 @@
+[run]
+omit = freqtrade/tests/*

.dockerignore

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

.travis.yml

@@ -4,10 +4,6 @@ os:
 language: python
 python:
 - 3.6
-- nightly
-matrix:
-  allow_failures:
-  - python: nightly
 addons:
   apt:
     packages:
@@ -19,9 +15,12 @@ install:
 - tar zxvf ta-lib-0.4.0-src.tar.gz
 - cd ta-lib && ./configure && sudo make && sudo make install && cd ..
 - export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
+- pip install coveralls
 - pip install -r requirements.txt
 script:
-- python -m unittest
+- pytest --cov=freqtrade --cov-config=.coveragerc freqtrade/tests/
+after_success:
+- coveralls
 notifications:
   slack:
     secure: 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

Dockerfile

@@ -1,17 +1,23 @@
-FROM    python:3.6.2
+FROM python:3.6.2
 
-RUN pip install numpy
-RUN apt-get update
-RUN apt-get -y install build-essential  
-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
+# Install TA-lib
+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
 
-RUN     mkdir -p /freqtrade
+# Prepare environment
+RUN mkdir /freqtrade
 WORKDIR /freqtrade
 
-ADD     ./requirements.txt /freqtrade/requirements.txt
-RUN     pip install -r requirements.txt
-ADD     . /freqtrade
-CMD     python main.py
+# Install dependencies
+COPY requirements.txt /freqtrade/
+RUN pip install -r requirements.txt
+
+# Install and execute
+COPY . /freqtrade/
+RUN pip install -e .
+CMD ["freqtrade"]

MANIFEST.in

@@ -0,0 +1,5 @@
+include LICENSE
+include README.md
+include config.json.example
+recursive-include freqtrade *.py
+include freqtrade/tests/testdata/*.json

README.md

@@ -1,6 +1,8 @@
 # freqtrade
 
 [![Build Status](https://travis-ci.org/gcarq/freqtrade.svg?branch=develop)](https://travis-ci.org/gcarq/freqtrade)
+[![Coverage Status](https://coveralls.io/repos/github/gcarq/freqtrade/badge.svg?branch=develop)](https://coveralls.io/github/gcarq/freqtrade?branch=develop)
+
 
 Simple High frequency trading bot for crypto currencies.
 Currently supports trading on Bittrex exchange.
@@ -28,15 +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.
 
@@ -45,7 +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.
@@ -56,6 +59,11 @@ if not feel free to raise a github issue.
 * [TA-lib](https://github.com/mrjbq7/ta-lib#dependencies) binaries
 
 #### Install
+
+`master` branch contains the latest stable release.
+
+`develop` branch has often new features, but might also cause breaking changes. To use it, you are encouraged to join our [slack channel](https://join.slack.com/t/highfrequencybot/shared_invite/enQtMjQ5NTM0OTYzMzY3LWMxYzE3M2MxNDdjMGM3ZTYwNzFjMGIwZGRjNTc3ZGU3MGE3NzdmZGMwNmU3NDM5ZTNmM2Y3NjRiNzk4NmM4OGE).
+
 ```
 $ cd freqtrade/
 # copy example config. Dont forget to insert your api keys
@@ -63,7 +71,8 @@ $ cp config.json.example config.json
 $ python -m venv .env
 $ source .env/bin/activate
 $ pip install -r requirements.txt
-$ ./main.py
+$ pip install -e .
+$ ./freqtrade/main.py
 ```
 
