Conditional Exceedance Probabilities for Event-Driven Trading

This repository contains the full code, notebooks, and experiments for an independent quantitative research program built around the Conditional Probability of Exceedance (CPE) framework — a live, production-deployed nonparametric signal system covering 161 instruments across equities, fixed income, gold, cryptocurrencies, commodities, volatility, and FX — plus further research threads that test complementary angles on the same underlying question: what is predictable in financial markets, and over what horizon. The latest, Paper 17, turns the framework's own claim on weather and climate: does a rare, extreme conditioning event give a better signal than a frequent, everyday one, and does anything in weather pay? Paper 18 then applies the same discipline to a local question: is there a measurable right time to buy a car or a home in Singapore?

Author: Arun Ramanathan, PhD (Independent Researcher | Singapore)

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Overview

The work here spans five lines of research, documented as 18 numbered papers and two standalone research notes: (a) the CPE framework and its live dashboards (Papers 1–5); (b) climate-finance and event-study extensions, from temperature and moisture predictors to the test of everyday weather against rare extremes (Papers 6–10 and 17); (c) predictability limits and AI/ML forecasting (Papers 11–16); (d) a local application of the framework, Singapore buy timing (Paper 18); and (e) standalone research notes (the El Niño sugar test and the AI narrative). The list is in the order the work was published:

1. The CPE framework (Papers 1–5) — a nonparametric tail-co-movement signal system, described in full below, that powers the live dashboards.

2. Climate-finance and event-study extensions (Papers 6–10) — apply CPE's tail-exceedance logic to physical/weather predictors (temperature extremes, vapour pressure deficit, growing-season geography), plus one event study of hurricane landfalls against reinsurer equity using a different, CAR-based methodology.

3. Multifractal predictability limits (Paper 11) — adapts atmospheric turbulence cascade theory to ask how far ahead financial markets are structurally predictable at all, cross-validated against CPE's own signal density.

4. Regime-conditioned price forecasting (Paper 12) — a fully independent, ML-based (LightGBM quantile regression) forecasting system for 22 instruments, reusing Paper 11's multifractal features and two causally-validated market-regime signals. Its central finding: real, genuine statistical skill for roughly half the panel does not translate into demonstrated tradeable alpha for any instrument, under five independently designed trading-strategy tests — a result treated as the paper's main contribution rather than suppressed, and now deployed as a forecast-only (no buy/sell signal) live dashboard.

5. Predictability limits as AI/ML regime detectors (Paper 13) — repurposes Paper 11's empirical predictability limit as a practical, instrument-specific rule for how much history any AI/ML model should train on, and when its training data should be considered stale. Demonstrated across 12 instruments via direct predicted-vs-actual curve comparison (deliberately no significance testing), and framed explicitly as a general concept-drift/training-window rule applicable to any non-stationary AI/ML pipeline, not just financial markets.

6. Does sophistication beat the limit? Testing AI/ML architecture, depth, and training-window size (Paper 14) — asks the natural next question after Paper 13: can real architectural sophistication buy its way past the measured predictability limit anyway? Five architecturally distinct models — climatology, an unregularized tree, a reinforcement-learning forecaster, a conditional GAN, and a conditional VAE — given an identical fair training budget show no separation in skill; a follow-up giving three of them a genuine, hand-built hidden layer of nonlinearity at the same budget changes nothing; and sweeping training-window size from inside the limit out to 8x beyond it shows error climbing, not falling, as more data is added — the opposite of what a small-sample explanation would predict. Modeled explicitly on how Lorenz's atmospheric predictability limit was itself confirmed decades after being proposed, not by argument but by real, increasingly sophisticated weather models running into the same wall.

7. Loss functions and calibrated uncertainty against the same limit (Paper 15) — tests two further candidates for buying past Paper 14's ceiling. Alternative loss functions (quantile/pinball loss, escalating-order Lq loss) recover no genuine skill or tail behavior. A purpose-built generative downscaler is more informative: its point forecast is, by mathematical construction, provably identical to plain climatology's, but its calibrated predictive distribution — matched to each instrument's own predictability limit, corrected for decoder-variance miscalibration, and rescaled for real markets' sub-linear variance growth — beats all five Paper 14 architectures on the Continuous Ranked Probability Score when those architectures are held to the certainty they implicitly claim. Once each architecture instead gets its own uncalibrated uncertainty estimate, the downscaler still beats every sophisticated architecture, but loses outright to climatology's own unmodeled empirical distribution on 7 of 12 instruments — evidence that calibrated uncertainty, not sophistication, is what's being rewarded, and that an unprocessed sample of real recent history is often harder to beat than a carefully calibrated synthetic approximation of the same thing.

8. Predictability-informed momentum, and the ceiling holds at the decision layer too (Paper 16) — asks whether decision-layer sophistication (position sizing, instrument selection, signal combination) can buy past the same measured limit rather than better prediction itself. A large-scale reinforcement-learning and genetic-algorithm policy search, predictability-informed conviction weighting, and breadth expansion all fail, extending the wall to a sixth lever. But testing two published momentum strategies the same way this program tests its own methods reveals a general lesson: the replication unit has to match the phenomenon's own claimed mechanism — Time-Series Momentum's real, decades-old edge is invisible on arbitrary calendar blocks (0 of 5) but clear on real crisis episodes (9 of 13). Applied to this program's own instrument selection, restricting standard TSMOM to only the instruments it can actually predict well (via Paper 11's measured predictability limit) beats the unfiltered version in 3 of 5 real historical periods, full-sample Sharpe 0.32 vs. 0.29 — the paper's central result. A narrower, secondary finding survives the same scrutiny on two individual instruments (JPM, XLB), once recomputed as a real point estimate rather than the industry-standard, beta-adjusted statistic that had inflated four other candidates.

9. A genuinely prospective test: does an El Niño-driven Indian monsoon deficit predict global sugar prices? (standalone research note, not part of the numbered series) — a specific, checkable claim published in July 2026 before its outcome was known: five historical El Niño years with price data (1997–2023; 2004 no longer clears NOAA's revised index threshold and is dropped, and 1991 predates the price series) show global sugar prices rising over the following 1–3 months more often than not (3-month hit rate 0.70–0.80 vs. an unconditional 0.35–0.45, replicated across held-out years). 2026 has since resolved: India's monsoon finished at −12.6% of normal (from −42% in June), close to the −10% IMD forecast, El Niño strengthened (JJAS-mean +2.0°C), and every price window that has closed so far is a real increase (June's 3-month window: +28.8%), all six sitting above the 65th-percentile threshold set from pre-2026 history alone. 1997 remains the standing counter-example. The published version reports real point estimates and genuine leave-one-year-out replication throughout, matching the standard used in Papers 12–16.

10. The AI Narrative, Tested Three Ways (standalone research note, not part of the numbered series) — tests three common assumptions about AI-exposed stocks against real data: that they now move together more than before ChatGPT, that NVIDIA reliably rallies on its own earnings, and that a theme with this much capital flowing into it should carry exploitable intraday structure. All three fail. Daily tail co-movement in a real, dated AI basket (AIQ ETF holdings as of December 2022) falls after ChatGPT's launch, not rises, and NVIDIA, Microsoft, Meta, Amazon, and Apple each show zero surviving tail-level predictability from the rest of the basket post-launch. NVIDIA's real excess return across all 71 of its quarterly earnings reports since 2006 beats the market and its sector peers on only 44–55% of occasions, and its sector-relative performance has not improved during the 2023–2026 AI earnings supercycle. And applying the exact two methods that already found nothing in crypto intraday data (naive conditional-exceedance, multifractal/structure-function machine learning) to real 1-minute NVDA/AMD/SPY data finds the same result, extending this program's "signals only at long horizons" finding to equities for the first time.

11. Why Everyday Weather Does Not Move Markets, and Where Rare Extremes Might (Paper 17) — tests this framework's own claim that rare, extreme conditioning events give better signals than frequent, everyday ones, with everyday weather as the frequent-event case. Everyday weather earns nothing out of sample (12,824 weather-and-price pairs and about 630,000 walk-forward rules all fail, and weather scores zero under a gate-free, season-matched causal test). At the rare end, 32 slow climate series in four batches show the predicted rarity gradient inside the CPE framework's own admission (-3.7 to +12.1 points over base in a causal walk-forward), which disappears under a gate-free rule, and a decision rule on the rarest rows lost 1.4% a year. The result is a candidate, scored forward: four frozen tables of climate conditions (97 conditions) and a registered paper-portfolio experiment have been scoring since 12 October 2026.

12. The Ten-Year Echo in Singapore's COE Premium, and Why Housing Timing Signals Fail Out of Sample (Paper 18) — a local test of the framework on when to buy a car or a home in Singapore, using only public data and a causal walk-forward with every mechanism registered before any result. A COE lasts ten years, so registrations today become scheduled expiries a decade later, which track de-registrations, which set quota: the premium lags the supply cycle by 18 to 24 months (correlation -0.72 for Category A, -0.67 for Category B) and has swung about every 11.6 years since 2002. One condition is supported: after a quota cut the premium is in its top 20% of moves 49% of the time twelve months later, against 24% normally, robust to all 27 tail thresholds and held through 2021, but not detected since 2022 (17 entries; the quota formula changed in August 2022 and February 2023). Nothing in private property (1,622 condition-years, lift 0.96 overall and 0.64 since 2017) or the HDB resale index (474 condition-years, lift 0.82 overall and 0.28 since 2017) survives out of sample: momentum scored 3.56 up to 2016 and 0.69 since. A causal replay shows the ten-year base rate is a poor guide for property. A public dashboard scores its own forecasts on a forward ledger, with the first outcomes due in early 2027.

The CPE framework

Rather than forecasting prices or returns, CPE asks a fundamentally different question:

When asset X is in the extreme upper tail of its historical distribution over the past N days, what is the empirical conditional probability that asset Y exceeds its own threshold over the next M days?

This is a nonparametric, empirical conditional frequency — computed directly from historical co-occurrences, with no distribution assumption, no fitted parameters, and no extrapolation beyond the data. The tail co-movement structure itself is the signal.

The framework sweeps 51 million candidate configurations across 161 instruments and retains only those where:

• Conditional probability exceeds 0.80
• Lift over the unconditional rate exceeds 1.5×
• At least 100 training-period observations support the estimate

This produces 169,357 pairwise signals. A greedy joint-conditioning procedure then identifies multi-predictor configurations — e.g. "when IBIT and BITB are both simultaneously in their upper tails, gold exceeds its 252-day median return with probability 0.94 vs 0.37 unconditionally" — a 2.54× lift confirmed genuinely calibrated out-of-sample in Paper 4.

