freqtrade_origin/freqtrade/templates/FreqaiExampleStrategy.py

import logging
import talib.abstract as ta
from pandas import DataFrame
import pandas as pd
from technical import qtpylib
import numpy as np
from freqtrade.strategy import (merge_informative_pair)
from freqtrade.strategy.interface import IStrategy
from freqtrade.freqai.strategy_bridge import CustomModel
from functools import reduce
logger = logging.getLogger(__name__)

class FreqaiExampleStrategy(IStrategy):
    """
    Example strategy showing how the user connects their own 
    IFreqaiModel to the strategy. Namely, the user uses:
    self.model = CustomModel(self.config)
    self.model.bridge.start(dataframe, metadata)

    to make predictions on their data. populate_any_indicators() automatically 
    generates the variety of features indicated by the user in the
    canonical freqtrade configuration file under config['freqai'].
    """

    minimal_roi = {
          "0": 0.01,
          "240": -1
     }

    plot_config = {
        'main_plot': {
        },
        'subplots': {
            "prediction":{
                'prediction':{'color':'blue'}
            },
            "target_roi":{
                'target_roi':{'color':'brown'},
            },
            "do_predict":{
                'do_predict':{'color':'brown'},
            },
        }
    }

    stoploss = -0.05
    use_sell_signal = True
    startup_candle_count: int = 1000 


    def informative_pairs(self):
        pairs = self.freqai_info['corr_pairlist'] 
        informative_pairs = []
        for tf in self.timeframes:
            informative_pairs.append([(pair, tf) for pair in pairs])
        return informative_pairs

    def populate_any_indicators(self, pair, df, tf, informative=None,coin=''):
        """
        Function designed to automatically generate, name and merge features
        from user indicated timeframes in the configuration file. User can add
        additional features here, but must follow the naming convention.
        :params:
        :pair: pair to be used as informative
        :df: strategy dataframe which will receive merges from informatives
        :tf: timeframe of the dataframe which will modify the feature names
        :informative: the dataframe associated with the informative pair
        :coin: the name of the coin which will modify the feature names.
        """
        if informative is None:
            informative = self.dp.get_pair_dataframe(pair, tf)

        informative[coin+'rsi'] = ta.RSI(informative, timeperiod=14)
        informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25)
        informative[coin+'adx'] = ta.ADX(informative, window=20)

        informative[coin+'20sma'] = ta.SMA(informative,timeperiod=20)
        informative[coin+'21ema'] = ta.EMA(informative,timeperiod=21)
        informative[coin+'bmsb'] = np.where(informative[coin+'20sma'].lt(informative[coin+'21ema']),1,0)
        informative[coin+'close_over_20sma'] = informative['close']/informative[coin+'20sma']

        informative[coin+'mfi'] = ta.MFI(informative, timeperiod=25)

        informative[coin+'ema21'] = ta.EMA(informative, timeperiod=21)
        informative[coin+'sma20'] = ta.SMA(informative, timeperiod=20)
        stoch = ta.STOCHRSI(informative, 15, 20, 2, 2)
        informative[coin+'srsi-fk'] = stoch['fastk']
        informative[coin+'srsi-fd'] = stoch['fastd']

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=14, stds=2.2)
        informative[coin+'bb_lowerband'] = bollinger['lower']
        informative[coin+'bb_middleband'] = bollinger['mid']
        informative[coin+'bb_upperband'] = bollinger['upper']
        informative[coin+'bb_width'] = ((informative[coin+"bb_upperband"] - informative[coin+"bb_lowerband"]) / informative[coin+"bb_middleband"])
        informative[coin+'close-bb_lower'] = informative['close'] / informative[coin+'bb_lowerband']

        informative[coin+'roc'] = ta.ROC(informative, timeperiod=3)
        informative[coin+'adx'] = ta.ADX(informative, window=14)

        macd = ta.MACD(informative)
        informative[coin+'macd'] = macd['macd']
        informative[coin+'pct-change'] = informative['close'].pct_change()
        informative[coin+'relative_volume'] = informative['volume'] / informative['volume'].rolling(10).mean()

        informative[coin+'pct-change'] = informative['close'].pct_change()

        indicators = [col for col in informative if col.startswith(coin)]

        for n in range(self.freqai_info['feature_parameters']['shift']+1):
            if n==0: continue
            informative_shift = informative[indicators].shift(n)
            informative_shift = informative_shift.add_suffix('_shift-'+str(n))
            informative = pd.concat((informative,informative_shift),axis=1)

        df = merge_informative_pair(df, informative, self.config['timeframe'], tf, ffill=True)
        skip_columns = [(s + '_'+tf) for s in
                        ['date', 'open', 'high', 'low', 'close', 'volume']]
        df = df.drop(columns=skip_columns)

        return df


    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # the configuration file parameters are stored here
        self.freqai_info = self.config['freqai']

        # the model is instantiated here
        self.model = CustomModel(self.config)

        print('Populating indicators...')

        # the following loops are necessary for building the features 
        # indicated by the user in the configuration file.
        for tf in self.freqai_info['timeframes']:
            dataframe = self.populate_any_indicators(metadata['pair'],
                                                        dataframe.copy(), tf)
            for i in self.freqai_info['corr_pairlist']:
                dataframe = self.populate_any_indicators(i,
                            dataframe.copy(), tf, coin=i.split("/")[0]+'-')

        # the model will return 4 values, its prediction, an indication of whether or not the prediction 
        # should be accepted, the target mean/std values from the labels used during each training period.
        (dataframe['prediction'], dataframe['do_predict'], 
            dataframe['target_mean'], dataframe['target_std']) = self.model.bridge.start(dataframe, metadata)

        dataframe['target_roi'] = dataframe['target_mean']+dataframe['target_std']*0.5
        dataframe['sell_roi'] = dataframe['target_mean']-dataframe['target_std']*1.5
        return dataframe


    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        buy_conditions = [
                    (dataframe['prediction'] > dataframe['target_roi'])
                    &
                    (dataframe['do_predict'] == 1)
        ]

        if buy_conditions:
            dataframe.loc[reduce(lambda x, y: x | y, buy_conditions), 'buy'] = 1

        return dataframe


    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
       # sell_goal = eval('self.'+metadata['pair'].split("/")[0]+'_sell_goal.value')
        sell_conditions = [
                    (dataframe['prediction'] < dataframe['sell_roi'])
                    &
                    (dataframe['do_predict'] == 1)
        ]
        if sell_conditions:
            dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), 'sell'] = 1

        return dataframe

    def get_ticker_indicator(self):
        return int(self.config['timeframe'][:-1])