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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

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expH: validation ladder closes + first atlas entries + Publications tab

expH (validation 2016-2021, opened once; holdout stays sealed):
- negative finding REPLICATES: 0 net survivors at full half-spread OOS
- CKX fails pre-registered sub-period clause (kappa* 0.445->0.263)
- ES->SPY flips sign OOS (+0.003, p=0.68) — regime-fragile
- daily reversal family: VR30 excess -0.143 -> +0.023 (crisis-regime)
- figure: alphas collapse OOS, the SPX staleness artifact persists

Atlas (via new_finding.py, Level 2 each):
- F001 nothing in the searched universe survives the full ladder
- F002 SPX->SPY lead is index content staleness (Fisher), survives SPA

Publications: new tab + P001 'The Artifact Frontier, Part I' — formal
write-up of expA->expG with live {{figure:...}} tokens rendered from
committed results.json

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Simon-Pierre Boucher committed 2 h ago (Aug 12, 2026) parent 881399d

Showing 18 changed files with +3,565 and −28

added atlas/F001-short-horizon-survival/v1/confidence.md +36 −0
@@ -0,0 +1,36 @@
1 +---
2 +project: anomaly-atlas
3 +document: F001 confidence
4 +author: Simon-Pierre Boucher
5 +contact: contact@spboucher.ai
6 +data_source: hfmarketdata.io
7 +created: 2026-08-12
8 +status: reviewed
9 +---
10 +
11 +Level: 2
12 +
13 +# Confidence — F001 (negative finding)
14 +
15 +**Level 2 — robust** (corrected & OOS & robust). Evidence per layer:
16 +
17 +* **Corrected**: the claim is made against the FULL searched universe (372
18 + signed rules), with BH-FDR + White RC + Hansen SPA (500 stationary
19 + bootstraps) + DSR; the 22-hypothesis budget was declared before scanning
20 + (research_gaps.md) and never expanded.
21 +* **Artifact-adjusted**: scans ran net of measured nulls (taxonomy T1-T7:
22 + bounce/EDGE variance-consistent MA(1) null, both-fresh synchronization,
23 + permuted calendar); detectors passed 29 synthetic gate tests first.
24 +* **Out-of-sample**: the 2016-2021 validation split, frozen before any scan
25 + and opened exactly once (expH), REPLICATES the negative: zero net
26 + survivors at full half-spread; the lone 0.25x survivor fails its
27 + pre-registered sub-period clause; ES->SPY flips sign; the daily reversal
28 + family collapses (-0.143 -> +0.023 VR30 excess).
29 +* **Robust**: holds across 4 timeframes, 2 train sub-periods, 2 validation
30 + sub-periods, liquidity buckets, futures splice variants, and both close
31 + conventions where applicable.
32 +
33 +**Why not Level 3**: Level 3 is reserved for confirmation on the sealed
34 +holdout (2022->), which this project deliberately did not spend on an
35 +already-settled negative. Costs are estimated (EDGE), not quoted — the
36 +cost-real qualifier is model-based.
added atlas/F001-short-horizon-survival/v1/finding.json +28 −0
@@ -0,0 +1,28 @@
1 +{
2 + "anomaly_id": "F001-short-horizon-survival",
3 + "title": "Nothing in the searched universe survives the full validation ladder",
4 + "summary": "Across 22 pre-registered hypotheses (372 searched rules: reversion, lead-lag, calendar; 2000-2016 train), every effect that beats the measured artifact nulls and Hansen-SPA search correction dies at a fraction of its own half-spread (median breakeven = 1.1% of one half-spread; zero intraday rules survive paying the full half-spread), and this negative result REPLICATES on the untouched 2016-2021 validation split (zero net survivors at full spread; the lone marginal 0.25x survivor fails its pre-registered sub-period clause; the splice-invariant ES-SPY train effect flips sign OOS; the daily 2008-2015 reversal family was crisis-regime).",
5 + "negative_result": true,
6 + "key_numbers": {
7 + "searched_rules": 372,
8 + "naive_significant": 232,
9 + "fdr_survivors": 226,
10 + "spa_step1_survivors": 68,
11 + "median_breakeven_kappa": 0.0114,
12 + "net_survivors_at_full_halfspread_train": 0,
13 + "net_survivors_at_full_halfspread_validation": 0
14 + },
15 + "evidence": [
16 + "results/expC_reversion_scan/",
17 + "results/expD_leadlag_scan/",
18 + "results/expE_calendar_scan/",
19 + "results/expF_multiple_testing/",
20 + "results/expG_cost_frontier/",
21 + "results/expH_oos_stability/"
22 + ],
23 + "reproduce": "make setup && python experiments/micro/expF_multiple_testing/benchmark.py (cache-served); full chain expA->expH per experiments/micro/*/hypothesis.md",
24 + "author": "Simon-Pierre Boucher",
25 + "contact": "contact@spboucher.ai",
26 + "data_source": "hfmarketdata.io",
27 + "confidence_level": 2
28 +}
added atlas/F001-short-horizon-survival/v1/provenance.json +11 −0
@@ -0,0 +1,11 @@
1 +{
2 + "commit": "881399dca21ef0ca7e30b858a85fc54a3e1fafa1",
3 + "config": "pre-registered protocol: charter CLAUDE.md; splits frozen in src/anomaly_atlas/data/universe.py (train 2000-2016, validation 2016-2021, holdout 2022-> sealed); seeds 42 throughout",
4 + "data_manifest_hash": "f94a641cda2c8e9eb4ea0fded4ac80f3d0505a20fd73be0d33b6a1aff1109e1c",
5 + "hardware_manifest": "embedded in each cited results.json (Apple Silicon, macOS; see manifest field)",
6 + "generated_utc": "2026-08-12T08:20:00Z",
7 + "n_hypotheses_tested": 22,
8 + "correction_method": "BH-FDR (scans) + Hansen SPA/White RC over the 372-rule searched universe + DSR; artifact nulls per taxonomy T1-T7; EDGE cost model",
9 + "oos_status": "validation split (2016-2021) opened once by expH and confirms; holdout (2022->) sealed",
10 + "experiments": "expA-expH"
11 +}
added atlas/F002-spx-spy-staleness-artifact/v1/confidence.md +33 −0
@@ -0,0 +1,33 @@
1 +---
2 +project: anomaly-atlas
3 +document: F002 confidence
4 +author: Simon-Pierre Boucher
5 +contact: contact@spboucher.ai
6 +data_source: hfmarketdata.io
7 +created: 2026-08-12
8 +status: reviewed
9 +---
10 +
11 +Level: 2
12 +
13 +# Confidence — F002 (artifact demonstration)
14 +
15 +**Level 2 — robust.** This is a finding-type C/D entry (artifact
16 +demonstration / methodological), published as a negative result about a
17 +would-be anomaly.
18 +
19 +* **Corrected**: the effect itself survives Hansen SPA — which is the
20 + demonstration: statistical correction cannot detect mechanism.
21 +* **Artifact mechanism established**: measured in expB (+0.065) and expD
22 + (+0.132) with print-synchronization applied — the lead persists at
23 + fresh_fraction 0.996 with artifact-share-from-print-gaps ~0, isolating
24 + CONTENT staleness (Fisher 1966) as the mechanism; the synthetic gate
25 + demonstrates the same signature on planted staleness.
26 +* **Out-of-sample**: replicates on the validation split (+0.080) — the
27 + artifact is structural, unlike every "alpha" in the same pipeline.
28 +* **Robust**: present in two independent windows plus validation, at both
29 + raw and synchronized treatments.
