stable/freqtrade/analyze.py
2017-10-11 20:04:31 +02:00

153 lines
5.0 KiB
Python

import logging
import time
from datetime import timedelta
import arrow
import talib.abstract as ta
from pandas import DataFrame
from freqtrade import exchange
from freqtrade.exchange import Bittrex, get_ticker_history
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame:
"""
Analyses the trend for the given pair
:param pair: pair as str in format BTC_ETH or BTC-ETH
:return: 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]
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
dataframe['adx'] = ta.ADX(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
dataframe['cci'] = ta.CCI(dataframe, timeperiod=5)
dataframe['sma'] = ta.SMA(dataframe, timeperiod=100)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=4)
dataframe['mfi'] = ta.MFI(dataframe)
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.loc[
(dataframe['close'] < dataframe['sma']) &
(dataframe['cci'] < -100) &
(dataframe['tema'] <= dataframe['blower']) &
(dataframe['mfi'] < 30) &
(dataframe['fastd'] < 20) &
(dataframe['adx'] > 20),
'buy'] = 1
dataframe.loc[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
"""
minimum_date = arrow.utcnow().shift(hours=-24)
data = get_ticker_history(pair, minimum_date)
dataframe = parse_ticker_dataframe(data['result'], minimum_date)
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_dataframe(dataframe: DataFrame, pair: str) -> None:
"""
Plots the given dataframe
:param dataframe: DataFrame
:param pair: pair as str
:return: None
"""
import matplotlib
matplotlib.use("Qt5Agg")
import matplotlib.pyplot as plt
# Two subplots sharing x axis
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['sar'], 'g_', label='pSAR')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['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()
# 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:
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)