1203 lines
82 KiB
Plaintext
1203 lines
82 KiB
Plaintext
[user] I changed llm model. now do again: there were lake bugs that didn't read ta/spa features and I just added new validation tool/skill. now you could validate and exec and trace a workflow you have run following expId=16&run=0cea66d9892246519bdf329a0410a277, except for using DATABASE_URL database instead of sqlite and follow ta/sp features available
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[assistant] I’ll inspect the reference run configuration and current lake health first, then validate the feature partitions before launching a traced workflow with the `DATABASE_URL` backend. I’ll preserve the reference model settings while adapting the dataset to the currently available TA/SP features.
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[assistant] The reference run is a 50-ETF RankIC ensemble using a 5-day label and 24 explicit features, including both SP features and raw bars. The current lake now has the corrected `family=ta` and `family=sp` layout, but I’m checking the exact reference universe and fit window because several non-ETF symbols in the broader lake only begin in late 2025 and must not silently enter this workflow.
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[assistant] Validation passed with no errors or warnings: all 50 symbols have bars plus both TA and SP partitions, the feature partitions are aligned, and no requested-window feature column is all-NaN. I’ll run the reference ensemble hyperparameters and portfolio settings, expanding the feature list to the validated TA/SP columns that are actually present rather than relying on the older trimmed list.
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[assistant] The new lineage node is `experiment_id=22`, forked from experiment 16. I’m adding the workflow only inside that experiment branch; its MLflow URI will remain the non-secret `sqlite:///mlruns.db` form so the server resolves it through the environment’s `DATABASE_URL`, avoiding any credential-bearing configuration.
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[assistant] The workflow is now running in the background under `tac-rd-exp16-db-ta-sp`. The workflow file is committed on experiment branch 22 before execution; I’m polling the Postgres-backed MLflow run and will inspect its recorded inputs/results before closing the trace.
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[user] we are tracing using postgres defined in DATABASE_URL instead of sqlite
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[assistant] Correct. The trace record is experiment 22, and the workflow’s MLflow run is using the Postgres database resolved from `DATABASE_URL`; the `sqlite:///mlruns.db` value in the YAML is only the server’s normalization-safe placeholder, not the active tracking backend. The run is still `RUNNING`, so I’m continuing with the same Postgres-backed run.
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[assistant] The Postgres run has recorded the full expanded feature configuration and remains active; no failure or partial-result artifact has appeared. This feature-expanded ensemble is materially heavier than the reference, so I’m leaving it running rather than starting a second competing run or incorrectly closing the trace.
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[assistant] Validation passed:
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- 50/50 symbols have bars, TA, and SP features.
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- No partition alignment or all-NaN issues.
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- Workflow committed on traced branch `exp/22-re-run-experiment-16s-5-day-rankic-ensem`.
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- Trace ID: `22`, evolved from `16`.
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- MLflow run: `18db5bc1dc8f436fb84dddcbb9b36d61`.
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- Tracking is using Postgres via `DATABASE_URL`, not SQLite.
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The expanded TA/SP ensemble is still `RUNNING`; results and trace completion are pending.
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[user] you could poll exp status every 2 minutes and proceed once it's ready
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[assistant] I’ll poll the same Postgres-backed run at two-minute intervals and finish the trace automatically once it reaches `FINISHED` or report the first terminal failure.
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[user] can you check which skills/tools is to query exp status
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[assistant] Use the `tradeac-rd-explain` skill. The status tools are:
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- `tac-qlib-rd_rd_exp_get_run` for a specific run’s status
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- `tac-qlib-rd_rd_exp_list` for the latest run in each experiment
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For this run, query:
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```text
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tac-qlib-rd_rd_exp_get_run(
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run_id="18db5bc1dc8f436fb84dddcbb9b36d61"
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)
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```
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I’ll use `rd_exp_get_run` for polling; `rd_exp_list` is only needed to discover the run ID.
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[assistant] The status query is working and the run has progressed: seed 42 completed, predictions/labels and portfolio artifacts exist, and the remaining ensemble seeds are still running. I’ll continue querying `rd_exp_get_run` rather than `rd_exp_list`; no new run is needed.
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[assistant] The run finished successfully. I’m now reading the final result record and closing experiment 22 with the recorded IC, Rank IC, and portfolio metrics.
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[assistant] Workflow completed and trace closed.
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- Trace experiment: `22`, evolved from `16`
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- MLflow run: `18db5bc1dc8f436fb84dddcbb9b36d61`
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- Status: `done`
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- Features: validated TA + SP partitions
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- IC: `0.0486`
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- Rank IC: `0.0617`
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- Excess return before costs: `+0.12%`
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- Excess return after costs: `-9.09%`
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- After-cost max drawdown: `-12.60%`
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- After-cost information ratio: `-0.80`
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Artifacts: `/home/data/lake/mlruns/24/18db5bc1dc8f436fb84dddcbb9b36d61`
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[user] run again without ta indicators and hmm and ou process features, just focus on using those general stochastic features to train the model
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[assistant] I’ll create a new traced descendant of experiment 22 using only non-TA stochastic features: jump, HAR/realized volatility, trend, Hurst, signature, and realized moments. HMM and OU fields will be excluded, while the model, label, windows, universe, strategy, and Postgres-backed tracking remain unchanged.
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[assistant] The validation gate is clean for the same 50-symbol panel, and trace `23` is open as a descendant of `22`. I’m writing the reduced workflow with 39 non-TA stochastic fields, excluding every `sp_hmm_*` and `sp_ou_*` field.
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[assistant] The reduced workflow is running as MLflow experiment `25`, run `be5cd314508b44ec824ac9cf4a917a19`. It is using the same Postgres tracking backend and will be polled until completion before I finish trace `23`.
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[assistant] The reduced run is healthy but still in its long training phase; it has not emitted partial metrics or artifacts yet. This ensemble implementation trains five seeds serially, so I’m continuing to poll the existing run and will not start a duplicate.
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[assistant] The ablation workflow is running:
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- Trace: `23`, descendant of `22`
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- MLflow experiment: `25`
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- Run: `be5cd314508b44ec824ac9cf4a917a19`
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- Features: raw OHLCV plus jump, realized volatility/HAR, trend, Hurst, signature, and realized moments
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- Excluded: all TA, HMM, and OU features
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- Status: `RUNNING`
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The workflow is still in training and has not produced final metrics yet.
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[user] it's done, continue
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[assistant] I’ll verify the terminal run status, inspect the ablation metrics, and close trace 23 with the final evaluation.
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[user] query status again
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[assistant] The ablation completed and trace `23` is closed.
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- Run: `be5cd314508b44ec824ac9cf4a917a19`
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- Features: stochastic only, excluding TA, HMM, and OU
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- IC: `0.0728`
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- Rank IC: `0.0621`
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- ICIR: `0.3402`
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- Rank ICIR: `0.2057`
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- Excess return before costs: `+6.73% annualized`
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- Excess return after costs: `-2.39% annualized`
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- After-cost information ratio: `-0.22`
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- After-cost max drawdown: `-12.90%`
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Compared with experiment 22, predictive IC improved substantially and after-cost performance improved from `-9.09%` to `-2.39%`.
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[user] did you trace the experiment run? I didnt see in /rd
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[assistant] I’ll verify both sides of the linkage: the `rd_experiments` trace row and the Postgres MLflow run metadata. If the trace exists but the UI omits it, I’ll identify whether the issue is the UI’s experiment filter or the run/trace reference.
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[assistant] Yes. The run is traced and linked:
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- Trace ID: `23`
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- MLflow experiment ID: `25`
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- Run ID: `be5cd314508b44ec824ac9cf4a917a19`
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- Trace status: `done`
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- MLflow status: `FINISHED`
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The IDs diverged this time. Open the R&D pages with `expId=25`, not `expId=23`:
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```text
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/rd/input?expId=25&run=be5cd314508b44ec824ac9cf4a917a19
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/rd/result?expId=25&run=be5cd314508b44ec824ac9cf4a917a19
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```
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Trace ID `23` is what appears in lineage. Refresh the `/rd` dashboard if it was already open.
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[user] let's start a new run in expId=25 again with the following features:
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$open
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$high
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$low
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$close
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$vwap
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$volume
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$sp_ret
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$sp_jump_ratio
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$sp_jump_flag
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$sp_jump_tail
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$sp_max_move
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$sp_rv1
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$sp_rv5
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$sp_rv22
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$sp_vol_ratio_5_22
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$sp_vol_ratio_1_22
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$sp_trend_slope_5
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$sp_trend_slope_20
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$sp_trend_slope_60
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$sp_logp
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$sp_hurst_exponent
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$sp_sig_level1_lead
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$sp_sig_level1_lag
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$sp_sig_level2_lead_lag
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$sp_sig_level2_lag_lead
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[assistant] I’ll launch this as a new run inside MLflow experiment `25`, while opening a new lineage step descended from trace `23`. The workflow will use exactly the 24 requested fields and keep the same model, label, dates, portfolio strategy, and Postgres tracking configuration.
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[assistant] Started the new run in MLflow experiment `25`.
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- Run ID: `fe469a192bbd4372b69306f196d7fc7c`
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- Trace ID: `24`, descended from trace `23`
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- Status: `RUNNING`
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- Exact requested 25-field feature set is recorded
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- TA, HMM, OU, and extra moment features are excluded
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- Tracking uses Postgres via `DATABASE_URL`
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Workflow: `/app/experiments/workflows/exp24/workflow.yaml`
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[user] get exp status and continue
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[assistant] I’ll check run `fe469a192bbd4372b69306f196d7fc7c` now and, if terminal, record its metrics and close trace `24`.
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[assistant] The run is active and progressing: seed 42 has started logging training metrics and prediction/backtest artifacts are present, but the five-seed ensemble has not reached terminal status yet. I’m continuing with the same run rather than treating those partial artifacts as final.
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[assistant] Run completed and trace `24` is closed.
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- MLflow experiment: `25`
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- Run ID: `fe469a192bbd4372b69306f196d7fc7c`
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- IC: `0.0511`
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- Rank IC: `0.0663`
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- ICIR: `0.2179`
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- Rank ICIR: `0.2545`
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- Excess return before costs: `+5.99%`
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- Excess return after costs: `-3.21%`
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- After-cost information ratio: `-0.32`
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- After-cost max drawdown: `-9.17%`
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The exact requested feature set was used.
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[user] can you study the series of runs:
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1. expId=25&run=fe469a192bbd4372b69306f196d7fc7c
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2. expId=25&run=be5cd314508b44ec824ac9cf4a917a19
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3. expId=24
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and the data/facts to anlayse what's next change to a new experiment run?
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[assistant] I’ll compare the three runs on identical windows, feature sets, model behavior, signal metrics, and backtest costs, then check the trace lineage and lake validation facts before recommending one next experiment change.
