LoadRLModel.py

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement

# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy.interface import IStrategy

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from stable_baselines import ACER

# This class is a sample. Feel free to customize it.
class LoadRLModel(IStrategy):
    stoploss = -0.50

    trailing_stop = False

    ticker_interval = '5m'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False

    startup_candle_count: int = 20

    model = ACER.load('model')


    def informative_pairs(self):
        return []

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

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

        # Plus Directional Indicator / Movement
        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
        dataframe['plus_di'] = ta.PLUS_DI(dataframe)

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

        # Aroon, Aroon Oscillator
        aroon = ta.AROON(dataframe)
        dataframe['aroonup'] = aroon['aroonup']
        dataframe['aroondown'] = aroon['aroondown']
        dataframe['aroonosc'] = ta.AROONOSC(dataframe)

        # Awesome Oscillator
        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)

        # # Keltner Channel
        # keltner = qtpylib.keltner_channel(dataframe)
        # dataframe["kc_upperband"] = keltner["upper"]
        # dataframe["kc_lowerband"] = keltner["lower"]
        # dataframe["kc_middleband"] = keltner["mid"]
        # dataframe["kc_percent"] = (
        #     (dataframe["close"] - dataframe["kc_lowerband"]) /
        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
        # )
        # dataframe["kc_width"] = (
        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
        # )

        # Ultimate Oscillator
        dataframe['uo'] = ta.ULTOSC(dataframe)

        # Commodity Channel Index: values [Oversold:-100, Overbought:100]
        dataframe['cci'] = ta.CCI(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'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

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

        # Stochastic Slow
        stoch = ta.STOCH(dataframe)
        dataframe['slowd'] = stoch['slowd']
        dataframe['slowk'] = stoch['slowk']

        # Stochastic Fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

        # Stochastic RSI
        stoch_rsi = ta.STOCHRSI(dataframe)
        dataframe['fastd_rsi'] = stoch_rsi['fastd']
        dataframe['fastk_rsi'] = stoch_rsi['fastk']

        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

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

        # # ROC
        dataframe['roc'] = ta.ROC(dataframe)

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

        # # 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']
        # dataframe["bb_percent"] = (
        #     (dataframe["close"] - dataframe["bb_lowerband"]) /
        #     (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        # )
        # dataframe["bb_width"] = (
        #     (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
        # )

        # # Bollinger Bands - Weighted (EMA based instead of SMA)
        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
        #     qtpylib.typical_price(dataframe), window=20, stds=2
        # )
        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
        # dataframe["wbb_percent"] = (
        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
        # )
        # dataframe["wbb_width"] = (
        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
        #     dataframe["wbb_middleband"]
        # )

        # # EMA - Exponential Moving Average
        # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
        # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)

        # # SMA - Simple Moving Average
        # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
        # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
        # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
        # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
        # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)

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

        # 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
        # # ------------------------------------
        # # Heikin Ashi Strategy
        # heikinashi = qtpylib.heikinashi(dataframe)
        # dataframe['ha_open'] = heikinashi['open']
        # dataframe['ha_close'] = heikinashi['close']
        # dataframe['ha_high'] = heikinashi['high']
        # dataframe['ha_low'] = heikinashi['low']

        # Retrieve best bid and best ask from the orderbook
        # ------------------------------------
        """
        # first check if dataprovider is available
        if self.dp:
            if self.dp.runmode in ('live', 'dry_run'):
                ob = self.dp.orderbook(metadata['pair'], 1)
                dataframe['best_bid'] = ob['bids'][0][0]
                dataframe['best_ask'] = ob['asks'][0][0]
        """

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        # dataframe.loc[
        #     (
        #         (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
        #         (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
        #         (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising
        #         (dataframe['volume'] > 0)  # Make sure Volume is not 0
        #     ),
        #     'buy'] = 1
        action, nan_list = self.rl_model_redict(dataframe)
        dataframe.loc[action == 1, 'buy'] =1
        dataframe.loc[nan_list == True, 'buy'] = 0
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """        
        # dataframe.loc[
        #     (
        #         (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
        #         (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
        #         (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling
        #         (dataframe['volume'] > 0)  # Make sure Volume is not 0
        #     ),
        #     'sell'] = 1
        action, nan_list = self.rl_model_redict(dataframe)
        dataframe.loc[action == 2, 'sell'] =1
        dataframe.loc[nan_list == True, 'sell'] = 0
        return dataframe

    def rl_model_redict(self, dataframe):
        data = np.array([
            dataframe['adx'],
            dataframe['plus_dm'],
            dataframe['plus_di'],
            dataframe['minus_dm'],
            dataframe['minus_di'],
            dataframe['aroonup'],
            dataframe['aroondown'],
            dataframe['aroonosc'],
            dataframe['ao'],
            # dataframe['kc_percent'],
            # dataframe['kc_width'],
            dataframe['uo'],
            dataframe['cci'],
            dataframe['rsi'],
            dataframe['fisher_rsi'],
            dataframe['slowd'],
            dataframe['slowk'],
            dataframe['fastd'],
            dataframe['fastk'],
            dataframe['fastd_rsi'],
            dataframe['fastk_rsi'],
            dataframe['macd'],
            dataframe['macdsignal'],
            dataframe['macdhist'],
            dataframe['mfi'],
            dataframe['roc'],
            # row['bb_percent'],
            # row['bb_width'],
            # row['wbb_percent'],
            # row['wbb_width'],
            # dataframe['htsine'],
            # dataframe['htleadsine'],
            # row['CDLHAMMER'],
            # row['CDLINVERTEDHAMMER'],
            # row['CDLDRAGONFLYDOJI'],
            # row['CDLPIERCING'],
            # row['CDLMORNINGSTAR'],
            # row['CDL3WHITESOLDIERS'],
            # row['CDLHANGINGMAN'],
            # row['CDLSHOOTINGSTAR'],
            # row['CDLGRAVESTONEDOJI'],
            # row['CDLDARKCLOUDCOVER'],
            # row['CDLEVENINGDOJISTAR'],
            # row['CDLEVENINGSTAR'],
            # row['CDL3LINESTRIKE'],
            # row['CDLSPINNINGTOP'],
            # row['CDLENGULFING'],
            # row['CDLHARAMI'],
            # row['CDL3OUTSIDE'],
            # row['CDL3INSIDE'],
            # trad_status,
            # (self.trade != None)
        ], dtype=np.float)

        data = data.reshape(-1, 24)

        nan_list = np.isnan(data).any(axis=1)
        data = np.nan_to_num(data)
        action, _ = self.model.predict(data, deterministic=True)

        return action, nan_list