tac-exp-dev/book

TradeAC Quant Trading Guide — Table of Contents & Status

A practitioner's guide to quantitative trading written the only way it is worth reading: grounded in a real research loop and a real execution trail. Every number in this book was either reproduced from a recorded TradeAC experiment (MLflow run + traced git branch) on the clean lake (exp 21+) or a reconciled post-reset live round, or it is explicitly labeled a hypothesis. See AGENTS.md (repo root) for the truth contract; EVIDENCE.md for the ledger; CLAIMS.md for the proven-vs-hypothesis matrix.

Evidence boundary and living-document status

• The clean-lake boundary (2026-08-18, exp 21) is the evidence watermark. Anything before it — exp 8–18 and their backtests, pre-reset live rounds — is historical context and idea material only, never cited as fact (they were demonstrably inflated by lake data-quality problems, EVIDENCE#010 → exp 21). Pre-reset experiments and all opencode chat transcripts (see data/chat_mining/ and references/chat-ideas.md) feed the book's hypothesis pipeline.
• Every section is living. As new experimental results land on the clean lake, chapters are updated; a chapter marked done is done for its window, not forever.

What this book is for

A quant-desk reader should be able to act on this book: replicate a signal pipeline, size a book, gate it with risk limits, execute it, and reconcile what actually happened. The book's spine is how performance improved with research-proved truth — the actual arc of TradeAC's campaign from a baseline that barely cleared costs to a live, reconciled round.

How to read evidence tags

• PROVEN — reproduced from a recorded run or reconciled round. Citation is an experiment_id/run_id or round_id.
• HYPOTHESIS — plausible but not yet reproduced; never stated as fact.
• REFERENCED — industry/academic practice; citation is an external source.
• TODO(evidence-needed: …) — an open question the desk should settle.

Table of contents

#	Chapter	Status	Core experiments cited	Core lesson
00	Why a real execution trail matters	drafting	round 3	A book claims nothing it cannot reconcile
01	Metrics: the vocabulary of a price series	drafting	exp 21–31 + dataset studies	Every claim reduces to a falsifiable statistic
02	The research loop: lake → experiment → live	drafting	exp 8–31	Traceability is the methodology
03	Baseline and the cost reality	drafting	exp 22–26, 28–31	A signal that dies after 5bp/15bp is not a signal
04	Prune, don't add: feature-family ablation	drafting	exp 9, 10, 11, 25, 43	On a 50-name panel, generic beats model-specific; standalone reversal never existed
05	Ensembles and the seed-count effect	drafting	exp 12, 28, 34	Averaging raises ICIR; more seeds raise breadth but not net — cost is the ceiling
06	The clean-lake reset: data quality as first-order risk	drafting	exp 21–24	If it doesn't reproduce on clean data, it was noise
07	Isolation runs: single-variable discipline	drafting	exp 26, 29–31, 33–37, 43	Most additions fail; the discipline is the value
08	Portfolio construction: dropout vs the rest	drafting	exp 13, 14, 15, 35, 38, 39, 41	Turnover-sensitive construction bleeds the edge; weekly recompute wins
09	The cost/turnover frontier	drafting	exp 26, 39, 41	Cut turnover before adding signal; weekly rebalance is the proven lever
10	Risk limits and gates that work	drafting	exp 18, 20, 40, 42	Limits are a safety net, not alpha; gates churn without signal
11	Live execution and reconciliation	drafting	exp 27, round 3	4.54 bps slippage realized; funnel 10→10→10→9
12	Synthesis: how proved truth compounds	drafting	all, exp 33–43	Cost relief > signal; the Q-campaign scoreboard

Status legend: drafting → in-review → done.

Per-chapter claim inventory (expected truth status)

Each chapter opens with its claims. The inventory below is the working contract: what the chapter asserts, and what evidence tier it must land in. It is updated as chapters pass their HITL review gate.

