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"""
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V28 ORB Volatility/Compression Features Diagnostic
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Tests three features already computed in enrich_daily_bars — no new data needed:
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gap_zscore_20d : How unusual is today's gap vs prior 20d gap history.
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High = anomalous gap-up = fresh institutional demand?
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V24 uses raw gap_pct (weight_gap=0.20) but not z-score.
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range_compression_10_60: avg_range_10 / avg_range_60. Low = compressed (coiling)
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before breakout at the DAILY level. Orthogonal to ORB
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tape ignition (1-min range coil, V26 diagnostic).
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atr_ratio_10_60 : ATR(10) / ATR(60). Low = recent volatility below long-term.
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Measures whether stock has "calmed down" before the gap event.
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Context: V25 (FINRA short-vol) FAILED. V26 (tape ignition) FAILED. V27 (RSI/BB) FAILED —
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RSI-14 redundant with OBV-slope (ρ=0.726). Next: orthogonal axes that avoid volume/momentum.
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Gates:
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G1: |Pearson| ≥ 0.07 on ≥ 120 trades (relaxed to 60 if coverage < 80%)
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G2: |top − bottom tercile avg_R| ≥ 0.30R
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G3: |top − bottom tercile WR| ≥ 5pp
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G4: feature coverage ≥ 50%
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G5a: |ρ(feature, obv_slope_20)| < 0.70
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G5b: |ρ(feature, avg_daily_vol_14d)| < 0.70
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"""
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from __future__ import annotations
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import concurrent.futures
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import datetime as dt
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import os
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import sys
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from pathlib import Path
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import pyarrow.parquet as pq
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import yaml
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from zoneinfo import ZoneInfo
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")))
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from libs.common.time_utils import trading_days_between
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from libs.intraday.domain import ORBStrategyParams
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from libs.intraday.features import compute_obv_slope_approx, enrich_daily_bars
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from libs.intraday.orb_simulator import ORBSimulationState, run_orb_simulation_with_state
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from libs.intraday.screener import orb_pre_screen_candidates
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# ── Config ──────────────────────────────────────────────────────────────────
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V24_CONFIG = "configs/intraday/strategies/orb_gainers_v24_quality_overlay.yaml"
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UNIVERSE_FILE = "configs/symbols_midlarge_snapshot_exact.yaml"
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INTRADAY_CACHE_DIR = "data/cache/intraday"
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LOOKBACK_DAYS = 400
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FEATURE_LOOKBACK_TRADING = 30
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_ET = ZoneInfo("America/New_York")
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_MKT_OPEN = dt.time(9, 30)
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_MKT_CLOSE = dt.time(16, 0)
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# ── Daily Bar Builder ─────────────────────────────────────────────────────────
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def _parse_ts(ts_raw: object) -> dt.datetime:
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s = str(ts_raw)
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if s.endswith("Z"):
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s = s[:-1] + "+00:00"
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return dt.datetime.fromisoformat(s).astimezone(_ET)
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def _build_daily_bar(path: Path, date: str) -> dict | None:
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try:
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table = pq.read_table(str(path))
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rows = table.to_pydict()
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except Exception:
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return None
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opens, highs, lows, closes, vols = [], [], [], [], []
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for i, ts_raw in enumerate(rows.get("timestamp", [])):
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try:
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ts = _parse_ts(ts_raw)
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except Exception:
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continue
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if _MKT_OPEN <= ts.time() < _MKT_CLOSE:
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opens.append(float(rows["open"][i] or 0))
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highs.append(float(rows["high"][i] or 0))
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lows.append(float(rows["low"][i] or 0))
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closes.append(float(rows["close"][i] or 0))
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vols.append(float(rows["volume"][i] or 0))
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if not opens:
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return None
