# source: https://raw.githubusercontent.com/davidcagoh/algo-traders/b33178a6a98e8a453e8d0a6d5ab70ae271aeefae/backtesting/user_data/strategies/HmmSmaSlopeV3.py """ Github_davidcagoh_algo_traders__HmmSmaSlopeV3__20260604_163820 — concave (sqrt) slope sizing. V2 used linear sizing `clip(slope_pct / 0.005, 0, 1)`. Result: bear MDD 4.44% (passes kill rule) but bull return dropped to +33.44% — best/worst trades matched V1 exactly, meaning the linear penalty was applied to entries that were profitable in aggregate. V3 hypothesis: the slope-strength magnitude carries no useful information beyond sign — so the right sizing curve is **concave**, pulling weak-positive slopes back toward full size while keeping the zero/negative cutoff. Using `size_factor = clip((slope_pct / SLOPE_REF) ** 0.5, 0, 1)`: slope_pct = 0.001 → V2 size 0.20, V3 size 0.45 (2.25× the V2 size) slope_pct = 0.005 → V2 size 1.00, V3 size 1.00 (equal at the strong end) slope_pct = ≤ 0 → V2 size 0.00, V3 size 0.00 (both skip) If the diagnosis is right, V3 should recover most of V2's lost bull return while keeping bear MDD under the 5.5% kill threshold. """ from __future__ import annotations from datetime import datetime from typing import Optional import numpy as np import pandas as pd import talib.abstract as ta from pandas import DataFrame from freqtrade.strategy import IStrategy try: from hmmlearn.hmm import GaussianHMM _HMM_AVAILABLE = True except ImportError: _HMM_AVAILABLE = False # HMM block (matches HmmRegime4Rolling) RETURN_WINDOW = 24 N_COMPONENTS = 4 BULL_THRESHOLD = 0.65 EXIT_THRESHOLD = 0.45 FIT_WINDOW = 1000 REFIT_EVERY = 168 # SMA-slope block (matches SmaRegime180) SMA_PERIOD = 180 SLOPE_LOOKBACK = 6 # Size scaling. SLOPE_STRONG = the slope-pct value (slope / sma180) at which # we'd want full size. 0.005 = +0.5% drift of the SMA over the 24h lookback. # For BTC at $50k that's ~$250 over a day — comfortable bull-trend threshold. # MIN_SIZE_FACTOR = 0 means we skip the trade entirely when slope is # nonpositive; the entry signal still triggers but the position never opens. SLOPE_STRONG = 0.005 MIN_SIZE_FACTOR = 0.0 SIZING_EXPONENT = 0.5 # 0.5 = sqrt (concave); 1.0 = linear (V2); 2.0 = convex class Github_davidcagoh_algo_traders__HmmSmaSlopeV3__20260604_163820(IStrategy): INTERFACE_VERSION = 3 can_short = False timeframe = "4h" startup_candle_count = max(FIT_WINDOW + RETURN_WINDOW, SMA_PERIOD + SLOPE_LOOKBACK) minimal_roi = {"0": 100} stoploss = -0.10 trailing_stop = False process_only_new_candles = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not _HMM_AVAILABLE: raise ImportError( "hmmlearn is required for Github_davidcagoh_algo_traders__HmmSmaSlopeV3__20260604_163820. " "Activate the freqtrade venv and run: pip install hmmlearn" ) # ---- SMA slope (matches SmaRegime180) ---- dataframe["sma180"] = ta.SMA(dataframe, timeperiod=SMA_PERIOD) dataframe["sma180_slope"] = ( dataframe["sma180"] - dataframe["sma180"].shift(SLOPE_LOOKBACK) ) # Slope as a fraction of the SMA itself — scale-free across coins. dataframe["slope_pct"] = dataframe["sma180_slope"] / dataframe["sma180"] # Concave sizing: raise the slope ratio to SIZING_EXPONENT (< 1 = concave). # Negative slope clipped to 0 BEFORE the power to avoid complex numbers. ratio = (dataframe["slope_pct"] / SLOPE_STRONG).clip(lower=0.0) dataframe["size_factor"] = ( (ratio ** SIZING_EXPONENT).clip(lower=MIN_SIZE_FACTOR, upper=1.0) ) # ---- Rolling HMM (matches HmmRegime4Rolling) ---- log_return = np.log( dataframe["close"] / dataframe["close"].shift(RETURN_WINDOW) ) log_vol = np.log(dataframe["volume"].clip(lower=1e-9)) log_vol_z = (log_vol - log_vol.mean()) / max(log_vol.std(), 1e-9) dataframe["_log_return"] = log_return dataframe["_log_vol_z"] = log_vol_z valid_mask = dataframe[["_log_return", "_log_vol_z"]].notna().all(axis=1) dataframe["bull_prob"] = np.nan valid_idx = np.where(valid_mask.values)[0] if len(valid_idx) < FIT_WINDOW + REFIT_EVERY: return dataframe X_full = dataframe[["_log_return", "_log_vol_z"]].values first_refit = valid_idx[0] + FIT_WINDOW last_row = len(dataframe) bull_prob = np.full(last_row, np.nan) for r in range(first_refit, last_row, REFIT_EVERY): fit_start = r - FIT_WINDOW X_fit = X_full[fit_start:r] if np.isnan(X_fit).any(): continue try: model = GaussianHMM( n_components=N_COMPONENTS, covariance_type="full", n_iter=200, random_state=42, ) model.fit(X_fit) except Exception: continue bull_states = [i for i in range(N_COMPONENTS) if model.means_[i, 0] > 0] if not bull_states: bull_states = [int(np.argmax(model.means_[:, 0]))] seg_end = min(r + REFIT_EVERY, last_row) for t in range(r, seg_end): if np.isnan(X_full[t]).any(): continue X_score = X_full[fit_start:t + 1] try: post = model.predict_proba(X_score)[-1] except Exception: continue bull_prob[t] = post[bull_states].sum() dataframe["bull_prob"] = bull_prob return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Entry signal is HMM-only — slope handled in custom_stake_amount. dataframe.loc[ (dataframe["bull_prob"] >= BULL_THRESHOLD) & (dataframe["bull_prob"].shift(1) < BULL_THRESHOLD), "enter_long", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Exit on EITHER HMM-low OR slope-flipped-negative (matches V1). dataframe.loc[ (dataframe["bull_prob"] < EXIT_THRESHOLD) | (dataframe["sma180_slope"] <= 0), "exit_long", ] = 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: """ Scale position by the slope strength at the current bar. Returning 0 (or anything < min_stake) signals freqtrade to skip the trade — which is what we want when slope is nonpositive. """ df, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) if df is None or df.empty: return proposed_stake size_factor = df["size_factor"].iloc[-1] if pd.isna(size_factor) or size_factor <= 0: return 0.0 # freqtrade will skip — slope is nonpositive return proposed_stake * float(size_factor)