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