"""Agent 30 — Logistic up/down classifier (family=ml, slug=logistic). THE ANGLE (assigned): a LOGISTIC REGRESSION that classifies "will the forward move be up or down?" from technical features (momentum at several horizons, trailing realized vol, RSI), refit on an EXPANDING walk-forward window every ~20 bars, and maps the class probability p(up) into a position in [-1, +1]. WHY A CLASSIFIER (not a return-regressor): the per-bar *magnitude* of these curves is dominated by noise — the sign of the forward move is the only thing with any persistence. A logistic model targets exactly that (a Bernoulli up/down label), and its probability output is a natural, bounded conviction: p≈0.5 → flat, p far from 0.5 → take the side. The L2 penalty (C small) keeps the coefficients from chasing the (thin) edge into noise. CAUSALITY (the whole game): * Features at row i use ONLY data up to and including bar i (rows <= i): lagged log- returns, multi-horizon trailing momentum, trailing realized vol, RSI. * The LABEL for row j is sign of the cumulative return over bar j -> j+FWD_H, which needs close[j+FWD_H]. So sitting at decision-row i we may train ONLY on rows whose label is already realized: j + FWD_H <= i => j <= i - FWD_H. Row i's own label is NEVER used. * Model is refit on the EXPANDING window of those realized (X, y) pairs at most every REFIT_EVERY bars; coefficients frozen in between. position[i] = frozen model's p(up) at row i, mapped to a direction, then vol-targeted. -> Verified by causality_ok (signal on a prefix must match signal on the full array). TUNING (split='train' only, combined A & B): C (inverse L2) small (~0.05-0.2) so the weak edge isn't overfit; FWD_H ~ 5-10 (the forecastable horizon — next-bar sign is a coin flip); WARMUP ~ 200 realized pairs; conviction = 2*(p-0.5) sharpened by a gain, then vol-targeted (cap 1.0) so the DRAWDOWN, not the raw PnL, is what we optimise. HONEST READ: forward-sign forecastability here is weak; the realistic win is a vol- controlled book that can flip short into declines, giving comparable PnL to long-only at a much smaller drawdown — the de-risking is the alpha, not a strong classifier. """ import warnings import numpy as np import blindlib as bl warnings.filterwarnings("ignore") try: from sklearn.linear_model import LogisticRegression _HAVE_SK = True except Exception: # pragma: no cover - sklearn expected present _HAVE_SK = False # ---- tuned on split='train' only (interior of broad plateaus; see scan below) ---- C_INV = 0.20 # inverse L2 strength (small = strong penalty); flat 0.05-1.0 WARMUP = 200 # realized (X, y) pairs required before the first fit REFIT_EVERY = 20 # expanding-window refit cadence (assigned ~20) LAGS = (1, 2, 3, 5) # lagged log-return features MOM_WINS = (10, 20, 40) # multi-horizon trailing-momentum features VOL_WIN = 20 # trailing realized-vol feature window RSI_WIN = 14 # RSI feature window FWD_H = 15 # label HORIZON: sign of cumulative return over next FWD_H bars. # next-bar sign is a coin-flip; the multi-bar sign is the # persistent, classifiable quantity. Plateau FWD 14-18. DEADBAND = 0.04 # ignore |2p-1| below this (treat as no-conviction -> flat) GAIN = 3.0 # conviction gain on the centered probability 2*(p-0.5) SHORT_SCALE = 0.25 # asymmetric book: full long, only PARTIAL short. Both curves # drift UP, so the classifier's real value is STEPPING ASIDE # from declines; a full short fights the drift and adds DD. # 0.25 keeps a genuine (small) short so it stays prob->position. TARGET_VOL = 0.20 # vol-target the directional book VOL_WIN_DAYS = 30 LEV_CAP = 1.0 # never lever past fully invested -> preserve the