signal_log: persist agent panel verdict + per-persona takes to SQLite
Add panel_verdict, panel_confidence, panel_rationale, panel_takes_json columns to the signals table (additive, idempotent ALTER TABLE migration for existing DBs). _report_row now reads the panel fields attach_panel writes onto the SignalReport, so LLM panel verdicts stop evaporating on process exit. Also documents the Oanda re-pin (round_trip_cost stays $0.30 for cross-book comparability and the live TP gate; $0.65 is the measured headline) and adds the feed-divergence / parity / shadow-scan study tooling referenced by CLAUDE.md.
This commit is contained in:
Harwinder Sekhon
@@ -0,0 +1,246 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Phase 2 — signal-level replay parity (data source change test)
|
||||
===============================================================
|
||||
Replays the REAL decision core (`SignalAggregator.analyze_from_dataframes`,
|
||||
news off, use_news=False) over the same window for two providers and
|
||||
compares the *decisions* bar-by-bar: direction, composite/final score,
|
||||
quality grade, gate/setup presence, strategy tag, and entry/SL/TP levels.
|
||||
|
||||
This turns the Phase-1 price-diff stats into flip rates. Feeds are read
|
||||
from the Phase-1 parquet cache (scripts/feed_divergence_study.py writes
|
||||
them); pass --no-cache-fetch only if you want to fetch fresh via
|
||||
fetch_range first.
|
||||
|
||||
Slicing matches gpu_backtest.py exactly: decision at close of bar `pos`,
|
||||
each TF sliced to bars fully closed by that decision time, primary at the
|
||||
shared grid, so the two feeds are compared on identical timestamps.
|
||||
|
||||
Usage:
|
||||
venv/bin/python scripts/signal_parity_study.py --a oanda --b tiingo \
|
||||
--tf M15 --stride 8 --days 365
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
from config import Timeframe, XAUUSD
|
||||
from pair_signals.xauusd import make_config
|
||||
from signal_aggregator import SignalAggregator
|
||||
|
||||
REPO = Path(__file__).resolve().parent.parent
|
||||
CACHE_DIR = REPO / "backtest" / "cache"
|
||||
RESEARCH_DIR = REPO / "backtest" / "research"
|
||||
|
||||
BAR_SECONDS = {
|
||||
Timeframe.M1: 60, Timeframe.M5: 300, Timeframe.M15: 900,
|
||||
Timeframe.M30: 1800, Timeframe.H1: 3600, Timeframe.H4: 14400,
|
||||
Timeframe.D1: 86400, Timeframe.W1: 604800,
|
||||
}
|
||||
_TF_LOOKUP = {t.value: t for t in Timeframe}
|
||||
_TF_LOOKUP.update({"M1": Timeframe.M1, "M5": Timeframe.M5,
|
||||
"M15": Timeframe.M15, "M30": Timeframe.M30,
|
||||
"H1": Timeframe.H1, "H4": Timeframe.H4,
|
||||
"D1": Timeframe.D1, "W1": Timeframe.W1})
|
||||
_CONFIRMS = {
|
||||
Timeframe.M5: [Timeframe.M15, Timeframe.H1],
|
||||
Timeframe.M15: [Timeframe.M5, Timeframe.H1],
|
||||
Timeframe.H1: [Timeframe.M15, Timeframe.H4],
|
||||
Timeframe.H4: [Timeframe.H1, Timeframe.D1],
|
||||
}
|
||||
|
||||
|
||||
def load_cached(name: str, tf: Timeframe, start: pd.Timestamp,
|
||||
end: pd.Timestamp) -> pd.DataFrame:
|
||||
cp = CACHE_DIR / f"{name}_XAU_USD_{tf.value}_{start:%Y%m%d}_{end:%Y%m%d}.parquet"
|
||||
if not cp.exists():
|
||||
sys.exit(f"missing cache {cp}; run scripts/feed_divergence_study.py "
|
||||
f"--a oanda --b {name} --tfs {tf.value} --days ... first")
|
||||
return pd.read_parquet(cp)
|
||||
|
||||
|
||||
def tag_of(report) -> str:
|
||||
first_bull = report.bullish_reasons[0] if report.bullish_reasons else ""
|
||||
first_bear = report.bearish_reasons[0] if report.bearish_reasons else ""
|
||||
if "XAU PULLBACK" in first_bull or "XAU PULLBACK" in first_bear:
|
||||
return "XAU_PB"
|
||||
if "XAU CHART PATTERN" in first_bull or "XAU CHART PATTERN" in first_bear:
|
||||
return "XAU_CP"
|
||||
if "XAU STRATEGY" in first_bull:
|
||||
return "XAU"
|
||||
return "GENERIC"
|
||||
|
||||
|
||||
def decide(agg, dfs, tss, stride, warmup) -> list:
|
||||
"""Replay decisions over the shared grid, returning one row per bar."""
