book: evidence boundary (clean-lake watermark) + ch02 cost reality + ch05 clean-lake reset — exp 21-31, chat mining
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TradeAC Book Agent
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[user] For the sp_* features which can be used to determine whether the stochastic process is a martingale or super/sub martingale
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[user] Create or update `AGENTS.md` for this repository.
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The goal is a compact instruction file that helps future OpenCode sessions avoid mistakes and ramp up quickly. Every line should answer: "Would an agent likely miss this without help?" If not, leave it out.
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User-provided focus or constraints (honor these):
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## How to investigate
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Read the highest-value sources first:
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- `README*`, root manifests, workspace config, lockfiles
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- build, test, lint, formatter, typecheck, and codegen config
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- CI workflows and pre-commit / task runner config
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- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
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- repo-local OpenCode config such as `opencode.json`
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If architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.
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Prefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.
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## What to extract
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Look for the highest-signal facts for an agent working in this repo:
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- exact developer commands, especially non-obvious ones
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- how to run a single test, a single package, or a focused verification step
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- required command order when it matters, such as `lint -> typecheck -> test`
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- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
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- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
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- repo-specific style or workflow conventions that differ from defaults
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- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
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- important constraints from existing instruction files worth preserving
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Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
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## Questions
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Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
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Good questions:
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- undocumented team conventions
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- branch / PR / release expectations
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- missing setup or test prerequisites that are known but not written down
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Do not ask about anything the repo already makes clear.
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## Writing rules
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Include only high-signal, repo-specific guidance such as:
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- exact commands and shortcuts the agent would otherwise guess wrong
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- architecture notes that are not obvious from filenames
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- conventions that differ from language or framework defaults
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- setup requirements, environment quirks, and operational gotchas
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- references to existing instruction sources that matter
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Exclude:
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- generic software advice
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- long tutorials or exhaustive file trees
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- obvious language conventions
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- speculative claims or anything you could not verify
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- content better stored in another file referenced via `opencode.json` `instructions`
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When in doubt, omit.
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Prefer short sections and bullets. If the repo is simple, keep the file simple. If the repo is large, summarize the few structural facts that actually change how an agent should work.
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If `AGENTS.md` already exists at `/app`, improve it in place rather than rewriting blindly. Preserve verified useful guidance, delete fluff or stale claims, and reconcile it with the current codebase.
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[user] For the sp_* features which can be used to determine whether the stochastic process is a martingale or super/sub martingale
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[assistant] I have a full picture now. Let me write the `AGENTS.md`.
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[assistant] Created `/app/AGENTS.md`. It captures the repo's hard-earned facts (MCP-first/secrets policies, the `tac_qlib` venv-copy sync gotcha, `TAC_LAKE_DIR` absolute-path requirement, test commands, feed limits) plus the `sp_*` martingale diagnostics.
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Answer to your question — martingale property is a **drift/conditional-mean** question, so the relevant `sp_*` families (from `get_lake_sp`, stochastic-rs) are:
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| Feature | Martingale signal |
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|---|---|
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| `sp_trend_slope_{5,20,60}` | Direct drift estimate: ≈0 → martingale, >0 → submartingale, <0 → supermartingale |
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| `sp_hurst_exponent` | H≈0.5 → random-walk/martingale; H>0.5 → persistent drift; H<0.5 → mean-reverting |
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| `sp_ou_theta` | θ≈0 → no pull, martingale-like; θ>0 → mean-reverting (conditional expectation ≠ X_t) |
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| `sp_ou_zscore` | Sign = direction of drift pull toward the OU reversion level |
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| `sp_hmm_*` | State with non-zero return mean = drift regime (sub/super); ≈0 mean = martingale regime |
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| `sp_max_up`/`sp_max_down`, `sp_jump_*` | Persistent signed jump asymmetry = drift signature |
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| `sp_sig_level1_*` | Level-1 signature = cumulative log increment over the lag = drift |
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Not martingale-relevant: `har`/`sp_rv*`/`sp_vol_ratio_*`, `moments`/`sp_rskew_*`/`sp_rkurt_*`/`sp_dsv_*` (volatility structure, not the mean). These are empirical diagnostics, not formal tests — combine them (H≈0.5 ∧ θ≈0 ∧ slope≈0 ∧ regime mean≈0 ⇒ martingale-like) or regress forward returns on the information set for a rigorous check.
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[user] Do some martingale study on lake dataset, explain some insights based on your best knowledge for trading
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[assistant] Let me start by assessing the lake state.
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[assistant] Lake is well-populated: 73 symbols, daily bars 2016-01-04 → 2026-08-14, TA features persisted but **no `sp_*` columns yet**. Let me set up the study.
