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.
2026-08-14 20:41:54 -04:00
parent 7ad5129f2a
commit 37ff5b383d
9 changed files with 874 additions and 19 deletions
@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+"""
+Scratchpad — Oanda vs Tiingo candle parity check
+=================================================
+Fetches the last N closed XAU/USD candles from Oanda and Tiingo for
+M5/M15/H1 via the normal provider stack (make_data_provider →
+fetch_ohlcv: forming-candle drop + validate/clean included, i.e. exactly
+what the signal pipeline consumes) and reports how well they line up:
+
+  * last-bar timestamp per provider (feed-lag check)
+  * timestamp mismatches (bars present in one feed only)
+  * per-bar OHLC diff table for the common tail
+  * max/mean |diff| per column, with the Close diff also in % of price
+
+Both feeds are mid-price (Oanda mid of MBA candles; Tiingo resampled
+mid), so small diffs are expected — this measures how small.
+
+Usage:
+    venv/bin/python scripts/compare_oanda_tiingo.py [--n 10]
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+from config import PlatformConfig, Timeframe, XAUUSD
+from providers import make_data_provider
+
+TIMEFRAMES = [Timeframe.M5, Timeframe.M15, Timeframe.H1]
+OHLC = ["Open", "High", "Low", "Close"]
+
+
+def fetch_tail(provider, tf, n):
+    """Last n closed bars from one provider, or None on failure."""
+    try:
+        df = provider.fetch_ohlcv(symbol=XAUUSD, timeframe=tf)
+    except Exception as e:  # scratchpad: report, don't crash
+        print(f"  !! {provider.name} {tf.value} fetch failed: {e}")
+        return None
+    return df.tail(n)
+
+
+def compare_tf(tf, oanda_df, tiingo_df, n):
+    print(f"\n{'=' * 78}\n{tf.value} — last {n} closed bars\n{'=' * 78}")
+    if oanda_df is None or tiingo_df is None:
+        print("  skipped (a provider failed)")
+        return
+
+    print(f"  Oanda last bar: {oanda_df.index[-1]}   "
+          f"Tiingo last bar: {tiingo_df.index[-1]}")
+
+    common = oanda_df.index.intersection(tiingo_df.index)
+    only_o = oanda_df.index.difference(tiingo_df.index)
+    only_t = tiingo_df.index.difference(oanda_df.index)
+    if len(only_o):
+        print(f"  bars only in Oanda : {[str(t) for t in only_o]}")
+    if len(only_t):
+        print(f"  bars only in Tiingo: {[str(t) for t in only_t]}")
+    if not len(common):
+        print("  NO common timestamps — feeds are on different bar grids!")
+        return
+
+    o = oanda_df.loc[common, OHLC]
+    t = tiingo_df.loc[common, OHLC]
+    diff = o - t
+
+    print(f"\n  {len(common)}/{n} bars share timestamps "
+          f"(window {common[0]} → {common[-1]})\n")
+
+    # Per-bar Close table — the number that matters most.
+    hdr = f"  {'bar close (UTC)':<20}{'Oanda C':>12}{'Tiingo C':>12}{'ΔC ($)':>10}"
+    print(hdr + f"{'ΔO':>8}{'ΔH':>8}{'ΔL':>8}")
+    for ts in common:
+        row = (f"  {str(ts):<20}{o.loc[ts, 'Close']:>12.2f}"
+               f"{t.loc[ts, 'Close']:>12.2f}{diff.loc[ts, 'Close']:>10.3f}")
+        row += (f"{diff.loc[ts, 'Open']:>8.3f}{diff.loc[ts, 'High']:>8.3f}"
+                f"{diff.loc[ts, 'Low']:>8.3f}")
+        print(row)
+
+    print("\n  summary over common bars:")
+    for col in OHLC:
+        d = diff[col].abs()
+        print(f"    {col:<6} max |Δ| = {d.max():8.4f}   mean |Δ| = {d.mean():8.4f}")
+    px = o["Close"].iloc[-1]
+    dc = diff["Close"].abs()
+    print(f"    Close Δ as % of price: max {dc.max() / px * 100:.4f}%   "
+          f"mean {dc.mean() / px * 100:.4f}%")
+    exact = (diff[OHLC].abs() < 1e-9).all(axis=1).sum()
+    print(f"    bars with identical OHLC: {exact}/{len(common)}")
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--n", type=int, default=10,
+                    help="bars per timeframe to compare (default 10)")
+    args = ap.parse_args()
+
+    cfg = PlatformConfig()
+    oanda = make_data_provider(cfg, "oanda")
+    tiingo = make_data_provider(cfg, "tiingo")
+
+    for tf in TIMEFRAMES:
+        o_df = fetch_tail(oanda, tf, args.n)
+        t_df = fetch_tail(tiingo, tf, args.n)
+        compare_tf(tf, o_df, t_df, args.n)
+
+    print("\nDone. Note: Oanda=mid of MBA candles, Tiingo=resampled mid — "
+          "sub-$1 diffs on gold are normal feed divergence, not a bug.")
+
+
+if __name__ == "__main__":
+    sys.exit(main())