# source: https://raw.githubusercontent.com/jaredrsommer/freqtradestrategies/9d66f71ce21f2f65feaad6bb967215be22a3cfd6/GKD_FisherTransform.py from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, RealParameter, merge_informative_pair, informative) from pandas_ta import ema import pandas as pd import numpy as np import talib from typing import Dict, List from pandas import DataFrame class Github_jaredrsommer_freqtradestrategies__GKD_FisherTransform__20260518_195114(IStrategy): # Strategy parameters timeframe = "4h" minimal_roi = {} stoploss = -0.10 trailing_stop = False trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 # Custom parameters fisher_period = IntParameter(10, 15, default=12, space="buy") # Lookback period for Fisher Transform fisher_smooth = IntParameter(5, 10, default=8, space="buy") # EMA smoothing period for Fisher Transform fisher_sell_threshold = DecimalParameter(2.0, 3.9, default=2.5, decimals=2, space="sell") fisher_buy_threshold = DecimalParameter(-1.0, 2.5, default=-0.5, decimals=2, space="buy") baseline_period = IntParameter(5, 21, default=21, space="buy") # Period for baseline EMA atr_period = IntParameter(10, 21, default=14, space="buy") # Period for ATR (volatility filter) goldie_locks = DecimalParameter(1.5, 3.0, default=2.0, decimals=2, space="buy") # Max multiplier for Goldie Locks Zone def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate Fisher Transform dataframe["fisher"] = self.calculate_fisher(dataframe, self.fisher_period.value) # Smooth Fisher with EMA dataframe["fisher_smooth"] = ema(dataframe["fisher"], length=self.fisher_smooth.value) dataframe["fisher_trend"] = ema(dataframe["fisher_smooth"], length=21) # Baseline (EMA) dataframe["baseline"] = ema(dataframe["close"], length=self.baseline_period.value) dataframe["baseline_diff"] = dataframe["baseline"].diff() dataframe["baseline_up"] = dataframe["baseline_diff"] > 0 dataframe["baseline_down"] = dataframe["baseline_diff"] < 0 # Volatility (ATR for Goldie Locks Zone) dataframe["atr"] = talib.ATR( dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=self.atr_period.value ) dataframe["goldie_min"] = dataframe["baseline"] - (dataframe["atr"] * self.goldie_locks.value) dataframe["goldie_max"] = dataframe["baseline"] + (dataframe["atr"] * self.goldie_locks.value) return dataframe def calculate_fisher(self, dataframe: DataFrame, period: int) -> pd.Series: # Fisher Transform calculation median_price = (dataframe["high"] + dataframe["low"]) / 2 fisher = pd.Series(0.0, index=dataframe.index) for i in range(period, len(dataframe)): # Normalize price price_window = median_price.iloc[i-period:i] price_min = price_window.min() price_max = price_window.max() if price_max != price_min: norm = (median_price.iloc[i] - price_min) / (price_max - price_min) norm = 2 * norm - 1 # Scale to [-0.999, 0.999] norm = max(min(norm, 0.999), -0.999) # Prevent division by zero # Apply Fisher Transform fisher.iloc[i] = 0.5 * np.log((1 + norm) / (1 - norm)) else: fisher.iloc[i] = 0.0 return fisher def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Standard Entry Logic (GKD-C Confirmation) dataframe.loc[ # (dataframe["fisher_smooth"] > self.fisher_buy_threshold.value) & # (dataframe["fisher_smooth"] < self.fisher_sell_threshold.value) & # (dataframe["fisher_smooth"] > 0) & # (dataframe["fisher_smooth"].shift() < -0) & (dataframe["fisher"] < self.fisher_sell_threshold.value) & (dataframe["fisher_smooth"] < dataframe['fisher']), # & # Fisher indicates bullish reversal # (dataframe["baseline_up"]) & # Baseline confirms uptrend # (dataframe["close"] >= dataframe["goldie_min"]) & # Within Goldie Locks Zone # (dataframe["close"] <= dataframe["goldie_max"]), "enter_long" ] = 1 if self.can_short == True: dataframe.loc[ (dataframe["fisher_smooth"] < self.fisher_sell_threshold.value) & # Fisher indicates bearish reversal (dataframe["baseline_down"]) & # Baseline confirms downtrend (dataframe["close"] >= dataframe["goldie_min"]) & # Within Goldie Locks Zone (dataframe["close"] <= dataframe["goldie_max"]), "enter_short" ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Exit Logic: Fisher reverses or crosses neutral zone dataframe.loc[ ( (dataframe["fisher_smooth"].shift() > 0) & (dataframe["fisher_smooth"] < 0) & (dataframe["fisher_smooth"] > dataframe['fisher']) ), # Fisher indicates weakening or bearish reversal "exit_long" ] = 1 # dataframe.loc[ # ( # (dataframe["fisher_smooth"] < self.fisher_buy_threshold.value) # ), # Fisher indicates weakening or bearish reversal # "exit_long" # ] = 1 if self.can_short == True: dataframe.loc[ (dataframe["fisher_smooth"] > 0), # Fisher indicates weakening or bullish reversal "exit_short" ] = 1 return dataframe # 193/1000: 358 trades. 190/25/143 Wins/Draws/Losses. Avg profit 0.87%. Median profit 0.17%. Total profit 31413.09084858 USDT ( 157.07%). Avg duration 23:52:00 min. Objective: -31413.09085 # # Buy hyperspace params: # buy_params = { # "atr_period": 21, # "baseline_period": 16, # "fisher_buy_threshold": -3.5, # "fisher_period": 12, # "fisher_smooth": 8, # "goldie_locks": 1.61, # } # # Sell hyperspace params: # sell_params = { # "fisher_sell_threshold": 3.74, # } # # ROI table: # minimal_roi = { # "0": 1.035, # "1166": 0.25, # "2510": 0.176, # "3562": 0 # } # # Stoploss: # stoploss = -0.21 # # Trailing stop: # trailing_stop = True # trailing_stop_positive = 0.011 # trailing_stop_positive_offset = 0.045 # trailing_only_offset_is_reached = False # # Max Open Trades: # max_open_trades = 3 # 248/1000: 298 trades. 176/0/122 Wins/Draws/Losses. Avg profit 0.81%. Median profit 2.48%. Total profit 21364.22232610 USDT ( 106.82%). Avg duration 1 day, 2:44:00 min. Objective: -21364.22233 # # Buy hyperspace params: # buy_params = { # "atr_period": 5, # "baseline_period": 9, # "fisher_buy_threshold": -2.86, # "fisher_period": 9, # "fisher_smooth": 9, # "goldie_locks": 2.38, # } # # Sell hyperspace params: # sell_params = { # "fisher_sell_threshold": 0.23, # } # # ROI table: # minimal_roi = { # "0": 1.234, # "2209": 0.438, # "3223": 0.166, # "12718": 0 # } # # Stoploss: # stoploss = -0.22 # # Trailing stop: # trailing_stop = True # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.034 # trailing_only_offset_is_reached = False # # Max Open Trades: # max_open_trades = 3