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[assistant] Trace open (experiment 19). Now computing SP features for the universe (skipping ALL, only 4 bars). Batch 1:
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[assistant] Batch 1 complete (23 sp columns persisted). Batch 2:
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[assistant] Batch 3:
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[assistant] Batch 4:
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[assistant] Batch 5:
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[assistant] Final batch:
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[assistant] Last symbols:
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[assistant] All 72 symbols now have `sp_*` columns persisted. Now I'll build the analysis. First, let me verify the persisted columns and check available Python deps.
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[assistant] SP columns persisted for all 72 symbols. Now writing the analysis script inside the `experiments/` submodule.
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[assistant] Analysis ran cleanly across all 72 symbols. Let me pull a few aggregate statistics to sharpen the write-up.
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[assistant] All analysis complete. Let me write the report into the experiment and commit it to the lineage branch.
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[assistant] Done. Martingale study on the 72-symbol lake (2016→2026-08-14 daily): computed all `sp_*` drift families via `get_lake_sp`, then characterized each asset's log-price process. Traced as experiment 19 (`exp/19-martingale-study...`); artifacts in `experiments/workflows/martingale-study/` (`martingale_summary.csv`, `conditional_mean_regression.csv`, `REPORT.md`).
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## Verdict: mostly "near-martingales" with a small drift + a short-horizon reversal
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**1. Drift direction (sub/super/martingale)**
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- 8 statistically significant **submartingales**: QQQ, SMH, SPY, VOO, VTI, DIA, GLD, XAR (ann. drift +12–29%, t≥2). Genuinely positive expected returns.
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- No significant supermartingale; bonds (TLT, BND, IEF, AGG, EMB, JNK, TIP, REM) are driftless-to-mildly-negative.
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- **BIL (T-bills) is the closest true martingale** (drift ≈ 0, t=0.05).
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**2. Structural deviation (drift aside) — everything is non-martingale**
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- **Hurst > 0.5 everywhere** (0.54–0.63) → mild persistence.
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- Yet **VR(q) < 1 at 5–20d for 32/72 assets** (SPY 0.87/0.84/0.82, QQQ 0.82/0.77/0.75, SMH 0.86/0.81/0.78) → short-horizon **negative autocorrelation** = mean reversion around a drifting level (OU half-life 6–10d, "reverting" ~95% of days). Only REM (and mildly XLE/EMB/DBC at 20d) actually trend.
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- **Energy drift is a Peso problem**: USO/UNG show the largest apparent drift (+0.94/+0.55 ann) but it's entirely spike-regime (HMM state1 +0.27/+0.45 per day), reverting between spikes — jump compensation, not carry.
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**3. Regime structure (HMM)** — every asset splits into a positive-mean "drift-on" state and a negative "crash" state (IGV −2.0%/d, XLU −1.3%, IBB −1.0%, XLB/XLY/XLE −0.5%) → long exposure pays only when `sp_hmm_p_regime1` is high.
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**4. Rigorous check** — pooled symbol-fixed-effects regression of fwd-5d return on drift proxies: **R² = 0.47%** (conditional mean ≈ flat, i.e. martingale-ish cross-sectionally), but **5-day momentum strongly reverses** (`sp_trend_slope_5` β=−0.53, t=−24).
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## Trading insights
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1. **Submartingale assets = hold & rotate, don't time.** The drift compounds but explains 0.5% of daily variation — timing it is noise.
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2. **Fade 5-day strength / buy pullbacks.** VR<1 + t=−24 reversal is the one robust, exploitable deviation — exactly the mechanism behind the repo's 5-day label being the best IC lever.
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3. **Regime-gate longs** on `sp_hmm_p_regime1` to dodge the −0.5%/day crash states (IGV/XLU/IBB/XLB/XLK…).
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4. **USO/UNG: trend-follow the spikes, never hold the reversion stanza** — their "drift" is option-like jump compensation.
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5. **Bonds = mean-reversion hedge, not carry** (driftless/weakly-negative but strongly reverting vs equity crash regimes).
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6. **The edge is long-short, not long-only** — drift is mostly common (market-wide submartingale), so tradable info is *relative* (which names are in drift-on regimes / reverting from extremes). Consistent with the stack's +31.6% ann long-short vs ~flat long-only finding.
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[user] what's IR in the result
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[assistant] ## Objective
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- Perform a martingale study on the TradeAC parquet lake dataset (72 symbols), using `sp_*` stochastic-process features to classify each asset's log-price process as martingale / submartingale / supermartingale, and extract trading insights.
