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spb/anomaly-atlas Public License

Systematic discovery & rigorous validation of statistical anomalies in open HF market data (hfmarketdata.io) — pre-registered, artifact-null-driven, fully reproducible. Live atlas: www.anomaly-atlas.io

quant-researchmarket-microstructureanomaliesreproducibilityduckdbapple-silicon
Python 61.4% JavaScript 28.7% CSS 8.6% Shell 0.7% Makefile 0.5%

Web: server-rendered SVG figures from results.json (dataviz-validated)

- web/lib/charts.js: expB staleness scatters, expC VR30-excess strip plot
  (FDR emphasis), expD raw-vs-fresh dumbbells; palette validated
  (validate_palette.js all-pairs PASS on card surface), native tooltips,
  ink-token text, selective direct labels
- figures injected on experiment pages + latest figure on home
- rendered and eyeballed (rsvg): fixed tick-label collision

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Simon-Pierre Boucher committed 4 h ago (Aug 12, 2026) parent c4977f2

Showing 7 changed files with +771 and −26

added benchmarks/synthetic/test_gate_expd.py +98 −0
@@ -0,0 +1,98 @@
1 +# =============================================================================
2 +# Project : anomaly-atlas
3 +# File : benchmarks/synthetic/test_gate_expd.py
4 +# Purpose : §8.1 gate for expD: grids, LOCF masks, both-fresh de-artifacting
5 +# Author : Simon-Pierre Boucher
6 +# Contact : contact@spboucher.ai
7 +# Data src : hfmarketdata.io (sole data source)
8 +# Created : 2026-08-12
9 +# Modified : 2026-08-12
10 +# Platform : macOS / Apple Silicon (arm64)
11 +# License : All rights reserved (research code)
12 +# =============================================================================
13 +"""Gate the expD alignment primitives before real data:
14 +
15 + * grid building from bar dicts is exact (slots, NaN gaps, RTH filter);
16 + * LOCF returns carry correct fresh-masks; missing days never leak;
17 + * the both-fresh treatment REMOVES the planted non-synchronicity artifact
18 + that the raw LOCF join manufactures (the core expD claim).
19 +"""
20 +
21 +from __future__ import annotations
22 +
23 +import sys
24 +from pathlib import Path
25 +
26 +import numpy as np
27 +
28 +sys.path.insert(0, str(Path(__file__).resolve().parent))
29 +
30 +from generators import correlated_pair # noqa: E402
31 +
32 +from anomaly_atlas.data.cleaning import (
33 + both_fresh,
34 + locf_day_returns,
35 + nan_xcorr,
36 + rth_day_grids,
37 +)
38 +
39 +SEEDS = [1, 2, 3]
40 +
41 +
42 +def bars_for_day(day: str, prices: dict[str, float]) -> list[dict]:
43 + return [{"datetime": f"{day} {hhmm}:00", "close": p} for hhmm, p in prices.items()]
44 +
45 +
46 +def test_grid_building_slots_and_rth_filter():
47 + bars = bars_for_day("2024-01-02", {"09:30": 100.0, "09:32": 101.0, "15:59": 102.0})
48 + bars += bars_for_day("2024-01-02", {"04:00": 99.0, "16:00": 103.0}) # outside RTH
49 + days = rth_day_grids(bars)
50 + g = days["2024-01-02"]
51 + assert np.isfinite(g[[0, 2, 389]]).all()
52 + assert np.isnan(g[1]) and np.isnan(g[3])
53 + assert np.isfinite(g).sum() == 3 # extended-hours bars excluded
54 +
55 +
56 +def test_locf_masks_and_missing_days():
57 + bars = bars_for_day("2024-01-02", {"09:30": 100.0, "09:32": 101.0, "09:33": 101.5})
58 + days = rth_day_grids(bars)
59 + r, fresh = locf_day_returns(days, ["2024-01-02", "2024-01-03"])
60 + assert len(r) == 389 * 2
61 + assert r[0] == 0.0 and not fresh[0] # 09:31 carried forward
62 + # 09:31->09:32 return ends fresh but STARTS on a carried value -> not fresh
63 + assert abs(r[1]) > 0 and not fresh[1]
64 + # 09:32->09:33: both endpoints fresh -> a genuine 1-minute return
65 + assert abs(r[2]) > 0 and fresh[2]
66 + assert np.isnan(r[389:]).all() # absent day never leaks
67 + assert not fresh[389:].any()
68 +
69 +
70 +def synthetic_pair_day_grids(n_days: int, rho: float, p_obs: float, seed: int):
71 + """Correlated 1min walks; y observed sparsely. Returns (days_x, days_y, day_list)."""