 There is also an [article](https://www.sales4k.com/blockchain/high-frequency-trading-bot-tutorial/) about how to setup the bot (thanks [@gurghet](https://github.com/gurghet)).
@@ -71,16 +80,62 @@ There is also an [article](https://www.sales4k.com/blockchain/high-frequency-tra
 #### Execute tests
 
 ```
-$ python -m unittest
+$ pytest
+```
+This will by default skip the slow running backtest set. To run backtest set:
+
+```
+$ 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:

bin/freqtrade

@@ -0,0 +1,4 @@
+#!/usr/bin/env python
+
+from freqtrade.main import main
+main()

config.json.example

@@ -4,16 +4,17 @@
     "stake_amount": 0.05,
     "dry_run": false,
     "minimal_roi": {
-        "2880": 0.005,
-        "720":  0.01,
-        "0":  0.02
+        "50":  0.0,
+        "40":  0.01,
+        "30":  0.02,
+        "0":  0.045
     },
-    "stoploss": -0.10,
+    "stoploss": -0.40,
     "bid_strategy": {
         "ask_last_balance": 0.0
     },
-    "bittrex": {
-        "enabled": true,
+    "exchange": {
+        "name": "bittrex",
         "key": "key",
         "secret": "secret",
         "pair_whitelist": [

exchange.py

@@ -1,170 +0,0 @@
-import enum
-import logging
-from typing import List
-
-from bittrex.bittrex import Bittrex
-
-logger = logging.getLogger(__name__)
-
-# Current selected exchange
-EXCHANGE = None
-_API = None
-_CONF = {}
-
-
-class Exchange(enum.Enum):
-    BITTREX = 1
-
-
-def init(config: dict) -> None:
-    """
-    Initializes this module with the given config,
-    it does basic validation whether the specified
-    exchange and pairs are valid.
-    :param config: config to use
-    :return: None
-    """
-    global _API, EXCHANGE
-
-    _CONF.update(config)
-
-    if config['dry_run']:
-        logger.info('Instance is running with dry_run enabled')
-
-    use_bittrex = config.get('bittrex', {}).get('enabled', False)
-    if use_bittrex:
-        EXCHANGE = Exchange.BITTREX
-        _API = Bittrex(api_key=config['bittrex']['key'], api_secret=config['bittrex']['secret'])
-    else:
-        raise RuntimeError('No exchange specified. Aborting!')
-
-    # Check if all pairs are available
-    markets = get_markets()
-    exchange_name = EXCHANGE.name.lower()
-    for pair in config[exchange_name]['pair_whitelist']:
-        if pair not in markets:
-            raise RuntimeError('Pair {} is not available at {}'.format(pair, exchange_name))
-
-
-def buy(pair: str, rate: float, amount: float) -> str:
-    """
-    Places a limit buy order.
-    :param pair: Pair as str, format: BTC_ETH
-    :param rate: Rate limit for order
-    :param amount: The amount to purchase
-    :return: order_id of the placed buy order
-    """
-    if _CONF['dry_run']:
-        return 'dry_run'
-    elif EXCHANGE == Exchange.BITTREX:
-        data = _API.buy_limit(pair.replace('_', '-'), amount, rate)
-        if not data['success']:
-            raise RuntimeError('BITTREX: {}'.format(data['message']))
-        return data['result']['uuid']
-
-
-def sell(pair: str, rate: float, amount: float) -> str:
-    """
-    Places a limit sell order.
-    :param pair: Pair as str, format: BTC_ETH
-    :param rate: Rate limit for order
-    :param amount: The amount to sell
-    :return: None
-    """
-    if _CONF['dry_run']:
-        return 'dry_run'
-    elif EXCHANGE == Exchange.BITTREX:
-        data = _API.sell_limit(pair.replace('_', '-'), amount, rate)
-        if not data['success']:
-            raise RuntimeError('BITTREX: {}'.format(data['message']))
-        return data['result']['uuid']
-
-
-def get_balance(currency: str) -> float:
-    """
-    Get account balance.
-    :param currency: currency as str, format: BTC
-    :return: float
-    """
-    if _CONF['dry_run']:
-        return 999.9
-    elif EXCHANGE == Exchange.BITTREX:
-        data = _API.get_balance(currency)
-        if not data['success']:
-            raise RuntimeError('BITTREX: {}'.format(data['message']))
-        return float(data['result']['Balance'] or 0.0)
-
-
-def get_ticker(pair: str) -> dict:
-    """
-    Get Ticker for given pair.
-    :param pair: Pair as str, format: BTC_ETC
-    :return: dict
-    """
-    if EXCHANGE == Exchange.BITTREX:
-        data = _API.get_ticker(pair.replace('_', '-'))
-        if not data['success']:
-            raise RuntimeError('BITTREX: {}'.format(data['message']))
-        return {
-            'bid': float(data['result']['Bid']),
-            'ask': float(data['result']['Ask']),
-            'last': float(data['result']['Last']),
-        }
-
-
-def cancel_order(order_id: str) -> None:
-    """
-    Cancel order for given order_id
-    :param order_id: id as str
-    :return: None
-    """
-    if _CONF['dry_run']:
-        pass
-    elif EXCHANGE == Exchange.BITTREX:
-        data = _API.cancel(order_id)
-        if not data['success']:
-            raise RuntimeError('BITTREX: {}'.format(data['message']))
-
-
-def get_open_orders(pair: str) -> List[dict]:
-    """
-    Get all open orders for given pair.
-    :param pair: Pair as str, format: BTC_ETC
-    :return: list of dicts
-    """
-    if _CONF['dry_run']:
-        return []
-    elif EXCHANGE == Exchange.BITTREX:
-        data = _API.get_open_orders(pair.replace('_', '-'))
-        if not data['success']:
-            raise RuntimeError('BITTREX: {}'.format(data['message']))
-        return [{
-            'id': entry['OrderUuid'],
-            'type': entry['OrderType'],
-            'opened': entry['Opened'],
-            'rate': entry['PricePerUnit'],
-            'amount': entry['Quantity'],
-            'remaining': entry['QuantityRemaining'],
-        } for entry in data['result']]
-
-
-def get_pair_detail_url(pair: str) -> str:
-    """
-    Returns the market detail url for the given pair
-    :param pair: pair as str, format: BTC_ANT
-    :return: url as str
-    """
-    if EXCHANGE == Exchange.BITTREX:
-        return 'https://bittrex.com/Market/Index?MarketName={}'.format(pair.replace('_', '-'))
-
-
-def get_markets() -> List[str]:
-    """
-    Returns all available markets
-    :return: list of all available pairs
-    """
-    if EXCHANGE == Exchange. BITTREX:
-        data = _API.get_markets()
-        if not data['success']:
-            raise RuntimeError('BITTREX: {}'.format(data['message']))
-        return [m['MarketName'].replace('-', '_') for m in data['result']]

freqtrade/__init__.py

@@ -0,0 +1,3 @@
+__version__ = '0.13.0'
+
+from . import main

freqtrade/analyze.py

@@ -1,37 +1,21 @@
+import logging
 import time
 from datetime import timedelta
-import logging
-import arrow
-import requests
-from pandas import DataFrame
-import talib.abstract as ta
 
+import arrow
+import talib.abstract as ta
+from pandas import DataFrame, to_datetime
+
+from freqtrade import exchange
+from freqtrade.exchange import Bittrex, get_ticker_history
+from freqtrade.vendor.qtpylib.indicators import awesome_oscillator
 
 logging.basicConfig(level=logging.DEBUG,
                     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
 
-def get_ticker(pair: str, minimum_date: arrow.Arrow) -> dict:
-    """
-    Request ticker data from Bittrex for a given currency pair
-    """
-    url = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks'
-    headers = {
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36',
-    }
-    params = {
-        'marketName': pair.replace('_', '-'),
-        'tickInterval': 'fiveMin',
-        '_': minimum_date.timestamp * 1000
-    }
-    data = requests.get(url, params=params, headers=headers).json()
-    if not data['success']:
-        raise RuntimeError('BITTREX: {}'.format(data['message']))
-    return data
-
-
-def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame:
+def parse_ticker_dataframe(ticker: list) -> DataFrame:
     """
     Analyses the trend for the given pair
     :param pair: pair as str in format BTC_ETH or BTC-ETH
@@ -39,19 +23,37 @@ def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame
     """
     df = DataFrame(ticker) \
         .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]
+        .rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'})
+    df['date'] = to_datetime(df['date'], utc=True, infer_datetime_format=True)
+    df.sort_values('date', inplace=True)
+    return df
 
 
 def populate_indicators(dataframe: DataFrame) -> DataFrame:
     """
     Adds several different TA indicators to the given DataFrame
     """
-    dataframe['ema'] = ta.EMA(dataframe, timeperiod=33)
-    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['sma'] = ta.SMA(dataframe, timeperiod=40)
+    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+    dataframe['mfi'] = ta.MFI(dataframe)
+    dataframe['cci'] = ta.CCI(dataframe)
+    dataframe['rsi'] = ta.RSI(dataframe)
+    dataframe['mom'] = ta.MOM(dataframe)
+    dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+    dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+    dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+    dataframe['ao'] = awesome_oscillator(dataframe)
+    macd = ta.MACD(dataframe)
+    dataframe['macd'] = macd['macd']
+    dataframe['macdsignal'] = macd['macdsignal']
+    dataframe['macdhist'] = macd['macdhist']
     return dataframe
 
 
@@ -61,28 +63,14 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
     :param dataframe: DataFrame
     :return: DataFrame with buy column
     """
-    prev_sar = dataframe['sar'].shift(1)
-    prev_close = dataframe['close'].shift(1)
-    prev_sar2 = dataframe['sar'].shift(2)
-    prev_close2 = dataframe['close'].shift(2)
-
-    # wait for stable turn from bearish to bullish market
-    dataframe.loc[
-        (dataframe['close'] > dataframe['sar']) &
-        (prev_close > prev_sar) &
-        (prev_close2 < prev_sar2),
-        'swap'
-    ] = 1
-
-    # consider prices above ema to be in upswing
-    dataframe.loc[dataframe['ema'] <= dataframe['close'], 'upswing'] = 1
-
-    dataframe.loc[
-        (dataframe['upswing'] == 1) &
-        (dataframe['swap'] == 1) &
-        (dataframe['adx'] > 25), # adx over 25 tells there's enough momentum
+    dataframe.ix[
+        (dataframe['close'] < dataframe['sma']) &
+        (dataframe['tema'] <= dataframe['blower']) &
+        (dataframe['mfi'] < 25) &
+        (dataframe['fastd'] < 25) &
+        (dataframe['adx'] > 30),
         'buy'] = 1
-    dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
+    dataframe.ix[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
 
     return dataframe
 
@@ -93,13 +81,19 @@ def analyze_ticker(pair: str) -> DataFrame:
     add several TA indicators and buy signal to it
     :return DataFrame with ticker data and indicator data
     """
-    minimum_date = arrow.utcnow().shift(hours=-6)
-    data = get_ticker(pair, minimum_date)
-    dataframe = parse_ticker_dataframe(data['result'], minimum_date)
+    minimum_date = arrow.utcnow().shift(hours=-24)
+    data = get_ticker_history(pair, minimum_date)
+    dataframe = parse_ticker_dataframe(data['result'])
+
+    if dataframe.empty:
+        logger.warning('Empty dataframe for pair %s', pair)
+        return dataframe
+
     dataframe = populate_indicators(dataframe)
     dataframe = populate_buy_trend(dataframe)
     return dataframe
 
+
 def get_buy_signal(pair: str) -> bool:
     """
     Calculates a buy signal based several technical analysis indicators
@@ -107,6 +101,10 @@ def get_buy_signal(pair: str) -> bool:
     :return: True if pair is good for buying, False otherwise
     """
     dataframe = analyze_ticker(pair)
+
+    if dataframe.empty:
+        return False
+
     latest = dataframe.iloc[-1]
 
     # Check if dataframe is out of date
@@ -133,19 +131,26 @@ 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['ema'], '--', label='EMA(20)')
-    ax1.plot(dataframe.index.values, dataframe['buy'], 'bo', label='buy')
+    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, [25] * len(dataframe.index.values))
+    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)
@@ -156,8 +161,9 @@ 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:
-        test_pair = 'BTC_ANT'
-        #for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
-        #   get_buy_signal(pair)
+        exchange.EXCHANGE = Bittrex({'key': '', 'secret': ''})
+        test_pair = 'BTC_ETH'
+        # for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
+        #     get_buy_signal(pair)
         plot_dataframe(analyze_ticker(test_pair), test_pair)
         time.sleep(60)

freqtrade/exchange/__init__.py

@@ -0,0 +1,119 @@
+import enum
+import logging
+from typing import List
+
+import arrow
+
+from freqtrade.exchange.bittrex import Bittrex
+from freqtrade.exchange.interface import Exchange
+
+logger = logging.getLogger(__name__)
+
+# Current selected exchange
+EXCHANGE: Exchange = None
+_CONF: dict = {}
+
+
+class Exchanges(enum.Enum):
+    """
+    Maps supported exchange names to correspondent classes.
+    """
+    BITTREX = Bittrex
+
+
+def init(config: dict) -> None:
+    """
+    Initializes this module with the given config,
+    it does basic validation whether the specified
+    exchange and pairs are valid.
+    :param config: config to use
+    :return: None
+    """
+    global _CONF, EXCHANGE
+
+    _CONF.update(config)
+
+    if config['dry_run']:
+        logger.info('Instance is running with dry_run enabled')
+
+    exchange_config = config['exchange']
+
+    # Find matching class for the given exchange name
+    name = exchange_config['name']
+    try:
+        exchange_class = Exchanges[name.upper()].value
+    except KeyError:
+        raise RuntimeError('Exchange {} is not supported'.format(name))
+