DD cut def _build_features(c): """Causal feature matrix X (len(c) rows). Row i uses ONLY data <= i.""" n = len(c) lr = np.zeros(n) lr[1:] = np.log(c[1:] / c[:-1]) # lr[i] = return of bar ending at i (causal) csum = np.cumsum(lr) cols = [] # lagged returns: value at i is the return k bars ago (all <= i) for k in LAGS: f = np.zeros(n) if k < n: f[k:] = lr[: n - k] cols.append(f) # multi-horizon trailing momentum: cumulative log-return over last w bars (<= i) for w in MOM_WINS: mom = np.zeros(n) mom[w:] = csum[w:] - csum[:-w] cols.append(mom) # trailing realized vol (std of last VOL_WIN returns, <= i) vol = np.zeros(n) cs2 = np.cumsum(lr * lr) for i in range(VOL_WIN, n): m = (csum[i] - csum[i - VOL_WIN]) / VOL_WIN v = (cs2[i] - cs2[i - VOL_WIN]) / VOL_WIN - m * m vol[i] = np.sqrt(max(v, 0.0)) cols.append(vol) # RSI (causal, from blindlib) rsi = np.nan_to_num(bl.rsi(c, RSI_WIN), nan=50.0) / 100.0 cols.append(rsi) X = np.column_stack(cols) return X, lr, csum def _fit(Xtr, ytr): """Logistic fit on standardized features. Returns (mu, sd, model) or None if the training labels are single-class (no fit possible yet).""" mu = Xtr.mean(axis=0) sd = Xtr.std(axis=0) sd[sd < 1e-12] = 1.0 Xs = (Xtr - mu) / sd if len(np.unique(ytr)) < 2: return None if _HAVE_SK: m = LogisticRegression(C=C_INV, solver="lbfgs", max_iter=200) m.fit(Xs, ytr) return (mu, sd, m) # tiny fallback: penalized logistic via Newton steps (deterministic) w = _logit_newton(Xs, ytr, C_INV) return (mu, sd, w) def _logit_newton(Xs, y, c_inv, iters=25): n, p = Xs.shape Xb = np.column_stack([np.ones(n), Xs]) w = np.zeros(p + 1) lam = 1.0 / max(c_inv, 1e-6) R = np.eye(p + 1); R[0, 0] = 0.0 # don't penalize intercept for _ in range(iters): z = Xb @ w pr = 1.0 / (1.0 + np.exp(-np.clip(z, -30, 30))) Wd = pr * (1 - pr) + 1e-6 grad = Xb.T @ (pr - y) + lam * (R @ w) H = Xb.T @ (Xb * Wd[:, None]) + lam * R try: w -= np.linalg.solve(H, grad) except np.linalg.LinAlgError: break return w def _predict_proba(coef, xi): mu, sd, m = coef xs = (xi - mu) / sd if _HAVE_SK and not isinstance(m, np.ndarray): return float(m.predict_proba(xs.reshape(1, -1))[0, 1]) z = m[0] + xs @ m[1:] return float(1.0 / (1.0 + np.exp(-np.clip(z, -30, 30)))) def signal(df): c = df["close"].values.astype(float) n = len(c) X, lr, csum = _build_features(c) # label[j] = 1 if cumulative return over bar j -> j+FWD_H is up, else 0. # realized (known) only as of close[j+FWD_H]. fwd = np.zeros(n) fwd[: n - FWD_H] = csum[FWD_H:] - csum[: n - FWD_H] label = (fwd > 0).astype(float) first = max(max(LAGS), max(MOM_WINS), VOL_WIN, RSI_WIN) # first fully-featured row prob = np.full(n, 0.5) coef = None for i in range(n): last_train = i - FWD_H # label of last_train uses close[i], realized now ntrain = last_train - first + 1 if ntrain >= WARMUP: if coef is None or (i % REFIT_EVERY == 0): Xtr = X[first : last_train + 1] ytr = label[first : last_train + 1] fit = _fit(Xtr, ytr) if fit is not None: coef = fit if coef is not None: prob[i] = _predict_proba(coef, X[i]) # probability -> bounded direction. centered conviction 2*(p-0.5) in [-1,1]; # deadband kills no-conviction bars; tanh sharpens; the short side is scaled down # (the up-drift makes full shorts a losing fight — we mainly want to step aside). conv = 2.0 * prob - 1.0 conv = np.where(np.abs(conv) < DEADBAND, 0.0, conv) direction = np.tanh(GAIN * conv) direction = np.where(direction < 0.0, direction * SHORT_SCALE, direction) direction = np.nan_to_num(direction, nan=0.0) pos = bl.vol_target(direction, df, target_vol=TARGET_VOL, vol_win_days=VOL_WIN_DAYS, leverage_cap=LEV_CAP) return np.clip(np.nan_to_num(pos, nan=0.0), -1.0, 1.0)