Key empirical results from the core CPE series (Papers 1–5):

• Calibration (Paper 4): 97.0% realised hit rate against 93.0% stated CPE across 103,983 resolved instances over 528 trading days. The framework is slightly conservative — it understated its own edge by ~4 percentage points.
• Gold dashboard: BULLISH throughout all of 2025 (gold: $2,629 → $5,318/oz, +102%). Pivoted BEARISH in early February 2026. Directional discrimination: +7.23% average gold return on BULLISH days vs +2.46% on BEARISH days.
• Portfolio tilt (Paper 3): Hold-to-horizon Sharpe 1.224 (pct_exceeding 1.8% vs 1,000 randomisation repetitions). Cross-sectional extension: Sharpe 1.613 across 61 episode-validated targets. See the Limitations section below — a 2026-08 four-year re-test, benchmarked against published momentum strategies, found this result showed up in one of the four real years tested and did not repeat in the other three.
• Portfolio tilt (Paper 4): Cumulative +30.6% vs +17.4% neutral equal-weight over 1.4 years. Sharpe 1.43 vs 1.03 (60/40), 0.94 (time-series momentum), 0.71 (risk parity). Same caveat applies.

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Method Summary

This section describes the core CPE methodology (Papers 1–5). Paper 9 reuses this exact method with geography-corrected coordinates for its temperature predictors, Paper 10 uses a separate event-study/cumulative-abnormal-return methodology, and Paper 11 uses multifractal cascade and structure-function decomposition — see their respective papers and notebooks/rein/ / notebooks/predictability_paper/ for method details.

For a fixed horizon τ and two assets X and Y:

1. Compute τ-day price increments for all 161 instruments
2. Define exceedance events using empirical quantile thresholds (50th–99th percentile, both directions)
3. Estimate conditional probabilities: P(ΔY_future > r_qY | ΔX_past > r_qX)
4. Filter estimates using three quality gates: CPE ≥ 0.80, lift ≥ 1.5×, n_conditioning ≥ 100
5. Apply greedy joint-conditioning to build multi-predictor configurations
6. Score each asset daily using w = CPE × lift × ln(n_episodes) × max(0, 2 × hit_rate − 1)
7. Translate conviction scores into portfolio tilts or directional dashboard signals

All probability tables are computed using pre-2025 data and remain frozen during evaluation. No parameters are tuned on test data.

Economic prior (Paper 3 refinement): A pre-specified admissibility gate of ~91 economically justified predictor→target channel pairs reduces the pairwise screen from 169,357 rows to 11,106 (6.6%), eliminating spurious relationships (e.g. silver predicting dogecoin) while retaining validated channels (e.g. vol-complex→equity, crypto→gold).

Episode-independence filter: Firing dates are clustered into genuinely separated episodes (gap > 1.5× longest conditioning window). Minimum 3 independent episodes required for nonzero conviction. This prevents overlapping observations from inflating nominal signal counts.

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Reproducibility

Core CPE framework (Papers 1–5):

• Data source: Daily adjusted close prices via yfinance
• Universe: 161 instruments across 6 asset classes
• Training period: Full history through 2024-12-31
• Evaluation period: 2025–2026 (strictly out-of-sample)
• Parameter sweep: 8 horizons (1–300 days) × 8 quantile thresholds × 2 tail directions × 161² instrument pairs ≈ 51 million configurations
• Surviving signals: 169,357 pairwise | 11,106 prior-gated
• No parameters are tuned on test data. All configurations frozen at training cutoff.

Climate-finance extensions (Papers 6–9):

• Weather data source: Open-Meteo archive API (free, no API key) for daily city/crop-zone temperatures, growing degree days, and vapour pressure deficit
• Financial data: Same yfinance universe and CPE gating thresholds (CPE ≥ 0.80, lift ≥ 1.5×, n ≥ 100) as the core framework, with weather variables added as conditioning predictors
• Paper 7 correction: rebuilds Paper 6's temperature predictors using ERA5-consistent gridded crop-zone coordinates (rather than city centroids) — see Limitations below for what this correction found. Paper 9 is a standalone research-note summary of the whole programme that revisits this same reversal as its lead example, rather than an independent correction of its own.

Hurricane/reinsurer event study (Paper 10):

• Event data: Our World in Data, adapted from NOAA HURDAT (1990–2022), supplemented with NOAA/NHC official season totals (2023–2025) — the 14 costliest US hurricane landfalls since 1995
• Equity data: yfinance, RenaissanceRe (RNR) and Munich Re (MUV2.DE)
• Method: market-model event study — OLS alpha/beta estimated over a 250-trading-day window ending 30 days before each event (standard gap), used to compute cumulative abnormal returns (CAR) over the event window

Multifractal predictability limits (Paper 11):

• Data: raw, untransformed daily price (no log/return/normalization transform) via yfinance, 15-instrument sample (SPY, QQQ, IWM, XLK, XLF, XLE, AAPL, MSFT, JPM, XOM, GLD, BTC-USD, TLT, EURUSD=X, ^VIX)
• Method: Double Trace Moment (DTM) cascade estimation, structure-function scan, and correlated/decorrelated moment decomposition across τ = 1–300 trading days (q = 2, 4), cross-validated against the core CPE framework's own signal density at SPY's ~252-day horizon
• Full pipeline documented in notebooks/predictability_paper/README.md

Regime-conditioned price forecasting (Paper 12):

• Data: yfinance daily adjusted close, 22-instrument universe (equities, sector ETFs, gold, FX), plus credit (HYG/LQD) and VIX-term-structure (VIXM/VIXY) regime proxies
• Method: LightGBM quantile regression (5 quantile levels) on each instrument's own multifractal features (reused from Paper 11) interacted with two causally-validated regime signals, selected per instrument from four candidates (climatology, credit-regime, VIX-regime, combined) via a genuine chronological selection/holdout split (HOLDOUT_START = 2022-01-01) — a data-snooping bug in an earlier selection procedure was caught and fixed mid-project (one instrument's headline skill score was ~12× inflated before the fix). Gradient-boosted trees were chosen over an earlier model-family comparison (OLS, random forest, XGBoost, LightGBM, a feed-forward neural net) that found tree ensembles broadly dominant; LightGBM specifically for its native quantile ("pinball loss") objective, which produces the full 5-quantile forecast band directly.
• Economic validation: five independently designed trading-strategy tests (directional, price-target, portfolio, Kelly-sized, cross-sectional relative-value) and five independently designed post-processing/bias-correction designs, all benchmarked against each instrument's own buy-and-hold return (a benchmark-specification bug — testing against a generic market index instead — produced one spurious "significant alpha" result, caught and corrected)
• Live-deployment bug, caught post-launch: the five quantile levels are fit as independent models with no constraint that they stay ordered ("quantile crossing") — found via direct inspection of live dashboard output (one instrument's median forecast briefly fell below its own 10th-percentile forecast), invisible to every backtest metric since those only score the median. Fixed with monotone rearrangement (Chernozhukov, Fernández-Val & Galichon, 2010); verified 0/22 instruments affected after the fix.
• Full pipeline documented in notebooks/predictor_v1/ and notebooks/predictor_v1_paper_draft.md (PDF also available, ready for Zenodo submission)

Predictability limits as AI/ML regime detectors (Paper 13):

• Data: reuses Paper 11's already-published predictability limits directly (no new estimation) for the 12 instruments where both a master-model decision and a predictability limit exist
• Method: a deliberately unregularized decision tree (max_depth=None, min_samples_leaf=1, min_samples_split=2; verified 0.000000 training error, one leaf per row) trained on a w = ⌊τ*/2⌋-day window immediately preceding each test window ("fresh") versus an equally-sized window drawn from ≥2τ* days earlier ("stale"), both evaluated on the identical test window, walked forward across each instrument's full history; tested on two independent feature variants (the full multifractal/regime-interaction feature set, and price-only lagged returns)
• Full pipeline documented in notebooks/predictor_v1/ (scripts 59–64) and notebooks/regime_detector_paper_draft.md (PDF also available, published: zenodo.org/records/21482869)

Testing AI/ML architecture, depth, and training-window size (Paper 14):

• Data: yfinance daily adjusted close, reusing Paper 12's 22-instrument universe and Paper 11's already-published predictability-limit results directly (no new estimation performed)
• Method: three tests against the same measured limit — (1) five architecturally distinct models (climatology, an unregularized tree, RL policy-gradient, conditional GAN, conditional VAE) given an identical fair training budget, on the 3 instruments with the largest measured predictability limits; (2) a direct follow-up giving three of those models a genuine one-hidden-layer nonlinearity, hand-implemented with manually derived backpropagation and validated on synthetic data before use; (3) a training-window-size sweep from 0.5x to 8x the predictability limit across all 12 instruments, with fixed test segmentation throughout so only training-data amount and staleness vary
• Full pipeline documented in notebooks/predictor_v1/ (scripts 65–70) and notebooks/architecture_ceiling_paper_draft.md (PDF also available, published: zenodo.org/records/21696948)

Loss functions and calibrated uncertainty against the same limit (Paper 15):

• Data: yfinance daily adjusted close, reusing Paper 12's 22-instrument universe and Paper 11's already-published predictability-limit results directly (no new estimation performed)
• Method: two further tests against the same measured limit — (1) quantile (pinball) loss at 5 quantile levels with monotone rearrangement, and escalating-order Lq loss (q = 2, 4, 6, 8), each on the largest-limit instruments; (2) a two-stage generative downscaler — climatology for the point forecast, plus a conditional VAE trained at each instrument's own predictability-limit scale (rather than the forecast horizon) to generate a calibrated distribution — with independent τ*-scale blocks chained into full-horizon ensembles, decoder variance accounted for via the law of total variance, and ensemble dispersion rescaled against each instrument's real recent-window historical variance to reflect markets' sub-linear variance growth beyond the limit. Scored against all five Paper 14 architectures via the Continuous Ranked Probability Score (CRPS, an unbiased ensemble energy-score estimator), first holding those architectures to a single point forecast, then giving each its own natural uncertainty source (leaf-mate ensembles for the tree, the annealed policy variance for the RL forecaster, raw internal samples for the GAN/VAE) for a fair comparison
• Full pipeline documented in notebooks/predictor_v1/ (scripts 73–80) and notebooks/loss_uncertainty_ceiling_paper_draft.md (PDF also available, published: zenodo.org/records/21802729)

Predictability-informed momentum and decision-layer sophistication (Paper 16):

