114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
import logging
|
|
from datetime import timedelta
|
|
|
|
import arrow
|
|
import talib.abstract as ta
|
|
from pandas import DataFrame, to_datetime
|
|
|
|
from freqtrade.exchange import get_ticker_history
|
|
from freqtrade.vendor.qtpylib.indicators import awesome_oscillator
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def parse_ticker_dataframe(ticker: list) -> DataFrame:
|
|
"""
|
|
Analyses the trend for the given ticker history
|
|
:param ticker: See exchange.get_ticker_history
|
|
:return: DataFrame
|
|
"""
|
|
columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
|
|
frame = DataFrame(ticker) \
|
|
.drop('BV', 1) \
|
|
.rename(columns=columns)
|
|
frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
|
|
frame.sort_values('date', inplace=True)
|
|
return frame
|
|
|
|
|
|
def populate_indicators(dataframe: DataFrame) -> DataFrame:
|
|
"""
|
|
Adds several different TA indicators to the given DataFrame
|
|
"""
|
|
dataframe['sar'] = ta.SAR(dataframe)
|
|
dataframe['adx'] = ta.ADX(dataframe)
|
|
stoch = ta.STOCHF(dataframe)
|
|
dataframe['fastd'] = stoch['fastd']
|
|
dataframe['fastk'] = stoch['fastk']
|
|
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
|
|
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
|
|
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
|
|
dataframe['mfi'] = ta.MFI(dataframe)
|
|
dataframe['cci'] = ta.CCI(dataframe)
|
|
dataframe['rsi'] = ta.RSI(dataframe)
|
|
dataframe['mom'] = ta.MOM(dataframe)
|
|
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
|
|
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
|
|
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
|
|
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
|
|
dataframe['ao'] = awesome_oscillator(dataframe)
|
|
macd = ta.MACD(dataframe)
|
|
dataframe['macd'] = macd['macd']
|
|
dataframe['macdsignal'] = macd['macdsignal']
|
|
dataframe['macdhist'] = macd['macdhist']
|
|
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
|
|
"""
|
|
dataframe.ix[
|
|
(dataframe['close'] < dataframe['sma']) &
|
|
(dataframe['tema'] <= dataframe['blower']) &
|
|
(dataframe['mfi'] < 25) &
|
|
(dataframe['fastd'] < 25) &
|
|
(dataframe['adx'] > 30),
|
|
'buy'] = 1
|
|
dataframe.ix[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
|
|
"""
|
|
data = get_ticker_history(pair)
|
|
dataframe = parse_ticker_dataframe(data)
|
|
|
|
if dataframe.empty:
|
|
logger.warning('Empty dataframe for pair %s', pair)
|
|
return dataframe
|
|
|
|
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)
|
|
|
|
if dataframe.empty:
|
|
return False
|
|
|
|
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
|