Optimiser Guide

The Optimiser automatically tests hundreds of parameter combinations and finds the settings that produced the best historical performance — saving you from manually running backtests one by one.

Note

The Optimiser is available on Silver and Gold plans.

How it works 

Normally your strategy uses fixed values — RSI period 14, Stop Loss 5%, rebalance every 2 weeks. The Optimiser lets you provide multiple values for each parameter, then automatically runs every combination and returns a ranked table of results.

Example: If you set:

RSI Period: 7, 14, 21
Stop Loss: 0.03, 0.05, 0.10
Primary Interval: 1w, 2w

The Optimiser runs 3 × 3 × 2 = 18 separate backtests and shows you all 18 results ranked by Sharpe Ratio.

Enabling the Optimiser 

Open your Config block.
Set the Optimizer dropdown from Off → On.
The parameter fields that support optimisation are highlighted in a different colour.

Once enabled, you can enter comma-separated values in any highlighted field:

Stop Loss:          0.03, 0.05, 0.07, 0.10
Take Profit:        0.10, 0.15, 0.20
Primary Interval:   1w, 2w

Tip

You can also enter comma-separated values in indicator blocks — for example, RSI Period: 7, 14, 21 or Bollinger Bands Period: 15, 20, 25. All optimisable fields across the entire workspace are combined.

Reading the results table 

After the Optimiser run completes, a results table appears with one row per parameter combination:

Column	What it means
Parameters	The specific values used in this combination.
Sharpe Ratio	Risk-adjusted return. Primary sorting metric.
Total Return	Raw percentage gain over the test period.
Max Drawdown	Worst peak-to-trough decline.
# Trades	Total number of trades executed.
Win Rate	Percentage of profitable trades.

Sort by Sharpe Ratio, not Total Return. Total Return is easy to inflate with a lucky parameter set; Sharpe penalises excessive risk-taking.

Choosing the best parameters 

Do not blindly pick the top-ranked row. Instead, look for stability clusters:

RSI 14, SL 5%  → Sharpe 1.42  ← Best
RSI 14, SL 7%  → Sharpe 1.39
RSI 21, SL 5%  → Sharpe 1.35
RSI 7,  SL 3%  → Sharpe 1.61  ← Higher but isolated
RSI 7,  SL 3%  → Sharpe 0.82 (different period test)

The top result (RSI 7, SL 3%) has the highest Sharpe but only works in one narrow configuration — classic overfitting. The RSI 14 cluster consistently delivers ~1.35–1.42 across multiple combinations — a much more reliable choice.

Rule: Pick parameters that appear in a neighbourhood of good results, not an isolated peak.

How many combinations is too many?

More combinations = more chances to accidentally find a parameter set that worked historically by pure luck.

Combinations	Risk level
< 50	Low. Results are usually meaningful.
50 – 200	Moderate. Look for stability clusters.
> 200	High. Significant overfitting risk — validate carefully.

A simple rule: fewer, wider-spaced values are better than many closely-spaced ones.

Good: 7, 14, 28 (spread across different regimes)
Risky: 13, 14, 15 (nearly identical — just noise)

Walk-Forward Validation 

The Optimiser’s most powerful feature is Walk-Forward testing — repeatedly optimise on one window of data and test on the next window you haven’t seen:

Window 1:  Optimise on 2005–2010  → Test on 2010–2012
Window 2:  Optimise on 2007–2012  → Test on 2012–2014
Window 3:  Optimise on 2009–2014  → Test on 2014–2016
...and so on.

This produces a series of out-of-sample results — the only truly honest way to evaluate whether your optimal parameters generalise to unseen data.

If the walk-forward results closely match the full-period optimised results → the strategy is robust. If they diverge sharply → the strategy is overfit.

Note

Walk-Forward is available on Silver and Gold plans and coming soon to the Optimiser interface.

Practical workflow 

Here is the recommended process for using the Optimiser effectively:

Split your data. Reserve the last 20–30% of your date range as an out-of-sample test period. For example, if you have data 2000–2022, optimise on 2000–2016 and test on 2016–2022.
Run the Optimiser on the in-sample period (2000–2016).
Pick parameters from a stability cluster, not the single best row.
Freeze those parameters and run a single backtest on the out-of-sample period (2016–2022).
Compare results. If out-of-sample Sharpe is within ~30% of in-sample Sharpe, the strategy passes. If it collapses completely, the strategy is overfit — go back to step 2 with simpler parameters.

Warning

Once you look at out-of-sample results, that period is “spent” — it can no longer serve as a clean test. Do not re-optimise based on out-of-sample results. If you do, you need a third, untouched period to validate.

Common mistakes 

Mistake 1 — Optimising too many parameters at once

Each additional parameter multiplies the search space. With 5 parameters and 4 values each, that’s 1,024 combinations — most of which are spurious. Optimise 2–3 key parameters at most in a single run.

Mistake 2 — Picking the single best result

The #1 result is almost always overfit. Look for the parameter cluster where many nearby combinations also perform well.

Mistake 3 — Using the full date range for optimisation

If you optimise on all available data and report those results, you have no honest out-of-sample test. Always hold back a test period before you start.

Mistake 4 — Ignoring trade count

A combination with 8 trades and Sharpe 2.5 is statistically meaningless. The Sharpe calculation needs at least 30–50 trades to be reliable. Filter out any row with fewer than 30 trades.