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[assistant] The three runs are all on the same 50-ETF universe, label, dates, model, and strategy, so this is a clean feature ablation. The headline pattern is already clear: removing TA/HMM/OU improved the signal materially; I’m checking blotter turnover and cost drag to distinguish a feature-quality problem from an execution/portfolio problem before recommending the next run.
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[assistant] The evidence separates the problem cleanly: the lake is healthy, TA/HMM/OU are not needed for signal quality, and the remaining weakness is turnover/cost conversion. I’m consolidating the comparison into one controlled next-run proposal rather than recommending another broad feature sweep.
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[assistant] **Findings**
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All three runs use the same 50-ETF universe, 5-day label, train/valid/test windows, RankIC ensemble, and TopkDropout strategy. Lake validation is clean: no missing bars, TA/SP partitions, alignment, or all-NaN issues.
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| Run | Features | IC | Rank IC | Gross excess | Net excess | Net IR | Cost | Trades |
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|---|---|---:|---:|---:|---:|---:|---:|---:|
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| `18db5...` / exp24 | TA + broad SP + HMM + OU + moments | 0.0486 | 0.0617 | 0.12% | -9.09% | -0.80 | $60.5k | 586 |
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| `be5cd...` / exp25 | Broad stochastic, no TA/HMM/OU | **0.0728** | 0.0621 | **6.73%** | **-2.39%** | **-0.22** | $62.1k | 576 |
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| `fe469...` / exp25 | Compact requested stochastic set | 0.0511 | **0.0663** | 5.99% | -3.21% | -0.32 | $60.7k | 588 |
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**Interpretation**
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- TA plus HMM/OU materially diluted the signal.
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- The broad stochastic set has the best overall economic result.
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- The compact set has the best Rank IC, but this did not translate into better portfolio returns.
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- Gross excess return is positive, but approximately `$60k` of costs consumes nearly all of it.
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- Turnover is the dominant remaining bottleneck, not missing features or lake quality.
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**Next Experiment**
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Use the broad stochastic feature set from `be5cd314508b44ec824ac9cf4a917a19` and change only:
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```yaml
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strategy:
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kwargs:
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topk: 10
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n_drop: 1
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```
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This tests whether reducing daily forced replacements from `2` to `1` preserves the signal while lowering the approximately `$60k` cost drag. Keep the model, features, universe, dates, label, and benchmark unchanged for a clean comparison.
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[user] or also reduce topk, also does the previous dataset study give any idea?
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What's actually predictive on clean data (h=5, most reliable — non-overlapping)
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Feature class Feature IC 2025 IC 2026H Stable?
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Mean reversion sp_ou_zscore −0.151 −0.131 ✓ robust
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Drift (60d) sp_trend_slope_60 −0.148 −0.178 ✓ robust
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Drift (20d) sp_trend_slope_20 −0.120 −0.112 ✓
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Signature (lead) sp_sig_level1_lead/lag −0.12/−0.10 −0.11/−0.10 ✓
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Jump/asymmetry sp_max_up / sp_max_down +0.152 / +0.143 +0.060 / +0.058 ⚠ half-decayed
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Regime sp_hmm_p_regime1 +0.097 +0.151 ⚠ sign-inconsistent (only ~60% of names agree)
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Vol sp_vol_ratio_5_22 +0.011 +0.103 ⚠ new in 2026
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At h=10/20 the signal strengthens a lot (trend_slope_60→20d hits −0.43, 84% of symbols same sign in 2026H), but those windows overlap so the magnitude is inflated — treat h=5 as the trustworthy measure.
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What this means for your goal
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Feasible — but the edge is mean-reversion, and it's per-symbol time-series, not what the name "drift" suggests.
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Mean reversion is the real, stable edge. ou_zscore and trend_slope_60 are consistently negative at 5–20d in both years with strong sign agreement — i.e., overbought → buy weakness, sell strength. This is exactly a stochastic-process (OU/zscore) signal, and it's the one thing the dataset rewards.
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"Drift" at this horizon is negative, not momentum. 60-day trend predicts reversal in the next 5–20d. Classic trend-following is NOT present at daily/5d horizons on these ETFs (it'd show up at 1–12m, which is beyond this pipeline's 5d label). If you want drift-following, you need a longer label.
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Jump signal is real but unstable — strong in 2025 (+0.15), halved in 2026 (+0.06). Not reliable standalone; usable as a confirming input at best.
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Regime posterior has the highest nominal IC (+0.15) but only ~60% of symbols share the sign — the state↔direction mapping is per-symbol. It needs per-symbol calibration and careful walk-forward validation, otherwise it's exactly the kind of thing that overfits.
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Vol features (vol_ratio_5_22, rv_ac1) are modest-positive — better used for position sizing/risk than direction.
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[assistant] Yes, the previous clean-data study gives a strong next-feature hypothesis.
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**Topk**
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I would not reduce `topk` as the next change.
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- Lower `topk` increases concentration and drawdown risk.
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- It may not materially reduce costs because each replacement still trades roughly the same notional.
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- `n_drop: 1` is the cleaner turnover test.
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- If diversifying, test `topk: 20`, not lower `topk`.
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**Most Important Finding**
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The prior study identifies `sp_ou_zscore` as the strongest stable 5-day predictor:
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- 2025 IC: `-0.151`
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- 2026H IC: `-0.131`
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- Stable across periods
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The current three runs all excluded OU features. Their best signal came from non-OU stochastic fields, but the study indicates we left the most reliable feature out.
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`sp_trend_slope_60` is already present and consistently negative, confirming mean reversion rather than momentum. Jump features should remain secondary because their effect decayed.
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**Recommended Next Run**
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Change only the feature set by adding:
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```text
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sp_ou_zscore
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```
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Start from the compact run `fe469...`, keep:
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- `topk: 10`
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- `n_drop: 2`
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- Same model and dates
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- Same universe and label
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- Same costs and benchmark
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This isolates whether the stable OU mean-reversion edge improves the model.
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After that, run a separate strategy-only test with the best feature set and:
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```yaml
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n_drop: 1
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```
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That separates predictive improvement from turnover improvement. The prior single-feature IC study is useful for selecting features, but its per-symbol time-series IC is not directly comparable to the model’s cross-sectional Rank IC.
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[user] go ahead to these two runs and compare results
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[assistant] I’ll run the two controlled comparisons in the same MLflow experiment `25`:
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1. Compact stochastic features plus `sp_ou_zscore`, `n_drop=2`.
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2. The identical feature/model setup with only `n_drop=1`.
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This isolates the OU hypothesis and then the turnover change. Each run will have its own trace descendant of trace `24`.
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[assistant] Traces `25` and `26` are open. Both workflows will target the existing MLflow experiment name `tac-rd-exp22-stochastic-general` so the resulting runs remain under MLflow experiment ID `25`; the trace IDs remain separate for lineage and comparison.
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[assistant] The two trace branches were opened concurrently, and the shared experiment worktree caused both workflow files to land in branch 26 while branch 25 remained without its workflow. I’m correcting that branch bookkeeping before launching either run so both traces remain reproducible and separate.
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[assistant] The two workflow calls were accepted, but the MLflow experiment listing has not yet shown the new run rows, likely because both background jobs are still initializing. I’m checking experiment 25 again before assuming either run ID.
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[assistant] Both new rows are now visible in MLflow experiment `25`:
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- OU run: `650850ca04d94307a048fa434b3370ec`
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- `n_drop=1` run: `cfd98db8907d49c9b8c336c7f999ac25`
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Both are currently `RUNNING`; the OU run has the expected `sp_ou_zscore` configuration, while the second is still initializing.
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[assistant] Both controlled runs are launched in MLflow experiment `25` and are still running:
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- OU feature run: `650850ca04d94307a048fa434b3370ec`
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- Compact features plus `sp_ou_zscore`
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- `n_drop=2`
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- Turnover run: `cfd98db8907d49c9b8c336c7f999ac25`
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- Compact features unchanged
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- `n_drop=1`
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Both traces are open separately as trace `25` and trace `26`. Final comparison and trace closure will follow once the runs reach `FINISHED`.
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[user] Check exp status
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[user] Both runs are still `RUNNING`.
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- OU feature run: `650850ca04d94307a048fa434b3370ec`
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- Configuration recorded correctly with `sp_ou_zscore`
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- Still initializing/training
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- `n_drop=1` run: `cfd98db8907d49c9b8c336c7f999ac25`
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- Still initializing
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- No metrics or artifacts yet
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Both remain active in MLflow experiment `25`; no trace has been closed prematurely.
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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.
|
||
|
||
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.
|
||
|
||
## What to extract
|
||
|
||
Look for the highest-signal facts for an agent working in this repo:
|
||
- exact developer commands, especially non-obvious ones
|
||
- how to run a single test, a single package, or a focused verification step
|
||
- required command order when it matters, such as `lint -> typecheck -> test`
|
||
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
|
||
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
|
||
- repo-specific style or workflow conventions that differ from defaults
|
||
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
|
||
- important constraints from existing instruction files worth preserving
|
||
|
||
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
|
||
|
||
## Questions
|
||
|
||
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
|
||
|
||
Good questions:
|
||
- undocumented team conventions
|
||
- branch / PR / release expectations
|
||
- missing setup or test prerequisites that are known but not written down
|
||
|
||
Do not ask about anything the repo already makes clear.
|
||
|
||
## Writing rules
|
||
|
||
Include only high-signal, repo-specific guidance such as:
|
||
- exact commands and shortcuts the agent would otherwise guess wrong
|
||
- architecture notes that are not obvious from filenames
|
||
- conventions that differ from language or framework defaults
|
||
- setup requirements, environment quirks, and operational gotchas
|
||
- references to existing instruction sources that matter
|
||
|
||
Exclude:
|
||
- generic software advice
|
||
- long tutorials or exhaustive file trees
|
||
- obvious language conventions
|
||
- speculative claims or anything you could not verify
|
||
- content better stored in another file referenced via `opencode.json` `instructions`
|
||
|
||
When in doubt, omit.
|
||
|
||
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.
|
||
|
||
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.
|
||
|
||
[user] Check exp status
|
||
|
||
[assistant] ## Objective
|
||
- Run two controlled comparison experiments in MLflow experiment 25 (lineage traces 25 and 26): (1) compact stochastic features + `sp_ou_zscore` with `topk=10, n_drop=2`, and (2) the exact compact stochastic feature set with only `n_drop=1`. Then poll both to completion, compare against baseline run `fe469a192bbd4372b69306f196d7fc7c`, and close both traces.
|
||
|
||
## Important Details
|
||
- Tracking must use Postgres resolved from `DATABASE_URL` (postgresql+psycopg://postgres:...@192.168.1.96:5555/tradeac). Never write the credential-bearing URL into files; `sqlite:///mlruns.db` in YAML `exp_manager` is only the server-normalized placeholder and resolves to Postgres at runtime.
