From ab34c3ef1b5b04242680592c06f176c9ca679d71 Mon Sep 17 00:00:00 2001 From: I Luk Kim Date: Tue, 21 Apr 2026 23:05:25 -0700 Subject: [PATCH] =?UTF-8?q?Diagnose=20RSI-14/BB%B=20axis=20for=20V27:=20AB?= =?UTF-8?q?ORT=20=E2=80=94=20redundant=20with=20OBV-slope?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit RSI-14: Pearson=+0.157, G2 passes (+0.316R gap, direction confirmed). BUT ρ(RSI-14, obv_slope_20)=+0.726 — G5a FAIL. RSI captures same momentum information as V24's OBV-slope. BB %B is ρ=0.889 with RSI — identical axis. Both fail G1 (n=98 < 120 threshold). V24 remains champion. Co-Authored-By: Claude Sonnet 4.6 --- .../scripts/diag_orb_momentum_features.py | 462 ++++++++++++++++++ 1 file changed, 462 insertions(+) create mode 100644 apps/intraday_bt/scripts/diag_orb_momentum_features.py diff --git a/apps/intraday_bt/scripts/diag_orb_momentum_features.py b/apps/intraday_bt/scripts/diag_orb_momentum_features.py new file mode 100644 index 0000000..701b219 --- /dev/null +++ b/apps/intraday_bt/scripts/diag_orb_momentum_features.py @@ -0,0 +1,462 @@ +""" +V27 ORB Momentum Features Diagnostic — RSI-14 and Bollinger Band %B + +Hypotheses: + RSI-14 (prior day): + Low RSI (<30 = oversold): post-selloff gap-up → strong reversion drive + High RSI (>70 = overbought): momentum continuation → gap adds to trend + Mid RSI (40-60): neutral — empirically uncertain + BB %B (prior day): + Low %B (<0.3): near lower band = compressed/reset before gap-up → explosive + High %B (>0.7): near/above upper band = existing momentum + gap = continuation + Direction: empirically determined, not assumed + +Both features use daily bars from `prev_bars` (strictly before entry date — lookahead-free). +These are the next natural axes after: + - V25: FINRA short-volume axis NULL (Pearson max 0.062) + - V26: ORB tape ignition NULL (range_coil_orb Pearson=-0.128 but p~0.10, n=101 < 120) + +Method: + 1. Run V24 simulation over 200d window → 101 trades with r_multiple + 2. For each trade, compute RSI-14 and BB %B from prev_bars (daily bars before entry) + 3. Also compute RSI-OBV interaction: RSI × obv_slope_20 / sqrt(2) — additive signal? + 4. Report Pearson, tercile WR/avg_R, gate outcomes + 5. Pairwise: vs obv_slope_20 and avg_daily_vol_14d (G5a/G5b) + +Gates: + G1: |Pearson| ≥ 0.07 on ≥ 120 trades (relaxed to 60 if coverage < 80%) + G2: |top − bottom tercile avg_R| ≥ 0.30R + G3: |top − bottom tercile WR| ≥ 5pp + G4: feature coverage ≥ 50% + G5a: |ρ(feature, obv_slope_20)| < 0.70 + G5b: |ρ(feature, avg_daily_vol_14d)| < 0.70 +""" +from __future__ import annotations + +import concurrent.futures +import datetime as dt +import os +import sys +from pathlib import Path + +import pyarrow.parquet as pq +import yaml +from zoneinfo import ZoneInfo + +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +from libs.common.time_utils import trading_days_between +from libs.intraday.domain import ORBStrategyParams +from libs.intraday.features import compute_obv_slope_approx, enrich_daily_bars +from libs.intraday.orb_simulator import ORBSimulationState, run_orb_simulation_with_state +from libs.intraday.screener import orb_pre_screen_candidates + +# ── Config ────────────────────────────────────────────────────────────────── +V24_CONFIG = "configs/intraday/strategies/orb_gainers_v24_quality_overlay.yaml" +UNIVERSE_FILE = "configs/symbols_midlarge_snapshot_exact.yaml" +INTRADAY_CACHE_DIR = "data/cache/intraday" +LOOKBACK_DAYS = 200 +FEATURE_LOOKBACK_TRADING = 30 # RSI-14 needs 15 prior bars; BB needs 20 + +_ET = ZoneInfo("America/New_York") +_MKT_OPEN = dt.time(9, 30) +_MKT_CLOSE = dt.time(16, 0) + + +# ── Daily