30 +
31 +**Why not Level 3**: holdout untouched (sealed); magnitude varies with
32 +index-constituent liquidity regime, so the number (not the mechanism)
33 +should be re-measured per period.
added atlas/F002-spx-spy-staleness-artifact/v1/finding.json +22 −0
@@ -0,0 +1,22 @@
1 +{
2 + "anomaly_id": "F002-spx-spy-staleness-artifact",
3 + "title": "The SPX->SPY minute-scale 'lead' is index content staleness, not price discovery",
4 + "summary": "SPY leads the SPX index print by one minute (+0.132 cross-correlation in train 2014-2015, +0.080 on the 2016-2021 validation split) at ~0.95 contemporaneous correlation, and the lead SURVIVES both-fresh print synchronization because the staleness lives inside the index computation (Fisher 1966, measured live). It also survives Hansen SPA - demonstrating that statistical correction cannot detect mechanism. Any 'ETF leads the index' claim on bar data is presumptively this artifact.",
5 + "negative_result": true,
6 + "key_numbers": {
7 + "train_lead_plus1min": 0.132,
8 + "validation_lead_plus1min": 0.08,
9 + "contemporaneous_corr": 0.965,
10 + "artifact_share_from_print_gaps": 0.0
11 + },
12 + "evidence": [
13 + "results/expB_artifact_baselines/",
14 + "results/expD_leadlag_scan/",
15 + "results/expH_oos_stability/"
16 + ],
17 + "reproduce": "python experiments/micro/expD_leadlag_scan/benchmark.py; expH xcorr section",
18 + "author": "Simon-Pierre Boucher",
19 + "contact": "contact@spboucher.ai",
20 + "data_source": "hfmarketdata.io",
21 + "confidence_level": 2
22 +}
added atlas/F002-spx-spy-staleness-artifact/v1/provenance.json +11 −0
@@ -0,0 +1,11 @@
1 +{
2 + "commit": "881399dca21ef0ca7e30b858a85fc54a3e1fafa1",
3 + "config": "pre-registered protocol: charter CLAUDE.md; splits frozen in src/anomaly_atlas/data/universe.py (train 2000-2016, validation 2016-2021, holdout 2022-> sealed); seeds 42 throughout",
4 + "data_manifest_hash": "f94a641cda2c8e9eb4ea0fded4ac80f3d0505a20fd73be0d33b6a1aff1109e1c",
5 + "hardware_manifest": "embedded in each cited results.json (Apple Silicon, macOS; see manifest field)",
6 + "generated_utc": "2026-08-12T08:20:00Z",
7 + "n_hypotheses_tested": 22,
8 + "correction_method": "BH-FDR (scans) + Hansen SPA/White RC over the 372-rule searched universe + DSR; artifact nulls per taxonomy T1-T7; EDGE cost model",
9 + "oos_status": "validation split (2016-2021) opened once by expH and confirms; holdout (2022->) sealed",
10 + "experiments": "expB, expD, expH"
11 +}
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modified experiments/micro/expH_oos_stability/README.md +1 −1
@@ -12,4 +12,4 @@ status: draft
12 12
13 13 Out-of-sample stability: once-touched holdout and sub-period evaluation of survivors
14 14
15 −Status: scaffolded 2026-08-12, not yet run.
15 +Status: **completed 2026-08-12** — validation split opened once: negative finding replicates (0 net survivors at kappa=1), CKX fails robustness, ES->SPY does not replicate, daily reversal was crisis-regime. First atlas entries issued.
modified experiments/micro/expH_oos_stability/analysis.md +39 −2
@@ -5,9 +5,46 @@ 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: reviewed
9 10 ---
10 11
11 12 # Analysis — expH_oos_stability
12 13
13 −*To be written after results exist. Must include the seven-field block and the evidence standard of CLAUDE.md §10 (never report an in-sample number as a finding).*
14 +Run: `results/expH_oos_stability/20260812T072518Z/results.json`. The
15 +validation split (2016-2021), frozen before the first scan, was opened here
16 +for the first time (418 requests, 18.8M rows). The holdout (2022→) remains
17 +sealed. All four pre-registered questions resolved:
18 +
19 +| Question | Train | Validation | Verdict |
20 +|---|---|---|---|
21 +| Q-b nothing survives κ=1 | 0 survivors | **0 survivors** | negative finding **replicates** |
22 +| Q-a CKX 1day net@0.25 | κ*=0.28 | +16.3 bp/d CI [1.4, 26.5], but 2019-21 CI [−19.5, +22.0] | **fails robustness clause** — not confirmed |
23 +| Q-c ES→SPY fresh corr | −0.032 (FDR, splice-invariant) | **+0.003** (p=0.68) | **does not replicate** |
24 +| Q-d daily reversal family | VR30 excess −0.143 | **+0.023** | crisis-regime story **confirmed** |
25 +
26 +## Reading
27 +
28 +1. **The ladder closes on the honest outcome.** Artifact nulls → search
29 + correction → costs → OOS: nothing survives all four. This is the
30 + charter's anticipated result ("a well-documented 'it's all artifacts'
31 + is a success"), now demonstrated rather than assumed — and it becomes
32 + the atlas's first Level-2 entry.
33 +2. **CKX was exactly what it looked like.** The lone cost survivor decays
34 + within validation (κ* 0.445 → 0.263), fails its pre-registered
35 + two-sub-period clause, and lives in a name whose capacity rounds to
36 + zero. Not escalated; the sealed holdout is not spent on it.
37 +3. **ES→SPY is a lesson in regime-fragility**: a splice-invariant,
38 + FDR-significant train effect that flips sign OOS. Splice-invariance
39 + guards against one artifact — it says nothing about stationarity.
40 +4. **The artifact replicates even though the anomalies do not.** SPY still
41 + leads SPX on validation (+0.080 vs +0.132 in train) — content staleness
42 + is structural. That contrast (artifacts stable, "alphas" not) is the
43 + project's cleanest single sentence.
44 +
45 +## Hand-off
46 +
47 +Two atlas entries created via `tools/new_finding.py` (F001 negative
48 +finding, Level 2; F002 SPX artifact demonstration, Level 2). Candidate 02
49 +(turn-of-month) and candidate 01 (intraday momentum) keep their protocols
50 +for future work; the holdout stays sealed until a claim earns it.
modified experiments/micro/expH_oos_stability/benchmark.py +198 −10
@@ -1,7 +1,7 @@
1 1 # =============================================================================
2 2 # Project : anomaly-atlas
3 3 # File : experiments/micro/expH_oos_stability/benchmark.py
4 −# Purpose : Benchmark runner: Out-of-sample stability: once-touched holdout and sub-period eva…
4 +# Purpose : Out-of-sample stability on the untouched validation split
5 5 # Author : Simon-Pierre Boucher
6 6 # Contact : contact@spboucher.ai
7 7 # Data src : hfmarketdata.io (sole data source)
@@ -10,25 +10,213 @@
10 10 # Platform : macOS / Apple Silicon (arm64)
11 11 # License : All rights reserved (research code)
12 12 # =============================================================================
13 +"""Experiment H — the validation split (2016-2021), opened for the first time.
13 14
14 −"""Benchmark entry point for expH_oos_stability.
15 −
16 −Must embed the hardware manifest in all result output
17 −(see benchmarks/hardware_manifest.py) and write results to
18 −results/expH_oos_stability/<timestamp>/. Uses hfmarketdata.io data ONLY, exclusively
19 −through src/anomaly_atlas/data/hf_client.py.
15 +Re-evaluates, with machinery identical to expC/expD/expG (imported from
16 +their committed benchmarks, not re-implemented): the expG pool net of
17 +costs, the ES->SPY basis effect, and the daily reversal family. The sealed
18 +HOLDOUT (2022->) is not touched.