• Data: yfinance daily adjusted close, a 12-instrument universe for the decision-layer search and TSMOM/XSMOM benchmarking, the full 22-instrument Paper 12 panel for the broader replication-unit and statistic-choice demonstrations
• Method: two threads. (1) Decision-layer sophistication tested directly at real scale — a PPO reinforcement-learning agent and a genetic algorithm (800 candidates × 1,500 generations) sizing positions from every predictive signal this program has produced, predictability-informed conviction weighting, and breadth expansion from 5 to 40 candidate targets — none produce a repeatable edge, a sixth lever added to Papers 12–15's wall. (2) A methodology demonstration: Time-Series Momentum and Cross-Sectional Momentum, both published and unfitted, tested against arbitrary calendar blocks versus real, independently identified crisis episodes, showing the replication unit alone can flip a verdict from a total loss to a 9-of-13 win; the same discipline applied to instrument selection (restricting TSMOM to instruments with a genuine predictability pocket near its own 252-day lookback) produces this paper's central result, and applied to the choice of statistic (real mean-difference versus Jensen's alpha) shows 12 of 22 instruments flip sign depending on which is used — the basis for never using Jensen's alpha to evaluate this program's own methods, at any sample size.
• Full pipeline documented in notebooks/predictor_v1/ (scripts 82–89) and notebooks/decision_layer_ceiling_paper_draft.md (PDF also available, published: zenodo.org/records/21842311)

El Niño monsoon deficit and global sugar prices (standalone research note):

• Data: NOAA Niño 3.4 index (ONI v6/ERSSTv6 vintage, 1990–2026), IMD monsoon rainfall bulletins (2026 season), IMF Global Sugar No. 11 benchmark (FRED, through 2026-07) extended through September 2026 with ICE Sugar No. 11 futures (validated at 0.997 level correlation before use)
• Method: a monsoon-month (June–September) El Niño flag (NOAA's own ≥0.5°C threshold) tested against 1-, 2-, and 3-month forward global sugar price returns, real point estimates and leave-one-year-out replication throughout. Published as a specific, checkable claim in July 2026 before the 2026 season's outcome was known; this version resolves it, corrects the historical year count (2004 no longer clears the current index threshold), and reports two further design iterations (a harvest-season-lag specification, a cross-asset check against gold and soybean futures) in full, both superseded by the concurrent, sugar-price design used in the main result
• Full pipeline documented in notebooks/sugar_paper/ and notebooks/sugar_paper/elnino_sugar_paper_draft.md (PDF also available, published: zenodo.org/records/23053552)

The AI Narrative, Tested Three Ways (standalone research note):

• Data: a real, dated 52-stock AI universe (Global X's AIQ ETF holdings as of December 1, 2022, recovered from an archived Wayback Machine snapshot of the fund's own holdings page, not hand-picked); all 71 of NVIDIA's resolved quarterly earnings reports since 2006 (Nasdaq/Yahoo Finance); real 1-minute consolidated-tape (SIP) bars for NVDA, AMD, and SPY, October 2023–September 2026 (Alpaca's free market-data tier)
• Method: three independent tests. (1) This program's core conditional-exceedance methodology applied within the AI basket, length-matched windows immediately before and after ChatGPT's launch, thresholds computed separately within each regime; robustness-checked by excluding the COVID crash from the pre-window (the decline survives, and sharpens). (2) A real mean-difference excess-return event study on NVIDIA's own earnings reports against the S&P 500, AMD, and the semiconductor sector, no regression-based market model. (3) The identical naive-conditional-exceedance and multifractal/structure-function machine-learning methods already used to test crypto intraday feasibility, applied unchanged to real equities data
• Full pipeline documented in notebooks/ai_stocks_cpe/ and notebooks/ai_stocks_cpe/ai_narrative_three_tests_paper_draft.md (PDF also available, published: zenodo.org/records/23092634)

Why Everyday Weather Does Not Move Markets, and Where Rare Extremes Might (Paper 17):

• Data: 32 free slow climate series in four batches (NOAA CPC and PSL ocean and polar indices, NCEI regional drought, NASA GISTEMP, Rutgers snow cover, NSIDC sea ice, HURDAT2 cyclone energy) and the program's own 161-instrument price panel; for the frequent-event comparison, ECMWF open forecasts, ERA5 observed weather, EIA-930 demand and generation, EIA gas storage and USDA crop condition
• Method: the CPE engine run unchanged on the slow climate series, with economic channels registered before each batch; a causal walk-forward (rows admitted each 1 January from outcomes already resolved) by the rarity of the conditioning tail; a gate-free horizon ladder and season-matched walk-forward applied identically to weather, climate and market predictors; a registered decision rule scored forward. The frequent-event side is a self-scoring weather pipeline tested against prices (pre-specified gas rules, a 12,824-configuration weather-tail scan, about 630,000 walk-forward decision rules, machine-learning models, a weather-to-gas-to-storage chain)
• Live: the Climate Signals dashboard holds four frozen tables of climate conditions (97 conditions), logs each when it fires, scores outcomes as horizons resolve and runs a registered paper-portfolio experiment, all since 12 October 2026; new distinct conditions are frozen weekly by a fixed rule
• Full pipeline documented in notebooks/weather_finance/ and notebooks/paper17_*.py; the paper is notebooks/weather_impacts_paper_draft.md (PDF also available, published: zenodo.org/records/23301263), rebuilt by notebooks/paper17_build.sh. Large intermediate files (price panels, ladder and walk-forward result tables) are regenerated by the scripts and are not committed

The Ten-Year Echo in Singapore's COE Premium, and Why Housing Timing Signals Fail Out of Sample (Paper 18):

• Data: public Singapore data only: COE results and vehicle flows (LTA, Department of Statistics) and government yields (MAS) via data.gov.sg from 2002; the URA private-residential and rental indices and supply series (from 1975), the HDB Resale Price Index and every HDB resale transaction since 2012 (data.gov.sg); and URA Data Service transactions for executive condominiums (only monthly aggregates are committed)
• Method: eight registered predictor families for the COE premium and nine for property and the HDB index, with a direction fixed in advance by mechanism; a causal walk-forward (thresholds and admission use only outcomes resolved before 1 January of the test year); a support rule of lift of at least 1.4 overall and since 2017 on at least 100 entries; robustness to tail thresholds, sub-periods and Newey-West regression; causal replay of base-rate forecasts scored by Brier score; post-hoc choices disclosed in the paper
• Live: the Singapore Buy-Timing Dashboard (verdict: wait and watch) is rebuilt daily, scores its own forward ledger (18 base-rate forecasts logged on 16 October 2026) and has a lookup for any HDB block; the repository holds the code and the paper build
• Paper: zenodo.org/records/23418466; the manuscript, build and figures are in the dashboard repository's paper/ folder (paper_numbers.py recomputes every number); the posts are notebooks/paper18_substack_post.md and notebooks/paper18_linkedin_post.md

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Limitations

This repository is intended for research purposes. Honest limitations reported across the paper series.

A note on method for every 2026-08 entry below: this project studies rare, extreme, non-stationary market behavior, which is properly the domain of extreme-value statistics, not mainstream large-n/Gaussian-adjacent inference. Findings here are reported as real point estimates (alpha, Sharpe, drawdown, episode counts) judged by whether they replicate across genuinely separate real out-of-sample evidence — a different real year, a different real instrument, more real independent episodes — never by a classical significance test (t-statistics, p-values, confidence-interval pass/fail gates, bootstrap or permutation nulls) layered on top. This is a deliberate, standing choice, not an oversight: rarity of extremes is this field's expected signature, not evidence of an untrustworthy small-sample estimate that a significance test needs to adjudicate.

Core CPE framework (Papers 1–5):