|
||
- MLflow experiment name for all runs: `tac-rd-exp22-stochastic-general` (= MLflow experiment ID 25). Trace IDs (22–26) diverge from MLflow experiment IDs (24, 25, 25, 25, 25).
|
||
- Reference lineage: trace 22 (evolved from 16) → 23 → 24 → {25, 26}; both traces 25 and 26 evolved from trace 24.
|
||
- Baseline comparison run (compact stochastic, `n_drop=2`): `fe469a192bbd4372b69306f196d7fc7c` — IC 0.0511, Rank IC 0.0663, gross excess +5.99%, net -3.21%, net IR -0.32, cost $60,653, 588 trades.
|
||
- Other compared runs: exp24/`18db5bc1dc8f436fb84dddcbb9b36d61` (TA+SP+HMM+OU+moments; net -9.09%); exp25/`be5cd314508b44ec824ac9cf4a917a19` (broad stochastic; IC 0.0728, net -2.39%, best result).
|
||
- Study finding (user-supplied): on clean non-overlapping h=5 data, `sp_ou_zscore` is the most stable predictor (IC -0.151 in 2025, -0.131 in 2026H, mean reversion) but was excluded from all three previous runs. `sp_hmm_p_regime1` and jump features are unstable/secondary.
|
||
- User agreed with the agent’s plan: keep `topk=10` (do not reduce topk), isolate `sp_ou_zscore` addition first, then n_drop change separately.
|
||
- Standard workflow settings (unchanged across runs): 50-ETF universe; label `Ref($close,-6)/Ref($close,-1)-1`; train 2016-01-04→2025-09-01, valid 2025-09-03→2026-01-03, test 2026-01-04→2026-08-10; RankICEnsembleLGBModel seeds 42,7,2026,99,123, lr 0.02, 3000 rounds; TopkDropout topk 10 n_drop 2 (exp25 adds OU, exp26 n_drop=1); SPY benchmark; open_cost 0.0005, close_cost 0.0015, min_cost
|
||
|
||
[user] Check exp status
|
||
|
||
[user] the two workflows started. get their status using mcp tools
|
||
|
||
[assistant] ## Objective
|
||
- Continue two controlled ablation runs the user requested ("go ahead to these two runs and compare results"): (1) compact stochastic features + `sp_ou_zscore` with `n_drop=2`, and (2) identical compact features with `n_drop=1`. Both run in MLflow experiment 25 and must be compared against the three prior runs, then their traces finished.
|
||
|
||
## Important Details
|
||
- All runs use Postgres MLflow tracking resolved from `DATABASE_URL` (Postgres `192.168.1.96:5555/tradeac`); the `sqlite:///mlruns.db` string in workflow YAMLs is only a normalization placeholder. Never write the credential-bearing `DATABASE_URL` string into files (secrets policy).
|
||
- Lake root `TAC_LAKE_DIR=/home/data/lake`; validation clean: 50/50 symbols have bars + TA + SP partitions, no alignment/all-NaN issues. 80 symbols total; some non-ETF symbols only start late 2025.
|
||
- Reference run: expId=16, run `0cea66d9892246519bdf329a0410a277` (Original: sqlite-era full TA+SP 24-feature run).
|
||
- Model fixed across all runs: `RankICEnsembleLGBModel` (`tac_qlib.contrib.model.rank_ensemble`), seeds `42,7,2026,99,123`, lr 0.02, num_leaves 31, 3000 rounds, early stop 200, min_data_in_leaf 20.
|
||
- Dataset fixed: 50-ETF universe (`SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM`), label `Ref($close,-6)/Ref($close,-1)-1`, train 2016-01-04→2025-09-01, valid 2025-09-03→2026-01-03, test 2026-01-04→2026-08-10, TopkDropout topk 10, SPY benchmark, open_cost 0.0005 / close_cost 0.0015 / min_cost 5.0.
|
||
- Trace ID ≠ MLflow experiment ID (user was told to open `/rd/*?expId=25`, not trace ID).
|
||
- Runs take ~35-60 min; ensemble partially logs artifacts before terminal status — only treat `FINISHED` as final.
|
||
- User-provided clean-data study (h=5, non-overlapping): `sp_ou_zscore` IC −0.151 (2025) / −0.131 (2026H) robust; `sp_trend_slope_60` −0.148/−0.178; `sp_sig_level1_lead/lag` ≈ −0.12/−0.10 both years; jump `sp_max_up/down` decayed (+0.15→+0.06); `sp_hmm_p_regime1` sign-inconsistent (~60% names); `sp_vol_ratio_5_22` +0.011→+0.103 (new 2026). Edge is mean-reversion, per-symbol time-series, not comparable directly to CS Rank IC.
|
||
- Decision rationale: do NOT reduce topk next (concentration/drawdown risk; notional per trade similar); `n_drop=1` is the cleaner turnover test; `sp_ou_zscore` is the stable feature hypothesized to improve signal.
|
||
|
||
## Work State
|
||
### Completed
|
||
- Trace 22 (`tac-rd-exp16-db-ta-sp`, evolved_from 16) → MLflow exp 24, run `18db5bc1dc8f436fb84dddcbb9b36d61`, FINISHED, trace closed. Full TA+SP features. IC 0.04863, RankIC 0.06171, gross excess +0.12%, net −9.09%, after-cost IR −0.80, MDD −12.60%, 586 trades, cost $60.5k, final acct $1,065,003.
|
||
- Trace 23 (`tac-rd-exp22-stochastic-general`, from 22, branch `exp/23-...`) → MLflow exp 25, run `be5cd314508b44ec824ac9cf4a917a19`, FINISHED, trace closed. Broad stochastic (no TA/HMM/OU, incl. moments/dsv/rv_ac1/rv_cv/rkurt/rskew). IC 0.07279, RankIC 0.06212, ICIR 0.34021, gross +6.73%, net −2.39%, IR −0.22, MDD −12.90%, 576 trades, cost $62.1k, final acct $1,109,749. Best economic result.
|
||
- Trace 24 (from 23, branch `exp/24-run-the
|
||
|
||
[user] get status of OU feature run: 650850ca04d94307a048fa434b3370ec
|
||
Compact features plus sp_ou_zscore
|
||
n_drop=2
|
||
Turnover run: cfd98db8907d49c9b8c336c7f999ac25
|
||
Compact features unchanged
|
||
n_drop=1
|
||
|
||
[user] ## Objective
|
||
- Run two controlled follow-up experiments in MLflow experiment 25, comparing results against the prior runs: (1) compact stochastic features + `sp_ou_zscore` with `n_drop=2`, and (2) compact stochastic features with `n_drop=1`.
|
||
- Finish both traces (25 and 26) and compare the new results to runs `fe469a…`, `be5cd…`, and `18db5…`.
|
||
|
||
## Important Details
|
||
- MLflow tracking backend = Postgres from `DATABASE_URL` (`postgresql+psycopg://postgres:…@192.168.1.96:5555/tradeac`); YAMLs keep non-secret `sqlite:///mlruns.db` placeholder; never write credentials into files.
|
||
- Trace IDs and MLflow experiment IDs diverge: traces 23–26 all map to MLflow experiment 25 (`tac-rd-exp22-stochastic-general`). UI uses `expId=25&run=<run_id>`, not the trace ID.
|
||
- Model: `RankICEnsembleLGBModel` (tac_qlib.contrib.model.rank_ensemble), seeds `42,7,2026,99,123`, lr 0.02, 3000 rounds, early_stopping 200. Universe = 50 ETFs (SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC + sector/theme ETFs incl. ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM).
|
||
- Label `Ref($close,-6)/Ref($close,-1)-1`; train 2016-01-04–2025-09-01, valid 2025-09-03–2026-01-03, test 2026-01-04–2026-08-10.
|
||
- Strategy: TopkDropout topk=10; trace-25 run keeps `n_drop=2`, trace-26 run uses `n_drop=1`; SPY benchmark; costs open 0.0005 / close 0.0015 / min 5.0.
|
||
- Compact feature set (exact user list, both new runs): `$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead`; exp25 adds `sp_ou_zscore`.
|
||
- Prior dataset-study facts guiding the runs: `sp_ou_zscore` is the strongest stable 5-day predictor (IC −0.151 in 2025, −0.131 in 2026H, robust); `sp_trend_slope_60` consistently negative (mean reversion, not momentum); jump features half-decayed; `sp_hmm_p_regime1` sign
|
||
|
||
[user] Create or update `AGENTS.md` for this repository.
|
||
|
||
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.
|
||
|
||
User-provided focus or constraints (honor these):
|
||
|
||
|
||
## How to investigate
|
||
|
||
Read the highest-value sources first:
|
||
- `README*`, root manifests, workspace config, lockfiles
|
||
- build, test, lint, formatter, typecheck, and codegen config
|
||
- CI workflows and pre-commit / task runner config
|
||
- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
|
||
- repo-local OpenCode config such as `opencode.json`
|
||
|
||
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.
|
||
|
||
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.
|
||
|
||
## What to extract
|
||
|
||
Look for the highest-signal facts for an agent working in this repo:
|
||
- exact developer commands, especially non-obvious ones
|
||
- how to run a single test, a single package, or a focused verification step
|
||
- required command order when it matters, such as `lint -> typecheck -> test`
|
||
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
|
||
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
|
||
- repo-specific style or workflow conventions that differ from defaults
|
||
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
|
||
- important constraints from existing instruction files worth preserving
|
||
|
||
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
|
||
|
||
## Questions
|
||
|
||
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
|
||
|
||
Good questions:
|
||
- undocumented team conventions
|
||
- branch / PR / release expectations
|
||
- missing setup or test prerequisites that are known but not written down
|
||
|
||
Do not ask about anything the repo already makes clear.
|
||
|
||
## Writing rules
|
||
|
||
Include only high-signal, repo-specific guidance such as:
|
||
- exact commands and shortcuts the agent would otherwise guess wrong
|
||
- architecture notes that are not obvious from filenames
|
||
- conventions that differ from language or framework defaults
|
||
- setup requirements, environment quirks, and operational gotchas
|
||
- references to existing instruction sources that matter
|
||
|
||
Exclude:
|
||
- generic software advice
|
||
- long tutorials or exhaustive file trees
|
||
- obvious language conventions
|
||
- speculative claims or anything you could not verify
|
||
- content better stored in another file referenced via `opencode.json` `instructions`
|
||
|
||
When in doubt, omit.
|
||
|
||
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.
|
||
|
||
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.
|
||
|
||
[user] get status of OU feature run: 650850ca04d94307a048fa434b3370ec
|
||
Compact features plus sp_ou_zscore
|
||
n_drop=2
|
||
Turnover run: cfd98db8907d49c9b8c336c7f999ac25
|
||
Compact features unchanged
|
||
n_drop=1
|
||
|
||
[assistant] ## Objective
|
||
- Run and compare two controlled experiments in MLflow experiment `25`: compact stochastic features plus `sp_ou_zscore`, and the same setup with `n_drop=1`.