72 + rng = np.random.default_rng(seed)
73 + px, py = correlated_pair(n_days * 390, rho=rho, sigma=0.001, seed=seed)
74 + days_x, days_y, day_list = {}, {}, []
75 + for d in range(n_days):
76 + day = f"2024-02-{d + 1:02d}"
77 + day_list.append(day)
78 + gx = px[d * 390 : (d + 1) * 390].copy()
79 + gy = py[d * 390 : (d + 1) * 390].copy()
80 + mask = rng.random(390) < p_obs
81 + mask[0] = True
82 + gy[~mask] = np.nan
83 + days_x[day] = gx
84 + days_y[day] = gy
85 + return days_x, days_y, day_list
86 +
87 +
88 +def test_both_fresh_removes_planted_nonsync_artifact():
89 + for seed in SEEDS:
90 + dx, dy, dl = synthetic_pair_day_grids(25, rho=0.7, p_obs=0.3, seed=seed)
91 + rx, fx = locf_day_returns(dx, dl)
92 + ry, fy = locf_day_returns(dy, dl)
93 + raw = nan_xcorr(rx, ry, 2)
94 + assert raw[1] > 0.10 # artifact present in the raw LOCF join
95 + bx, by = both_fresh(rx, fx, ry, fy)
96 + sync = nan_xcorr(bx, by, 2)
97 + assert abs(sync[1]) < 0.05 # and gone on the synchronized subsample
98 + assert sync[0] > 0.6 # true contemporaneous corr (rho=0.7) recovered
modified data_manifest/index.jsonl +198 −0
@@ -453,3 +453,201 @@
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650 +{"author": "Simon-Pierre Boucher", "cache_key": "b87017846cfb2f60b4cf2ead3e232b24", "data_source": "hfmarketdata.io", "endpoint": "/v1/bars/futures/ES", "fetched_utc": "2026-08-12T06:46:02Z", "first": "2015-06-10 20:23:00", "last": "2015-07-31 17:14:00", "params": {"adjustment": "contin_UNadj", "end": "2016-01-01", "limit": 50000, "order": "asc", "start": "2015-06-10 20:23:00", "timeframe": "1min"}, "rows": 50000, "sha256": "d1ff7e740b3a63c1b68cd66bd86ab63a94daba37e4be04177505987288b61bad"}
651 +{"author": "Simon-Pierre Boucher", "cache_key": "e0663a26b52935dd0f8051c9aadb4ec0", "data_source": "hfmarketdata.io", "endpoint": "/v1/bars/futures/ES", "fetched_utc": "2026-08-12T06:46:03Z", "first": "2015-07-31 17:14:00", "last": "2015-09-22 07:44:00", "params": {"adjustment": "contin_UNadj", "end": "2016-01-01", "limit": 50000, "order": "asc", "start": "2015-07-31 17:14:00", "timeframe": "1min"}, "rows": 50000, "sha256": "db08bb2f91a67cbb534224e6636eeb392baec609d3f98ac7fdbb656c7182ac52"}
652 +{"author": "Simon-Pierre Boucher", "cache_key": "445e288bf8143ffb040e377f273cd8cd", "data_source": "hfmarketdata.io", "endpoint": "/v1/bars/futures/ES", "fetched_utc": "2026-08-12T06:46:05Z", "first": "2015-09-22 07:44:00", "last": "2015-11-12 01:05:00", "params": {"adjustment": "contin_UNadj", "end": "2016-01-01", "limit": 50000, "order": "asc", "start": "2015-09-22 07:44:00", "timeframe": "1min"}, "rows": 50000, "sha256": "60b1af5b39fcff997d033db731e833290a1ec47bdbc613859191067c8f548016"}
653 +{"author": "Simon-Pierre Boucher", "cache_key": "feba5806d5ceea82ac362ed3f089b499", "data_source": "hfmarketdata.io", "endpoint": "/v1/bars/futures/ES", "fetched_utc": "2026-08-12T06:46:06Z", "first": "2015-11-12 01:05:00", "last": "2015-12-31 16:59:00", "params": {"adjustment": "contin_UNadj", "end": "2016-01-01", "limit": 50000, "order": "asc", "start": "2015-11-12 01:05:00", "timeframe": "1min"}, "rows": 46407, "sha256": "e4174e785a531002bd4ea75c32b00544a709b2875062f9329631b1818b60baff"}
modified experiments/micro/expD_leadlag_scan/benchmark.py +186 −10
@@ -1,7 +1,7 @@
1 1 # =============================================================================
2 2 # Project : anomaly-atlas
3 3 # File : experiments/micro/expD_leadlag_scan/benchmark.py
4 −# Purpose : Benchmark runner: Lead-lag scan: lagged cross-correlations and Granger, separating…
4 +# Purpose : Lead-lag scan — raw LOCF vs both-fresh, artifact share, FDR
5 5 # Author : Simon-Pierre Boucher
6 6 # Contact : contact@spboucher.ai
7 7 # Data src : hfmarketdata.io (sole data source)
@@ -10,25 +10,201 @@
10 10 # Platform : macOS / Apple Silicon (arm64)
11 11 # License : All rights reserved (research code)
12 12 # =============================================================================
13 +"""Experiment D — lead-lag scan (protocol pre-specified in hypothesis.md).
13 14
14 −"""Benchmark entry point for expD_leadlag_scan.
15 −
16 −Must embed the hardware manifest in all result output
17 −(see benchmarks/hardware_manifest.py) and write results to
18 −results/expD_leadlag_scan/<timestamp>/. Uses hfmarketdata.io data ONLY, exclusively
19 −through src/anomaly_atlas/data/hf_client.py.
15 +The design measures T3 directly: every pair reports its raw-LOCF lead, its
16 +both-fresh lead, and the difference (artifact share). Level 0 throughout.
