feat(C12): 1-day horizon; FAILED

1-day triple barrier labels: dir_acc 18.0% (-23.3pp), up_prec 20.4% (-31.8pp).
5-day horizon is essential — shorter horizon can't overcome next-day noise.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

docs/c12_1day_result.json

@@ -0,0 +1,112 @@
+{
+  "results": {
+    "2330": {
+      "5day": {
+        "accuracy": 33.3,
+        "dir_accuracy": 36.7,
+        "up_precision": 52.9,
+        "dn_precision": 27.8,
+        "n_signals": 196,
+        "n_predictions": 231
+      },
+      "1day": {
+        "accuracy": 55.4,
+        "dir_accuracy": 24.6,
+        "up_precision": 29.4,
+        "dn_precision": 19.4,
+        "n_signals": 65,
+        "n_predictions": 231
+      }
+    },
+    "0050": {
+      "5day": {
+        "accuracy": 38.1,
+        "dir_accuracy": 43.1,
+        "up_precision": 62.8,
+        "dn_precision": 29.9,
+        "n_signals": 195,
+        "n_predictions": 231
+      },
+      "1day": {
+        "accuracy": 56.3,
+        "dir_accuracy": 24.6,
+        "up_precision": 30.0,
+        "dn_precision": 6.7,
+        "n_signals": 65,
+        "n_predictions": 231
+      }
+    },
+    "2317": {
+      "5day": {
+        "accuracy": 39.0,
+        "dir_accuracy": 40.5,
+        "up_precision": 50.8,
+        "dn_precision": 28.4,
+        "n_signals": 220,
+        "n_predictions": 231
+      },
+      "1day": {
+        "accuracy": 55.4,
+        "dir_accuracy": 11.9,
+        "up_precision": 8.7,
+        "dn_precision": 13.9,
+        "n_signals": 59,
+        "n_predictions": 231
+      }
+    },
+    "2454": {
+      "5day": {
+        "accuracy": 39.4,
+        "dir_accuracy": 41.6,
+        "up_precision": 41.3,
+        "dn_precision": 41.8,
+        "n_signals": 190,
+        "n_predictions": 231
+      },
+      "1day": {
+        "accuracy": 51.5,
+        "dir_accuracy": 11.9,
+        "up_precision": 16.1,
+        "dn_precision": 8.3,
+        "n_signals": 67,
+        "n_predictions": 231
+      }
+    },
+    "2881": {
+      "5day": {
+        "accuracy": 40.3,
+        "dir_accuracy": 44.8,
+        "up_precision": 53.0,
+        "dn_precision": 36.9,
+        "n_signals": 203,
+        "n_predictions": 231
+      },
+      "1day": {
+        "accuracy": 54.5,
+        "dir_accuracy": 17.2,
+        "up_precision": 17.9,
+        "dn_precision": 16.0,
+        "n_signals": 64,
+        "n_predictions": 231
+      }
+    }
+  },
+  "aggregate": {
+    "5day": {
+      "accuracy": 38.0,
+      "dir_accuracy": 41.3,
+      "up_precision": 52.2,
+      "dn_precision": 33.0,
+      "n_signals": 200.8
+    },
+    "1day": {
+      "accuracy": 54.6,
+      "dir_accuracy": 18.0,
+      "up_precision": 20.4,
+      "dn_precision": 12.9,
+      "n_signals": 64.0
+    }
+  },
+  "passed": false,
+  "pass_criterion": "1day dir_accuracy >= 44.0 AND up_precision >= 54.0"
+}

docs/improvements_log.md

@@ -13,3 +13,4 @@
 | C9 | multi-stock training universe (20 stocks) | dir_acc: 41.3 → 40.4, up_prec: 52.2 → 52.9 | FAILED — cross-stock label noise hurts more than extra data helps |
 | C10 | asymmetric triple barrier grid (pt_ratio=1.0..3.0, sl fixed 1.0) | up_prec: 52.2 @ pt=1.0, 43.5 @ pt=1.5, 37.2 @ pt=2.0 — monotonically worse | FAILED — wider UP barrier degrades precision; symmetric pt=1.0 remains best |
 | C11 | full ensemble walk-forward (RF + XGB + LGBM) | dir_acc: 41.3 → 43.1 (+1.8pp), up_prec: 52.2 → 52.2 (flat) | FAILED — dir misses 43.5 threshold; RF already representative; ↑prec flat |
+| C12 | 1-day label horizon (vs 5-day triple barrier) | dir_acc: 41.3 → 18.0% (-23.3pp), up_prec: 52.2 → 20.4% (-31.8pp) | FAILED — 1-day horizon catastrophically degrades; model can't predict next-day direction; 5-day horizon absorbs noise better |

