Qlib Strategy

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qlib_strategy inherits qlib_factor and qlib_a158 to study portfolio policies using fixed predictions. The shared framework owns quotes, price limits, valuation, cash, side fees, orders, positions and trades.

Rank-retention policy

• Retain holdings inside the current eligible TopN.
• After minimum_holding_days elapsed market dates, exit the worst-ranked holdings outside the buffered rank threshold first.
• Cap fills per side per day at max(1,floor(N × replacement_fraction)); initial construction is exempt.
• Blocked exits retain capital. Unfilled entries are not replaced, and sells precede buys. New positions receive at most 1/N equity; retained holdings are not rebalanced.

Defaults: minimum_holding_days=10, replacement_fraction=0.2, rank_buffer=1. Top5/10/20/30 permit 1/2/4/6 fills per side per day; this is a count cap, not a notional turnover cap. There is no fixed-expiry parameter; planned_exit_date is null.

Install and run

Research plugins are not published to PyPI. Install the AxonX core and clone the repository; run the following commands from its root. For local Agent development, install in dependency order in the execution service's Python environment:

axonx plugin install -e plugins/qlib_a158
axonx plugin install -e plugins/qlib_factor
axonx plugin install -e plugins/qlib_strategy

Restart persistent services after Python source changes; reinstall after dependency or entry-point metadata changes. Omit -e for fixed-version experiments. Deploy remotely with ordinary installation, installing upstream plugins in order on the same target service; configure AXONX_TARGET_TOKEN first and replace the example address below:

axonx plugin install plugins/qlib_a158 --target http://research.example:1024
axonx plugin install plugins/qlib_factor --target http://research.example:1024
axonx plugin install plugins/qlib_strategy --target http://research.example:1024
axonx submit --task qlib_strategy_backtest --source-tasks '<predict_task_id>' \
  --top-ns '[20,30]' --as-of-date 20261008 \
  --minimum-holding-days 3 --replacement-fraction 0.2 --rank-buffer 1 \
  --buy-cost-rate 0.0005 --sell-cost-rate 0.0015 --target http://research.example:1024

Reuse successful base or factor predictions. Record returned Task / Run IDs and wait for success before reading results. Upstream qlib_strategy_etl, qlib_strategy_analysis, qlib_strategy_train and qlib_strategy_predict register factor-layer implementations; policy comparisons reuse predictions. Task-only wheel updates through the remote installation Job apply without restarting; restart when restart_required is true and verify Task definitions. Direct pip/source changes require restart. Use compatible AxonX core and plugin versions, following the plugin package requirements.

Experiments

Settings, full results and artifact provenance are maintained in the research experiment guide. The three configurations were rerun on machine 45 with 0.05% buy / 0.15% sell fees.

AxonX owns protocol v1 in axonx.task.protocol, using the existing TaskType as task_type. Training exposes output_params.protocol and one models.json collection; member/checkpoint metadata sidecars are removed. Offline and online predictions share one model_identity (models_sha256, features_sha256), dates, score_columns and file hash. Feature contracts, semantic fingerprints and verified model snapshots are shared AxonX helpers. Missing headers and old formats are rejected; regenerate artifacts. Package versions remain independent.