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03

Backtesting

The backtesting engine should answer one question: does the strategy behave well enough, under realistic assumptions, to deserve more attention? The job is to pressure-test the claim, not to prove that the idea is special.

3
Modules 3
Lessons Reject or advance
Focus
Module 01 · Basics

Signals of a useful backtest

Good validation gives you reasons to trust or reject the process. Bad validation gives you a screenshot.

Assumptions
Make the environment believable

Date range, instrument set, and capital base should resemble the desk you plan to run rather than the dataset that flatters the strategy most.

Path
The equity curve should be readable

A strong terminal profit can still hide intolerable drawdowns, unstable streaks, or fragile dependence on one period of market behavior.

Decision
A pass means worthy of monitoring, not guaranteed

Backtesting narrows uncertainty. It does not remove slippage, changing regimes, liquidity shifts, or execution drift in live conditions.

Why this matters

Every idea in this path reduces to one question: what are the explicit rules, and what stops the trade if the thesis is wrong? Keep that lens as you read.

Module 02 · Core Concepts

How to use the engine like a filter instead of a marketing tool

The fastest path to better deployment is learning to reject more strategies earlier.

Step 01
Lock the strategy before the run

Freeze the logic and capital assumptions before you inspect performance so the test reflects the original thesis instead of a moving target.

Step 02
Run broad enough scenarios

Test multiple periods, symbols, and market states to see whether the system's behavior survives outside the one obvious window.

Step 03
Review stress, not just summary stats

Drawdown duration, loss clustering, and recovery quality usually matter more operationally than the headline return number.

Step 04
Decide pass, revise, or kill

A clean workflow ends with an explicit decision. Strategies that stay forever in maybe-mode consume time and add noise to the desk.

Module 03 · Practical Understanding

The backtest questions SFZ should help you answer fast

These checks keep the engine aligned with real operational decisions.

Metrics that matter
  • Expected drawdown versus capital budget.
  • Consistency across symbols or market slices.
  • Recovery speed after weak periods.
Reasons to fail the strategy
  • Performance depends on one short historical burst.
  • Small parameter changes collapse the result.
  • The strategy only looks good when sizing is unrealistic.
Ready for the terminal when
  • The rule set remains understandable after testing.
  • The drawdown path is uncomfortable but still acceptable.
  • You know exactly what live behavior would invalidate the thesis.
Try it — do costs eat the edge?

A backtest that ignores costs lies. Each round-trip pays commission and slippage. See how a thin gross edge survives — or doesn't — once real costs are modelled.

Gross return
—
Net return
—
Cost drag
—
Backtesting trap to avoid

Do not tune until the bad period disappears.

  • Do not compare systems with incompatible capital assumptions.
  • Do not treat one high-performing run as robust evidence.
Test your knowledge

Use the engine like a filter. If you cannot reject weak ideas, the workflow is not doing its job.

Question 1

What is the best primary outcome of a backtest?

Question 2

Which matters more operationally than headline return?

Question 3

What should make you suspicious of a backtest result?

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