# source: https://raw.githubusercontent.com/IvPalmer/Master-Trader/c7955a15d4bfe82e567bc2bd315a372000a442fd/ft_userdata/user_data/strategies/BearCrashShortV1.py # directory_url: https://github.com/IvPalmer/Master-Trader/blob/main/ft_userdata/user_data/strategies/ # User: IvPalmer # Repository: Master-Trader # --------------------""" Github_IvPalmer_Master_Trader__BearCrashShortV1__20260923_012354 - Bear Regime Short-Only Strategy + Bounce-Long Mode ====================================================================== PRIMARY: Short-only during confirmed bear regimes. SECONDARY: Rare bounce-long on bear-to-bull regime transitions (SMB Capital 5/5 track record). Entry pattern (short): "Failed Rally Short" - trend-following shorts on dead cat bounces - BTC must be in confirmed bear regime (below SMA200, ADX>25, RSI<45) for 4-of-6 candles - Pair: -DI > +DI, ADX > 25, RSI 40-70 (bear "overbought"), below SMA200 - Anti-squeeze: RSI > 40 (entry signal), BTC RSI > 20, F&G > 10 (confirm_trade_entry) Entry pattern (bounce-long): Post-bear recovery bet - BTC was in confirmed bear regime on previous candle, now flipped bullish - Pair: +DI > -DI, RSI 40-70, volume >= 1.5x 20-period average Exit: RSI < 25, 2-candle +DI > -DI confirmation, BTC flips bullish, volatility spike, or time-based Risk: -5% stoploss on exchange, 2% trail at 3%, 48h hard exit, $22 stake, 2x leverage """ import json import logging from datetime import datetime from pathlib import Path from freqtrade.strategy import IStrategy, informative from pandas import DataFrame import talib.abstract as ta from market_intelligence import FearGreedIndex, PositionTracker, MAX_BOTS_PER_PAIR logger = logging.getLogger(__name__) KILL_SWITCH_FILE = Path("/freqtrade/user_data/kill_switch_Github_IvPalmer_Master_Trader__BearCrashShortV1__20260923_012354.json") DAILY_LOSS_LIMIT = 0.10 # 10% of wallet (with 2x leverage, a -5% SL = ~10% of stake) CONSECUTIVE_LOSS_LIMIT = 3 class Github_IvPalmer_Master_Trader__BearCrashShortV1__20260923_012354(IStrategy): INTERFACE_VERSION = 3 can_short = True timeframe = "1h" # Tighter ROI for shorts - crypto crashes are fast, take profits early minimal_roi = { "0": 0.08, # 8% - grab big moves immediately "720": 0.05, # 5% after 12h "1440": 0.03, # 3% after 24h "2160": 0.01, # 1% after 36h "2880": 0.0, # Break-even at 48h - hard exit } stoploss = -0.05 # -5% hard stop (tighter than longs due to squeeze risk) trailing_stop = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count = 200 # BTC SMA200 @property def protections(self): return [ {"method": "CooldownPeriod", "stop_duration_candles": 3}, {"method": "StoplossGuard", "lookback_period_candles": 48, "trade_limit": 2, "stop_duration_candles": 24, "only_per_pair": True}, {"method": "MaxDrawdown", "lookback_period_candles": 48, "max_allowed_drawdown": 0.15, "stop_duration_candles": 24, "trade_limit": 1}, ] # -- BTC informative pair ------------------------------------------------- @informative("1h", "BTC/{stake}:{stake}") def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["sma200"] = ta.SMA(dataframe["close"], timeperiod=200) dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14) dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14) return dataframe # -- Indicators ------------------------------------------------------------ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # DMI / ADX dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14) dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14) # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # MACD macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] # SMA 200 for pair-level trend dataframe["sma200"] = ta.SMA(dataframe["close"], timeperiod=200) # ATR for volatility spike exits dataframe["atr_14"] = ta.ATR(dataframe, timeperiod=14) dataframe["atr_sma_50"] = dataframe["atr_14"].rolling(50).mean() # -- BTC Bear Regime Detection (4-of-6 rolling window) -- btc_bear_single = ( (dataframe["btc_usdt_close_1h"] < dataframe["btc_usdt_sma200_1h"]) & (dataframe["btc_usdt_adx_1h"] > 25) & (dataframe["btc_usdt_rsi_1h"] < 45) ).astype(int) # BTC must also be declining (not just sitting below SMA200) btc_declining = ( dataframe["btc_usdt_close_1h"] < dataframe["btc_usdt_close_1h"].shift(6) ) # Require 4 out of 6 candles bearish AND BTC actively declining # (tolerates 1-2 candle flicker without losing regime signal) dataframe["btc_bear_confirmed"] = ( (btc_bear_single.rolling(6).sum() >= 4) & btc_declining ).astype(int) # -- Regime Flip Detection (bear → bull transition) -- # Previous candle was confirmed bear, current candle shows bullish flip dataframe["btc_was_bear"] = dataframe["btc_bear_confirmed"].shift(1).fillna(0) dataframe["btc_now_bull"] = ( (dataframe["btc_usdt_close_1h"] > dataframe["btc_usdt_sma200_1h"]) & (dataframe["btc_usdt_rsi_1h"] > 45) ).astype(int) dataframe["bear_to_bull"] = ( (dataframe["btc_was_bear"] == 1) & (dataframe["btc_now_bull"] == 1) ).astype(int) # Volume ratio for bounce confirmation dataframe["volume_sma_20"] = dataframe["volume"].rolling(window=20).mean() dataframe["volume_ratio"] = dataframe["volume"] / (dataframe["volume_sma_20"] + 1e-10) return dataframe # -- Kill Switch ----------------------------------------------------------- def _read_kill_switch(self) -> dict: try: if KILL_SWITCH_FILE.exists(): return json.loads(KILL_SWITCH_FILE.read_text()) except (json.JSONDecodeError, OSError): pass return { "daily_loss": 0.0, "daily_loss_date": datetime.now().strftime("%Y-%m-%d"), "consecutive_losses": 0, "killed": False, } def _write_kill_switch(self, data: dict): try: KILL_SWITCH_FILE.parent.mkdir(parents=True, exist_ok=True) KILL_SWITCH_FILE.write_text(json.dumps(data, indent=2)) except OSError as e: logger.warning("Kill switch write failed: %s", e) def _is_killed(self) -> bool: ks = self._read_kill_switch() today = datetime.now().strftime("%Y-%m-%d") if ks.get("daily_loss_date") != today: ks["daily_loss"] = 0.0 ks["daily_loss_date"] = today ks["killed"] = False ks["consecutive_losses"] = 0 self._write_kill_switch(ks) return False return ks.get("killed", False) def _record_trade_result(self, profit_ratio: float): ks = self._read_kill_switch() today = datetime.now().strftime("%Y-%m-%d") if ks.get("daily_loss_date") != today: ks = {"daily_loss": 0.0, "daily_loss_date": today, "consecutive_losses": 0, "killed": False} if profit_ratio < 0: ks["daily_loss"] = ks.get("daily_loss", 0.0) + abs(profit_ratio) ks["consecutive_losses"] = ks.get("consecutive_losses", 0) + 1 else: ks["consecutive_losses"] = 0 if ks["daily_loss"] >= DAILY_LOSS_LIMIT: ks["killed"] = True logger.warning("KILL SWITCH: Daily loss %.1f%% >= %.1f%% limit", ks["daily_loss"] * 100, DAILY_LOSS_LIMIT * 100) if ks["consecutive_losses"] >= CONSECUTIVE_LOSS_LIMIT: ks["killed"] = True logger.warning("KILL SWITCH: %d consecutive losses", ks["consecutive_losses"]) self._write_kill_switch(ks) # -- Entry Gate ------------------------------------------------------------ def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: str | None, side: str, **kwargs) -> bool: is_live = self.dp and self.dp.runmode.value in ("live", "dry_run") if is_live: if self._is_killed(): logger.info("BLOCKED %s: Kill switch active", pair) return False bot_name = self.config.get("bot_name", "BearCrashShort") other_bots = PositionTracker.count_bots_holding(pair, exclude_bot=bot_name) if other_bots >= MAX_BOTS_PER_PAIR: logger.info("BLOCKED %s: %d other bots already hold this pair", pair, other_bots) return False # Anti-squeeze: block during deep capitulation (F&G <= 10) fg = FearGreedIndex.get()["value"] if fg <= 10: logger.info("BLOCKED %s: F&G capitulation (%d) - squeeze risk", pair, fg) return False PositionTracker.register(bot_name, pair, amount * rate) return True def confirm_trade_exit(self, pair: str, trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: is_live = self.dp and self.dp.runmode.value in ("live", "dry_run") if is_live: bot_name = self.config.get("bot_name", "BearCrashShort") PositionTracker.unregister(bot_name, pair) if trade.close_profit is not None: self._record_trade_result(trade.close_profit) return True # -- Entry Signal ---------------------------------------------------------- def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # -- BTC REGIME GATE (persistent bear) -- (dataframe["btc_bear_confirmed"] == 1) # -- PAIR-LEVEL SIGNALS -- & (dataframe["minus_di"] > dataframe["plus_di"]) # Bears dominating & (dataframe["adx"] > 25) # Confirmed trend & (dataframe["rsi"] > 40) & (dataframe["rsi"] < 70) # Bear "overbought" zone & (dataframe["close"] < dataframe["sma200"]) # Below SMA200 # -- ANTI-SQUEEZE FILTERS -- & (dataframe["btc_usdt_rsi_1h"] > 20) # BTC not at floor # -- VOLUME -- & (dataframe["volume"] > 0) ), "enter_short", ] = 1 # === BOUNCE LONG: Post-bear recovery (SMB Capital 5/5 track record) === # Rare signal: only fires on the candle where bear regime flips to bullish dataframe.loc[ ( (dataframe["bear_to_bull"] == 1) & (dataframe["rsi"] > 40) & (dataframe["rsi"] < 70) & (dataframe["plus_di"] > dataframe["minus_di"]) & (dataframe["volume_ratio"] >= 1.5) & (dataframe["volume"] > 0) ), "enter_long", ] = 1 return dataframe # -- Exit Signal ----------------------------------------------------------- def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( # Bear regime ending (dataframe["btc_usdt_close_1h"] > dataframe["btc_usdt_sma200_1h"]) # OR bulls taking over pair (2-candle confirmation to avoid DI flicker) | ( (dataframe["plus_di"] > dataframe["minus_di"]) & (dataframe["plus_di"].shift(1) > dataframe["minus_di"].shift(1)) ) # OR extremely oversold (bounce imminent) | (dataframe["rsi"] < 25) # OR volatility spike | (dataframe["atr_14"] > 2.5 * dataframe["atr_sma_50"]) ) & (dataframe["volume"] > 0), "exit_short", ] = 1 return dataframe # -- Time-Based Exit ------------------------------------------------------- def custom_exit(self, pair: str, trade, current_time, current_rate, current_profit, **kwargs): if not trade.is_short: return None hours = (current_time - trade.open_date_utc).total_seconds() / 3600 if hours >= 48: return "time_exit_48h" if hours >= 24 and current_profit > 0.02: return "time_profit_24h" if hours >= 36 and current_profit > 0: return "time_breakeven_36h" return None # -- Leverage -------------------------------------------------------------- def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str | None, side: str, **kwargs) -> float: return 2.0