Bar Builder ───────────────────────────────────────────────────────── + +def _parse_ts(ts_raw: object) -> dt.datetime: + s = str(ts_raw) + if s.endswith("Z"): + s = s[:-1] + "+00:00" + return dt.datetime.fromisoformat(s).astimezone(_ET) + + +def _build_daily_bar_from_intraday(path: Path, date: str) -> dict | None: + try: + table = pq.read_table(str(path)) + rows = table.to_pydict() + except Exception: + return None + opens, highs, lows, closes, vols = [], [], [], [], [] + for i, ts_raw in enumerate(rows.get("timestamp", [])): + try: + ts = _parse_ts(ts_raw) + except Exception: + continue + if _MKT_OPEN <= ts.time() < _MKT_CLOSE: + opens.append(float(rows["open"][i] or 0)) + highs.append(float(rows["high"][i] or 0)) + lows.append(float(rows["low"][i] or 0)) + closes.append(float(rows["close"][i] or 0)) + vols.append(float(rows["volume"][i] or 0)) + if not opens: + return None + return { + "date": date, "open": opens[0], "high": max(highs), + "low": min(lows), "close": closes[-1], "volume": sum(vols), + } + + +def build_daily_bars(tickers: list[str], dates: list[str], workers: int = 8) -> dict[str, list[dict]]: + root = Path(INTRADAY_CACHE_DIR) + + def _load(ticker: str) -> tuple[str, list[dict]]: + d_path = root / ticker + if not d_path.is_dir(): + return ticker, [] + bars: list[dict] = [] + for date in dates: + p = d_path / f"{date}.parquet" + if not p.exists(): + continue + bar = _build_daily_bar_from_intraday(p, date) + if bar and bar["close"] > 0: + bars.append(bar) + return ticker, bars + + result: dict[str, list[dict]] = {} + with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as ex: + for ticker, bars in ex.map(_load, tickers): + if bars: + result[ticker] = bars + return result + + +def load_intraday_bulk(candidates: dict[str, list[str]]) -> dict[str, dict[str, list[dict]]]: + import pandas as pd + result: dict[str, dict[str, list[dict]]] = {} + for date, tickers in candidates.items(): + day_bars: dict[str, list[dict]] = {} + for ticker in tickers: + p = Path(INTRADAY_CACHE_DIR) / ticker / f"{date}.parquet" + if not p.exists(): + continue + try: + df = pd.read_parquet(str(p)) + if not df.empty and len(df) >= 5: + day_bars[ticker] = df.to_dict("records") + except Exception: + pass + if day_bars: + result[date] = day_bars + return result + + +# ── Feature Computations (dict-based daily bars, lookahead-free) ────────────── + +def compute_rsi_14(prev_bars: list[dict]) -> float | None: + """RSI(14) from last 15 bars in prev_bars (14 price changes). Lookahead-free.""" + if len(prev_bars) < 15: + return None + recent = sorted(prev_bars, key=lambda b: b["date"])[-15:] + gains, losses = [], [] + for i in range(14): + chg = recent[i + 1]["close"] - recent[i]["close"] + if chg >= 0: + gains.append(chg); losses.append(0.0) + else: + gains.append(0.0); losses.append(abs(chg)) + avg_gain = sum(gains) / 14 + avg_loss = sum(losses) / 14 + if avg_loss == 0: + return 100.0 + rs = avg_gain / avg_loss + return 100.0 - (100.0 / (1.0 + rs)) + + +def compute_bb_pct_b(prev_bars: list[dict], window: int = 20) -> float | None: + """BB %B: (last_close - lower_band) / (upper_band - lower_band). >1 = above upper.""" + if len(prev_bars) < window: + return None + recent = sorted(prev_bars, key=lambda b: b["date"])[-window:] + closes = [b["close"] for b in recent] + sma = sum(closes) / window + if sma <= 0: + return None + std = (sum((c - sma) ** 2 for c in closes) / window) ** 0.5 + if std <= 0: + return 0.5 + band_w = 4 * std # upper - lower = 4σ + if band_w <= 0: + return 0.5 + return (closes[-1] - (sma - 2 * std)) / band_w + + +# ── Stats Helpers ───────────────────────────────────────────────────────────── + +def pearson(xs: list[float], ys: list[float]) -> float | None: + if len(xs) != len(ys) or len(xs) < 2: + return None + n = len(xs) + xm, ym = sum(xs) / n, sum(ys) / n + num = sum((xs[i] - xm) * (ys[i] - ym) for i in range(n)) + dx = (sum((x - xm) ** 2 for x in xs)) ** 0.5 + dy = (sum((y - ym) ** 2 for y in ys)) ** 0.5 + if dx <= 0 or dy <= 0: + return None + return num / (dx * dy) + + +def tercile_stats(vals: list[float], outcomes_r: list[float]) -> dict: + if len(vals) < 6: + return {} + pairs = sorted(zip(vals, outcomes_r), key=lambda p: p[0]) + n = len(pairs) + t = n // 3 + + def stats(sub): + ys = [p[1] for p in sub] + wr = sum(1 for y in ys if y > 0) / len(ys) if ys else 0.0 + avg = sum(ys) / len(ys) if ys else 0.0 + return {"n": len(ys), "wr": wr, "avg_r": avg} + + return {"low": stats(pairs[:t]), "mid": stats(pairs[t:2*t]), "high": stats(pairs[2*t:])} + + +# ── Main ───────────────────────────────────────────────────────────────────── + +def main() -> None: + print("=== V27 ORB Momentum Features Diagnostic (RSI-14 + BB %B) ===\n") + + # 1. Load V24 config + with open(V24_CONFIG) as f: + raw = yaml.safe_load(f) + params = ORBStrategyParams(**raw["orb_strategy"]) + print(f"V24 config loaded. weight_obv_slope={params.weight_obv_slope}") + + # 2. Determine 200d trading window + today = dt.date(2026, 4, 21) + all_td = trading_days_between(today - dt.timedelta(days=400), today) + trading_days_list = [d.isoformat() for d in all_td[-LOOKBACK_DAYS:]] + print(f"Window: {trading_days_list[0]} → {trading_days_list[-1]} ({len(trading_days_list)} trading days)") + + extended_td = [d.isoformat() for d in all_td[-(LOOKBACK_DAYS + FEATURE_LOOKBACK_TRADING + 10):]] + first_cal = dt.date.fromisoformat(extended_td[0]) + last_cal = dt.date.fromisoformat(trading_days_list[-1]) + needed_dates: list[str] = [] + d = first_cal + while d <= last_cal: + needed_dates.append(d.isoformat()) + d += dt.timedelta(days=1) + + # 3. Load universe + with open(UNIVERSE_FILE) as f: + udata = yaml.safe_load(f) + universe = udata.get("symbols", udata) if isinstance(udata, dict) else udata + if "QQQ" not in universe: + universe = list(universe) + ["QQQ"] + print(f"Universe: {len(universe)} tickers") + + # 4. Build daily bars + enrichment + print(f"\nBuilding daily bars ({len(needed_dates)} calendar days)...") + daily_bars = build_daily_bars(universe, needed_dates) + print(f"Built daily bars for {len(daily_bars)} tickers") + print("Computing enrichment...") + enrichment = enrich_daily_bars(daily_bars, trading_days_list) + print(f"Enrichment for {len(enrichment)} tickers") + + # 5. Pre-screen + load intraday + candidates = orb_pre_screen_candidates( + daily_bars, trading_days_list, enrichment, + min_price=params.min_price, + min_atr=params.min_atr_14, + min_avg_dollar_vol=params.min_avg_dollar_volume, + max_per_day=None, + ) + total_pairs = sum(len(v) for v in candidates.values()) + print(f"Pre-screened: {total_pairs} ticker-days") + print("Loading intraday bars...") + all_intraday = load_intraday_bulk(candidates) + print(f"Loaded: {sum(len(v) for v in all_intraday.values())} ticker-days") + + # 6. Run V24 simulation + print("\nRunning V24 simulation...") + state = ORBSimulationState(equity=params.initial_capital) + day_results, _ = run_orb_simulation_with_state( + all_intraday, trading_days_list, params, enrichment, state=state, + ) + all_trades = [t for dr in day_results for t in dr.trades] + trades_with_r = [t for t in all_trades if getattr(t, "r_multiple_at_exit", None) is not None] + print(f"Total trades: {len(all_trades)}, with r_multiple: {len(trades_with_r)}") + + if