• Overlapping weekly observations: Portfolio return series use overlapping weekly observations, which inflate the apparent independent sample size of any classical significance test — one further reason this project reports real point estimates and out-of-sample replication rather than significance verdicts throughout (see the standing methodology note at the top of this section). Update: a follow-up re-test (below) checked whether the portfolio-tilt engine's single large 2025 result held up in other real years, rather than leaning on any corrected test statistic — it held up in that one year and did not repeat in the other three tested.
• Bitcoin ETF concentration: 103,983 calibration instances are dominated by IBIT, BITB, and FBTC — three near-identical instruments. Effective independent count is substantially smaller than nominal.
• Bearish signal failure: Bearish CPE signals for gold achieved only 29% realised hit rate against 83.7% stated CPE. Root cause: UVXY/VIXY structural regime change in 2025, where volatility spikes coincided with gold surges rather than gold weakness as in the training period.
• Non-stationary predictor thresholds: Leveraged VIX ETPs (UVXY, VIXY) lose value continuously through roll decay. Their 252-day quantile thresholds become structurally unachievable in live evaluation, causing high-CPE training configurations to never fire out-of-sample.
• Single regime: 1.4 years of evaluation covers one sustained gold bull market. Sustained bear markets, credit crises, and deflationary environments have not been tested.
• Data sufficiency: The validated vol→equity channel rests on 4 independent training-period episodes. Five observations (including the 2025 OOS result) cannot distinguish genuine predictive content from a well-supported coincidence. Approximately 3–5 additional independent episodes are needed.
• Transaction costs: Not modelled. Transaction cost break-even is ~10.1 bps per one-way leg for the 5-sleeve strategy — above realistic ETF costs but sensitive to AUM and operational overhead.
• Multi-year re-test (2026-08): the originally reported portfolio-tilt Sharpe has not yet repeated out-of-sample. Both portfolio-tilt mechanisms (files/backtest_engine.py, the current, independent reimplementation built from the Portfolio Tilt paper's own spec, since the original scoring code no longer exists) were re-run across four independent years (2022–2025, each trained honestly through the prior year-end), comparing real point estimates year by year rather than the original randomisation-test methodology: hold-to-horizon (Paper 3's mechanism) showed a large result in exactly 1 of the 4 years (2025, +23.87%/yr — concentrated almost entirely in gold, which opened 28 holds that year against 0 in 2022) and small, mixed-sign results the other 3 years (+0.16%, -1.53%, +1.73%); static tilt (Paper 4's mechanism) showed small, mixed-sign results close to zero in all 4 years (+0.13%, +0.93%, -0.86%, +0.56%). Benchmarked over the same years against two published, unfitted control strategies — Time-Series Momentum (Moskowitz, Ooi & Pedersen, 2012) and Cross-Sectional Momentum (Jegadeesh & Titman, 1993) — TSMOM showed the identical pattern (small, mixed-sign, near-zero results in every recent year: +0.02%, -2.37%, +1.68%, -1.78%, despite a large full-sample result of +2.57%/yr over 1994–2026 earned almost entirely during the 2008 crisis alone), while XSMOM showed large negative results in 2 of the 4 years (-15.88%, -15.42%). This does not invalidate the original Papers 3–4 results, which used a different methodology and evaluation window, but judged by real year-by-year replication, the portfolio-tilt strategy has not yet shown a repeatable, demonstrated edge. Full methodology and all four years' results: files/cpe_hth_multi_year_test.py, files/cpe_static_tilt_multi_year_test.py, tsmom_benchmark.py, xsmom_benchmark.py, tsmom_crisis_alpha_check.py, master_alpha_comparison.py.
• Breadth expansion (2026-08): nominal breadth is not effective breadth. The production portfolio-tilt engine trades only 5 sleeves, but the underlying, already economic-prior-gated joint screen (joint_cpe_results.parquet — confirmed directly from joint_cpe_engine.py's own source to already apply the Paper 3 is_admissible() gate, not an unrestricted screen) has real, supported configurations for 132 distinct tradeable targets. Expanding to the top 40 of these by supporting-configuration count (a mechanical, pre-registered selection criterion — chosen by evidence volume, not by which targets happened to perform well) and re-running the same four-year test found small, mixed-sign results in every year (-0.19%, -0.00%, -0.22%, +0.84%), smaller in magnitude than the 5-sleeve version. The reason: at most 5 of the 40 targets ever placed a real tilt in any single year — the EPISODE_MIN_OBS_FOR_CONVICTION=3 floor (a deliberate anti-luck gate, not an oversight) leaves most targets neutral most of the time, so more instruments in the universe did not translate into more independent bets. Lowering that floor to 2 (the only other defensible value — floor=1 is a shown-degenerate case: 2,987 one-episode configurations show a suspicious 95.2% "perfect" hit rate, an unmistakable small-sample/selection artifact) roughly doubled how many targets were actually active and moved every year's result positive (+0.21%, +0.03%, +1.35%, +1.03%), but magnitudes stayed small, and at a quantified reliability cost: two-episode configurations show their one out-of-training episode landing wrong entirely (0% hit rate) 7.3% of the time, more than 3× the three-episode rate of 2.1%. Read together: breadth is very likely the right lever in principle (Grinold's IR ≈ IC·√breadth; both changes moved every single year's result in the same direction) but the framework's own strict admissibility/episode-independence gates — the same rigor that makes any individual signal trustworthy — currently starve it of enough genuinely independent, reliable signal to realize that benefit at scale; closing this gap needs a much larger admissible universe or more years of accumulated episodes, not a looser threshold. Full methodology: files/cpe_breadth_expansion_test.py, files/cpe_breadth_expansion_floor2_test.py.
• Crisis-type diversification, part 1 (2026-08): the original admissible-screen search found nothing, but that search itself was incomplete. A systematic search of the existing joint screen for a mechanistically different crisis signal (credit-spread-widening / inflation-shock, the 2022 regime, where bonds sell off with stocks rather than rallying against them) found none among already-computed configurations. economic_prior.py already admits ("credit_hy", "broad_equity_us") — no prior expansion needed, contrary to what was first stated here — the real gap was that the existing screen had never surfaced a bearish-direction configuration using it.
• Crisis-type diversification, part 2 (2026-08): a pre-registered, targeted search for that missing configuration, complete with two of my own real errors caught and corrected along the way — documented in full because they're instructive, not despite that. Hypothesis, fixed before touching any 2022+ data: high-yield credit (HYG/JNK) bearish and long-duration Treasuries (TLT/TLH) also bearish, jointly, predicts equity weakness. Error 1: the first version of the search silently discarded any configuration with fewer than 100 raw joint observations, a threshold borrowed from the original decades-spanning screen — structurally guaranteed to reject exactly the "rare, regime-specific" pattern being searched for. Removing that filter revealed the joint condition fires 36-52 times across all of available history, concentrated entirely in 2022 — a real, historically-corroborated fact (2022 was independently the worst joint stock/bond year in decades) that the first search never surfaced. Error 2: a quick manual check of this single-episode result used a sign-inverted forward-return calculation and was reported here as confirming the hypothesis (subsequent equity weakness). Direct, careful re-verification against raw price levels (not shift-based tricks) showed the opposite: the one real 2022 episode anchors within days of the actual market bottom (Sept-Nov 2022), and SPY/QQQ/IWM/DIA subsequently rose sharply at every horizon tested (median +8% to +19%) — confirmed robustly across ~75 of ~84 credit/rate/target/horizon combinations checked directly against price data. The corrected, final finding: this joint credit+rate extreme is real and historically singular to 2022, but it behaves as a capitulation/bottom-marking signal, not a crisis-continuation one — the mechanistic opposite of the original hypothesis. It remains, honestly, an n=1 finding (one real episode) and cannot be validated as reliable regardless of how directionally clean its single outcome is — the same standard this program already applies to any 1-2 episode configuration. Broader (less extreme, q=0.20) versions of the same joint condition do have enough real episodes (4-5) to be evaluable, and their actual episode-level hit rate is poor (0.0-0.25, below the 50% baseline) — so no version of this hypothesis, at any threshold tested, is both statistically evaluable and predictively reliable. Full methodology, including both bugs left visible in the script's own history rather than quietly fixed: files/credit_stress_channel_search.py.
• Combining CPE's crisis detector with TSMOM's crisis-period return (2026-08): tested directly, does not help, and the reason is a genuine timing mismatch, not a tuning failure. Hypothesis: scale up TSMOM's exposure specifically when CPE's vol-complex channel detects a crisis starting, rather than running TSMOM at constant weight. Tested at 1.5x and 2.0x scale-up (two round, non-optimized multipliers) on CPE's 127 active days (2014–2026 OOS): Sharpe fell slightly at every scale (0.277 → 0.264 → 0.249), and within 2020 — the only year the signal ever fired — TSMOM's own return went from -0.05%/yr unscaled to -0.13%/yr at 2x. Diagnosed directly: TSMOM's own large crisis-period return (established above) is earned over sustained, multi-month drawdowns (the 2008 episode alone, 543 days, carries almost the entire full-sample result), while CPE's detector fires at the onset of a fast, V-shaped shock (2020's crash resolved in weeks) — a 12-month-trailing trend signal is structurally too slow to have repositioned correctly by the time a sudden crisis is already underway, so scaling up exposure at onset just amplifies whatever TSMOM happens to be doing at that moment, which in 2020 was not yet right. A different combination logic (e.g. scaling on crisis duration survived, not onset) might align better, but that is a new, untested hypothesis. Full methodology: files/tsmom_cpe_crisis_combo_test.py.
• A better-motivated TSMOM combination (2026-08): a small, real, replicated improvement, built from what the two failed attempts above actually diagnosed rather than a fresh guess. Two design changes, each grounded in an already-validated finding rather than the mechanisms already shown not to work: (1) restrict TSMOM to only the instruments whose own measured predictability structure (Ramanathan 2026a) shows a genuine pocket near TSMOM's own 252-day lookback — SPY, IWM, AAPL, MSFT, GLD — rather than applying the identical lookback to every instrument regardless of its own structure (using tau* to select which instruments to trade, not to change how the signal is built, since the earlier tau*-as-lookback attempt failed for a specific, diagnosed reason: tau* measures magnitude/volatility persistence, not directional momentum persistence); (2) blend CPE's crisis detector into the book additively, as its own small, independent sleeve, rather than as a multiplier on TSMOM's exposure — avoiding the precise-timing requirement that broke the multiplicative combination above. Tested on 12 tau*-measured instruments, 2006–2026 (the period all 12 genuinely have data — an earlier, incorrect version of this test used 1963 as the start date, which meant the 1963–2006 portion was silently running on 1–2 real instruments instead of a genuine multi-instrument portfolio; caught by inspecting the output directly and fixed before reporting): pocket-filtered TSMOM beat the unfiltered 12-instrument baseline in 3 of 5 real, non-overlapping historical blocks (full-sample Sharpe 0.32 vs. 0.29). Adding the CPE sleeve won outright in only 1 of 5 blocks — it is a drag on raw return most of the time, visibly so in the equity curve, exactly as expected of a sleeve that sits in cash over 96% of the time — but full-sample Sharpe still improved (0.36) and max drawdown shrank (-13.1% vs. -16.1% pocket-filtered alone), consistent with a genuine small diversifier rather than a return enhancer. Not a demonstrated edge on this evidence alone (one full-sample estimate, five blocks), but a small, real, mechanistically-coherent improvement that moved in the direction its own diagnosed logic predicted, unlike either of the two combinations tested before it. Full methodology: combined_pocket_tsmom_cpe_strategy.py.
• Every strategy's real alpha, plotted on one scale (2026-08): no single approach shows a clean, broadly-replicated edge; most cluster near zero; both published controls sit clearly negative on this specific test. Every individual real observation (a real year, a real historical block, a real instrument) for every strategy above is plotted as its own point, not collapsed into one number: alpha_generated_plot.png. Three things stand out directly from the plot. First, CPE hold-to-horizon's positive median is carried by one isolated point (+23.87%, 2025) sitting far from its other three observations clustered near zero — visible directly, not hidden by the median. Second, the per-instrument strategies (Paper 12 Kelly-sized and master model, RL sizing) show the widest real spread of any strategy tested — some instruments show real double-digit alpha in both directions — which is a different, more specific finding than "no signal": it points to real instrument-level inconsistency rather than a uniformly broken approach. Third, both published controls sit clearly on the negative side on this specific test: XSMOM's calendar-block observations cluster tightly negative with no outlier doing the work, and TSMOM's calendar-block alpha is also negative throughout — but this specific chart uses calendar blocks as the replication unit for TSMOM, already shown above to be the wrong unit for a strategy whose real edge concentrates in real crisis episodes (9 of 13) rather than spreading evenly across arbitrary chronological time; TSMOM's negative showing here should be read as "no edge distributed evenly across calendar time," not "no edge at all." Full methodology: alpha_generated_plot.py.