20 19 """
21 20
21 +from __future__ import annotations
22 +
23 +import importlib.util
24 +import json
22 25 import sys
26 +from datetime import UTC, datetime
23 27 from pathlib import Path
24 28
25 −sys.path.insert(0, str(Path(__file__).resolve().parents[3] / "benchmarks"))
29 +import numpy as np
30 +
31 +REPO_ROOT = Path(__file__).resolve().parents[3]
32 +sys.path.insert(0, str(REPO_ROOT / "benchmarks"))
33 +sys.path.insert(0, str(REPO_ROOT / "src"))
34 +
26 35 from hardware_manifest import collect_manifest # noqa: E402
27 36
37 +from anomaly_atlas.data.cleaning import rth_day_grids # noqa: E402
38 +from anomaly_atlas.data.hf_client import HFMarketDataClient # noqa: E402
39 +from anomaly_atlas.data.universe import LIQUID_ETF, LIQUID_STOCK, VALIDATION # noqa: E402
40 +from anomaly_atlas.stats.bootstrap import moving_block_bootstrap, percentile_ci # noqa: E402
41 +from anomaly_atlas.stats.multiple_testing import bootstrap_pvalue # noqa: E402
42 +from anomaly_atlas.stats.reversion import ac1, variance_ratio # noqa: E402
43 +from anomaly_atlas.validation.artifacts import edge_spread # noqa: E402
44 +
45 +
46 +def load_module(name: str, rel: str):
47 + spec = importlib.util.spec_from_file_location(name, REPO_ROOT / rel)
48 + mod = importlib.util.module_from_spec(spec)
49 + spec.loader.exec_module(mod)
50 + return mod
51 +
52 +
53 +expg = load_module("expg_bench", "experiments/micro/expG_cost_frontier/benchmark.py")
54 +expd = load_module("expd_bench", "experiments/micro/expD_leadlag_scan/benchmark.py")
55 +
56 +ADJ = "adj_split"
57 +VAL_SUBS = {"2016-2018": ("2016-01-01", "2019-01-01"),
58 + "2019-2021": ("2019-01-01", "2022-01-01")}
59 +KAPPA_CHECK = ("0.25", "1.0")
60 +
61 +
62 +def eval_pool_on(client, rev_pool, ll_pool, start, end, label) -> list[dict]:
63 + items = []
64 + for r in rev_pool:
65 + bars = client.get_bars(r["asset"], r["ticker"], r["timeframe"], ADJ, start, end)
66 + stream = expg.contrarian_stream(bars, r["timeframe"])
67 + hs = expg.half_spread(client, r["asset"], r["ticker"], ADJ, start, end)
68 + if len(stream) < 150:
69 + continue
70 + item = {"rule": f"R:{r['ticker']}:{r['timeframe']}", "family": "reversion",
71 + "window": label} | expg.sweep(stream, hs)
72 + items.append(item)
73 + needed = {"SPY"} | {p["leader"] for p in ll_pool} | {p["follower"] for p in ll_pool}
74 + grids = {}
75 + for name in sorted(needed):
76 + if name == "ES":
77 + g = rth_day_grids(client.get_bars("futures", "ES", "1min",
78 + "contin_adj_ratio", start, end))
79 + else:
80 + asset = ("etf" if name in ("SPY", "QQQ") or name.startswith("XL") else "stock")
81 + g = rth_day_grids(client.get_bars(asset, name, "1min", ADJ, start, end))
82 + grids[name] = g
83 + day_list = sorted(grids["SPY"].keys())
84 + for p in ll_pool:
85 + if p["leader"] not in grids or p["follower"] not in grids:
86 + continue
87 + stream = expg.leadlag_stream(grids[p["leader"]], grids[p["follower"]],
88 + day_list, p["sign"])
89 + if len(stream) < 150:
90 + continue
91 + traded = p["follower"]
92 + asset = ("futures" if traded == "ES" else
93 + "etf" if traded in ("SPY", "QQQ") or traded.startswith("XL") else "stock")
94 + adj = "contin_adj_ratio" if traded == "ES" else ADJ
95 + hs = expg.half_spread(client, asset, traded, adj, start, end)
96 + item = {"rule": f"L:{p['pair']}:{'+' if p['sign'] > 0 else '-'}",
97 + "family": "leadlag", "window": label} | expg.sweep(stream, hs)
98 + items.append(item)
99 + return items, grids, day_list
100 +
28 101
29 102 def main() -> None:
30 − collect_manifest() # embedded in results once implemented
31 − raise NotImplementedError("experiment not yet implemented")
103 + run_utc = datetime.now(UTC)
104 + client = HFMarketDataClient()
105 + rev_pool, ll_pool = expg.pool_from_committed_results()
106 + s, e = VALIDATION
107 +
108 + # (a,b) pool on validation
109 + items, grids, day_list = eval_pool_on(client, rev_pool, ll_pool, s, e, "validation")
110 + for it in items:
111 + print(it["rule"], "kappa* =", it.get("kappa_star"))
112 +
113 + # CKX sub-period check (Q-a robustness clause)
114 + ckx_subs = {}
115 + for sub, (ss, ee) in VAL_SUBS.items():
116 + bars = client.get_bars("stock", "CKX", "1day", ADJ, ss, ee)
117 + stream = expg.contrarian_stream(bars, "1day")
118 + hs = expg.half_spread(client, "stock", "CKX", ADJ, ss, ee)
119 + ckx_subs[sub] = expg.sweep(stream, hs) if len(stream) >= 100 else {"n_days": len(stream)}
120 +
121 + # (c) ES->SPY and SPX->SPY fresh xcorr on validation
122 + xcorr_out = {}
123 + spy_mat = expd.day_matrix(grids["SPY"], day_list)
124 + for name in ("ES", "SPX"):
125 + if name == "SPX":
126 + g = rth_day_grids(client.get_bars("index", "SPX", "1min", None, s, e))
127 + else:
128 + g = grids.get("ES") or rth_day_grids(
129 + client.get_bars("futures", "ES", "1min", "contin_adj_ratio", s, e))
130 + mat = expd.day_matrix(g, day_list)
131 + m = expd.analyze_pair(mat[0], mat[1], spy_mat[0], spy_mat[1])
132 + if m:
133 + xcorr_out[f"{name}->SPY"] = {k: m[k] for k in
134 + ("fresh_xcorr", "raw_xcorr", "p_fresh_+1",
135 + "p_fresh_-1", "n_fresh_pairs")}
136 + # ES splice invariance
137 + for adj in ("contin_adj_absolute", "contin_UNadj"):
138 + g = rth_day_grids(client.get_bars("futures", "ES", "1min", adj, s, e))
139 + mat = expd.day_matrix(g, day_list)
140 + m = expd.analyze_pair(mat[0], mat[1], spy_mat[0], spy_mat[1])
141 + if m:
142 + xcorr_out[f"ES[{adj}]->SPY"] = {"fresh_-1": m["fresh_xcorr"]["-1"],
143 + "fresh_+1": m["fresh_xcorr"]["1"]}
144 +
145 + # (d) daily reversal family on validation vs train values
146 + rc = expg.latest(str(REPO_ROOT / "results/expC_reversion_scan/*/results.json"))
147 + train_daily = {c["ticker"]: c for c in rc["cells"]
148 + if c["timeframe"] == "1day" and c["period"] == "2008-2015"}
149 + family = []
150 + for asset, ticker in [("stock", t) for t in LIQUID_STOCK] + \
151 + [("etf", t) for t in LIQUID_ETF]:
152 + bars = client.get_bars(asset, ticker, "1day", ADJ, s, e)
153 + if len(bars) < 500:
154 + continue
155 + closes = np.array([b["close"] for b in bars])
156 + r = np.diff(np.log(closes))
157 + a = ac1(r)
158 + vr30 = variance_ratio(r, 30)
159 + o = np.array([b["open"] for b in bars])
160 + h = np.array([b["high"] for b in bars])
161 + lo = np.array([b["low"] for b in bars])
162 + c = closes
163 + sp = edge_spread(o, h, lo, c)
164 + var = r.var()
165 + bounce = -(sp**2) / 4 / var if np.isfinite(sp) and var > 0 else 0.0
166 + boot = moving_block_bootstrap(r, ac1, block=21, n_boot=300, seed=42)
167 + lo_ci, hi_ci = percentile_ci(boot - bounce)
168 + tr = train_daily.get(ticker, {})
169 + family.append({
170 + "ticker": ticker,
171 + "val_ac1": round(a, 5), "val_excess_ac1": round(a - bounce, 5),
172 + "val_excess_ci95": [round(lo_ci, 5), round(hi_ci, 5)],
173 + "val_p_excess": bootstrap_pvalue(boot - bounce, 0.0),
174 + "val_vr30_excess": round(vr30 - (1 + 2 * a * (1 - 1 / 30)), 4),
175 + "train_excess_ac1": tr.get("excess_ac1"),
176 + "train_vr30_excess": round(tr["vr30"] - (1 + 2 * tr["ac1"] * (1 - 1 / 30)), 4)
177 + if tr else None,
178 + })
179 +
180 + ck = {str(k): [i["rule"] for i in items if i["net"].get(str(k), {}).get("positive")
181 + and i["net"].get(str(k), {}).get("ci95_bp", [1])[0] > 0]
182 + for k in (0.25, 1.0)}
183 + med_val = float(np.median([f["val_vr30_excess"] for f in family]))
184 + med_train = float(np.median([f["train_vr30_excess"] for f in family
185 + if f["train_vr30_excess"] is not None]))
186 + summary = {
187 + "pool_evaluated": len(items),
188 + "net_pos_ci_at": ck,
189 + "ckx_validation": next((i for i in items if i["rule"].startswith("R:CKX")), None),
190 + "ckx_subperiods": ckx_subs,
191 + "es_spy_val_fresh_-1": xcorr_out.get("ES->SPY", {}).get("fresh_xcorr", {}).get("-1"),
192 + "es_spy_val_p_-1": xcorr_out.get("ES->SPY", {}).get("p_fresh_-1"),
193 + "spx_spy_val_fresh_-1": xcorr_out.get("SPX->SPY", {}).get("fresh_xcorr", {}).get("-1"),
194 + "daily_family_median_vr30_excess": {"train_2008_2015": round(med_train, 4),
195 + "validation": round(med_val, 4)},
196 + }
197 +
198 + results = {
199 + "experiment": "expH_oos_stability",
200 + "run_utc": run_utc.isoformat(),
201 + "author": "Simon-Pierre Boucher",
202 + "contact": "contact@spboucher.ai",
203 + "data_source": "hfmarketdata.io",
204 + "confidence_level": "evaluates Level-1/2 claims on the validation split",
205 + "protocol": {"validation": VALIDATION, "subs": VAL_SUBS,
206 + "holdout_untouched": True},
207 + "pool_items": items,
208 + "xcorr": xcorr_out,
209 + "daily_family": family,
210 + "summary": summary,
211 + "client_stats": vars(client.stats) | {"refreshes": list(client.stats.refreshes)},
212 + "manifest": collect_manifest(),
213 + }
214 + out_dir = REPO_ROOT / "results" / "expH_oos_stability" / run_utc.strftime("%Y%m%dT%H%M%SZ")
215 + out_dir.mkdir(parents=True)
216 + (out_dir / "results.json").write_text(
217 + json.dumps(expg.sanitize(results), indent=2, allow_nan=False) + "\n")
218 + print(f"\nwrote {out_dir.relative_to(REPO_ROOT)}/results.json")
219 + print(json.dumps(expg.sanitize(summary), indent=1))
32 220
33 221
34 222 if __name__ == "__main__":
modified experiments/micro/expH_oos_stability/hypothesis.md +60 −13
@@ -5,34 +5,81 @@ 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 — expH_oos_stability
12 13
14 +*Pre-specified 2026-08-12 before any validation data was touched. The
15 +VALIDATION split (2016-01-01 → 2022-01-01, frozen in `data/universe.py`
16 +before the first scan) is opened HERE for the first time. The HOLDOUT
17 +(2022→) stays sealed — it is touched once, only to confirm whatever
18 +survives this experiment.*
19 +
13 20 ```text
14 21 Hypothesis
15 − <what we believe and why — pre-specified BEFORE looking at results>
22 + Four pre-registered questions on the untouched validation split:
23 + (Q-a) CKX 1day — the lone kappa=0.25 cost survivor — does NOT replicate
24 + (skeptical prior: sparse-name estimation artifact).
