Neat_RL.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
import neat
import pickle

# This class is a sample. Feel free to customize it.
class Neat_RL(IStrategy):
    """
    This is a sample strategy to inspire you.
    More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
    populate_sell_trend, hyperopt_space, buy_strategy_generator
    """
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    # INTERFACE_VERSION = 2

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    # minimal_roi = {
    #     "60": 0.01,
    #     "30": 0.02,
    #     "0": 0.04
    # }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    # stoploss = -10
    stoploss = -0.50

    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # Optimal ticker interval for the strategy.
    ticker_interval = '5m'

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

    # These values can be overridden in the "ask_strategy" section in the config.
    # use_sell_signal = True
    # sell_profit_only = False
    # ignore_roi_if_buy_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 20

    with open('winner-feedforward', 'rb') as f:
        nc = pickle.load(f)

    # print(nc)

    nconfig = neat.Config(
        neat.DefaultGenome, 
        neat.DefaultReproduction,
        neat.DefaultSpeciesSet, 
        neat.DefaultStagnation,
        "neat_config"
        )

    model = neat.nn.FeedForwardNetwork.create(nc, nconfig)

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> 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.
        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """

        # 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'],
            dataframe['bb_percent'],
            dataframe['bb_width'],
            dataframe['wbb_percent'],
            dataframe['wbb_width'],
            dataframe['htsine'],
            dataframe['htleadsine'],
            dataframe['CDLHAMMER'],
            dataframe['CDLINVERTEDHAMMER'],
            dataframe['CDLDRAGONFLYDOJI'],
            dataframe['CDLPIERCING'],
            dataframe['CDLMORNINGSTAR'],
            dataframe['CDL3WHITESOLDIERS'],
            dataframe['CDLHANGINGMAN'],
            dataframe['CDLSHOOTINGSTAR'],
            dataframe['CDLGRAVESTONEDOJI'],
            dataframe['CDLDARKCLOUDCOVER'],
            dataframe['CDLEVENINGDOJISTAR'],
            dataframe['CDLEVENINGSTAR'],
            dataframe['CDL3LINESTRIKE'],
            dataframe['CDLSPINNINGTOP'],
            dataframe['CDLENGULFING'],
            dataframe['CDLHARAMI'],
            dataframe['CDL3OUTSIDE'],
            dataframe['CDL3INSIDE'],
            # trad_status,
            # (self.trade != None)
        ], dtype=np.float)

        data = data.reshape(-1, 55)

        nan_list = np.isnan(data).any(axis=1)
        data = np.nan_to_num(data)
        # action, _ = self.model.predict(data, deterministic=True)
        actions = []
        for d in data:
            pred = self.model.activate(d)
            actions.append(np.argmax(pred))
        actions = np.array(actions)
        # print(actions)
        return actions, nan_list