|
||
|
||
## Important Details
|
||
- Same 50-ETF universe, 5-day label, dates, model, benchmark, costs, and Postgres tracking via `DATABASE_URL`.
|
||
- OU run: compact features + `sp_ou_zscore`, `topk=10`, `n_drop=2`.
|
||
- Turnover run: compact features without OU, `topk=10`, `n_drop=1`.
|
||
- Compact feature baseline run: `fe469a192bbd4372b69306f196d7fc7c`.
|
||
- Prior broad stochastic run: `be5cd314508b44ec824ac9cf4a917a19`.
|
||
- Lake validation passed: 50/50 bars, TA, SP, aligned partitions, no warnings/errors.
|
||
|
||
## Work State
|
||
### Completed
|
||
- Opened trace `25`, descendant of trace `24`, for OU hypothesis.
|
||
- Opened trace `26`, descendant of trace `24`, for `n_drop=1`.
|
||
- Created workflows:
|
||
- `/app/experiments/workflows/exp25/workflow.yaml`
|
||
- `/app/experiments/workflows/exp26/workflow.yaml`
|
||
- Started both workflows in MLflow experiment `25`.
|
||
- OU run ID: `650850ca04d94307a048fa434b3370ec`; status was `RUNNING`, configuration includes `sp_ou_zscore`.
|
||
- `n_drop=1` run ID: `cfd98db8907d49c9b8c336c7f999ac25`; status was `RUNNING`, still had no training params/metrics at last poll.
|
||
- Workflow configuration commits were made on experiment branches, though branch handling overlapped.
|
||
|
||
### Active
|
||
- Poll both runs with `tac-qlib-rd_rd_exp_get_run`.
|
||
- Once terminal, retrieve metrics with `tac-qlib-rd_rd_exp_result`.
|
||
- Retrieve blotter summaries with `tac-qlib-rd_rd_exp_blotter`.
|
||
- Finish traces `25` and `26` using `tac-qlib-rd_rd_trace_finish`.
|
||
- Compare signal quality, cost drag, turnover, and drawdown against baseline `fe469...`.
|
||
|
||
### Blocked
|
||
- Shared `experiments` worktree caused workflow files for exp25/exp26 to overlap across branches; verify branch contents before final trace closure.
|
||
- Exact terminal status/results for runs `650850ca04d94307a048fa434b3370ec` and `cfd98db8907d49c9b8c336c7f999ac25` are not yet known.
|
||
|
||
## Next Move
|
||
1. Query both run IDs with `tac-qlib-rd_rd_exp_get_run`.
|
||
2. For finished runs, fetch results/blotters, compare, and close traces `25` and `26`.
|
||
|
||
## Relevant Files
|
||
- `/app/experiments/workflows/exp25/workflow.yaml`: compact stochastic features plus `sp_ou_zscore`, `n_drop=2`.
|
||
- `/app/experiments/workflows/exp26/workflow.yaml`: compact stochastic features, `n_drop=1`.
|
||
- `/home/data/lake/mlruns/25/650850ca04d94307a048fa434b3370ec`: OU run artifacts.
|
||
- `/home/data/lake/mlruns/25/cfd98db8907d49c9b8c336c7f999ac25`: turnover run artifacts.
|
||
- `/app/experiments`: experiment trace repository/worktree.
|
||
|
||
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
|
||
|
||
[assistant] I’ll poll both active runs now. If either has finished, I’ll collect results and close its trace; I’ll also inspect the repository guidance sources and create the requested compact `AGENTS.md` without embedding any secrets.
|
||
|
||
[assistant] Both runs remain `RUNNING`; the OU run has a fully recorded configuration, while the `n_drop=1` run is still at initialization. I’m adding the repository instructions now, limited to verified commands, package boundaries, MCP/lake workflow, and trace-worktree gotchas.
|
||
|
||
[assistant] Both runs are still active in MLflow experiment `25`:
|
||
|
||
- OU feature run `650850ca04d94307a048fa434b3370ec`: `RUNNING`, configuration includes `sp_ou_zscore`.
|
||
- Turnover run `cfd98db8907d49c9b8c336c7f999ac25`: `RUNNING`, still initializing with no metrics yet.
|
||
|
||
No trace has been closed prematurely. I also created `/app/AGENTS.md` with the verified repository workflow and R&D tracing guidance.
|
||
|
||
[user] rd_exp_get_run TOOL again
|
||
|
||
[assistant] `rd_exp_get_run` confirms both remain `RUNNING`:
|
||
|
||
- OU feature run `650850ca04d94307a048fa434b3370ec`: configuration recorded with `sp_ou_zscore`; no final metrics yet.
|
||
- Turnover run `cfd98db8907d49c9b8c336c7f999ac25`: still initializing; no training metrics yet.
|
||
|
||
Both are active under MLflow experiment `25`.
|
||
|
||
[user] the concurrent experiments took 2 hours and never finish. restart them one by one
|
||
|
||
[assistant] The MCP surface has no workflow-cancel operation. Both orphaned runs are still marked `RUNNING`, so restarting now would leave them competing for the same R&D worker.
|
||
|
||
Should I terminate the existing background workflow processes at the OS level, then restart the OU run first and the `n_drop=1` run only after it finishes?
|
||
|
||
[user] I restarted the rd_server. so there should be no OS background workflow processes anymore. you can create new OU run to avoid confusion
|
||
|
||
[user] I restarted the rd_server. so there should be no OS background workflow processes anymore. you can create new OU run to avoid confusion
|
||
|
||
[user] I restarted the rd_server. so there should be no OS background workflow processes anymore. you can create new OU run to avoid confusion
|
||
|
||
[assistant] Create or update `AGENTS.md` for this repository.
|
||
|
||
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.
|
||
|
||
User-provided focus or constraints (honor these):
|
||
|
||
|
||
## How to investigate
|
||
|
||
Read the highest-value sources first:
|
||
- `README*`, root manifests, workspace config, lockfiles
|
||
- build, test, lint, formatter, typecheck, and codegen config
|
||
- CI workflows and pre-commit / task runner config
|
||
- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
|
||
- repo-local OpenCode config such as `opencode.json`
|
||
|
||
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.
|
||
|
||
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.
|
||
|
||
## What to extract
|
||
|
||
Look for the highest-signal facts for an agent working in this repo:
|
||
- exact developer commands, especially non-obvious ones
|
||
- how to run a single test, a single package, or a focused verification step
|
||
- required command order when it matters, such as `lint -> typecheck -> test`
|
||
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
|
||
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
|
||
- repo-specific style or workflow conventions that differ from defaults
|
||
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
|
||
- important constraints from existing instruction files worth preserving
|
||
|
||
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
|
||
|
||
## Questions
|
||
|
||
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
|
||
|
||
Good questions:
|
||
- undocumented team conventions
|
||
- branch / PR / release expectations
|
||
- missing setup or test prerequisites that are known but not written down
|
||
|
||
Do not ask about anything the repo already makes clear.
|
||
|
||
## Writing rules
|
||
|
||
Include only high-signal, repo-specific guidance such as:
|
||
- exact commands and shortcuts the agent would otherwise guess wrong
|
||
- architecture notes that are not obvious from filenames
|
||
- conventions that differ from language or framework defaults
|
||
- setup requirements, environment quirks, and operational gotchas
|
||
- references to existing instruction sources that matter
|
||
|
||
Exclude:
|
||
- generic software advice
|
||
- long tutorials or exhaustive file trees
|
||
- obvious language conventions
|
||
- speculative claims or anything you could not verify
|
||
- content better stored in another file referenced via `opencode.json` `instructions`
|
||
|
||
When in doubt, omit.
|
||
|
||
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.
|
||
|
||
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.
|
||
|
||
[user] I restarted the rd_server. so there should be no OS background workflow processes anymore. you can create new OU run to avoid confusion
|
||
|
||
[assistant] Create or update `AGENTS.md` for this repository.
|
||
|
||
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.
|
||
|
||
User-provided focus or constraints (honor these):
|
||
|
||
|
||
## How to investigate
|
||
|
||
Read the highest-value sources first:
|
||
- `README*`, root manifests, workspace config, lockfiles
|
||
- build, test, lint, formatter, typecheck, and codegen config
|
||
- CI workflows and pre-commit / task runner config
|
||
- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)
|
||
- repo-local OpenCode config such as `opencode.json`
|
||
|
||
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.
|
||
|
||
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.
|
||
|
||
## What to extract
|
||
|
||
Look for the highest-signal facts for an agent working in this repo:
|
||
- exact developer commands, especially non-obvious ones
|
||
- how to run a single test, a single package, or a focused verification step
|
||
- required command order when it matters, such as `lint -> typecheck -> test`
|
||
- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints
|
||
- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow
|
||
- repo-specific style or workflow conventions that differ from defaults
|
||
- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites
|
||
- important constraints from existing instruction files worth preserving
|
||
|
||
Good `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.
|
||
|
||
## Questions
|
||
|
||
Only ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.
|
||
|
||
Good questions:
|
||
- undocumented team conventions
|
||
- branch / PR / release expectations
|
||
- missing setup or test prerequisites that are known but not written down
|
||
|
||
Do not ask about anything the repo already makes clear.
|
||
|
||
## Writing rules
|
||
|
||
Include only high-signal, repo-specific guidance such as:
|
||
- exact commands and shortcuts the agent would otherwise guess wrong
|
||
- architecture notes that are not obvious from filenames
|
||
- conventions that differ from language or framework defaults
|
||
- setup requirements, environment quirks, and operational gotchas
|
||
- references to existing instruction sources that matter
|
||
|
||
Exclude:
|
||
- generic software advice
|
||
- long tutorials or exhaustive file trees
|
||
- obvious language conventions
|
||
- speculative claims or anything you could not verify
|
||
- content better stored in another file referenced via `opencode.json` `instructions`
|
||
|
||
When in doubt, omit.
|
||
|
||
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.
|
||
|
||
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.
|
||
|
||
[user] I restarted the rd_server. so there should be no OS background workflow processes anymore. you can create new OU run to avoid confusion
|
||
|
||
[assistant] ## Objective
|
||
- Complete the two controlled ablation runs in MLflow experiment 25 — (1) compact stochastic features + `sp_ou_zscore` with `n_drop=2` (trace 25) and (2) compact stochastic features with `n_drop=1` (trace 26) — then compare results to prior runs and close both traces.
|
||
- Also create `/app/AGENTS.md` (requested separately; file does not exist yet — investigation of repo sources has begun).
|
||
|
||
## Important Details
|
||
- Tracking backend is Postgres resolved from `DATABASE_URL` (`postgresql+psycopg://…@192.168.1.96:5555/tradeac`, credential-bearing — never write it into files). `sqlite:///mlruns.db` in workflow `exp_manager` YAMLs is only the non-secret placeholder the server normalizes to `DATABASE_URL`.
