2017-11-18 07:34:32 +00:00
|
|
|
"""
|
|
|
|
Functions to analyze ticker data with indicators and produce buy and sell signals
|
|
|
|
"""
|
2017-11-14 17:06:03 +00:00
|
|
|
from enum import Enum
|
2017-10-06 10:22:04 +00:00
|
|
|
import logging
|
2017-05-24 19:52:41 +00:00
|
|
|
from datetime import timedelta
|
2017-10-06 10:22:04 +00:00
|
|
|
|
2017-08-27 14:12:28 +00:00
|
|
|
import arrow
|
2017-09-01 18:40:12 +00:00
|
|
|
import talib.abstract as ta
|
2017-10-29 08:16:53 +00:00
|
|
|
from pandas import DataFrame, to_datetime
|
2017-09-01 18:40:12 +00:00
|
|
|
|
2017-11-09 21:29:23 +00:00
|
|
|
from freqtrade.exchange import get_ticker_history
|
2017-11-18 07:34:57 +00:00
|
|
|
from freqtrade.vendor.qtpylib.indicators import awesome_oscillator, crossed_above
|
2017-05-24 19:52:41 +00:00
|
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
2017-11-14 17:06:03 +00:00
|
|
|
class SignalType(Enum):
|
2017-11-18 07:34:32 +00:00
|
|
|
""" Enum to distinguish between buy and sell signals """
|
2017-11-14 17:06:03 +00:00
|
|
|
BUY = "buy"
|
|
|
|
SELL = "sell"
|
|
|
|
|
2017-05-24 19:52:41 +00:00
|
|
|
|
2017-10-29 07:36:03 +00:00
|
|
|
def parse_ticker_dataframe(ticker: list) -> DataFrame:
|
2017-09-09 09:26:33 +00:00
|
|
|
"""
|
2017-10-31 23:12:18 +00:00
|
|
|
Analyses the trend for the given ticker history
|
|
|
|
:param ticker: See exchange.get_ticker_history
|
2017-09-09 09:26:33 +00:00
|
|
|
:return: DataFrame
|
|
|
|
"""
|
2017-11-07 19:13:36 +00:00
|
|
|
columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
|
|
|
|
frame = DataFrame(ticker) \
|
2017-09-10 06:51:56 +00:00
|
|
|
.drop('BV', 1) \
|
2017-11-07 19:13:36 +00:00
|
|
|
.rename(columns=columns)
|
|
|
|
frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
|
|
|
|
frame.sort_values('date', inplace=True)
|
|
|
|
return frame
|
2017-09-09 10:02:47 +00:00
|
|
|
|
2017-10-06 10:22:04 +00:00
|
|
|
|
2017-09-09 10:02:47 +00:00
|
|
|
def populate_indicators(dataframe: DataFrame) -> DataFrame:
|
|
|
|
"""
|
|
|
|
Adds several different TA indicators to the given DataFrame
|
|
|
|
"""
|
2017-10-20 09:56:44 +00:00
|
|
|
dataframe['sar'] = ta.SAR(dataframe)
|
2017-09-12 08:47:23 +00:00
|
|
|
dataframe['adx'] = ta.ADX(dataframe)
|
2017-09-29 06:37:45 +00:00
|
|
|
stoch = ta.STOCHF(dataframe)
|
|
|
|
dataframe['fastd'] = stoch['fastd']
|
|
|
|
dataframe['fastk'] = stoch['fastk']
|
|
|
|
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
|
2017-10-20 09:56:44 +00:00
|
|
|
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
|
2017-10-15 13:54:26 +00:00
|
|
|
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
|
2017-09-29 06:37:45 +00:00
|
|
|
dataframe['mfi'] = ta.MFI(dataframe)
|
2017-10-28 13:14:01 +00:00
|
|
|
dataframe['rsi'] = ta.RSI(dataframe)
|
|
|
|
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
|
|
|
|
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
|
2017-10-28 13:52:26 +00:00
|
|
|
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
|
|
|
|
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
|
2017-10-25 15:24:20 +00:00
|
|
|
dataframe['ao'] = awesome_oscillator(dataframe)
|
2017-10-28 13:43:34 +00:00
|
|
|
macd = ta.MACD(dataframe)
|
|
|
|
dataframe['macd'] = macd['macd']
|
|
|
|
dataframe['macdsignal'] = macd['macdsignal']
|
|
|
|
dataframe['macdhist'] = macd['macdhist']
|
2017-11-12 07:13:54 +00:00
|
|
|
hilbert = ta.HT_SINE(dataframe)
|
|
|
|
dataframe['htsine'] = hilbert['sine']
|
|
|
|
dataframe['htleadsine'] = hilbert['leadsine']
|
2017-05-24 19:52:41 +00:00
|
|
|
return dataframe
|
|
|
|
|
|
|
|
|
