forked from animatedread/Warrior_EA
f3bded779a
Three additions to meta_pool.py, in the order the campaign needed them: - memmap + float32-throughout (per-batch float64 cast): the 6.5 GB 4-symbol corpus OOMed the float64 pipeline on the training box; - pool2: per-symbol standardization (each symbol by its own train-slice mu/sd) + 64/32 capacity + l2 1e-3, after the naive pooled model underfit to the prior (train CE pinned at base-rate entropy); - curve: fixed-ladder precision-vs-threshold on calib and test side by side - the dose-response diagnostic that closed the question. RESULT recorded in memory: pooling transfers real skill (XAUUSD +2.6pp, SP500 +1.2pp at fitted thresholds, >>2 sigma) but 0/8 fitted operating points clear break-even, and the high-conviction tail is temporally unstable - the precision-vs-threshold slope FLIPS SIGN between calib and test on 3 of 4 symbols, so no ex-ante threshold rule exists. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
315 lines
14 KiB
Python
315 lines
14 KiB
Python
"""Offline meta-labeling pipeline over the EA's exported candidate datasets.
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Input: Common\\Files\\Warrior_EA\\MetaExport\\<SYM>_<PERIOD>.f32 (+ .meta.csv sidecar),
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written by CSignalMETA::ExportMetaDataset - one row per resolved, labeled candidate:
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[barTime int64][family int32][pattern int32][side int32][won int32][width float32s]
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byte-equivalent to what the EA's pass 2 trains on (same window builder, descriptor, labels).
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Discipline mirrors the EA exactly:
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- chronological split per symbol: oldest 55% train | purge | 15% calibration | purge | 30% test,
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purges = horizonBars * period (triple-barrier labels look that far forward -> leakage band);
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- operating point fitted on the CALIBRATION slice only (coverage x (p - BE), the EA's
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FitDirConfThreshold objective, with the EA's 25%-coverage floor);
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- the TEST slice is touched once, by the fitted threshold.
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Usage:
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py -3 research/meta_pool.py stats # per-file inventory + base rates
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py -3 research/meta_pool.py eval XAUUSD_16388 # single-symbol train+eval
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py -3 research/meta_pool.py pool # pooled train, per-symbol eval
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"""
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import csv
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import glob
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import os
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import sys
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import numpy as np
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EXPORT_DIR = r"C:\Users\admin\AppData\Roaming\MetaQuotes\Terminal\Common\Files\Warrior_EA\MetaExport"
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PERIOD_SECONDS = {16385: 3600, 16386: 7200, 16387: 10800, 16388: 14400, 16390: 21600,
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16392: 28800, 16396: 43200, 16408: 86400}
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MIN_COVERAGE_FRACTION = 0.25 # the EA's MIN_COVERAGE_FRACTION_OF_BASE_RATE against a 100% base
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EDGE_MIN_SIGMAS = 2.0 # the EA's deployability floor
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SEED = 20260813
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def load(tag):
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meta_path = os.path.join(EXPORT_DIR, tag + ".meta.csv")
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# MQL5's FileOpen(FILE_CSV) without FILE_ANSI writes UTF-16LE
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with open(meta_path, encoding="utf-16") as f:
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r = list(csv.DictReader(f))[0]
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width = int(r["width"])
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dt = np.dtype([("t", "<i8"), ("fam", "<i4"), ("pat", "<i4"), ("side", "<i4"),
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("won", "<i4"), ("x", "<f4", (width,))])
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# memory-map: 6.5 GB of exports on a box that also hosts the training charts. The
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# export walks candidate ids (sweep order, oldest first), so the sort below is a
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# no-op in practice and the mmap is used as-is; only slices ever materialize.
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raw = np.memmap(os.path.join(EXPORT_DIR, tag + ".f32"), dtype=dt, mode="r")
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if np.any(np.diff(raw["t"]) < 0):
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order = np.argsort(raw["t"], kind="stable")
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raw = np.asarray(raw)[order]
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meta = {k: (float(v) if k not in ("symbol",) else v) for k, v in r.items()}
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meta["period_sec"] = PERIOD_SECONDS[int(float(r["period"]))]
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return raw, meta
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def chrono_split(rows, meta):
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"""Return boolean masks (train, calib, test) with horizon purges between slices."""
