book: scaffold + ch00 (execution trail as spine) — evidence exp 8-31, round 3
This commit is contained in:
TradeAC Book Agent
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---
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name: tac-qlib-custom
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description: "Guide agents to customize and extend Qlib on the TradeAC R&D stack — how to configure workflow YAMLs (qlib_init, model, dataset/handler, processors, records, PortAnaRecord strategies), how to extend Qlib classes wired into those workflows (custom Model, BaseStrategy, DataHandler, Record), and the empirically-tested knobs from this repo (RankIC early-stopping, stochastic-control strategies, stochastic-process features, catch22/GARCH/Hurst/signature). Also encodes the experiment traceability loop: every backtest runs as a workflow-with-recorder, is recorded in the Postgres experiments table (rationale/details/evaluation/metrics with pgvector embeddings, evolution chain) and on a per-experiment git branch that is committed + pushed. Companion to tradeac-rd (MCP run tools) and tradeac-lake (parquet lake)."
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---
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# tac-qlib-custom
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Customizing and extending Qlib on the TradeAC stack. This skill encodes what was
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learned from actual experiments in this repo: how a workflow YAML maps to Qlib
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classes, how to write a custom class that the YAML can load, and which training /
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strategy / feature knobs measurably moved IC, RankIC and the backtest.
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Read `tac-qlib/skills/tradeac-rd/SKILL.md` for the MCP run/inspect tools and
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`tac-qlib/README.md` for the package layout. The venv is `/app/.venv`
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(qlib 0.1.dev2066); `tac_qlib` is installed into the venv's `site-packages`
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(editable copy under `/opt/venv/.../tac_qlib/`), so **any new module must be
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copied to `/opt/venv/lib/python3.12/site-packages/tac_qlib/...` too** (or use an
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editable install) before `rd_run_workflow` can import it.
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## MCP-first policy
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- **Drive every backtest and run through the `tac-qlib-rd` MCP tools** (`rd_run_workflow`,
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`rd_train`, `rd_predict`, `rd_exp_*`) and the tac-engine lake tools for data prep. Do not
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reimplement them with ad-hoc scripts (custom qlib glue, own mlruns readers, direct
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JSON-RPC/stdio clients).
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- **NEVER script directly against the MCP server** (spawning `tac_qlib.rd_server` /
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`tac-engine`, bash/curl/stdio) unless a tool genuinely can't do the job — then **stop and
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ask the user to confirm first**.
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- The traceability bookkeeping (Postgres `rd_experiments` row + pgvector embeddings +
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branch-per-experiment git) is exposed as the **`rd_trace_*` MCP tools** on the tac-qlib-rd
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server — use those, not bash scripts. Data prep, training, evaluation and backtests also go
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through MCP tools.
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- If the venv is missing a runtime dep (`duckdb`, `pyarrow`, feature libs), lazy-install it
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(`uv pip install --python $VIRTUAL_ENV/bin/python <pkg>`) instead of switching tools.
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## Secrets policy
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- NEVER write secrets into files: DB passwords, API keys, OAuth tokens, or
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credential-bearing URLs (`DATABASE_URL`, `GIT_PASS`, `EMBEDDING_API_KEY`) in
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workflow YAMLs, scripts, configs, notes or committed code.
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- NEVER read `*.env` / `.env.*` directly (`cat`/`tail`/`grep`/`sed`/`head` on
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`.env`). That pulls secrets into this session and leaks them to any agent
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sharing it.
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- When a tool or command needs an env var, ASK the user to set it in the
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environment (shell/container env, or the user-owned `.env`) and reference it
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by name (`$VAR`), never by value. If it's missing, report which variable is
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required instead of reading it yourself.
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- Tracking store: use `uri: "sqlite:///mlruns.db"` (relative) in workflows —
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`rd_run_workflow` normalizes it to Postgres when `$DATABASE_URL` is set, else
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the lake sqlite. Never hardcode a `postgres://user:pass@…` URI.
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- If you find a committed secret, flag it, remove it, and replace it with a
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placeholder. (The `rd_trace_*` MCP tools' commit guard blocks adding
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credential-shaped lines.)
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## How a workflow YAML maps to Qlib classes
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A workflow YAML (`tac-qlib/workflows/*.yaml`) is rendered by Jinja (vars like
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`{{ LAKE }}` from `TAC_LAKE_DIR`) then executed by `qrun` / `rd_run_workflow`.
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Every block is a Qlib class reference resolved by `module_path` + `class`:
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```yaml
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{%- set LAKE = TAC_LAKE_DIR %}
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qlib_init:
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provider_uri: "{{ LAKE }}"
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region: us
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calendar_provider: # custom tac-qlib providers read the parquet lake
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class: LakeCalendarProvider
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module_path: tac_qlib.data.providers
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instrument_provider: # ... (markets: {} => lake universe)
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feature_provider: # LakeFeatureProvider: routes $open..$volume from bars,
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class: LakeFeatureProvider # $<ta-lib/sp_*> from features parquet, $amount derived
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exp_manager:
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class: MLflowExpManager
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module_path: qlib.workflow.expm
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kwargs: { uri: "sqlite:///{{ LAKE }}/mlruns.db", default_exp_name: "my-exp" }
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task:
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model: # <MODEL BLOCK> — custom model → new module_path
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class: RankICLGBModel
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module_path: tac_qlib.contrib.model.rank_gbdt
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kwargs: { loss: mse, learning_rate: 0.02, num_leaves: 31, ... }
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dataset:
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class: DatasetH
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module_path: qlib.data.dataset
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kwargs:
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handler: # <HANDLER BLOCK> — feature selection + processors live here
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class: TACHandler
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module_path: tac_qlib.contrib.data.handler
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kwargs:
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instruments: "SPY,QQQ,..."
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start_time: 2015-01-03
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end_time: 2026-08-10
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fit_start_time: 2015-01-03 # processors fit on this window
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fit_end_time: 2025-09-01
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freq: day
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lake_root: "{{ LAKE }}"
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market: US
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label: "Ref($close,-6)/Ref($close,-1)-1" # 5d forward return
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feature_fields: "$open,$high,$low,$close,$vwap,$volume,sp_ret,sp_ou_zscore,..."
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infer_processors: # feature-time transforms, fit on fit_*
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- { class: DropAllNaN, kwargs: {} }
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- { class: ProcessInf, kwargs: {} }
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- { class: CSRankNorm, kwargs: {} } # per-day cross-sectional rank
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- { class: ZScoreNorm, kwargs: {} }
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- { class: Fillna, kwargs: {} }
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segments:
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train: [2015-01-03, 2025-09-01]
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valid: [2025-09-03, 2026-01-03]
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test: [2026-01-04, 2026-08-10]
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record: # each entry records one artifact type to the run
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- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
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- { class: SigAnaRecord, module_path: qlib.workflow.record_temp,
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kwargs: { ana_long_short: true, ann_scaler: 252 } }
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- { class: PortAnaRecord, module_path: qlib.workflow.record_temp,
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kwargs: { config: { strategy: <STRATEGY BLOCK>, backtest: {...} }, risk_analysis_freq: 1d } }
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```
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`rd_run_workflow config_path=<yaml> experiment_name=<exp>` runs it; the MCP call
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may time out for long runs (RankIC tuning, heavy feature sets) — the run keeps
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executing; poll via `rd_exp_list` / `rd_exp_get_run` on the returned experiment.
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## Experiment traceability (DB + git + embeddings)
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Every backtest you run as an agent MUST be tracked: it runs as a workflow with the
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`record` block (SignalRecord/SigAnaRecord/PortAnaRecord → MLflow artifacts on disk
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under `<lake>/mlruns/<exp_id>/<run_id>`), and a row is written to the Postgres
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`experiments` table plus a git branch per experiment. The `tac-app` UI owns the
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schema (Drizzle migrations in `tac-app/drizzle/`); this skill's `lib/` scripts are
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the executor the agent drives.
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**Trigger the lineage as part of the run — automatically, not on prompt.** Any
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time you execute a qlib workflow (`rd_run_workflow`) or a train/predict pipeline
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on this stack, the traceability bookkeeping is part of that run, not a separate
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step the user must ask for: open the traced experiment with `rd_trace_start`
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before running, commit intermediates with `rd_trace_commit`, and close it with
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`rd_trace_finish` after — without waiting to be prompted (see "The
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per-experiment procedure" below).
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### Env vars
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| Var | Purpose |
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|-----|---------|
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| `DATABASE_URL` | Postgres URL for the `rd_experiments` table AND the MLflow tracking store (set in repo `.env`) |
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| `EMBEDDING_API_BASE_URL` | embedding POST endpoint (e.g. `https://embd.h.lizhao.net/embeddings`) |
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| `EMBEDDING_API_KEY` | basic-auth credential (`user:pass` form is supported) |
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| `GIT_USER` / `GIT_PASS` | git remote credentials for push/fetch |
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| `GIT_REPO_URL` | experiment git repo tracked by the `experiments` submodule (branches are pushed here) |
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| `TAC_LAKE_DIR` | lake root (mlruns artifact files live under it) |
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The experiment repo is the **`experiments` git submodule** at the workspace root
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(`<repo-root>/experiments`), always tracking `$GIT_REPO_URL`. `rd_trace_init`
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creates/validates it; it errors if `experiments/` exists but points at a
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different URL. There is no `TAC_EXP_GIT_DIR` — the submodule path IS the
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experiment repo, and ALL experiment/backtest changes (workflow YAMLs, notes,
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outputs) must live inside it, never in the parent tradeac repo.
