# source: https://raw.githubusercontent.com/TraderAlice/Auto-Quant/34ba6b6ee6aa69813a50a72158d4c089d97afb96/versions/0.4.1/strategies/PerPairMR.py """ Github_TraderAlice_Auto_Quant__PerPairMR__20260508_103218 — first per-pair-conditional strategy: BNB→RSI MR, alts→BB-lower MR Paradigm: mean-reversion (per-pair conditional) Hypothesis: This run's strongest findings were both MR-flavored: (a) BNBMeanRevertSharp r0: RSI<25 + 1d EMA200*0.85 gate on BNB gave clean cross-regime positive (robust 0.079) (b) AltsBollLowerMR r10: BB-lower + RSI<35 + slope-up filter on SOL+AVAX gave bull Sharpe 1.33 (highest in run) but winter-fragile v0.4.0's "MR is BNB-skewed" finding plus v0.4.1's r6 fork confirmed RSI doesn't generalize beyond BNB. But Bollinger excursion DOES work on SOL+AVAX bull. So each MR signal has its preferred pair shape — different signal families fit different volatility profiles. v0.4.1's per-pair-conditional entries (via metadata['pair']) lets us EXPRESS this finding structurally: one strategy that uses RSI mechanism on BNB and BB-lower mechanism on SOL+AVAX. The aggregate is then two independent edge sources, not a forced single mechanism. Critically — both branches use the same winter defense (1d slope-up + close>EMA200) so the entire strategy is regime-gated. Equal-weight sizing per the v0.4.0 r7 / v0.4.1 r9 ablation findings. Parent: root (synthesis of BNBMeanRevertSharp + AltsBollLowerMR structures, novel per-pair conditional architecture) Created: pending — fill in after first commit Status: active Uses MTF: yes """ from pandas import DataFrame import talib.abstract as ta from freqtrade.strategy import IStrategy, informative class Github_TraderAlice_Auto_Quant__PerPairMR__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 startup_candle_count: int = 250 # r18: expand to full 5-pair. BTC+ETH branches use Donchian-48 sustained # break + 4h macro filter (transfer of AltsBollBreak r1 finding) since # majors don't have the same MR profile. This makes the strategy # paradigm-mixed AND universe-conditional simultaneously. pair_basket = ["BTC/USDT", "ETH/USDT", "BNB/USDT", "SOL/USDT", "AVAX/USDT"] test_timeranges = [ ("bull_2021", "20210101-20211231"), ("winter_2022", "20220101-20221231"), ("recovery_23_25", "20230101-20251231"), ("full_5y", "20210101-20251231"), ] @informative("4h") def populate_indicators_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # r14: 4h trend filter for alts branch — kills sideways-recovery # entries that were SOL -0.14, AVAX -0.05. dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) return dataframe @informative("1d") def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) # r28: revert r27 14-bar → 7-bar slope. r27 finding: 14-bar slope # is over-restrictive (robust 0.052→0.036). 7-bar window is the # local optimum. dataframe["ema200_slope_up"] = ( dataframe["ema200"] > dataframe["ema200"].shift(7) ).astype(int) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # All branches need RSI; alts also need Bollinger; majors need # Donchian-48 + volume. dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) bb_period = 20 bb_std = 2.0 sma = dataframe["close"].rolling(bb_period).mean() std = dataframe["close"].rolling(bb_period).std() dataframe["bb_lower"] = sma - bb_std * std dataframe["bb_mid"] = sma dataframe["donchian_high_48"] = dataframe["high"].rolling(48).max().shift(1) 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: pair = metadata.get("pair", "") if pair == "BNB/USDT": # BNB-RSI mechanism: dataframe.loc[ (dataframe["rsi"] < 25) & (dataframe["close"] > dataframe["ema200_1d"] * 0.85), "enter_long", ] = 1 elif pair in ("SOL/USDT", "AVAX/USDT"): # Alts BB-lower MR with full regime gate. dataframe.loc[ (dataframe["close"] < dataframe["bb_lower"]) & (dataframe["rsi"] < 35) & (dataframe["ema200_slope_up_1d"] == 1) & (dataframe["close"] > dataframe["ema200_1d"]) & (dataframe["ema50_4h"] > dataframe["ema200_4h"]), "enter_long", ] = 1 elif pair in ("BTC/USDT", "ETH/USDT"): # r18: majors Donchian-48 sustained break + 4h macro + volume. # Different paradigm (breakout, not MR) for majors which have # smaller MR opportunity space. Strategy now paradigm-conditional. prior_above = dataframe["close"].shift(1) > dataframe["donchian_high_48"].shift(1) dataframe.loc[ (dataframe["close"] > dataframe["donchian_high_48"]) & prior_above & (dataframe["ema50_4h"] > dataframe["ema200_4h"]) & (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: pair = metadata.get("pair", "") if pair == "BNB/USDT": dataframe.loc[dataframe["rsi"] > 55, "exit_long"] = 1 elif pair in ("SOL/USDT", "AVAX/USDT"): dataframe.loc[dataframe["close"] > dataframe["bb_mid"], "exit_long"] = 1 elif pair in ("BTC/USDT", "ETH/USDT"): # Majors patient SMA50 exit (paradigm-specific from # AltsBollBreak r1 finding — breakouts ride trends). dataframe.loc[dataframe["close"] < dataframe["sma50"], "exit_long"] = 1 return dataframe # r22: removed custom_stake_amount entirely. r20+r21 transfer # experiment finding: BNB sizing doesn't compose linearly inside # multi-pair strategy. Plain 25/RSI: robust 0.052→0.038 (worse). # Regime-aware (slope_up gate): robust 0.052→0.044 (better than # plain but still worse than baseline). Net: sizing is best applied # at strategy isolation (single-pair) rather than embedded in # multi-pair containers. Cross-pair attribution is meaningfully # different from single-pair attribution.