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Inverse

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Foxel05/freqtrade-stuff/strategies/Inverse_converted.py · ★127 · ⑂31 · first seen 2026-07-28 · repo updated 2021-11-19

Basics mode: spot timeframe: 1h interface version: 3
Settings stoploss: -0.2 has minimal roi trailing process only new candles: false startup candle count: 200 hyperopt hyperopt params: 5
Indicators ADX ATR CCI EMA WMA talib
Concepts mean_reversion trailing
Methods SSLChannels confirm_trade_exit informative_indicators normal_tf_indicators
10 related strategies (⧉ identical code, ≈ similar name)

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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame, Series
from freqtrade.strategy import IStrategy
from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa
from freqtrade.persistence import Trade
from datetime import datetime, timedelta

class Inverse(IStrategy):
    INTERFACE_VERSION = 3
    # Buy hyperspace params:
    buy_params = {'buy_fisher_cci_1': -0.42, 'buy_fisher_cci_2': 0.41, 'buy_fisher_length': 31}
    # Sell hyperspace params:
    sell_params = {'sell_fisher_cci_1': 0.42, 'sell_fisher_cci_2': -0.34}
    # ROI table:  
    minimal_roi = {'0': 100}
    # Stoploss:
    stoploss = -0.2
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.078
    trailing_stop_positive_offset = 0.174
    trailing_only_offset_is_reached = False
    # Optimal timeframe for the strategy.
    timeframe = '1h'
    info_timeframe = '4h'
    # 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_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    plot_config = {'main_plot': {}, 'subplots': {'fisher': {'fisher_stoch': {'color': 'blue'}, 'fisher_cci': {'color': 'red'}, 'fisher_rsi': {'color': 'black'}, 'fisher_mfi': {'color': 'purple'}}}}
    # Hyperoptable parameters
    buy_fisher_length = IntParameter(low=13, high=55, default=34, space='buy', optimize=True, load=True)
    buy_fisher_cci_1 = DecimalParameter(low=-0.6, high=-0.3, decimals=2, default=-0.5, space='buy', optimize=True, load=True)
    buy_fisher_cci_2 = DecimalParameter(low=0.3, high=0.6, decimals=2, default=0.5, space='buy', optimize=True, load=True)
    sell_fisher_cci_1 = DecimalParameter(low=0.3, high=0.6, decimals=2, default=0.5, space='sell', optimize=True, load=True)
    sell_fisher_cci_2 = DecimalParameter(low=-0.6, high=-0.3, decimals=2, default=-0.5, space='sell', optimize=True, load=True)

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1]
        previous_candle_1 = dataframe.iloc[-2]
        if last_candle is not None:
            #            if (sell_reason in ['roi','sell_signal','trailing_stop_loss']):
            if exit_reason in ['exit_signal']:
                if last_candle['di_up'] and last_candle['adx'] > previous_candle_1['adx']:
                    return False
        return True

    def informative_pairs(self):
        # get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()
        # Assign tf to each pair so they can be downloaded and cached for strategy.
        informative_pairs = [(pair, self.info_timeframe) for pair in pairs]
        # informative_pairs.append(('BTC/USDT', self.btc_info_timeframe))
        return informative_pairs

    def informative_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, 'DataProvider is required for multiple timeframes.'
        # Get the informative pair
        informative_p = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.info_timeframe)
        # EMA
        informative_p['ema_50'] = ta.EMA(informative_p, timeperiod=50)
        informative_p['ema_100'] = ta.EMA(informative_p, timeperiod=100)
        informative_p['ema_200'] = ta.EMA(informative_p, timeperiod=200)
        # SSL Channels
        ssl_down, ssl_up = self.SSLChannels(informative_p, 20)
        informative_p['ssl_down'] = ssl_down
        informative_p['ssl_up'] = ssl_up
        return informative_p