+    EXCHANGE = exchange_class(exchange_config)
+
+    # Check if all pairs are available
+    validate_pairs(config['exchange']['pair_whitelist'])
+
+
+def validate_pairs(pairs: List[str]) -> None:
+    """
+    Checks if all given pairs are tradable on the current exchange.
+    Raises RuntimeError if one pair is not available.
+    :param pairs: list of pairs
+    :return: None
+    """
+    markets = EXCHANGE.get_markets()
+    for pair in pairs:
+        if pair not in markets:
+            raise RuntimeError('Pair {} is not available at {}'.format(pair, EXCHANGE.name.lower()))
+
+
+def buy(pair: str, rate: float, amount: float) -> str:
+    if _CONF['dry_run']:
+        return 'dry_run'
+
+    return EXCHANGE.buy(pair, rate, amount)
+
+
+def sell(pair: str, rate: float, amount: float) -> str:
+    if _CONF['dry_run']:
+        return 'dry_run'
+
+    return EXCHANGE.sell(pair, rate, amount)
+
+
+def get_balance(currency: str) -> float:
+    if _CONF['dry_run']:
+        return 999.9
+
+    return EXCHANGE.get_balance(currency)
+
+
+def get_balances():
+    return EXCHANGE.get_balances()
+
+
+def get_ticker(pair: str) -> dict:
+    return EXCHANGE.get_ticker(pair)
+
+
+def get_ticker_history(pair: str, minimum_date: arrow.Arrow):
+    return EXCHANGE.get_ticker_history(pair, minimum_date)
+
+
+def cancel_order(order_id: str) -> None:
+    if _CONF['dry_run']:
+        return
+
+    return EXCHANGE.cancel_order(order_id)
+
+
+def get_open_orders(pair: str) -> List[dict]:
+    if _CONF['dry_run']:
+        return []
+
+    return EXCHANGE.get_open_orders(pair)
+
+
+def get_pair_detail_url(pair: str) -> str:
+    return EXCHANGE.get_pair_detail_url(pair)
+
+
+def get_markets() -> List[str]:
+    return EXCHANGE.get_markets()

freqtrade/exchange/bittrex.py

@@ -0,0 +1,115 @@
+import logging
+from typing import List, Optional
+
+import arrow
+import requests
+from bittrex.bittrex import Bittrex as _Bittrex
+
+from freqtrade.exchange.interface import Exchange
+
+logger = logging.getLogger(__name__)
+
+_API: _Bittrex = None
+_EXCHANGE_CONF: dict = {}
+
+
+class Bittrex(Exchange):
+    """
+    Bittrex API wrapper.
+    """
+    # Base URL and API endpoints
+    BASE_URL: str = 'https://www.bittrex.com'
+    TICKER_METHOD: str = BASE_URL + '/Api/v2.0/pub/market/GetTicks'
+    PAIR_DETAIL_METHOD: str = BASE_URL + '/Market/Index'
+    # Ticker inveral
+    TICKER_INTERVAL: str = 'fiveMin'
+    # Sleep time to avoid rate limits, used in the main loop
+    SLEEP_TIME: float = 25
+
+    @property
+    def sleep_time(self) -> float:
+        return self.SLEEP_TIME
+
+    def __init__(self, config: dict) -> None:
+        global _API, _EXCHANGE_CONF
+
+        _EXCHANGE_CONF.update(config)
+        _API = _Bittrex(api_key=_EXCHANGE_CONF['key'], api_secret=_EXCHANGE_CONF['secret'])
+
+    def buy(self, pair: str, rate: float, amount: float) -> str:
+        data = _API.buy_limit(pair.replace('_', '-'), amount, rate)
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+        return data['result']['uuid']
+
+    def sell(self, pair: str, rate: float, amount: float) -> str:
+        data = _API.sell_limit(pair.replace('_', '-'), amount, rate)
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+        return data['result']['uuid']
+
+    def get_balance(self, currency: str) -> float:
+        data = _API.get_balance(currency)
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+        return float(data['result']['Balance'] or 0.0)
+
+    def get_balances(self):
+        data = _API.get_balances()
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+        return data['result']
+    
+    def get_ticker(self, pair: str) -> dict:
+        data = _API.get_ticker(pair.replace('_', '-'))
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+        return {
+            'bid': float(data['result']['Bid']),
+            'ask': float(data['result']['Ask']),
+            'last': float(data['result']['Last']),
+        }
+
+    def get_ticker_history(self, pair: str, minimum_date: Optional[arrow.Arrow] = None):
+        url = self.TICKER_METHOD
+        headers = {
+            # TODO: Set as global setting
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36'
+        }
+        params = {
+            'marketName': pair.replace('_', '-'),
+            'tickInterval': self.TICKER_INTERVAL,
+            # TODO: Timestamp has no effect on API response
+            '_': minimum_date.timestamp * 1000
+        }
+        data = requests.get(url, params=params, headers=headers).json()
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+        return data
+
+    def cancel_order(self, order_id: str) -> None:
+        data = _API.cancel(order_id)
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+
+    def get_open_orders(self, pair: str) -> List[dict]:
+        data = _API.get_open_orders(pair.replace('_', '-'))
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+        return [{
+            'id': entry['OrderUuid'],
+            'type': entry['OrderType'],
+            'opened': entry['Opened'],
+            'rate': entry['PricePerUnit'],
+            'amount': entry['Quantity'],
+            'remaining': entry['QuantityRemaining'],
+        } for entry in data['result']]
+
+    def get_pair_detail_url(self, pair: str) -> str:
+        return self.PAIR_DETAIL_METHOD + '?MarketName={}'.format(pair.replace('_', '-'))
+
+    def get_markets(self) -> List[str]:
+        data = _API.get_markets()
+        if not data['success']:
+            raise RuntimeError('{}: {}'.format(self.name.upper(), data['message']))
+        return [m['MarketName'].replace('-', '_') for m in data['result']]

freqtrade/exchange/interface.py

@@ -0,0 +1,142 @@
+from abc import ABC, abstractmethod
+from typing import List, Optional
+
+import arrow
+
+
+class Exchange(ABC):
+    @property
+    def name(self) -> str:
+        """
+        Name of the exchange.
+        :return: str representation of the class name
+        """
+        return self.__class__.__name__
+
+    @property
+    @abstractmethod
+    def sleep_time(self) -> float:
+        """
+        Sleep time in seconds for the main loop to avoid API rate limits.
+        :return: float
+        """
+
+    @abstractmethod
+    def buy(self, pair: str, rate: float, amount: float) -> str:
+        """
+        Places a limit buy order.
+        :param pair: Pair as str, format: BTC_ETH
+        :param rate: Rate limit for order
+        :param amount: The amount to purchase
+        :return: order_id of the placed buy order
+        """
+
+    @abstractmethod
+    def sell(self, pair: str, rate: float, amount: float) -> str:
+        """
+        Places a limit sell order.
+        :param pair: Pair as str, format: BTC_ETH
+        :param rate: Rate limit for order
+        :param amount: The amount to sell
+        :return: order_id of the placed sell order
+        """
+
+    @abstractmethod
+    def get_balance(self, currency: str) -> float:
+        """
+        Gets account balance.
+        :param currency: Currency as str, format: BTC
+        :return: float
+        """
+
+    @abstractmethod
+    def get_balances(self) -> List[dict]:
+        """
+        Gets account balances across currencies
+        :return: List of dicts, format: [
+          {
+            'Currency': str,
+            'Balance': float,
+            'Available': float,
+            'Pending': float,
+          }
+          ...
+        ]
+        """
+
+    @abstractmethod
+    def get_ticker(self, pair: str) -> dict:
+        """
+        Gets ticker for given pair.
+        :param pair: Pair as str, format: BTC_ETC
+        :return: dict, format: {
+            'bid': float,
+            'ask': float,
+            'last': float
+        }
+        """
+
+    @abstractmethod
+    def get_ticker_history(self, pair: str, minimum_date: Optional[arrow.Arrow] = None) -> dict:
+        """
+        Gets ticker history for given pair.
+        :param pair: Pair as str, format: BTC_ETC
+        :param minimum_date: Minimum date (optional)
+        :return: dict, format: {
+            'success': bool,
+            'message': str,
+            'result': [
+                {
+                    'O': float,       (Open)
+                    'H': float,       (High)
+                    'L': float,       (Low)
+                    'C': float,       (Close)
+                    'V': float,       (Volume)
+                    'T': datetime,    (Time)
+                    'BV': float,      (Base Volume)
+                },
+                ...
+            ]
+        }
+        """
+
+    @abstractmethod
+    def cancel_order(self, order_id: str) -> None:
+        """
+        Cancels order for given order_id.
+        :param order_id: ID as str
+        :return: None
+        """
+
+    @abstractmethod
+    def get_open_orders(self, pair: str) -> List[dict]:
+        """
+        Gets all open orders for given pair.
+        :param pair: Pair as str, format: BTC_ETC
+        :return: List of dicts, format: [
+            {
+                'id': str,
+                'type': str,
+                'opened': datetime,
+                'rate': float,
+                'amount': float,
+                'remaining': int,
+            },
+            ...
+        ]
+        """
+
+    @abstractmethod
+    def get_pair_detail_url(self, pair: str) -> str:
+        """
+        Returns the market detail url for the given pair.
+        :param pair: Pair as str, format: BTC_ETC
+        :return: URL as str
+        """
+
+    @abstractmethod
+    def get_markets(self) -> List[str]:
+        """
+        Returns all available markets.
+        :return: List of all available pairs
+        """

freqtrade/main.py

@@ -1,29 +1,25 @@
 #!/usr/bin/env python
+import copy
 import json
 import logging
 import time
 import traceback
 from datetime import datetime
 from typing import Dict, Optional
+from signal import signal, SIGINT, SIGABRT, SIGTERM
 
 from jsonschema import validate
 
-import exchange
-import persistence
-from persistence import Trade
-from analyze import get_buy_signal
-from misc import CONF_SCHEMA, get_state, State, update_state
-from rpc import telegram
+from freqtrade import __version__, exchange, persistence
+from freqtrade.analyze import get_buy_signal
+from freqtrade.misc import CONF_SCHEMA, State, get_state, update_state
+from freqtrade.persistence import Trade
+from freqtrade.rpc import telegram
 
 logging.basicConfig(level=logging.DEBUG,
                     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
-__author__ = "gcarq"
-__copyright__ = "gcarq 2017"
-__license__ = "GPLv3"
-__version__ = "0.10.0"
-
 _CONF = {}
 
 
@@ -39,7 +35,7 @@ def _process() -> None:
         if len(trades) < _CONF['max_open_trades']:
             try:
                 # Create entity and execute trade
-                trade = create_trade(float(_CONF['stake_amount']), exchange.EXCHANGE)
+                trade = create_trade(float(_CONF['stake_amount']))
                 if trade:
                     Trade.session.add(trade)
                 else:
@@ -94,12 +90,9 @@ def execute_sell(trade: Trade, current_rate: float) -> None:
     # Get available balance
     currency = trade.pair.split('_')[1]
     balance = exchange.get_balance(currency)
-    whitelist = _CONF[trade.exchange.name.lower()]['pair_whitelist']
-
     profit = trade.exec_sell_order(current_rate, balance)
-    whitelist.append(trade.pair)
     message = '*{}:* Selling [{}]({}) at rate `{:f} (profit: {}%)`'.format(
-        trade.exchange.name,
+        trade.exchange,
         trade.pair.replace('_', '/'),
         exchange.get_pair_detail_url(trade.pair),
         trade.close_rate,
@@ -150,6 +143,7 @@ def handle_trade(trade: Trade) -> None:
     except ValueError:
         logger.exception('Unable to handle open order')
 
+
 def get_target_bid(ticker: Dict[str, float]) -> float:
     """ Calculates bid target between current ask price and last price """
     if ticker['ask'] < ticker['last']:
@@ -158,15 +152,14 @@ def get_target_bid(ticker: Dict[str, float]) -> float:
     return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
 
 
-def create_trade(stake_amount: float, _exchange: exchange.Exchange) -> Optional[Trade]:
+def create_trade(stake_amount: float) -> Optional[Trade]:
     """
     Checks the implemented trading indicator(s) for a randomly picked pair,
     if one pair triggers the buy_signal a new trade record gets created
     :param stake_amount: amount of btc to spend
-    :param _exchange: exchange to use
     """
     logger.info('Creating new trade with stake_amount: %f ...', stake_amount)
-    whitelist = _CONF[_exchange.name.lower()]['pair_whitelist']
+    whitelist = copy.deepcopy(_CONF['exchange']['pair_whitelist'])
     # Check if stake_amount is fulfilled
     if exchange.get_balance(_CONF['stake_currency']) < stake_amount:
         raise ValueError(
@@ -174,11 +167,7 @@ def create_trade(stake_amount: float, _exchange: exchange.Exchange) -> Optional[
         )
 
     # Remove currently opened and latest pairs from whitelist
-    trades = Trade.query.filter(Trade.is_open.is_(True)).all()
-    latest_trade = Trade.query.filter(Trade.is_open.is_(False)).order_by(Trade.id.desc()).first()
-    if latest_trade:
-        trades.append(latest_trade)
-    for trade in trades:
+    for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
         if trade.pair in whitelist:
             whitelist.remove(trade.pair)
             logger.debug('Ignoring %s in pair whitelist', trade.pair)
@@ -199,7 +188,7 @@ def create_trade(stake_amount: float, _exchange: exchange.Exchange) -> Optional[
 