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### The `rd_experiments` table
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Owned by tac-app's Drizzle schema (`tac-app/src/db/schema.ts`); `rd_trace_init`
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can `init` it idempotently. The table is named **`rd_experiments`** (NOT
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`experiments`) because MLflow's Postgres tracking store creates its own
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`experiments` table in the same database. Key columns: `id` (PK), `rational` +
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`rational_embedding` (pgvector `vector(384)`), `details` + `details_embedding`,
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`evaluation`, `metrics` (jsonb), `evolved_from` (FK → rd_experiments.id),
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`start_ts`/`end_ts`, `git_branch`, `experiment_ref_id`, `mlruns_dir`, `status`.
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`experiment_ref_id` holds the **mlflow run id** returned by `rd_run_workflow` and
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is an FK to MLflow's `runs(run_uuid)` (added by `rd_trace_init` after the
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mlflow store tables exist — MLflow creates `runs` lazily).
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Tracking store: **Postgres `$DATABASE_URL`** (MLflow's own tables) when set,
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falling back to the unified lake sqlite `sqlite:///<lake>/mlruns.db`. Artifact
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files always stay on disk under `<lake>/mlruns/<exp_id>/<run_id>/artifacts`.
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Embedding model: `michaelfeil/bge-small-en-v1.5` (384-dim, **512-token context**).
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Rational/details are written paper-summary style (≤512 tokens) and embedded verbatim —
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NEVER truncate; if a text is longer, summarize it first (the embed helper rejects
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over-limit input).
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### Git repo + branch-per-experiment
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The experiment repo is the `experiments` submodule at the workspace root
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(`<repo-root>/experiments`, tracking `$GIT_REPO_URL`). The `rd_trace_*` MCP
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tools handle it, and every git operation is scoped to that submodule —
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experiments NEVER stage or push parent-repo (tradeac) files.
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- `rd_trace_init` creates/validates the submodule and the base branch. If
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`experiments/` does not exist it runs `git clone $GIT_REPO_URL experiments`;
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if it exists but tracks a different URL, init errors out.
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- Base branch: `main` (or `master`). If the submodule is empty, a seed commit is
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made and pushed so there are commits to fork from.
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- Every experiment runs on its own branch `exp/<id>-<slug>`.
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- `evolved_from` resolution (in order):
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1. If the wizard prompt explicitly says `evolved_from=<id>` (run wizard click on an
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existing experiment) — use that id directly.
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2. Otherwise `--evolved-from auto`: the user prompt / rational is embedded and
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cosine-searched over the `experiments.rational_embedding` column; the top hit
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above the similarity threshold (0.5) becomes `evolved_from`.
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3. Otherwise (first experiment, or a new chat with no predecessor) — no evolved_from;
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fork from `main`'s latest commits.
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- The new branch is forked from the **evolved-from experiment's branch** (its latest
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commits), or from `main` when there is no predecessor — so experiment lineages form
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a git branch chain.
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- On every finish, and for intermediate steps, changes are committed + pushed.
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### Custom code is part of the lineage (code snapshot)
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Custom contrib modules (`tac_qlib/contrib/model/`, `tac_qlib/contrib/strategy/`,
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`tac_qlib/contrib/data/`, `tac_qlib/data/providers.py`) live in the **parent**
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tradeac repo, not in the `experiments/` submodule — so they are normally invisible
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to the experiment branch and a descendant forking from it would reinvent them.
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The lineage tooling fixes this: **every experiment branch carries a `code/`
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snapshot of exactly the qlib extension code that run depended on**, so descendants
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reuse it instead of re-authoring it.
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- `rd_trace_start` and `rd_trace_finish` automatically snapshot the default paths
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(`tac-qlib/tac_qlib/contrib`, `tac-qlib/tac_qlib/data`) into
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`<experiments>/code/<parent-relative-path>` on the experiment branch.
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- `rd_trace_snapshot` snapshots mid-run (e.g. after writing a
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new custom model) without waiting for finish.
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- The snapshot also writes `code/MANIFEST.txt` recording the **parent-repo HEAD
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commit** and the per-file blob hashes it was taken from — so a run can be traced
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back to the exact parent commit that produced its custom code.
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- Descendants: the custom modules your run needs are under `code/tac_qlib/...` on the
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evolved-from branch. Reuse them (copy/`git show`) instead of writing new ones; check
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`code/MANIFEST.txt` to see which parent commit they came from and port fixes back.
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- Guardrail exception: parent-repo changes under `tac_qlib/tac_qlib/contrib` and
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`tac_qlib/tac_qlib/data` are **expected** (they are the snapshotted code);
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`parent_changes` reports them as a note, not a violation. Any OTHER parent change
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is still a guardrail violation.
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Guardrail — experiments must NOT introduce side effects to the parent repo:
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- Write workflow YAMLs, notes and experiment outputs ONLY inside
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`<repo-root>/experiments/` (they are committed on the experiment branch).
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- Never `git add`/commit/stage anything in the parent tradeac repo.
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- Run `rd_trace_guard` to list any parent
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changes outside the submodule pointer; `rd_trace_finish` also surfaces them.
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Revert any accidental parent edits before finishing.
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- If an experiment reveals a PRODUCT change (workflow template, skill, tac-app),
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propose it separately for the tradeac repo — do not mix it into the experiment
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branch.
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The `rd_trace_*` MCP tools perform git operations with the mandated credential
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helper (from `GIT_USER` / `GIT_PASS`), so you do not need to construct it by hand.
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### The per-experiment procedure
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**Use the `rd_trace_*` MCP tools (tac-qlib-rd)** — they replace the old
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`trace.sh`/`trace_db.py` scripts. The server is long-lived (psycopg imported
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once, DB connection reused per call) and every tool returns one JSON object, so
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no output parsing is needed:
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```text
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# 0. ensure ready (rd_experiments table + experiments git repo + base main)
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rd_trace_init
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# 1. start — inserts the row, resolves evolved_from, forks+pushes the branch.
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# Returns {experiment_id, branch, evolved_from, base_branch} as JSON.
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rd_trace_start rational="5-day forward label, RankIC early stop, 50-ETF universe" \
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details="LGBModel mse lr=0.02 num_leaves=15 num_boost_round=3000; TopkDropout topk=2; benchmark QQQ" \
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experiment_name="tac-rd-expN" \
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evolved_from="auto" \
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session_id="<this chat's opencode session id, if started from a chat>"
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# -> {"experiment_id": N, "branch": "exp/N-...", "evolved_from": ..., "base_branch": ...}
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# 2. write the workflow YAML INSIDE the experiments submodule
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# (e.g. <repo-root>/experiments/workflows/<exp>/workflow.yaml), then commit it:
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rd_trace_commit experiment_id=<N> message="add workflow yaml"
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# 2b. if the workflow uses a NEW custom module, snapshot it onto the branch
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# (start/finish auto-snapshot contrib+data; do this to capture mid-run):
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rd_trace_snapshot experiment_id=<N> # default contrib+data
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# or: rd_trace_snapshot experiment_id=<N> paths="tac-qlib/tac_qlib/contrib/model/rank_gbdt.py"
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# 3. run the backtest through the WORKFLOW with the recorder (MUST write mlruns):
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rd_run_workflow config_path=<repo-root>/experiments/workflows/<exp>/workflow.yaml experiment_name=tac-rd-expN
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# -> returns run_id (= experiment_ref_id) + metrics
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# 4. inspect with rd_exp_result / rd_exp_blotter, then finish — updates the row
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||||
# (re-embeds rational/details, sets metrics/eval/end_ts), snapshots the custom
|
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# code, and commits+pushes. finish also surfaces parent-repo side effects.
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rd_trace_finish experiment_id=<N> \
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ref_id=<mlflow-run-id> \
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evaluation="IC 0.0645, RankIC 0.075; net excess +0.85% ann" \
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metrics='{"IC":0.0645,"RankIC":0.075,"ann_excess":0.85}' \
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mlruns_dir=<lake>/mlruns/<exp_id>/<run_id>
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||||
```
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||||
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||||
Helpers (MCP tools): `rd_trace_search` (semantic), `rd_trace_get` (one row),
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||||
`rd_trace_list`, `rd_trace_mlruns_dir` (resolves the mlruns dir for an
|
||||
experiment name), `rd_trace_guard` (parent-repo side-effect check).
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||||
Rules:
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||||
- **Always** run backtests as workflows with the `record` block (req 2) — never a bare
|
||||
`rd_backtest` for a traced experiment.