00 — Why a real execution trail matters

Claim	Expected status
A book's claims must be reconcilable to a real trail (targets→decisions→fills)	PROVEN — round 3 funnel
Backtest claims without live reconciliation are hypotheses about execution	HYPOTHESIS → settled by round 3
The funnel (targets→decided→placed→filled) is the minimal honesty structure	REFERENCED (industry ops practice) + PROVEN via tac-rd-book schema

01 — Metrics: the vocabulary of a price series

Claim	Expected status
Every chapter claim reduces to a statistic computable on the lake (drift, jump, vol, regime, reversion, memory, risk, error, probability, timeline, decay)	PROVEN (chapters 03–12) + HYPOTHESIS (dataset-study magnitudes, chat-derived)
Generic scale-free statistics beat model-specific machinery on a small daily panel	PROVEN (exp 23/24/25/29/31) + HYPOTHESIS (generality)
A statistic is only as good as the falsification it survives (null z-scores, reproduction)	PROVEN (exp 21 detection playbook) + REFERENCED
The strongest single-feature signal (OU z-score) can be worthless inside a rank model — the "OU paradox"	PROVEN (exp 25) + open mechanism TODO(evidence-needed)

02 — The research loop

Claim	Expected status
Experiments must be traced: branch + MLflow run + notes (hypothesis before run)	PROVEN — traceability loop used on exp 8–31
Pre-registration protects against post-hoc cherry-picking	REFERENCED (research practice; see CLAIMS for multiple-testing note)
The lake is the single source of bar/feature truth	PROVEN — exp 21 showed dirty-lake risk
One variable changes per run (isolation); verdicts attributable	PROVEN — exp 26→28/29/30/31 design

03 — Baseline and the cost reality

Claim	Expected status
Baseline 1-day LGB signal is weak on 2026 OOS (RankIC ≈ 0.04, below the 0.2 ICIR noise threshold)	PROVEN — exp 8
Costs erase most of the raw edge: +6.2% ann gross → +1.6% net	PROVEN — exp 8
A viable signal must clear realistic execution costs	PROVEN (exp 8, exp 26) + REFERENCED

04 — Prune, don't add

Claim	Expected status
Dropping model-specific feature families (ou, hmm) improves the rank signal (RankIC 0.030→0.064)	PROVEN — exp 9
Adding moment/volatility families regresses the signal (exp 11), same failure mode as ou/hmm	PROVEN — exp 11
Adding OU mean-reversion (sp_ou_zscore) hurts on clean data	PROVEN — exp 25
Standalone 5d reversal (single feature sp_trend_slope_5) is not learnable — model trains positive IC	PROVEN — exp 43 (Q11)
More features ≠ better signal on a small cross-section	HYPOTHESIS (supported by 3+ runs, still panel-specific)

05 — Ensembles

Claim	Expected status
5-seed RankIC ensemble raises net-of-cost performance vs single model on the ablated set	PROVEN — exp 12 (pre-clean-lake), re-validated exp 22–24
Seed count is load-bearing: 2 seeds lose to 5 seeds on clean data	PROVEN — exp 28
10 seeds raise rank breadth (RankIC 0.0671, L/S Sharpe 4.58) but the book stays negative net	PROVEN — exp 34 (Q02)
Ensemble averaging's benefit is separable from feature expansion	PROVEN — exp 12 isolation design

06 — Clean-lake reset

Claim	Expected status
The reference signal did not reproduce on a rebuilt lake (IC 0.035→0.002)	PROVEN — exp 21
Data-quality problems had inflated earlier results; post-reset signal is the only valid one	PROVEN — exp 21 + exp 22–24 reproduction
Signal work must be re-validated after any data rebuild	PROVEN (exp 21) + HYPOTHESIS for generality

07 — Isolation runs

Claim	Expected status
Single-variable changes isolate what moved performance	PROVEN — exp 26→29/30/31 + exp 33–43 Q-runs design
Multi-horizon momentum degrades the reference (net IR 0.21→-1.12)	PROVEN — exp 29
Risk-adjusted 22d Sharpe drift (M2): reproduced on the compact set by Q01	PROVEN — exp 30 + exp 33 (Q01)
GARCH(1,1) vol-regime features add no signal	PROVEN — exp 31
Longer labels raise IC monotonically but net worsens under daily turnover (10d/22d)	PROVEN — exp 36/37 (Q04/Q05)
Standalone reversal feature does not reproduce	PROVEN — exp 43 (Q11)

