151 lines
4.9 KiB
Python
151 lines
4.9 KiB
Python
import logging
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import time
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from datetime import timedelta
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import arrow
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import talib.abstract as ta
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from pandas import DataFrame
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from freqtrade.exchange import get_ticker_history
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame:
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"""
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Analyses the trend for the given pair
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:param pair: pair as str in format BTC_ETH or BTC-ETH
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:return: DataFrame
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"""
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df = DataFrame(ticker) \
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.drop('BV', 1) \
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.rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'}) \
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.sort_values('date')
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return df[df['date'].map(arrow.get) > minimum_date]
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def populate_indicators(dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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"""
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dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
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dataframe['adx'] = ta.ADX(dataframe)
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stoch = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch['fastd']
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dataframe['fastk'] = stoch['fastk']
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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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dataframe['cci'] = ta.CCI(dataframe, timeperiod=5)
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=100)
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=4)
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dataframe['mfi'] = ta.MFI(dataframe)
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return dataframe
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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"""
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Based on TA indicators, populates the buy trend for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(dataframe['close'] < dataframe['sma']) &
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(dataframe['cci'] < -100) &
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(dataframe['tema'] <= dataframe['blower']) &
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(dataframe['mfi'] < 30) &
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(dataframe['fastd'] < 20) &
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(dataframe['adx'] > 20),
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'buy'] = 1
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dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
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return dataframe
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def analyze_ticker(pair: str) -> DataFrame:
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"""
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Get ticker data for given currency pair, push it to a DataFrame and
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add several TA indicators and buy signal to it
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:return DataFrame with ticker data and indicator data
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"""
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minimum_date = arrow.utcnow().shift(hours=-24)
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data = get_ticker_history(pair, minimum_date)
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dataframe = parse_ticker_dataframe(data['result'], minimum_date)
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if dataframe.empty:
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logger.warning('Empty dataframe for pair %s', pair)
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return dataframe
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dataframe = populate_indicators(dataframe)
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dataframe = populate_buy_trend(dataframe)
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return dataframe
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def get_buy_signal(pair: str) -> bool:
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"""
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Calculates a buy signal based several technical analysis indicators
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:param pair: pair in format BTC_ANT or BTC-ANT
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:return: True if pair is good for buying, False otherwise
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"""
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dataframe = analyze_ticker(pair)
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if dataframe.empty:
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return False
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latest = dataframe.iloc[-1]
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# Check if dataframe is out of date
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signal_date = arrow.get(latest['date'])
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if signal_date < arrow.now() - timedelta(minutes=10):
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return False
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signal = latest['buy'] == 1
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logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
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return signal
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def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
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"""
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Plots the given dataframe
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:param dataframe: DataFrame
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:param pair: pair as str
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:return: None
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"""
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import matplotlib
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matplotlib.use("Qt5Agg")
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import matplotlib.pyplot as plt
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# Two subplots sharing x axis
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fig, (ax1, ax2) = plt.subplots(2, sharex=True)
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fig.suptitle(pair, fontsize=14, fontweight='bold')
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ax1.plot(dataframe.index.values, dataframe['sar'], 'g_', label='pSAR')
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ax1.plot(dataframe.index.values, dataframe['close'], label='close')
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# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
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ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
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ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
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ax1.legend()
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# ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
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ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
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# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
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ax2.legend()
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# Fine-tune figure; make subplots close to each other and hide x ticks for
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# all but bottom plot.
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fig.subplots_adjust(hspace=0)
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plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
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plt.show()
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if __name__ == '__main__':
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# Install PYQT5==5.9 manually if you want to test this helper function
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while True:
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test_pair = 'BTC_ETH'
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# for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
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# get_buy_signal(pair)
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plot_dataframe(analyze_ticker(test_pair), test_pair)
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time.sleep(60)
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