research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer
Ondata di ricerca onesta a largo spettro su BTC/ETH+DVOL certificati: 104 ipotesi distinte (11 famiglie), un agente-finder per ipotesi, verifica avversariale a 3 scettici sui promettenti, sintesi (153 agenti totali). Esito: NIENTE di nuovo regge -> conferma del soffitto strutturale ~1.3 BTC/ETH-direzionale; lo stack TP01+XS01+VRP01 resta imbattuto. - altlib.py: harness condiviso vettoriale leak-free (eval_weights/study_weights, fee-sweep, both-asset + hold-out 2025+). Riproduce i numeri canonici di TP01. - MARGINAL SCORER (study_marginal/marginal_vs_tp01): Sharpe INCREMENTALE vs baseline TP01 (corr, blend uplift OOS, alpha residua) + jackknife OOS (clean-year + drop-best-month). earns_slot = abs!=FAIL & ADDS & robust_oos. Smaschera gli overlay su TSMOM con PASS assoluti fasulli (CMB04, VOL11, ...) e il falso positivo KAMA (ADDS ma muore al jackknife). - runs/*.py (104) script riproducibili per ipotesi; wf_altstrat.js workflow. - Verdetto: 0 candidati deployabili; 2 LEAD fragili (VOL08, STA05_LS) da forward-monitor. - test_marginal_scorer.py blocca baseline + invarianti. Suite: 32 verde. Diario: docs/diary/2026-06-20-alt-strategies-100agent-sweep.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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"""STA02 — Walk-forward Logistic Regression on TA features (1d).
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Idea: a logistic classifier is periodically re-fit on features
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{rsi, zscore_price, momentum, realized_vol} all computed causally.
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Predict P(next bar up) -> long if P > 0.5, else flat (long-only, no short).
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Causal contract
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---------------
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At decision bar d (close[d] known):
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- features use data up to and including close[d]
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- we predict: will close[d+1] > close[d] ?
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- target[d] = position held during bar d+1
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- altlib eval_weights shifts by 1 for us -> no double shift
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Feature construction (all using data <= close[d]):
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- rsi_14: RSI(14) at bar d
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- zscore_20: (close[d] - sma_20[d]) / std_20[d]
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- mom_10: log(close[d] / close[d-10]) (10-bar momentum)
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- rvol_20: realized annualized vol, 20-bar window
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Training label:
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- y[k] = 1 if close[k+1] > close[k], else 0
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- Train on (X[k], y[k]) for k in [warmup .. d-1]
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Grid (4 configs x 1 TF = 4 total backtests <= 6 limit):
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- min_train_years: 1.0 or 2.0
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- C (inverse regularization): 0.1 or 1.0
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Best config by min(BTC, ETH) hold-out Sharpe.
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"""
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
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import altlib as al
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import numpy as np
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import warnings
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warnings.filterwarnings("ignore")
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler
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def logistic_target(df, min_train_years: float = 1.0, C: float = 1.0) -> np.ndarray:
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"""
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Walk-forward logistic on {rsi14, zscore20, mom10, rvol20}.
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Returns vol-targeted position array (target[i] decided at close[i]).