• Which instruments show real edge, and added value vs. an existing quant-shop tool on those same instruments (2026-08). Checking Paper 12's own two independently-designed per-instrument methods (Kelly-sized, master model) for convergence — the same instrument, positive alpha, in both, on the same 2022+ holdout — found six: JPM (Kelly +11.64%, master model +2.78%, RL sizing +0.13% — the only instrument positive across all three independently-built methods tested this session), GLD (+6.84%/+2.08%), XLE (+6.55%/+2.88%), XLB (+2.53%/+1.49%), XLU (+1.16%/+0.91%), MSFT (+0.48%/+0.12%). Standard, unfitted TSMOM (the same 252d/63d spec used throughout this session) was then applied to each of these exact six instruments, individually, over the identical 2022+ holdout, against the identical own-buy-and-hold benchmark: TSMOM was negative on every single one, often sharply (JPM -10.99%, GLD -8.32%, XLE -17.46%, XLB -9.64%, XLU -5.06%, MSFT -9.90%) — see added_value_vs_tsmom_plot.png. Read honestly, not as "beats TSMOM in general": TSMOM's own established edge (above) comes from a diversified, 29-instrument book and from rare, sustained crisis episodes, not from trading one instrument alone — stripped of diversification and applied single-name in a non-trending 2022–2025 stretch, standard trend-following struggles badly, which is what shows up here. The fair statement of added value is narrower and more specific: on the instruments where Paper 12's regime-conditioned, single-instrument methods show real convergent signal, they meaningfully outperform a diversification-dependent tool used outside the conditions its own edge depends on — not evidence of general superiority, evidence of being better suited to a different, specific problem. Full methodology: added_value_vs_tsmom.py.
• Correction, same day: TSMOM was only tested on the 6 already-selected instruments above — running it against the other 16 changes the claim. The prior entry's comparison had a real selection issue, flagged before it was run: it only checked TSMOM on instruments already chosen for showing the user's own convergent signal. Running the identical TSMOM spec against the remaining 16 of Paper 12's 22 instruments, same 2022+ holdout, same benchmark, found TSMOM negative on 15 of those 16 too (only IYR positive, +2.76%) — TSMOM is negative on 21 of 22 instruments in this period, essentially regardless of whether the user's own methods find signal there. This means the "added value" finding above is not "our methods work specifically where TSMOM struggles" — TSMOM struggles almost everywhere single-name in this period, which is a separate fact about single-instrument trend-following in 2022–2025, not something caused by or specific to where the user's own edge lives. There is a smaller, real, honest signal underneath that broader fact worth keeping: TSMOM's median alpha is meaningfully worse on the 6 own-signal instruments (-9.77%) than on the other 16 (-4.65%), more than double — so TSMOM's already-broad struggles are notably worse, in degree, on exactly the instruments where the user's own methods find real signal. The correctly-scoped claim is a differential in degree, not an exclusive or causal relationship. Full methodology: tsmom_all22_vs_own_methods.py.
• Final synthesis of the entire 2026-08 investigation, stated plainly rather than left implicit across ~15 separate entries above. What's now firmly established: five prediction-layer levers (Papers 12–15: architecture, depth, data volume, loss function, calibrated uncertainty) and a second wave of decision-layer levers tested here (RL sizing, tau*-informed conviction, breadth expansion, direct combination with a real published strategy) all fail to buy a demonstrated, broadly-repeatable, portfolio-level edge past the measured predictability limit — the single most triangulated finding in the whole program, from roughly a dozen independently-designed tests converging on the same wall. What's also been genuinely validated, not merely asserted: this program's founding premise that extreme, regime-specific, non-Gaussian structure is real and matters. CPE's vol-complex channel is a real, EVT-verified crisis detector; TSMOM's own established edge is itself proof of the thesis, earned almost entirely in one 543-day stretch of 2008, invisible to any smoothed analysis; JPM and five other instruments show real, convergent signal across independently-built methods, outperforming a diversification-dependent tool on those exact instruments; real, independently-measured predictability structure (SPY's own 252-day pocket) tracks real where momentum concentrates. One real, modest, honestly-validated construction came out of it: pocket-filtered TSMOM (instrument selection via real predictability structure) plus an additive CPE crisis sleeve, a small improvement (3/5 real blocks, Sharpe 0.29→0.36, reduced drawdown) built from what two failed combinations actually diagnosed. What is not yet shown: a demonstrated, portfolio-level, tradeable strategy that clearly and repeatably beats existing quant-shop tools — every positive finding here is real but narrow (specific instruments, specific episodes, one specific combination) and modest in magnitude. Closing that gap needs a genuinely different, validated crisis-type detector (searched for directly — see the credit-stress-channel entries above — and found something real but statistically unvalidatable from a single episode) and more real, live track record, which no amount of further backtesting substitutes for. The most durable outcome of this investigation may not be any single number: every finding above is now reported as a real point estimate plus genuine out-of-sample replication, matched to the right unit for each phenomenon, rather than a classical significance verdict — and negative results now get the same adversarial scrutiny as suspiciously good ones, a discipline that caught two real errors in the credit-stress-channel search alone.
• Decision-layer search, six real attempts, one answer (2026-08, formalized in Paper 16, now published: zenodo.org/records/21842311): large-scale automated policy search does not buy past the ceiling either, and the reason is consistent across methods. A genetic-algorithm search (population 800 × generations 1,500 = 1.2M real backtested policies, all three predictive signal types — price forecasts, τ*/pocket structure, CPE probability) showed genuine training-period learning that overfit decisively out of sample (1/12 instruments positive, pooled alpha -10.86%/yr). Two further GA fixes — regime-diverse fitness (selecting for consistency across 4 real training sub-periods) and training extended back to 2005 to include the 2008 GFC — were tried directly: the first made the result worse (cross-validating within a window can't manufacture exposure to a regime absent from all of it), the second gave a small genuine improvement (2/12, -9.73%/yr) but still no edge. A structurally different fourth attempt (real PPO — GAE, a learned value function, genuinely path-dependent state a GA population can't use) converged to the same place by a different route: after fixing a leverage-gaming reward bug, the trained policy still settles into a near-constant, state-independent position for 6 of 12 instruments, with real signal-conditioned variation only for JPM, XOM, and EURUSD=X. Full methodology: notebooks/predictor_v1/82_ga_decision_layer_search.py, 83_ga_regime_diverse_fitness.py, 84_ga_gfc_extended_training.py, 85_ppo_decision_layer.py.
• A second, independent methodological finding from the same investigation: OLS/Jensen's-alpha is never used to evaluate this program's own methods, at any sample size — not a style preference, a demonstrated instability. Computing TSMOM's own alpha two ways on identical return series — a real mean-difference versus Jensen's alpha (OLS regression intercept, beta-adjusted) — flips sign on 12 of 22 instruments in this program's own panel (JPM: -10.99% vs. +2.21%), with no relationship to how much exposure the strategy carried. The mechanism: least-squares is the maximum-likelihood estimator specifically under a Gaussian-error assumption, and squaring residuals lets a handful of extreme days (this program's entire subject matter) dominate the fit; its standard-error/t-statistic inference layer is separately an explicit large-n construct that structurally cannot certify a real, small effect regardless of true size. A larger sample only hides the second problem — it leaves the first, the estimator's own point estimate being distorted rather than informed by extreme events, fully intact. Full methodology: notebooks/tsmom_stat_choice_check.py.
• Paper 16 published (2026-08): the six-instrument/Jensen's-alpha framing in the two entries above is superseded by the paper's final, correctly-scoped result. Recomputing the six-instrument convergence claim above as a real mean-difference (per the OLS finding directly above) rather than the originally-cited Jensen's alpha changes the picture: only JPM and XLB hold up positive under every method and every statistic checked (JPM master-model +0.78%/yr vs. TSMOM's -10.99%/yr; XLB master-model +0.97%/yr vs. TSMOM's -9.64%/yr). GLD, XLE, XLU, and MSFT looked positive under Jensen's alpha but did not survive recomputation. Kelly-sized position sizing shows larger numbers for all six but runs meaningfully elevated market exposure (beta 1.3–2.0 across the six checked), so part of that edge is leverage, not timing skill. The paper's actual headline result is separate from this instrument-level finding: restricting standard TSMOM to only the instruments it can actually predict well (the predictability-limit-informed instrument selection described above) beats the unfiltered baseline in 3 of 5 real historical periods, Sharpe 0.32 vs. 0.29 — the paper is titled and framed around this broader result, not the narrower two-instrument finding. Full methodology: notebooks/alpha_generated_plot.py (recomputes Kelly-sized/master-model alpha as real mean differences), notebooks/decision_layer_ceiling_paper_draft.md (published: zenodo.org/records/21842311).
• Block-count robustness check on Paper 16's central result (2026-08): the win-count framing is more fragile than the underlying edge. Paper 16's headline "3 of 5 real historical blocks" was tested at only one specific block count. Re-running the identical pocket-filtered-vs-baseline comparison at 10 different block counts (3 to 20, mechanical equal-length splits of the same 2006–2026 history — no randomisation involved, block edges are a deterministic function of the count) found the win-count itself ranges from 40% to 67% across those splits, dipping below 50% at two of them (3 of 7, 4 of 10) — the paper's own 5-block number sits on the favorable end of that range, not the middle. The average annual excess return, in contrast, stayed positive and stable at every single block count tested (+0.41% to +0.46%/yr), with pocket-filtered TSMOM's mean return per block above the baseline's at all 10 splits. The mechanism: at the block counts where the win-count dips below 50%, the median individual block still shows pocket-filtered trailing, but a handful of blocks carry large enough wins to keep the average positive — the same crisis-concentration signature already documented for TSMOM itself in Section 4.1, showing up one level down in the filtered version too. Full methodology and a companion infographic: notebooks/tsmom_block_count_sensitivity.py, notebooks/paper16_pitsmom_infographic.html.
• Everyday weather versus rare extremes (2026-10, Paper 17, published: zenodo.org/records/23301263): everyday weather carries no price signal, and the rare-extreme gradient exists only inside the framework's own admission. A free weather-forecast pipeline is skilful for about a week but earns nothing out of sample, and under a gate-free, season-matched causal test weather scores zero at every tail size. Across 32 slow climate series the hit rate over base rises with the rarity of the conditioning tail inside the CPE framework's admission (-3.7 to +12.1 points in a causal walk-forward; +4.9 to +32 for the program's own market predictors), but the gradient disappears under one lift-based rule applied to all predictors, rests on 5 to 12 episodes per condition, is concentrated in 2020-2023, and a decision rule on the rarest rows lost 1.4% a year. It is treated as a candidate and scored forward on the Climate Signals dashboard.
• Singapore buy timing (2026-10, Paper 18, published: zenodo.org/records/23418466): the COE premium is a ten-year echo of its own supply, one quota-cut effect is supported, and no housing signal survives out of sample. After a quota cut the premium sits in its top 20% of moves 49% of the time twelve months later against 24% normally (all 27 threshold combinations above lift 1.4, about 2.5 at six months in both 2010-2016 and 2017-2021), but it is not detected from 2022 (lift 1.3 on 17 entries) and rests on roughly twelve non-overlapping six-month windows. Private property (1,622 condition-years) scores 0.96 overall and 0.64 since 2017, the HDB index 0.82 and 0.28; momentum, rents, rates and vacancy worked before 2017 and not after. Two choices were made after seeing results (the supply-cycle family and the since-2017 support rule) and are disclosed. Scored forward on the dashboard's own ledger from 16 October 2026. Full methodology: the sg-buy-timing-dashboard repository.
• The AI narrative, tested three ways (2026-10, standalone research note, published: zenodo.org/records/23092634): common assumptions about AI stocks checked against real data, all three fail. Applying this program's core conditional-exceedance methodology to a real, dated AI basket (Global X's AIQ ETF holdings as of December 2022, recovered from an archived fund holdings page, not hand-picked) in length-matched windows either side of ChatGPT's launch, real tail-level co-movement falls from 2,132 surviving configurations pre-launch to 522 after — surviving a robustness check that excludes the COVID crash from the pre-window (the decline sharpens, to 2,678 → 522, rather than disappearing). NVIDIA, Microsoft, Meta, Amazon, and Apple each show zero surviving tail-level predictability from the rest of the basket post-launch. Separately, a real mean-difference event study across all 71 of NVIDIA's quarterly earnings reports since 2006 finds the stock beats the market and its sector peers on only 44–55% of occasions, with no improvement in sector-relative performance during the 2023–2026 AI earnings supercycle (if anything, worse). Finally, applying the identical naive-conditional-exceedance and multifractal-feature machine-learning methods already used to find no intraday signal in crypto (BTC/ETH/SOL/BNB, mid-2026) to real 1-minute NVDA/AMD/SPY data (Alpaca, free tier) finds the same result — zero surviving configurations and directional accuracy at chance (49.2–51.4%) — extending the "signals only at long horizons" finding to equities for the first time. Full methodology: notebooks/ai_stocks_cpe/.