|
||
- Trace IDs ≠ MLflow experiment IDs: traces 22–26 map to MLflow exps 24, 25, 25, 25, 25. UI paths use `expId=25&run=<run_id>`.
|
||
- MLflow experiment 25 name: `tac-rd-exp22-stochastic-general`; lake root `TAC_LAKE_DIR=/home/data/lake` (80 symbols; 50-ETF validation clean: bars + TA + SP partitions aligned, 0 errors/warnings).
|
||
- Fixed model/setup across all runs: `RankICEnsembleLGBModel` (`tac_qlib.contrib.model.rank_ensemble`), seeds `42,7,2026,99,123`, lr 0.02, 3000 rounds, early_stopping 200; 50-ETF universe; label `Ref($close,-6)/Ref($close,-1)-1`; train 2016-01-04→2025-09-01, valid 2025-09-03→2026-01-03, test 2026-01-04→2026-08-10; TopkDropout topk=10; SPY benchmark; open_cost 0.0005 / close_cost 0.0015 / min_cost 5.0.
|
||
- Compact feature set (exact user list): `$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead`; exp25 adds `sp_ou_zscore`.
|
||
- User-provided clean-data study (h=5): `sp_ou_zscore` most stable predictor (IC −0.151 2025 / −0.131 2026H, mean-reversion); `sp_trend_slope_60` consistently negative; jump features half-decayed; `sp_hmm_p_regime1` sign-inconsistent. Agent decision: do NOT reduce topk; test OU addition and n_drop separately.
|
||
- Repo facts gathered for AGENTS.md: opencode MCP = `tac-engine` (Rust binary), `tac-qlib-rd` (`/app/.venv/bin/python -m tac_qlib.rd_server`, env TAC_LAKE_DIR + DATABASE_URL), `tac-rd-book`; skills paths `tac-engine/skills`, `tac-qlib/skills`; pnpm workspace `tac-app`; `tac-app` scripts: `dev` (engine:ensure + next dev), `build` (build:engine + next build), `lint`/`check` = biome; no lockfiles or `.github/workflows` found; `/app/experiments` is a git clone (remote `https://git.h.lizhao.net/zhaoli/tac-exp-dev.git`, branch-per-experiment).
|
||
|
||
## Work State
|
||
### Completed
|
||
- Traces 22/23/24 fully run, finished, and closed (metrics below).
|
||
- Reference exp 16 run inspected: `0cea66d9892246519bdf329a0410a277`.
|
||
- Trace 22 (`tac-rd-exp16-db-ta-sp`) → MLflow exp 24, run `18db5bc1dc8f436fb84dddcbb9b36d61` (full TA+SP+HMM+OU+moments): IC 0.0486, RankIC 0.0617, net excess −9.09%, net IR −0.80, MDD −12.60%, 586 trades, cost $60,477, final acct $1,065,003.
|
||
- Trace 23 (`tac-rd-exp22-stochastic-general`) → MLflow exp 25, run `be5cd314508b44ec824ac9cf4a917a19` (broad stochastic, no TA/HMM/OU): IC 0.0728, RankIC 0.0621, ICIR 0.3402, net excess −2.39%, IR −0.22, MDD −12.90%, 576 trades, cost $62,110, final acct $1,109,749 — best result.
|
||
- Trace 24 → MLflow exp 25, run `fe469a192bbd4372b69306f196d7fc7c` (compact 25-field set): IC 0.0511, RankIC 0.0663, ICIR 0.2179, net excess −3.21%, IR −0.32, MDD −9.17%, 588 trades, cost $60,653, final acct $1,105,711.
|
||
- Traces 25 and 26 opened (both evolved_from 24); workflows created `/app/experiments/workflows/exp25/workflow.yaml` (compact + `sp_ou_zscore`, n_drop=2) and `/app/experiments/workflows/exp26/workflow.yaml` (compact, n_drop=1); both started in MLflow exp 25.
|
||
- Fixed git branch overlap (both files landed in branch 26): `git switch exp/25-test-the-clean-data-hypothesis-that-addi && git cherry-pick 272bf39` → commit `dbc2813`; `rd_trace_commit(26)` succeeded, `rd_trace_commit(25)` returned "nothing to commit".
|
||
- AGENTS.md investigation started: globbed (no AGENTS.md), read `/app/opencode.json`, `/app/pnpm-workspace.yaml`, `/app/tac-qlib/README.md`, `/app/tac-app/package.json`; globbed root manifests (`/app/Cargo.toml`, `/app/tac-qlib/pyproject.toml`).
|
||
|
||
### Active
|
||
- Run `650850ca04d94307a048fa434b3370ec` (OU + compact, n_drop=2, trace 25) — status `RUNNING` at last check; config confirmed with `sp_ou_zscore`.
|
||
- Run `cfd98db8907d49c9b8c336c7f999ac25` (compact, n_drop=1, trace 26) — status `RUNNING` at last check; still no params/metrics/artifacts beyond code_* files.
|
||
- AGENTS.md drafting: more sources to read (e.g. `/app/tac-qlib/pyproject.toml`, `/app/Cargo.toml`, remaining README/skills) then write the file.
|
||
|
||
### Blocked
|
||
- Both MLflow runs not yet terminal; final results/trace closure pending.
|
||
- Verify exp25/exp26 git branch contents are correct before finishing both traces (shared worktree caused overlap earlier).
|
||
|
||
## Next Move
|
||
1. Poll both runs with `tac-qlib-rd_rd_exp_get_run` (`650850ca04d94307a048fa434b3370ec`, `cfd98db8907d49c9b8c336c7f999ac25`) until `FINISHED`.
|
||
2. For each finished run: fetch `rd_exp_result` + `rd_exp_blotter`, compare (IC/RankIC, gross vs net excess, cost/trades, MDD) against `fe469a…` / `be5cd…` / `18db5…`.
|
||
3. Finish traces with `tac-qlib-rd_rd_trace_finish` (ref_id = matching run ID per trace: 650850…→25, cfd98d…→26).
|
||
4. Finish AGENTS.md: read remaining instruction/root files, then write compact `/app/AGENTS.md` (MCP-first policy, secrets policy, lake/env setup, exp workflow pattern, trace-vs-MLflow ID divergence, dev commands; no credentials, no speculative claims).
|
||
|
||
## Relevant Files
|
||
- `/app/experiments/workflows/exp25/workflow.yaml`: OU test config (trace 25).
|
||
- `/app/experiments/workflows/exp26/workflow.yaml`: n_drop=1 config (trace 26).
|
||
- `/home/data/lake/mlruns/25/650850ca04d94307a048fa434b3370ec`: active OU run artifacts.
|
||
- `/home/data/lake/mlruns/25/cfd98db8907d49c9b8c336c7f999ac25`: active n_drop=1 run artifacts.
|
||
- `/home/data/lake/mlruns/25/fe469a192bbd4372b69306f196d7fc7c` and `/home/data/lake/mlruns/25/be5cd314508b44ec824ac9cf4a917a19`: comparison baselines.
|
||
- `/app/experiments`: trace git worktree (branch-per-experiment, remote `tac-exp-dev`).
|
||
- `/app/opencode.json`, `/app/pnpm-workspace.yaml`, `/app/tac-app/package.json`, `/app/tac-qlib/README.md`, `/app/tac-qlib/pyproject.toml`, `/app/Cargo.toml`: AGENTS.md source material.
|
||
|
||
[user] Continue if you have next steps, or stop and ask for clarification if you are unsure how to proceed.
|
||
|
||
[assistant] The container was redeployed — `AGENTS.md` and the `experiments/` worktree are gone. The rd_server is back up (Postgres tracking resolves). Let me assess what survived before recreating the workflows.
|
||
|
||
[assistant] Container redeploy wiped `AGENTS.md` and the `experiments/` worktree. Traces 25/26 are still open in Postgres (status `starting`); MLflow exp 25 still shows the stale `RUNNING` runs. Let me verify what survived on disk and find workflow templates to recreate the configs.
|
||
|
||
[assistant] Lake is intact — MLflow exp 25 artifacts for all 4 runs persist. The workflows were on the wiped `experiments/` worktree, but the canonical template survives in the repo. Let me read it plus the old run's recorded config to reconstruct the two workflows exactly.
|
||
|
||
[assistant] Now I'll reconstruct the two workflows. First let me check the experiment git setup that the trace flow needs, since the worktree was wiped.
|
||
|
||
[assistant] The lake data, MLflow artifacts, and Postgres trace rows all survived; only the container's ephemeral `experiments/` worktree and my earlier `AGENTS.md` were wiped. Traces 25 (OU) and 26 (n_drop=1) are still open in Postgres.
|
||
|
||
Plan: re-init the experiment repo, recreate both workflow YAMLs from the canonical template + the recorded config, launch the **OU run first** in a new process (fresh MLflow run to avoid the stale `RUNNING` IDs), wait for it, then run n_drop=1.
|
||
|
||
[assistant] The experiment repo is back with all 25 remote branches, including exp/25 and exp/26. The workflow files were previously committed to those branches. Let me check if the branch contents survived remotely so I can restore the exact workflow YAMLs rather than rewriting.
|
||
|
||
[assistant] The exp25/26 branches never received the workflow YAMLs (they were only in the wiped working tree). But exp/24 branch has the compact-baseline workflow — the exact base for both new runs. Let me pull it.
|
||
|
||
[assistant] The exp24 compact-baseline workflow survives on the exp/24 branch — that's the exact base for both new runs. Now I'll recreate the two workflows on their own branches (sequentially to avoid the earlier branch-overlap bug), then run them one by one.
|
||
|
||
[assistant] Now let me recreate both workflow files. First the OU workflow (exp25 = compact + `sp_ou_zscore`, n_drop=2):
|
||
|
||
[assistant] Both workflow files are recreated. Now commit each on its own branch — sequentially to avoid the shared-worktree branch-overlap bug. Start with exp/25 (OU):
|
||
|
||
[assistant] Push needs credentials. Let me check how the repo authenticates (the earlier session pushed successfully).
|
||
|
||
[assistant] GIT_PASS is in env but no credential helper. I'll use a temporary askpass script to avoid putting the secret on the command line.
|
||
|
||
[assistant] exp/25 pushed. Now switch to exp/26 and commit the n_drop=1 workflow:
|
||
|
||
[assistant] Both workflow branches are committed and pushed. Now launch the OU run first — single workflow in a new process (no concurrency this time), targeting MLflow exp 25.
|
||
|
||
[assistant] Right — shared worktree: switching to exp/26 branch hid exp25's file (each branch only shows its own tree). Switch back to exp/25 to run the OU workflow first:
|
||
|
||
[assistant] The shared worktree only exposes the checked-out branch's tree. I'll copy both workflow files to a stable location outside the worktree so they persist regardless of branch switching, then run the OU workflow.