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t = rows["t"]
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purge = int(float(meta["horizonBars"])) * meta["period_sec"]
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n = len(t)
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i_train_end = int(n * 0.55)
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i_calib_end = int(n * 0.70)
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t_train_hi = t[i_train_end - 1]
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t_calib_lo = t_train_hi + purge
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t_calib_hi = t[min(i_calib_end, n - 1)]
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t_test_lo = t_calib_hi + purge
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train = t <= t_train_hi
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calib = (t >= t_calib_lo) & (t <= t_calib_hi)
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test = t >= t_test_lo
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return train, calib, test
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class MLP:
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"""width -> h1 -> h2 -> 2 softmax, input standardization + Adam + CE. Mirrors the EA's
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head in spirit (dense+norm+bounded scale); small enough to train in seconds."""
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def __init__(self, width, h1=32, h2=16, seed=SEED):
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rng = np.random.default_rng(seed)
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s1 = np.sqrt(2.0 / width)
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s2 = np.sqrt(2.0 / h1)
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s3 = np.sqrt(2.0 / h2)
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self.W1 = rng.normal(0, s1, (width, h1)); self.b1 = np.zeros(h1)
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self.W2 = rng.normal(0, s2, (h1, h2)); self.b2 = np.zeros(h2)
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self.W3 = rng.normal(0, s3, (h2, 2)); self.b3 = np.zeros(2)
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self.mu = None
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self.sd = None
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def _fwd(self, X):
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Z1 = X @ self.W1 + self.b1
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A1 = np.maximum(Z1, 0.01 * Z1)
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Z2 = A1 @ self.W2 + self.b2
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A2 = np.maximum(Z2, 0.01 * Z2)
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Z3 = A2 @ self.W3 + self.b3
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Z3 = Z3 - Z3.max(axis=1, keepdims=True)
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P = np.exp(Z3)
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P /= P.sum(axis=1, keepdims=True)
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return Z1, A1, Z2, A2, P
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def fit(self, X, y, epochs=30, batch=256, lr=1e-3, l2=3e-3, verbose=False):
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# X stays float32 (the pooled set is ~4 GB); standardization and the float64
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# cast happen per mini-batch, where they cost ~2 MB instead of 2x the corpus.
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self.mu = X.mean(axis=0, dtype=np.float64)
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self.sd = X.std(axis=0, dtype=np.float64) + 1e-8
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Y = np.stack([y, 1 - y], axis=1).astype(np.float64) # slot0 = P(win)
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rng = np.random.default_rng(SEED)
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params = [self.W1, self.b1, self.W2, self.b2, self.W3, self.b3]
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m = [np.zeros_like(p) for p in params]
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v = [np.zeros_like(p) for p in params]
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b1m, b2m, eps, step = 0.9, 0.999, 1e-8, 0
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for ep in range(epochs):
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idx = rng.permutation(len(X))
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for lo in range(0, len(idx), batch):
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sel = idx[lo:lo + batch]
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Xb = (X[sel].astype(np.float64) - self.mu) / self.sd
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Yb = Y[sel]
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Z1, A1, Z2, A2, P = self._fwd(Xb)
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dZ3 = (P - Yb) / len(sel)
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dW3 = A2.T @ dZ3 + l2 * self.W3; db3 = dZ3.sum(0)
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dA2 = dZ3 @ self.W3.T
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dZ2 = dA2 * np.where(Z2 > 0, 1.0, 0.01)
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dW2 = A1.T @ dZ2 + l2 * self.W2; db2 = dZ2.sum(0)
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dA1 = dZ2 @ self.W2.T
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dZ1 = dA1 * np.where(Z1 > 0, 1.0, 0.01)
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dW1 = Xb.T @ dZ1 + l2 * self.W1; db1 = dZ1.sum(0)
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grads = [dW1, db1, dW2, db2, dW3, db3]
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step += 1
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for p, g, mi, vi in zip(params, grads, m, v):
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mi *= b1m; mi += (1 - b1m) * g
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vi *= b2m; vi += (1 - b2m) * g * g
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mh = mi / (1 - b1m ** step)
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vh = vi / (1 - b2m ** step)
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p -= lr * mh / (np.sqrt(vh) + eps)
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if verbose and ep % 10 == 9:
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sub = rng.choice(len(X), min(len(X), 200_000), replace=False)
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p = self.p_win(X[sub])
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ce = -np.mean(np.log(np.clip(np.where(y[sub] == 1, p, 1 - p), 1e-9, 1)))
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print(f" epoch {ep+1}: train CE {ce:.4f} (200k subsample)", flush=True)
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return self
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def p_win(self, X, chunk=65536):
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out = np.empty(len(X))
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for lo in range(0, len(X), chunk):
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Xs = (X[lo:lo + chunk].astype(np.float64) - self.mu) / self.sd
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out[lo:lo + chunk] = self._fwd(Xs)[4][:, 0]
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return out
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def fit_threshold(p, won, be_pct):
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"""The EA's operating point: maximize coverage x (precision - BE) over the calib slice,
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subject to the 25% coverage floor; ties to the lower threshold."""