2017-09-09 13:32:53 +00:00
|
|
|
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
|
2017-05-24 19:52:41 +00:00
|
|
|
"""
|
2017-11-14 18:28:31 +00:00
|
|
|
Based on TA indicators, populates the buy signal for the given dataframe
|
2017-09-02 08:56:56 +00:00
|
|
|
:param dataframe: DataFrame
|
2017-09-09 13:32:53 +00:00
|
|
|
:return: DataFrame with buy column
|
2017-05-24 23:11:35 +00:00
|
|
|
"""
|
2017-11-16 18:16:54 +00:00
|
|
|
dataframe.loc[
|
2017-09-29 06:37:45 +00:00
|
|
|
(dataframe['tema'] <= dataframe['blower']) &
|
2017-11-18 06:45:57 +00:00
|
|
|
(dataframe['rsi'] < 37) &
|
|
|
|
(dataframe['fastd'] < 48) &
|
|
|
|
(dataframe['adx'] > 31),
|
2017-09-12 08:47:23 +00:00
|
|
|
'buy'] = 1
|
|
|
|
|
2017-05-24 19:52:41 +00:00
|
|
|
return dataframe
|
|
|
|
|
2017-11-14 18:28:31 +00:00
|
|
|
def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
|
|
|
|
"""
|
|
|
|
Based on TA indicators, populates the sell signal for the given dataframe
|
|
|
|
:param dataframe: DataFrame
|
|
|
|
:return: DataFrame with buy column
|
|
|
|
"""
|
2017-11-16 18:16:54 +00:00
|
|
|
dataframe.loc[
|
2017-11-14 18:28:31 +00:00
|
|
|
(crossed_above(dataframe['rsi'], 70)),
|
|
|
|
'sell'] = 1
|
|
|
|
|
|
|
|
return dataframe
|
|
|
|
|
2017-05-24 19:52:41 +00:00
|
|
|
|
2017-09-09 10:16:14 +00:00
|
|
|
def analyze_ticker(pair: str) -> DataFrame:
|
2017-09-09 13:32:53 +00:00
|
|
|
"""
|
|
|
|
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
|
|
|
|
"""
|
2017-11-13 18:54:09 +00:00
|
|
|
ticker_hist = get_ticker_history(pair)
|
|
|
|
if not ticker_hist:
|
|
|
|
logger.warning('Empty ticker history for pair %s', pair)
|
|
|
|
return DataFrame()
|
2017-10-06 10:22:04 +00:00
|
|
|
|
2017-11-13 18:54:09 +00:00
|
|
|
dataframe = parse_ticker_dataframe(ticker_hist)
|
2017-09-09 10:16:14 +00:00
|
|
|
dataframe = populate_indicators(dataframe)
|
2017-09-09 13:32:53 +00:00
|
|
|
dataframe = populate_buy_trend(dataframe)
|
2017-11-14 18:28:31 +00:00
|
|
|
dataframe = populate_sell_trend(dataframe)
|
2017-11-17 10:08:23 +00:00
|
|
|
# TODO: buy_price and sell_price are only used by the plotter, should probably be moved there
|
|
|
|
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
|
|
|
|
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
|
2017-09-09 10:16:14 +00:00
|
|
|
return dataframe
|
|
|
|
|
2017-10-06 10:22:04 +00:00
|
|
|
|
2017-11-14 17:06:03 +00:00
|
|
|
def get_signal(pair: str, signal: SignalType) -> bool:
|
2017-05-24 19:52:41 +00:00
|
|
|
"""
|
2017-11-14 17:06:03 +00:00
|
|
|
Calculates current signal based several technical analysis indicators
|
2017-05-24 19:52:41 +00:00
|
|
|
:param pair: pair in format BTC_ANT or BTC-ANT
|
2017-09-09 13:32:53 +00:00
|
|
|
:return: True if pair is good for buying, False otherwise
|
2017-05-24 19:52:41 +00:00
|
|
|
"""
|
2017-11-21 19:37:29 +00:00
|
|
|
try:
|
|
|
|
dataframe = analyze_ticker(pair)
|
|
|
|
except ValueError as ex:
|
|
|
|
logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
|
|
|
|
return False
|
|
|
|
|
2017-09-27 22:43:32 +00:00
|
|
|
if dataframe.empty:
|
|
|
|
return False
|
|
|
|
|
2017-05-24 19:52:41 +00:00
|
|
|
latest = dataframe.iloc[-1]
|
2017-05-24 23:11:35 +00:00
|
|
|
|
|
|
|
# Check if dataframe is out of date
|
|
|
|
signal_date = arrow.get(latest['date'])
|
|
|
|
if signal_date < arrow.now() - timedelta(minutes=10):
|
|
|
|
return False
|
|
|
|
|
2017-11-14 17:06:03 +00:00
|
|
|
result = latest[signal.value] == 1
|
|
|
|
logger.debug('%s_trigger: %s (pair=%s, signal=%s)', signal.value, latest['date'], pair, result)
|
|
|
|
return result
|