2017-06-05 19:17:10 +00:00
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import time
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2017-05-24 19:52:41 +00:00
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from datetime import timedelta
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import logging
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2017-08-27 14:12:28 +00:00
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import arrow
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2017-05-24 19:52:41 +00:00
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import requests
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from pandas.io.json import json_normalize
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2017-09-02 08:56:56 +00:00
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from pandas import DataFrame
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2017-09-01 18:40:12 +00:00
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import talib.abstract as ta
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2017-05-24 19:52:41 +00:00
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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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2017-09-09 09:26:33 +00:00
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def get_ticker(pair: str, minimum_date: arrow.Arrow) -> dict:
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2017-05-24 19:52:41 +00:00
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"""
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2017-09-09 09:26:33 +00:00
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Request ticker data from Bittrex for a given currency pair
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2017-05-24 19:52:41 +00:00
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"""
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url = 'https://bittrex.com/Api/v2.0/pub/market/GetTicks'
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2017-05-24 20:23:20 +00:00
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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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2017-05-24 19:52:41 +00:00
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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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2017-05-24 20:23:20 +00:00
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data = requests.get(url, params=params, headers=headers).json()
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2017-05-24 19:52:41 +00:00
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if not data['success']:
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raise RuntimeError('BITTREX: {}'.format(data['message']))
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2017-09-09 09:26:33 +00:00
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return data
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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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2017-09-10 06:51:56 +00:00
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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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2017-09-09 10:02:47 +00:00
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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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2017-09-04 07:19:05 +00:00
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dataframe['close_30_ema'] = ta.EMA(dataframe, timeperiod=30)
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dataframe['close_90_ema'] = ta.EMA(dataframe, timeperiod=90)
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2017-09-01 18:40:12 +00:00
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dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.2)
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2017-05-24 19:52:41 +00:00
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# calculate StochRSI
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2017-09-02 08:41:30 +00:00
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stochrsi = ta.STOCHRSI(dataframe)
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dataframe['stochrsi'] = stochrsi['fastd'] # values between 0-100, not 0-1
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2017-09-02 08:56:56 +00:00
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macds'] = macd['macdsignal']
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dataframe['macdh'] = macd['macdhist']
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2017-05-24 19:52:41 +00:00
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return dataframe
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2017-09-09 13:32:53 +00:00
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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2017-05-24 19:52:41 +00:00
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"""
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2017-09-09 13:32:53 +00:00
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Based on TA indicators, populates the buy trend for the given dataframe
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2017-09-02 08:56:56 +00:00
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:param dataframe: DataFrame
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2017-09-09 13:32:53 +00:00
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:return: DataFrame with buy column
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2017-05-24 23:11:35 +00:00
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"""
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dataframe.loc[
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2017-09-02 08:41:30 +00:00
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(dataframe['stochrsi'] < 20)
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2017-09-09 10:02:47 +00:00
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& (dataframe['macd'] > dataframe['macds'])
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2017-09-01 18:40:12 +00:00
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& (dataframe['close'] > dataframe['sar']),
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2017-09-09 13:32:53 +00:00
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'buy'
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2017-05-24 19:52:41 +00:00
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] = 1
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2017-09-09 13:32:53 +00:00
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dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
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2017-05-24 19:52:41 +00:00
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return dataframe
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2017-09-09 10:16:14 +00:00
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def analyze_ticker(pair: str) -> DataFrame:
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2017-09-09 13:32:53 +00:00
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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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2017-09-10 06:51:56 +00:00
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minimum_date = arrow.utcnow().shift(hours=-6)
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2017-09-09 10:16:14 +00:00
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data = get_ticker(pair, minimum_date)
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dataframe = parse_ticker_dataframe(data['result'], minimum_date)
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dataframe = populate_indicators(dataframe)
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2017-09-09 13:32:53 +00:00
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dataframe = populate_buy_trend(dataframe)
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2017-09-09 10:16:14 +00:00
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return dataframe
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2017-09-01 19:11:46 +00:00
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def get_buy_signal(pair: str) -> bool:
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2017-05-24 19:52:41 +00:00
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"""
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2017-09-09 13:32:53 +00:00
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Calculates a buy signal based several technical analysis indicators
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2017-05-24 19:52:41 +00:00
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:param pair: pair in format BTC_ANT or BTC-ANT
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2017-09-09 13:32:53 +00:00
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:return: True if pair is good for buying, False otherwise
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2017-05-24 19:52:41 +00:00
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"""
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2017-09-09 10:16:14 +00:00
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dataframe = analyze_ticker(pair)
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2017-05-24 19:52:41 +00:00
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latest = dataframe.iloc[-1]
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2017-05-24 23:11:35 +00:00
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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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2017-09-09 13:32:53 +00:00
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signal = latest['buy'] == 1
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2017-08-27 13:50:59 +00:00
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logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
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2017-05-24 19:52:41 +00:00
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return signal
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2017-09-02 08:56:56 +00:00
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def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
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2017-05-24 19:52:41 +00:00
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"""
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Plots the given dataframe
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2017-09-02 08:56:56 +00:00
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:param dataframe: DataFrame
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2017-05-24 19:52:41 +00:00
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:param pair: pair as str
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:return: None
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"""
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2017-06-05 19:17:10 +00:00
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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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2017-05-24 19:52:41 +00:00
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# Three subplots sharing x axe
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2017-08-27 13:40:27 +00:00
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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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2017-05-24 19:52:41 +00:00
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ax1.plot(dataframe.index.values, dataframe['close'], label='close')
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2017-09-04 07:19:05 +00:00
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ax1.plot(dataframe.index.values, dataframe['close_30_ema'], label='EMA(30)')
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ax1.plot(dataframe.index.values, dataframe['close_90_ema'], label='EMA(90)')
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2017-05-24 19:52:41 +00:00
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# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
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2017-09-09 13:32:53 +00:00
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ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
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2017-05-24 19:52:41 +00:00
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ax1.legend()
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2017-06-05 19:17:10 +00:00
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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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2017-05-24 21:28:40 +00:00
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ax2.legend()
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2017-05-24 19:52:41 +00:00
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2017-06-05 19:17:10 +00:00
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ax3.plot(dataframe.index.values, dataframe['stochrsi'], label='StochRSI')
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2017-09-02 08:41:30 +00:00
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ax3.plot(dataframe.index.values, [80] * len(dataframe.index.values))
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ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
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2017-06-05 19:17:10 +00:00
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ax3.legend()
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2017-05-24 19:52:41 +00:00
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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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2017-08-27 13:40:27 +00:00
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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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2017-05-24 19:52:41 +00:00
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plt.show()
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if __name__ == '__main__':
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2017-09-08 21:10:22 +00:00
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# Install PYQT5==5.9 manually if you want to test this helper function
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2017-05-24 19:52:41 +00:00
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while True:
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pair = 'BTC_ANT'
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2017-06-05 19:17:10 +00:00
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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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2017-09-09 10:16:14 +00:00
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plot_dataframe(analyze_ticker(pair), pair)
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2017-06-05 19:17:10 +00:00
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time.sleep(60)
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