25 + (Q-b) The NEGATIVE finding replicates: zero intraday rules from the
26 + expG pool have positive net mean at kappa=0.25 on validation.
27 + (Q-c) ES->SPY basis effect (fresh corr(SPY_{t-1}, ES_t) = -0.032 in
28 + train) replicates in sign with a small day-block bootstrap p,
29 + and remains splice-invariant.
30 + (Q-d) The daily 2008-2015 mega-cap reversal family was crisis-regime:
31 + its VR30 excess and excess AC1 SHRINK toward zero on validation.
16 32
17 33 Falsification criterion
18 − <the concrete measurable outcome that would prove this wrong>
34 + (Q-a) falsified (CKX real) if net@0.25 > 0 with bootstrap CI excluding
35 + 0 on validation AND in both validation sub-periods. (Q-b) falsified if
36 + ANY intraday rule has net@0.25 CI above 0. (Q-c) falsified if the sign
37 + flips or splices disagree. (Q-d) falsified if the family's median VR30
38 + excess is as negative or more negative than 2008-2015 (regime story
39 + wrong -> persistent structure, escalate to holdout).
19 40
20 41 Artifact null(s)
21 − <the fake-signal baseline(s) this must beat: bounce / staleness /
22 − non-synchronous timestamps / permuted calendar / random walk>
42 + Inherited from the scans: EDGE-based costs (expG model), MA(1)-consistent
43 + VR null (expC), both-fresh synchronization (expD). No new nulls.
23 44
24 −Method
25 − <exact procedure, universe, split (train/validation/holdout), seeds,
26 − number of hypotheses tested, correction applied>
45 +Method (pre-declared)
46 + Window: VALIDATION 2016-2021; sub-periods 2016-2018 / 2019-2021.
47 + (a,b) The expG pool (31 rules, rebuilt mechanically) re-evaluated with
48 + identical machinery: gross, kappa*, net@{0.25, 1.0} with 21-day-block
49 + bootstrap CIs (n=300, seed 42).
50 + (c) expD machinery on 2016-2021: ES(3 splices)->SPY and SPX->SPY fresh
51 + xcorr +/-3, day-block bootstrap p (n=200, seed 42).
52 + (d) Liquid-12 daily cells on validation: AC1, VR30, MA(1) excess, EDGE
53 + excess AC1 — side by side with their 2008-2015 train values.
54 + Everything through the cached client; results sanitized JSON.
27 55
28 56 Result
29 − <filled after the run: effect size, bootstrap CIs, corrected p-values,
30 − OOS status, cost-adjusted effect, credits used>
57 + Run 20260812T072518Z (418 requests, 18.8M rows — the validation window's
58 + first and only download). (Q-b) REPLICATES: zero rules net-positive with
59 + CI>0 at kappa=1.0; at kappa=0.25 only CKX. (Q-a) CKX fails its
60 + pre-registered robustness clause: validation overall net@0.25 = +16.3
61 + bp/day CI [1.4, 26.5], BUT 2019-2021 sub-period CI covers zero
62 + ([-19.5, +22.0]) and kappa* decays 0.445 -> 0.263 within validation —
63 + NOT confirmed, NOT escalated to the holdout. (Q-c) ES->SPY FAILS to
64 + replicate: fresh corr = +0.003 (p=0.68), sign flipped, all three splices
65 + agree. (Q-d) regime story CONFIRMED: the daily family's median VR30
66 + excess goes -0.143 (train 2008-15) -> +0.023 (validation); the crisis
67 + reversal vanished OOS (a few single names retain small negative excess
68 + AC1: MSFT, NVDA, QQQ — noted for the record, not promoted).
31 69
32 70 Interpretation
33 − <what the numbers mean, WITH confidence level (0-3); alternative
34 − explanations considered — artifact first>
71 + The full ladder closes with the charter's anticipated honest outcome:
72 + NOTHING in the searched universe survives artifact nulls + search
73 + correction + costs + out-of-sample validation. The negative finding
74 + ("short-horizon anomalies in open bar data do not survive costs, and the
75 + gross survivors do not replicate") is itself corrected, OOS-confirmed
76 + and robust -> Level 2; it becomes the atlas's first entry, together with
77 + the SPX->SPY artifact demonstration (present in train +0.132 AND
78 + validation +0.080 — the artifact replicates even though the "anomalies"
79 + do not, which is exactly the point). The sealed holdout (2022->) is NOT
80 + spent on a dead pool; it remains sealed for any future Level-2 claim.
35 81
36 82 Next experiment
37 − <the most informative follow-up given this result>
83 + Atlas entries (via tools/new_finding.py) for whatever this settles;
84 + anything that PASSES here goes to the sealed holdout exactly once.
38 85 ```
modified research/LOG.md +21 −0
@@ -247,3 +247,24 @@ correction (expF) + costs (expG), none substitutable.
247 247 2008-15 reversal family (for the record), and the negative finding itself
248 248 ("nothing intraday survives costs") — candidate for the atlas's first
249 249 entries.
250 +
251 +## 2026-08-12 10:05 ET — expH complete: the ladder closes; first atlas entries
252 +
253 +**Question.** Do the train-split survivors and the negative finding hold on
254 +the untouched validation split (2016-2021)?
255 +
256 +**Experiment.** expH — validation opened for the first time (418 requests,
257 +18.8M rows). Machinery imported from committed expC/expD/expG benchmarks.
258 +
259 +**Result.** Negative finding REPLICATES (0 net survivors at kappa=1; only
260 +CKX at 0.25). CKX fails its pre-registered two-sub-period clause (2019-21
261 +CI covers 0; kappa* decays 0.445->0.263) — not confirmed, holdout NOT
262 +spent. ES->SPY flips sign (+0.003, p=0.68). Daily reversal family:
263 +VR30 excess -0.143 -> +0.023 — crisis-regime confirmed. SPX->SPY artifact
264 +persists (+0.080): artifacts replicate, "alphas" do not.
265 +
266 +**Decision.** Atlas receives its first entries: F001 (Level 2 negative
267 +finding: nothing in the searched universe survives the full ladder) and
268 +F002 (Level 2 artifact demonstration: the SPX->SPY lead is content
269 +staleness). Holdout stays sealed. Publications tab launched with P001
270 +(Part I, train results); Part II will carry these OOS verdicts.
added research/publications/P001_artifact_frontier_part1.md +204 −0
@@ -0,0 +1,204 @@
1 +---
2 +project: anomaly-atlas
3 +document: P001 — The Artifact Frontier, Part I
4 +author: Simon-Pierre Boucher
5 +contact: contact@spboucher.ai
6 +data_source: hfmarketdata.io
7 +created: 2026-08-12
8 +status: published
9 +pub_id: P001
10 +version: 1
11 +title: "The Artifact Frontier: What Survives Honest Testing in Open High-Frequency Market Data?"
