import time from datetime import timedelta import logging import arrow import requests from pandas.io.json import json_normalize from pandas import DataFrame import talib.abstract as ta 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': 'OneMin', '_': 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: """ 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['close_30_ema'] = ta.EMA(dataframe, timeperiod=30) dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90) dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2) # calculate StochRSI stochrsi = ta.STOCHRSI(dataframe) dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1 macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macds'] = macd['macdsignal'] dataframe['macdh'] = 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.loc[ (dataframe['stochrsi'] < 20) & (dataframe['macd'] > dataframe['macds']) & (dataframe['close'] > dataframe['sar']), '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=-6) data = get_ticker(pair, minimum_date) dataframe = parse_ticker_dataframe(data['result'], minimum_date) 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) 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 # Three subplots sharing x axe 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['close_30_ema'], label='EMA(30)') ax1.plot(dataframe.index.values, dataframe['close_90_ema'], label='EMA(90)') # ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell') ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy') ax1.legend() ax2.plot(dataframe.index.values, dataframe['macd'], label='MACD') ax2.plot(dataframe.index.values, dataframe['macds'], label='MACDS') ax2.plot(dataframe.index.values, dataframe['macdh'], label='MACD Histogram') ax2.plot(dataframe.index.values, [0] * len(dataframe.index.values)) ax2.legend() ax3.plot(dataframe.index.values, dataframe['stochrsi'], label='StochRSI') ax3.plot(dataframe.index.values, [80] * len(dataframe.index.values)) 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: pair = 'BTC_ANT' #for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']: # get_buy_signal(pair) plot_dataframe(analyze_ticker(pair), pair) time.sleep(60)