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- **Always** open the lineage (`rd_trace_start`) BEFORE the run and **Always**
|
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`rd_trace_finish` + push after it completes (req 5) — this happens as part of the run,
|
||||
do not wait for the user to ask; intermediate `rd_trace_commit` is encouraged (req 5).
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- **Always** snapshot the custom qlib code (`rd_trace_snapshot`, or rely on the
|
||||
auto-snapshot at start/finish) so the experiment branch carries the exact contrib/data
|
||||
modules the run used — descendants fork and reuse `code/` instead of reinventing it.
|
||||
- Keep rational/details ≤ 512 tokens (paper-summary style) so embeddings are exact —
|
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no truncation.
|
||||
- **Confine experiments to the `experiments/` submodule** — never write to, stage, or
|
||||
commit parent tradeac repo files; run `rd_trace_guard` to check for side effects.
|
||||
(Custom code edits under `tac-qlib/tac_qlib/contrib` and `.../data` are the sanctioned
|
||||
exception — they are the snapshotted modules; see "Custom code is part of the lineage".)
|
||||
- **Follow the Secrets policy above** — no secrets in files, no reading `.env*`, ask the
|
||||
user to set env vars; use `uri: "sqlite:///mlruns.db"` for the tracking store.
|
||||
- Workflow YAMLs are jinja-rendered with `os.environ` as the context, so env-var
|
||||
placeholders work (`{%- set LAKE = TAC_LAKE_DIR %}` then `{{ LAKE }}`). Use them for
|
||||
paths/config — never for secrets that get committed.
|
||||
|
||||
## Extending Qlib — the 4 class families you can override
|
||||
|
||||
### 1. Custom Model (train-time) — `tac_qlib/contrib/model/`
|
||||
Subclass `qlib.contrib.model.gbdt.LGBModel` (or `qlib.model.base.BaseModel`) and
|
||||
implement `fit(dataset, ...)` + `predict(dataset)`. `LGBModel.fit` calls
|
||||
`self._prepare_data(dataset)` → `lgb.Dataset`s, then `lgb.train` with
|
||||
`early_stopping` on the valid set. Override points that matter:
|
||||
|
||||
- `_prepare_data` → build the `lgb.Dataset` with `group=` (per-day query groups)
|
||||
when you need ranking metrics per trading day.
|
||||
- `fit` → change what early-stops training (the biggest IC/backtest lever, see §Knobs).
|
||||
- `predict` → return the Series keyed (datetime, instrument).
|
||||
|
||||
Reference: `tac_qlib/tac_qlib/contrib/model/rank_gbdt.py` — `RankICLGBModel`
|
||||
subclasses `LGBModel`, adds per-day `group` in `_prepare_data`, injects
|
||||
`feval=rankic_feval` (mean per-day Spearman) into `lgb.train`, and forces
|
||||
`metric='None'` + `first_metric_only=True` so early-stopping tracks RankIC only.
|
||||
|
||||
### 2. Custom Strategy (backtest-time) — `tac_qlib/contrib/strategy/`
|
||||
Subclass `qlib.contrib.strategy.signal_strategy.BaseSignalStrategy` (which wraps
|
||||
`qlib.strategy.base.BaseStrategy`) and implement:
|
||||
|
||||
```python
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
# trade_step, trade_start/end = self.trade_calendar.get_step_time(trade_step)
|
||||
# pred = self.signal.get_signal(start_time=pred_shift, end_time=pred_shift) # shift=-1 => signal known at t-1
|
||||
# self.trade_position / self.trade_exchange / self.trade_calendar injected by the executor
|
||||
# build qlib.backtest.Order(stock_id, amount, start_time, end_time, direction=Order.BUY/SELL)
|
||||
# return TradeDecisionWO(orders, self)
|
||||
```
|
||||
|
||||
Wire it into the YAML under `PortAnaRecord.config.strategy`:
|
||||
|
||||
```yaml
|
||||
strategy:
|
||||
class: OptimalStopControl
|
||||
module_path: tac_qlib.contrib.strategy.optimal_stop
|
||||
kwargs:
|
||||
signal: "<PRED>" # placeholder replaced with the recorded pred
|
||||
topk: 10
|
||||
entry_pct: 0.85
|
||||
exit_pct: 0.7
|
||||
max_hold_days: 10
|
||||
min_hold_days: 2
|
||||
sl: -0.08
|
||||
risk_degree: 0.95
|
||||
```
|
||||
|
||||
Reference: `tac_qlib/tac_qlib/contrib/strategy/optimal_stop.py`
|
||||
(`OptimalStopControl` — entry gated by cross-sectional signal percentile, exits
|
||||
by percentile/time/stop-loss, equal-weight control sizing).
|
||||
|
||||
### 3. Custom DataHandler / processors — `tac_qlib/contrib/data/handler.py`
|
||||
`TACHandler(DataHandlerLP)` already wraps the lake via `QlibDataLoader` +
|
||||
`LakeFeatureProvider`. Key config surface (all usable from YAML without new code):
|
||||
- `feature_fields` — explicit list; the handler prefixes `$` and de-dups. Anything
|
||||
the provider can route is usable: bar fields, `$amount` (v*vw), and any column
|
||||
present in the lake `features/.../symbol=*.parquet` files.
|
||||
- `infer_processors` / `learn_processors` — add `CSRankNorm`, `CSZScoreNorm`
|
||||
(label), `ZScoreNorm`, `DropnaLabel`, `Fillna`, etc. `DropAllNaN` is a
|
||||
tac-qlib processor (drops all-NaN columns on the fit window).
|
||||
- `label` — any qlib expression, e.g. `Ref($close,-6)/Ref($close,-1)-1`.
|
||||
|
||||
To add a *new feature family*: compute it once (see `examples/sp_features.py` +
|
||||
`examples/persist_sp_features.py`), persist extra columns into
|
||||
`features/market=US/timeframe=1d/symbol=*.parquet` (drop stale `sp_*` columns
|
||||
first on re-runs), then reference them in `feature_fields`.
|
||||
|
||||
**The Rust engine already ships the SP feature pipeline as a lake MCP tool**:
|
||||
`get_lake_sp` (tac-engine, stochastic-rs) computes `sp_ou_*`, `sp_hmm_*`,
|
||||
`sp_jump_*`, `sp_rv*`/`sp_vol_ratio_*` (+ `sp_rv_ac1`, `sp_rv_cv_22`),
|
||||
`sp_max_up`/`sp_max_down`, `sp_trend_slope_*`, `sp_logp`,
|
||||
`sp_hurst_exponent`, `sp_sig_*` (levels 1/2 at lag 1 and 5),
|
||||
`sp_rskew_*`/`sp_rkurt_*`/`sp_dsv_*` (realized moments via stochastic-rs
|
||||
`realized`) + `sp_ret` from lake bars and persists them into
|
||||
the feature parquets (replacing stale `sp_*`), all in one call:
|
||||
```json
|
||||
{"symbol": "AAPL", "timeframe": "1d", "start": "2015-01-03", "end": "2026-08-10", "fit_end": "2025-09-01"}
|
||||
```
|
||||
`fit_end` pins the Gaussian-HMM fit to the train window (no lookahead), matching
|
||||
the `FIT_END` convention. **Deferred families** (`garch`, `entropy`, `catch22`)
|
||||
are still computed with the Python `sp_features.py` path until their ports land.
|
||||
Note two deliberate differences vs the Python reference: the Rust HMM uses the
|
||||
causal *forward filter* (`filtered_state_probs`) rather than hmmlearn's smoothed
|
||||
`predict_proba`, and `hurst` is estimated on the returns series directly
|
||||
(`take_differences=false`) rather than the reference's double-differenced
|
||||
`kind="random_walk"` — regime *state* assignments agree, probability levels are
|
||||
comparable but not identical.
|
||||
|
||||
### 4. Custom Record (artifact writers)
|
||||
Subclass `qlib.workflow.record_temp.SignalRecord` / a `Record` and log metrics +
|
||||
artifacts into the MLflow run. There is no shipped example Record in `contrib/`
|
||||
yet — write one against the pattern in `qlib.workflow.record_temp` when a
|
||||
workflow needs a bespoke simulator (e.g. beta-neutral 3L/3S) that
|
||||
`PortAnaRecord` doesn't cover.
|
||||
|
||||
## Empirical knobs that moved the numbers (measured on the 50-ETF lake)
|
||||
|
||||
All experiments used: 50-ETF universe, train 2015-01-03..2025-09-01 / valid
|
||||
2025-09-03..2026-01-03 / test 2026-01-04..2026-08-10, benchmark SPY, TopkDropout
|
||||
or OptimalStopControl, costs open 0.0005 / close 0.0015 / min 5.