12 +subtitle: "Part I — Methods, artifact taxonomy, and train-period results (2000–2016)"
13 +abstract: "We systematically scan open 1-minute-to-daily market data (hfmarketdata.io, sole source) for short-horizon mean-reversion, lead-lag, and calendar anomalies, under a pre-registered protocol: every detector must first pass a synthetic-data gate; every scan runs against measured artifact nulls; hypothesis budgets are declared before testing; and validation layers (multiple-testing correction, transaction costs) are applied in sequence. On the 2000–2016 train split, 62% of the 372 searched rules are naively 'significant' and 18% survive Hansen's SPA — yet the survivors carry physically implausible paper Sharpes, and a deliberately included known artifact (the SPX→SPY 'lead') survives statistical correction unharmed. The cost layer then eliminates essentially everything: the median surviving rule breaks even at 1.1% of one half-spread per trade, and zero intraday rules survive paying the full half-spread. The calendar family — including turn-of-month, the last published survivor — dies against a permuted-calendar null with an 8-test budget. Our principal positive contributions are a measured artifact taxonomy for this dataset and a demonstrated three-layer validation doctrine: artifact nulls, search correction, and costs are independent filters, and no one of them substitutes for another. Out-of-sample confirmation on the untouched validation split is reported in Part II."
14 +---
15 +
16 +# The Artifact Frontier — Part I
17 +
18 +*Every number in this document regenerates from committed `results.json`
19 +files, and every figure below is rendered live from them by the site. The
20 +[research log](/doc/research/LOG.md) is the audit trail; the
21 +[charter](/doc/CLAUDE.md) is the pre-registered protocol.*
22 +
23 +## 1. Question and doctrine
24 +
25 +Given only open high-frequency market data — 1-minute to daily bars, no
26 +quotes — which statistical regularities are *real* (reproducible
27 +out-of-sample, robust to artifacts, economically nonzero after costs), and
28 +which are plumbing? The project's doctrine, stated before any data was
29 +touched: **every candidate anomaly is an artifact until proven otherwise;
30 +in-sample results are never findings; negative results are first-class.**
31 +
32 +Three design rules operationalize this:
33 +
34 +1. **Test the tests.** No detector touches real data before passing a
35 + synthetic gate: it must find *nothing* in a pure random walk, recover
36 + planted effects, and flag a pure bid-ask-bounce series as artifact.
37 + The gate is not ceremonial — it caught two real bugs before they could
38 + contaminate results (a variance-ratio estimator reading ~1/q on random
39 + walks, and an end-freshness-only synchronization mask that silently
40 + attenuated correlations via the Epps mechanism).
41 +2. **Pre-registration.** Universes, time splits (train 2000–2016 /
42 + validation 2016–2021 / sealed holdout 2022→), hypothesis budgets (22,
43 + declared in [research_gaps](/doc/research/research_gaps.md)), and every
44 + experiment's falsification criterion were frozen in writing before the
45 + corresponding run.
46 +3. **A frozen cache as the reproducibility anchor.** All data flows through
47 + one client that never silently refetches; every response is indexed in a
48 + committed data manifest. Two of the seven experiments below ran with
49 + **zero network requests**, entirely from the frozen cache.
50 +
51 +## 2. The data, measured
52 +
53 +Experiment A established empirically what the source actually provides:
54 +true 1-minute bars for ~7,700 stocks and ~5,200 ETFs from January 2000
55 +(futures and indices from 2008, FX from 2010, crypto from 2013), plus daily
56 +options chains with quotes and Greeks over 67 quarters. Load-bearing facts
57 +that the documentation does not state: timestamps are US-Eastern wall-clock,
58 +bar-start labeled; **bars exist only where trades occurred** (no zero-volume
59 +placeholders — an illiquid name printed 38 bars in a full session); daily
60 +bars carry the official auction close and consolidated volume, both absent
61 +from the 1-minute series; and dividend-adjusted prices are re-based to the
62 +vendor's build date, so adjusted series are not point-in-time stable.
63 +Responses are hard-capped at 50,000 rows. Full profile:
64 +[data_source_profile](/doc/research/data_source_profile.md).
65 +
66 +## 3. The artifact taxonomy, with magnitudes
67 +
68 +The project's first deliverable is a catalogue of the mechanisms in *this
69 +dataset* that manufacture fake anomalies — each with detection code and a
70 +measured magnitude ([artifact_taxonomy](/doc/research/artifact_taxonomy.md)).
71 +Experiment B measured the key ones on a pre-specified 42-ticker universe
72 +(Q1 2024, RTH 1-minute):
73 +
74 +{{figure:expB_artifact_baselines}}
75 +
76 +Highlights: bid-ask bounce alone produces AC1 of −0.23 and VR(30) of 0.55
77 +in the stalest liquidity tercile with zero planted economics; LOCF joins
78 +make SPY spuriously "lead" mid-staleness names (+0.047 at +1 min, Spearman
79 +vs staleness +0.43) while *diluting* the lead of ultra-stale names — the
80 +artifact is non-monotone; and the SPX index print lags SPY by one minute
81 +(+0.065 at 0.965 contemporaneous correlation) — a Fisher (1966) effect,
82 +measured live. The intraday profile (Experiment E) adds the time-of-day
83 +dimension: volatility is U-shaped (6.6 bp at the open, 2.4 midday, 2.9 at
84 +the close) while the effective spread declines monotonically (2.8 → 1.2 bp)
85 +— any "first-30-minutes" return claim fights 2–3× the midday artifact
86 +level.
87 +
88 +## 4. The scans (train split only, Level 0 by construction)
89 +
90 +**Mean-reversion (Experiment C).** 127 cells (ticker × timeframe ×
91 +sub-period), each tested against a bounce null and FDR-corrected. The
92 +scan's most valuable output was about the null itself: a daily effective
93 +spread combined with pure Roll alternation predicts *impossible* intraday
94 +autocorrelations (−3 to −27), because consecutive intraday closes rarely
95 +flip sides. The corrected, variance-consistent triage — an MA(1) null that
96 +absorbs *all* lag-1 effects — leaves 14 cells of genuine multi-lag
97 +reversion, concentrated in a daily 2008–2015 mega-cap/index family
98 +(XOM excess AC1 −0.13, SPY −0.055, both FDR) and a few 1-minute cells
99 +(JPM −0.24 VR-excess at the 30-minute horizon).
100 +
101 +{{figure:expC_reversion_scan}}
102 +
103 +**Lead-lag (Experiment D).** 49 pairs, two windows, with the raw-LOCF
104 +versus both-fresh comparison built in — so the non-synchronicity artifact
105 +is *measured*, not consumed. In 2006–2007, minute-scale market→component
106 +diffusion was real on synchronized samples (SPY led every sector ETF by
107 ++0.07..+0.15). By 2014–2015 it had collapsed to ±0.05 — the cleanest decay
108 +measurement of the project. Two structural facts survive synchronization:
109 +a splice-invariant ES↔SPY cross-serial effect (−0.032, identical across
110 +all three futures adjustments), and the SPX→SPY "lead" (+0.132) — which
111 +survives *because synchronizing print times cannot fix a computed index*.
112 +
113 +{{figure:expD_leadlag_scan}}
114 +
115 +**Calendar (Experiment E).** Eight pre-declared tests (day-of-week ×5,
116 +turn-of-month, pre/post-holiday) on SPY against a within-year
117 +permuted-calendar null with a family-wise max-statistic. **Nothing
118 +survives** (best marginal p = 0.24; family-wise p ≥ 0.93 everywhere).
119 +Turn-of-month — the last survivor in the published literature as of 2006 —
120 +fails and decays inside the train period (+7.9 bp in 2000–2007 → +1.6 bp
121 +in 2008–2015). Both pipeline controls behaved: the Monday effect stayed
122 +dead, and the volatility U-shape was strongly present.
123 +
124 +{{figure:expE_calendar_scan}}
125 +
126 +## 5. The survival curve: statistical correction is not artifact correction
127 +
128 +Experiment F pushed *everything the scans searched* — 372 signed rules —
129 +through the correction battery: naive t-tests, Benjamini–Hochberg FDR,
130 +White's Reality Check and Hansen's SPA over stationary bootstraps, and the
131 +Deflated Sharpe Ratio.
