|
|
"""
|
|
|
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__), "../../..")))
|
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|
|
|
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
|
|
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|
|
|
_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)
|
|
|
|
|
|
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|
|
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}")
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print(f" Top: n={h['n']}, WR={h['wr']*100:.1f}%, avg_R={h['avg_r']:+.3f}")
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# Empirically determine best tercile (higher avg_R wins)
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if h["avg_r"] >= lo["avg_r"]:
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best_tercile = "high"
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worst_tercile = "low"
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else:
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best_tercile = "low"
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worst_tercile = "high"
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direction_match = best_tercile == hypothesized_best
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print(f" Empirical best: '{best_tercile}' tercile (hypothesis: '{hypothesized_best}' → {'✓ confirmed' if direction_match else '✗ INVERTED'})")
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best = tstat[best_tercile]
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worst = tstat[worst_tercile]
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min_trades = 120 if coverage_pct >= 0.80 else 60
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g1 = rho_abs >= 0.07 and len(valid) >= min_trades
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g2 = best["avg_r"] - worst["avg_r"] >= 0.30
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g3 = best["wr"] >= worst["wr"] + 0.05
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g4 = coverage_pct >= 0.50
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n_failed = sum([not g1, not g2, not g3, not g4])
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overall_pass = n_failed == 0
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print(f" Gates (best='{best_tercile}' tercile):")
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print(f" G1 |Pearson|≥0.07 + n≥{min_trades}: {rho_abs:.4f}, n={len(valid)} → {'PASS ✓' if g1 else 'FAIL ✗'}")
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print(f" G2 avg_R gap ≥ 0.30R: {best['avg_r'] - worst['avg_r']:+.3f} → {'PASS ✓' if g2 else 'FAIL ✗'}")
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print(f" G3 WR gap ≥ 5pp: {(best['wr'] - worst['wr'])*100:+.1f}pp → {'PASS ✓' if g3 else 'FAIL ✗'}")
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print(f" G4 coverage ≥ 50%: {coverage_pct*100:.0f}% → {'PASS ✓' if g4 else 'FAIL ✗'}")
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print(f" VERDICT: {'ALL GATES PASS → PROCEED TO PHASE 2' if overall_pass else f'FAIL ({n_failed} gate(s) failed)'}")
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results[feat_name] = {
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"pass": overall_pass, "pearson": rho, "stats": tstat,
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"n": len(valid), "coverage": coverage_pct,
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"best_tercile": best_tercile, "hypothesized_best": hypothesized_best,
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}
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# 9. 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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|
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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()
|