Backtesting

Evaluate strategies on historical candles before risking capital. OpenTrade supports native Python marketplace strategies and external Freqtrade strategies.

From the chart

Open Chart and select an instrument
Open the Strategies tab in the right sidebar
Install or enable a strategy artifact
Run backtest with parameters and date range
Review markers on the chart and job results in the panel

Strategy types

Type	Runtime	Notes
Marketplace Python	`strategy-worker`	Author in sidebar or versions page
Freqtrade catalog	`freqtrade-worker`	Git-pinned `IStrategy` repos

Builtin examples ship in the marketplace seeds; Freqtrade strategies import via the strategy catalog API (operator workflow).

Freqtrade features (shipped)

Single backtest jobs
Backtest matrix — compare up to 50 versions across a pair list (caps apply)
Chart overlay markers for results
Async overlay preview for installed Freqtrade strategies (debounced on chart)

Native Python backtesting

Available for marketplace strategies with marker output. Narrower than the Freqtrade path in some edge cases — prefer Freqtrade for complex IStrategy ecosystems.

Paper trading

Related but separate: simulate fills on new candles. Partial — sessions can be created; full automated evaluation is not finished. Do not treat as production-ready.

Not available: live exchange execution from backtest results, Monte Carlo / walk-forward UI wizards, or guaranteed parity with Freqtrade live bots.

Metrics and analysis

Job results expose trade lists and summary stats depending on strategy output. Use chart markers for visual review. For deep quant metrics (Sharpe, Sortino, etc.), export or extend strategy output — not all metrics are shown in UI today.

Best practices

Include realistic commission and slippage in strategy design where supported
Test multiple time periods and pairs
Watch for overfitting — keep parameter count modest
For Freqtrade strategies, respect startup_candle_count and lookahead constraints from Freqtrade docs