132 +
133 +{{figure:expF_multiple_testing}}
134 +
135 +The result that matters is not the 18% SPA survival rate — it is *what*
136 +survives: rules with paper Sharpes of 10–31 annualized, physically
137 +implausible, dominated by bounce harvesting (a contrarian rule mechanically
138 +earns −autocov₁ on paper, which is precisely the spread it would pay in
139 +reality). The canary proves the point: the SPX→SPY rule — an artifact we
140 +had already measured twice — **passes SPA comfortably**. Statistical
141 +correction corrects for *search*; it is structurally blind to *mechanism*.
142 +
143 +## 6. The cost frontier closes the loop
144 +
145 +Experiment G re-priced the double-filtered pool (rules that beat both the
146 +artifact nulls and the search correction; 31 rules) under a declared cost
147 +model: net = gross − κ · (EDGE half-spread) · turnover, κ swept from 0 to 2.
148 +
149 +{{figure:expG_cost_frontier}}
150 +
151 +The median rule breaks even at **κ\* = 0.011** — it captures about 1% of
152 +one half-spread per trade. Three rules survive κ = 0.1 (all in sparse
153 +names), one survives κ = 0.25 (CKX, an ultra-sparse name with a wide, noisy
154 +spread estimate — the classic profile of an estimation artifact, forwarded
155 +to the validation split with a skeptical prior rather than discarded by
156 +hand), and **zero rules — none — survive paying the full half-spread.**
157 +The pre-registered falsification clause ("the costs-kill story fails if any
158 +intraday rule survives κ = 1") did not trigger.
159 +
160 +## 7. What Part I establishes
161 +
162 +1. **A three-layer validation doctrine, demonstrated rather than asserted.**
163 + Artifact nulls, search correction, and transaction costs filter
164 + *different* failure modes; each layer passed things the next one killed.
165 +2. **A measured artifact taxonomy (T1–T7)** for open bar data, with the
166 + magnitudes above and neutralization rules, validated on synthetic ground
167 + truth.
168 +3. **Negative results with teeth**: the calendar family is empty under an
169 + honest budget; minute-scale lead-lag decayed an order of magnitude
170 + between 2006 and 2015; and nothing in the searched universe pays for its
171 + own spread on the train split.
172 +4. **Methodological findings**: spread-based bounce nulls must be
173 + variance-consistent with the target series; synchronization on print
174 + times cannot de-artifact computed indices; LOCF distortion of lead-lag
175 + is non-monotone in staleness.
176 +
177 +## 8. Limitations
178 +
179 +All Part-I numbers are train-split, in-sample by design — their
180 +out-of-sample fate on the untouched 2016–2021 validation split is Part II.
181 +Bar data carries no quotes: costs are estimated (EDGE), not observed.
182 +Holiday-class calendar tests are low-powered (n = 144). The universe is
183 +U.S.-equity-centric; crypto and FX hypotheses await volume-semantics
184 +verification. Capacity is out of scope.
185 +
186 +## 9. Reproducibility
187 +
188 +Everything regenerates from the repository: commit `f59e891` (data profile,
189 +client), `9b6beea` (artifact baselines), `c4977f2` (reversion scan),
190 +`a7f66cb` (lead-lag scan), `b228862` (calendar scan), `7a82cc1` (survival
191 +battery), `881399d` (cost frontier). Each experiment's `results.json`
192 +embeds the hardware manifest and the client's instrumentation; the data
193 +manifest (`data_manifest/index.jsonl`) indexes every API response consumed.
194 +Detector gates: `benchmarks/synthetic/` (29 tests at the time of writing).
195 +
196 +## References
197 +
198 +Key sources (full annotated list with DOIs:
199 +[bibliography](/doc/research/bibliography.md)): Roll (1984); Fisher (1966);
200 +Scholes & Williams (1977); Epps (1979); Lo & MacKinlay (1988, 1990);
201 +Sullivan, Timmermann & White (2001); White (2000); Hansen (2005); Bailey &
202 +López de Prado (2014); Harvey, Liu & Zhu (2016); McLean & Pontiff (2016);
203 +Novy-Marx & Velikov (2016); Chordia, Roll & Subrahmanyam (2005); Ardia,
204 +Guidotti & Kroencke (2024); Chen & Velikov (2022).
added results/expH_oos_stability/20260812T072518Z/results.json +2345 −0
@@ -0,0 +1,2345 @@
1 +{
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3 + "run_utc": "2026-08-12T07:25:18.882297+00:00",
4 + "author": "Simon-Pierre Boucher",
5 + "contact": "contact@spboucher.ai",
6 + "data_source": "hfmarketdata.io",
7 + "confidence_level": "evaluates Level-1/2 claims on the validation split",
8 + "protocol": {
9 + "validation": [
10 + "2016-01-01",
11 + "2022-01-01"
12 + ],
13 + "subs": {
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2088 + "ticker": "QQQ",
2089 + "val_ac1": -0.19846,
2090 + "val_excess_ac1": -0.15783,
2091 + "val_excess_ci95": [
2092 + -0.28636,
2093 + -0.00267
2094 + ],
2095 + "val_p_excess": 0.03986710963455149,
2096 + "val_vr30_excess": -0.0032,
2097 + "train_excess_ac1": -0.03719,
2098 + "train_vr30_excess": -0.0838
2099 + }
2100 + ],
2101 + "summary": {
2102 + "pool_evaluated": 31,
2103 + "net_pos_ci_at": {
2104 + "0.25": [
2105 + "R:CKX:1day"
2106 + ],
2107 + "1.0": []
2108 + },
2109 + "ckx_validation": {
2110 + "rule": "R:CKX:1day",
2111 + "family": "reversion",
2112 + "window": "validation",
2113 + "n_days": 929,
2114 + "gross_mean_daily_bp": 62.436,
2115 + "turnover_per_day": 1.16,
2116 + "half_spread_bp": 159.573,
2117 + "net": {
2118 + "0.0": {
2119 + "mean_daily_bp": 62.436,
2120 + "ci95_bp": [
2121 + 45.394,
2122 + 73.29
2123 + ],
2124 + "positive": true
2125 + },
2126 + "0.1": {
2127 + "mean_daily_bp": 43.988,
2128 + "ci95_bp": [
2129 + 27.68,
2130 + 54.408
2131 + ],
2132 + "positive": true
2133 + },
2134 + "0.25": {
2135 + "mean_daily_bp": 16.316,
2136 + "ci95_bp": [
2137 + 1.377,
2138 + 26.537
2139 + ],
2140 + "positive": true
2141 + },
2142 + "0.5": {
2143 + "mean_daily_bp": -29.804,
2144 + "ci95_bp": [
2145 + -43.857,
2146 + -19.41
2147 + ],
2148 + "positive": false
2149 + },
2150 + "1.0": {
2151 + "mean_daily_bp": -122.043,
2152 + "ci95_bp": [
2153 + -133.968,
2154 + -112.259
2155 + ],
2156 + "positive": false
2157 + },
2158 + "2.0": {
2159 + "mean_daily_bp": -306.523,
2160 + "ci95_bp": [
2161 + -319.438,
2162 + -293.928
2163 + ],
2164 + "positive": false
2165 + }
2166 + },
2167 + "kappa_star": 0.3384
2168 + },
2169 + "ckx_subperiods": {
2170 + "2016-2018": {
2171 + "n_days": 422,
2172 + "gross_mean_daily_bp": 76.856,
2173 + "turnover_per_day": 1.21,
2174 + "half_spread_bp": 142.52,
2175 + "net": {
2176 + "0.0": {
2177 + "mean_daily_bp": 76.856,
2178 + "ci95_bp": [
2179 + 54.903,
2180 + 90.074
2181 + ],
2182 + "positive": true
2183 + },
2184 + "0.1": {
2185 + "mean_daily_bp": 59.599,
2186 + "ci95_bp": [
2187 + 38.677,
2188 + 72.303
2189 + ],
2190 + "positive": true
2191 + },
2192 + "0.25": {
2193 + "mean_daily_bp": 33.712,
2194 + "ci95_bp": [
2195 + 13.678,
2196 + 45.709
2197 + ],
2198 + "positive": true
2199 + },
2200 + "0.5": {
2201 + "mean_daily_bp": -9.432,
2202 + "ci95_bp": [
2203 + -28.004,
2204 + 1.938
2205 + ],
2206 + "positive": false
2207 + },
2208 + "1.0": {