20 17 """
21 18
19 +from __future__ import annotations
20 +
21 +import json
22 22 import sys
23 +from datetime import UTC, datetime
23 24 from pathlib import Path
24 25
25 −sys.path.insert(0, str(Path(__file__).resolve().parents[3] / "benchmarks"))
26 +import numpy as np
27 +
28 +REPO_ROOT = Path(__file__).resolve().parents[3]
29 +sys.path.insert(0, str(REPO_ROOT / "benchmarks"))
30 +sys.path.insert(0, str(REPO_ROOT / "src"))
31 +
26 32 from hardware_manifest import collect_manifest # noqa: E402
27 33
34 +from anomaly_atlas.data.cleaning import ( # noqa: E402
35 + RTH_SLOTS,
36 + both_fresh,
37 + nan_xcorr,
38 + rth_day_grids,
39 +)
40 +from anomaly_atlas.data.hf_client import HFMarketDataClient # noqa: E402
41 +from anomaly_atlas.data.universe import random_stock_universe # noqa: E402
42 +from anomaly_atlas.stats.multiple_testing import ( # noqa: E402
43 + benjamini_hochberg,
44 + bootstrap_pvalue,
45 +)
46 +
47 +ADJ = "adj_split"
48 +LIQUID = ["AAPL", "MSFT", "NVDA", "AMZN", "GOOGL", "META", "TSLA", "JPM", "XOM", "UNH", "QQQ"]
49 +SECTORS = ["XLF", "XLE", "XLK", "XLV", "XLI", "XLY", "XLP", "XLU", "XLB"]
50 +WINDOWS = {"2006-2007": ("2006-01-01", "2008-01-01"),
51 + "2014-2015": ("2014-01-01", "2016-01-01")}
52 +MAX_LAG = 3
53 +BOOT_N, BOOT_SEED = 200, 42
54 +MIN_DAYS, MIN_FRESH = 200, 10_000
55 +
56 +
57 +def day_matrix(days: dict[str, np.ndarray], day_list: list[str]) -> tuple[np.ndarray, np.ndarray]:
58 + """(n_days, 389) LOCF returns + interval-fresh masks, day-aligned."""
59 + rets = np.full((len(day_list), RTH_SLOTS - 1), np.nan)
60 + fresh = np.zeros((len(day_list), RTH_SLOTS - 1), dtype=bool)
61 + for i, day in enumerate(day_list):
62 + p = days.get(day)
63 + if p is None:
64 + continue
65 + observed = np.isfinite(p)
66 + filled = p.copy()
67 + for t in range(1, RTH_SLOTS):
68 + if not observed[t]:
69 + filled[t] = filled[t - 1]
70 + rets[i] = np.diff(filled)
71 + fresh[i] = observed[1:] & observed[:-1]
72 + return rets, fresh
73 +
74 +
75 +def day_block_boot_xcorr(
76 + rx: np.ndarray, fx: np.ndarray, ry: np.ndarray, fy: np.ndarray, lag: int
77 +) -> np.ndarray:
78 + """Bootstrap distribution of the both-fresh xcorr at `lag`, resampling days."""
79 + rng = np.random.default_rng(BOOT_SEED)
80 + n_days = rx.shape[0]
81 + out = np.empty(BOOT_N)
82 + for b in range(BOOT_N):
83 + idx = rng.integers(0, n_days, n_days)
84 + bx, by = both_fresh(rx[idx].ravel(), fx[idx].ravel(), ry[idx].ravel(), fy[idx].ravel())
85 + out[b] = nan_xcorr(bx, by, abs(lag)).get(lag, np.nan)
86 + return out
87 +
88 +
89 +def analyze_pair(rx, fx, ry, fy) -> dict | None:
90 + bx, by = both_fresh(rx.ravel(), fx.ravel(), ry.ravel(), fy.ravel())
91 + n_fresh = int(np.isfinite(bx).sum())
92 + if n_fresh < MIN_FRESH:
93 + return None
94 + raw = nan_xcorr(rx.ravel(), ry.ravel(), MAX_LAG)
95 + fresh_xc = nan_xcorr(bx, by, MAX_LAG)
96 + boot_p1 = day_block_boot_xcorr(rx, fx, ry, fy, 1)
97 + boot_m1 = day_block_boot_xcorr(rx, fx, ry, fy, -1)
98 + return {
99 + "n_fresh_pairs": n_fresh,
100 + "fresh_fraction": round(n_fresh / rx.size, 4),
101 + "raw_xcorr": {str(k): round(v, 5) if np.isfinite(v) else None for k, v in raw.items()},
102 + "fresh_xcorr": {str(k): round(v, 5) if np.isfinite(v) else None
103 + for k, v in fresh_xc.items()},
104 + "artifact_share_+1": round(raw[1] - fresh_xc[1], 5)
105 + if np.isfinite(raw[1]) and np.isfinite(fresh_xc[1]) else None,
106 + "p_fresh_+1": bootstrap_pvalue(boot_p1, 0.0),
107 + "p_fresh_-1": bootstrap_pvalue(boot_m1, 0.0),
108 + }
109 +
28 110
29 111 def main() -> None:
30 − collect_manifest() # embedded in results once implemented
31 − raise NotImplementedError("experiment not yet implemented")
112 + run_utc = datetime.now(UTC)
113 + client = HFMarketDataClient()
114 + random10 = random_stock_universe(
115 + client.tickers("stock", timeframe="1min", adjustment=ADJ))[:10]
116 +
117 + series_spec: dict[str, tuple[str, str, str | None]] = (
118 + {"SPY": ("etf", "SPY", ADJ), "SPX": ("index", "SPX", None)}
119 + | {t: ("stock", t, ADJ) for t in LIQUID if t != "QQQ"}
120 + | {"QQQ": ("etf", "QQQ", ADJ)}
121 + | {t: ("etf", t, ADJ) for t in SECTORS}
122 + | {t: ("stock", t, ADJ) for t in random10}
123 + | {f"ES[{a}]": ("futures", "ES", a)
124 + for a in ("contin_adj_ratio", "contin_adj_absolute", "contin_UNadj")}
125 + )
126 +
127 + pairs: list[dict] = []
128 + for window, (s, e) in WINDOWS.items():
129 + grids: dict[str, dict] = {}
130 + for name, (asset, ticker, adj) in series_spec.items():
131 + if window == "2006-2007" and (name == "SPX" or name.startswith("ES[")):
132 + continue
133 + bars = client.get_bars(asset, ticker, "1min", adj, s, e)
134 + g = rth_day_grids(bars)
135 + if len(g) >= MIN_DAYS:
136 + grids[name] = g
137 + print(f"{window} {name}: {len(g)} days")
138 + if "SPY" not in grids:
139 + continue
140 + day_list = sorted(grids["SPY"].keys())
141 + mats = {n: day_matrix(g, day_list) for n, g in grids.items()}
142 + rx, fx = mats["SPY"]
143 + for name in mats:
144 + if name == "SPY":
145 + continue
146 + ry, fy = mats[name]
147 + # convention: x = the hypothesized leader.