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"""
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c = df["close"].values.astype(float)
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n = len(c)
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bpy = al.bars_per_year(df)
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bpd = al.bars_per_day(df)
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# --- build features (all causal at bar i) ---
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# RSI 14
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feat_rsi = al.rsi(c, win=14)
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# Z-score of close over 20-bar window
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feat_zsc = al.zscore(c, win=20)
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# 10-bar log-momentum: log(close[i] / close[i-10])
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# Using lag=10 bars; only valid for i >= 10
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feat_mom = np.full(n, np.nan)
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lag = 10
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feat_mom[lag:] = np.log(c[lag:] / c[:-lag])
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# Realized annualized vol (20-bar)
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r = al.simple_returns(c)
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feat_rvol = al.realized_vol(r, win=20, bars_per_year=bpy)
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# Stack into feature matrix [n x 4]
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X_all = np.column_stack([feat_rsi, feat_zsc, feat_mom, feat_rvol])
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# Label: 1 if next bar close > current close, else 0
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# y[i] = 1 if close[i+1] > close[i] — shape (n,), y[n-1] is undefined
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y_all = np.zeros(n, dtype=float)
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y_all[:-1] = (c[1:] > c[:-1]).astype(float)
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min_train_bars = int(min_train_years * bpy)
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# Need at least warmup + lags for first valid sample
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first_valid = max(20, lag) # 20 for zscore/rvol, 10 for mom
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# first training sample k: k >= first_valid AND feature X[k] fully defined
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# first prediction at bar d: d >= first_valid + min_train_bars
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first_pred = first_valid + min_train_bars
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# Refit quarterly
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refit_every = max(1, int(bpy / 4))
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direction = np.zeros(n, dtype=float)
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last_refit = -refit_every # force first refit
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model = LogisticRegression(C=C, solver="lbfgs", max_iter=500,
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random_state=42, class_weight="balanced")
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scaler = StandardScaler()
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trained = False
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for d in range(first_pred, n - 1):
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if d - last_refit >= refit_every:
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# Build training set: samples k in [first_valid, d-1] (predict y[k] from X[k])
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# X[k] causal (uses data <= close[k]), y[k] requires close[k+1] (NOT at k, at k+1)
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# So the last valid training sample is k = d-1 (we know close[d] = close[(d-1)+1])
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k_start = first_valid
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k_end = d # exclusive, so training on [k_start, d-1]
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if k_end - k_start < 30:
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continue
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X_tr = X_all[k_start:k_end]
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y_tr = y_all[k_start:k_end]
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# Drop rows with NaN features
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valid_mask = np.all(np.isfinite(X_tr), axis=1) & np.isfinite(y_tr)
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if valid_mask.sum() < 20:
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continue
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X_tr = X_tr[valid_mask]
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y_tr = y_tr[valid_mask]
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# Check both classes present
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if len(np.unique(y_tr)) < 2:
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continue
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try:
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scaler.fit(X_tr)
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X_tr_scaled = scaler.transform(X_tr)
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model.fit(X_tr_scaled, y_tr)
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trained = True
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last_refit = d
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except Exception:
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continue
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if not trained:
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continue
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# Predict at bar d: features X_all[d]
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x_d = X_all[d]
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if not np.all(np.isfinite(x_d)):
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continue
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x_scaled = scaler.transform(x_d.reshape(1, -1))
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prob_up = model.predict_proba(x_scaled)[0]
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# class order: model.classes_ = [0, 1]
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idx_up = list(model.classes_).index(1) if 1 in model.classes_ else 1
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p_up = prob_up[idx_up]
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# Long if P(up) > 0.5, else flat (long-only, no short)
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direction[d] = 1.0 if p_up > 0.5 else 0.0
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# Vol-target the direction signal
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target = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return target
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def run_grid():
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configs = [
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dict(min_train_years=1.0, C=0.1),
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dict(min_train_years=1.0, C=1.0),
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dict(min_train_years=2.0, C=0.1),
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dict(min_train_years=2.0, C=1.0),
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]
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best_rep = None
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best_holdout = -999.0
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for cfg in configs:
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name = f"STA02(train={cfg['min_train_years']}y,C={cfg['C']})"
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print(f"\n--- Running {name} ---")
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rep = al.study_weights(
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name,
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lambda df, c=cfg: logistic_target(df, **c),
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tfs=("1d",)
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)
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print(al.fmt(rep))
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min_hold = rep["verdict"].get("best_holdout_sharpe", -999.0)
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if min_hold > best_holdout:
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best_holdout = min_hold
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best_rep = rep
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best_rep["_cfg"] = cfg
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return best_rep
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if __name__ == "__main__":
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best = run_grid()
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print("\n\n=== BEST CONFIG ===")
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print(al.fmt(best))
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print("JSON:", al.as_json(best))
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