freqtrade_origin/freqtrade/analyze.py

"""
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
import talib.abstract as ta
from pandas import DataFrame, to_datetime

import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exchange import get_ticker_history

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) \
        .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

    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.
    """

    # Momentum Indicator
    # ------------------------------------

    # ADX
    dataframe['adx'] = ta.ADX(dataframe)

    # Awesome oscillator
    dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
    """
    # Commodity Channel Index: values Oversold:<-100, Overbought:>100
    dataframe['cci'] = ta.CCI(dataframe)
    """
    # MACD
    macd = ta.MACD(dataframe)
    dataframe['macd'] = macd['macd']
    dataframe['macdsignal'] = macd['macdsignal']
    dataframe['macdhist'] = macd['macdhist']

    # MFI
    dataframe['mfi'] = ta.MFI(dataframe)

    # Minus Directional Indicator / Movement
    dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
    dataframe['minus_di'] = ta.MINUS_DI(dataframe)

    # Plus Directional Indicator / Movement
    dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
    dataframe['plus_di'] = ta.PLUS_DI(dataframe)
    """
    # ROC
    dataframe['roc'] = ta.ROC(dataframe)
    """
    # RSI
    dataframe['rsi'] = ta.RSI(dataframe)
    """
    # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
    rsi = 0.1 * (dataframe['rsi'] - 50)
    dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)

    # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
    dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)

    # Stoch
    stoch = ta.STOCH(dataframe)
    dataframe['slowd'] = stoch['slowd']
    dataframe['slowk'] = stoch['slowk']
    """
    # Stoch fast
    stoch_fast = ta.STOCHF(dataframe)
    dataframe['fastd'] = stoch_fast['fastd']
    dataframe['fastk'] = stoch_fast['fastk']
    """
    # Stoch RSI
    stoch_rsi = ta.STOCHRSI(dataframe)
    dataframe['fastd_rsi'] = stoch_rsi['fastd']
    dataframe['fastk_rsi'] = stoch_rsi['fastk']
    """

    # Overlap Studies
    # ------------------------------------

    # Previous Bollinger bands
    # Because ta.BBANDS implementation is broken with small numbers, it actually
    # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
    # and use middle band instead.
    dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
    """
    # Bollinger bands
    bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
    dataframe['bb_lowerband'] = bollinger['lower']
    dataframe['bb_middleband'] = bollinger['mid']
    dataframe['bb_upperband'] = bollinger['upper']
    """

    # EMA - Exponential Moving Average
    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)

    # SAR Parabol
    dataframe['sar'] = ta.SAR(dataframe)

    # SMA - Simple Moving Average
    dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)

    # TEMA - Triple Exponential Moving Average
    dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)

    # Cycle Indicator
    # ------------------------------------
    # Hilbert Transform Indicator - SineWave
    hilbert = ta.HT_SINE(dataframe)
    dataframe['htsine'] = hilbert['sine']
    dataframe['htleadsine'] = hilbert['leadsine']

    # Pattern Recognition - Bullish candlestick patterns
    # ------------------------------------
    """
    # Hammer: values [0, 100]
    dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)

    # Inverted Hammer: values [0, 100]
    dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)

    # Dragonfly Doji: values [0, 100]
    dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)

    # Piercing Line: values [0, 100]
    dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]

    # Morningstar: values [0, 100]
    dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]

    # Three White Soldiers: values [0, 100]
    dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
    """

    # Pattern Recognition - Bearish candlestick patterns
    # ------------------------------------
    """
    # Hanging Man: values [0, 100]
    dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)

    # Shooting Star: values [0, 100]
    dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)

    # Gravestone Doji: values [0, 100]
    dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)

    # Dark Cloud Cover: values [0, 100]
    dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)

    # Evening Doji Star: values [0, 100]
    dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)

    # Evening Star: values [0, 100]
    dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
    """

    # Pattern Recognition - Bullish/Bearish candlestick patterns
    # ------------------------------------
    """
    # Three Line Strike: values [0, -100, 100]
    dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)

    # Spinning Top: values [0, -100, 100]
    dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]

    # Engulfing: values [0, -100, 100]
    dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]

    # Harami: values [0, -100, 100]
    dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]

    # Three Outside Up/Down: values [0, -100, 100]
    dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]

    # Three Inside Up/Down: values [0, -100, 100]
    dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
    """

    # Chart type
    # ------------------------------------
    """
    # Heikinashi stategy
    heikinashi = qtpylib.heikinashi(dataframe)
    dataframe['ha_open'] = heikinashi['open']
    dataframe['ha_close'] = heikinashi['close']
    dataframe['ha_high'] = heikinashi['high']
    dataframe['ha_low'] = heikinashi['low']
    """

    return 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
    """
    dataframe.loc[
        (
            (dataframe['rsi'] < 35) &
            (dataframe['fastd'] < 35) &
            (dataframe['adx'] > 30) &
            (dataframe['plus_di'] > 0.5)
        ) |
        (
            (dataframe['adx'] > 65) &
            (dataframe['plus_di'] > 0.5)
        ),
        'buy'] = 1

    return 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
    """
    dataframe.loc[
        (
            (
                (qtpylib.crossed_above(dataframe['rsi'], 70)) |
                (qtpylib.crossed_above(dataframe['fastd'], 70))
            ) &
            (dataframe['adx'] > 10) &
            (dataframe['minus_di'] > 0)
        ) |
        (
            (dataframe['adx'] > 70) &
            (dataframe['minus_di'] > 0.5)
        ),
        'sell'] = 1
    return 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, signal: SignalType) -> bool:
    """
    Calculates current 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
    """
    ticker_hist = get_ticker_history(pair)
    if not ticker_hist:
        logger.warning('Empty ticker history for pair %s', pair)
        return 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
    except Exception as ex:
        logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
        return False

    if dataframe.empty:
        logger.warning('Empty dataframe for pair %s', pair)
        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):
        logger.warning('Too old dataframe for pair %s', pair)
        return False

    # FIX: 20180109, there could be some confusion because we will make a
    #      boolean result (execute the action or not depending on the signal).
    #      But the above checks can also return False, and we hide that.
    result = latest[signal.value] == 1
    logger.debug('%s_trigger: %s (pair=%s, signal=%s)', signal.value, latest['date'], pair, result)
    return result