stable/analyze.py

165 lines
5.4 KiB
Python

import time
from datetime import timedelta
import logging
import arrow
import requests
from pandas.io.json import json_normalize
from pandas import DataFrame
import talib.abstract as ta
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def get_ticker(pair: str, minimum_date: arrow.Arrow) -> dict:
"""
Request ticker data from Bittrex for a given currency pair
"""
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']))
return data
def parse_ticker_dataframe(ticker: list, minimum_date: arrow.Arrow) -> DataFrame:
"""
Analyses the trend for the given pair
:param pair: pair as str in format BTC_ETH or BTC-ETH
:return: DataFrame
"""
df = DataFrame(ticker) \
.drop('BV', 1) \
.rename(columns={'C':'close', 'V':'volume', 'O':'open', 'H':'high', 'L':'low', 'T':'date'}) \
.sort_values('date')
return df[df['date'].map(arrow.get) > minimum_date]
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['ema'] = ta.EMA(dataframe, timeperiod=33)
dataframe['sar'] = ta.SAR(dataframe, 0.02, 0.22)
dataframe['adx'] = ta.ADX(dataframe)
return dataframe
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy trend for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
prev_sar = dataframe['sar'].shift(1)
prev_close = dataframe['close'].shift(1)
prev_sar2 = dataframe['sar'].shift(2)
prev_close2 = dataframe['close'].shift(2)
# wait for stable turn from bearish to bullish market
dataframe.loc[
(dataframe['close'] > dataframe['sar']) &
(prev_close > prev_sar) &
(prev_close2 < prev_sar2),
'swap'
] = 1
# consider prices above ema to be in upswing
dataframe.loc[dataframe['ema'] <= dataframe['close'], 'upswing'] = 1
dataframe.loc[
(dataframe['upswing'] == 1) &
(dataframe['swap'] == 1) &
(dataframe['adx'] > 25), # adx over 25 tells there's enough momentum
'buy'] = 1
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
return dataframe
def analyze_ticker(pair: str) -> DataFrame:
"""
Get ticker data for given currency pair, push it to a DataFrame and
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
minimum_date = arrow.utcnow().shift(hours=-6)
data = get_ticker(pair, minimum_date)
dataframe = parse_ticker_dataframe(data['result'], minimum_date)
dataframe = populate_indicators(dataframe)
dataframe = populate_buy_trend(dataframe)
return dataframe
def get_buy_signal(pair: str) -> bool:
"""
Calculates a buy signal based several technical analysis indicators
:param pair: pair in format BTC_ANT or BTC-ANT
:return: True if pair is good for buying, False otherwise
"""
dataframe = analyze_ticker(pair)
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['buy'] == 1
logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
return signal
def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
"""
Plots the given dataframe
:param dataframe: DataFrame
:param pair: pair as str
:return: None
"""
import matplotlib
matplotlib.use("Qt5Agg")
import matplotlib.pyplot as plt
# Two subplots sharing x axis
fig, (ax1, ax2) = plt.subplots(2, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['sar'], 'g_', label='pSAR')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['ema'], '--', label='EMA(20)')
ax1.plot(dataframe.index.values, dataframe['buy'], 'bo', label='buy')
ax1.legend()
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.legend()
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()
if __name__ == '__main__':
# Install PYQT5==5.9 manually if you want to test this helper function
while True:
test_pair = 'BTC_ANT'
#for pair in ['BTC_ANT', 'BTC_ETH', 'BTC_GNT', 'BTC_ETC']:
# get_buy_signal(pair)
plot_dataframe(analyze_ticker(test_pair), test_pair)
time.sleep(60)