     # Create trade entity and return
     message = '*{}:* Buying [{}]({}) at rate `{:f}`'.format(
-        _exchange.name,
+        exchange.EXCHANGE.name.upper(),
         pair.replace('_', '/'),
         exchange.get_pair_detail_url(pair),
         open_rate
@@ -211,7 +200,7 @@ def create_trade(stake_amount: float, _exchange: exchange.Exchange) -> Optional[
                  open_rate=open_rate,
                  open_date=datetime.utcnow(),
                  amount=amount,
-                 exchange=_exchange,
+                 exchange=exchange.EXCHANGE.name.upper(),
                  open_order_id=order_id,
                  is_open=True)
 
@@ -235,10 +224,27 @@ def init(config: dict, db_url: Optional[str] = None) -> None:
     else:
         update_state(State.STOPPED)
 
+    # Register signal handlers
+    for sig in (SIGINT, SIGTERM, SIGABRT):
+        signal(sig, cleanup)
+
+
+def cleanup(*args, **kwargs) -> None:
+    """
+    Cleanup the application state und finish all pending tasks
+    :return: None
+    """
+    telegram.send_msg('*Status:* `Stopping trader...`')
+    logger.info('Stopping trader and cleaning up modules...')
+    update_state(State.STOPPED)
+    persistence.cleanup()
+    telegram.cleanup()
+    exit(0)
+
 
 def app(config: dict) -> None:
     """
-    Main function which handles the application state
+    Main loop which handles the application state
     :param config: config as dict
     :return: None
     """
@@ -260,17 +266,26 @@ def app(config: dict) -> None:
             elif new_state == State.RUNNING:
                 _process()
                 # We need to sleep here because otherwise we would run into bittrex rate limit
-                time.sleep(25)
+                time.sleep(exchange.EXCHANGE.sleep_time)
             old_state = new_state
     except RuntimeError:
-        telegram.send_msg('*Status:* Got RuntimeError: ```\n{}\n```'.format(traceback.format_exc()))
+        telegram.send_msg(
+            '*Status:* Got RuntimeError:\n```\n{}\n```'.format(traceback.format_exc())
+        )
         logger.exception('RuntimeError. Trader stopped!')
-    finally:
-        telegram.send_msg('*Status:* `Trader has stopped`')
 
 
-if __name__ == '__main__':
+def main():
+    """
+    Loads and validates the config and starts the main loop
+    :return: None
+    """
+    global _CONF
     with open('config.json') as file:
         _CONF = json.load(file)
         validate(_CONF, CONF_SCHEMA)
         app(_CONF)
+
+
+if __name__ == '__main__':
+    main()

freqtrade/misc.py

@@ -60,7 +60,7 @@ CONF_SCHEMA = {
             },
             'required': ['ask_last_balance']
         },
-        'bittrex': {'$ref': '#/definitions/exchange'},
+        'exchange': {'$ref': '#/definitions/exchange'},
         'telegram': {
             'type': 'object',
             'properties': {
@@ -76,7 +76,7 @@ CONF_SCHEMA = {
         'exchange': {
             'type': 'object',
             'properties': {
-                'enabled': {'type': 'boolean'},
+                'name': {'type': 'string'},
                 'key': {'type': 'string'},
                 'secret': {'type': 'string'},
                 'pair_whitelist': {
@@ -85,11 +85,11 @@ CONF_SCHEMA = {
                     'uniqueItems': True
                 }
             },
-            'required': ['enabled', 'key', 'secret', 'pair_whitelist']
+            'required': ['name', 'key', 'secret', 'pair_whitelist']
         }
     },
     'anyOf': [
-        {'required': ['bittrex']}
+        {'required': ['exchange']}
     ],
     'required': [
         'max_open_trades',

freqtrade/persistence.py

@@ -6,10 +6,7 @@ from sqlalchemy.ext.declarative import declarative_base
 from sqlalchemy.orm.scoping import scoped_session
 from sqlalchemy.orm.session import sessionmaker
 
-from sqlalchemy.types import Enum
-
-import exchange
-
+from freqtrade import exchange
 
 _CONF = {}
 
@@ -39,11 +36,19 @@ def init(config: dict, db_url: Optional[str] = None) -> None:
     Base.metadata.create_all(engine)
 
 
+def cleanup() -> None:
+    """
+    Flushes all pending operations to disk.
+    :return: None
+    """
+    Trade.session.flush()
+
+
 class Trade(Base):
     __tablename__ = 'trades'
 
     id = Column(Integer, primary_key=True)
-    exchange = Column(Enum(exchange.Exchange), nullable=False)
+    exchange = Column(String, nullable=False)
     pair = Column(String, nullable=False)
     is_open = Column(Boolean, nullable=False, default=True)
     open_rate = Column(Float, nullable=False)

freqtrade/rpc/telegram.py

@@ -4,21 +4,20 @@ from typing import Callable, Any
 
 import arrow
 from sqlalchemy import and_, func, text
+from telegram import ParseMode, Bot, Update
 from telegram.error import NetworkError
 from telegram.ext import CommandHandler, Updater
-from telegram import ParseMode, Bot, Update
 
-from misc import get_state, State, update_state
-from persistence import Trade
-
-import exchange
+from freqtrade import exchange
+from freqtrade.misc import get_state, State, update_state
+from freqtrade.persistence import Trade
 
 # Remove noisy log messages
 logging.getLogger('requests.packages.urllib3').setLevel(logging.INFO)
 logging.getLogger('telegram').setLevel(logging.INFO)
 logger = logging.getLogger(__name__)
 
-_updater = None
+_updater: Updater = None
 _CONF = {}
 
 
@@ -31,18 +30,23 @@ def init(config: dict) -> None:
     :return: None
     """
     global _updater
-    _updater = Updater(token=config['telegram']['token'], workers=0)
 
     _CONF.update(config)
+    if not _CONF['telegram']['enabled']:
+        return
+
+    _updater = Updater(token=config['telegram']['token'], workers=0)
 
     # Register command handler and start telegram message polling
     handles = [
         CommandHandler('status', _status),
         CommandHandler('profit', _profit),
+        CommandHandler('balance', _balance),
         CommandHandler('start', _start),
         CommandHandler('stop', _stop),
         CommandHandler('forcesell', _forcesell),
         CommandHandler('performance', _performance),
+        CommandHandler('help', _help),
     ]
     for handle in handles:
         _updater.dispatcher.add_handler(handle)
@@ -58,6 +62,14 @@ def init(config: dict) -> None:
     )
 
 
+def cleanup() -> None:
+    """
+    Stops all running telegram threads.
+    :return: None
+    """
+    _updater.stop()
+
+
 def authorized_only(command_handler: Callable[[Bot, Update], None]) -> Callable[..., Any]:
     """
     Decorator to check if the message comes from the correct chat_id
@@ -191,6 +203,27 @@ def _profit(bot: Bot, update: Update) -> None:
     send_msg(markdown_msg, bot=bot)
 
 
+@authorized_only
+def _balance(bot: Bot, update: Update) -> None:
+    """
+    Handler for /balance
+    Returns current account balance per crypto
+    """
+    output = ""
+    balances = exchange.get_balances()
+    for currency in balances:
+        if not currency['Balance'] and not currency['Available'] and not currency['Pending']:
+            continue
+        output += """*Currency*: {Currency}
+*Available*: {Available}
+*Balance*: {Balance}
+*Pending*: {Pending}
+
+""".format(**currency)
+
+    send_msg(output)
+
+
 @authorized_only
 def _start(bot: Bot, update: Update) -> None:
     """
@@ -255,7 +288,7 @@ def _forcesell(bot: Bot, update: Update) -> None:
         # Execute sell
         profit = trade.exec_sell_order(current_rate, balance)
         message = '*{}:* Selling [{}]({}) at rate `{:f} (profit: {}%)`'.format(
-            trade.exchange.name,
+            trade.exchange,
             trade.pair.replace('_', '/'),
             exchange.get_pair_detail_url(trade.pair),
             trade.close_rate,
@@ -299,6 +332,28 @@ 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`
+*/balance:* `Show account balance per currency`
+*/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_analyze.py

@@ -0,0 +1,41 @@
+# pragma pylint: disable=missing-docstring
+from datetime import datetime
+import json
+import pytest
+from pandas import DataFrame
+
+from freqtrade.analyze import parse_ticker_dataframe, populate_buy_trend, populate_indicators, \
+    get_buy_signal
+
+
+@pytest.fixture
+def result():
+    with open('freqtrade/tests/testdata/btc-eth.json') as data_file:
+        data = json.load(data_file)
+
+    return parse_ticker_dataframe(data['result'])
+
+
+def test_dataframe_has_correct_columns(result):
+    assert result.columns.tolist() == \
+                        ['close', 'high', 'low', 'open', 'date', 'volume']
+
+
+def test_dataframe_has_correct_length(result):
+    assert len(result.index) == 5751
+
+
+def test_populates_buy_trend(result):
+    dataframe = populate_buy_trend(populate_indicators(result))
+    assert 'buy' in dataframe.columns
+    assert 'buy_price' in dataframe.columns
+
+
+def test_returns_latest_buy_signal(mocker):
+    buydf = DataFrame([{'buy': 1, 'date': datetime.today()}])
+    mocker.patch('freqtrade.analyze.analyze_ticker', return_value=buydf)
+    assert get_buy_signal('BTC-ETH')
+
+    buydf = DataFrame([{'buy': 0, 'date': datetime.today()}])
+    mocker.patch('freqtrade.analyze.analyze_ticker', return_value=buydf)
+    assert not get_buy_signal('BTC-ETH')

freqtrade/tests/test_backtesting.py

@@ -0,0 +1,82 @@
+# pragma pylint: disable=missing-docstring
+import json
+import logging
+import os
+
+import pytest
+import arrow
+from pandas import DataFrame
+
+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 format_results(results):
+    return '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
+    )
+
+
+def print_pair_results(pair, results):
+    print('For currency {}:'.format(pair))
+    print(format_results(results[results.currency == pair]))
+
+
+@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": {
+            "50":  0.0,
+            "40":  0.01,
+            "30":  0.02,
+            "0":  0.045
+        },
+        "stoploss": -0.40
+    }
+
+
+def backtest(conf, pairs, mocker):
+    trades = []
+    mocked_history = mocker.patch('freqtrade.analyze.get_ticker_history')
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
+    for pair in pairs:
+        with open('freqtrade/tests/testdata/'+pair+'.json') as data_file:
+            data = json.load(data_file)
+            mocked_history.return_value = data
+            ticker = analyze_ticker(pair)[['close', 'date', 'buy']].copy()
+            # for each buy point
+            for row in ticker[ticker.buy == 1].itertuples(index=True):
+                trade = Trade(open_rate=row.close, open_date=row.date, amount=1)
+                # calculate win/lose forwards from buy point
+                for row2 in ticker[row.Index:].itertuples(index=True):
+                    if should_sell(trade, row2.close, row2.date):
+                        current_profit = (row2.close - trade.open_rate) / trade.open_rate
+
+                        trades.append((pair, current_profit, row2.Index - row.Index))
+                        break
+    labels = ['currency', 'profit', 'duration']
+    results = DataFrame.from_records(trades, columns=labels)
+    return results
+
+
+@pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
+def test_backtest(conf, pairs, mocker, report=True):
+    results = backtest(conf, pairs, mocker)
+
+    print('====================== BACKTESTING REPORT ================================')
+    [print_pair_results(pair, results) for pair in pairs]
+    print('TOTAL OVER ALL TRADES:')
+    print(format_results(results))

freqtrade/tests/test_hyperopt.py

@@ -0,0 +1,154 @@
+# pragma pylint: disable=missing-docstring
+import logging
+import os
+from functools import reduce
+from math import exp
+from operator import itemgetter
+
+import pytest
+from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
+from pandas import DataFrame
+
+from freqtrade.tests.test_backtesting import backtest, format_results
+from freqtrade.vendor.qtpylib.indicators import crossed_above
+
+logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
+
+# set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
+TARGET_TRADES = 1200
+
+
+@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 buy_strategy_generator(params):
+    print(params)
+
+    def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+        conditions = []
+        # GUARDS AND TRENDS
+        if params['uptrend_long_ema']['enabled']:
+            conditions.append(dataframe['ema50'] > dataframe['ema100'])
+        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['rsi']['enabled']:
+            conditions.append(dataframe['rsi'] < params['rsi']['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),
+            'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
+            'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
+            'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
+        }
+        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):
+    mocked_buy_trend = mocker.patch('freqtrade.analyze.populate_buy_trend')
+
+    def optimizer(params):
+        mocked_buy_trend.side_effect = buy_strategy_generator(params)
+
+        results = backtest(conf, pairs, mocker)
+
+        result = format_results(results)
+        print(result)
+
+        total_profit = results.profit.sum() * 1000
+        trade_count = len(results.index)
+
+        trade_loss = 1 - 0.8 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5)
+        profit_loss = exp(-total_profit**3 / 10**11)
+
+        return {
+            'loss': trade_loss + profit_loss,
+            'status': STATUS_OK,
+            'result': result
+        }
+
+    space = {
+        'mfi': hp.choice('mfi', [
+            {'enabled': False},