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return {
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"date": date, "open": opens[0], "high": max(highs),
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"low": min(lows), "close": closes[-1], "volume": sum(vols),
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}
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def build_daily_bars(tickers: list[str], dates: list[str], workers: int = 8) -> dict[str, list[dict]]:
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root = Path(INTRADAY_CACHE_DIR)
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def _load(ticker: str) -> tuple[str, list[dict]]:
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d_path = root / ticker
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if not d_path.is_dir():
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return ticker, []
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bars: list[dict] = []
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for date in dates:
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p = d_path / f"{date}.parquet"
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if not p.exists():
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continue
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bar = _build_daily_bar(p, date)
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if bar and bar["close"] > 0:
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bars.append(bar)
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return ticker, bars
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result: dict[str, list[dict]] = {}
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with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as ex:
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for ticker, bars in ex.map(_load, tickers):
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if bars:
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|
result[ticker] = bars
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return result
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def load_intraday_bulk(candidates: dict[str, list[str]]) -> dict[str, dict[str, list[dict]]]:
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|
import pandas as pd
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|
result: dict[str, dict[str, list[dict]]] = {}
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|
for date, tickers in candidates.items():
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|
day_bars: dict[str, list[dict]] = {}
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|
for ticker in tickers:
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p = Path(INTRADAY_CACHE_DIR) / ticker / f"{date}.parquet"
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|
if not p.exists():
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|
continue
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try:
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|
df = pd.read_parquet(str(p))
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|
if not df.empty and len(df) >= 5:
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|
day_bars[ticker] = df.to_dict("records")
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|
except Exception:
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|
|
pass
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|
if day_bars:
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|
result[date] = day_bars
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|
return result
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|
# ── Stats Helpers ─────────────────────────────────────────────────────────────
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|
|
|
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
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|
num = sum((xs[i] - xm) * (ys[i] - ym) for i in range(n))
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|
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:
|
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|
return None
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|
|
return num / (dx * dy)
|
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|
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|
|
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])
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|
|
n = len(pairs)
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|
|
t = n // 3
|
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|
|
|
|
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
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|
avg = sum(ys) / len(ys) if ys else 0.0
|
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|
return {"n": len(ys), "wr": wr, "avg_r": avg}
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|
|
return {"low": stats(pairs[:t]), "mid": stats(pairs[t:2*t]), "high": stats(pairs[2*t:])}
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|
# ── Main ─────────────────────────────────────────────────────────────────────
|
|
|
|
|
|
def main() -> None:
|
|
|
print("=== V28 ORB Volatility/Compression Features Diagnostic ===\n")
|
|
|
|
|
|
with open(V24_CONFIG) as f:
|
|
|
raw = yaml.safe_load(f)
|
|
|
params = ORBStrategyParams(**raw["orb_strategy"])
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|
|
print(f"V24 config loaded. weight_obv_slope={params.weight_obv_slope}")
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|
|
today = dt.date(2026, 4, 21)
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|
|
all_td = trading_days_between(today - dt.timedelta(days=700), today)
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|
|
trading_days_list = [d.isoformat() for d in all_td[-LOOKBACK_DAYS:]]
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|
|
print(f"Window: {trading_days_list[0]} → {trading_days_list[-1]} ({len(trading_days_list)} trading days)")
|
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|
|
extended_td = [d.isoformat() for d in all_td[-(LOOKBACK_DAYS + FEATURE_LOOKBACK_TRADING + 10):]]
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|
|
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)
|
|
|
|
|
|
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")
|
|
|
|
|
|
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")
|
|
|
|
|
|
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")
|
|
|
|
|
|
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
|
|
|
|
|
|
# Features from enrichment — zero extra computation needed
|
|
|
print("\nExtracting enrichment-based 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] = {