148 + # ES/SPX pairs: x = ES or SPX, y = SPY. Others: x = SPY, y = ticker.
149 + if name.startswith("ES[") or name == "SPX":
150 + m = analyze_pair(ry, fy, rx, fx)
151 + else:
152 + m = analyze_pair(rx, fx, ry, fy)
153 + if m is None:
154 + continue
155 + bucket = ("es" if name.startswith("ES[") else "index" if name == "SPX"
156 + else "sector" if name in SECTORS
157 + else "liquid" if name in LIQUID else "random")
158 + m |= {"pair": f"{name}->SPY" if bucket in ("es", "index") else f"SPY->{name}",
159 + "bucket": bucket, "window": window}
160 + pairs.append(m)
161 + print(f"{window}: {len(pairs)} pair-cells so far")
162 +
163 + # FDR over all fresh ±1 tests jointly
164 + tests = [(i, "p_fresh_+1") for i in range(len(pairs))] + \
165 + [(i, "p_fresh_-1") for i in range(len(pairs))]
166 + mask = benjamini_hochberg(np.array([pairs[i][k] for i, k in tests]), alpha=0.05)
167 + for (i, k), rej in zip(tests, mask, strict=True):
168 + pairs[i][f"fdr_{k[2:]}"] = bool(rej)
169 +
170 + fdr_survivors = [
171 + {"pair": p["pair"], "window": p["window"], "bucket": p["bucket"],
172 + "fresh_+1": p["fresh_xcorr"]["1"], "fresh_-1": p["fresh_xcorr"]["-1"],
173 + "which": [k for k in ("fresh_+1", "fresh_-1") if p[f"fdr_{k}"]]}
174 + for p in pairs if p.get("fdr_fresh_+1") or p.get("fdr_fresh_-1")
175 + ]
176 + summary = {
177 + "pair_cells": len(pairs),
178 + "tests": len(tests),
179 + "fdr_alpha": 0.05,
180 + "fdr_survivors": fdr_survivors,
181 + "median_artifact_share_by_bucket": {
182 + b: round(float(np.median([p["artifact_share_+1"] for p in pairs
183 + if p["bucket"] == b and p["artifact_share_+1"] is not None])), 5)
184 + for b in ("liquid", "sector", "random")
185 + },
186 + }
187 +
188 + results = {
189 + "experiment": "expD_leadlag_scan",
190 + "run_utc": run_utc.isoformat(),
191 + "author": "Simon-Pierre Boucher",
192 + "contact": "contact@spboucher.ai",
193 + "data_source": "hfmarketdata.io",
194 + "confidence_level": 0,
195 + "protocol": {"windows": WINDOWS, "adjustment": ADJ, "max_lag": MAX_LAG,
196 + "boot": [BOOT_N, BOOT_SEED], "random10": random10,
197 + "min_days": MIN_DAYS, "min_fresh": MIN_FRESH},
198 + "summary": summary,
199 + "pairs": pairs,
200 + "client_stats": vars(client.stats) | {"refreshes": list(client.stats.refreshes)},
201 + "manifest": collect_manifest(),
202 + }
203 + out_dir = REPO_ROOT / "results" / "expD_leadlag_scan" / run_utc.strftime("%Y%m%dT%H%M%SZ")
204 + out_dir.mkdir(parents=True)
205 + (out_dir / "results.json").write_text(json.dumps(results, indent=2) + "\n")
206 + print(f"\nwrote {out_dir.relative_to(REPO_ROOT)}/results.json")
207 + print(json.dumps(summary, indent=1))
32 208
33 209
34 210 if __name__ == "__main__":
modified experiments/micro/expD_leadlag_scan/hypothesis.md +38 −14
@@ -5,34 +5,58 @@ author: Simon-Pierre Boucher
5 5 contact: contact@spboucher.ai
6 6 data_source: hfmarketdata.io
7 7 created: 2026-08-12
8 −status: draft
8 +modified: 2026-08-12
9 +status: final
9 10 ---
10 11
11 12 # Hypothesis — expD_leadlag_scan
12 13
14 +*Pre-specified 2026-08-12 before the scan ran. Alignment primitives
15 +(interval-fresh masks, both-fresh de-artifacting) passed the §8.1 gate
16 +first — the gate caught an end-freshness-only mask bug (Epps attenuation)
17 +before real data.*
18 +
13 19 ```text
14 20 Hypothesis
15 − <what we believe and why — pre-specified BEFORE looking at results>
21 + Tests H07/H08 (scan half) and previews H09/H11. Expectations:
22 + (i) raw LOCF joins will show SPY "leading" everything, in proportion to
23 + staleness (T3); (ii) on both-fresh subsamples, liquid and sector-ETF
24 + pairs will show NO lead-lag at ±1..3 min in 2014-2015 (H08
25 + expected-negative); (iii) 2006-2007 fresh pairs MAY retain a small lead
26 + (pre-decay regime, Chordia et al. 2005); (iv) ES→SPY: any 1min-visible
27 + lead is expected to be tiny or absent, and must be invariant to the three
28 + futures splice variants; (v) SPX→SPY: the raw lead persists even
29 + both-fresh (index prints are CONTENT-stale though print-fresh) — the H11
30 + artifact case.