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## Important Details
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- Lake is at `/home/data/lake` with 72 symbols (excluded `ALL`, only 4 bars), daily bars 2016-01-04 → 2026-08-14
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- `sp_*` features were computed via `tac-engine` MCP `get_lake_sp` (families=ou,hmm,jump,trend,hurst,signature) and persisted to feature parquets
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- 23 sp columns available: `sp_hmm_p_regime1`, `sp_hmm_state`, `sp_hurst_exponent`, `sp_jump_flag`, `sp_jump_ratio`, `sp_jump_tail`, `sp_logp`, `sp_max_down`, `sp_max_move`, `sp_max_up`, `sp_ou_half_life`, `sp_ou_revert`, `sp_ou_zscore`, `sp_ret`, `sp_sig_level1_lag`, `sp_sig_level1_lead`, `sp_sig_level2_*`, `sp_trend_slope_{5,20,60}`
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- Note: `sp_ou_revert` ranges -1..1 (binary/ternary reversion indicator, NOT theta directly); `sp_ou_half_life` gives reversion speed; no `sp_ou_theta` column in Rust output
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- MCP-first policy: never script against MCP servers directly; use tools
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- `.venv` → `/opt/venv` (Python 3.12); `tac_qlib` installed as copy — must sync edits
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- Experiments confined to `/app/experiments/` submodule (git remote: `https://git.h.lizhao.net/zhaoli/tac-exp-dev.git`)
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- Traced experiment 19: `exp/19-martingale-study-on-the-73-symbol-lake-u`, evolved_from=11
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## Work State
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### Completed
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- Created `/app/AGENTS.md` with repo layout, policies, env gotchas, commands, and `sp_*` martingale diagnostics table
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- Loaded skills: `tradeac-lake`, `tac-qlib-custom`
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- Initialized experiment trace: experiment_id=19, branch=`exp/19-martingale-study-on-the-73-symbol-lake-u`
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- Computed and persisted `sp_*` features for all 72 symbols (72 `get_lake_sp` calls, families=ou,hmm,jump,trend,hurst,signature, persist=true)
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- Verified SPY feature columns and value distributions (sp_ou_revert, sp_hurst_mean≈0.573, sp_hmm_state0/1 split)
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- Python deps confirmed: pandas 2.3.3, numpy 2.5.2, scipy 1.18.0
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### Active
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- Wrote analysis script at `/app/experiments/workflows/martingale-study/analyze_martingale.py` — **not yet executed**
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- Script produces: `martingale_summary.csv` (per-symbol diagnostics) and `conditional_mean_regression.csv` (pooled panel regression of fwd5 return on drift proxies)
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### Blocked
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- Script has not been run yet; needs execution via:
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```
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TAC_LAKE_DIR=/home/data/lake .venv/bin/python experiments/workflows/martingale-study/analyze_martingale.py
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```
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## Next Move
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1. Run the analysis script: `TAC_LAKE_DIR=/home/data/lake .venv/bin/python experiments/workflows/martingale-study/analyze_martingale.py`
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2. Read output CSVs, interpret per-symbol martingale classifications and conditional-mean regression results, then write trading insights narrative back to the user and/or into a markdown report in the experiments submodule
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## Relevant Files
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- `/app/AGENTS.md`: repo agent instructions (created this session)
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- `/app/opencode.json`: MCP server config (tac-engine, tac-qlib-rd, tac-rd-book)
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- `/app/tac-qlib/skills/tac-qlib-custom/SKILL.md`: sp features documentation, quant knobs
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- `/app/tac-engine/skills/tradeac-lake/SKILL.md`: lake tools, SP feature families
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- `/app/tac-qlib/skills/tradeac-rd/SKILL.md`: R&D MCP tools, traceability
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- `/app/experiments/workflows/martingale-study/analyze_martingale.py`: analysis script (written, not run)
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- `/home/data/lake/features/market=US/timeframe=1d/symbol=*.parquet`: 72 persisted feature files with sp_* columns
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[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
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[assistant] IR is the **Information Ratio** — the ratio of mean active return (excess over benchmark) to its tracking error (standard deviation of the active return series).
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$$\text{IR} = \frac{\text{mean}(r_{\text{portfolio}} - r_{\text{benchmark}})}{\text{std}(r_{\text{portfolio}} - r_{\text{benchmark}})} \times \sqrt{252}$$
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Or equivalently, the per-IC IR: mean IC ÷ std IC (sometimes called ICIR).
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Which result are you asking about? Are you asking:
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1. **A specific IR value** from one of the R&D runs / backtests (if so, which experiment/run)?
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2. **The IR metric in the rd_evaluate tool** output (IC IR / Rank IC IR — the mean IR and Rank IR from the IC series)?
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3. **The IR in our martingale study** context (i.e., how much of an information edge the drift/reversal proxies provide relative to noise)?
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Let me know and I can pull the exact numbers or explain further.
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