|
|
|
"gap_zscore_20d": 0, "range_compression_10_60": 0,
|
|
|
"atr_ratio_10_60": 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)
|
|
|
|
|
|
enrich_day = enrichment.get(ticker, {}).get(date, {})
|
|
|
f_gap_z = enrich_day.get("gap_zscore_20d")
|
|
|
f_range_comp = enrich_day.get("range_compression_10_60")
|
|
|
f_atr_ratio = enrich_day.get("atr_ratio_10_60")
|
|
|
avg_vol = enrich_day.get("avg_daily_vol_14d")
|
|
|
|
|
|
bars_t = sorted_daily.get(ticker, [])
|
|
|
prev_bars = [b for b in bars_t if b["date"][:10] < date]
|
|
|
f_obv = compute_obv_slope_approx(prev_bars, lookback=20)
|
|
|
|
|
|
for fname, fval in [
|
|
|
("gap_zscore_20d", f_gap_z),
|
|
|
("range_compression_10_60", f_range_comp),
|
|
|
("atr_ratio_10_60", f_atr_ratio),
|
|
|
("obv_slope", f_obv),
|
|
|
]:
|
|
|
if fval is None:
|
|
|
missing[fname] += 1
|
|
|
|
|
|
annotated.append({
|
|
|
"ticker": ticker, "date": date, "r": r, "win": r > 0,
|
|
|
"gap_zscore_20d": f_gap_z,
|
|
|
"range_compression_10_60": f_range_comp,
|
|
|
"atr_ratio_10_60": f_atr_ratio,
|
|
|
"obv_slope": f_obv,
|
|
|
"avg_daily_vol": avg_vol,
|
|
|
})
|
|
|
|
|
|
total = len(annotated)
|
|
|
print(f"Annotated: {total} trades")
|
|
|
for fname in ["gap_zscore_20d", "range_compression_10_60", "atr_ratio_10_60"]:
|
|
|
print(f" Missing {fname}: {missing[fname]}/{total}")
|
|
|
|
|
|
# Feature analysis
|
|
|
feature_defs = [
|
|
|
("gap_zscore_20d", "Today's gap z-score vs 20d history — anomalous gap = fresh demand?", "high"),
|
|
|
("range_compression_10_60", "avg_range(10d) / avg_range(60d) — low = coiling before breakout", "low"),
|
|
|
("atr_ratio_10_60", "ATR(10) / ATR(60) — low = volatility compressed below long-term avg", "low"),
|
|
|
]
|
|
|
|
|
|
print("\n" + "=" * 90)
|
|
|
print(f"FEATURE ANALYSIS — V24 {LOOKBACK_DAYS}d 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
|
|
|
rho_abs = abs(rho) if rho is not None else 0.0
|
|
|
|
|
|
# Empirically determine best tercile
|
|
|
if tstat:
|
|
|
best_tercile = "high" if tstat["high"]["avg_r"] >= tstat["low"]["avg_r"] else "low"
|
|
|
worst_tercile = "low" if best_tercile == "high" else "high"
|
|
|
direction_match = best_tercile == hypothesized_best
|
|
|
|
|
|
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}%")
|
|
|
print(f" Pearson(feature, r_multiple) = {rho:.4f}" if rho is not None else " Pearson = n/a")
|
|
|
print(f" Tercile breakdown (low→high feature value):")
|
|
|
h, m, lo = tstat["high"], tstat["mid"], tstat["low"]
|
|
|
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}")
|
|
|
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,
|
|
|
}
|
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# Pairwise correlations
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print(f"\n{'─'*60}")
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print("PAIRWISE CORRELATIONS:")
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for feat_name in [fd[0] for fd in feature_defs]:
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for ref_key, ref_label, gate_name in [
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("obv_slope", "obv_slope_20", "G5a"),
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("avg_daily_vol", "avg_daily_vol_14d", "G5b"),
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]:
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combined = [
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(a[feat_name], a[ref_key]) for a in annotated
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if a[feat_name] is not None and a[ref_key] is not None
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]
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if len(combined) >= 10:
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rho_x = pearson([c[0] for c in combined], [c[1] for c in combined])
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if rho_x is not None:
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gate_pass = abs(rho_x) < 0.70
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print(f" ρ({feat_name[:28]:28s}, {ref_label}): {rho_x:+.4f} {gate_name}: {'PASS ✓' if gate_pass else 'FAIL ✗'}")
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|
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print("\n Inter-feature correlations:")
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|
|
feat_keys = [fd[0] for fd in feature_defs]
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|
|
for i, f1 in enumerate(feat_keys):
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|
for f2 in feat_keys[i+1:]:
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|
combined = [(a[f1], a[f2]) for a in annotated if a[f1] is not None and a[f2] is not None]
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|
|
if len(combined) >= 10:
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|
|
rho_x = pearson([c[0] for c in combined], [c[1] for c in combined])
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|
|
if rho_x is not None:
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|
|
print(f" ρ({f1[:26]:26s}, {f2[:26]:26s}): {rho_x:+.4f}")
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|
|
|
|
|
passing = [name for name, r in results.items() if r is not None and r["pass"]]
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|
failed = [name for name, r in results.items() if r is not None and not r["pass"]]
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|
|
skipped = [name for name, r in results.items() if r is None]
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|
|
|
|
|
print(f"\n{'='*90}")
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|
|
print("SUMMARY")
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|
|
print(f"{'='*90}")
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|
|
print(f"Features passing all gates: {passing if passing else 'NONE'}")
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|
|
print(f"Features failing gates: {failed if failed else 'NONE'}")
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|
|
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_vol_signal in V28 config: {weight_sign * weight_magnitude:+.3f}")
|
|
|
print(f" → '{best}' is already in enrich_daily_bars — wire directly into orb_simulator scoring")
|
|
|
else:
|
|
|
print(f"\nVERDICT: ABORT — Volatility/compression axis null on V24 200d trade set")
|
|
|
print(" → V24 remains champion.")
|
|
|
print(" → Next Ralph iteration: gravitational pull from libs/features/market_features.py")
|
|
|
print(" or expand to 400d window to increase n above 120 threshold")
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
main()
|