16 31
17 32 Falsification criterion
18 − <the concrete measurable outcome that would prove this wrong>
33 + (ii) falsified if any liquid/sector both-fresh pair survives FDR(5%) at
34 + |lag| ∈ 1..3 in 2014-2015. (iv) falsified if the ES→SPY fresh lead
35 + changes sign/significance across splice variants (that would mean the
36 + measurement is splice-dominated, not economic).
19 37
20 38 Artifact null(s)
21 − <the fake-signal baseline(s) this must beat: bounce / staleness /
22 − non-synchronous timestamps / permuted calendar / random walk>
23 −
24 −Method
25 − <exact procedure, universe, split (train/validation/holdout), seeds,
26 − number of hypotheses tested, correction applied>
39 + T3 non-synchronicity: measured DIRECTLY as artifact share =
40 + raw_xcorr[+1] − fresh_xcorr[+1] per pair. Day-block bootstrap null
41 + (resampled days, n=200, seed=42) for fresh xcorr[±1] vs 0.
42 +
43 +Method (grid pre-declared)
44 + Pairs vs SPY: 11 liquid (AAPL MSFT NVDA AMZN GOOGL META TSLA JPM XOM UNH
45 + QQQ), 9 sector ETFs (XLF XLE XLK XLV XLI XLY XLP XLU XLB), 10 random
46 + (first 10, sorted, of the seed-42 random-30). Plus ES→SPY (all three
47 + splice variants) and SPX→SPY. Windows: W1 2006-01-01→2008-01-01,
48 + W2 2014-01-01→2016-01-01 (ES/SPX: W2 only — history starts 2008).
49 + 1min RTH grids; raw-LOCF vs both-fresh (interval-fresh) treatments;
50 + lags ±3; min 200 covered days and 10k fresh pairs per cell.
51 + Correction: BH-FDR over all fresh xcorr[+1] and xcorr[-1] tests jointly.
52 + Cell count ≤ (30×2 + 4 + 1) ≈ 65 pairs-windows × 2 lags.
27 53
28 54 Result
29 − <filled after the run: effect size, bootstrap CIs, corrected p-values,
30 − OOS status, cost-adjusted effect, credits used>
55 + <filled after the run>
31 56
32 57 Interpretation
33 − <what the numbers mean, WITH confidence level (0-3); alternative
34 − explanations considered — artifact first>
58 + <filled after the run — Level 0; survivors go to expF/expH>
35 59
36 60 Next experiment
37 − <the most informative follow-up given this result>
61 + expE calendar scan (pre-counted budget); then expF correction battery.
38 62 ```
added web/lib/charts.js +228 −0
@@ -0,0 +1,228 @@
1 +// ============================================================================
2 +// Project : anomaly-atlas
3 +// File : web/lib/charts.js
4 +// Purpose : Server-rendered SVG figures built from experiment results.json
5 +// Author : Simon-Pierre Boucher
6 +// Contact : contact@spboucher.ai
7 +// Data src : hfmarketdata.io (sole data source)
8 +// Created : 2026-08-12
9 +// Modified : 2026-08-12
10 +// Platform : macOS / Apple Silicon (arm64) — Node.js (deployed on MacLustr)
11 +// License : All rights reserved (research code)
12 +// ============================================================================
13 +"use strict";
14 +
15 +/* Figures follow the dataviz method: form by job, validated palette
16 + * (slots: blue #2a78d6, orange #eb6834 — all-pairs PASS on #fffdf9; gray
17 + * #898781 is de-emphasis ink, not a series), thin marks with 2px surface
18 + * rings, hairline grid, text in ink tokens (never series color), native
19 + * <title> tooltips, selective direct labels. Every figure is generated from
20 + * the latest committed results.json — never hand-typed numbers. */
21 +
22 +const path = require("path");
23 +
24 +const BLUE = "#2a78d6";
25 +const ORANGE = "#eb6834";
26 +const GRAY = "#898781";
27 +const SURFACE = "#fffdf9";
28 +const GRID = "#e5e1d6";
29 +const INK = "#1a1c20";
30 +const MUTED = "#5d6167";
31 +
32 +function esc(s) {
33 + return String(s).replace(/[&<>"']/g, (c) => ({ "&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;", "'": "&#39;" }[c]));
34 +}
35 +
36 +function latestResults(C, experiment) {
37 + const run = C.listResultRuns().find((r) => r.experiment === experiment);
38 + if (!run) return null;
39 + const data = C.readJson(path.posix.join(run.rel, "results.json"));
40 + return data ? { data, run: run.timestamp } : null;
41 +}
42 +
43 +function fig(svg, caption) {
44 + return `<figure class="chart-fig">${svg}<figcaption>${caption}</figcaption></figure>`;
45 +}
46 +
47 +const AXIS_TXT = `font-size="11" fill="${MUTED}"`;
48 +
49 +// ------------------------------------------------------------------- expB
50 +function expBScatterPanel(rows, key, title, W, H, xLabelBottom) {
51 + const ML = 46, MR = 14, MT = 26, MB = 34;
52 + const iw = W - ML - MR, ih = H - MT - MB;
53 + const ys = rows.map((r) => r[key]);
54 + const ymin = Math.min(...ys, 0), ymax = Math.max(...ys, 0);
