# source: https://raw.githubusercontent.com/saifbelaarbi/trader-google-c/97f35ead8ee505df554ce720791a1417bf93539e/ftbot/strategies/ClaudePullback.py """ Github_saifbelaarbi_trader_google_c__ClaudePullback__20260625_021644 — V2 strategy, redesigned from the ClaudeConsensus backtest (2024-06 → 2026-06: -96.79%, PF 0.39, 3413 trades). Measured leaks in V1 and the V2 response: V1 leak V2 change ------------------------------------------------ -------------------------------- ~$127 of $194 loss was fees (3413 trades) pullback entries → far fewer trades 1451 stop-outs, 0% winners, 6 min avg duration stop widened to 2×ATR 8/8 consensus won 12% vs 5/8 won 27% — entries enter on pullback to EMA20 in an fired at max momentum extension (tops) established trend, not on extension consensus-flip exit won 4.6% (vol/ADX counted exit only on confirmed 15m EMA toward both directions) trend flip account churned below min stake by Sept 2024 stake = 20% of equity, capped $40 partial TP1 doubled fee events, capped winners removed — single position, trail Entry (long; short is the mirror): - EMA20 > EMA50 on 15m AND 1h (established trend, both timeframes) - ADX > 20 (trend has strength) - close at/below EMA20 (+0.2%) (pullback into value, not extension) - StochRSI K < 35 and turning up (dip exhausted, momentum resuming) - RSI > 35 (pullback, not a breakdown) Exits: SL : 2×ATR from entry; trails 1×ATR once profit ≥ 1.5×ATR Trend: confirmed 15m EMA20/EMA50 flip → exit """ from datetime import datetime from typing import Optional import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy import IStrategy, informative, stoploss_from_open from pandas import DataFrame class Github_saifbelaarbi_trader_google_c__ClaudePullback__20260625_021644(IStrategy): INTERFACE_VERSION = 3 timeframe = "15m" can_short = True # Hard failsafe only — the real stop is ATR-based in custom_stoploss. stoploss = -0.10 use_custom_stoploss = True # ROI disabled — exits are the ATR trail + trend-flip signal. minimal_roi = {"0": 100} process_only_new_candles = True startup_candle_count = 120 max_stake_usdt = 40.0 wallet_fraction = 0.20 # stake scales with equity so drawdowns shrink size @property def protections(self): return [ {"method": "CooldownPeriod", "stop_duration_candles": 4}, { "method": "StoplossGuard", "lookback_period_candles": 96, "trade_limit": 3, "stop_duration_candles": 24, "only_per_pair": False, }, { "method": "MaxDrawdown", "lookback_period_candles": 96, "trade_limit": 4, "stop_duration_candles": 96, "max_allowed_drawdown": 0.10, }, ] @informative("1h") def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["atr_pct"] = dataframe["atr"] / dataframe["close"] stoch = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3) dataframe["stoch_k"] = stoch["fastk"] dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: long_mask = ( (dataframe["ema20"] > dataframe["ema50"]) & (dataframe["ema20_1h"] > dataframe["ema50_1h"]) & (dataframe["adx"] > 20) & (dataframe["close"] <= dataframe["ema20"] * 1.002) & (dataframe["stoch_k"] < 35) & (dataframe["stoch_k"] > dataframe["stoch_k"].shift(1)) & (dataframe["rsi"] > 35) & (dataframe["volume"] > 0) ) short_mask = ( (dataframe["ema20"] < dataframe["ema50"]) & (dataframe["ema20_1h"] < dataframe["ema50_1h"]) & (dataframe["adx"] > 20) & (dataframe["close"] >= dataframe["ema20"] * 0.998) & (dataframe["stoch_k"] > 65) & (dataframe["stoch_k"] < dataframe["stoch_k"].shift(1)) & (dataframe["rsi"] < 65) & (dataframe["volume"] > 0) ) dataframe.loc[long_mask, "enter_long"] = 1 dataframe.loc[long_mask, "enter_tag"] = "pullback_long" dataframe.loc[short_mask, "enter_short"] = 1 dataframe.loc[short_mask, "enter_tag"] = "pullback_short" return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Structural exit only: the trend that justified the entry no longer exists. dataframe.loc[dataframe["ema20"] < dataframe["ema50"], "exit_long"] = 1 dataframe.loc[dataframe["ema20"] > dataframe["ema50"], "exit_short"] = 1 return dataframe def custom_stake_amount( self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs, ) -> float: total_equity = self.wallets.get_total_stake_amount() if self.wallets else 200.0 stake = min(self.max_stake_usdt, total_equity * self.wallet_fraction) if min_stake: stake = max(stake, min_stake) return min(stake, max_stake) def leverage( self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs, ) -> float: return 1.0 def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs, ) -> Optional[float]: atr_pct = self._last_candle_value(pair, "atr_pct") or 0.005 atr_pct = max(atr_pct, 0.001) if current_profit >= 1.5 * atr_pct: # In profit past 1.5×ATR → trail 1×ATR behind price. return -atr_pct # Initial stop: 2×ATR from entry, outside the 15m noise band. return stoploss_from_open( -2 * atr_pct, current_profit, is_short=trade.is_short, leverage=trade.leverage ) def _last_candle_value(self, pair: str, column: str): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if dataframe is None or dataframe.empty or column not in dataframe.columns: return None value = dataframe[column].iloc[-1] try: return float(value) except (TypeError, ValueError): return None