from datetime import timedelta import time import arrow import matplotlib import logging matplotlib.use("Qt5Agg") import matplotlib.pyplot as plt import requests from pandas.io.json import json_normalize from stockstats import StockDataFrame logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) def get_ticker_dataframe(pair): """ Analyses the trend for the given pair :param pair: pair as str in format BTC_ETH or BTC-ETH :return: StockDataFrame """ minimum_date = arrow.now() - timedelta(hours=2) 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'])) data = [{ 'close': t['C'], 'volume': t['V'], 'open': t['O'], 'high': t['H'], 'low': t['L'], 'date': t['T'], } for t in data['result'] if arrow.get(t['T']) > minimum_date] dataframe = StockDataFrame(json_normalize(data)) # calculate StochRSI rsi = dataframe['rsi_{}'.format(14)] rolling = rsi.rolling(window=14, center=False) low = rolling.min() high = rolling.max() dataframe['stochrsi'] = (rsi - low) / (high - low) return dataframe def populate_trends(dataframe): """ Populates the trends for the given dataframe :param dataframe: StockDataFrame :return: StockDataFrame with populated trends """ dataframe.loc[ (dataframe['stochrsi'] < 0.20), 'underpriced' ] = 1 dataframe.loc[dataframe['underpriced'] == 1, 'buy'] = dataframe['close'] return dataframe def get_buy_signal(pair): """ Calculates a buy signal based on StochRSI indicator :param pair: pair in format BTC_ANT or BTC-ANT :return: True if pair is underpriced, False otherwise """ dataframe = get_ticker_dataframe(pair) dataframe = populate_trends(dataframe) latest = dataframe.iloc[-1] signal = latest['underpriced'] == 1 logger.debug('buy_trigger: {} (pair={}, signal={})'.format(latest['date'], pair, signal)) return signal def plot_dataframe(dataframe, pair): """ Plots the given dataframe :param dataframe: StockDataFrame :param pair: pair as str :return: None """ # Three subplots sharing x axe f, (ax1, ax2, ax3) = plt.subplots(3, sharex=True) f.suptitle(pair, fontsize=14, fontweight='bold') ax1.plot(dataframe.index.values, dataframe['close'], label='close') ax1.plot(dataframe.index.values, dataframe['close_12_ema'], label='EMA(12)') ax1.plot(dataframe.index.values, dataframe['close_26_ema'], label='EMA(26)') # ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell') ax1.plot(dataframe.index.values, dataframe['buy'], '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, [0.80] * len(dataframe.index.values)) ax3.plot(dataframe.index.values, [0.20] * len(dataframe.index.values)) ax3.legend() # Fine-tune figure; make subplots close to each other and hide x ticks for # all but bottom plot. f.subplots_adjust(hspace=0) plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False) plt.show() if __name__ == '__main__': while True: pair = 'BTC_ANT' for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']: get_buy_signal(pair) time.sleep(60)