scripts/backtest_c12.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+"""C12: 1-day vs 5-day label horizon comparison."""
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(ROOT))
+
+try:
+    from dotenv import load_dotenv; load_dotenv(ROOT / ".env")
+except ImportError:
+    pass
+
+import warnings; warnings.filterwarnings("ignore")
+import json
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+
+from scripts.improvement_harness import (
+    fetch_df, build_triple_barrier_labels, walk_forward,
+    compute_metrics, BASELINE_FEATURES, DEFAULT_STOCKS,
+    RF_PARAMS, MIN_TRAIN, STEP, LABEL_HORIZON,
+)
+from models.predictor import _build_features
+
+PASS_DIR = 44.0
+PASS_UP  = 54.0
+
+
+def build_1day_labels(close: np.ndarray, vol_lookback: int = 20, pt_sl_ratio: float = 1.0) -> np.ndarray:
+    """Triple barrier with 1-day vertical barrier instead of 5-day."""
+    n = len(close)
+    log_ret = np.diff(np.log(close + 1e-9))
+    labels = np.full(n, np.nan)
+    for i in range(n - 1):  # horizon=1
+        start_v = max(0, i - vol_lookback)
+        window = log_ret[start_v:i]
+        vol = float(np.std(window)) if len(window) >= 5 else 0.015
+        vol = max(vol, 0.001)
+        upper = close[i] * (1.0 + vol * pt_sl_ratio)
+        lower = close[i] * (1.0 - vol * pt_sl_ratio)
+        c = close[i + 1]
+        if c >= upper:
+            labels[i] = 1
+        elif c <= lower:
+            labels[i] = -1
+        else:
+            labels[i] = 0
+    return labels
+
+
+def walk_forward_1d(feat_df: pd.DataFrame, label_arr: np.ndarray, cols: list) -> dict:
+    LABEL_HORIZON_1D = 1
+    avail = [c for c in cols if c in feat_df.columns]
+    X_all = feat_df[avail].fillna(0).values
+    n = len(feat_df)
+    y_true_all, y_pred_all = [], []
+    cutoff = MIN_TRAIN
+    while cutoff + STEP + LABEL_HORIZON_1D <= n:
+        y_tr = label_arr[:cutoff]
+        valid = ~np.isnan(y_tr)
+        y_v = y_tr[valid]
+        if len(y_v) < 10 or len(np.unique(y_v)) < 2:
+            cutoff += STEP; continue
+        clf = RandomForestClassifier(**RF_PARAMS)
+        clf.fit(X_all[:cutoff][valid], y_v.astype(int))
+        test_end = min(cutoff + STEP, n - LABEL_HORIZON_1D)
+        y_te = label_arr[cutoff:test_end]
+        valid_te = ~np.isnan(y_te)
+        if valid_te.sum() == 0:
+            cutoff += STEP; continue
+        y_pred = clf.predict(X_all[cutoff:test_end][valid_te])
+        y_true_all.extend(y_te[valid_te].tolist())
+        y_pred_all.extend(y_pred.tolist())
+        cutoff += STEP
+    if not y_true_all:
+        return {}
+    return compute_metrics(np.array(y_true_all), np.array(y_pred_all))
+
+
+def main():
+    hdr = f"{'Stock':>6}  {'5day_dir%':>9}  {'1day_dir%':>9}  {'Δdir':>5}  {'5day_↑prec%':>11}  {'1day_↑prec%':>11}  {'Δprec':>6}  {'1day_sigs':>9}"
+    print(f"\n{hdr}\n{'-'*len(hdr)}")
+
+    per_stock = {}
+    rows_5d, rows_1d = [], []
+
+    for stock_no in DEFAULT_STOCKS:
+        print(f"  computing {stock_no}...", end="\r", flush=True)
+        df = fetch_df(stock_no)
+        if df is None or df.empty:
+            print(f"{stock_no:>6}  no data")
+            continue
+
+        feat = _build_features(df)
+        close = (df.set_index("date")["close"] if "date" in df.columns else df["close"]).values
+
+        labels_5d = build_triple_barrier_labels(close)
+        r5 = walk_forward(feat, labels_5d, BASELINE_FEATURES)
+
+        labels_1d = build_1day_labels(close)
+        r1 = walk_forward_1d(feat, labels_1d, BASELINE_FEATURES)
+
+        per_stock[stock_no] = {"5day": r5, "1day": r1}
+        if r5:
+            rows_5d.append(r5)
+        if r1:
+            rows_1d.append(r1)
+
+        d5 = r5.get("dir_accuracy", float("nan"))
+        d1 = r1.get("dir_accuracy", float("nan"))
+        p5 = r5.get("up_precision", float("nan"))
+        p1 = r1.get("up_precision", float("nan"))
+        dd = d1 - d5 if not (np.isnan(d1) or np.isnan(d5)) else float("nan")
+        dp = p1 - p5 if not (np.isnan(p1) or np.isnan(p5)) else float("nan")
+        sigs = r1.get("n_signals", 0)
+        print(f"{stock_no:>6}  {d5:>9.1f}  {d1:>9.1f}  {dd:>+5.1f}  {p5:>11.1f}  {p1:>11.1f}  {dp:>+6.1f}  {sigs:>9}")
+
+    def _mean(rows, k):
+        vals = [r[k] for r in rows if isinstance(r.get(k), (int, float)) and not np.isnan(r.get(k, float("nan")))]
+        return round(sum(vals) / len(vals), 1) if vals else float("nan")
+
+    agg5 = {k: _mean(rows_5d, k) for k in ("accuracy", "dir_accuracy", "up_precision", "dn_precision", "n_signals")}
+    agg1 = {k: _mean(rows_1d, k) for k in ("accuracy", "dir_accuracy", "up_precision", "dn_precision", "n_signals")}
+
+    dd_agg = agg1["dir_accuracy"] - agg5["dir_accuracy"]
+    dp_agg = agg1["up_precision"] - agg5["up_precision"]
+
+    print(f"\n{'=== AGGREGATE ==='}")
+    print(f"  5day: dir={agg5['dir_accuracy']}%  ↑prec={agg5['up_precision']}%  signals={agg5['n_signals']}")
+    print(f"  1day: dir={agg1['dir_accuracy']}%  ↑prec={agg1['up_precision']}%  signals={agg1['n_signals']}")
+    print(f"  Δ   : dir={dd_agg:+.1f}pp  ↑prec={dp_agg:+.1f}pp")
+
+    passed = bool((agg1["dir_accuracy"] >= PASS_DIR) and (agg1["up_precision"] >= PASS_UP))
+    status = "PASSED" if passed else "FAILED"
+    print(f"\n  Pass criterion: 1day dir ≥ {PASS_DIR} AND ↑prec ≥ {PASS_UP}")
+    print(f"  C12 {status}")
+
+    result = {
+        "results": per_stock,
+        "aggregate": {"5day": agg5, "1day": agg1},
+        "passed": passed,
+        "pass_criterion": f"1day dir_accuracy >= {PASS_DIR} AND up_precision >= {PASS_UP}",
+    }
+
+    out = ROOT / "docs" / "c12_1day_result.json"
+    out.parent.mkdir(exist_ok=True)
+    with open(out, "w") as f:
+        json.dump(result, f, indent=2)
+    print(f"\n  Written: {out}")
+
+    print(f"\nC12 {status} — 1day dir: {agg1['dir_accuracy']}%, ↑prec: {agg1['up_precision']}%, signals/stock: {agg1['n_signals']}")
+
+
+if __name__ == "__main__":
+    main()