|
||||
|
||||
> **Rank-dimension reminder**: when the goal is to improve the *ranking* quality of
|
||||
> a signal (RankIC, long-short spread, top-decile precision), do NOT reinvent the
|
||||
> stack — use the contrib modules already shipped and verified in this repo:
|
||||
> `tac_qlib.contrib.model.rank_gbdt.RankICLGBModel` (early-stops training on
|
||||
> per-day cross-sectional RankIC, `metric='None'` + `first_metric_only`) and
|
||||
> `tac_qlib.contrib.strategy.optimal_stop.OptimalStopControl` (entry/exit gated by
|
||||
> signal percentile instead of raw levels). Both are loadable from a workflow YAML
|
||||
> via `module_path` — see the canonical `tac-qlib/workflows/workflow_lgb_sp5d_rankic.yaml`
|
||||
> (rank dimension: model) and `workflow_lgb_sp5d_optstop.yaml` (rank dimension:
|
||||
> portfolio construction). Verified end-to-end on 2026-01-04..2026-08-10:
|
||||
> RankIC 0.071 / net-of-cost excess +20.7% ann (IR 0.70) vs SPY. Only write a new
|
||||
> custom Model/Strategy when these proven paths are insufficient.
|
||||
|
||||
### Label
|
||||
- **5-day forward return `Ref($close,-6)/Ref($close,-1)-1` ≫ 2-day.** IC nearly
|
||||
tripled (0.0207 → 0.0645 standalone; the biggest single lever found). The 2-day
|
||||
target is too noisy.
|
||||
|
||||
### Features
|
||||
- **Stochastic-process features beat hand-rolled TA.** 55-feature set: OU
|
||||
(`sp_ou_*`), 2-state HMM (`sp_hmm_*`), jump intensity (`sp_jump_*`, incl.
|
||||
`sp_max_up`/`sp_max_down`), HARRV vol (`sp_rv*` + `sp_rv_ac1`/`sp_rv_cv_22`),
|
||||
trend (`sp_trend_slope_*`, `sp_logp`), GARCH (`sp_garch_*`), Hurst
|
||||
(`sp_hurst_exponent`), path signatures (`sp_sig_*`, lag 1 & 5), entropy
|
||||
(`sp_ent_*`), realized moments (`sp_rskew_*`/`sp_rkurt_*`/`sp_dsv_*`),
|
||||
catch22 (`sp_c22_*`). IC 0.036 → 0.047 vs the 19-feature v1.
|
||||
- **Do NOT add ta-lib indicators on top** (SP+TA, 74 feats): IC dropped
|
||||
0.047 → 0.031, RankIC 0.047 → 0.020. They're redundant with rv22/hmm/garch/catch22
|
||||
and dilute CSRankNorm + LGBM.
|
||||
- **CSRankNorm** (per-day cross-sectional rank) is important for the rank signal.
|
||||
- Warm-up rows persist as all-NaN feature rows — expected; DropAllNaN/DropnaLabel
|
||||
handle them.
|
||||
|
||||
### Model / training loop
|
||||
- **LambdaRank / rank_xendcg objectives FAIL here** (RankIC → ~0): with only ~50
|
||||
"documents" per query the rank gradient is noise.
|
||||
- **Early-stopping metric beats objective.** MSE objective + early-stop on a
|
||||
**RankIC feval** (mean per-day Spearman) lifted RankIC 0.047 → 0.075 (standalone).
|
||||
- **The workflow gap was qlib's training loop**: `lgb.train` default
|
||||
`first_metric_only=False` + `metric=l2` keeps training while l2 improves after
|
||||
RankIC peaks. `RankICLGBModel` sets `metric='None'` + `first_metric_only=True`
|
||||
so early-stopping tracks RankIC only.
|
||||
- **RankIC-only early stop + bigger/smaller budget is the win**: `num_boost_round
|
||||
3000`, `learning_rate 0.02`, `early_stopping_rounds 200`, `min_data_in_leaf 20`,
|
||||
`lambda_l2 0.5` → test excess **+9.1% ann w/o cost (IR 1.03, maxDD −3.8%)** and
|
||||
**+0.85% ann after costs** — the only config that beat SPY net. Note IC/RankIC
|
||||
themselves were slightly lower (0.042) than the 500-tree run (0.051); the tuned
|
||||
budget selects the iteration maximizing *valid* RankIC, converting to realized
|
||||
excess return.
|
||||
|
||||
### Strategy / portfolio construction
|
||||
- **Long-only construction leaves the edge on the table.** The SP-5d signal has
|
||||
long-short **+31.6% ann (Sharpe 2.51)**, but TopkDropout long-only ≈ flat vs SPY,
|
||||
and OptimalStopControl underperformed (valid-window threshold overfit: valid
|
||||
+7.5% → test −17.7% on one calibration).
|
||||
- **Costs eat most of the gross edge** (+9.1% → +0.85% net). Reduce turnover or go
|
||||
long-short to widen the net edge.
|
||||
- OptimalStopControl thresholds must be calibrated on the *valid* window and are
|
||||
sensitive to overfit — prefer robust defaults or penalize turnover in selection.
|
||||
|
||||
## Gotchas
|
||||
|
||||
- **Installed package copy**: `tac_qlib` in the venv is a copy under
|
||||
`/opt/venv/lib/python3.12/site-packages/tac_qlib/`. After editing any
|
||||
`tac_qlib/contrib/**` module, `cp` it there or the workflow imports the stale
|
||||
version. New subpackages need `mkdir -p` first.
|
||||
- `qlib.backtest` exports `Order` but not `OrderDir`/`Position` at top level —
|
||||
import `Order` from `qlib.backtest`, `OrderDir`/`TradeDecisionWO` from
|
||||
`qlib.backtest.decision`, `Position` from `qlib.backtest.position`.
|
||||
- `qlib.backtest.high_performance_ds` may not export `Order` in this build — don't
|
||||
import from it.
|
||||
- HMM / GARCH / catch22 features must not see test data at fit time: fit the HMM
|
||||
on the train window only (`fit_end=FIT_END`), and compute rolling windows ending
|
||||
at each day. GARCH/entropy use a stride + forward-fill for speed (~5x).
|
||||
- `pycatch22`, `arch`, `hurst`, `antropy`, `hmmlearn` are required for the full
|
||||
feature set; install with `uv pip install --python /app/.venv/bin/python <pkg>`
|
||||
(a C compiler is needed for `pycatch22`). `duckdb` and `pyarrow` are declared in
|
||||
`tac-qlib/pyproject.toml`; if a workflow import fails on either, lazy-install with
|
||||
`uv pip install --python /app/.venv/bin/python duckdb pyarrow`.
|
||||
- `rd_run_workflow` defaults to `wait=false`: it returns immediately with
|
||||
`status: started` and the workflow runs in a background thread — poll
|
||||
`rd_exp_get_run` / `rd_exp_list` for the newest run of the experiment
|
||||
(status `RUNNING` until it finishes), then reuse its `run_id`. Pass
|
||||
`wait=true` only for small windows that finish within the MCP call timeout.
|
||||
- After fixing a YAML model/handler change, remember both `/app/tac-qlib/...` and
|
||||
the `/opt/venv` copy stay in sync.
|
||||
|
||||
## Files this skill is based on
|
||||
|
||||
Minimal, runnable examples live next to this skill in `examples/` — they are the
|
||||
canonical reference for every artifact the skill describes:
|
||||
|
||||
- Workflows (full `record` block → MLflow on disk):
|
||||
- `examples/workflow_minimal.yaml` — the canonical backtest template (req: every
|
||||
traced backtest runs through a workflow like this via `rd_run_workflow`)
|
||||
- `examples/workflow_rankic.yaml` — RankIC-early-stop model wired in
|
||||
- Repo workflows for reference: `tac-qlib/workflows/workflow_lgb_taclake.yaml`,
|
||||
`tune_run1_wider_5d.yaml`, `tune_run2_regularized.yaml`, `tune_run3_label5d_clean_universe.yaml`,
|
||||
`tune_run4_fix_universe_longtrain.yaml`, `tune_run5_longtest.yaml`
|
||||
- Models: `examples/model_rank_gbdt.py` (`RankICLGBModel`: per-day groups +
|
||||
`feval=rankic` + `metric='None'`). Repo: `tac_qlib/contrib/model/rank_gbdt.py`
|
||||
- Strategies: `examples/strategy_optimal_stop.py` (`OptimalStopControl`),
|
||||
`examples/strategy_beta_neutral.py` (doc-only 3L/3S stub — pattern for a
|
||||
custom strategy + Record; not wired into the package)
|
||||
- Handler: `examples/handler.py` (how to subclass `TACHandler`); repo:
|
||||
`tac_qlib/contrib/data/handler.py`; providers: `tac_qlib/data/providers.py`
|
||||
- Feature engineering: `examples/sp_features.py` (OU + Hurst) and
|
||||
`examples/persist_sp_features.py` (persist `sp_*` into the lake features parquet)
|
||||
- Ranking experiments: `examples/run_rank_objectives.py` (mse vs lambdarank vs
|
||||
rank_xendcg ablation on the lake)
|
||||
- Optstop calibration: `examples/run_optstop_compare.py` (valid-window grid +
|
||||
overfit warning)
|
||||
- Traceability tooling: the `rd_trace_*` MCP tools (tac-qlib-rd,
|
||||
`tac_qlib/trace.py`) — see the traceability section above
|
||||
@@ -0,0 +1,70 @@
|
||||
"""Minimal custom DataHandler — how to extend TACHandler for a new feature family.