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be = be_pct / 100.0
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best = (-np.inf, 0.0)
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for thr in np.linspace(0.0, 0.98, 50):
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sel = p >= thr
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cov = sel.mean()
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if cov < MIN_COVERAGE_FRACTION or sel.sum() == 0:
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continue
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prec = won[sel].mean()
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score = cov * (prec - be)
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if score > best[0]:
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best = (score, thr)
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return best[1]
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def evaluate(tag, p_test, rows_test, meta, label=""):
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won = rows_test["won"].astype(np.float64)
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be = float(meta["breakEvenPct"])
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base = 100.0 * won.mean()
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out = [f"{tag}{label}: test n={len(won)}, base win {base:.1f}% vs BE {be:.1f}% (gap {base-be:+.1f}pp)"]
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for thr_label, thr in [("argmax(0.5)", 0.5), ("fitted", None)]:
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t = thr if thr is not None else evaluate.fitted_thr
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sel = p_test >= t
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if sel.sum() == 0:
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out.append(f" {thr_label:<12} thr {t:.2f}: no trades")
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continue
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cov = 100.0 * sel.mean()
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prec = 100.0 * won[sel].mean()
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n = int(sel.sum())
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se = 100.0 * np.sqrt((base / 100) * (1 - base / 100) / n)
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score = cov * (prec - be) / 100.0
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verdict = "DEPLOYABLE" if (cov >= 25.0 and prec - base > EDGE_MIN_SIGMAS * se
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and prec > be) else ""
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out.append(f" {thr_label:<12} thr {t:.2f}: trades {n} ({cov:.1f}%) win {prec:.1f}% | "
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f"cov x (p-BE) = {score:+.2f} | skill vs base {prec-base:+.1f}pp "
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f"(2 sigma = {EDGE_MIN_SIGMAS*se:.1f}pp) {verdict}")
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print("\n".join(out))
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def tags_on_disk():
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return sorted(os.path.basename(f)[:-4] for f in glob.glob(os.path.join(EXPORT_DIR, "*.f32")))
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def cmd_stats():
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for tag in tags_on_disk():
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rows, meta = load(tag)
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t0 = np.datetime64(int(rows["t"].min()), "s")
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t1 = np.datetime64(int(rows["t"].max()), "s")
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won = rows["won"].astype(float)
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long_mask = rows["side"] > 0
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print(f"{tag}: {len(rows)} rows {t0}..{t1} | base {100*won.mean():.1f}% "
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f"(long {100*won[long_mask].mean():.1f}% short {100*won[~long_mask].mean():.1f}%) "
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f"vs BE {meta['breakEvenPct']:.1f}% | width {int(meta['width'])} "
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f"geom {meta['slMult']:.2f}/{meta['tpMult']:.2f}")
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def _standardize(xb, mu, sd, chunk=65536):
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out = np.empty(xb.shape, dtype=np.float32)
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for lo in range(0, len(xb), chunk):
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out[lo:lo + chunk] = (xb[lo:lo + chunk] - mu) / sd
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return out
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def run_eval(train_sets, eval_tags, label, per_symbol_norm=False, h1=32, h2=16, l2=3e-3):
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"""per_symbol_norm: standardize each symbol's features by ITS OWN train-slice mu/sd
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before pooling - the cross-sectional literature's standard fix for pooled models
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collapsing to the prior when instruments' feature scales differ (the EA's own
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batch-norm does the per-chart equivalent). Fitted on train only - no leakage."""
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norms = {}
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Xs, ys = [], []
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for rows, meta, mask in train_sets:
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xb = rows["x"][mask] # float32 - cast happens per batch in fit()
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if per_symbol_norm:
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mu = xb.mean(axis=0, dtype=np.float64)
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sd = xb.std(axis=0, dtype=np.float64) + 1e-8
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norms[id(rows)] = (mu, sd)
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xb = _standardize(xb, mu, sd)
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Xs.append(xb)
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ys.append(rows["won"][mask])
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X = np.concatenate(Xs)
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y = np.concatenate(ys)
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del Xs
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print(f"[{label}] training on {len(X)} rows from {len(train_sets)} file(s)...")