|
||||
primary = dfs[agg.config.primary_timeframe]
|
||||
dur = pd.Timedelta(seconds=BAR_SECONDS[agg.config.primary_timeframe])
|
||||
|
||||
# positions of sampled bars in the primary frame
|
||||
primary_pos = primary.index.get_indexer(tss)
|
||||
rows = []
|
||||
for i in range(0, len(tss), stride):
|
||||
ts = tss[i]
|
||||
pos = int(primary_pos[i])
|
||||
if pos < warmup:
|
||||
continue
|
||||
slices = {}
|
||||
for tf, full in dfs.items():
|
||||
cutoff = ts # decision_time = ts + dur; bars closed by ts
|
||||
cut = int(full.index.searchsorted(cutoff, side="right"))
|
||||
if cut <= 0:
|
||||
continue
|
||||
slices[tf] = full.iloc[:cut]
|
||||
if agg.config.primary_timeframe not in slices:
|
||||
continue
|
||||
r = agg.analyze_from_dataframes(XAUUSD, slices, use_news=False)
|
||||
ts_setup = r.trade_setup
|
||||
rows.append({
|
||||
"ts": ts,
|
||||
"direction": r.final_signal.value,
|
||||
"final_score": r.final_score,
|
||||
"primary_score": r.primary_score,
|
||||
"grade": r.signal_quality,
|
||||
"has_setup": ts_setup is not None,
|
||||
"tag": tag_of(r) if ts_setup else "",
|
||||
"entry": ts_setup.entry_price if ts_setup else np.nan,
|
||||
"sl": ts_setup.stop_loss if ts_setup else np.nan,
|
||||
"tp1": ts_setup.take_profit_1 if ts_setup else np.nan,
|
||||
"tp2": ts_setup.take_profit_2 if ts_setup else np.nan,
|
||||
})
|
||||
return rows
|
||||
|
||||
|
||||
def main():
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--a", required=True)
|
||||
ap.add_argument("--b", required=True)
|
||||
ap.add_argument("--tf", default="M15")
|
||||
ap.add_argument("--stride", type=int, default=1,
|
||||
help="sample every Nth shared bar (default 1)")
|
||||
ap.add_argument("--days", type=int, default=365)
|
||||
ap.add_argument("--end", default=None)
|
||||
ap.add_argument("--warmup", type=int, default=300)
|
||||
args = ap.parse_args()
|
||||
|
||||
tf = _TF_LOOKUP.get(args.tf)
|
||||
if tf is None:
|
||||
sys.exit(f"unknown tf {args.tf}")
|
||||
confirms = _CONFIRMS[tf]
|
||||
tfs = [tf] + confirms
|
||||
|
||||
end = pd.Timestamp(args.end, tz="UTC") if args.end \
|
||||
else pd.Timestamp.now(tz="UTC")
|
||||
start = end - pd.Timedelta(days=args.days)
|
||||
|
||||
cfg = make_config(tf, confirms)
|
||||
agg = SignalAggregator(cfg)
|
||||
|
||||
a_dfs, b_dfs = {}, {}
|
||||
for t in tfs:
|
||||
a_dfs[t] = load_cached(args.a, t, start, end)
|
||||
b_dfs[t] = load_cached(args.b, t, start, end)
|
||||
|
||||
# shared grid = primary timestamps present in both feeds
|
||||
shared = a_dfs[tf].index.intersection(b_dfs[tf].index)
|
||||
|
||||
t0 = time.time()
|
||||
print(f"replaying {len(shared)} shared {tf.value} bars "
|
||||
f"(stride {args.stride} → ~{len(shared)//args.stride}) on {args.a}...")