Climate-finance extensions (Papers 6–9):

• Geographic mismatch (found in Paper 6, corrected in Paper 7): Paper 6's city-centroid temperature predictors did not align with the actual sugar-growing regions driving CANE futures — a signal with real statistical properties (lift 1.42–1.63×) but no plausible geographic transmission mechanism. Paper 7 rebuilt the predictor set using ERA5 gridded crop-zone coordinates; the sugar signal disappeared entirely (zero surviving configurations), while genuine wheat, corn, and natural-gas channels emerged instead. Reported as a negative result rather than suppressed.
• Small independent-episode counts: Several climate-predictor findings (e.g. the El Niño/monsoon → sugar analysis) rest on fewer than 10 historical episodes since 1990. Directionally consistent, but not enough to rule out coincidence to the same standard as the core CPE framework's larger-N signals.
• The El Niño/monsoon → sugar analysis, resolved (2026-09, standalone research note, published: zenodo.org/records/23053552): a specific claim, published before its outcome was known, checked against what actually happened. In July 2026, with India's monsoon in a severe deficit under a confirmed El Niño, this program flagged 2026 as a live instance of the historical pattern above (3-month hit rate 0.70–0.80 vs. an unconditional 0.35–0.45 across five historical years with price data — 2004 no longer clears NOAA's revised ONI threshold and is dropped, and 1991 predates the price series). The season has since resolved: India's monsoon finished at −12.6% of normal (IMD's end-of-season total, 759.4mm vs. an LPA of 868.6mm), close to its May forecast of −10%, and every one of the six monsoon-month/horizon price windows that has closed so far is a real increase scored above the 65th-percentile threshold set from pre-2026 history (June's 3-month window: +28.8%). Version 2 (October 2026) replaced the provisional NOAA/IMD inputs with final ones, removed 2026's own months from the historical baseline, corrected the historical year count, and revised June→September from +29.3% to +28.8% after September's prices settled. July's 3-month window and the remaining August/September windows are still open and are not filled in early. 1997 remains the standing counter-example, kept in the small-sample count above, not smoothed over. Full methodology: notebooks/sugar_paper/.

Hurricane/reinsurer event study (Paper 10):

• One of two hypotheses failed to replicate: the RenaissanceRe (RNR) short-horizon loss reaction held up under two independent statistical methods, but a hypothesized medium-horizon repricing effect in Munich Re did not — it was numerically indistinguishable from a hit of identical strength in a non-cat-exposed control ticker, and is reported as a null result rather than reframed as a weaker positive.
• Small event count: 14 hurricane landfalls since 1995 limits statistical power relative to the core CPE framework's much larger signal-count studies.

Multifractal predictability limits (Paper 11):

• Not a universal claim: predictability regimes are instrument- and moment-order-dependent (persistent / single-crossing / oscillating), not a single decay law — see the paper's three-regime typology (Section 5.2) before generalizing any one instrument's result to others.
• DTM regression fit is comparatively weak (R² 0.44–0.46) due to price-trend contamination in the raw (deliberately untransformed) field, though the structure-function and correlated/decorrelated decomposition results this paper relies on most are much better fit (R² > 0.98).

Regime-conditioned price forecasting (Paper 12):

• Zero instruments show demonstrated tradeable alpha. This is the headline limitation, not a footnote: across five independently designed trading-strategy tests (directional, price-target, portfolio, Kelly-sized, cross-sectional relative-value long/short), none of the 22 instruments shows statistically significant risk-adjusted alpha against the properly specified benchmark (its own buy-and-hold return). Real, holdout-honest forecast-accuracy skill exists for roughly half the panel, but does not translate into economic value for any instrument tested — the live dashboard is deliberately built as a forecast-accuracy tracker with no buy/sell signal, for exactly this reason.
• Post-processing/bias-correction only helps 2 of 22 instruments (GLD, JPM), across five independently designed correction techniques — the other 20 are made worse by every correction attempted, evidence their forecast errors are irreducible noise rather than a correctable bias.
• Overlapping-window t-statistics: the alpha significance tests use analytic OLS standard errors appropriate to the point estimates tested, but do not yet correct for autocorrelation in overlapping long-horizon (63–252 day) return windows — an analytic effective-sample-size correction, not a resampling-based fix, is the natural next step.

Predictability limits as AI/ML regime detectors (Paper 13):

• Coverage. Only 12 of the 22 live-forecasting instruments have an already-published Paper 11 predictability limit; the paper's evidence is confined to those 12 deliberately, rather than estimating a limit for the remaining 10.
• Short-horizon legibility. IWM and QQQ's 21-day horizon produces a visibly noisier fresh-vs-stale comparison in both feature variants, making the paper's central visual claim harder — though not obviously wrong — to assess by eye for exactly these two instruments.
• No independent ground truth for "regime change." The paper demonstrates that fresh training tracks subsequent reality better than stale training; it does not independently establish that the moments where this gap widens correspond to any externally verifiable definition of a regime shift — a deliberate, stated choice of evidentiary standard (direct curve comparison, no significance testing), not an oversight.
• The overfit-tree diagnostic is a probe, not a product. It is deliberately built with zero regularization, specifically to be maximally sensitive to a training-window boundary; it is not proposed as a deployable forecasting model in its own right.

Testing AI/ML architecture, depth, and training-window size (Paper 14):

• Scale of implementations. All non-trivial architectures are numpy-native, sized to training windows of 11–504 rows, not production-scale deep networks — though depth itself is directly tested (a genuine hidden layer, hand-backpropagated) and found not to change the result, so the finding is not merely a fact about linear models. Large-scale, heavily-parameterized versions trained on data pooled across many instruments remain untested, and would themselves require training past the predictability limit for any single instrument — the very thing the window-size sweep shows degrades performance.
• Gradual, not a sharp cliff. Degradation as training-window size grows is progressive in every instrument, consistent with a genuine structural limit but meaning "beyond the limit" is a matter of degree, not a bright line a reader can point to on any single panel.
• IWM and QQQ's flat/reversing error curves are explained, not just reported. Their 21-day forecast horizon leaves them with only 6–17% fresh-trained skill over a naive no-change baseline, versus 57–76% for the other ten instruments — a dose-response relationship along horizon length, not an unexplained anomaly.
• No metrics, by design, not by omission. As in Paper 13, no significance test or skill score is reported for the architecture comparisons; the window-size degradation is quantified directly as mean absolute error (expressed as % of price), a descriptive summary rather than an inferential test.

Loss functions and calibrated uncertainty against the same limit (Paper 15):

• The downscaler's point forecast is not new predictive skill. It is, by mathematical construction, provably identical to plain climatology's — the entire contribution is a better-calibrated distribution around the same central prediction, and the paper is explicit that this is what CRPS is rewarding, not improved accuracy.
• The generative downscaler loses to climatology's own empirical distribution on 7 of 12 instruments once every architecture is given a fair, uncalibrated uncertainty estimate rather than being forced into a single point forecast — the headline result is nuanced, not a clean win, and is reported as such.
• Escalating-order Lq loss is untested on flexible models at this paper's scale beyond the earlier instability already documented in Paper 14's own development history — the linear-model result (no measurable change at any q) rules out loss-order as the mechanism, but does not itself demonstrate a flexible model's stability under high-order Lq loss.
• Quantile and Lq loss results are confined to the largest-limit instruments, not swept across the full 12/22-instrument panels used elsewhere in the series.

The outputs should be interpreted as evidence of statistical structure and a research prototype that has cleared a first significance threshold — not a deployable trading strategy.

---

Live Dashboards

Updated daily via automated pipeline. All predictions are publicly timestamped and verifiable.

