Your strategy.
Your rules.
ModelsBuild momentum systems, volatility targets and models of models. Work with your agent or bring your own code. ENSEMBLE provides the data, execution and research record.
Combine equity momentum, a bond trend model and a commodity strategy. Balance their risk, cap any one strategy’s weight, and recalculate each week.
Start with your models
Use entire strategies as the building blocks of another model.
Choose how they work together
Apply a reusable optimizer to the child models’ return histories.
Make it your own
Set the lookback, position limits and rebalance rules in your model.
Illustrative logic, not a built model or investment recommendation.
Choose how you build.
Describe the logic
Explain your universe, allocation rules and constraints in natural language. Review the generated Python and build result before relying on them.
Supply your code
Bring Python that meets the model contract. Validate its inputs and dependencies, then build and inspect it within the same lifecycle.
Work with an agent
Connect an MCP client or use the API and CLI. Your agent can discover assets, create models and inspect stored results through supported interfaces.
Composition + optimizers
Make good research reusable.
Combine child models into a parent allocation. Store custom optimizer code and reference it consistently across models. Inspect both the underlying assets and the logic that combines them.
Composition has depth and runtime limits. Changes to a dependency can change later results; inspect referenced code and rebuild or refresh deliberately.
Two child models. One allocation.
Equity sleeve
60%
VTI + VXUS
Defensive sleeve
40%
BND + GLD
Bring your own optimizer
Store its source. Reference it across models. Review dependencies when it changes.
Follow the work.
Build → Run → AllocationSource
Script + hash
See the logic that ran.
Inputs
Parameters + dependencies
Understand the choices behind it.
Execution
Run + data as of
Check completion and freshness.
Result
Allocations + return series
Compare and inspect the output.
Stored work
Understand the result.
Then improve it.
Read the script, inspect inputs, compare stored runs and examine model backtests. Builds and script hashes help connect an output to the logic that produced it.
Backtests remain hypothetical. Data dates provide context, not immutable snapshots of every dependency or historical data revision.
Maintained allocations
Continue beyond the first build.
Enable a schedule to refresh the model on supported trading days. Inspect the latest successful allocation and its data date. Retrieve outputs through the platform interfaces or download a broker-format file on Business.
Schedules respect readiness, available data and spending caps. Exports do not place trades or confirm an asset’s acceptance at a broker.
Research that keeps working.
Schedule
Enabled trading-day refresh
Subject to readiness, data availability and your spending cap.
Latest successful run
Allocation available
Inspect the run date and data as of before using its weights.
To your next step