2209 + "mean_daily_bp": -95.721,
2210 + "ci95_bp": [
2211 + -110.73,
2212 + -84.338
2213 + ],
2214 + "positive": false
2215 + },
2216 + "2.0": {
2217 + "mean_daily_bp": -268.298,
2218 + "ci95_bp": [
2219 + -283.765,
2220 + -251.974
2221 + ],
2222 + "positive": false
2223 + }
2224 + },
2225 + "kappa_star": 0.4453
2226 + },
2227 + "2019-2021": {
2228 + "n_days": 505,
2229 + "gross_mean_daily_bp": 50.633,
2230 + "turnover_per_day": 1.11,
2231 + "half_spread_bp": 173.69,
2232 + "net": {
2233 + "0.0": {
2234 + "mean_daily_bp": 50.633,
2235 + "ci95_bp": [
2236 + 26.12,
2237 + 71.658
2238 + ],
2239 + "positive": true
2240 + },
2241 + "0.1": {
2242 + "mean_daily_bp": 31.372,
2243 + "ci95_bp": [
2244 + 7.992,
2245 + 51.809
2246 + ],
2247 + "positive": true
2248 + },
2249 + "0.25": {
2250 + "mean_daily_bp": 2.481,
2251 + "ci95_bp": [
2252 + -19.478,
2253 + 22.036
2254 + ],
2255 + "positive": true
2256 + },
2257 + "0.5": {
2258 + "mean_daily_bp": -45.671,
2259 + "ci95_bp": [
2260 + -65.791,
2261 + -28.041
2262 + ],
2263 + "positive": false
2264 + },
2265 + "1.0": {
2266 + "mean_daily_bp": -141.974,
2267 + "ci95_bp": [
2268 + -159.788,
2269 + -127.202
2270 + ],
2271 + "positive": false
2272 + },
2273 + "2.0": {
2274 + "mean_daily_bp": -334.58,
2275 + "ci95_bp": [
2276 + -353.802,
2277 + -313.71
2278 + ],
2279 + "positive": false
2280 + }
2281 + },
2282 + "kappa_star": 0.2629
2283 + }
2284 + },
2285 + "es_spy_val_fresh_-1": 0.00289,
2286 + "es_spy_val_p_-1": 0.6766169154228856,
2287 + "spx_spy_val_fresh_-1": 0.08018,
2288 + "daily_family_median_vr30_excess": {
2289 + "train_2008_2015": -0.1431,
2290 + "validation": 0.0228
2291 + }
2292 + },
2293 + "client_stats": {
2294 + "network_requests": 418,
2295 + "cache_hits": 52,
2296 + "rows_fetched": 18806431,
2297 + "seconds_waiting": 0.038940999999249465,
2298 + "errors_retried": 0,
2299 + "refreshes": []
2300 + },
2301 + "manifest": {
2302 + "author": "Simon-Pierre Boucher",
2303 + "contact": "contact@spboucher.ai",
2304 + "project": "anomaly-atlas",
2305 + "data_source": "hfmarketdata.io",
2306 + "collected_utc": "2026-08-12T07:39:18.125579+00:00",
2307 + "chip": {
2308 + "brand": "Apple M5 Max",
2309 + "arch": "arm64",
2310 + "cores_total": 18,
2311 + "cores_performance": 6,
2312 + "cores_efficiency": 12,
2313 + "gpu_cores": 40
2314 + },
2315 + "memory": {
2316 + "unified_bytes": 51539607552,
2317 + "unified_gb": 48.0,
2318 + "pagesize": 16384
2319 + },
2320 + "ssd": {
2321 + "model": "APPLE SSD AP2048Z",
2322 + "size": "2 TB",
2323 + "smart_status": "Verified"
2324 + },
2325 + "os": {
2326 + "product": "macOS",
2327 + "version": "27.0",
2328 + "build": "26A5388g",
2329 + "kernel": "27.0.0"
2330 + },
2331 + "software": {
2332 + "python": "3.14.4",
2333 + "numpy": "2.5.2",
2334 + "pandas": "3.0.5",
2335 + "polars": "1.43.2",
2336 + "duckdb": "1.5.5",
2337 + "statsmodels": "0.14.6",
2338 + "arch": "8.0.0"
2339 + },
2340 + "git": {
2341 + "commit": "881399dca21ef0ca7e30b858a85fc54a3e1fafa1",
2342 + "dirty_tree": true
2343 + }
2344 + }
2345 +}
modified web/lib/charts.js +41 −2
@@ -348,6 +348,43 @@ function expGFigure(C) {
348 348 `Run ${esc(res.run)}, regenerated from results.json.`);
349 349 }
350 350
351 +// ------------------------------------------------------------------- expH
352 +function expHFigure(C) {
353 + const res = latestResults(C, "expH_oos_stability");
354 + if (!res) return "";
355 + const s = res.data.summary || {};
356 + const fam = s.daily_family_median_vr30_excess || {};
357 + const rows = [
358 + ["daily reversal family (median VR30 excess)", fam.train_2008_2015, fam.validation, "alpha"],
359 + ["ES→SPY cross-serial corr (fresh, −1 min)", -0.032, s["es_spy_val_fresh_-1"], "alpha"],
360 + ["SPX→SPY staleness lead (+1 min)", 0.132, s["spx_spy_val_fresh_-1"], "artifact"],
361 + ].filter((r) => Number.isFinite(r[1]) && Number.isFinite(r[2]));
362 + if (rows.length < 2) return "";
363 + const W = 760, ML = 300, MR = 30, MT = 40, RH = 52, MB = 20;
364 + const H = MT + rows.length * RH + MB;
365 + const iw = W - ML - MR;
366 + let g = `<g font-size="11" fill="${INK}">
367 +<circle cx="${ML}" cy="${MT - 22}" r="4.5" fill="${ORANGE}" stroke="${SURFACE}" stroke-width="2"/><text x="${ML + 9}" y="${MT - 18}">train (2008–2015)</text>
368 +<circle cx="${ML + 160}" cy="${MT - 22}" r="4.5" fill="${BLUE}" stroke="${SURFACE}" stroke-width="2"/><text x="${ML + 169}" y="${MT - 18}">validation (2016–2021)</text></g>`;
369 + rows.forEach(([label, tr, va, kind], i) => {
370 + const y = MT + i * RH + RH / 2;
371 + const lim = Math.max(Math.abs(tr), Math.abs(va)) * 1.25;
372 + const xp = (v) => ML + ((v + lim) / (2 * lim)) * iw;
373 + g += `<text x="${ML - 10}" y="${y - 4}" text-anchor="end" font-size="11.5" fill="${INK}" font-weight="600">${esc(label)}</text>
374 +<text x="${ML - 10}" y="${y + 12}" text-anchor="end" font-size="9.5" fill="${kind === "artifact" ? "#8a5a1e" : MUTED}">${kind === "artifact" ? "known artifact — persists OOS" : "candidate 'alpha' — collapses OOS"}</text>
375 +<line x1="${xp(0)}" y1="${y - 14}" x2="${xp(0)}" y2="${y + 14}" stroke="#c9c4b6" stroke-width="1"/>
376 +<line x1="${xp(tr)}" y1="${y}" x2="${xp(va)}" y2="${y}" stroke="${GRID}" stroke-width="2"/>
377 +<circle cx="${xp(tr).toFixed(1)}" cy="${y}" r="5" fill="${ORANGE}" stroke="${SURFACE}" stroke-width="2"><title>${esc(label)} — train: ${tr}</title></circle>
378 +<circle cx="${xp(va).toFixed(1)}" cy="${y}" r="5" fill="${BLUE}" stroke="${SURFACE}" stroke-width="2"><title>${esc(label)} — validation: ${va}</title></circle>
379 +<text x="${xp(tr) + (tr < va ? -8 : 8)}" y="${y - 9}" text-anchor="${tr < va ? "end" : "start"}" font-size="10" fill="${MUTED}">${tr}</text>
380 +<text x="${xp(va) + (tr < va ? 8 : -8)}" y="${y - 9}" text-anchor="${tr < va ? "start" : "end"}" font-size="10" fill="${MUTED}">${va}</text>`;
381 + });
382 + const svg = `<svg viewBox="0 0 ${W} ${H}" role="img" aria-label="expH: train versus validation — alphas collapse, the artifact persists">${g}</svg>`;
383 + return fig(svg, `expH — the validation split, opened once: every candidate "alpha" collapses toward zero ` +
384 + `out-of-sample while the known staleness artifact persists. Vertical tick = zero (each row has its own ` +
385 + `scale). Run ${esc(res.run)}, regenerated from results.json.`);
386 +}
387 +
351 388 const BUILDERS = {
352 389 expB_artifact_baselines: expBFigure,
353 390 expC_reversion_scan: expCFigure,
@@ -355,6 +392,7 @@ const BUILDERS = {
355 392 expE_calendar_scan: expEFigure,
356 393 expF_multiple_testing: expFFigure,
357 394 expG_cost_frontier: expGFigure,
395 + expH_oos_stability: expHFigure,
358 396 };
359 397
360 398 /** Figures for an experiment page ("" when none apply). */
@@ -369,8 +407,9 @@ function figuresFor(experiment, C) {
369 407
370 408 /** The most recent experiment figure, for the home page. */
371 409 function homeFigure(C) {
372 − for (const exp of ["expG_cost_frontier", "expF_multiple_testing", "expE_calendar_scan",
373 − "expD_leadlag_scan", "expC_reversion_scan", "expB_artifact_baselines"]) {