len(trades_with_r) < 20: + print("ABORT: fewer than 20 trades with r_multiple") + return + + # 7. Compute features per trade + print("\nComputing RSI-14 and BB %B features per trade...") + sorted_daily: dict[str, list[dict]] = { + t: sorted(bars, key=lambda b: b["date"]) + for t, bars in daily_bars.items() + } + + annotated: list[dict] = [] + missing: dict[str, int] = {"rsi_14": 0, "bb_pct_b": 0, "obv_slope": 0} + + for trade in trades_with_r: + ticker = trade.ticker + date = str(trade.date)[:10] + r = float(trade.r_multiple_at_exit) + + bars_t = sorted_daily.get(ticker, []) + prev_bars = [b for b in bars_t if b["date"][:10] < date] + + f_rsi = compute_rsi_14(prev_bars) + f_bb = compute_bb_pct_b(prev_bars) + f_obv = compute_obv_slope_approx(prev_bars, lookback=20) + avg_vol = enrichment.get(ticker, {}).get(date, {}).get("avg_daily_vol_14d") + + for fname, fval in [("rsi_14", f_rsi), ("bb_pct_b", f_bb), ("obv_slope", f_obv)]: + if fval is None: + missing[fname] += 1 + + annotated.append({ + "ticker": ticker, "date": date, "r": r, "win": r > 0, + "rsi_14": f_rsi, + "bb_pct_b": f_bb, + "obv_slope": f_obv, + "avg_daily_vol": avg_vol, + }) + + total = len(annotated) + print(f"Annotated: {total} trades") + for fname in ["rsi_14", "bb_pct_b", "obv_slope"]: + print(f" Missing {fname}: {missing[fname]}/{total}") + + # 8. Feature analysis — empirically determine best tercile direction + feature_defs = [ + # (key, description, hypothesized best tercile — empirically verified) + ("rsi_14", "RSI-14 prior day: momentum state before gap-up", "high"), + ("bb_pct_b", "BB %B prior day: price position in Bollinger Band", "low"), + ] + + print("\n" + "=" * 90) + print("FEATURE ANALYSIS — V24 200d trade set") + print("=" * 90) + + results: dict[str, dict | None] = {} + for feat_name, description, hypothesized_best in feature_defs: + valid = [(a[feat_name], a["r"]) for a in annotated if a[feat_name] is not None] + if len(valid) < 20: + print(f"\n{feat_name}: SKIP — only {len(valid)} valid trades (need ≥20)") + results[feat_name] = None + continue + + vals = [v[0] for v in valid] + rs = [v[1] for v in valid] + overall_wr = sum(1 for v in valid if v[1] > 0) / len(valid) + rho = pearson(vals, rs) + tstat = tercile_stats(vals, rs) + coverage_pct = len(valid) / total + + print(f"\n{'─'*60}") + print(f"FEATURE: {feat_name}") + print(f" Description: {description}") + print(f" n={len(valid)}/{total} ({coverage_pct*100:.0f}% coverage), overall WR={overall_wr*100:.1f}%") + rho_abs = abs(rho) if rho is not None else 0.0 + print(f" Pearson(feature, r_multiple) = {rho:.4f}" if rho is not None else " Pearson = n/a") + + if tstat: + h, m, lo = tstat["high"], tstat["mid"], tstat["low"] + print(f" Tercile breakdown (low→high feature value):") + print(f" Bottom: n={lo['n']}, WR={lo['wr']*100:.1f}%, avg_R={lo['avg_r']:+.3f}") + print(f" Middle: n={m['n']}, WR={m['wr']*100:.1f}%, avg_R={m['avg_r']:+.3f}") + print(f" Top: n={h['n']}, WR={h['wr']*100:.1f}%, avg_R={h['avg_r']:+.3f}") + + # Empirically determine best tercile (higher avg_R wins) + if h["avg_r"] >= lo["avg_r"]: + best_tercile = "high" + worst_tercile = "low" + else: + best_tercile = "low" + worst_tercile = "high" + direction_match = best_tercile == hypothesized_best + print(f" Empirical best: '{best_tercile}' tercile (hypothesis: '{hypothesized_best}' → {'✓ confirmed' if direction_match else '✗ INVERTED'})") + + best = tstat[best_tercile] + worst = tstat[worst_tercile] + min_trades = 120 if coverage_pct >= 0.80 else 60 + g1 = rho_abs >= 0.07 and len(valid) >= min_trades + g2 = best["avg_r"] - worst["avg_r"] >= 0.30 + g3 = best["wr"] >= worst["wr"] + 0.05 + g4 = coverage_pct >= 0.50 + n_failed = sum([not g1, not g2, not g3, not g4]) + overall_pass = n_failed == 0 + + print(f" Gates (best='{best_tercile}' tercile):") + print(f" G1 |Pearson|≥0.07 + n≥{min_trades}: {rho_abs:.4f}, n={len(valid)} → {'PASS ✓' if g1 else 'FAIL ✗'}") + print(f" G2 avg_R gap ≥ 0.30R: {best['avg_r'] - worst['avg_r']:+.3f} → {'PASS ✓' if g2 else 'FAIL ✗'}") + print(f" G3 WR gap ≥ 5pp: {(best['wr'] - worst['wr'])*100:+.1f}pp → {'PASS ✓' if g3 else 'FAIL ✗'}") + print(f" G4 coverage ≥ 50%: {coverage_pct*100:.0f}% → {'PASS ✓' if g4 else 'FAIL ✗'}") + print(f" VERDICT: {'ALL GATES PASS → PROCEED TO PHASE 2' if overall_pass else f'FAIL ({n_failed} gate(s) failed)'}") + + results[feat_name] = { + "pass": overall_pass, "pearson": rho, "stats": tstat, + "n": len(valid), "coverage": coverage_pct, + "best_tercile": best_tercile, "hypothesized_best": hypothesized_best, + } + + # 9. Pairwise correlations + print(f"\n{'─'*60}") + print("PAIRWISE CORRELATIONS:") + for feat_name in [fd[0] for fd in feature_defs]: + for ref_key, ref_label, gate_name in [ + ("obv_slope", "obv_slope_20", "G5a"), + ("avg_daily_vol", "avg_daily_vol_14d", "G5b"), + ]: + combined = [ + (a[feat_name], a[ref_key]) for a in annotated + if a[feat_name] is not None and a[ref_key] is not None + ] + if len(combined) >= 10: + rho_x = pearson([c[0] for c in combined], [c[1] for c in combined]) + if rho_x is not None: + gate_pass = abs(rho_x) < 0.70 + print(f" ρ({feat_name[:18]:18s}, {ref_label}): {rho_x:+.4f} {gate_name}: {'PASS ✓' if gate_pass else 'FAIL ✗'}") + + print("\n Feature intercorrelation:") + combined_rr = [ + (a["rsi_14"], a["bb_pct_b"]) for a in annotated + if a["rsi_14"] is not None and a["bb_pct_b"] is not None + ] + if combined_rr: + rho_cross = pearson([c[0] for c in combined_rr], [c[1] for c in combined_rr]) + print(f" ρ(rsi_14, bb_pct_b): {rho_cross:.4f}" if rho_cross is not None else " ρ(rsi_14, bb_pct_b): n/a") + + # 10. Summary + passing = [name for name, r in results.items() if r is not None and r["pass"]] + failed = [name for name, r in results.items() if r is not None and not r["pass"]] + skipped = [name for name, r in results.items() if r is None] + + print(f"\n{'='*90}") + print("SUMMARY") + print(f"{'='*90}") + print(f"Features passing all gates: {passing if passing else 'NONE'}") + print(f"Features failing gates: {failed if failed else 'NONE'}") + print(f"Features skipped: {skipped if skipped else 'NONE'}") + + if passing: + best = max(passing, key=lambda n: abs(results[n]["pearson"] or 0)) + pearson_val = results[best]["pearson"] + obv_pearson_ref = 0.2349 + weight_magnitude = round(0.05 * min(1.0, abs(pearson_val) / obv_pearson_ref), 2) + weight_magnitude = max(weight_magnitude, 0.02) + best_direction = results[best]["best_tercile"] + weight_sign = +1 if best_direction == "high" else -1 + + print(f"\nVERDICT: PROCEED TO PHASE 2") + print(f" Best feature: {best}") + print(f" Pearson: {pearson_val:.4f}") + print(f" Direction: '{best_direction}' tercile is best → weight = {weight_sign * weight_magnitude:+.3f}") + print(f" Suggested weight_momentum_signal in V27 config: {weight_sign * weight_magnitude:+.3f}") + print(f" → Wire '{best}' into enrich_daily_bars as momentum signal") + print(f" → Add weight_momentum_signal to V27 config (parent V24)") + else: + print(f"\nVERDICT: ABORT — RSI/BB momentum axis null on V24 200d trade set") + print(" → V24 remains champion. Momentum overlay exhausted.") + print(" → Next Ralph iteration: gravitational pull or gap z-score from libs/features/market_features.py") + + +if __name__ == "__main__": + main()