# source: https://raw.githubusercontent.com/whitneyjohn61/freqtrade-strategy-lab/cd9ba36b9b9d121c6002194e02dfa41f5fc72ff0/user_data/strategies/StarterStrategy_V01.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa # isort: skip_file """ Starter FreqAI strategy for freqtrade-strategy-lab. Simplified regression setup: targets &s_close / &s_volatility, XGBoostRegressor in config. Replace thresholds and logic as you iterate — see CLAUDE.md and user_data/info/PROJECT_NOTES.md. """ import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime import talib.abstract as ta from freqtrade.strategy import IStrategy from freqtrade.persistence import Trade class Github_whitneyjohn61_freqtrade_strategy_lab__StarterStrategy_V01__20260323_214946(IStrategy): minimal_roi = {"0": 0.10, "240": 0.05, "480": 0} stoploss = -0.12 use_custom_stoploss = True trailing_stop = True trailing_stop_positive = 0.015 trailing_stop_positive_offset = 0.04 trailing_only_offset_is_reached = True timeframe = "15m" inf_1h = "1h" can_short = True startup_candle_count = 1000 # Fixed lab thresholds — move to Hyperopt DecimalParameter when ready ai_direction_threshold = 0.012 ai_volatility_threshold = 0.012 di_max_threshold = 0.45 stoploss_multiplier = 1.4 stoploss_hard_cap = 0.04 def feature_engineering_expand_all( self, dataframe: DataFrame, period: int, metadata: dict, **kwargs ) -> DataFrame: dataframe[f"%-rsi-{period}"] = ta.RSI(dataframe, timeperiod=period) dataframe[f"%-roc-{period}"] = ta.ROC(dataframe, timeperiod=period) dataframe[f"%-atr-{period}"] = ta.ATR(dataframe, timeperiod=period) safe_atr = dataframe[f"%-atr-{period}"].replace(0, np.nan) dataframe[f"%-rsi_atr_ratio-{period}"] = dataframe[f"%-rsi-{period}"] / safe_atr ema = ta.EMA(dataframe, timeperiod=period) dataframe[f"%-ema_dist-{period}"] = (dataframe["close"] - ema) / ema bollinger = ta.BBANDS(dataframe, timeperiod=period, nbdevup=2.0, nbdevdn=2.0, matype=0) dataframe[f"%-bb_width-{period}"] = ( bollinger["upperband"] - bollinger["lowerband"] ) / bollinger["middleband"] return dataframe def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs) -> DataFrame: dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek dataframe["%-hour_of_day"] = dataframe["date"].dt.hour return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame: label_period = self.freqai_info["feature_parameters"]["label_period_candles"] dataframe["&s_close"] = np.log(dataframe["close"].shift(-label_period) / dataframe["close"]) future_high = dataframe["high"].rolling(label_period).max().shift(-label_period) future_low = dataframe["low"].rolling(label_period).min().shift(-label_period) dataframe["&s_volatility"] = (future_high - future_low) / dataframe["close"] return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.freqai.start(dataframe, metadata, self) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=9) dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=21) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not self.config.get("freqai", {}).get("enabled", False): return dataframe if "do_predict" not in dataframe.columns: return dataframe valid_prediction = dataframe["do_predict"] == 1 if "DI_values" in dataframe.columns: safe_situation = dataframe["DI_values"] < self.di_max_threshold else: safe_situation = pd.Series(True, index=dataframe.index) if "&s_close" not in dataframe.columns or "&s_volatility" not in dataframe.columns: return dataframe ai_long = ( (dataframe["&s_close"] > self.ai_direction_threshold) & (dataframe["&s_volatility"] > self.ai_volatility_threshold) ) tech_long = (dataframe["rsi"] < 70) & (dataframe["ema_fast"] > dataframe["ema_slow"]) dataframe.loc[ valid_prediction & safe_situation & ai_long & tech_long & (dataframe["volume"] > 0), "enter_long", ] = 1 ai_short = ( (dataframe["&s_close"] < -self.ai_direction_threshold) & (dataframe["&s_volatility"] > self.ai_volatility_threshold) ) tech_short = (dataframe["rsi"] > 30) & (dataframe["ema_fast"] < dataframe["ema_slow"]) dataframe.loc[ valid_prediction & safe_situation & ai_short & tech_short & (dataframe["volume"] > 0), "enter_short", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not self.config.get("freqai", {}).get("enabled", False): return dataframe if "&s_close" not in dataframe.columns or "DI_values" not in dataframe.columns: return dataframe di_breach = dataframe["DI_values"] >= self.di_max_threshold dataframe.loc[(dataframe["&s_close"] < 0) | di_breach, "exit_long"] = 1 dataframe.loc[(dataframe["&s_close"] > 0) | di_breach, "exit_short"] = 1 return dataframe def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs, ) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if "atr" in last_candle and pd.notna(last_candle["atr"]): atr_pct = last_candle["atr"] / trade.open_rate dynamic_stop_pct = atr_pct * self.stoploss_multiplier dynamic_stop_pct = max(0.010, min(self.stoploss_hard_cap, dynamic_stop_pct)) if trade.is_short: stop_price = trade.open_rate * (1 + dynamic_stop_pct) rel_stop = (current_rate - stop_price) / current_rate else: stop_price = trade.open_rate * (1 - dynamic_stop_pct) rel_stop = (stop_price - current_rate) / current_rate return rel_stop return -0.025 def leverage( self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str, side: str, **kwargs, ) -> float: return min(3.0, max_leverage)