|
||||
|
||||
`TACHandler(DataHandlerLP)` already routes lake bars + ta-lib features via
|
||||
`LakeFeatureProvider` (see tac_qlib/contrib/data/handler.py). To add a NEW
|
||||
feature family (computed once, persisted into the lake features parquet — see
|
||||
examples/persist_sp_features.py), you only need to:
|
||||
|
||||
1. persist extra columns into features/market=US/timeframe=1d/symbol=*.parquet
|
||||
2. list them in `feature_fields` (they are prefixed with `$` and de-duped)
|
||||
|
||||
A subclass is only needed when the feature must be computed *inside* the qlib
|
||||
pipeline (e.g. as an extra processor). This file sketches that pattern.
|
||||
|
||||
Reference handler structure (from tac_qlib/contrib/data/handler.py):
|
||||
|
||||
class TACHandler(DataHandlerLP):
|
||||
def __init__(self, instruments, start_time, end_time, freq,
|
||||
fit_start_time=None, fit_end_time=None,
|
||||
feature_fields=None, label=None, lake_root=None, market="US",
|
||||
infer_processors=None, learn_processors=None, **kwargs):
|
||||
loader = QlibDataLoader(configured=(feature_fields or self.DEFAULT_FIELDS), freq=freq)
|
||||
super().__init__(instruments, start_time, end_time, freq=freq,
|
||||
data_loader=loader,
|
||||
infer_processors=infer_processors or DEFAULT_INFER_PROCESSORS,
|
||||
learn_processors=learn_processors or DEFAULT_LEARN_PROCESSORS,
|
||||
fit_start_time=fit_start_time, fit_end_time=fit_end_time,
|
||||
process_type=DataHandlerLP.PTYPE_A, **kwargs)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from tac_qlib.contrib.data.handler import DEFAULT_INFER_PROCESSORS, DEFAULT_LEARN_PROCESSORS, TACHandler
|
||||
|
||||
|
||||
class CustomFeaturesHandler(TACHandler):
|
||||
"""TACHandler variant that also loads the lake feature columns passed in.
|
||||
|
||||
Usage from YAML — only the handler kwargs change:
|
||||
|
||||
handler:
|
||||
class: CustomFeaturesHandler
|
||||
module_path: tac_qlib.contrib.data.handler # after adding this class there
|
||||
kwargs:
|
||||
instruments: AAPL,MSFT,QQQ
|
||||
start_time: 2026-03-01
|
||||
end_time: 2026-08-06
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
feature_fields: "$close,sp_ou_alpha,sp_hurst_exponent"
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
feature_fields: Optional[List[str]] = None,
|
||||
infer_processors: Optional[List[Any]] = None,
|
||||
learn_processors: Optional[List[Any]] = None,
|
||||
**kwargs: Any,
|
||||
):
|
||||
# `feature_fields` are passed through with the leading `$` stripped by
|
||||
# TACHandler; infer/learn default to the lake-tuned processor stacks.
|
||||
super().__init__(
|
||||
feature_fields=feature_fields,
|
||||
infer_processors=infer_processors or DEFAULT_INFER_PROCESSORS,
|
||||
learn_processors=learn_processors or DEFAULT_LEARN_PROCESSORS,
|
||||
**kwargs,
|
||||
)
|
||||
@@ -0,0 +1,78 @@
|
||||
"""Minimal RankIC early-stopping LightGBM model (the biggest IC/backtest lever).
|
||||
|
||||
Drop-in replacement for `qlib.contrib.model.gbdt.LGBModel` in a workflow YAML:
|
||||
|
||||
task.model:
|
||||
class: RankICLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_gbdt
|
||||
kwargs: { loss: mse, learning_rate: 0.02, num_boost_round: 3000,
|
||||
early_stopping_rounds: 200, lambda_l2: 0.5 }
|
||||
|
||||
What it changes vs stock LGBModel:
|
||||
* `_prepare_data` builds `lgb.Dataset` with per-day `group` query groups, so
|
||||
metrics are computed per trading day.
|
||||
* `fit` injects `feval=rankic_feval` (mean per-day Spearman) into `lgb.train`
|
||||
and forces `metric='None'` + `first_metric_only=True` so early stopping
|
||||
tracks RankIC — not l2, which keeps improving after RankIC peaks.
|
||||
|
||||
Why: with ~50 instruments per day, ranking objectives (lambda_rank/xendcg)
|
||||
produce near-zero RankIC; MSE objective + RankIC early-stop is what lifts it.
|
||||
|
||||
Install: copy to tac_qlib/contrib/model/rank_gbdt.py AND the /opt/venv copy.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from qlib.contrib.model.gbdt import LGBModel
|
||||
|
||||
|
||||
def rankic_feval(preds: np.ndarray, dataset) -> tuple[str, float, bool]:
|
||||
"""Mean per-day Spearman rank IC between predictions and the label."""
|
||||
label = dataset.get_label()
|
||||
group = dataset.get_group() if hasattr(dataset, "get_group") else None
|
||||
if group is None:
|
||||
return "rankic", _spearman(preds, label), False
|
||||
|
||||
start = 0
|
||||
ics = []
|
||||
for g in group:
|
||||
sl = slice(start, start + g)
|
||||
start += g
|
||||
ics.append(_spearman(preds[sl], label[sl]))
|
||||
return "rankic", float(np.mean(ics)), False
|
||||
|
||||
|
||||
def _spearman(x: np.ndarray, y: np.ndarray) -> float:
|
||||
if len(x) < 2:
|
||||
return 0.0
|
||||
from scipy.stats import spearmanr
|
||||
|
||||
rho, _ = spearmanr(x, y)
|
||||
return float(rho) if rho == rho else 0.0
|
||||
|
||||
|
||||
class RankICLGBModel(LGBModel):
|
||||
"""LGBModel with per-day query groups and RankIC-only early stopping."""
|
||||
|
||||
def _prepare_data(self, dataset, *args, **kwargs):
|
||||
"""Attach per-day group sizes to the train/valid lgb.Dataset."""
|
||||
dtrain, dvalid = super()._prepare_data(dataset, *args, **kwargs)
|
||||
for d, index in ((dtrain, dataset.get_index_by_segment("train")), (dvalid, dataset.get_index_by_segment("valid"))):
|
||||
if d is not None and index is not None:
|
||||
# group by calendar day in order
|
||||
days = pd.Series([i[0] for i in index])
|
||||
group = days.value_counts().sort_index().tolist()
|
||||
d.set_group(np.array(group, dtype=np.int32))
|
||||
return dtrain, dvalid
|
||||
|
||||
def fit(self, dataset, evals_result: Dict[str, Any] | None = None, **kwargs):
|
||||
# force RankIC-only early stopping
|
||||
kwargs.setdefault("feval", rankic_feval)
|
||||
kwargs.setdefault("metric", "None")
|
||||
kwargs.setdefault("first_metric_only", True)
|
||||
return super().fit(dataset, evals_result=evals_result, **kwargs)
|
||||
@@ -0,0 +1,66 @@
|
||||
"""Minimal persistence of computed SP features into the lake features parquet.
|
||||
|
||||
Flow: compute sp_* features per symbol (examples/sp_features.py) and MERGE them
|
||||
into features/market=US/timeframe=1d/symbol=*.parquet so TACHandler /
|
||||
LakeFeatureProvider can route `$sp_ou_theta` etc. from the workflow YAML.
|
||||
|
||||
Run after backfilling bars; re-run drops stale sp_* columns first (see note).
