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Strategy Lab

Build and validate a strategy in one workspace.

Strategy Lab brings the complete strategy definition, historical test and platform export into one visual workspace. Configure the market assumptions, express the entry logic, define risk controls and inspect the result without writing code.

Design visually Build BUY and SELL entries from reusable price, indicator and logical condition blocks.

Test the full contract Include data, costs, risk, limits, trading windows and exit behavior in the historical test.

Continue to the broker Save the strategy and use the available platform export after reviewing its evidence.

TR-GAB Strategy Lab showing strategy configuration controls and an empty backtest result workspace

Strategy Lab captured automatically from the current development version with safe demo data and no backtest executed.

Define the test environment

Start by selecting the market-data source, symbol and timeframe. The data mode defines the historical window, while spread, slippage and engine precision describe how orders should be simulated. These settings are part of the strategy contract and are preserved with the definition.

Optional Macro Channel controls let you calculate and attach a higher-timeframe regime to the strategy. Use it to visualize the broader market context or to guard entries during the backtest.

Build the entry logic

BUY and SELL entries are composed from condition blocks. A condition can compare:

  • Price with an indicator.
  • An indicator with a numeric threshold.
  • One indicator with another indicator.
  • Crosses and other supported logical relationships over configured periods, shifts and timeframes.

Conditions can be enabled independently and combined inside an entry. Reusable condition banks help apply the same idea across Labs, miners and Workflows without rebuilding it by hand.

Configure execution and risk

The strategy editor keeps the controls that materially change a result next to the entry logic:

  • Money-management method, initial capital, leverage and maximum lots.
  • Static or dynamic account limits, daily pauses and trade limits.
  • Stop-loss, take-profit, breakeven and trailing-stop behavior.
  • Broker commission and swap assumptions when enabled.
  • Trading windows, Friday exits and end-of-day exits.

These controls are evaluated as strategy behavior, not presentation-only metadata. Review them before comparing results or exporting to a target platform.

Run and inspect the backtest

Select Run Backtest when the strategy definition and data range are ready. Strategy Lab returns the KPI summary, equity curve, price chart and trade list for the internal engine. Individual trades can be inspected to understand why and when a position opened or closed.

Use the result as evidence, not as a promise. A useful next step is to retest on a different period or market, then apply robustness and walk-forward validation in a Workflow.

ConfigureBuild entriesBacktestValidateExport

Save, reuse and export

The Library can save and load the full Strategy Lab configuration. Copy Config makes the current setup available to compatible product surfaces, while automatic naming creates a consistent name from the configured strategy.

Platform actions are separate from the internal backtest. They generate the native artifact for the selected target and use the configured broker connection and destination. Always review the target-platform test before deploying an exported strategy.

A practical first strategy

  1. Select the source, symbol, timeframe and historical window.
  2. Set realistic spread, slippage, commission and swap assumptions.
  3. Add one focused BUY or SELL entry and define its exits.
  4. Configure money management, account limits and the trading window.
  5. Run the internal backtest and inspect KPIs, trades and the equity curve.
  6. Retest and validate the candidate in a Workflow.
  7. Export only after the target-platform result is understood.

Keep the first test explainable. A small strategy definition with realistic costs is easier to diagnose, compare and improve than a large rule set optimized around one historical result.