08 — Portfolio construction

Claim	Expected status
TopkDropout beats stochastic-control OptimalStopControl on the ensemble signal	PROVEN — exp 13, 14
Stop-control constructions churn and bleed costs (cost drag ≈ −11.3pp)	PROVEN — exp 13
Weekly rebalance recompute of the daily signal is the campaign's best construction (net +12.51%, IR 1.24)	PROVEN — exp 39 (Q07)
Fractional-Kelly sizing (exp 15) is refuted on the clean lake (net +1.04%, IR 0.11)	PROVEN — exp 38 (Q06)
Widening the book (topk 20) adds no edge; long-short top/bottom is destroyed by turnover	PROVEN — exp 35/41 (Q03/Q09)

09 — Cost/turnover frontier

Claim	Expected status
n_drop 2→1 flips net excess from −3.21% to +2.13% with identical signal metrics	PROVEN — exp 26
Cost drag is the binding constraint, not signal quality	PROVEN — exp 26 (IC/RankIC identical between n_drop variants)
Weekly recompute cuts cost drag to ~1.1pp and unlocks +12.51% net	PROVEN — exp 39 (Q07)
Long-short daily turnover costs 9.7% of NAV ($96.7k); fill rate 0.40	PROVEN — exp 41 (Q09)

10 — Risk limits

Claim	Expected status
$5M liquidity floor improves net IR 0.81→0.98 and cuts drawdown 7.9%→5.4%	PROVEN — exp 18 (pre-clean-lake; see note in chapter)
Size/concentration caps hurt by cutting deployed capital	PROVEN — exp 18; re-confirmed clean-lake exp 40 (Q08)
Entry/risk gates are no-ops when the signal is the bottleneck	PROVEN — exp 20 (R2/R3 byte-identical)
Exp-18 numbers are not comparable to post-reset runs due to env non-determinism	PROVEN — exp 20 R0 note
Post-reset A/B: the floor binds but adds no IR edge; DD relief is pure defunding	PROVEN — exp 40 (Q08)
HMM regime gate meets only the drawdown leg and churns	PROVEN — exp 42 (Q10)

11 — Live execution and reconciliation

Claim	Expected status
Live funnel held: 10 targets → 10 decided → 10 placed → 9 filled, 1 cancelled, 1 skipped	PROVEN — round 3
Realized slippage ≈ 4.54 bps, estimated cost ≈ $45, turnover 0.74	PROVEN — round 3 metrics
Live beats backtest: execution claims trace to round_id, not to backtest	PROVEN — methodology

12 — Synthesis

Claim	Expected status
The largest performance deltas came from data quality, cost/turnover relief, feature pruning, and risk limits — not from adding features	PROVEN — composite of exp 9, 18, 21, 26, 39
The campaign's refuted runs (exp 11, 13, 14, 20, 25, 29, 31, Q02–Q06, Q09–Q11) were as valuable as wins	REFERENCED + PROVEN (they stopped wrong directions)
Turnover reduction is the dominant net-performance lever (weekly rebalance +12.51% vs daily −3.21%–+2.13%)	PROVEN — exp 26 vs 39
Generalizability of the 50-ETF panel results is an open question	HYPOTHESIS — TODO(evidence-needed: out-of-panel universe)

Repository layout

book/
  README.md          # this file
  EVIDENCE.md        # ledger: id → claim → source → verified?
  CLAIMS.md          # proven-vs-hypothesis matrix, updated every chapter
  chapters/00-intro.md ...   # one file per chapter
  data/              # ad-hoc validation scripts + outputs
  data/chat_mining/  # raw opencode chat transcripts (idea sources)
  references/chat-ideas.md   # distilled ideas/hypotheses from chats + pre-reset experiments
  references/        # external citations

Open questions for the desk

• TODO(evidence-needed: weekly-rebalance result (exp 39) reproduced on a second window before promotion to a live round)
• TODO(evidence-needed: long-horizon label (10d/22d) paired with a low-turnover construction — the signal edge is proven, the cost kills daily churn)
• TODO(evidence-needed: out-of-universe (non-ETF) validation of the compact stochastic feature set)
• TODO(evidence-needed: exp 18 risk-limit spec reconciliation — post-reset A/B (exp 40) shows it is a safety net, not alpha)