55 + const pad = (ymax - ymin) * 0.12 || 0.01;
56 + const y0 = ymin - pad, y1 = ymax + pad;
57 + const xp = (v) => ML + v * iw;
58 + const yp = (v) => MT + ih - ((v - y0) / (y1 - y0)) * ih;
59 +
60 + let g = `<text x="${ML}" y="${MT - 10}" font-size="12" font-weight="600" fill="${INK}">${esc(title)}</text>`;
61 + for (const t of [y0 + pad, 0, y1 - pad]) {
62 + const v = Math.round(t * 1000) / 1000;
63 + g += `<line x1="${ML}" y1="${yp(v)}" x2="${ML + iw}" y2="${yp(v)}" stroke="${GRID}" stroke-width="1"/>`;
64 + // skip an extreme tick label that would collide with the zero label
65 + if (v === 0 || Math.abs(yp(v) - yp(0)) > 14) {
66 + g += `<text x="${ML - 6}" y="${yp(v) + 4}" text-anchor="end" ${AXIS_TXT}>${v}</text>`;
67 + }
68 + }
69 + g += `<line x1="${ML}" y1="${yp(0)}" x2="${ML + iw}" y2="${yp(0)}" stroke="#c9c4b6" stroke-width="1"/>`;
70 + for (const t of [0, 0.5, 1]) {
71 + g += `<text x="${xp(t)}" y="${MT + ih + 16}" text-anchor="middle" ${AXIS_TXT}>${t}</text>`;
72 + }
73 + if (xLabelBottom) g += `<text x="${ML + iw / 2}" y="${H - 4}" text-anchor="middle" ${AXIS_TXT}>staleness (share of RTH minutes without a fresh print)</text>`;
74 + const extreme = rows.reduce((a, b) => (Math.abs(b[key]) > Math.abs(a[key]) ? b : a), rows[0]);
75 + for (const r of rows) {
76 + g += `<circle cx="${xp(r.staleness).toFixed(1)}" cy="${yp(r[key]).toFixed(1)}" r="4.5" fill="${BLUE}" stroke="${SURFACE}" stroke-width="2"><title>${esc(r.ticker)} — staleness ${r.staleness}, ${esc(title)} ${r[key]}</title></circle>`;
77 + }
78 + for (const r of [extreme]) {
79 + g += `<text x="${Math.min(xp(r.staleness) + 8, W - 44)}" y="${yp(r[key]) + 4}" font-size="10.5" fill="${MUTED}">${esc(r.ticker)}</text>`;
80 + }
81 + return g;
82 +}
83 +
84 +function expBFigure(C) {
85 + const res = latestResults(C, "expB_artifact_baselines");
86 + if (!res) return "";
87 + const rows = Object.entries(res.data.per_ticker || {})
88 + .map(([ticker, m]) => ({ ticker, ...m }))
89 + .filter((r) => Number.isFinite(r.staleness));
90 + if (rows.length < 5) return "";
91 + const W = 760, H = 250;
92 + const half = 372;
93 + const svg = `<svg viewBox="0 0 ${W} ${H}" role="img" aria-label="Measured artifact levels versus staleness">
94 +<g>${expBScatterPanel(rows.filter((r) => Number.isFinite(r.ac1)), "ac1", "1min return AC1", half, H, true)}</g>
95 +<g transform="translate(${W - half},0)">${expBScatterPanel(rows.filter((r) => Number.isFinite(r["spy_leads_+1"])), "spy_leads_+1", "SPY leads +1 min (LOCF join)", half, H, true)}</g>
96 +</svg>`;
97 + return fig(svg, `expB — artifact null levels, one dot per ticker (Q1 2024, RTH 1min). ` +
98 + `Bounce pushes AC1 negative and LOCF joins make SPY spuriously lead, both in proportion to staleness. ` +
99 + `Run ${esc(res.run)}, regenerated from results.json.`);
100 +}
101 +
102 +// ------------------------------------------------------------------- expC
103 +function expCFigure(C) {
104 + const res = latestResults(C, "expC_reversion_scan");
105 + if (!res) return "";
106 + const cells = (res.data.cells || []).filter((c) => Number.isFinite(c.vr30) && Number.isFinite(c.ac1));
107 + if (!cells.length) return "";
108 + for (const c of cells) c.vr30_excess = c.vr30 - (1 + 2 * c.ac1 * (1 - 1 / 30));
109 + const rowsDef = [];
110 + for (const tf of ["1day", "30min", "5min", "1min"]) {
111 + for (const per of ["2000-2007", "2008-2015", "2014-2015"]) {
112 + const cs = cells.filter((c) => c.timeframe === tf && c.period === per);
113 + if (cs.length) rowsDef.push({ label: `${tf} · ${per}`, cells: cs });
114 + }
115 + }
116 + const W = 760, ML = 150, MR = 16, MT = 30, RH = 30, MB = 46;
117 + const H = MT + rowsDef.length * RH + MB;
118 + const xmin = -0.32, xmax = 0.16;
119 + const iw = W - ML - MR;
120 + const xp = (v) => ML + ((Math.max(xmin, Math.min(xmax, v)) - xmin) / (xmax - xmin)) * iw;
121 + let g = "";
122 + for (const t of [-0.3, -0.2, -0.1, 0, 0.1]) {
123 + g += `<line x1="${xp(t)}" y1="${MT - 6}" x2="${xp(t)}" y2="${MT + rowsDef.length * RH}" stroke="${t === 0 ? "#c9c4b6" : GRID}" stroke-width="1"/>
124 +<text x="${xp(t)}" y="${MT + rowsDef.length * RH + 16}" text-anchor="middle" ${AXIS_TXT}>${t}</text>`;
125 + }
126 + g += `<text x="${ML + iw / 2}" y="${H - 6}" text-anchor="middle" ${AXIS_TXT}>VR(30) excess over the MA(1)-consistent null · negative = multi-lag reversion beyond any lag-1 effect</text>`;
127 + const labeled = new Set(
128 + cells.filter((c) => c.fdr_vr30 && c.vr30_excess < -0.05)
129 + .sort((a, b) => a.vr30_excess - b.vr30_excess).slice(0, 3).map((c) => c.ticker + c.period + c.timeframe));
