Trading costs
Include fees, expected slippage and funding in the trade model.
INTELLIGENCE / MEASURE
How to backtest a crypto strategy honestly.
Compare the hypothesis with Buy & Hold and a baseline, declare fees, slippage and funding upfront, then inspect next-bar execution and every trade.
HYPOTHESIS SPACE1,248 trials
01Trend Momentum0.9302Funding Regime0.8903Volatility Breakout0.68
explorerankfreeze
SYSTEM DOMAIN02 / DISCOVER
Search, rank and freeze a candidate
- DISCOVER
- OPTIMIZE
- VALIDATE
- RUN
- OBSERVE
A result is a record, not a green badge.
The page follows the same order as the application: declare the question, inspect the run, then state what the data cannot prove.
- 01
Trading costs
Include fees, expected slippage and funding in the trade model.
- 02
Next-bar execution
A close-bar signal is not filled at an already known close price.
- 03
Every trade
Inspect entries, exits, closing reasons and modelled costs.
- 04
Comparable runs
Save and compare configurations using risk and robustness metrics.
WORKSPACE EVIDENCE
Open the report behind the explanation.
The screenshot is a recorded product state. Its values are historical and illustrative; use the Laboratory to run your own configuration.
Open laboratory
Next-bar execution
A close-bar signal is not filled at an already known close price.
Every trade
Inspect entries, exits, closing reasons and modelled costs.
Comparable runs
Save and compare configurations using risk and robustness metrics.
DECISION FRAMEWORK
Did the candidate improve evidence or only the training score?
Read the candidate against its anchor, neighbouring parameters, sample size, costs and complete trial history.
Bound the search space
Compare several objectives
Freeze before opening holdout
Candidate record
Important
Historical, statistical and model results do not promise future returns. kquant provides computational tools and technical execution, but does not assess whether a specific trade is suitable for a user.