146 lines
4.7 KiB
Python
146 lines
4.7 KiB
Python
import time
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from datetime import timedelta
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import arrow
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import logging
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import requests
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from pandas.io.json import json_normalize
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from stockstats import StockDataFrame
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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 get_ticker_dataframe(pair):
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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: StockDataFrame
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"""
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minimum_date = arrow.now() - timedelta(hours=6)
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url = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks'
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headers = {
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'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',
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}
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params = {
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'marketName': pair.replace('_', '-'),
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'tickInterval': 'OneMin',
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'_': minimum_date.timestamp * 1000
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}
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data = requests.get(url, params=params, headers=headers).json()
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if not data['success']:
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raise RuntimeError('BITTREX: {}'.format(data['message']))
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data = [{
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'close': t['C'],
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'volume': t['V'],
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'open': t['O'],
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'high': t['H'],
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'low': t['L'],
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'date': t['T'],
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} for t in sorted(data['result'], key=lambda k: k['T']) if arrow.get(t['T']) > minimum_date]
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dataframe = StockDataFrame(json_normalize(data))
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# calculate StochRSI
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window = 14
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rsi = dataframe['rsi_{}'.format(window)]
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rolling = rsi.rolling(window=window, center=False)
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low = rolling.min()
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high = rolling.max()
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dataframe['stochrsi'] = (rsi - low) / (high - low)
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return dataframe
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def populate_trends(dataframe):
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"""
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Populates the trends for the given dataframe
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:param dataframe: StockDataFrame
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:return: StockDataFrame with populated trends
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"""
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"""
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dataframe.loc[
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(dataframe['stochrsi'] < 0.20)
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& (dataframe['close_30_ema'] > (1 + 0.0025) * dataframe['close_60_ema']),
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'underpriced'
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] = 1
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"""
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dataframe.loc[
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(dataframe['stochrsi'] < 0.20)
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& (dataframe['macd'] > dataframe['macds']),
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'underpriced'
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] = 1
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dataframe.loc[dataframe['underpriced'] == 1, 'buy'] = dataframe['close']
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return dataframe
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def get_buy_signal(pair):
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"""
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Calculates a buy signal based on StochRSI indicator
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:param pair: pair in format BTC_ANT or BTC-ANT
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:return: True if pair is underpriced, False otherwise
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"""
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dataframe = get_ticker_dataframe(pair)
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dataframe = populate_trends(dataframe)
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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['underpriced'] == 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, pair):
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"""
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Plots the given dataframe
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:param dataframe: StockDataFrame
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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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# Three subplots sharing x axe
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fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
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fig.suptitle(pair, fontsize=14, fontweight='bold')
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ax1.plot(dataframe.index.values, dataframe['close'], label='close')
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ax1.plot(dataframe.index.values, dataframe['close_30_ema'], label='EMA(60)')
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ax1.plot(dataframe.index.values, dataframe['close_90_ema'], label='EMA(120)')
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# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
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ax1.plot(dataframe.index.values, dataframe['buy'], 'bo', label='buy')
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ax1.legend()
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ax2.plot(dataframe.index.values, dataframe['macd'], label='MACD')
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ax2.plot(dataframe.index.values, dataframe['macds'], label='MACDS')
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ax2.plot(dataframe.index.values, dataframe['macdh'], label='MACD Histogram')
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ax2.plot(dataframe.index.values, [0] * len(dataframe.index.values))
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ax2.legend()
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ax3.plot(dataframe.index.values, dataframe['stochrsi'], label='StochRSI')
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ax3.plot(dataframe.index.values, [0.80] * len(dataframe.index.values))
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ax3.plot(dataframe.index.values, [0.20] * len(dataframe.index.values))
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ax3.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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while True:
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pair = 'BTC_ANT'
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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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dataframe = get_ticker_dataframe(pair)
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dataframe = populate_trends(dataframe)
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plot_dataframe(dataframe, pair)
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time.sleep(60)
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