+            {'enabled': True, 'value': hp.uniform('mfi-value', 5, 15)}
+        ]),
+        'fastd': hp.choice('fastd', [
+            {'enabled': False},
+            {'enabled': True, 'value': hp.uniform('fastd-value', 5, 40)}
+        ]),
+        'adx': hp.choice('adx', [
+            {'enabled': False},
+            {'enabled': True, 'value': hp.uniform('adx-value', 10, 30)}
+        ]),
+        'cci': hp.choice('cci', [
+            {'enabled': False},
+            {'enabled': True, 'value': hp.uniform('cci-value', -150, -100)}
+        ]),
+        'rsi': hp.choice('rsi', [
+            {'enabled': False},
+            {'enabled': True, 'value': hp.uniform('rsi-value', 20, 30)}
+        ]),
+        'uptrend_long_ema': hp.choice('uptrend_long_ema', [
+            {'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'},
+            {'type': 'ao_cross_zero'},
+            {'type': 'ema5_cross_ema10'},
+            {'type': 'macd_cross_signal'},
+        ]),
+    }
+    trials = Trials()
+    best = fmin(fn=optimizer, space=space, algo=tpe.suggest, max_evals=40, trials=trials)
+    print('\n\n\n\n====================== HYPEROPT BACKTESTING REPORT ================================')
+    print('Best parameters {}'.format(best))
+    newlist = sorted(trials.results, key=itemgetter('loss'))
+    print('Result: {}'.format(newlist[0]['result']))

freqtrade/tests/test_main.py

@@ -0,0 +1,132 @@
+# pragma pylint: disable=missing-docstring
+import copy
+from unittest.mock import MagicMock, call
+
+import pytest
+from jsonschema import validate
+
+from freqtrade.exchange import Exchanges
+from freqtrade.main import create_trade, handle_trade, close_trade_if_fulfilled, init, \
+    get_target_bid
+from freqtrade.misc import CONF_SCHEMA
+from freqtrade.persistence import Trade
+
+
+@pytest.fixture
+def conf():
+    configuration = {
+        "max_open_trades": 3,
+        "stake_currency": "BTC",
+        "stake_amount": 0.05,
+        "dry_run": True,
+        "minimal_roi": {
+            "2880": 0.005,
+            "720": 0.01,
+            "0": 0.02
+        },
+        "bid_strategy": {
+            "ask_last_balance": 0.0
+        },
+        "exchange": {
+            "name": "bittrex",
+            "enabled": True,
+            "key": "key",
+            "secret": "secret",
+            "pair_whitelist": [
+                "BTC_ETH",
+                "BTC_TKN",
+                "BTC_TRST",
+                "BTC_SWT",
+            ]
+        },
+        "telegram": {
+            "enabled": True,
+            "token": "token",
+            "chat_id": "chat_id"
+        }
+    }
+    validate(configuration, CONF_SCHEMA)
+    return configuration
+
+
+def test_create_trade(conf, mocker):
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    buy_signal = mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True)
+    mocker.patch.multiple('freqtrade.main.telegram', init=MagicMock(), send_msg=MagicMock())
+    mocker.patch.multiple('freqtrade.main.exchange',
+                          validate_pairs=MagicMock(),
+                          get_ticker=MagicMock(return_value={
+                              'bid': 0.07256061,
+                              'ask': 0.072661,
+                              'last': 0.07256061
+                          }),
+                          buy=MagicMock(return_value='mocked_order_id'))
+    # Save state of current whitelist
+    whitelist = copy.deepcopy(conf['exchange']['pair_whitelist'])
+
+    init(conf, 'sqlite://')
+    for pair in ['BTC_ETH', 'BTC_TKN', 'BTC_TRST', 'BTC_SWT']:
+        trade = create_trade(15.0)
+        Trade.session.add(trade)
+        Trade.session.flush()
+        assert trade is not None
+        assert trade.open_rate == 0.072661
+        assert trade.pair == pair
+        assert trade.exchange == Exchanges.BITTREX.name
+        assert trade.amount == 206.43811673387373
+        assert trade.stake_amount == 15.0
+        assert trade.is_open
+        assert trade.open_date is not None
+        assert whitelist == conf['exchange']['pair_whitelist']
+
+    buy_signal.assert_has_calls(
+        [call('BTC_ETH'), call('BTC_TKN'), call('BTC_TRST'), call('BTC_SWT')]
+    )
+
+
+def test_handle_trade(conf, mocker):
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    mocker.patch.multiple('freqtrade.main.telegram', init=MagicMock(), send_msg=MagicMock())
+    mocker.patch.multiple('freqtrade.main.exchange',
+                          validate_pairs=MagicMock(),
+                          get_ticker=MagicMock(return_value={
+                              'bid': 0.17256061,
+                              'ask': 0.172661,
+                              'last': 0.17256061
+                          }),
+                          buy=MagicMock(return_value='mocked_order_id'))
+    trade = Trade.query.filter(Trade.is_open.is_(True)).first()
+    assert trade
+    handle_trade(trade)
+    assert trade.close_rate == 0.17256061
+    assert trade.close_profit == 137.4872490056564
+    assert trade.close_date is not None
+    assert trade.open_order_id == 'dry_run'
+
+
+def test_close_trade(conf, mocker):
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    trade = Trade.query.filter(Trade.is_open.is_(True)).first()
+    assert trade
+
+    # Simulate that there is no open order
+    trade.open_order_id = None
+
+    closed = close_trade_if_fulfilled(trade)
+    assert closed
+    assert not trade.is_open
+
+
+def test_balance_fully_ask_side(mocker):
+    mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 0.0}})
+    assert get_target_bid({'ask': 20, 'last': 10}) == 20
+
+
+def test_balance_fully_last_side(mocker):
+    mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
+    assert get_target_bid({'ask': 20, 'last': 10}) == 10
+
+
+def test_balance_when_last_bigger_than_ask(mocker):
+    mocker.patch.dict('freqtrade.main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}})
+    assert get_target_bid({'ask': 5, 'last': 10}) == 5

freqtrade/tests/test_persistence.py

@@ -0,0 +1,21 @@
+# pragma pylint: disable=missing-docstring
+from freqtrade.exchange import Exchanges
+from freqtrade.persistence import Trade
+
+
+def test_exec_sell_order(mocker):
+    api_mock = mocker.patch('freqtrade.main.exchange.sell', side_effect='mocked_order_id')
+    trade = Trade(
+        pair='BTC_ETH',
+        stake_amount=1.00,
+        open_rate=0.50,
+        amount=10.00,
+        exchange=Exchanges.BITTREX,
+        open_order_id='mocked'
+    )
+    profit = trade.exec_sell_order(1.00, 10.00)
+    api_mock.assert_called_once_with('BTC_ETH', 1.0, 10.0)
+    assert profit == 100.0
+    assert trade.close_rate == 1.0
+    assert trade.close_profit == profit
+    assert trade.close_date is not None

freqtrade/tests/test_telegram.py

@@ -0,0 +1,223 @@
+# pragma pylint: disable=missing-docstring
+from datetime import datetime
+from unittest.mock import MagicMock
+
+import pytest
+from jsonschema import validate
+from telegram import Bot, Update, Message, Chat
+
+from freqtrade.main import init, create_trade
+from freqtrade.misc import update_state, State, get_state, CONF_SCHEMA
+from freqtrade.persistence import Trade
+from freqtrade.rpc.telegram import _status, _profit, _forcesell, _performance, _start, _stop, _balance
+
+
+@pytest.fixture
+def conf():
+    configuration = {
+        "max_open_trades": 3,
+        "stake_currency": "BTC",
+        "stake_amount": 0.05,
+        "dry_run": True,
+        "minimal_roi": {
+            "2880": 0.005,
+            "720": 0.01,
+            "0": 0.02
+        },
+        "bid_strategy": {
+            "ask_last_balance": 0.0
+        },
+        "exchange": {
+            "name": "bittrex",
+            "enabled": True,
+            "key": "key",
+            "secret": "secret",
+            "pair_whitelist": [
+                "BTC_ETH"
+            ]
+        },
+        "telegram": {
+            "enabled": True,
+            "token": "token",
+            "chat_id": "0"
+        },
+        "initial_state": "running"
+    }
+    validate(configuration, CONF_SCHEMA)
+    return configuration
+
+@pytest.fixture
+def update():
+    _update = Update(0)
+    _update.message = Message(0, 0, datetime.utcnow(), Chat(0, 0))
+    return _update
+
+
+class MagicBot(MagicMock, Bot):
+    pass
+
+
+def test_status_handle(conf, update, mocker):
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True)
+    msg_mock = MagicMock()
+    mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock)
+    mocker.patch.multiple('freqtrade.main.exchange',
+                          validate_pairs=MagicMock(),
+                          get_ticker=MagicMock(return_value={
+                              'bid': 0.07256061,
+                              'ask': 0.072661,
+                              'last': 0.07256061
+                          }),
+                          buy=MagicMock(return_value='mocked_order_id'))
+    init(conf, 'sqlite://')
+
+    # Create some test data
+    trade = create_trade(15.0)
+    assert trade
+    Trade.session.add(trade)
+    Trade.session.flush()
+
+    _status(bot=MagicBot(), update=update)
+    assert msg_mock.call_count == 2
+    assert '[BTC_ETH]' in msg_mock.call_args_list[-1][0][0]
+
+
+def test_profit_handle(conf, update, mocker):
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True)
+    msg_mock = MagicMock()
+    mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock)
+    mocker.patch.multiple('freqtrade.main.exchange',
+                          validate_pairs=MagicMock(),
+                          get_ticker=MagicMock(return_value={
+                              'bid': 0.07256061,
+                              'ask': 0.072661,
+                              'last': 0.07256061
+                          }),
+                          buy=MagicMock(return_value='mocked_order_id'))
+    init(conf, 'sqlite://')
+
+    # Create some test data
+    trade = create_trade(15.0)
+    assert trade
+    trade.close_rate = 0.07256061
+    trade.close_profit = 100.00
+    trade.close_date = datetime.utcnow()
+    trade.open_order_id = None
+    trade.is_open = False
+    Trade.session.add(trade)
+    Trade.session.flush()
+
+    _profit(bot=MagicBot(), update=update)
+    assert msg_mock.call_count == 2
+    assert '(100.00%)' in msg_mock.call_args_list[-1][0][0]
+
+
+def test_forcesell_handle(conf, update, mocker):
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True)
+    msg_mock = MagicMock()
+    mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock)
+    mocker.patch.multiple('freqtrade.main.exchange',
+                          validate_pairs=MagicMock(),
+                          get_ticker=MagicMock(return_value={
+                              'bid': 0.07256061,
+                              'ask': 0.072661,
+                              'last': 0.07256061
+                          }),
+                          buy=MagicMock(return_value='mocked_order_id'))
+    init(conf, 'sqlite://')
+
+    # Create some test data
+    trade = create_trade(15.0)
+    assert trade
+    Trade.session.add(trade)
+    Trade.session.flush()
+
+    update.message.text = '/forcesell 1'
+    _forcesell(bot=MagicBot(), update=update)
+
+    assert msg_mock.call_count == 2
+    assert 'Selling [BTC/ETH]' in msg_mock.call_args_list[-1][0][0]
+    assert '0.072561' in msg_mock.call_args_list[-1][0][0]
+
+
+def test_performance_handle(conf, update, mocker):
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    mocker.patch('freqtrade.main.get_buy_signal', side_effect=lambda _: True)
+    msg_mock = MagicMock()
+    mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock)
+    mocker.patch.multiple('freqtrade.main.exchange',
+                          validate_pairs=MagicMock(),
+                          get_ticker=MagicMock(return_value={
+                              'bid': 0.07256061,
+                              'ask': 0.072661,
+                              'last': 0.07256061
+                          }),
+                          buy=MagicMock(return_value='mocked_order_id'))
+    init(conf, 'sqlite://')
+
+    # Create some test data
+    trade = create_trade(15.0)
+    assert trade
+    trade.close_rate = 0.07256061
+    trade.close_profit = 100.00
+    trade.close_date = datetime.utcnow()
+    trade.open_order_id = None
+    trade.is_open = False
+    Trade.session.add(trade)
+    Trade.session.flush()
+
+    _performance(bot=MagicBot(), update=update)
+    assert msg_mock.call_count == 2
+    assert 'Performance' in msg_mock.call_args_list[-1][0][0]
+    assert 'BTC_ETH	100.00%' in msg_mock.call_args_list[-1][0][0]
+
+
+def test_start_handle(conf, update, mocker):
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    msg_mock = MagicMock()
+    mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock)
+    mocker.patch.multiple('freqtrade.main.exchange', _CONF=conf, init=MagicMock())
+    init(conf, 'sqlite://')
+
+    update_state(State.STOPPED)
+    assert get_state() == State.STOPPED
+    _start(bot=MagicBot(), update=update)
+    assert get_state() == State.RUNNING
+    assert msg_mock.call_count == 0
+
+
+def test_stop_handle(conf, update, mocker):
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    msg_mock = MagicMock()
+    mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock)
+    mocker.patch.multiple('freqtrade.main.exchange', _CONF=conf, init=MagicMock())
+    init(conf, 'sqlite://')
+
+    update_state(State.RUNNING)
+    assert get_state() == State.RUNNING
+    _stop(bot=MagicBot(), update=update)
+    assert get_state() == State.STOPPED
+    assert msg_mock.call_count == 1