|
||||
|
||||
python examples/persist_sp_features.py --market US --timeframe 1d
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from tac_qlib.data.config import LakeConfig, NON_FEATURE_COLUMNS
|
||||
from examples.sp_features import build_sp_features
|
||||
|
||||
#: columns owned by this feature family (replaced on re-runs, never duplicated)
|
||||
SP_PREFIX = "sp_"
|
||||
|
||||
|
||||
def persist_symbol(lake: LakeConfig, timeframe: str, symbol: str) -> None:
|
||||
bars_path = lake.bar_path(timeframe, symbol)
|
||||
feats_path = lake.features_path(timeframe, symbol)
|
||||
if not bars_path.exists():
|
||||
return
|
||||
bars = pd.read_parquet(bars_path)
|
||||
feats = build_sp_features(bars)
|
||||
# bars have a single 't'/'date' column; align feature rows to it
|
||||
feats = feats.drop(columns=[c for c in NON_FEATURE_COLUMNS if c in feats.columns], errors="ignore")
|
||||
|
||||
feats_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
if feats_path.exists():
|
||||
existing = pd.read_parquet(feats_path)
|
||||
# drop stale sp_* columns before merging (idempotent re-runs)
|
||||
existing = existing[[c for c in existing.columns if not c.startswith(SP_PREFIX)]]
|
||||
merged = pd.merge(existing, feats, on="t", how="left", suffixes=("", "_dup"))
|
||||
merged = merged.loc[:, ~merged.columns.str.endswith("_dup")]
|
||||
# keep original column order + new sp_* appended
|
||||
merged.to_parquet(feats_path, index=False)
|
||||
else:
|
||||
feats.to_parquet(feats_path, index=False)
|
||||
print(f"persisted {symbol}: {len(feats.columns) - 1} sp_* features")
|
||||
|
||||
|
||||
def main() -> None:
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--market", default="US")
|
||||
ap.add_argument("--timeframe", default="1d")
|
||||
ap.add_argument("--symbols", default="", help="comma-separated; default: all lake symbols")
|
||||
args = ap.parse_args()
|
||||
lake_root = os.environ.get("TAC_LAKE_DIR")
|
||||
if not lake_root:
|
||||
raise SystemExit("TAC_LAKE_DIR is required")
|
||||
lake = LakeConfig(lake_root, args.market)
|
||||
symbols = [s.strip().upper() for s in args.symbols.split(",") if s.strip()] or lake.load_symbols()
|
||||
for symbol in symbols:
|
||||
persist_symbol(lake, args.timeframe, symbol)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,69 @@
|
||||
"""Minimal OptimalStopControl threshold calibration — valid-window grid search.
|
||||
|
||||
This repo found OptimalStopControl thresholds overfit the valid window (valid
|
||||
+7.5% → test −17.7% on one calibration). This script runs a small grid over
|
||||
(entry_pct, exit_pct, max_hold_days) on the VALID window, reports per-config
|
||||
excess return + turnover, and warns when the best valid config is a spike.
|
||||
|
||||
Reference repo impl: tac-qlib/examples/run_optstop_compare.py.
|
||||
|
||||
python examples/run_optstop_compare.py --universe AAPL,MSFT,QQQ
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import itertools
|
||||
import os
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
GRID = {
|
||||
"entry_pct": [0.7, 0.85, 0.95],
|
||||
"exit_pct": [0.5, 0.7],
|
||||
"max_hold_days": [5, 10],
|
||||
}
|
||||
|
||||
|
||||
def evaluate_config(lake_root: str, universe: list[str], window: tuple, config: dict) -> dict:
|
||||
"""Simplified stand-in: train the RankIC model, backtest OptimalStopControl
|
||||
on `window`, return (ann_excess_return, turnover, max_drawdown).
|
||||
|
||||
The real repo impl calls qlib.backtest with the strategy and reads
|
||||
report_normal.csv + risk.csv. Keep the interface here so the grid loop is
|
||||
reusable.
|
||||
"""
|
||||
# placeholder — plug in the real backtest here
|
||||
return {"ann_excess": 0.0, "turnover": 0.0, "max_dd": 0.0}
|
||||
|
||||
|
||||
def main() -> None:
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--lake-root", default=os.environ.get("TAC_LAKE_DIR", ""))
|
||||
ap.add_argument("--universe", default="AAPL,MSFT,QQQ,IVV,SMH,TLT")
|
||||
args = ap.parse_args()
|
||||
universe = [s.strip().upper() for s in args.universe.split(",")]
|
||||
valid = ("2026-06-01", "2026-06-30")
|
||||
test = ("2026-07-01", "2026-08-06")
|
||||
|
||||
keys = list(GRID)
|
||||
results = []
|
||||
for combo in itertools.product(*[GRID[k] for k in keys]):
|
||||
config = dict(zip(keys, combo))
|
||||
v = evaluate_config(args.lake_root, universe, valid, config)
|
||||
t = evaluate_config(args.lake_root, universe, test, config)
|
||||
results.append({**config, "valid_excess": v["ann_excess"], "test_excess": t["ann_excess"]})
|
||||
|
||||
df = pd.DataFrame(results).sort_values("valid_excess", ascending=False)
|
||||
print(df.head(10).to_string(index=False))
|
||||
# Overfit check: how far is the best-valid config from the median test config?
|
||||
med = df["test_excess"].median()
|
||||
best = df.iloc[0]
|
||||
print(f"\nmedian test excess: {med:+.3f} | best-valid test excess: {best['test_excess']:+.3f}")
|
||||
if abs(best["test_excess"] - med) > 0.10:
|
||||
print("WARNING: best-valid config is an outlier on test — likely overfit, prefer robust defaults")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,90 @@
|
||||
"""Minimal ranking-objective ablation loop — why lambda_rank fails here.
|
||||
|
||||
This repo found that with only ~50 instruments per day the rank-gradient
|
||||
objectives (lambdarank / rank_xendcg) produce near-zero RankIC, while MSE
|
||||
objective + RankIC early-stop is the winner. This script replays that check by
|
||||
training a few LightGBM variants on the same lake split and printing RankIC.
|
||||
|
||||
Reference repo impl: tac-qlib/examples/run_rank_objectives.py.
|
||||
|
||||
python examples/run_rank_objectives.py --universe AAPL,MSFT,QQQ
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
OBJECTIVES = ["mse", "lambdarank", "rank_xendcg"]
|
||||
|
||||
|
||||
def load_frame(lake_root: str, universe: list[str], start: str, end: str) -> pd.DataFrame:
|
||||
"""Stack lake bars into a qlib-like (datetime, instrument) frame."""
|
||||
from tac_qlib.data.config import LakeConfig
|
||||
|
||||
lake = LakeConfig(lake_root, "US")
|
||||
frames = []
|
||||
for sym in universe:
|
||||
p = lake.bar_path("1d", sym)
|
||||
if p.exists():
|
||||
df = pd.read_parquet(p)[["t", "c"]].rename(columns={"t": "datetime", "c": "close"})
|
||||
df["instrument"] = sym
|
||||
frames.append(df)
|
||||
out = pd.concat(frames, ignore_index=True)
|
||||
out["datetime"] = pd.to_datetime(out["datetime"])
|
||||
out = out[(out["datetime"] >= start) & (out["datetime"] <= end)]
|
||||
return out.set_index(["datetime", "instrument"])
|
||||
|
||||
|
||||
def label_5d(frame: pd.DataFrame) -> pd.Series:
|
||||
close = frame["close"].unstack()
|
||||
lbl = close.shift(-6) / close.shift(-1) - 1
|
||||
return lbl.stack().rename("label")
|
||||
|
||||
|
||||
def train_one(lake_root: str, universe: list[str], objective: str, train: tuple, test: tuple):
|
||||
import lightgbm as lgb
|
||||
|
||||
frame = load_frame(lake_root, universe, train[0], test[1])
|
||||
label = label_5d(frame)
|
||||
data = pd.concat([frame["close"], label], axis=1).dropna()
|
||||
|
||||
tr = data.loc[(data.index.get_level_values(0) >= train[0]) & (data.index.get_level_values(0) <= train[1])]
|
||||
te = data.loc[(data.index.get_level_values(0) >= test[0]) & (data.index.get_level_values(0) <= test[1])]
|
||||
|
||||
dtrain = lgb.Dataset(tr[["close"]], label=tr["label"])
|
||||
dtest = lgb.Dataset(te[["close"]], label=te["label"])
|
||||
params = {"objective": objective, "learning_rate": 0.05, "num_leaves": 15, "verbosity": -1}
|
||||
model = lgb.train(params, dtrain, num_boost_round=100, valid_sets=[dtest])
|
||||
|
||||
pred = model.predict(te[["close"]], num_iteration=model.best_iteration)
|
||||
label_te = te["label"].to_numpy()
|
||||
# per-day RankIC
|
||||
days = te.index.get_level_values(0).unique()
|
||||
ics = []
|
||||
for d in days:
|
||||
m = te.index.get_level_values(0) == d
|
||||
if m.sum() >= 3:
|
||||
ics.append(pd.Series(pred[m]).rank().corr(pd.Series(label_te[m]).rank()))
|
||||
return float(np.nanmean(ics))
|
||||
|
||||
|
||||
def main() -> None:
|
||||
ap = argparse.ArgumentParser()
|
||||
ap.add_argument("--lake-root", default=os.environ.get("TAC_LAKE_DIR", ""))
|
||||
ap.add_argument("--universe", default="AAPL,MSFT,QQQ,IVV,SMH,TLT")
|
||||
args = ap.parse_args()
|
||||
universe = [s.strip().upper() for s in args.universe.split(",")]
|
||||
train = ("2026-03-01", "2026-05-31")
|
||||
test = ("2026-07-01", "2026-08-06")
|
||||
print(f"{'objective':<14}{'test RankIC':>12}")
|
||||
for obj in OBJECTIVES:
|
||||
ic = train_one(args.lake_root, universe, obj, train, test)
|
||||
print(f"{obj:<14}{ic:>12.4f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,80 @@
|
||||
"""Minimal stochastic-process feature computation — OU mean-reversion + Hurst.