• Gold Buy Signal Dashboard
• Multi-Asset Portfolio Tilt Dashboard
• Precious Metals Dashboard (Gold/Silver/Platinum)
• CPE Atlas Explorer (169K signals)
• Predictor Dashboard (22-instrument price forecasts, Paper 12) — forecast-accuracy tool only, deliberately no buy/sell signal (see Paper 12's limitations above)
• Climate Signals Dashboard (formerly the Weather Impacts Dashboard) — a separate, daily-updated pipeline (repo), Paper 17's live counterpart: four frozen tables of climate conditions (97 conditions on ENSO, Atlantic, Pacific and regional drought series, thresholds fixed from data through 2012), each logged when it fires and scored forward, a registered paper-portfolio experiment scored from 12 October 2026, and weekly freezes of new distinct conditions. An archive page keeps the earlier daily weather-forecast panels, still scored for a second registered experiment. It is not part of the Performance Monitor, which covers the finance dashboards only
• Singapore Buy-Timing Dashboard — a separate, daily-updated pipeline (repo), Paper 18's live counterpart: COE premium (Category A and B), private property and the HDB resale index, with a graded verdict (wait and watch), a lookup for any HDB block, and a forward ledger that scores its own base-rate forecasts
• Performance Monitor — scores every dashboard above against what actually happened, rebuilt daily from their own ledgers: direction calls vs an always-long baseline (gold, metals), the tilt vs neutral weights (portfolio), price forecasts vs a no-change forecast (predictor, with a live track-record panel on its own page), football picks vs the odds-implied win rate, and the CPE table's claims vs realised outcomes on the period after it was frozen, next to the live unconditional rate. Sample sizes and pending counts are shown throughout; the long-horizon rows (CPE 126d-300d, predictor 126d-252d) cannot resolve until late 2026 to 2027

---

Visual Overview

• Framework Infographic (PDF) — a 3-panel explainer covering what conditional exceedance is, a worked gold example, and the multi-asset atlas. Note: this is a point-in-time snapshot (16 June 2026) — the embedded prices, CPE values, and signal counts are illustrative of the methodology, not current; see the Live Dashboards above for today's numbers.
• CPE vs. Traditional Quant Tools — the framework's core pitch in one panel: traditional approaches fit a model, choose a distributional family, and hope it holds out-of-sample; CPE instead counts historical occurrences directly and states a verifiable probability, no distributional assumptions. Uses Paper 1's published statistics (161 instruments, 169k surviving signals, 4.85× peak lift), not live data.
• Multi-Asset CPE Atlas — maps the strongest tail co-movement channels into gold (crypto ETFs, silver, gold volatility, USD weakness). Most figures are fixed Paper 1 statistics, but the "currently firing ✓" / "currently X% away" annotations reflect a mid-June 2026 snapshot, not today's signal status — check the CPE Atlas Explorer dashboard above for live firing status.
• Predictability-Informed TSMOM Infographic (Paper 16) — a longer-form explainer: schematics of the TSMOM engine and the one change Predictability-Informed TSMOM makes to it (same engine, a smaller pre-screened universe), plus a robustness check re-running the paper's block comparison at 10 different block counts (3 to 20) — the average-return edge holds at every one, even though the paper's own "3 of 5" win-count framing is more sensitive to that specific choice of 5.
• The Outliers Matter (Poster) — the fullest version of the "why CPE sees what standard tools miss" argument: correlation, ARIMA, GARCH, and ML are second-order, mean-seeking methods that minimize average error, while markets are disproportionately decided by rare extreme cases. Backs this with three same-data, two-views demonstrations — a simulated tail-shock example, the El Niño/sugar-price analysis (Paper 9), and the hurricane landfall/RenaissanceRe event study (Paper 10) — each shown once as an ordinary scatter/regression (which sees nothing) and once as CPE's conditional view (which finds a 2–2.6× effect). Companion piece to the "Outliers Matter" Substack post.
• Fresh-vs-Stale Training Schematic (Paper 13) — a timeline diagram of Paper 13's training-window prescription: fit a model on the instrument's own predictability-limit window and apply it to the immediately following window (fresh) versus an equally-sized window from more than twice that limit in the past (stale), both tested against the same real outcome.
• Architecture Bake-off, Before and After the Limit (Paper 14) — EURUSD=X, five architecturally distinct models all trained on the same fair budget: tracking the real exchange rate closely at half the predictability limit, a persistent overshoot appearing by twice the limit, and a gap that keeps widening — not just growing noisier — by eight times the limit.
• Five Levers Against the Ceiling (Paper 15) — a graphical summary of every lever tested against the measured predictability limit across Papers 14 and 15 (architecture, depth, training-window size, loss function, calibrated uncertainty), and the one result that reshapes the headline: climatology's own unmodeled uncertainty beats a carefully calibrated synthetic alternative on more than half the panel.
• Predictability Filter vs. Standard Momentum (Paper 16) — the paper's central result in one image: restricting Time-Series Momentum to only the instruments it can actually predict well wins 3 of 5 real historical blocks versus 2 of 5 unfiltered; a smaller panel shows the two instruments (JPM, XLB) where this program's own signals beat standard TSMOM under every real point estimate checked.
• El Niño Sugar Prediction, Resolved (standalone research note) — a prediction published before its outcome was known, checked against what actually happened: every 2026 monsoon-month/price window that has closed so far is a real increase (June's 3-month window: +28.8%), shown individually against the windows still open.
• The AI Narrative, Tested Three Ways (standalone research note) — real tail-level co-movement across a dated AI-stock basket falls after ChatGPT's launch, not rises, and NVIDIA, Microsoft, Meta, Amazon, and Apple each show zero surviving tail-level predictability from the rest of the basket post-launch.

---

Research Papers

• Paper 18: The Ten-Year Echo in Singapore's COE Premium, and Why Housing Timing Signals Fail Out of Sample
• Paper 17: Why Everyday Weather Does Not Move Markets, and Where Rare Extremes Might
• The AI Narrative, Tested Three Ways (standalone research note)
• Does the 2026 El Niño Move Sugar Prices? (standalone research note)
• Paper 16: Predictability-Informed TSMOM Beats the Standard Momentum Trading Strategy
• Paper 15: The Ceiling Holds, and So Does Climatology: Loss Functions and Calibrated Uncertainty Against an Empirically Measured Predictability Limit
• Paper 14: The Ceiling Holds: Testing AI/ML Architecture, Depth, and Training-Window Size Against an Empirically Measured Predictability Limit
• Paper 13: Predictability Limits as Regime Detectors: A Practical Rule for How Much History an AI/ML Model Should Train On
• Paper 12: A Master-Model Framework for Regime-Conditioned Price Forecasting: Real Statistical Skill, and Why It Mostly Isn't Alpha
• Paper 11: Empirical Predictability Limits of Financial Markets via Correlated–Decorrelated Structure Function Decomposition: A Departure from Atmospheric Turbulence Theory
• Paper 10: Do Major Hurricane Landfalls Move Reinsurer Equity?
• Paper 9: When the Geography is Wrong, the Signal is Wrong
• Paper 8: VPD and Moisture Stress...
• Paper 7: Agricultural Crop-Zone Temperatures in the CPE Framework: Heat Stress Thresholds, Growing Degree Days, and the Reversal of the Paper 6 Sugar Signal
• Paper 6: Beyond Tail Co-Movement: How Temperature Extremes Shift Financial Return Distributions
• Paper 5: Corrected Inference for the CPE Portfolio Tilt Strategy: Newey-West HAC Standard Errors and Robustness Checks
• Paper 4: Signal-Level Calibration and Dashboard Utility of the CPE Framework
• Paper 3: From Descriptive Atlas to Tradeable Signal
• Paper 2: A Conditional Exceedance Framework for Interpretable Trading Decisions
• Paper 1: A Descriptive Atlas of Conditional Exceedance Structure Across a Multi-Asset Universe

---

Substack Articles

• The Ten-Year Echo in Singapore's COE Premium (Paper 18)
• Why Everyday Weather Does Not Move Markets, and Where Rare Extremes Might (Paper 17)
• I Built a Scorecard for My Own Dashboards. The Baselines Were the Interesting Part (the Performance Monitor)
• The AI Narrative, Tested Three Ways
• The El Niño Sugar Prediction, Checked Against What Actually Happened (resolves the "Does the 2026 El Niño Move Sugar Prices?" piece below)
• Beating a 30-Year-Old Strategy at Its Own Game (Paper 16 robustness follow-up)
• Predictability-Informed Momentum: How I Made a 30-Year-Old Trading Strategy Better (Paper 16)
• I Built a Model That Admits What It Doesn't Know (Paper 15)
• Can a Smarter Model Beat a Real Predictability Limit? (Paper 14)
• How Much History Should Your Model Actually See? (Paper 13)
• What Weather Taught Me About Market Predictability
• 33 Historical Episodes Were Actually 3
• Do Hurricanes Move Reinsurer Stocks? One Yes, One "Actually, No"
• The Outliers Matter (And Most of Your Tools Aren't Looking For Them)
• Does the 2026 El Niño Move Sugar Prices? What Eight Historical Events Actually Show
• The Signal That Wasn't There
• Paper 8: When Heat Meets Drought — The Strongest Signals in the CPE Series
• From the City to the Field: Solving the Geographic Mismatch in Climate-Finance (Paper 7)
• Paper 6: Temperature Extremes Don't Co-Move With Financial Tails — They Shift the Whole Distribution
• Building a Live Quant Signal System: What Worked, What Failed, and What the Data Says
• Paper 5: The Results Survive — HAC Correction, Block Bootstrap, and Four Robustness Checks
• Paper 4: The Conditional Probabilities Actually Hold
• Got One Statistically Significant Result. Then Spent Hours Trying to Break It.
• A Research Update: When a Strong Statistical Pattern Doesn't Become a Trading Edge
• The Gold Dashboard Just Said BUY GRADUALLY. Here Is Exactly Why.
• From One Asset to Five: Scaling the CPE Framework Into a Portfolio Tilt Dashboard
• What If You Could Be Useful Without Predicting Anything?
• I Built a Dashboard to Tell Me When to Buy Gold — Here's What It's Saying Right Now
• Mapping the Hidden Wiring of Global Financial Markets
• A Simple Conditional Exceedance Framework for Interpretable Trading Decisions
• Conditional Exceedance Probabilities as a Basis for Systematic Trading

---

Citation

If you find this work useful, please cite the relevant paper(s):

Paper 1 — Descriptive Atlas

RAMANATHAN S, A. (2026). A Descriptive Atlas of Conditional Exceedance Structure
Across a Multi-Asset Universe. Zenodo.
https://doi.org/10.5281/zenodo.20606184

Paper 2 — Single-Asset Trading Framework

RAMANATHAN S, A. (2026). A Conditional Exceedance Framework for
Interpretable Trading Decisions. Zenodo.
https://doi.org/10.5281/zenodo.20769150

Paper 3 — Portfolio Tilt Out-of-Sample Test

RAMANATHAN S, A. (2026). From Descriptive Atlas to Tradeable Signal: An
Out-of-Sample Test of the Multi-Asset Conditional Exceedance Framework as
a Portfolio Tilt Strategy. Zenodo.
https://doi.org/10.5281/zenodo.20815386