+    assert 'Stopping trader' in msg_mock.call_args_list[0][0][0]
+
+
+def test_balance_handle(conf, update, mocker):
+    mock_balance = [{
+        'Currency': 'BTC',
+        'Balance': 10.0,
+        'Available': 12.0,
+        'Pending': 0.0,
+        'CryptoAddress': 'XXXX'}]
+    mocker.patch.dict('freqtrade.main._CONF', conf)
+    msg_mock = MagicMock()
+    mocker.patch.multiple('freqtrade.main.telegram', _CONF=conf, init=MagicMock(), send_msg=msg_mock)
+    mocker.patch.multiple('freqtrade.main.exchange',
+                          get_balances=MagicMock(return_value=mock_balance))
+
+    _balance(bot=MagicBot(), update=update)
+    assert msg_mock.call_count == 1
+    assert '*Currency*: BTC' in msg_mock.call_args_list[0][0][0]
+    assert 'Balance' in msg_mock.call_args_list[0][0][0]

freqtrade/tests/testdata/download_backtest_data.py

@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+
+"""This script generate json data from bittrex"""
+
+from urllib.request import urlopen
+
+CURRENCIES = ["ok", "neo", "dash", "etc", "eth", "snt"]
+OUTPUT_DIR = 'freqtrade/tests/testdata/'
+
+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')
+    output = OUTPUT_DIR + 'btc-'+cur+'.json'
+    with open(output, 'w') as file:
+        file.write(json_str)

freqtrade/vendor/qtpylib/indicators.py

@@ -0,0 +1,619 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# QTPyLib: Quantitative Trading Python Library
+# https://github.com/ranaroussi/qtpylib
+#
+# Copyright 2016 Ran Aroussi
+#
+# Licensed under the GNU Lesser General Public License, v3.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.gnu.org/licenses/lgpl-3.0.en.html
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+import pandas as pd
+import warnings
+import sys
+
+from datetime import datetime, timedelta
+from pandas.core.base import PandasObject
+
+# =============================================
+# check min, python version
+if sys.version_info < (3, 4):
+    raise SystemError("QTPyLib requires Python version >= 3.4")
+
+# =============================================
+warnings.simplefilter(action="ignore", category=RuntimeWarning)
+
+# =============================================
+
+
+def numpy_rolling_window(data, window):
+    shape = data.shape[:-1] + (data.shape[-1] - window + 1, window)
+    strides = data.strides + (data.strides[-1],)
+    return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
+
+
+def numpy_rolling_series(func):
+    def func_wrapper(data, window, as_source=False):
+        series = data.values if isinstance(data, pd.Series) else data
+
+        new_series = np.empty(len(series)) * np.nan
+        calculated = func(series, window)
+        new_series[-len(calculated):] = calculated
+
+        if as_source and isinstance(data, pd.Series):
+            return pd.Series(index=data.index, data=new_series)
+
+        return new_series
+
+    return func_wrapper
+
+
+@numpy_rolling_series
+def numpy_rolling_mean(data, window, as_source=False):
+    return np.mean(numpy_rolling_window(data, window), -1)
+
+
+@numpy_rolling_series
+def numpy_rolling_std(data, window, as_source=False):
+    return np.std(numpy_rolling_window(data, window), -1)
+
+# ---------------------------------------------
+
+
+def session(df, start='17:00', end='16:00'):
+    """ remove previous globex day from df """
+    if len(df) == 0:
+        return df
+
+    # get start/end/now as decimals
+    int_start = list(map(int, start.split(':')))
+    int_start = (int_start[0] + int_start[1] - 1 / 100) - 0.0001
+    int_end = list(map(int, end.split(':')))
+    int_end = int_end[0] + int_end[1] / 100
+    int_now = (df[-1:].index.hour[0] + (df[:1].index.minute[0]) / 100)
+
+    # same-dat session?
+    is_same_day = int_end > int_start
+
+    # set pointers
+    curr = prev = df[-1:].index[0].strftime('%Y-%m-%d')
+
+    # globex/forex session
+    if is_same_day == False:
+        prev = (datetime.strptime(curr, '%Y-%m-%d') -
+                timedelta(1)).strftime('%Y-%m-%d')
+
+    # slice
+    if int_now >= int_start:
+        df = df[df.index >= curr + ' ' + start]
+    else:
+        df = df[df.index >= prev + ' ' + start]
+
+    return df.copy()
+
+
+# ---------------------------------------------
+
+def heikinashi(bars):
+    bars = bars.copy()
+    bars['ha_close'] = (bars['open'] + bars['high'] +
+                        bars['low'] + bars['close']) / 4
+    bars['ha_open'] = (bars['open'].shift(1) + bars['close'].shift(1)) / 2
+    bars.loc[:1, 'ha_open'] = bars['open'].values[0]
+    bars.loc[1:, 'ha_open'] = (
+        (bars['ha_open'].shift(1) + bars['ha_close'].shift(1)) / 2)[1:]
+    bars['ha_high'] = bars.loc[:, ['high', 'ha_open', 'ha_close']].max(axis=1)
+    bars['ha_low'] = bars.loc[:, ['low', 'ha_open', 'ha_close']].min(axis=1)
+
+    return pd.DataFrame(index=bars.index, data={'open': bars['ha_open'],
+                                                'high': bars['ha_high'], 'low': bars['ha_low'], 'close': bars['ha_close']})
+
+
+# ---------------------------------------------
+
+def tdi(series, rsi_len=13, bollinger_len=34, rsi_smoothing=2, rsi_signal_len=7, bollinger_std=1.6185):
+    rsi_series = rsi(series, rsi_len)
+    bb_series = bollinger_bands(rsi_series, bollinger_len, bollinger_std)
+    signal = sma(rsi_series, rsi_signal_len)
+    rsi_series = sma(rsi_series, rsi_smoothing)
+
+    return pd.DataFrame(index=series.index, data={
+        "rsi": rsi_series,
+        "signal": signal,
+        "bbupper": bb_series['upper'],
+        "bblower": bb_series['lower'],
+        "bbmid": bb_series['mid']
+    })
+
+# ---------------------------------------------
+
+
+def awesome_oscillator(df, weighted=False, fast=5, slow=34):
+    midprice = (df['high'] + df['low']) / 2
+
+    if weighted:
+        ao = (midprice.ewm(fast).mean() - midprice.ewm(slow).mean()).values
+    else:
+        ao = numpy_rolling_mean(midprice, fast) - \
+            numpy_rolling_mean(midprice, slow)
+
+    return pd.Series(index=df.index, data=ao)
+
+
+# ---------------------------------------------
+
+def nans(len=1):
+    mtx = np.empty(len)
+    mtx[:] = np.nan
+    return mtx
+
+
+# ---------------------------------------------
+
+def typical_price(bars):
+    res = (bars['high'] + bars['low'] + bars['close']) / 3.
+    return pd.Series(index=bars.index, data=res)
+
+
+# ---------------------------------------------
+
+def mid_price(bars):
+    res = (bars['high'] + bars['low']) / 2.
+    return pd.Series(index=bars.index, data=res)
+
+
+# ---------------------------------------------
+
+def ibs(bars):
+    """ Internal bar strength """
+    res = np.round((bars['close'] - bars['low']) /
+                   (bars['high'] - bars['low']), 2)
+    return pd.Series(index=bars.index, data=res)
+
+
+# ---------------------------------------------
+
+def true_range(bars):
+    return pd.DataFrame({
+        "hl": bars['high'] - bars['low'],
+        "hc": abs(bars['high'] - bars['close'].shift(1)),
+        "lc": abs(bars['low'] - bars['close'].shift(1))
+    }).max(axis=1)
+
+
+# ---------------------------------------------
+
+def atr(bars, window=14, exp=False):
+    tr = true_range(bars)
+
+    if exp:
+        res = rolling_weighted_mean(tr, window)
+    else:
+        res = rolling_mean(tr, window)
+
+    res = pd.Series(res)
+    return (res.shift(1) * (window - 1) + res) / window
+
+
+# ---------------------------------------------
+
+def crossed(series1, series2, direction=None):
+    if isinstance(series1, np.ndarray):
+        series1 = pd.Series(series1)
+
+    if isinstance(series2, int) or isinstance(series2, float) or isinstance(series2, np.ndarray):
+        series2 = pd.Series(index=series1.index, data=series2)
+
+    if direction is None or direction == "above":
+        above = pd.Series((series1 > series2) & (
+            series1.shift(1) <= series2.shift(1)))
+
+    if direction is None or direction == "below":
+        below = pd.Series((series1 < series2) & (
+            series1.shift(1) >= series2.shift(1)))
+
+    if direction is None:
+        return above or below
+
+    return above if direction is "above" else below
+
+
+def crossed_above(series1, series2):
+    return crossed(series1, series2, "above")
+
+
+def crossed_below(series1, series2):
+    return crossed(series1, series2, "below")
+
+# ---------------------------------------------
+
+
+def rolling_std(series, window=200, min_periods=None):
+    min_periods = window if min_periods is None else min_periods
+    try:
+        if min_periods == window:
+            return numpy_rolling_std(series, window, True)
+        else:
+            try:
+                return series.rolling(window=window, min_periods=min_periods).std()
+            except:
+                return pd.Series(series).rolling(window=window, min_periods=min_periods).std()
+    except:
+        return pd.rolling_std(series, window=window, min_periods=min_periods)
+
+
+# ---------------------------------------------
+
+def rolling_mean(series, window=200, min_periods=None):
+    min_periods = window if min_periods is None else min_periods
+    try:
+        if min_periods == window:
+            return numpy_rolling_mean(series, window, True)
+        else:
+            try:
+                return series.rolling(window=window, min_periods=min_periods).mean()
+            except:
+                return pd.Series(series).rolling(window=window, min_periods=min_periods).mean()
+    except:
+        return pd.rolling_mean(series, window=window, min_periods=min_periods)
+
+
+# ---------------------------------------------
+
+def rolling_min(series, window=14, min_periods=None):
+    min_periods = window if min_periods is None else min_periods
+    try:
+        try:
+            return series.rolling(window=window, min_periods=min_periods).min()
+        except:
+            return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
+    except:
+        return pd.rolling_min(series, window=window, min_periods=min_periods)
+
+
+# ---------------------------------------------
+
+def rolling_max(series, window=14, min_periods=None):
+    min_periods = window if min_periods is None else min_periods
+    try:
+        try:
+            return series.rolling(window=window, min_periods=min_periods).min()
+        except:
+            return pd.Series(series).rolling(window=window, min_periods=min_periods).min()
+    except:
+        return pd.rolling_min(series, window=window, min_periods=min_periods)
+
+
+# ---------------------------------------------
+
+def rolling_weighted_mean(series, window=200, min_periods=None):
+    min_periods = window if min_periods is None else min_periods
+    try:
+        return series.ewm(span=window, min_periods=min_periods).mean()
+    except:
+        return pd.ewma(series, span=window, min_periods=min_periods)
+
+
+# ---------------------------------------------
+
+def hull_moving_average(series, window=200):
+    wma = (2 * rolling_weighted_mean(series, window=window / 2)) - \
+        rolling_weighted_mean(series, window=window)
+    return rolling_weighted_mean(wma, window=np.sqrt(window))
+
+
+# ---------------------------------------------
+
+def sma(series, window=200, min_periods=None):
+    return rolling_mean(series, window=window, min_periods=min_periods)
+
+
+# ---------------------------------------------
+
+def wma(series, window=200, min_periods=None):
+    return rolling_weighted_mean(series, window=window, min_periods=min_periods)
+
+
+# ---------------------------------------------
+
+def hma(series, window=200):
+    return hull_moving_average(series, window=window)
+
+
+# ---------------------------------------------
+
+def vwap(bars):
+    """
+    calculate vwap of entire time series
+    (input can be pandas series or numpy array)
+    bars are usually mid [ (h+l)/2 ] or typical [ (h+l+c)/3 ]
+    """
+    typical = ((bars['high'] + bars['low'] + bars['close']) / 3).values
+    volume = bars['volume'].values
+
+    return pd.Series(index=bars.index,
+                     data=np.cumsum(volume * typical) / np.cumsum(volume))
+
+
+# ---------------------------------------------
+
+def rolling_vwap(bars, window=200, min_periods=None):
+    """
+    calculate vwap using moving window
+    (input can be pandas series or numpy array)
+    bars are usually mid [ (h+l)/2 ] or typical [ (h+l+c)/3 ]
+    """
+    min_periods = window if min_periods is None else min_periods
+
+    typical = ((bars['high'] + bars['low'] + bars['close']) / 3)
+    volume = bars['volume']
+
+    left = (volume * typical).rolling(window=window,
+                                      min_periods=min_periods).sum()
+    right = volume.rolling(window=window, min_periods=min_periods).sum()
+
+    return pd.Series(index=bars.index, data=(left / right))
+
+
+# ---------------------------------------------
+
+def rsi(series, window=14):
+    """
+    compute the n period relative strength indicator
+    """
+    # 100-(100/relative_strength)
+    deltas = np.diff(series)
+    seed = deltas[:window + 1]
+
+    # default values
+    ups = seed[seed > 0].sum() / window
+    downs = -seed[seed < 0].sum() / window
+    rsival = np.zeros_like(series)
+    rsival[:window] = 100. - 100. / (1. + ups / downs)
+
+    # period values
+    for i in range(window, len(series)):
+        delta = deltas[i - 1]
+        if delta > 0:
+            upval = delta
+            downval = 0
+        else:
+            upval = 0
+            downval = -delta
+
+        ups = (ups * (window - 1) + upval) / window
+        downs = (downs * (window - 1.) + downval) / window
+        rsival[i] = 100. - 100. / (1. + ups / downs)
+
