freqtrade_origin/freqtrade/analyze.py
2017-11-07 20:13:36 +01:00

168 lines
5.7 KiB
Python

import logging
import time
from datetime import timedelta
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 parse_ticker_dataframe(ticker: list) -> DataFrame:
"""
Analyses the trend for the given ticker history
:param ticker: See exchange.get_ticker_history
:return: DataFrame
"""
columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
frame = DataFrame(ticker) \
.drop('BV', 1) \
.rename(columns=columns)
frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
frame.sort_values('date', inplace=True)
return frame
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
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
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy trend for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.ix[
(dataframe['close'] < dataframe['sma']) &
(dataframe['tema'] <= dataframe['blower']) &
(dataframe['mfi'] < 25) &
(dataframe['fastd'] < 25) &
(dataframe['adx'] > 30),
'buy'] = 1
dataframe.ix[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
return dataframe
def analyze_ticker(pair: str) -> DataFrame:
"""
Get ticker data for given currency pair, push it to a DataFrame and
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
data = get_ticker_history(pair)
dataframe = parse_ticker_dataframe(data)
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
:param pair: pair in format BTC_ANT or BTC-ANT
: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
signal_date = arrow.get(latest['date'])
if signal_date < arrow.now() - timedelta(minutes=10):
return False
signal = latest['buy'] == 1
logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
return signal
def plot_analyzed_dataframe(pair: str) -> None:
"""
Calls analyze() and plots the returned dataframe
:param pair: pair as str
:return: None
"""
import matplotlib
matplotlib.use("Qt5Agg")
import matplotlib.pyplot as plt
# Init Bittrex to use public API
exchange._API = Bittrex({'key': '', 'secret': ''})
dataframe = analyze_ticker(pair)
# Two subplots sharing x axis
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, dataframe['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)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()
if __name__ == '__main__':
# Install PYQT5==5.9 manually if you want to test this helper function
while True:
for p in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
plot_analyzed_dataframe(p)
time.sleep(60)