stable/analyze.py

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import time
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from datetime import timedelta
import logging
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import arrow
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import requests
from pandas.io.json import json_normalize
from pandas import DataFrame
import talib.abstract as ta
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logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def get_ticker_dataframe(pair: str) -> DataFrame:
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"""
Analyses the trend for the given pair
:param pair: pair as str in format BTC_ETH or BTC-ETH
:return: DataFrame
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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 = {
'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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params = {
'marketName': pair.replace('_', '-'),
'tickInterval': 'OneMin',
'_': minimum_date.timestamp * 1000
}
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data = requests.get(url, params=params, headers=headers).json()
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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 sorted(data['result'], key=lambda k: k['T']) if arrow.get(t['T']) > minimum_date]
dataframe = DataFrame(json_normalize(data))
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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)
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# calculate StochRSI
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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']
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return dataframe
def populate_trends(dataframe: DataFrame) -> DataFrame:
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"""
Populates the trends for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with populated trends
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"""
"""
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dataframe.loc[
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(dataframe['stochrsi'] < 20)
& (dataframe['close_30_ema'] > (1 + 0.0025) * dataframe['close_60_ema']),
'underpriced'
] = 1
"""
dataframe.loc[
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(dataframe['stochrsi'] < 20)
& (dataframe['macd'] > dataframe['macds'])
& (dataframe['close'] > dataframe['sar']),
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'underpriced'
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] = 1
dataframe.loc[dataframe['underpriced'] == 1, 'buy'] = dataframe['close']
return dataframe
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def get_buy_signal(pair: str) -> bool:
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"""
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]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
if signal_date < arrow.now() - timedelta(minutes=10):
return False
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signal = latest['underpriced'] == 1
logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
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return signal
def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
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"""
Plots the given dataframe
:param dataframe: DataFrame
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:param pair: pair as str
:return: None
"""
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import matplotlib
matplotlib.use("Qt5Agg")
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)
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(30)')
ax1.plot(dataframe.index.values, dataframe['close_90_ema'], label='EMA(90)')
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# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['buy'], 'bo', label='buy')
ax1.legend()
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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))
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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, [80] * len(dataframe.index.values))
ax3.plot(dataframe.index.values, [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
# all but bottom plot.
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fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
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plt.show()
if __name__ == '__main__':
while True:
pair = 'BTC_ANT'
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#for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
# get_buy_signal(pair)
dataframe = get_ticker_dataframe(pair)
dataframe = populate_trends(dataframe)
plot_dataframe(dataframe, pair)
time.sleep(60)