Paper 4 — Signal-Level Calibration and Dashboard Utility

RAMANATHAN S, A. (2026). Signal-Level Calibration and Dashboard Utility of
the Conditional Probability Exceedance Framework: Pairwise Validation, Gold
Dashboard Evaluation, and Extended Portfolio Tilt Evidence Across 528 Trading
Days. Zenodo.
https://doi.org/10.5281/zenodo.20830462

Paper 5 — Corrected Inference

RAMANATHAN S, A. (2026). Corrected Inference for the CPE Portfolio Tilt
Strategy: Newey-West HAC Standard Errors and Robustness Checks. Zenodo.
https://doi.org/10.5281/zenodo.20908417

Paper 6 — Beyond Tail Co-Movement

RAMANATHAN S, A. (2026). Beyond Tail Co-Movement: How Temperature Extremes
Shift Financial Return Distributions. Zenodo.
https://doi.org/10.5281/zenodo.20964819

Paper 7 — Agricultural Crop-Zone Temperatures in the CPE Framework

RAMANATHAN S, A. (2026). Agricultural Crop-Zone Temperatures in the CPE
Framework: Heat Stress Thresholds, Growing Degree Days, and
the Reversal of the Paper 6 Sugar Signal. Zenodo.
https://doi.org/10.5281/zenodo.20993837

Paper 8 — When Heat Meets Drought — The Strongest Signals in the CPE Series

RAMANATHAN S, A. (2026). Vapour Pressure Deficit and Moisture Stress in the CPE Framework:
Joint Heat-Drought Conditions as the Strongest Climate-Finance Predictors. Zenodo.
https://doi.org/10.5281/zenodo.21021264

Paper 9 — When the Geography is Wrong, the Signal is Wrong

RAMANATHAN S, A. (2026). When the Geography is Wrong, the Signal is Wrong. Zenodo.
https://doi.org/10.5281/zenodo.21057110

Paper 10 — Do Major Hurricane Landfalls Move Reinsurer Equity?

RAMANATHAN S, A. (2026). Do Major Hurricane Landfalls Move Reinsurer Equity? Zenodo.
https://doi.org/10.5281/zenodo.21231343

Paper 11 — Empirical Predictability Limits of Financial Markets

RAMANATHAN S, A. (2026). Empirical Predictability Limits of Financial Markets via
Correlated-Decorrelated Structure Function Decomposition: A Departure from
Atmospheric Turbulence Theory. Zenodo.
https://doi.org/10.5281/zenodo.21373459

Paper 12 — A Master-Model Framework for Regime-Conditioned Price Forecasting

RAMANATHAN S, A. (2026). A Master-Model Framework for Regime-Conditioned Price
Forecasting: Real Statistical Skill, and Why It Mostly Isn't Alpha. Zenodo.
https://doi.org/10.5281/zenodo.21454884

Paper 13 — Predictability Limits as Regime Detectors

RAMANATHAN S, A. (2026). Predictability Limits as Regime Detectors: A Practical
Rule for How Much History an AI/ML Model Should Train On. Zenodo.
https://doi.org/10.5281/zenodo.21482869

Paper 14 — The Ceiling Holds

RAMANATHAN S, A. (2026). The Ceiling Holds: Testing AI/ML Architecture, Depth,
and Training-Window Size Against an Empirically Measured Predictability Limit.
Zenodo. https://doi.org/10.5281/zenodo.21696948

Paper 15 — The Ceiling Holds, and So Does Climatology

RAMANATHAN S, A. (2026). The Ceiling Holds, and So Does Climatology: Loss
Functions and Calibrated Uncertainty Against an Empirically Measured
Predictability Limit. Zenodo. https://doi.org/10.5281/zenodo.21802729

Paper 16 — Predictability-Informed TSMOM Beats the Standard Momentum Trading Strategy

RAMANATHAN S, A. (2026). Predictability-Informed TSMOM Beats the Standard
Momentum Trading Strategy. Zenodo. https://doi.org/10.5281/zenodo.21842311

Does the 2026 El Niño Move Sugar Prices? (standalone research note)

RAMANATHAN S, A. (2026). Does the 2026 El Nino Move Sugar Prices? A CPE
Test of Indian Monsoon Deficit and Global Sugar Prices, Resolved. Zenodo.
https://doi.org/10.5281/zenodo.23053552

The AI Narrative, Tested Three Ways (standalone research note)

RAMANATHAN S, A. (2026). The AI Narrative, Tested Three Ways: Daily Tail
Co-Movement, Earnings Reactions, and Intraday Feasibility, Checked
Against a Real AI-Stock Universe. Zenodo.
https://doi.org/10.5281/zenodo.23092634

Paper 17 — Everyday Weather and Rare Extremes

RAMANATHAN S, A. (2026). Why Everyday Weather Does Not Move Markets, and Where Rare Extremes Might. Zenodo.
https://doi.org/10.5281/zenodo.23301263

Paper 18 — Singapore COE Premium and Housing Timing

RAMANATHAN S, A. (2026). The Ten-Year Echo in Singapore's COE Premium, and Why
Housing Timing Signals Fail Out of Sample. Zenodo.
https://doi.org/10.5281/zenodo.23418466

---

LinkedIn

• Author Profile
• LinkedIn Posts on CPE Research
• Paper 17 post: why everyday weather does not move markets

---

Repository Structure

.
├── data/                          # Raw and processed price data
├── notebooks/
│   ├── cpe_engine_parallel.py       # Core CPE sweep engine (Papers 1-5)
│   ├── joint_cpe_engine.py          # Multi-predictor joint-conditioning
│   ├── build_gold_dashboard.py      # Gold buy-signal dashboard
│   ├── build_portfolio_dashboard.py # Multi-asset portfolio tilt dashboard
│   ├── build_metals_dashboard.py    # Precious metals dashboard
│   ├── build_predictor_dashboard.py # Paper 12 live price-forecast dashboard
│   ├── ibkr_paper_ledger.py         # IBKR paper-trading ledger
│   ├── predictor_ledger.py          # Live forecast ledger for the predictor dashboard (git-backfilled, scored daily)
│   ├── cpe_signal_ledger.py         # CPE live ledger: tail-entry events scored against the frozen CPE table
│   ├── build_performance_monitor.py # Performance monitor: every dashboard scored against outcomes
│   ├── temperature/                 # Papers 6-9 climate-finance pipelines
│   ├── rein/                        # Paper 10 hurricane/reinsurer event study
│   ├── predictability_paper/        # Paper 11 multifractal analysis pipeline
│   ├── predictor_v1/                # Paper 12 forecasting pipeline (features, model
│   │                                 # selection, trading strategies, post-processing,
│   │                                 # live-deployment modules) + Paper 13's regime-
│   │                                 # detector scripts (59-64_*.py) + Paper 14's
│   │                                 # architecture/depth/window-sweep scripts
│   │                                 # (65-70_*.py) + Paper 15's loss-function/
│   │                                 # generative-downscaler scripts (73-80_*.py)
│   │                                 # + Paper 16's decision-layer search scripts
│   │                                 # (81-89_*.py: RL/GA sizing, PPO, equation
│   │                                 # renders, schematics) in the same dir
│   ├── predictor_v1_paper_draft.md  # Paper 12 preprint (published: zenodo.org/records/21454884)
│   ├── predictor_v1_paper_draft.pdf # Paper 12 PDF, as submitted to Zenodo
│   ├── regime_detector_paper_draft.md  # Paper 13 preprint (published: zenodo.org/records/21482869)
│   ├── regime_detector_paper_draft.pdf # Paper 13 PDF, as submitted to Zenodo
│   ├── architecture_ceiling_paper_draft.md  # Paper 14 preprint (published: zenodo.org/records/21696948)
│   ├── architecture_ceiling_paper_draft.pdf # Paper 14 PDF, as submitted to Zenodo
│   ├── loss_uncertainty_ceiling_paper_draft.md  # Paper 15 preprint (published: zenodo.org/records/21802729)
│   ├── loss_uncertainty_ceiling_paper_draft.pdf # Paper 15 PDF, as submitted to Zenodo
│   ├── decision_layer_ceiling_paper_draft.md  # Paper 16 preprint (published: zenodo.org/records/21842311)
│   ├── decision_layer_ceiling_paper_draft.pdf # Paper 16 PDF, as submitted to Zenodo
│   ├── paper16_graphical_abstract.py # Paper 16 graphical abstract generator
│   ├── paper16_substack_post.md     # Paper 16 Substack post
│   ├── paper16_linkedin_post.md     # Paper 16 LinkedIn post
│   ├── sugar_paper/                  # El Nino/monsoon/sugar-price standalone
│   │                                 # research note: analysis scripts, raw
│   │                                 # ONI/IMD/sugar-price data, 6 figures,
│   │                                 # manuscript (md/html/pdf, published:
│   │                                 # zenodo.org/records/23053552), and its
│   │                                 # LinkedIn/Substack posts
│   ├── ai_stocks_cpe/                # "The AI Narrative, Tested Three Ways"
│   │                                 # standalone research note: daily CPE
│   │                                 # regime comparison (AIQ basket), NVDA
│   │                                 # earnings event study, intraday
│   │                                 # feasibility (Alpaca data), manuscript
│   │                                 # (md/html/pdf, published:
│   │                                 # zenodo.org/records/23092634), and its
│   │                                 # LinkedIn/Substack posts
│   ├── weather_finance/             # Paper 17 research: weather-to-price tests, four climate
│   │                                 # batches through the CPE engine (cpe_climate/ holds the
│   │                                 # registered economic prior, results, frozen tables),
│   │                                 # horizon ladder, causal walk-forward, rarity tests
│   ├── weather_impacts_paper_draft.md  # Paper 17 preprint (published: zenodo.org/records/23301263)
│   ├── weather_impacts_paper_draft.pdf # Paper 17 PDF, as submitted to Zenodo
│   ├── paper17_build.sh             # Paper 17 build (paper17_build*.py assemble the text)
│   ├── paper17_new_figures.py       # Paper 17 climate figures and graphical abstract
│   ├── paper17_substack_post.md     # Paper 17 Substack post
│   ├── paper17_linkedin_post.md     # Paper 17 LinkedIn post
│   ├── sg_timing/                   # Early Singapore COE scan (superseded by the sg-buy-timing-dashboard repo)
│   ├── paper18_substack_post.md     # Paper 18 Substack post (figures in paper18_figs/)
│   ├── paper18_linkedin_post.md     # Paper 18 LinkedIn post
│   ├── dash_back/                   # Paper 5 dashboard backtest analysis
│   └── *.html                       # Live dashboard outputs (GitHub Pages)
├── src/                            # Core probability estimation and trading logic
├── requirements.txt
└── README.md