+    # return rsival
+    return pd.Series(index=series.index, data=rsival)
+
+
+# ---------------------------------------------
+
+def macd(series, fast=3, slow=10, smooth=16):
+    """
+    compute the MACD (Moving Average Convergence/Divergence)
+    using a fast and slow exponential moving avg'
+    return value is emaslow, emafast, macd which are len(x) arrays
+    """
+    macd = rolling_weighted_mean(series, window=fast) - \
+        rolling_weighted_mean(series, window=slow)
+    signal = rolling_weighted_mean(macd, window=smooth)
+    histogram = macd - signal
+    # return macd, signal, histogram
+    return pd.DataFrame(index=series.index, data={
+        'macd': macd.values,
+        'signal': signal.values,
+        'histogram': histogram.values
+    })
+
+
+# ---------------------------------------------
+
+def bollinger_bands(series, window=20, stds=2):
+    sma = rolling_mean(series, window=window)
+    std = rolling_std(series, window=window)
+    upper = sma + std * stds
+    lower = sma - std * stds
+
+    return pd.DataFrame(index=series.index, data={
+        'upper': upper,
+        'mid': sma,
+        'lower': lower
+    })
+
+
+# ---------------------------------------------
+
+def weighted_bollinger_bands(series, window=20, stds=2):
+    ema = rolling_weighted_mean(series, window=window)
+    std = rolling_std(series, window=window)
+    upper = ema + std * stds
+    lower = ema - std * stds
+
+    return pd.DataFrame(index=series.index, data={
+        'upper': upper.values,
+        'mid': ema.values,
+        'lower': lower.values
+    })
+
+
+# ---------------------------------------------
+
+def returns(series):
+    try:
+        res = (series / series.shift(1) -
+               1).replace([np.inf, -np.inf], float('NaN'))
+    except:
+        res = nans(len(series))
+
+    return pd.Series(index=series.index, data=res)
+
+
+# ---------------------------------------------
+
+def log_returns(series):
+    try:
+        res = np.log(series / series.shift(1)
+                     ).replace([np.inf, -np.inf], float('NaN'))
+    except:
+        res = nans(len(series))
+
+    return pd.Series(index=series.index, data=res)
+
+
+# ---------------------------------------------
+
+def implied_volatility(series, window=252):
+    try:
+        logret = np.log(series / series.shift(1)
+                        ).replace([np.inf, -np.inf], float('NaN'))
+        res = numpy_rolling_std(logret, window) * np.sqrt(window)
+    except:
+        res = nans(len(series))
+
+    return pd.Series(index=series.index, data=res)
+
+
+# ---------------------------------------------
+
+def keltner_channel(bars, window=14, atrs=2):
+    typical_mean = rolling_mean(typical_price(bars), window)
+    atrval = atr(bars, window) * atrs
+
+    upper = typical_mean + atrval
+    lower = typical_mean - atrval
+
+    return pd.DataFrame(index=bars.index, data={
+        'upper': upper.values,
+        'mid': typical_mean.values,
+        'lower': lower.values
+    })
+
+
+# ---------------------------------------------
+
+def roc(series, window=14):
+    """
+    compute rate of change
+    """
+    res = (series - series.shift(window)) / series.shift(window)
+    return pd.Series(index=series.index, data=res)
+
+
+# ---------------------------------------------
+
+def cci(series, window=14):
+    """
+    compute commodity channel index
+    """
+    price = typical_price(series)
+    typical_mean = rolling_mean(price, window)
+    res = (price - typical_mean) / (.015 * np.std(typical_mean))
+    return pd.Series(index=series.index, data=res)
+
+
+# ---------------------------------------------
+
+def stoch(df, window=14, d=3, k=3, fast=False):
+    """
+    compute the n period relative strength indicator
+    http://excelta.blogspot.co.il/2013/09/stochastic-oscillator-technical.html
+    """
+    highs_ma = pd.concat([df['high'].shift(i)
+                          for i in np.arange(window)], 1).apply(list, 1)
+    highs_ma = highs_ma.T.max().T
+
+    lows_ma = pd.concat([df['low'].shift(i)
+                         for i in np.arange(window)], 1).apply(list, 1)
+    lows_ma = lows_ma.T.min().T
+
+    fast_k = ((df['close'] - lows_ma) / (highs_ma - lows_ma)) * 100
+    fast_d = numpy_rolling_mean(fast_k, d)
+
+    if fast:
+        data = {
+            'k': fast_k,
+            'd': fast_d
+        }
+
+    else:
+        slow_k = numpy_rolling_mean(fast_k, k)
+        slow_d = numpy_rolling_mean(slow_k, d)
+        data = {
+            'k': slow_k,
+            'd': slow_d
+        }
+
+    return pd.DataFrame(index=df.index, data=data)
+
+
+# ---------------------------------------------
+
+def zscore(bars, window=20, stds=1, col='close'):
+    """ get zscore of price """
+    std = numpy_rolling_std(bars[col], window)
+    mean = numpy_rolling_mean(bars[col], window)
+    return (bars[col] - mean) / (std * stds)
+
+# ---------------------------------------------
+
+
+def pvt(bars):
+    """ Price Volume Trend """
+    pvt = ((bars['close'] - bars['close'].shift(1)) /
+           bars['close'].shift(1)) * bars['volume']
+    return pvt.cumsum()
+
+
+# =============================================
+
+PandasObject.session = session
+PandasObject.atr = atr
+PandasObject.bollinger_bands = bollinger_bands
+PandasObject.cci = cci
+PandasObject.crossed = crossed
+PandasObject.crossed_above = crossed_above
+PandasObject.crossed_below = crossed_below
+PandasObject.heikinashi = heikinashi
+PandasObject.hull_moving_average = hull_moving_average
+PandasObject.ibs = ibs
+PandasObject.implied_volatility = implied_volatility
+PandasObject.keltner_channel = keltner_channel
+PandasObject.log_returns = log_returns
+PandasObject.macd = macd
+PandasObject.returns = returns
+PandasObject.roc = roc
+PandasObject.rolling_max = rolling_max
+PandasObject.rolling_min = rolling_min
+PandasObject.rolling_mean = rolling_mean
+PandasObject.rolling_std = rolling_std
+PandasObject.rsi = rsi
+PandasObject.stoch = stoch
+PandasObject.zscore = zscore
+PandasObject.pvt = pvt
+PandasObject.tdi = tdi
+PandasObject.true_range = true_range
+PandasObject.mid_price = mid_price
+PandasObject.typical_price = typical_price
+PandasObject.vwap = vwap
+PandasObject.rolling_vwap = rolling_vwap
+PandasObject.weighted_bollinger_bands = weighted_bollinger_bands
+PandasObject.rolling_weighted_mean = rolling_weighted_mean
+
+PandasObject.sma = sma
+PandasObject.wma = wma
+PandasObject.hma = hma

requirements.txt

@@ -1,14 +1,23 @@
--e git+https://github.com/ericsomdahl/python-bittrex.git#egg=python-bittrex
-SQLAlchemy==1.1.13
-python-telegram-bot==7.0.1
+-e git+https://github.com/ericsomdahl/python-bittrex.git@d7033d0#egg=python-bittrex
+SQLAlchemy==1.1.14
+python-telegram-bot==8.1.1
 arrow==0.10.0
 requests==2.18.4
 urllib3==1.22
 wrapt==1.10.11
 pandas==0.20.3
-matplotlib==2.0.2
 scikit-learn==0.19.0
 scipy==0.19.1
 jsonschema==2.6.0
+numpy==1.13.3
 TA-Lib==0.4.10
+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==2.1.0
 #PYQT5==5.9

setup.py

@@ -0,0 +1,41 @@
+from setuptools import setup
+
+from freqtrade import __version__
+
+
+setup(name='freqtrade',
+      version=__version__,
+      description='Simple High Frequency Trading Bot for crypto currencies',
+      url='https://github.com/gcarq/freqtrade',
+      author='gcarq and contributors',
+      author_email='michael.egger@tsn.at',
+      license='GPLv3',
+      packages=['freqtrade'],
+      scripts=['bin/freqtrade'],
+      setup_requires=['pytest-runner'],
+      tests_require=['pytest', 'pytest-mock', 'pytest-cov'],
+      install_requires=[
+          'python-bittrex==0.1.3',
+          'SQLAlchemy==1.1.13',
+          'python-telegram-bot==8.1.1',
+          'arrow==0.10.0',
+          'requests==2.18.4',
+          'urllib3==1.22',
+          'wrapt==1.10.11',
+          'pandas==0.20.3',
+          'scikit-learn==0.19.0',
+          'scipy==0.19.1',
+          'jsonschema==2.6.0',
+          'TA-Lib==0.4.10',
+      ],
+      dependency_links=[
+          "git+https://github.com/ericsomdahl/python-bittrex.git@d7033d0#egg=python-bittrex-0.1.3"
+      ],
+      include_package_data=True,
+      zip_safe=False,
+      classifiers=[
+          'Programming Language :: Python :: 3.6',
+          'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
+          'Topic :: Office/Business :: Financial :: Investment',
+          'Intended Audience :: Science/Research',
+      ])

test/test_analyze.py

@@ -1,49 +0,0 @@
-# pragma pylint: disable=missing-docstring
-import unittest
-from unittest.mock import patch
-from pandas import DataFrame
-import arrow
-from analyze import parse_ticker_dataframe, populate_buy_trend, populate_indicators, analyze_ticker, get_buy_signal
-
-RESULT_BITTREX = {
-    'success': True,
-    'message': '',
-    'result': [
-        {'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 22.17210568, 'T': '2017-08-30T10:40:00', 'BV': 0.01448082},
-        {'O': 0.00066194, 'H': 0.00066195, 'L': 0.00066194, 'C': 0.00066195, 'V': 33.4727437, 'T': '2017-08-30T10:34:00', 'BV': 0.02215696},
-        {'O': 0.00065311, 'H': 0.00065311, 'L': 0.00065311, 'C': 0.00065311, 'V': 53.85127609, 'T': '2017-08-30T10:37:00', 'BV': 0.0351708},
-        {'O': 0.00066194, 'H': 0.00066194, 'L': 0.00065311, 'C': 0.00065311, 'V': 46.29210665, 'T': '2017-08-30T10:42:00', 'BV': 0.03063118},
-    ]
-}
-
-class TestAnalyze(unittest.TestCase):
-    def setUp(self):
-        self.result = parse_ticker_dataframe(RESULT_BITTREX['result'], arrow.get('2017-08-30T10:00:00'))
-
-    def test_1_dataframe_has_correct_columns(self):
-        self.assertEqual(self.result.columns.tolist(),
-                         ['close', 'high', 'low', 'open', 'date', 'volume'])
-
-    def test_2_orders_by_date(self):
-        self.assertEqual(self.result['date'].tolist(),
-                         ['2017-08-30T10:34:00',
-                          '2017-08-30T10:37:00',
-                          '2017-08-30T10:40:00',
-                          '2017-08-30T10:42:00'])
-
-    def test_3_populates_buy_trend(self):
-        dataframe = populate_buy_trend(populate_indicators(self.result))
-        self.assertTrue('buy' in dataframe.columns)
-        self.assertTrue('buy_price' in dataframe.columns)
-
-    def test_4_returns_latest_buy_signal(self):
-        buydf = DataFrame([{'buy': 1, 'date': arrow.utcnow()}])
-        with patch('analyze.analyze_ticker', return_value=buydf):
-            self.assertEqual(get_buy_signal('BTC-ETH'), True)
-        buydf = DataFrame([{'buy': 0, 'date': arrow.utcnow()}])
-        with patch('analyze.analyze_ticker', return_value=buydf):
-            self.assertEqual(get_buy_signal('BTC-ETH'), False)
-
-
-if __name__ == '__main__':
-    unittest.main()

test/test_backtesting.py

@@ -1,71 +0,0 @@
-# pragma pylint: disable=missing-docstring
-import unittest
-from unittest.mock import patch
-import os
-import json
-import logging
-import arrow
-from pandas import DataFrame
-from analyze import analyze_ticker
-from persistence import Trade
-from main import should_sell
-
-def print_results(results):
-    print('Made {} buys. Average profit {:.1f}%. Total profit was {:.3f}. Average duration {:.1f} mins.'.format(
-        len(results.index),
-        results.profit.mean() * 100.0,
-        results.profit.sum(),
-        results.duration.mean()*5
-    ))
-
-class TestMain(unittest.TestCase):
-    pairs = ['btc-neo', 'btc-eth', 'btc-omg', 'btc-edg', 'btc-pay', 'btc-pivx', 'btc-qtum', 'btc-mtl', 'btc-etc', 'btc-ltc']
-    conf = {
-        "minimal_roi": {
-            "2880": 0.005,
-            "720":  0.01,
-            "0":  0.02
-        },
-        "stoploss": -0.10
-    }
-
-    @classmethod
-    def setUpClass(cls):
-        logging.disable(logging.DEBUG) # disable debug logs that slow backtesting a lot
-
-    @unittest.skipIf(not os.environ.get('BACKTEST', False), "slow, should be run manually")
-    def test_backtest(self):
-        trades = []
-        with patch.dict('main._CONF', self.conf):
-            for pair in self.pairs:
-                with open('test/testdata/'+pair+'.json') as data_file:
-                    data = json.load(data_file)
-
-                    with patch('analyze.get_ticker', return_value=data):
-                        with patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00')):
-                            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('====================== BACKTESTING REPORT ================================')
-
-        for pair in self.pairs:
-            print('For currency {}:'.format(pair))
-            print_results(results[results.currency == pair])
-        print('TOTAL OVER ALL TRADES:')
-        print_results(results)

test/test_main.py

@@ -1,114 +0,0 @@
-import unittest
-from unittest.mock import patch, MagicMock
-
-from jsonschema import validate
-
-import exchange
-from main import create_trade, handle_trade, close_trade_if_fulfilled, init, get_target_bid
-from misc import CONF_SCHEMA
-from persistence import Trade
-
-
-class TestMain(unittest.TestCase):
-    conf = {
-        "max_open_trades": 3,
-        "stake_currency": "BTC",
-        "stake_amount": 0.05,
-        "dry_run": True,
-        "minimal_roi": {
-            "2880": 0.005,
-            "720": 0.01,
-            "0": 0.02
-        },
-        "bid_strategy": {
-            "ask_last_balance": 0.0
-        },
-        "bittrex": {
-            "enabled": True,
-            "key": "key",
-            "secret": "secret",
-            "pair_whitelist": [
-                "BTC_ETH"
-            ]
-        },
-        "telegram": {
-            "enabled": True,
-            "token": "token",