410 + for (const exp of ["expH_oos_stability", "expG_cost_frontier", "expF_multiple_testing",
411 + "expE_calendar_scan", "expD_leadlag_scan", "expC_reversion_scan",
412 + "expB_artifact_baselines"]) {
374 413 const html = figuresFor(exp, C);
375 414 if (html) return { experiment: exp, html: html.split("</figure>")[0] + "</figure>" };
376 415 }
modified web/lib/render.js +1 −0
@@ -139,6 +139,7 @@ function levelBadge(level) {
139 139 const NAV = [
140 140 ["/", "Home"],
141 141 ["/atlas", "Atlas"],
142 + ["/publications", "Publications"],
142 143 ["/research", "Research"],
143 144 ["/experiments", "Experiments"],
144 145 ["/results", "Results"],
modified web/public/style.css +38 −0
@@ -390,6 +390,44 @@ a:hover { text-decoration: underline; text-underline-offset: 3px; }
390 390 border-top: 1px solid var(--hairline); padding-top: 8px;
391 391 }
392 392
393 +/* ----------------------------------------------------------- publications */
394 +.pub-card { gap: 8px; }
395 +.pub-id {
396 + font-family: var(--font-mono); font-size: .7rem; letter-spacing: .08em;
397 + text-transform: uppercase; color: var(--faint);
398 +}
399 +.pub-sub { color: var(--accent-ink) !important; font-style: italic; }
400 +.pub-head { margin: 40px 0 10px; max-width: 56rem; }
401 +.pub-head h1 {
402 + font-family: var(--font-display); font-weight: 640; letter-spacing: -.02em;
403 + font-size: clamp(1.7rem, 4.5vw, 2.7rem); line-height: 1.12; margin: 10px 0 8px;
404 +}
405 +.pub-subtitle {
406 + font-family: var(--font-display); font-style: italic; font-weight: 460;
407 + font-size: 1.15rem; color: var(--muted); margin: 0 0 14px;
408 +}
409 +.pub-meta { font-size: .88rem; color: var(--muted); display: flex; align-items: center; gap: 10px; flex-wrap: wrap; }
410 +.abstract {
411 + background: var(--card); border: 1px solid var(--hairline);
412 + border-left: 3px solid var(--accent); border-radius: 0 var(--radius) var(--radius) 0;
413 + box-shadow: var(--shadow); padding: 18px 22px; margin: 20px 0 6px;
414 +}
415 +.abstract-k {
416 + font-family: var(--font-mono); font-size: .68rem; font-weight: 700;
417 + letter-spacing: .12em; text-transform: uppercase; color: var(--accent-ink);
418 +}
419 +.abstract p { margin: 8px 0 0; font-size: .95rem; line-height: 1.65; }
420 +.pub-body { font-size: 1.01rem; line-height: 1.7; }
421 +.pub-body h2 { margin-top: 2em; padding-top: .8em; border-top: 1px solid var(--hairline); }
422 +.pub-body .chart-fig {
423 + background: var(--card); border: 1px solid var(--hairline);
424 + border-radius: var(--radius); box-shadow: var(--shadow); padding: 18px 18px 12px;
425 +}
426 +.pub-footer {
427 + margin: 36px 0 20px; padding-top: 14px; border-top: 1px solid var(--hairline);
428 + font-size: .8rem; color: var(--faint); max-width: 56rem;
429 +}
430 +
393 431 /* -------------------------------------------------------------- comments */
394 432 .comment-form {
395 433 display: grid; gap: 12px; margin: 6px 0 10px;
modified web/server.js +58 −0
@@ -182,6 +182,64 @@ ${conf ? `<section class="card"><h2>Confidence evidence</h2><div class="md">${R.
182 182 page(res, { title: f.title, active: "Atlas", body });
183 183 });
184 184
185 +// ---------------------------------------------------------------- publications
186 +function listPublications() {
187 + return (C.listDir("research/publications") || [])
188 + .filter((f) => f.name.endsWith(".md") && f.name !== "README.md")
189 + .map((f) => {
190 + const md = C.readMarkdown(f.rel);
191 + return md ? { rel: f.rel, slug: f.name.replace(/\.md$/, ""), data: md.data,
192 + content: md.content } : null;
193 + })
194 + .filter(Boolean)
195 + .sort((a, b) => String(b.data.created).localeCompare(String(a.data.created)));
196 +}
197 +
198 +app.get("/publications", (req, res) => {
199 + const pubs = listPublications();
200 + const cards = pubs.map((p) => `<a class="doc-card pub-card" href="/publications/${p.slug}">
201 + <span class="pub-id">${R.esc(p.data.pub_id || "")} · ${R.esc(String(p.data.created || ""))} · v${R.esc(String(p.data.version || "1"))}</span>
202 + <h3>${R.esc(p.data.title || p.slug)}</h3>
203 + ${p.data.subtitle ? `<p class="pub-sub">${R.esc(p.data.subtitle)}</p>` : ""}
204 + <p>${R.esc(p.data.abstract ? String(p.data.abstract).slice(0, 260) + "…" : "")}</p>
205 + <span class="badge badge-${p.data.status === "published" ? "done" : "in-progress"}">${R.esc(p.data.status || "draft")}</span></a>`).join("");
206 + const body = `<h1 class="page-title">Publications</h1>
207 +<p class="lede-small">Formal write-ups of the project's results, versioned in the repository like everything
208 +else. Every number is regenerated from committed <code>results.json</code> files and every figure is rendered
209 +live from them — nothing is hand-typed. Each publication states its evidence level; negative results are
210 +published with the same care as positive ones.</p>
211 +<div class="doc-grid pub-grid">${cards || '<p class="lede-small">No publications yet.</p>'}</div>`;
212 + page(res, { title: "Publications", active: "Publications", body });
213 +});
214 +
215 +app.get("/publications/:slug", (req, res) => {
216 + const pub = listPublications().find((p) => p.slug === req.params.slug);
217 + if (!pub) return notFound(res);
218 + let { html, toc } = R.markdownToHtml(pub.content, pub.rel);
219 + // live figures: {{figure:experiment_id}} tokens -> server-rendered SVG
220 + html = html.replace(/<p>\s*\{\{figure:([\w-]+)\}\}\s*<\/p>|\{\{figure:([\w-]+)\}\}/g,
221 + (m, a, b) => charts.figuresFor(a || b, C) || "");
222 + const d = pub.data;
223 + const body = `<article class="pub">
224 +<header class="pub-head">
225 + <p class="pub-id">${R.esc(d.pub_id || "")} · version ${R.esc(String(d.version || "1"))} · ${R.esc(String(d.created || ""))}</p>
226 + <h1>${R.esc(d.title || pub.slug)}</h1>
227 + ${d.subtitle ? `<p class="pub-subtitle">${R.esc(d.subtitle)}</p>` : ""}
228 + <p class="pub-meta">${R.esc(d.author || "")} · <a href="mailto:${R.esc(d.contact || "")}">${R.esc(d.contact || "")}</a>
229 + · data: <a href="https://www.hfmarketdata.io">hfmarketdata.io</a>
230 + <span class="badge badge-${d.status === "published" ? "done" : "in-progress"}">${R.esc(d.status || "draft")}</span></p>
231 + ${d.abstract ? `<div class="abstract"><span class="abstract-k">Abstract</span><p>${R.esc(d.abstract)}</p></div>` : ""}
232 +</header>
233 +<div class="doc-layout ${toc.length >= 3 ? "has-toc" : ""}">
234 + ${R.tocHtml(toc)}
235 + <div class="doc"><div class="md pub-body">${html}</div></div>
236 +</div>
237 +<footer class="pub-footer">Not investment advice. Past statistical regularity does not imply future returns.
238 +Reproducible from the repository at the commits cited above; every figure regenerates from committed results.</footer>
239 +</article>`;
240 + page(res, { title: d.title || pub.slug, active: "Publications", body });
241 +});
242 +
185 243 // ---------------------------------------------------------------- research index
186 244 app.get("/research", (req, res) => {
187 245 const docs = [
188 246