130 + rowsDef.forEach((row, i) => {
131 + const y = MT + i * RH + RH / 2;
132 + g += `<text x="${ML - 10}" y="${y + 4}" text-anchor="end" font-size="11.5" fill="${INK}">${esc(row.label)}</text>`;
133 + for (const c of row.cells) {
134 + const surv = c.fdr_vr30 && c.vr30_excess < -0.05;
135 + const tip = `${c.ticker} ${c.timeframe} ${c.period}: VR30 ${c.vr30}, excess ${c.vr30_excess.toFixed(3)}${surv ? " (FDR survivor)" : ""}`;
136 + g += surv
137 + ? `<circle cx="${xp(c.vr30_excess).toFixed(1)}" cy="${y}" r="5" fill="${BLUE}" stroke="${SURFACE}" stroke-width="2"><title>${esc(tip)}</title></circle>`
138 + : `<circle cx="${xp(c.vr30_excess).toFixed(1)}" cy="${y}" r="3.5" fill="${SURFACE}" stroke="${GRAY}" stroke-width="1.4" opacity=".8"><title>${esc(tip)}</title></circle>`;
139 + if (labeled.has(c.ticker + c.period + c.timeframe)) {
140 + g += `<text x="${xp(c.vr30_excess) - 8}" y="${y - 8}" text-anchor="middle" font-size="10.5" fill="${MUTED}">${esc(c.ticker)}</text>`;
141 + }
142 + }
143 + });
144 + const legend = `<g font-size="11" fill="${MUTED}">
145 +<circle cx="${ML}" cy="${MT - 16}" r="5" fill="${BLUE}" stroke="${SURFACE}" stroke-width="2"/><text x="${ML + 10}" y="${MT - 12}" fill="${INK}">FDR survivor (excess &lt; −0.05)</text>
146 +<circle cx="${ML + 210}" cy="${MT - 16}" r="3.5" fill="${SURFACE}" stroke="${GRAY}" stroke-width="1.4"/><text x="${ML + 220}" y="${MT - 12}">other scan cells (Level 0)</text></g>`;
147 + const svg = `<svg viewBox="0 0 ${W} ${H}" role="img" aria-label="expC variance-ratio excess by timeframe and period">${legend}${g}</svg>`;
148 + return fig(svg, `expC — multi-lag reversion triage on TRAIN. One dot per ticker-cell; ` +
149 + `the MA(1)-consistent null absorbs all lag-1 effects (bounce included); values beyond the axis range ` +
150 + `pile at its edge. Run ${esc(res.run)}, regenerated from results.json.`);
151 +}
152 +
153 +// ------------------------------------------------------------------- expD
154 +function expDFigure(C) {
155 + const res = latestResults(C, "expD_leadlag_scan");
156 + if (!res) return "";
157 + const out = [];
158 + for (const window of ["2014-2015", "2006-2007"]) {
159 + const rows = (res.data.pairs || [])
160 + .filter((p) => p.window === window && p.raw_xcorr && p.fresh_xcorr &&
161 + Number.isFinite(p.raw_xcorr["1"]) && Number.isFinite(p.fresh_xcorr["1"]))
162 + .sort((a, b) => (a.bucket + "").localeCompare(b.bucket + "") || b.raw_xcorr["1"] - a.raw_xcorr["1"]);
163 + if (!rows.length) continue;
164 + const W = 760, ML = 170, MR = 16, MT = 34, RH = 19, MB = 44;
165 + const H = MT + rows.length * RH + MB;
166 + const vals = rows.flatMap((p) => [p.raw_xcorr["1"], p.fresh_xcorr["1"]]);
167 + const xmin = Math.min(...vals, 0) - 0.01, xmax = Math.max(...vals, 0) + 0.01;
168 + const iw = W - ML - MR;
169 + const xp = (v) => ML + ((v - xmin) / (xmax - xmin)) * iw;
170 + let g = "";
171 + const ticks = [0, 0.05, 0.1].filter((t) => t >= xmin && t <= xmax);
172 + for (const t of ticks) {
173 + g += `<line x1="${xp(t)}" y1="${MT - 6}" x2="${xp(t)}" y2="${MT + rows.length * RH}" stroke="${t === 0 ? "#c9c4b6" : GRID}" stroke-width="1"/>
174 +<text x="${xp(t)}" y="${MT + rows.length * RH + 16}" text-anchor="middle" ${AXIS_TXT}>${t}</text>`;
175 + }
176 + g += `<text x="${ML + iw / 2}" y="${H - 5}" text-anchor="middle" ${AXIS_TXT}>cross-correlation at +1 min (leader → follower)</text>`;
177 + let lastBucket = "";
178 + rows.forEach((p, i) => {
179 + const y = MT + i * RH + RH / 2;
180 + if (p.bucket !== lastBucket) {
181 + lastBucket = p.bucket;
182 + g += `<text x="${ML - 160}" y="${y + 4}" font-size="10" font-weight="700" fill="${MUTED}" letter-spacing=".08em">${esc(p.bucket.toUpperCase())}</text>`;
183 + }
184 + const raw = p.raw_xcorr["1"], fresh = p.fresh_xcorr["1"];
185 + const surv = p.fdr_fresh_p1 || p["fdr_fresh_+1"];
186 + g += `<text x="${ML - 10}" y="${y + 4}" text-anchor="end" font-size="11" fill="${INK}">${esc(p.pair)}</text>
187 +<line x1="${xp(raw)}" y1="${y}" x2="${xp(fresh)}" y2="${y}" stroke="${GRID}" stroke-width="2"/>
188 +<circle cx="${xp(raw).toFixed(1)}" cy="${y}" r="4.5" fill="${ORANGE}" stroke="${SURFACE}" stroke-width="2"><title>${esc(p.pair)} raw LOCF join: ${raw}</title></circle>
189 +<circle cx="${xp(fresh).toFixed(1)}" cy="${y}" r="4.5" fill="${BLUE}" stroke="${SURFACE}" stroke-width="2"><title>${esc(p.pair)} both-fresh: ${fresh}${surv ? " (FDR survivor)" : ""}</title></circle>`;
190 + });
191 + const legend = `<g font-size="11">