|
||||
a_rows = decide(agg, a_dfs, shared, args.stride, args.warmup)
|
||||
print(f" {args.a}: {len(a_rows)} decisions in {time.time()-t0:.1f}s")
|
||||
t0 = time.time()
|
||||
b_rows = decide(agg, b_dfs, shared, args.stride, args.warmup)
|
||||
print(f" {args.b}: {len(b_rows)} decisions in {time.time()-t0:.1f}s")
|
||||
|
||||
A = pd.DataFrame(a_rows).set_index("ts")
|
||||
B = pd.DataFrame(b_rows).set_index("ts")
|
||||
common = A.index.intersection(B.index)
|
||||
A, B = A.loc[common], B.loc[common]
|
||||
n = len(common)
|
||||
|
||||
print(f"\n{'=' * 78}")
|
||||
print(f"SIGNAL-LEVEL PARITY — {tf.value} — {args.a} vs {args.b} "
|
||||
f"({n:,} bars)")
|
||||
print("=" * 78)
|
||||
|
||||
def pct(x): return f"{100 * x:.2f}%"
|
||||
|
||||
same_dir = (A["direction"] == B["direction"])
|
||||
same_grade = (A["grade"] == B["grade"])
|
||||
same_setup = (A["has_setup"] == B["has_setup"])
|
||||
same_all = same_dir & same_grade & same_setup
|
||||
|
||||
print(f"\nIDENTITY")
|
||||
print(f" same direction : {pct(same_dir.mean())}")
|
||||
print(f" same grade : {pct(same_grade.mean())}")
|
||||
print(f" same setup presence : {pct(same_setup.mean())}")
|
||||
print(f" same dir+grade+setup : {pct(same_all.mean())}")
|
||||
|
||||
both_sig = (A["direction"] != 0) & (B["direction"] != 0)
|
||||
flips = both_sig & (A["direction"] != B["direction"])
|
||||
opp = flips & (A["direction"] == -B["direction"])
|
||||
print(f"\nDIRECTION")
|
||||
print(f" bars both actionable : {both_sig.sum():,} "
|
||||
f"({pct(both_sig.mean())})")
|
||||
print(f" direction flips : {flips.sum():,} "
|
||||
f"({pct(flips.sum()/max(both_sig.sum(),1))} of actionable)")
|
||||
print(f" opposite-sign flips : {opp.sum():,}")
|
||||
|
||||
grade_a = set(A.loc[A["has_setup"], "grade"])
|
||||
print(f"\nGRADE (of {args.a} setups, grades seen: {sorted(grade_a)})")
|
||||
changed = A.loc[both_sig, "grade"] != B.loc[both_sig, "grade"]
|
||||
print(f" grade changed on actionable bars: "
|
||||
f"{changed.sum():,} ({pct(changed.mean())})")
|
||||
|
||||
# score deltas on shared bars
|
||||
ds = (A["final_score"] - B["final_score"]).abs()
|
||||
dp = (A["primary_score"] - B["primary_score"]).abs()
|
||||
print(f"\nSCORE Δ ({args.a} − {args.b})")
|
||||
print(f" final_score mean {ds.mean():6.2f} p95 {ds.quantile(.95):6.2f} "
|
||||
f"max {ds.max():6.2f}")
|
||||
print(f" primary_score mean {dp.mean():6.2f} p95 {dp.quantile(.95):6.2f} "
|
||||
f"max {dp.max():6.2f}")
|
||||
|
||||
# setup price diffs on bars where BOTH generated a setup
|
||||
both_setup = A["has_setup"] & B["has_setup"]
|
||||
if both_setup.any():
|
||||
print(f"\nSETUP LEVELS (both-setup bars: {both_setup.sum():,})")
|
||||
for col, label in (("entry", "entry"), ("sl", "SL"), ("tp1", "TP1"),
|
||||
("tp2", "TP2")):
|
||||
d = (A.loc[both_setup, col] - B.loc[both_setup, col]).abs()
|
||||
print(f" {label:<6} mean ${d.mean():6.3f} p95 ${d.quantile(.95):7.3f} "
|
||||
f"max ${d.max():7.2f}")
|
||||
|
||||
# tag disagreement on both-setup bars
|
||||
if both_setup.any():
|
||||
same_tag = (A.loc[both_setup, "tag"] == B.loc[both_setup, "tag"])
|
||||
print(f" same strategy tag : {pct(same_tag.mean())}")
|
||||
|
||||
# where do flips cluster (hour)?
|
||||
flip_hours = A.loc[flips].index.hour.value_counts().sort_index()
|
||||
if len(flip_hours):
|
||||
print(f"\nFLIPS BY UTC HOUR")
|
||||
for h, c in flip_hours.items():
|
||||
print(f" {h:02d}: {c}")
|
||||
|
||||
out = {
|
||||
"tf": tf.value, "a": args.a, "b": args.b, "bars": n,
|
||||
"same_dir": same_dir.mean(), "same_grade": same_grade.mean(),
|
||||
"same_setup": same_setup.mean(), "same_all": same_all.mean(),
|
||||
"flip_rate_actionable": flips.sum() / max(both_sig.sum(), 1),
|
||||
"mean_abs_final_score_delta": ds.mean(),
|
||||
"mean_abs_primary_score_delta": dp.mean(),
|
||||
}
|
||||
print(f"\n{out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user