|
||||
|
||||
These are the features that beat hand-rolled TA in this repo's 50-ETF runs.
|
||||
Compute them per symbol on a rolling window ENDING at each day (never let them
|
||||
see test data at fit time — see the HMM/GARCH note in SKILL.md).
|
||||
|
||||
Reference repo impl: tac-qlib/examples/sp_features.py (full 55-feature set:
|
||||
OU, HMM, jump, HARRV, trend, GARCH, Hurst, path signatures, entropy, catch22).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
#: rolling window for feature computation (days)
|
||||
LOOKBACK = 250
|
||||
|
||||
|
||||
def compute_ou_features(close: pd.Series) -> pd.DataFrame:
|
||||
"""Ornstein-Uhlenbeck fit: theta (reversion speed), sigma (vol), residual z.
|
||||
|
||||
OU: dx_t = theta (mu - x_t) dt + sigma dW_t (theta is the mean-reversion
|
||||
speed; higher = faster reversion = tradable mean-reversion signal).
|
||||
|
||||
Rolling OLS of dx on lagged log-price gives theta = -b (reversion speed);
|
||||
sigma is the residual std. Vectorized via rolling cov/var.
|
||||
"""
|
||||
logp = np.log(close)
|
||||
dx = logp.diff()
|
||||
x_prev = logp.shift(1)
|
||||
df = pd.DataFrame({"dx": dx, "x": x_prev})
|
||||
|
||||
out = pd.DataFrame(index=close.index, dtype=float)
|
||||
cov = df["dx"].rolling(LOOKBACK, min_periods=30).cov(df["x"])
|
||||
var = df["x"].rolling(LOOKBACK, min_periods=30).var()
|
||||
theta = (-cov / var).rename("sp_ou_theta")
|
||||
out["sp_ou_theta"] = theta
|
||||
out["sp_ou_sigma"] = df["dx"].rolling(LOOKBACK, min_periods=30).std()
|
||||
# standardized residual z = (x - mu) / sigma of the fitted process
|
||||
mu = df["x"].rolling(LOOKBACK, min_periods=30).mean()
|
||||
scale = np.sqrt(np.clip(1 / (2 * theta + 1e-9), 0, None))
|
||||
out["sp_ou_zscore"] = (df["x"] - mu) / (out["sp_ou_sigma"] * scale)
|
||||
return out
|
||||
|
||||
|
||||
def compute_hurst(close: pd.Series, lookback: int = 100) -> pd.Series:
|
||||
"""Rolling Hurst exponent via rescaled range (R/S). H>0.5 = trending."""
|
||||
def _hurst(x: np.ndarray) -> float:
|
||||
if len(x) < 20:
|
||||
return np.nan
|
||||
lags = range(2, min(len(x) // 2, 50))
|
||||
tau = []
|
||||
for lag in lags:
|
||||
diff = x[lag:] - x[:-lag]
|
||||
tau.append(np.sqrt(np.std(diff)))
|
||||
tau = np.array(tau)
|
||||
lags = np.array(lags, dtype=float)
|
||||
poly = np.polyfit(np.log(lags), np.log(tau), 1)
|
||||
return float(poly[0])
|
||||
|
||||
return close.rolling(lookback, min_periods=20).apply(lambda w: _hurst(w.to_numpy()), raw=False).rename(
|
||||
"sp_hurst_exponent"
|
||||
)
|
||||
|
||||
|
||||
def build_sp_features(bars: pd.DataFrame) -> pd.DataFrame:
|
||||
"""bars: lake 1d bars indexed by (datetime, instrument) or a symbol frame."""
|
||||
if isinstance(bars.index, pd.MultiIndex):
|
||||
frames = []
|
||||
for inst, sub in bars.groupby(level=1):
|
||||
close = sub.droplevel(1)["close"]
|
||||
feats = pd.concat([compute_ou_features(close), compute_hurst(close)], axis=1)
|
||||
feats["instrument"] = inst
|
||||
frames.append(feats.reset_index())
|
||||
out = pd.concat(frames).set_index(["datetime", "instrument"])
|
||||
else:
|
||||
close = bars["close"]
|
||||
out = pd.concat([compute_ou_features(close), compute_hurst(close)], axis=1)
|
||||
return out
|
||||
@@ -0,0 +1,82 @@
|
||||
"""Minimal beta-neutral 3L/3S strategy + record — stub of tac_qlib/contrib/strategy/beta_neutral.py.
|
||||
|
||||
Strategy side: subclass BaseSignalStrategy, hold ~3 long + 3 short equally
|
||||
weighted (dollar-neutral) with TP/SL and a hard close at the horizon. The beta
|
||||
comes from regression of daily returns on the benchmark in `_prepare_betas`.
|
||||
|
||||
Record side (BetaNeutralRecord): a custom `Record` that simulates the 3L/3S
|
||||
portfolio after training and logs report / trades / risk.csv into the MLflow
|
||||
run — the pattern to follow for any custom Record.
|
||||
|
||||
Wire the record into the workflow YAML:
|
||||
|
||||
record:
|
||||
- class: BetaNeutralRecord
|
||||
module_path: tac_qlib.contrib.strategy.beta_neutral
|
||||
kwargs: { benchmark: QQQ, n_long: 3, n_short: 3 }
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from qlib.backtest import Order
|
||||
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import BaseSignalStrategy
|
||||
|
||||
|
||||
class BetaNeutralStrategy(BaseSignalStrategy):
|
||||
"""3 long / 3 short dollar-neutral template with TP/SL and hard close."""
|
||||
|
||||
def __init__(self, *, n_long: int = 3, n_short: int = 3, tp: float = 0.06, sl: float = -0.05, **kwargs: Any):
|
||||
super().__init__(**kwargs)
|
||||
self.n_long = n_long
|
||||
self.n_short = n_short
|
||||
self.tp = tp
|
||||
self.sl = sl
|
||||
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
start_time, end_time = self.trade_calendar.get_step_time(trade_step)
|
||||
pred_start, pred_end = self.trade_calendar.get_step_time(trade_step - 1)
|
||||
pred = self.signal.get_signal(start_time=pred_start, end_time=pred_end)
|
||||
|
||||
orders: List[Order] = []
|
||||
if pred is not None and len(pred):
|
||||
daily = pred.groupby(level=0).mean().iloc[-1].dropna().sort_values()
|
||||
longs = daily.tail(self.n_long).index.tolist()
|
||||
shorts = daily.head(self.n_short).index.tolist()
|
||||
for inst in longs:
|
||||
orders.append(self._order(inst, 1, start_time, end_time))
|
||||
for inst in shorts:
|
||||
orders.append(self._order(inst, -1, start_time, end_time))
|
||||
return TradeDecisionWO(orders, self)
|
||||
|
||||
def _order(self, inst, direction, start_time, end_time):
|
||||
price = self.trade_exchange.get_close(inst, end_time) or 1.0
|
||||
qty = int(self.trade_exchange.account.cash / (len(self.trade_exchange.get_positions()) + 1) / price)
|
||||
return Order(
|
||||
inst,
|
||||
qty,
|
||||
start_time,
|
||||
end_time,
|
||||
direction=OrderDir.BUY if direction > 0 else OrderDir.SELL,
|
||||
type="market",
|
||||
)
|
||||
|
||||
|
||||
class BetaNeutralRecord: # subclass qlib.workflow.record_temp.Record in the real impl
|
||||
"""Custom record that backtests 3L/3S and logs report/trades/risk.csv."""
|
||||
|
||||
def __init__(self, *, benchmark: str = "QQQ", n_long: int = 3, n_short: int = 3, **_: Any):
|
||||
self.benchmark = benchmark
|
||||
self.n_long = n_long
|
||||
self.n_short = n_short
|
||||
|
||||
def generate(self, **kwargs):
|
||||
# Real impl: run qlib.backtest with BetaNeutralStrategy on the recorded
|
||||
# pred, write report_normal.csv / positions_normal.csv / risk.csv into
|
||||
# the current MLflow run's artifact dir, then log the headline metrics.
|
||||
print("BetaNeutralRecord.generate: simulate 3L/3S and log artifacts")
|
||||
@@ -0,0 +1,77 @@
|
||||
"""Minimal OptimalStopControl strategy — a stub of tac_qlib/contrib/strategy/optimal_stop.py.
|
||||
|
||||
Subclasses qlib's BaseSignalStrategy; override `generate_trade_decision` to build
|
||||
`qlib.backtest.Order`s and return a `TradeDecisionWO`. The real implementation
|
||||
gates entry by cross-sectional signal percentile, exits by percentile / time /
|
||||
stop-loss, and sizes equal-weight with `risk_degree` control.
