stable/analyze.py

145 lines
4.7 KiB
Python

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=6)
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 sorted(data['result'], key=lambda k: k['T']) if arrow.get(t['T']) > minimum_date]
dataframe = StockDataFrame(json_normalize(data))
# calculate StochRSI
window = 14
rsi = dataframe['rsi_{}'.format(window)]
rolling = rsi.rolling(window=window, 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)
& (dataframe['close_30_ema'] > (1 + 0.0025) * dataframe['close_60_ema']),
'underpriced'
] = 1
"""
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]
# Check if dataframe is out of date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['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) = plt.subplots(2, 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_60_ema'], label='EMA(60)')
ax1.plot(dataframe.index.values, dataframe['close_120_ema'], label='EMA(120)')
# 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()
ax2.plot(dataframe.index.values, dataframe['stochrsi'], label='StochRSI')
ax2.plot(dataframe.index.values, [0.80] * len(dataframe.index.values))
ax2.plot(dataframe.index.values, [0.20] * len(dataframe.index.values))
ax2.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)
#dataframe = get_ticker_dataframe(pair)
#dataframe = populate_trends(dataframe)
#plot_dataframe(dataframe, pair)
#time.sleep(60)