    def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # RSI
        # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=self.buy_fisher_length.value)
        # # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
        # rsi = 0.1 * (dataframe['rsi'] - 50)
        # wmarsi = ta.WMA(rsi, timeperiod = 9)
        # dataframe['fisher_rsi'] = (numpy.exp(2 * wmarsi) - 1) / (numpy.exp(2 * wmarsi) + 1)
        # # MFI - Money Flow Index
        # dataframe['mfi'] = ta.MFI(dataframe, timeperiod=self.buy_fisher_length.value)
        # # Inverse Fisher transform on MFI
        # mfi = 0.1 * (dataframe['mfi'] - 50)
        # wmamfi = ta.WMA(mfi, timeperiod = 9)
        # dataframe['fisher_mfi'] = (numpy.exp(2 * wmamfi) - 1) / (numpy.exp(2 * wmamfi) + 1)
        # # Stochastic
        # stoch_fast = ta.STOCHF(dataframe, fastk_period=self.buy_fisher_length.value)
        # dataframe['fastk'] = stoch_fast['fastk']
        # # Inverse Fisher transform on Stochastic
        # stoch = 0.1 * (dataframe['fastk'] - 50)
        # wmastoch = ta.WMA(stoch, timeperiod = 9)
        # dataframe['fisher_stoch'] = (numpy.exp(2 * wmastoch) - 1) / (numpy.exp(2 * wmastoch) + 1)
        # Commodity Channel Index: values [Oversold:-100, Overbought:100]
        for cci_length in self.buy_fisher_length.range:
            dataframe[f'cci'] = ta.CCI(dataframe, timeperiod=cci_length)
            # Inverse Fisher transform on CCI
            cci = 0.1 * (dataframe[f'cci'] / 4)
            wmacci = ta.WMA(cci, timeperiod=9)
            dataframe[f'fisher_cci_{cci_length}'] = (numpy.exp(2 * wmacci) - 1) / (numpy.exp(2 * wmacci) + 1)
        # dataframe['fisher_average'] = (
        #     (dataframe['fisher_rsi'] + 
        #      dataframe['fisher_cci'] + 
        #      dataframe['fisher_mfi'] + 
        #      dataframe['fisher_stoch']
        #     ) / 4).astype(float)
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        # confirm_trade_exit
        dataframe['adx'] = ta.ADX(dataframe, timeperiod=3)
        dataframe['di_up'] = ta.PLUS_DI(dataframe, timeperiod=3) > ta.MINUS_DI(dataframe, timeperiod=3)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        --> Informative timeframe
        ___________________________________________________________________________________________
        """
        if self.info_timeframe != 'none':
            informative_p = self.informative_indicators(dataframe, metadata)
            dataframe = merge_informative_pair(dataframe, informative_p, self.timeframe, self.info_timeframe, ffill=True)
            drop_columns = [s + '_' + self.info_timeframe for s in ['date']]
            dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
        '\n        --> The indicators for the normal timeframe\n        ___________________________________________________________________________________________\n        '
        dataframe = self.normal_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Make sure Volume is not 0
        dataframe.loc[(qtpylib.crossed_above(dataframe[f'fisher_cci_{self.buy_fisher_length.value}'], self.buy_fisher_cci_1.value) | (qtpylib.crossed_below(dataframe[f'fisher_cci_{self.buy_fisher_length.value}'], self.buy_fisher_cci_2.value).rolling(8).max() == 1) & qtpylib.crossed_above(dataframe[f'fisher_cci_{self.buy_fisher_length.value}'], self.buy_fisher_cci_2.value)) & (dataframe[f'ssl_up_{self.info_timeframe}'] > dataframe[f'ssl_down_{self.info_timeframe}']) & (dataframe['ema_50'] > dataframe['ema_200']) & (dataframe[f'ema_50_{self.info_timeframe}'] > dataframe[f'ema_100_{self.info_timeframe}']) & (dataframe[f'ema_50_{self.info_timeframe}'] > dataframe[f'ema_200_{self.info_timeframe}']) & (dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Make sure Volume is not 0
        dataframe.loc[(qtpylib.crossed_below(dataframe[f'fisher_cci_{self.buy_fisher_length.value}'], self.sell_fisher_cci_1.value) | qtpylib.crossed_below(dataframe[f'fisher_cci_{self.buy_fisher_length.value}'], self.sell_fisher_cci_2.value)) & (dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe
    # SSL Channels

    def SSLChannels(self, dataframe, length=7):
        df = dataframe.copy()
        df['ATR'] = ta.ATR(df, timeperiod=14)
        df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
        df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
        df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.nan))
        df['hlv'] = df['hlv'].ffill()
        df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
        df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
        return (df['sslDown'], df['sslUp'])