freqtrade_origin/freqtrade/analyze.py
2018-01-22 20:51:39 -08:00

118 lines
3.8 KiB
Python

"""
Functions to analyze ticker data with indicators and produce buy and sell signals
"""
import logging
from datetime import timedelta
from enum import Enum
from typing import Dict, List
import arrow
from pandas import DataFrame, to_datetime
from freqtrade.exchange import get_ticker_history
from freqtrade.strategy.strategy import Strategy
logger = logging.getLogger(__name__)
class SignalType(Enum):
""" Enum to distinguish between buy and sell signals """
BUY = "buy"
SELL = "sell"
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) \
.rename(columns=columns)
if 'BV' in frame:
frame.drop('BV', 1, inplace=True)
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
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
strategy = Strategy()
return strategy.populate_indicators(dataframe=dataframe)
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
strategy = Strategy()
return strategy.populate_buy_trend(dataframe=dataframe)
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
"""
strategy = Strategy()
return strategy.populate_sell_trend(dataframe=dataframe)
def analyze_ticker(ticker_history: List[Dict]) -> DataFrame:
"""
Parses the given ticker history and returns a populated DataFrame
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
dataframe = parse_ticker_dataframe(ticker_history)
dataframe = populate_indicators(dataframe)
dataframe = populate_buy_trend(dataframe)
dataframe = populate_sell_trend(dataframe)
return dataframe
def get_signal(pair: str, interval: int) -> (bool, bool):
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format BTC_ANT or BTC-ANT
:return: (True, False) if pair is good for buying and not for selling
"""
ticker_hist = get_ticker_history(pair, interval)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return (False, False)
try:
dataframe = analyze_ticker(ticker_hist)
except ValueError as ex:
logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
return (False, False)
except Exception as ex:
logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
return (False, False)
if dataframe.empty:
return (False, 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, False)
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s', latest['date'], pair, str(buy), str(sell))
return (buy, sell)