192 +<circle cx="${ML}" cy="${MT - 18}" r="4.5" fill="${ORANGE}" stroke="${SURFACE}" stroke-width="2"/><text x="${ML + 9}" y="${MT - 14}" fill="${INK}">raw LOCF join (artifact included)</text>
193 +<circle cx="${ML + 230}" cy="${MT - 18}" r="4.5" fill="${BLUE}" stroke="${SURFACE}" stroke-width="2"/><text x="${ML + 239}" y="${MT - 14}" fill="${INK}">both-fresh (synchronized)</text></g>`;
194 + out.push(fig(
195 + `<svg viewBox="0 0 ${W} ${H}" role="img" aria-label="expD lead-lag: raw versus synchronized cross-correlation, ${window}">${legend}${g}</svg>`,
196 + `expD — lead at +1 min per pair, ${window}: the gap between the raw join and the both-fresh ` +
197 + `subsample is the non-synchronicity artifact (T3), measured. Run ${esc(res.run)}, regenerated from results.json.`
198 + ));
199 + }
200 + return out.join("");
201 +}
202 +
203 +const BUILDERS = {
204 + expB_artifact_baselines: expBFigure,
205 + expC_reversion_scan: expCFigure,
206 + expD_leadlag_scan: expDFigure,
207 +};
208 +
209 +/** Figures for an experiment page ("" when none apply). */
210 +function figuresFor(experiment, C) {
211 + const builder = BUILDERS[experiment];
212 + try {
213 + return builder ? builder(C) : "";
214 + } catch {
215 + return "";
216 + }
217 +}
218 +
219 +/** The most recent experiment figure, for the home page. */
220 +function homeFigure(C) {
221 + for (const exp of ["expD_leadlag_scan", "expC_reversion_scan", "expB_artifact_baselines"]) {
222 + const html = figuresFor(exp, C);
223 + if (html) return { experiment: exp, html: html.split("</figure>")[0] + "</figure>" };
224 + }
225 + return null;
226 +}
227 +
228 +module.exports = { figuresFor, homeFigure };
modified web/public/style.css +8 −0
@@ -382,6 +382,14 @@ a:hover { text-decoration: underline; text-underline-offset: 3px; }
382 382 }
383 383 .chip-k { color: var(--faint); margin-right: 5px; text-transform: uppercase; letter-spacing: .06em; }
384 384
385 +/* --------------------------------------------------------------- figures */
386 +.chart-fig { margin: 18px 0 6px; }
387 +.chart-fig svg { width: 100%; height: auto; display: block; font-family: var(--font-text); }
388 +.chart-fig figcaption {
389 + font-size: .78rem; color: var(--faint); margin-top: 8px; line-height: 1.5;
390 + border-top: 1px solid var(--hairline); padding-top: 8px;
391 +}
392 +
385 393 /* -------------------------------------------------------------- comments */
386 394 .comment-form {
387 395 display: grid; gap: 12px; margin: 6px 0 10px;
modified web/server.js +15 −2
@@ -18,6 +18,7 @@ const path = require("path");
18 18 const express = require("express");
19 19 const C = require("./lib/content");
20 20 const R = require("./lib/render");
21 +const charts = require("./lib/charts");
21 22
22 23 const app = express();
23 24 const PORT = process.env.PORT || 8150;
@@ -118,7 +119,15 @@ app.get("/", (req, res) => {
118 119 <a class="more" href="/doc/research/LOG.md">Full research log →</a></div>
119 120 <div class="log-grid">${logHtml}</div>
120 121 </section>`;
121 − page(res, { title: "Home", active: "Home", body });
122 + const homeFig = charts.homeFigure(C);
123 + const figSection = homeFig
124 + ? `<section class="card">
125 + <div class="card-head-row"><h2>Latest measured figure</h2>
126 + <a class="more" href="/experiments/${homeFig.experiment}">${homeFig.experiment} →</a></div>
127 + ${homeFig.html}
128 +</section>`
129 + : "";
130 + page(res, { title: "Home", active: "Home", body: body + figSection });
122 131 });
123 132
124 133 // ---------------------------------------------------------------- atlas
@@ -279,9 +288,13 @@ app.get("/experiments/:id", (req, res) => {
279 288 const codeLink = C.exists(path.posix.join(exp.rel, "benchmark.py"))
280 289 ? `<p><a class="more" href="/file/${exp.rel}/benchmark.py">View benchmark implementation (benchmark.py) →</a></p>`
281 290 : "";
291 + const figures = charts.figuresFor(exp.id, C);
292 + const figuresHtml = figures
293 + ? `<section class="card"><h2>Figures</h2><p class="lede-small">Generated server-side from the latest committed <code>results.json</code> — never hand-typed.</p>${figures}</section>`
294 + : "";
282 295 const body = `<p class="crumb"><a href="/experiments">Experiments</a> / ${R.esc(exp.id)}</p>
283 296 <h1 class="page-title">${R.esc(exp.id)}</h1><p class="lede-small">${R.esc(exp.purpose)}</p>
284 −${codeLink}${sections.join("")}${runsHtml}`;
297 +${codeLink}${figuresHtml}${sections.join("")}${runsHtml}`;
285 298 page(res, { title: exp.id, active: "Experiments", body });
286 299 });
287 300
288 301