Optimisation with seek

The seek command finds optimal strategy input parameters by running multiple backtests and maximising a chosen fitness metric.

Basic usage

algo seek --strategy macrossover --symbols @ES --fitness-metric sharpe

This runs every combination of LenFast and LenSlow values defined in the strategy’s algolang: struct tags and reports the combination that maximises the Sharpe ratio.

Fitness metrics

Metric	Description
`npdd`	Net Profit divided by Max Drawdown (default)
`sharpe`	Sharpe ratio
`sortino`	Sortino ratio
`rina`	RINA index
`calmar`	Calmar ratio
`profit`	Net profit
`profitfactor`	Profit factor
`pct-profitable`	Win percentage

Custom expressions are also supported using mathematical operators:

algo seek --strategy kbt --symbols @ES --fitness-metric "profit/maxdd"

Controlling inputs

Include specific inputs

# Only optimise LenFast
algo seek --strategy macrossover --symbols @ES --inputs-include LenFast

Exclude specific inputs

# Optimise everything except LenSlow
algo seek --strategy macrossover --symbols @ES --inputs-exclude LenSlow

Optimisation modes

Mode	Description
`exhaustive`	Tests every combination (default). Guaranteed to find the global optimum.
`genetic`	Uses a genetic algorithm to search the parameter space. Faster for large spaces.

Multi-threaded optimisation

# Use 16 threads
algo seek --strategy kbt --symbols @ES --nbr-threads 16

The default is 24 threads. Algolang uses test deduplication to avoid running identical parameter combinations.

Avoiding overfitting

Use --use-first and --use-last to split data for in-sample optimisation and out-of-sample validation:

# Optimise on first 60% of data
algo seek --strategy kbt --symbols @ES --use-first 60 --fitness-metric sharpe

# Then validate the best parameters on the last 40%
algo run --strategy kbt --symbols @ES --use-last 40 \
    --inputs LenFast=10,LenSlow=50 --output-mode summary

Configuration Files