Backtest

With your algorithm created, it’s time to see how it would have performed on historical data.

1. Configure a Backtest

   With your algorithm selected, open the backtest panel:

   • Symbol: BTC/USDC
   • Start Date: 6 months ago
   • End Date: Today
   • Starting Capital: $1,000 (or whatever notional you want to simulate)
   • Leverage: starts at 1x — increase if your live deployment will use higher leverage

   Caution

   Use at least 3-6 months of data for meaningful results. Short backtests can be misleading — a strategy might look great over 2 weeks but fail over longer periods.

2. Run the Backtest

   Click Run Backtest. The engine simulates your strategy against 6 months of historical BTC data using the exact same logic that would run live. Wait for it to complete.

3. Read Your Results

   The results panel shows everything you need:

   Key metrics — Check these first:

   Metric	What It Tells You	Good Range
   Win Rate	% of trades that were profitable	> 40% (depends on R:R)
   Profit Factor	Gross profit / gross loss	> 1.5
   Sharpe Ratio	Risk-adjusted return (annualized)	> 1.0
   Sortino Ratio	Like Sharpe but only penalizes downside	> 1.5
   Max Drawdown	Largest peak-to-trough decline	< 20%

   Equity curve — This is the visual story of your strategy. A steadily rising curve is what you want. Sharp drops are drawdowns.

   Indicator usefulness — Each indicator gets a score (0-100) showing how much it contributes. Low scores mean the indicator might be redundant.

4. Interpret What You See

   Ask yourself:

   • Sharpe > 1.0? The strategy has risk-adjusted edge.
   • Max drawdown < 20%? The risk is manageable.
   • Win rate + profit factor make sense together? A 35% win rate with 3:1 R:R is fine.
   • Equity curve smooth? Or does all profit come from 2-3 lucky trades?

   If the numbers don’t look great — that’s normal for a first attempt! The next step is to iterate: adjust thresholds, try different indicators, or test on a different timeframe.

   Note

   Keep a mental (or written) log of what you changed and why. It’s easy to lose track after several iterations. Version numbers help, but knowing why you made each change is what accelerates learning.

5. Switch to Optimize Mode

   Happy with your strategy logic but not sure about the exact parameter values? Instead of manually tweaking and re-running, let the optimizer find better settings for you.

   At the top of the Test Workspace, switch from Backtest to Optimize. The panel changes to show optimization configuration.

6. Configure Your Optimization

   Set up three things:

   1. Optimization Goal — What should the optimizer maximize?

   Goal	Best For
   Balanced (Sharpe)	General-purpose — good risk-adjusted returns (default)
   Max Sharpe	Maximizing risk-adjusted performance
   Min Drawdown	Conservative strategies — minimize worst-case loss
   Max PnL	Aggressive strategies — maximize raw profit

   2. Search Ranges — Which parameters should the optimizer vary, and within what bounds?

   The optimizer comes pre-loaded with sensible defaults:

   Parameter	Range	Step
   RSI Threshold	20–40	1
   Stop Loss %	0.5–3.0	0.1
   Take Profit %	0.5–5.0	0.1

   Use the range sliders to adjust bounds, or click + Add to include more parameters. Each parameter needs a name, a path to the algorithm setting it controls, and a range.

   3. Validation Settings — How should the optimizer split historical data?

   Setting	What It Does	Example
   Train Days	Window used to test each parameter set	90
   Test Days	Out-of-sample window to validate	20
   Step Days	How far the window slides forward between folds	20

   4. Trials — How many parameter combinations to try (default: 10, minimum: 5).

   Note

   Start with the defaults. 10 trials is enough to get initial results. Increase trials if you have a large parameter space.

7. Run the Optimization

   Click Run Optimize. The optimizer uses Bayesian search (Optuna TPE) to intelligently explore parameter combinations — each trial informs the next, so it converges faster than random search.

   Watch the progress bar as trials complete. You’ll see:

   • Trial count — how many have run out of the total
   • Best score — the highest score found so far
   • Top 5 leaderboard — the best parameter combinations ranked by score, with test PnL for each

   If you see the score plateau early, hit Stop Search — results from completed trials are preserved.

8. Read Your Optimization Results

   When the optimizer finishes, review the results:

   • Best parameters — the parameter values that scored highest
   • Leaderboard — top 5 trials with their scores and test PnL
   • Fold detail — performance breakdown per cross-validation fold (switch to the Folds tab)

   Caution

   Look at the Folds tab. If train metrics are much better than test metrics, the parameters may be overfit to historical data. Consistent performance across folds is what you want.

   Take the best parameters, update your algorithm, and run a fresh backtest to confirm. If the results hold up — you’re ready to deploy.

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What’s Next?

Want to…	Go to
Deploy your algorithm live	Deploy Walkthrough →
Learn all backtest metrics in depth	Backtest Features →
Understand optimization and cross-validation	Backtest Features → Optimization