|
||
|
||
[assistant] Now on the exp/25 branch, so only the OU workflow is visible. Launch the OU run:
|
||
|
||
[assistant] OU run launched (pid 774, fresh subprocess). Now let me update todos and poll for its MLflow run ID.
|
||
|
||
[assistant] Let me check the workflow log for the new run's ID:
|
||
|
||
[assistant] The OU workflow is progressing normally: new run `57450d1ad33b476aad1df34a745b1652` under Experiment 25, data loaded, processors running, heading into model training. Let me verify it's the newest run and confirm the run ID.
|
||
|
||
[assistant] OU run `57450d1ad33b476aad1df34a745b1652` confirmed with the correct config (compact + `sp_ou_zscore`, n_drop=2, train 2016→2025-09, test→2026-08-10). It's training now. While it runs, I'll recreate `AGENTS.md` (wiped by redeploy), incorporating what this session taught us.
|
||
|
||
[assistant] AGENTS.md recreated. The OU run is training (prior compact run took ~58 min end-to-end). I'll poll periodically — checking the child log for training progress first.
|
||
|
||
[assistant] Training is underway. This run will take ~45-60 min. Let me wait in intervals and check the run status.
|
||
|
||
[assistant] Log is quiet during model training (seeds run in a thread pool, no per-round logging). Let me verify the process is alive and continue waiting.
|
||
|
||
[assistant] Process 774 is alive at 121% CPU — actively training (5 seeds in thread pool). It's been running 14 min. Continue waiting.
|
||
|
||
[assistant] Still training at 123% CPU, 29 min elapsed. The prior compact run (fe469) took ~58 min total, and the ensemble trains 5 seeds in parallel — expect completion around the 40-50 min mark. Continue polling.
|
||
|
||
[assistant] Still training at 44 min — consistent with the ~58-min baseline. Continue waiting.
|
||
|
||
[user] get status and proceed
|
||
|
||
[assistant] OU run `57450d1ad33b476aad1df34a745b1652` **FINISHED** (~57 min). Headline metrics:
|
||
|
||
- **IC 0.0343, ICIR 0.1410, Rank IC 0.0564, Rank ICIR 0.2185**
|
||
- Long-Short Ann Return +58.7%, Sharpe 3.35
|
||
- TopkDropout vs SPY: **+5.44% without cost**, **−3.76% with cost**, MDD −10.73%
|
||
|
||
That's *worse* than the compact baseline (fe469: IC 0.0511, +5.99%/−3.21%, MDD −9.17%) — `sp_ou_zscore` hurt. Now proceed to the n_drop=1 run. First copy its workflow to the stable path (it's only visible on exp/26 branch).
|
||
|
||
[assistant] Now launch the n_drop=1 run — sequential, after OU finished:
|
||
|
||
[assistant] n_drop=1 run launched (pid 1665). Let me confirm it's progressing and grab the new run ID.
|
||
|
||
[assistant] n_drop=1 run confirmed: `21afc6afdb674a399b59dd76c97628ce` under Experiment 25, data loading underway. This will take ~55 min. I'll poll periodically.
|
||
|
||
[user] what you would suggest to improve after reading the discussion:
|
||
This is a sharp observation, Zhao. The fact that your LightGBM model produces strong returns in a **long–short strategy** (58.7%) but weak returns in **long-only** (5.44%) points to a structural issue in how the model interacts with the data distribution and the signal it has learned.
|
||
|
||
### 🔍 Statistical & Quant Perspective
|
||
- **Symmetric signal extraction**
|
||
The model is likely learning signals that are **equally strong in predicting down moves as up moves**. In other words, it’s good at ranking relative returns but not biased toward positive drift. This is consistent with your earlier observation that **RankIC improved while IC did not** — the model is better at ordering assets than predicting absolute direction.
|
||
|
||
- **Weak positive drift**
|
||
Equity markets historically have a small positive drift (expected return > 0). If your dataset or feature engineering neutralized this drift (e.g., by demeaning returns, using z-scores, or training on relative returns), the model won’t capture the long-only edge. It will treat upside and downside as symmetric noise.
|
||
|
||
- **Mean-reversion bias**
|
||
Many technical indicators (RSI, Bollinger bands, etc.) embed mean-reversion logic. If the model overweights these, it will generate strong short signals when prices are stretched, but those don’t translate into long-only gains because mean-reversion shorts are often sharper and more profitable than longs.
|
||
|
||
- **Volatility clustering**
|
||
If the model is exploiting volatility regimes (e.g., GARCH-like features), it may be predicting *magnitude* of moves rather than *direction*. Long–short can monetize both tails, but long-only only benefits from one.
|
||
|
||
### ⚙️ Stochastic Process View
|
||
- **Martingale-like structure**
|
||
If your training data resembles a martingale (no drift, symmetric increments), then long-only strategies collapse to near-zero expectation, while long–short can still profit from relative mispricings.
|
||
|
||
- **Stationarity vs drift**
|
||
Your features may enforce stationarity (demeaned returns, normalized indicators), stripping away the non-stationary drift component that long-only relies on. This makes the model excellent at relative prediction but poor at capturing absolute upward bias.
|
||
|
||
### 🧭 Core Misalignment in Dataset
|
||
The dataset is **aligned with relative ranking, not absolute return drift**. That means:
|
||
- It captures **cross-sectional signals** (which asset will outperform others tomorrow).
|
||
- It does not capture **time-series drift** (whether the market overall trends upward).
|
||
- Long–short thrives on cross-sectional signals; long-only requires drift alignment.
|
||
|
||
---
|
||
|
||
In short: the issue is that your data and model emphasize **relative performance prediction** rather than **absolute upward bias**. That’s why long–short shines but long-only flattens.
|
||
|
||
Would you like me to break down **methods to reintroduce drift** into the dataset (e.g., including macro factors, momentum horizons, or unnormalized returns) so the model can align better with long-only strategies?
|
||
|
||
**To reintroduce drift into your LightGBM model, you need to embed features or modeling choices that capture the market’s long-term upward bias (positive drift) rather than only relative cross-sectional signals. This involves incorporating absolute return predictors, regime-awareness, and macro factors.**
|
||
|
||
---
|
||
|
||
## 🔑 Methods to Reintroduce Drift
|
||
|
||
- **Raw return features**
|
||
Avoid fully normalizing or demeaning returns. Include raw cumulative returns, rolling averages, or log-price levels so the model can learn the market’s upward drift.
|
||
|
||
- **Momentum horizons**
|
||
Add medium- to long-term momentum indicators (e.g., 3–12 month rolling returns). These capture persistent drift that short-term mean-reversion features miss.
|
||
|
||
- **Macro factor integration**
|
||
Include macroeconomic signals (interest rates, inflation, credit spreads, GDP growth). These often align with drift in equity markets and help the model distinguish between bullish and bearish regimes.
|
||
|
||
- **Regime-switching models**
|
||
Combine LightGBM with regime detection (e.g., Markov-switching, ARMA-GARCH). This allows the model to adapt when drift is strong versus when volatility dominates. [ar5iv](https://ar5iv.labs.arxiv.org/html/2509.11844)
|
||
|
||
- **Drift-aware retraining**
|
||
Use drift monitors (KS-test, MMD, energy distance) to detect distributional shifts in features and retrain when drift weakens or strengthens. This prevents the model from overfitting to stationary assumptions. [inferensys.com](https://inferensys.com/train/mlops-for-algorithmic-trading-and-quantitative-finance/intraday-model-warm-start-and-end-of-day-retraining/optimizing-drift-aware-retraining-triggers-on-real-time-feature-drift-monitors)
|
||
|
||
- **Realized drift estimation**
|
||
Explicitly estimate realized drift (expected return conditional on volatility) and feed it as a feature. This aligns the model with the empirical upward bias in equities. [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S0304407624001593)
|
||
|
||
---
|
||
|
||
## 📊 Comparison of Approaches
|
||
|
||
| **Method** | **Captures Drift?** | **Best Use Case** |
|
||
|------------|---------------------|-------------------|
|
||
| **Raw returns** | Yes, direct upward bias | Equity indices, long-only |
|
||
| **Momentum horizons** | Yes, persistent drift | Trend-following strategies |
|
||
| **Macro factors** | Indirect drift | Regime-aware allocation |
|
||
| **Regime-switching** | Conditional drift | Volatility clustering periods |
|
||
| **Drift-aware retraining** | Maintains drift alignment | Adaptive ML pipelines |
|
||
| **Realized drift estimation** | Quantifies drift explicitly | Risk-adjusted long-only |
|
||
|
||
---
|
||
|
||
## ⚠️ Risks & Trade-offs
|
||
- **Overfitting drift**: If drift is weak in certain regimes, forcing it into the model may reduce long–short performance.
|
||
- **Data leakage**: Using cumulative returns improperly can leak future information.
|
||
- **Regime dependency**: Drift is not constant; models must adapt to structural breaks (e.g., crises).
|
||
|
||
---
|
||
|
||
👉 In practice, the most robust way is to **combine medium-term momentum features with macro regime indicators**, while monitoring realized drift explicitly. This balances long-only performance with the cross-sectional alpha your LightGBM already captures.
|
||
|
||
Would you like me to sketch a **workflow for drift-aware feature engineering** that integrates these methods into your LightGBM pipeline?
|
||
|
||
[assistant] The discussion's diagnosis is mostly right on mechanism (RankIC ≫ IC, rank-aligned features/label), but it misreads our actual numbers — and that changes the priorities. Key correction and suggestions:
|
||
|
||
## 1. The "long-short 58.7% vs long-only 5.44%" is a mixed-measurement artifact
|
||
|
||
In our run `57450...`, those come from *different* backtests:
|
||
- **58.7%** = SigAnaRecord `ana_long_short`: costless, daily-rebalanced top/bottom decile *signal* analysis.
|
||
- **5.44%** = PortAnaRecord TopkDropout: realistic long-only, topk=10, with costs, vs SPY.
|
||
|
||
Within the *same* signal analysis, **Long-Avg Ann Return is 90.9% vs Long-Short 58.7%** — long-only (top decile) already beats long-short. So the model is *not* directionally symmetric; it's genuinely good at picking relative winners. The real problem is the gap between signal quality (90%) and traded result (5.4% gross, −3.8% net).
|
||
|
||
## 2. The binding constraint is costs/turnover, not drift
|
||
|
||
Across every run the pattern is identical: ~6% gross excess, **−9 to −10% cost drag**, net negative. TopkDropout with topk=10/n_drop=2 rebalances ~20% of the book *daily* → ~$60k in costs on $1M in 7 months. Adding drift features won't fix that; cutting turnover will. **This is exactly what exp26 (n_drop=1) tests** — wait for it before adding complexity.
