Skip to content

Quantitative research ​

Use AxonX to turn research steps into Tasks with explicit inputs, inspectable artifacts, and upstream relationships. Start with a working service and the demo tutorial. Research algorithms and data requirements belong to installed plugins.

Choose the next step ​

GoalGuideResult
Execute the Alpha158 chainResearch workflowSuccessful ETL → Train → Predict → Backtest records
Prepare historical dataTushare dataRaw partitions and master data covering the experiment
Inspect stage outputsReading resultsFeature, model, prediction, and backtest evidence
Evaluate a changeExperiment designControls, ablations, a locked candidate, and independent confirmation
Interpret portfolio metricsBacktest methodologyReturns, costs, drawdown, and execution assumptions
Compare two strategiesStrategy comparisonMetrics over common valid dates

Factor analysis branches from ETL independently; it is not required before training. Submission returns a handle; wait for that execution to succeed before passing its Task ID downstream. Lineage records dependencies without automatically scheduling the chain.

Choose a research plugin ​

Plugin management explains installation and discovery in the execution environment. Alpha158 provides the baseline price/volume features, LightGBM training, and TopN backtest. Alpha158 Enhanced adds independently registered Tasks and configurable feature groups.

Discover the installed Task schemas before submitting. Keep parameters, algorithms, and artifact definitions with the plugin; use research artifact contracts when implementing outputs for Studio.

Follow the agent-developed experiment ​

The README benchmark follows Codex developing a separate plugin, running ablations, locking a configuration, and checking an independent period. Confirmation-period Top10 net annualized return rose from −5.74% to 28.21%, and Top20 from −3.24% to 24.93%.

Read experiment design for the reusable method and the plugin's complete results for recorded evidence. For agent access, continue with external agents.

Agent-native quant research.