-            "chat_id": "chat_id"
-        }
-    }
-
-    def test_1_create_trade(self):
-        with patch.dict('main._CONF', self.conf):
-            with patch('main.get_buy_signal', side_effect=lambda _: True) as buy_signal:
-                with patch.multiple('main.telegram', init=MagicMock(), send_msg=MagicMock()):
-                    with patch.multiple('main.exchange',
-                                        get_ticker=MagicMock(return_value={
-                                            'bid': 0.07256061,
-                                            'ask': 0.072661,
-                                            'last': 0.07256061
-                                        }),
-                                        buy=MagicMock(return_value='mocked_order_id')):
-                        init(self.conf, 'sqlite://')
-                        trade = create_trade(15.0, exchange.Exchange.BITTREX)
-                        Trade.session.add(trade)
-                        Trade.session.flush()
-                        self.assertIsNotNone(trade)
-                        self.assertEqual(trade.open_rate, 0.072661)
-                        self.assertEqual(trade.pair, 'BTC_ETH')
-                        self.assertEqual(trade.exchange, exchange.Exchange.BITTREX)
-                        self.assertEqual(trade.amount, 206.43811673387373)
-                        self.assertEqual(trade.stake_amount, 15.0)
-                        self.assertEqual(trade.is_open, True)
-                        self.assertIsNotNone(trade.open_date)
-                        buy_signal.assert_called_once_with('BTC_ETH')
-
-    def test_2_handle_trade(self):
-        with patch.dict('main._CONF', self.conf):
-            with patch.multiple('main.telegram', init=MagicMock(), send_msg=MagicMock()):
-                with patch.multiple('main.exchange',
-                                    get_ticker=MagicMock(return_value={
-                                        'bid': 0.17256061,
-                                        'ask': 0.172661,
-                                        'last': 0.17256061
-                                    }),
-                                    buy=MagicMock(return_value='mocked_order_id')):
-                    trade = Trade.query.filter(Trade.is_open.is_(True)).first()
-                    self.assertTrue(trade)
-                    handle_trade(trade)
-                    self.assertEqual(trade.close_rate, 0.17256061)
-                    self.assertEqual(trade.close_profit, 137.4872490056564)
-                    self.assertIsNotNone(trade.close_date)
-                    self.assertEqual(trade.open_order_id, 'dry_run')
-
-    def test_3_close_trade(self):
-        with patch.dict('main._CONF', self.conf):
-            trade = Trade.query.filter(Trade.is_open.is_(True)).first()
-            self.assertTrue(trade)
-
-            # Simulate that there is no open order
-            trade.open_order_id = None
-
-            closed = close_trade_if_fulfilled(trade)
-            self.assertTrue(closed)
-            self.assertEqual(trade.is_open, False)
-
-    def test_balance_fully_ask_side(self):
-        with patch.dict('main._CONF', {'bid_strategy': {'ask_last_balance': 0.0}}):
-            self.assertEqual(get_target_bid({'ask': 20, 'last': 10}), 20)
-
-    def test_balance_fully_last_side(self):
-        with patch.dict('main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}}):
-            self.assertEqual(get_target_bid({'ask': 20, 'last': 10}), 10)
-
-    def test_balance_when_last_bigger_than_ask(self):
-        with patch.dict('main._CONF', {'bid_strategy': {'ask_last_balance': 1.0}}):
-            self.assertEqual(get_target_bid({'ask': 5, 'last': 10}), 5)
-
-    @classmethod
-    def setUpClass(cls):
-        validate(cls.conf, CONF_SCHEMA)
-
-
-if __name__ == '__main__':
-    unittest.main()

test/test_persistence.py

@@ -1,28 +0,0 @@
-import unittest
-from unittest.mock import patch
-
-from exchange import Exchange
-from persistence import Trade
-
-
-class TestTrade(unittest.TestCase):
-    def test_1_exec_sell_order(self):
-        with patch('main.exchange.sell', side_effect='mocked_order_id') as api_mock:
-            trade = Trade(
-                pair='BTC_ETH',
-                stake_amount=1.00,
-                open_rate=0.50,
-                amount=10.00,
-                exchange=Exchange.BITTREX,
-                open_order_id='mocked'
-            )
-            profit = trade.exec_sell_order(1.00, 10.00)
-            api_mock.assert_called_once_with('BTC_ETH', 1.0, 10.0)
-            self.assertEqual(profit, 100.0)
-            self.assertEqual(trade.close_rate, 1.0)
-            self.assertEqual(trade.close_profit, profit)
-            self.assertIsNotNone(trade.close_date)
-
-
-if __name__ == '__main__':
-    unittest.main()

test/test_telegram.py

@@ -1,195 +0,0 @@
-import unittest
-from unittest.mock import patch, MagicMock
-from datetime import datetime
-
-from jsonschema import validate
-from telegram import Bot, Update, Message, Chat
-
-import exchange
-from main import init, create_trade
-from misc import CONF_SCHEMA, update_state, State, get_state
-from persistence import Trade
-from rpc.telegram import _status, _profit, _forcesell, _performance, _start, _stop
-
-
-class MagicBot(MagicMock, Bot):
-    pass
-
-
-class TestTelegram(unittest.TestCase):
-
-    conf = {
-        "max_open_trades": 3,
-        "stake_currency": "BTC",
-        "stake_amount": 0.05,
-        "dry_run": True,
-        "minimal_roi": {
-            "2880": 0.005,
-            "720": 0.01,
-            "0": 0.02
-        },
-        "bid_strategy": {
-            "ask_last_balance": 0.0
-        },
-        "bittrex": {
-            "enabled": True,
-            "key": "key",
-            "secret": "secret",
-            "pair_whitelist": [
-                "BTC_ETH"
-            ]
-        },
-        "telegram": {
-            "enabled": True,
-            "token": "token",
-            "chat_id": "0"
-        },
-        "initial_state": "running"
-    }
-
-    def test_1_status_handle(self):
-        with patch.dict('main._CONF', self.conf):
-            with patch('main.get_buy_signal', side_effect=lambda _: True):
-                msg_mock = MagicMock()
-                with patch.multiple('main.telegram', _CONF=self.conf, init=MagicMock(), send_msg=msg_mock):
-                    with patch.multiple('main.exchange',
-                                        get_ticker=MagicMock(return_value={
-                                            'bid': 0.07256061,
-                                            'ask': 0.072661,
-                                            'last': 0.07256061
-                                        }),
-                                        buy=MagicMock(return_value='mocked_order_id')):
-                        init(self.conf, 'sqlite://')
-
-                        # Create some test data
-                        trade = create_trade(15.0, exchange.Exchange.BITTREX)
-                        self.assertTrue(trade)
-                        Trade.session.add(trade)
-                        Trade.session.flush()
-
-                        _status(bot=MagicBot(), update=self.update)
-                        self.assertEqual(msg_mock.call_count, 2)
-                        self.assertIn('[BTC_ETH]', msg_mock.call_args_list[-1][0][0])
-
-    def test_2_profit_handle(self):
-        with patch.dict('main._CONF', self.conf):
-            with patch('main.get_buy_signal', side_effect=lambda _: True):
-                msg_mock = MagicMock()
-                with patch.multiple('main.telegram', _CONF=self.conf, init=MagicMock(), send_msg=msg_mock):
-                    with patch.multiple('main.exchange',
-                                        get_ticker=MagicMock(return_value={
-                                            'bid': 0.07256061,
-                                            'ask': 0.072661,
-                                            'last': 0.07256061
-                                        }),
-                                        buy=MagicMock(return_value='mocked_order_id')):
-                        init(self.conf, 'sqlite://')
-
-                        # Create some test data
-                        trade = create_trade(15.0, exchange.Exchange.BITTREX)
-                        self.assertTrue(trade)
-                        trade.close_rate = 0.07256061
-                        trade.close_profit = 100.00
-                        trade.close_date = datetime.utcnow()
-                        trade.open_order_id = None
-                        trade.is_open = False
-                        Trade.session.add(trade)
-                        Trade.session.flush()
-
-                        _profit(bot=MagicBot(), update=self.update)
-                        self.assertEqual(msg_mock.call_count, 2)
-                        self.assertIn('(100.00%)', msg_mock.call_args_list[-1][0][0])
-
-    def test_3_forcesell_handle(self):
-        with patch.dict('main._CONF', self.conf):
-            with patch('main.get_buy_signal', side_effect=lambda _: True):
-                msg_mock = MagicMock()
-                with patch.multiple('main.telegram', _CONF=self.conf, init=MagicMock(), send_msg=msg_mock):
-                    with patch.multiple('main.exchange',
-                                        get_ticker=MagicMock(return_value={
-                                            'bid': 0.07256061,
-                                            'ask': 0.072661,
-                                            'last': 0.07256061
-                                        }),
-                                        buy=MagicMock(return_value='mocked_order_id')):
-                        init(self.conf, 'sqlite://')
-
-                        # Create some test data
-                        trade = create_trade(15.0, exchange.Exchange.BITTREX)
-                        self.assertTrue(trade)
-                        Trade.session.add(trade)
-                        Trade.session.flush()
-
-                        self.update.message.text = '/forcesell 1'
-                        _forcesell(bot=MagicBot(), update=self.update)
-
-                        self.assertEqual(msg_mock.call_count, 2)
-                        self.assertIn('Selling [BTC/ETH]', msg_mock.call_args_list[-1][0][0])
-                        self.assertIn('0.072561', msg_mock.call_args_list[-1][0][0])
-
-    def test_4_performance_handle(self):
-        with patch.dict('main._CONF', self.conf):
-            with patch('main.get_buy_signal', side_effect=lambda _: True):
-                msg_mock = MagicMock()
-                with patch.multiple('main.telegram', _CONF=self.conf, init=MagicMock(), send_msg=msg_mock):
-                    with patch.multiple('main.exchange',
-                                        get_ticker=MagicMock(return_value={
-                                            'bid': 0.07256061,
-                                            'ask': 0.072661,
-                                            'last': 0.07256061
-                                        }),
-                                        buy=MagicMock(return_value='mocked_order_id')):
-                        init(self.conf, 'sqlite://')
-
-                        # Create some test data
-                        trade = create_trade(15.0, exchange.Exchange.BITTREX)
-                        self.assertTrue(trade)
-                        trade.close_rate = 0.07256061
-                        trade.close_profit = 100.00
-                        trade.close_date = datetime.utcnow()
-                        trade.open_order_id = None
-                        trade.is_open = False
-                        Trade.session.add(trade)
-                        Trade.session.flush()
-
-                        _performance(bot=MagicBot(), update=self.update)
-                        self.assertEqual(msg_mock.call_count, 2)
-                        self.assertIn('Performance', msg_mock.call_args_list[-1][0][0])
-                        self.assertIn('BTC_ETH	100.00%', msg_mock.call_args_list[-1][0][0])
-
-    def test_5_start_handle(self):
-        with patch.dict('main._CONF', self.conf):
-            msg_mock = MagicMock()
-            with patch.multiple('main.telegram', _CONF=self.conf, init=MagicMock(), send_msg=msg_mock):
-                init(self.conf, 'sqlite://')
-
-                update_state(State.STOPPED)
-                self.assertEqual(get_state(), State.STOPPED)
-                _start(bot=MagicBot(), update=self.update)
-                self.assertEqual(get_state(), State.RUNNING)
-                self.assertEqual(msg_mock.call_count, 0)
-
-    def test_6_stop_handle(self):
-        with patch.dict('main._CONF', self.conf):
-            msg_mock = MagicMock()
-            with patch.multiple('main.telegram', _CONF=self.conf, init=MagicMock(), send_msg=msg_mock):
-                init(self.conf, 'sqlite://')
-
-                update_state(State.RUNNING)
-                self.assertEqual(get_state(), State.RUNNING)
-                _stop(bot=MagicBot(), update=self.update)
-                self.assertEqual(get_state(), State.STOPPED)
-                self.assertEqual(msg_mock.call_count, 1)
-                self.assertIn('Stopping trader', msg_mock.call_args_list[0][0][0])
-
-    def setUp(self):
-        self.update = Update(0)
-        self.update.message = Message(0, 0, datetime.utcnow(), Chat(0, 0))
-
-    @classmethod
-    def setUpClass(cls):
-        validate(cls.conf, CONF_SCHEMA)
-
-
-if __name__ == '__main__':
-    unittest.main()