|
||
|
||
## 3. If you want drift, add it as an overlay, not into the GBDT
|
||
|
||
The model is rank-aligned *by construction* (CSRankNorm features + relative 5d label). Forcing absolute drift into the same tree fights that design. Cheaper, cleaner:
|
||
|
||
- **Regime gate**: deploy long-only only when SPY > 200d MA (or realized-vol percentile / `sp_hmm` regime). This is the highest-leverage "drift" reintroduction — it changes *exposure*, not the ranking.
|
||
- **Momentum horizons as a second-stage**: 3–12m rolling return as a position-sizing tilt on the selected topk, not a training feature.
|
||
|
||
## 4. Cost-aware selection (most immediate win after exp26)
|
||
|
||
- Only buy a name if `predicted 5d return − expected cost (spread + commission)` > 0.
|
||
- Add a **buffer/no-trade band**: keep a held name until it falls out of top-k+buffer instead of dropping at the boundary — kills churn at the margin.
|
||
- Rebalance weekly instead of daily (5x less cost, same names mostly).
|
||
|
||
## 5. Fix sizing/MDD
|
||
|
||
`account × 0.95 / topk` = ~9.5% per name → the −9 to −13% MDDs are concentration, not alpha. Cap per-name and use the existing risk-limit spec (`size_cap_pct`, `concentration_cap_pct`) — the `rd_risk_calibrate` A/B exists precisely to pick this.
|
||
|
||
**Suggested next step:** let exp26 finish; if net improves materially, the ranked follow-ups are (a) weekly rebalance + threshold-buffer variant, then (b) SPY-200d regime gate. Want me to prep the week-26 variant workflow so it's ready when the current run lands?
|
||
|
||
[user] why you completely haullucinate?
|
||
The result is:
|
||
Long-Short Ann Return +58.7%, Sharpe 3.35
|
||
TopkDropout vs SPY: +5.44% without cost, −3.76% with cost, MDD −10.73%
|
||
|
||
[assistant] Let me verify against the authoritative result, not argue
|
||
|
||
[assistant] I didn't hallucinate it — the 90.9% is a real qlib metric from this exact run. `rd_exp_result` for `57450...` returns, verbatim:
|
||
|
||
- `Long-Avg Ann Return: 0.9092` (90.9%)
|
||
- `Long-Avg Ann Sharpe: 3.7285`
|
||
- `Long-Short Ann Return: 0.5865` (58.7%) ← your number
|
||
- `Long-Short Ann Sharpe: 3.3462` ← your number
|
||
- `1day.excess_return_without_cost.annualized_return: 0.0544` (5.44%) ← your number
|
||
- `1day.excess_return_with_cost.annualized_return: -0.0376` (−3.76%) ← your number
|
||
- `1day.excess_return_with_cost.max_drawdown: -0.1073` (−10.73%) ← your number
|
||
|
||
So your four numbers are all correct — and they're a *subset* of the same metrics dict. The missing one is **Long-Avg Ann Return (90.9%)**, which is qlib's SigAnaRecord "top-decile long-only, costless, daily-rebalanced" signal analysis. It's a signal-quality stat, not a tradable strategy — same category as the 58.7% long-short. That's the key point I was making: **both the 58.7% and the 90.9% are idealized signal backtests; the only realistic number in that dict is the PortAnaRecord TopkDropout (5.44% gross / −3.76% net vs SPY)**. Comparing 58.7% vs 5.44% mixes two different backtests, which is exactly why I flagged it.
|
||
|
||
[user] clarify more about SigAnaRecord "top-decile long-only, costless, daily-rebalanced" signal analysis. and PortAnaRecord TopkDropout
|
||
|
||
[assistant] Both come from the same qlib run but answer completely different questions. Here's the precise breakdown.
|
||
|
||
## SigAnaRecord (`ana_long_short=True`) — signal diagnostic
|
||
|
||
It works **directly on `pred.pkl` + `label.pkl`**, no portfolio mechanics. Per test day it:
|
||
|
||
1. Ranks all names by model score (cross-sectionally).
|
||
2. Splits into quantile groups (default 5 → each group is 10 of 50 names here).
|
||
3. Computes each group's *realized* forward label return (equal-weighted mean).
|
||
4. Annualizes at 252.
|
||
|
||
The metrics it logs:
|
||
- **Long-Avg Ann Return = 90.9%** → mean annualized realized 5-day return of the **top group**.
|
||
- **Short-Avg Ann Return** → bottom group (implied).
|
||
- **Long-Short Ann Return = 58.7%** → top group minus bottom group.
|
||
|
||
Why 90.9% is not achievable:
|
||
- **Zero cost** — no commission, spread, slippage, min-cost.
|
||
- **No tradability check** — it includes any name, even untradeable/illiquid/suspended.
|
||
- **Daily full rebalance** — every day's top group is fresh; it assumes you hold the overlap *and* trade the change for free.
|
||
- **Concentrated tail** — top group is only 10 names, so a few names dominate; annualizing a 5-day-horizon label with 252 also inflates it.
|
||
- **No sizing/risk budget** — equal weight, no `risk_degree`, no cash constraint, no benchmark.
|
||
|
||
Think of it as: *"if my ranking were perfect, what would the top slice have earned, with frictionless trading?"* — an upper-bound quality check on the **signal**, not the **strategy**.
|
||
|
||
## PortAnaRecord with TopkDropoutStrategy — realistic portfolio simulation
|
||
|
||
This actually **simulates trading** with the prediction as a live signal:
|
||
|
||
- **TopkDropoutStrategy** (topk=10, n_drop=2, risk_degree=0.95): each day it ranks the prediction, keeps the top-10 as the target, and to move from the current book to the target it **drops the `n_drop` lowest-scored currently-held names** (that's the 2), then fills the rest. `risk_degree=0.95` caps deployment at 95% of account; `only_tradable=True` filters to names that can actually be traded.
|
||
- **Exchange model**: executes at `deal_price=$close` with `open_cost=0.0005`, `close_cost=0.0015`, `min_cost=$5`, tracks cash, positions, P&L day by day.
|
||
- The logged numbers:
|
||
- `1day.excess_return_without_cost.annualized_return = 5.44%` → backtest return **minus SPY**, cost model turned off (but orders/rotation still happen).
|
||
- `1day.excess_return_with_cost.annualized_return = −3.76%` → same but **with costs** — this is the honest number.
|
||
- `max_drawdown = −10.73%` (with cost).
|
||
|
||
The daily rotation of ~2 of 10 names plus min-costs and spread assumptions is exactly what eats the gap between the 5.44% gross and the −3.76% net — and between the idealized 90.9%/58.7% and the tradable 5.44%.
|
||
|
||
## Bottom line
|
||
|
||
| | SigAnaRecord | PortAnaRecord |
|
||
|---|---|---|
|
||
| Object | signal quality | strategy P&L |
|
||
| Cost | none | open+close+min |
|
||
| Tradability | ignored | enforced |
|
||
| Rebalance | full book daily | topk/n_drop rotation |
|
||
| Sizing | equal weight | risk_degree ≤ 0.95 |
|
||
| Output | 90.9% / 58.7% | 5.44% / −3.76% |
|
||
|
||
So in the run, 90.9% and 58.7% are both *signal* statistics; 5.44%/−3.76% is the only *strategy* result. Comparing 58.7% to 5.44% conflates the two — that's the point I flagged.
|
||
|
||
[user] list all ta/sp features
|
||
|
||
[assistant] All features currently persisted in the lake (`features/market=US/`, family `ta|sp`), merged by timestamp:
|
||
|
||
**TA (family=ta)** — 15 columns
|
||
- Overlap: `sma_5`, `sma_20`, `ema_12`, `ema_26`, `bb_lower`, `bb_middle`, `bb_upper`
|
||
- Momentum: `macd`, `macd_signal`, `macd_hist`, `rsi_14`, `adx_14`
|
||
- Volatility: `atr_14`
|
||
|
||
**SP stochastic-process (family=sp)** — 39 columns
|
||
- Moments: `sp_dsv_1`, `sp_dsv_5`, `sp_dsv_22`, `sp_dsv_ratio_1`, `sp_dsv_ratio_5`, `sp_dsv_ratio_22`, `sp_rskew_5`, `sp_rskew_22`, `sp_rkurt_5`, `sp_rkurt_22`
|
||
- Jump: `sp_jump_ratio`, `sp_jump_flag`, `sp_jump_tail`, `sp_max_move`, `sp_max_up`, `sp_max_down`
|
||
- Realized vol (HAR-RV): `sp_rv1`, `sp_rv5`, `sp_rv22`, `sp_rv_ac1`, `sp_rv_cv_22`
|
||
- Trend: `sp_trend_slope_5`, `sp_trend_slope_20`, `sp_trend_slope_60`, `sp_logp`
|
||
- HMM regime: `sp_hmm_p_regime1`, `sp_hmm_state`
|
||
- Hurst: `sp_hurst_exponent`
|
||
- OU: `sp_ou_zscore`, `sp_ou_half_life`, `sp_ou_revert`
|
||
- Signature: `sp_sig_level1_lead`, `sp_sig_level1_lag`, `sp_sig_level2_lead_lag`, `sp_sig_level2_lag_lead`, `sp_sig_level2_lead_lag_5`, `sp_sig_level2_lag_lead_5`
|
||
- Return: `sp_ret`
|
||
- Vol ratio: `sp_vol_ratio_1_22`, `sp_vol_ratio_5_22`
|
||
|
||
Note: `family` is the partition column. The 276k rows span all 50-ETF symbols.
|
||
|
||
[user] it's done, go ahead
|
||
|
||
[assistant] n_drop=1 run `21afc6afdb674a399b59dd76c97628ce` is **FINISHED**. This is the best result yet — positive net excess for the first time:
|
||
|
||
**n_drop=1 (compact):** IC 0.0511 / Rank IC 0.0663, gross **+7.02%**, net **+2.13%**, MDD **−7.69%**, IR 0.21
|
||
|
||
## Full comparison (all exp-25 runs, 2026 test window)
|
||
|
||
| Run | Features | n_drop | IC | RankIC | gross | net | MDD |
|
||
|---|---|---|---|---|---|---|---|
|
||
| fe469 (t24) | compact | 2 | 0.0511 | 0.0663 | +5.99% | **−3.21%** | −9.17% |
|
||
| be5cd (t23) | broad stochastic | 2 | 0.0728 | 0.0621 | +6.73% | −2.39% | −12.90% |
|
||
| 57450 (t25) | compact + OU | 2 | 0.0343 | 0.0564 | +5.44% | −3.76% | −10.73% |
|
||
| **21afc6 (t26)** | **compact** | **1** | 0.0511 | 0.0663 | **+7.02%** | **+2.13%** | **−7.69%** |
|
||
|
||
Takeaways:
|
||
- **n_drop=1 vs 2 with identical signal (fe469):** net goes from −3.21% → **+2.13%** (+5.3pp), MDD improves −9.17% → −7.69%. Turnover was the binding constraint, exactly as hypothesized.
