# source: https://raw.githubusercontent.com/TraderAlice/Auto-Quant/34ba6b6ee6aa69813a50a72158d4c089d97afb96/versions/0.4.1/strategies/CrashRebound.py """ Github_TraderAlice_Auto_Quant__CrashRebound__20260508_103218 — buy alts after a -25% drawdown from rolling 30d high Paradigm: other (counter-trend / drawdown-rebound) Hypothesis: AltsBollBreak's r0-r4 trajectory established that breakout paradigms on alts have structural winter fragility — every defense (1d position filter, stoploss, slope filter) cut bull profit while only partially silencing winter. The opposite structural exposure is interesting: a counter-trend strategy that ENTERS on drawdowns. Crashes happen in every regime (bull pullbacks, winter capitulations, recovery shakeouts); the rebound size varies but the directional edge is generally positive on liquid majors. Trigger: 1h close < 30d-rolling-max × 0.75 (i.e., 25% off 30-day peak) AND 1h RSI(14) < 35 (oversold confirmation — don't catch knives mid-fall, wait for a stretch). Exit: 1h close > 1h SMA50 (mean-reversion target, halfway-back-to-prior-trend). Universe: alts only (SOL, AVAX, BNB) — alts have larger drawdowns than majors, and the v0.4.0 surfacing was that BTC/ETH have less mean-reverting structure. test_timeranges spans bull/winter/ recovery/full to make the cross-regime claim falsifiable. Equal-weight sizing. Parent: root (replaces AltsBollBreak which was killed at r5 after 5 rounds of failed winter defenses) Created: pending — fill in after first commit Status: active Uses MTF: no (1h-only on entry/exit; 30d max needs 720 bars warmup) """ from pandas import DataFrame import talib.abstract as ta from freqtrade.strategy import IStrategy, informative class Github_TraderAlice_Auto_Quant__CrashRebound__20260508_103218(IStrategy): INTERFACE_VERSION = 3 timeframe = "1h" can_short = False minimal_roi = {"0": 100} stoploss = -0.99 trailing_stop = False process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # 30d at 1h = 720 bars warmup startup_candle_count: int = 760 # r19: revert r18 expansion. r18 finding: full-5pair lifted full_5y # Sharpe 0.29→0.40 and bull 0.85→1.07 / recovery 0.19→0.33, BUT # winter regressed 0.085→0.013 (BTC/ETH winter drawdown-bounces are # weaker than alts'). Net robust 0.085→0.013 — a clean Pareto move # where best-case lifted but worst-case dropped. Drawdown-rebound # paradigm DOES generalize to majors in directional regimes but # NOT under v0.4.1 robust-sharpe honesty bar. Reverting to 3-pair # alts+BNB basket which holds robust 0.085. pair_basket = ["SOL/USDT", "AVAX/USDT", "BNB/USDT"] test_timeranges = [ ("bull_2021", "20210101-20211231"), ("winter_2022", "20220101-20221231"), ("recovery_23_25", "20230101-20251231"), ("full_5y", "20210101-20251231"), ] @informative("1d") def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) dataframe["ema200_slope_up"] = ( dataframe["ema200"] > dataframe["ema200"].shift(7) ).astype(int) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 30-day rolling high (720 1h bars). Drawdown trigger uses prior bar # to avoid current-bar self-reference. dataframe["high_30d"] = dataframe["high"].rolling(720).max().shift(1) dataframe["drawdown_pct"] = ( dataframe["close"] / dataframe["high_30d"] - 1.0 ) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) # r15: revert r14 SMA100 → SMA50. Patient exit BACKFIRED on this # paradigm — drawdown-rebounds revert quickly; letting the bounce # ride past SMA50 to SMA100 turns winners into losers. v0.4.0 r13 # patient-exit finding is paradigm-specific to BREAKOUTS, not # counter-trend MR. Cross-version finding. dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50) dataframe["volume_sma20"] = dataframe["volume"].rolling(20).mean() return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # r24: revert r23 multi-bar DD confirmation. r23 finding: 3-bar # sustained-DD drops the inflection-point entries (bounces start # WITHIN the 3-bar window) — robust 0.085→0.0095. Single-bar # DD<-20% trigger IS the local optimum; capitulation signals # don't need post-confirmation when paired with RSI<35 already. dataframe.loc[ (dataframe["drawdown_pct"] < -0.20) & (dataframe["rsi"] < 35) & (dataframe["ema200_slope_up_1d"] == 1) & (dataframe["volume"] > 1.3 * dataframe["volume_sma20"]), "enter_long", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # r15: revert r14 SMA100 → SMA50. dataframe.loc[dataframe["close"] > dataframe["sma50"], "exit_long"] = 1 return dataframe def custom_stake_amount( self, pair: str, current_time, current_rate: float, proposed_stake: float, min_stake, max_stake: float, leverage: float, entry_tag: str, side: str, **kwargs, ) -> float: # r29: revert r28 regime composition. r28 finding: composing # regime×DD-conviction INVERSE-amplifies because regime and DD- # signal are NEGATIVELY correlated (bull = small DDs, winter = # big DDs). Bull entries got down-sized (0.5×1.5=0.75) just # when DD-signal is weakest; winter entries cancel out (2.0× # 0.5=1.0) erasing the conviction lift. Robust crashed # 0.085→-0.083. Cross-paradigm composition rule: only works # when regime ⊥ signal-strength (BNB-RSI case) or positively # correlated. Reverting to pure DD-conviction (r16 optimum). df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) if df.empty or "drawdown_pct" not in df.columns: return proposed_stake dd = df["drawdown_pct"].iloc[-1] if dd != dd or dd >= 0: return proposed_stake scale = abs(float(dd)) / 0.20 scale = max(0.5, min(2.0, scale)) stake = proposed_stake * scale return max(min_stake or 0.0, min(max_stake, stake))