|
||||
|
||||
Wire into a workflow YAML under PortAnaRecord.config.strategy:
|
||||
|
||||
strategy:
|
||||
class: OptimalStopControl
|
||||
module_path: tac_qlib.contrib.strategy.optimal_stop
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 10
|
||||
entry_pct: 0.85
|
||||
exit_pct: 0.7
|
||||
max_hold_days: 10
|
||||
min_hold_days: 2
|
||||
sl: -0.08
|
||||
risk_degree: 0.95
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
import numpy as np
|
||||
|
||||
from qlib.backtest import Order
|
||||
from qlib.backtest.decision import OrderDir, TradeDecisionWO
|
||||
from qlib.contrib.strategy.signal_strategy import BaseSignalStrategy
|
||||
|
||||
|
||||
class OptimalStopControl(BaseSignalStrategy):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
topk: int = 10,
|
||||
entry_pct: float = 0.85,
|
||||
exit_pct: float = 0.7,
|
||||
max_hold_days: int = 10,
|
||||
min_hold_days: int = 2,
|
||||
sl: float = -0.08,
|
||||
risk_degree: float = 0.95,
|
||||
**kwargs: Any,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.topk = topk
|
||||
self.entry_pct = entry_pct
|
||||
self.exit_pct = exit_pct
|
||||
self.max_hold_days = max_hold_days
|
||||
self.min_hold_days = min_hold_days
|
||||
self.sl = sl
|
||||
self.risk_degree = risk_degree
|
||||
|
||||
def generate_trade_decision(self, execute_result=None):
|
||||
"""Build orders for one trade step (minimal sketch — see repo impl)."""
|
||||
trade_step = self.trade_calendar.get_trade_step()
|
||||
# signal is known at t-1 via shift=-1 in the signal object
|
||||
start_time, end_time = self.trade_calendar.get_step_time(trade_step)
|
||||
pred_start, pred_end = self.trade_calendar.get_step_time(trade_step - 1)
|
||||
pred = self.signal.get_signal(start_time=pred_start, end_time=pred_end)
|
||||
|
||||
orders: List[Order] = []
|
||||
if pred is not None and len(pred):
|
||||
# take the top-k by cross-sectional percentile, equal-weight size
|
||||
cross = pred.groupby(level=0).rank(pct=True) # 0..1 per day
|
||||
keep = pred.index[cross >= 1.0 - self.entry_pct]
|
||||
for inst, (dt, _instr) in zip(keep, keep):
|
||||
price = self.trade_exchange.get_close(inst, end_time) or 1.0
|
||||
qty = int((self.risk_degree * self.trade_exchange.account.cash) / (self.topk * price))
|
||||
if qty > 0:
|
||||
orders.append(
|
||||
Order(inst, qty, start_time, end_time, direction=OrderDir.BUY, type="market")
|
||||
)
|
||||
return TradeDecisionWO(orders, self)
|
||||
@@ -0,0 +1,107 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# MINIMAL workflow — the canonical "run a backtest" template for the skill.
|
||||
#
|
||||
# Every traced backtest runs through a workflow YAML like this one via
|
||||
# rd_run_workflow, so the `record` blocks write MLflow artifacts to disk
|
||||
# (<lake>/mlruns/<exp_id>/<run_id>). The traced experiment's ref id IS the
|
||||
# mlflow run id returned by rd_run_workflow.
|
||||
#
|
||||
# Trigger:
|
||||
# rd_run_workflow config_path=examples/workflow_minimal.yaml \
|
||||
# experiment_name=tac-rd-minimal
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-minimal" }
|
||||
|
||||
task:
|
||||
model:
|
||||
class: LGBModel
|
||||
module_path: qlib.contrib.model.gbdt
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.05
|
||||
num_leaves: 15
|
||||
n_estimators: 200
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
reg_alpha: 0.01
|
||||
reg_lambda: 0.01
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: AAPL,MSFT,QQQ,IVV,SMH,TLT
|
||||
start_time: 2026-03-01
|
||||
end_time: 2026-08-06
|
||||
fit_start_time: 2026-03-01
|
||||
fit_end_time: 2026-05-31
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
segments:
|
||||
train: [2026-03-01, 2026-05-31]
|
||||
valid: [2026-06-01, 2026-06-30]
|
||||
test: [2026-07-01, 2026-08-06]
|
||||
|
||||
# Record block — REQUIRED. Each entry writes one artifact family to mlruns:
|
||||
# SignalRecord pred.pkl + label.pkl
|
||||
# SigAnaRecord IC / Rank IC series + long-short group returns
|
||||
# PortAnaRecord backtest report / positions / risk
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: { ana_long_short: true, ann_scaler: 252 }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 2
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-07-01
|
||||
end_time: 2026-08-06
|
||||
account: 1000000
|
||||
benchmark: QQQ
|
||||
exchange_kwargs:
|
||||
codes: AAPL,MSFT,QQQ,IVV,SMH,TLT
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
@@ -0,0 +1,113 @@
|
||||
# -----------------------------------------------------------------------------
|
||||
# RankIC early-stop workflow — minimal example wiring the custom model.
|
||||
#
|
||||
# model_rank_gbdt.py must be importable: copy it (or symlink) into
|
||||
# tac_qlib/contrib/model/ and sync to /opt/venv site-packages (see SKILL.md
|
||||
# "Installed package copy" gotcha). Then run:
|
||||
#
|
||||
# rd_run_workflow config_path=examples/workflow_rankic.yaml \
|
||||
# experiment_name=tac-rd-rankic
|
||||
# -----------------------------------------------------------------------------
|
||||
{%- set LAKE = TAC_LAKE_DIR %}
|
||||
|
||||
qlib_init:
|
||||
provider_uri: "{{ LAKE }}"
|
||||
region: us
|
||||
expression_cache: null
|
||||
dataset_cache: null
|
||||
|
||||
calendar_provider:
|
||||
class: tac_qlib.data.providers.LakeCalendarProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
instrument_provider:
|
||||
class: tac_qlib.data.providers.LakeInstrumentProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
|
||||
feature_provider:
|
||||
class: tac_qlib.data.providers.LakeFeatureProvider
|
||||
kwargs: { lake_root: "{{ LAKE }}", market: US }
|
||||
|
||||
exp_manager:
|
||||
class: MLflowExpManager
|
||||
module_path: qlib.workflow.expm
|
||||
kwargs: { uri: "sqlite:///mlruns.db", default_exp_name: "tac-rd-rankic" }
|
||||
|
||||
task:
|
||||
model:
|
||||
# Custom model — see examples/model_rank_gbdt.py (RankICLGBModel):
|
||||
# per-day query groups + feval=rankic + metric='None' so early-stopping
|
||||
# tracks mean per-day Spearman instead of l2.
|
||||
class: RankICLGBModel
|
||||
module_path: tac_qlib.contrib.model.rank_gbdt
|
||||
kwargs:
|
||||
loss: mse
|
||||
learning_rate: 0.02
|
||||
num_leaves: 15
|
||||
num_boost_round: 3000
|
||||
early_stopping_rounds: 200
|
||||
min_data_in_leaf: 20
|
||||
lambda_l1: 0.0
|
||||
lambda_l2: 0.5
|
||||
colsample_bytree: 0.8
|
||||
subsample: 0.8
|
||||
subsample_freq: 1
|
||||
seed: 2026
|
||||
|
||||
dataset:
|
||||
class: DatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: TACHandler
|
||||
module_path: tac_qlib.contrib.data.handler
|
||||
kwargs:
|
||||
instruments: AAPL,MSFT,QQQ,IVV,SMH,TLT
|
||||
start_time: 2026-03-01
|
||||
end_time: 2026-08-06
|
||||
fit_start_time: 2026-03-01
|
||||
fit_end_time: 2026-05-31
|
||||
freq: day
|
||||
lake_root: "{{ LAKE }}"
|
||||
market: US
|
||||
label: "Ref($close,-6)/Ref($close,-1)-1"
|
||||
infer_processors:
|
||||
- { class: DropAllNaN, kwargs: {} }
|
||||
- { class: ProcessInf, kwargs: {} }
|
||||
- { class: CSRankNorm, kwargs: {} }
|
||||
- { class: ZScoreNorm, kwargs: {} }
|
||||
- { class: Fillna, kwargs: {} }
|
||||
segments:
|
||||
train: [2026-03-01, 2026-05-31]
|
||||
valid: [2026-06-01, 2026-06-30]
|
||||
test: [2026-07-01, 2026-08-06]
|
||||
|
||||
record:
|
||||
- { class: SignalRecord, module_path: qlib.workflow.record_temp, kwargs: {} }
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: { ana_long_short: true, ann_scaler: 252 }
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config:
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy
|
||||
kwargs:
|
||||
signal: "<PRED>"
|
||||
topk: 2
|
||||
n_drop: 1
|
||||
only_tradable: true
|
||||
risk_degree: 0.95
|
||||
backtest:
|
||||
start_time: 2026-07-01
|
||||
end_time: 2026-08-06
|
||||
account: 1000000
|
||||
benchmark: QQQ
|
||||
exchange_kwargs:
|
||||
codes: AAPL,MSFT,QQQ,IVV,SMH,TLT
|
||||
deal_price: $close
|
||||
freq: day
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5.0
|
||||
risk_analysis_freq: 1d
|
||||
Reference in New Issue
Block a user