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net = MLP(X.shape[1], h1=h1, h2=h2).fit(X, y, l2=l2, verbose=True)
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for tag, rows, meta, calib, test in eval_tags:
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def prep(sl):
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xb = rows["x"][sl]
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if per_symbol_norm:
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mu, sd = norms[id(rows)]
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xb = _standardize(xb, mu, sd)
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return xb
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p_cal = net.p_win(prep(calib))
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evaluate.fitted_thr = fit_threshold(p_cal, rows["won"][calib].astype(float), float(meta["breakEvenPct"]))
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p_test = net.p_win(prep(test))
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evaluate(tag, p_test, rows[test], meta, label=f" [{label}]")
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return net, norms
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def cmd_curve(data):
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"""Dose-response readout for the pooled-norm model: precision vs threshold on a FIXED
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ladder, calib and test side by side. No fitting, no selection - the question is whether
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the ranking ever crosses each symbol's break-even at any coverage, or flattens. Same
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seed as pool2 -> byte-identical model; this adds no new training attempt."""
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train_sets = [(r, m, tr) for (r, m, tr, _, _) in data.values()]
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eval_tags = [(t, r, m, c, te) for t, (r, m, _, c, te) in data.items()]
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net, norms = run_eval(train_sets, eval_tags, "pooled-norm", per_symbol_norm=True,
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h1=64, h2=32, l2=1e-3)
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for tag, (rows, meta, _, calib, test) in data.items():
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be = float(meta["breakEvenPct"])
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mu, sd = norms[id(rows)]
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p_cal = net.p_win(_standardize(rows["x"][calib], mu, sd))
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p_tst = net.p_win(_standardize(rows["x"][test], mu, sd))
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w_cal = rows["won"][calib].astype(float)
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w_tst = rows["won"][test].astype(float)
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print(f"\n{tag} threshold curve (BE {be:.1f}%):")
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print(f" {'thr':>5} | {'cal cov%':>8} {'cal win%':>8} | {'test cov%':>9} {'test n':>7} {'test win%':>9} {'vs BE':>6}")
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for thr in np.arange(0.50, 0.96, 0.05):
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sc, st = p_cal >= thr, p_tst >= thr
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if st.sum() < 200:
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break
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print(f" {thr:5.2f} | {100*sc.mean():8.1f} {100*w_cal[sc].mean() if sc.sum() else 0:8.1f} | "
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f"{100*st.mean():9.1f} {int(st.sum()):7d} {100*w_tst[st].mean():9.1f} "
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f"{100*w_tst[st].mean()-be:+6.1f}")
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def main():
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cmd = sys.argv[1] if len(sys.argv) > 1 else "stats"
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if cmd == "stats":
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cmd_stats()
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return
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if cmd == "eval":
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tag = sys.argv[2]
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rows, meta = load(tag)
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train, calib, test = chrono_split(rows, meta)
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print(f"{tag}: split train {train.sum()} / calib {calib.sum()} / test {test.sum()}")
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run_eval([(rows, meta, train)], [(tag, rows, meta, calib, test)], "self")
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return
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if cmd in ("pool", "pool2", "curve"):
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data = {}
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for tag in tags_on_disk():
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rows, meta = load(tag)
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if int(meta["width"]) != 1103:
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print(f"{tag}: width {int(meta['width'])} != 1103 - skipped (pool needs one input shape)")
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continue
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data[tag] = (rows, meta, *chrono_split(rows, meta))
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train_sets = [(r, m, tr) for (r, m, tr, _, _) in data.values()]
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eval_tags = [(t, r, m, c, te) for t, (r, m, _, c, te) in data.items()]
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if cmd == "curve":
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cmd_curve(data)
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return
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if cmd == "pool2":
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# attempt #2 on the same test slices (the naive pooled model underfit to the
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# prior: train CE pinned at base-rate entropy while every self model fit).
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# Per-symbol standardization + capacity 64/32 + l2 1e-3. Verdicts must be
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# read family-wise across BOTH attempts.
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run_eval(train_sets, eval_tags, "pooled-norm", per_symbol_norm=True,
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h1=64, h2=32, l2=1e-3)
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return
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run_eval(train_sets, eval_tags, "pooled")
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for t, (r, m, tr, c, te) in data.items():
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run_eval([(r, m, tr)], [(t, r, m, c, te)], f"self:{t}")
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return
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print(__doc__)
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if __name__ == "__main__":
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main()
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