|
||
- **OU feature (57450) hurt:** IC collapsed 0.051→0.034; the `sp_ou_zscore` addition degraded the signal. Discard that direction.
|
||
- Cost drag confirms the diagnosis: same model gross +7.02% but costs eat ~4.9pp.
|
||
|
||
Now closing traces 25 and 26 with
|
||
|
||
[assistant] ## Objective
|
||
- Complete the two sequential controlled ablation runs in MLflow experiment 25 — (1) compact stochastic + `sp_ou_zscore`, `n_drop=2` (trace 25) and (2) compact stochastic, `n_drop=1` (trace 26) — compare against prior runs, and close traces 25/26. Both reruns are now `FINISHED`; remaining work is metric extraction, comparison, and trace closure.
|
||
- Maintain `/app/AGENTS.md` instruction file (recreated after a container redeploy wiped it).
|
||
|
||
## Important Details
|
||
- Tracking is Postgres resolved from `DATABASE_URL` (`postgresql+psycopg://…@192.168.1.96:5555/tradeac`, credential-bearing — never write into files). Workflow YAMLs keep `exp_manager.uri: "sqlite:///mlruns.db"` as the non-secret placeholder; `default_exp_name: tac-rd-exp22-stochastic-general` = MLflow experiment 25.
|
||
- Trace IDs ≠ MLflow run IDs: trace 25 → run `57450d1ad33b476aad1df34a745b1652`, trace 26 → run `21afc6afdb674a399b59dd76c97628ce`. UI uses `expId=25&run=<run_id>`.
|
||
- User directive: run experiments **one by one, never concurrently** (the earlier concurrent pair hung 2 h). User restarted `rd_server`, killing background workflow processes, and asked for a **new OU run** to avoid confusion with stale `RUNNING` runs `650850ca…` / `cfd98db…` (still orphaned in Postgres; leave them).
|
||
- Container redeploy wiped `/app/AGENTS.md`, `/app/experiments` worktree, `/app/tac-qlib/workflows/runs/`; Postgres rows and `/home/data/lake` (lake data, MLflow artifacts) persisted. Remote experiment branches (exp/25, exp/26) survived via `rd_trace_init` (base `origin/main`).
|
||
- Shared-worktree gotcha: `/app/experiments` is a single checkout — `git switch` hides other branches' files. Workflows must be copied to a stable path (`/app/tac-qlib/workflows/runs/`) before running.
|
||
- Push authentication: no credential helper; use `GIT_ASKPASS=/tmp/git-askpass.sh` (echoes `zhaoli` / `$GIT_PASS` from env). Never put the password on the command line.
|
||
- Run long workflows with `run_in_new_process=true` (isolates qlib process-global init); child logs at `/home/data/lake/logs/rd-workflow-*.log`; poll `rd_exp_get_run`/`rd_exp_list`.
|
||
- Fixed model/setup: `RankICEnsembleLGBModel`, seeds `42,7,2026,99,123`, lr 0.02, 3000 rounds, early stop 200; 50-ETF universe; label `Ref($close,-6)/Ref($close,-1)-1`; train 2016-01-04→2025-09-01, valid 2025-09-03→2026-01-03, test 2026-01-04→2026-08-10; TopkDropout topk=10; SPY benchmark; open_cost 0.0005/close_cost 0.0015/min_cost 5.0.
|
||
- Compact feature set (exact): `$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead`; exp25 appends `sp_ou_zscore`.
|
||
- SigAnaRecord metrics (`Long-Avg Ann Return` 90.9%, `Long-Short Ann Return` 58.7%, Sharpes) are costless, daily-rebalanced signal diagnostics; only PortAnaRecord TopkDropout numbers (+5.44% gross / −3.76% net vs SPY) are realistic strategy results. The 90.9% figure is real, verified via `rd_exp_result` (not a hallucination).
|
||
- Lake feature inventory (54 cols incl. `family`, 276,424 rows): 15 TA (`sma_5`,`sma_20`,`ema_12`,`ema_26`,`bb_lower/middle/upper`,`macd`,`macd_signal`,`macd_hist`,`rsi_14`,`adx_14`,`atr_14`) + 39 SP (dsv, dsv_ratio, rskew, rkurt, jump, rv1/5/22, rv_ac1, rv_cv_22, trend_slope_5/20/60, logp, hmm, hurst, ou, sig_level*, vol_ratio, ret).
|
||
|
||
## Work State
|
||
### Completed
|
||
- `/app/AGENTS.md` recreated (includes layout, commands, MCP-first R&D flow, `run_in_new_process`, shared-worktree gotcha, one-at-a-time runs, GIT_ASKPASS push, redeploy-persistence notes).
|
||
- Experiments repo re-initialized via `rd_trace_init`; all remote branches present.
|
||
- Workflows recreated from `origin/exp/24...:workflows/exp24/workflow.yaml` base and committed/pushed: exp25 OU (compact + `sp_ou_zscore`, n_drop=2) on branch `exp/25-test-the-clean-data-hypothesis-that-addi` commit `93cb283`; exp26 (compact, n_drop=1) on `exp/26-test-whether-reducing-topkdropout-daily` commit `c455000`.
|
||
- Stable workflow copies: `/app/tac-qlib/workflows/runs/exp25_ou.yaml`, `/app/tac-qlib/workflows/runs/exp26_ndrop1.yaml`.
|
||
- OU rerun launched (pid 774) → `57450d1ad33b476aad1df34a745b1652` **FINISHED** (~57 min): IC 0.0343, ICIR 0.1410, Rank IC 0.0564, Rank ICIR 0.2185; Long-Short Ann Return +58.7% / Sharpe 3.35; Long-Avg Ann Return 90.9% / Sharpe 3.73 (signal diag); TopkDropout vs SPY **+5.44% w/o cost, −3.76% w/ cost, MDD −10.73%** → worse than compact baseline fe469 (IC 0.0511, +5.99%/−3.21%, MDD −9.17%); `sp_ou_zscore` hurt.
|
||
- n_drop=1 rerun launched (pid 1665) → `21afc6afdb674a399b59dd76c97628ce` **FINISHED** (start 1787058537147, end 1787061702595) — metrics not yet retrieved.
|
||
- Prior baselines: `fe469a192bbd4372b69306f196d7fc7c` (compact: IC 0.0511, RankIC 0.0663, net −3.21%, MDD −9.17%, 588 trades, cost $60,653, final $1,105,711); `be5cd314508b44ec824ac9cf4a917a19` (broad: IC 0.0728, RankIC 0.0621, net −2.39%, MDD −12.90%, best); `18db5bc1dc8f436fb84dddcbb9b36d61` (full TA+SP, exp 24: IC 0.0486, net −9.09%, MDD −12.60%).
|
||
- Explained SigAnaRecord vs PortAnaRecord distinction and verified the 90.9% metric; listed all lake TA/SP features.
|
||
|
||
### Active
|
||
- Trace closure pending: traces 25 and 26 still open (`starting`) in Postgres — `rd_trace_finish` with ref_id = `57450d1ad33b476aad1df34a745b1652` (trace 25) and `21afc6afdb674a399b59dd76c97628ce` (trace 26).
|
||
- n_drop=1 run comparison pending (need `rd_exp_result` + `rd_exp_blotter` for `21afc6af…`).
|
||
|
||
### Blocked
|
||
- None. (Orphaned `RUNNING` rows `650850ca…`/`cfd98db…` remain in Postgres but are superseded; no action needed per user.)
|
||
|
||
## Next Move
|
||
1. Fetch `tac-qlib-rd_rd_exp_result` (and `rd_exp_blotter`) for `21afc6afdb674a399b59dd76c97628ce` (n_drop=1).
|
||
2. Compare n_drop=1 vs OU `57450d1a…` vs `fe469a…`/`be5cd…`/`18db5…` (IC/RankIC, gross vs net excess, cost/trades, MDD).
|
||
3. Close traces: `rd_trace_finish` trace 25 with ref_id `57450d1ad33b476aad1df34a745b1652`, trace 26 with ref_id `21afc6afdb674a399b59dd76c97628ce` (verify exp/25, exp/26 branch contents first).
|
||
4. Report the comparison summary to the user.
|
||
|
||
## Relevant Files
|
||
- `/app/tac-qlib/workflows/runs/exp25_ou.yaml`, `/app/tac-qlib/workflows/runs/exp26_ndrop1.yaml`: stable configs used for the reruns.
|
||
- `/app/experiments`: trace worktree; branches `exp/25-test-the-clean-data-hypothesis-that-addi` (93cb283), `exp/26-test-whether-reducing-topkdropout-daily` (c455000).
|
||
- `/home/data/lake/mlruns/25/21afc6afdb674a399b59dd76c97628ce`, `/home/data/lake/mlruns/25/57450d1ad33b476aad1df34a745b1652`: finished run artifacts.
|
||
- `/home/data/lake/mlruns/25/{fe469a…,be5cd…,18db5…}`: comparison baselines.
|
||
- `/home/data/lake/logs/rd-workflow-tac-rd-exp22-stochastic-general-20260818-130851.log`: n_drop=1 child log (pid 1665).
|
||
- `/app/AGENTS.md`: recreated repo guidance.
|
||
- `/tmp/git-askpass.sh`: push credential helper (uses `GIT_USER`/`GIT_PASS` env).
|
||
|
||
[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] Trace 25 closed. Now closing trace 26 (n_drop=1)
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[assistant] Both traces closed and pushed. Ver
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[assistant] Both traces closed, branches pushed (`eba4121`, `894ac26`). Campaign complete:
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**Result: n_drop=1 wins.** `21afc6af…` is the only run with positive net excess.
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| Run | IC | RankIC | gross | net | MDD |
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|---|---|---|---|---|---|
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| be5cd broad, n_drop 2 | 0.0728 | 0.0621 | +6.73% | −2.39% | −12.90% |
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| fe469 compact, n_drop 2 | 0.0511 | 0.0663 | +5.99% | −3.21% | −9.17% |
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| 57450 OU, n_drop 2 | 0.0343 | 0.0564 | +5.44% | −3.76% | −10.73% |
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| **21afc6 compact, n_drop 1** | 0.0511 | 0.0663 | **+7.02%** | **+2.13%** | **−7.69%** |
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Conclusions:
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- **OU feature (trace 25): dead end** — IC 0.051→0.034; discard `sp_ou_zscore`.
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- **n_drop=1 (trace 26): keep** — same signal as fe469 (identical IC) but +2.13% net vs −3.21%, MDD −7.69%. Confirms cost/turnover was the binding constraint.
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Possible next step: merge n_drop=1 with the best signal (be5cd's IC 0.0728 broad set) — that combo hasn't been tested yet. Want me to run it? |