1
System specification
Versioned rules, data contracts, event timing, accounting, and constraints.
Lesson · Backtesting and Trading Systems
Trading System Specification and Backtest Report
Combine hypothesis, data, rules, execution, sizing, costs, validation, metrics, limits, and acceptance criteria into a versioned system specification and report.
Core concepts
What to understand
Keep each definition operational so it can be implemented and tested.
2
Backtest report
Dates, assumptions, metrics, regimes, failures, and comparison results.
3
Acceptance criteria
Predefined thresholds determine whether the system advances, changes, or is rejected.
Interactive lab
Trading System Specification Builder
Define the complete system, validate contradictions, preview the test pipeline, and export a machine-readable JSON specification.
Trading System Specification Builder
Deterministic synthetic educational data · no live market connection
BACKTEST MODEL
Process
Freeze the test contract
The specification becomes the input to Algorithmic and Automated Trading automation.
Specify
Define data, rules, execution, portfolio accounting, and limits.
Validate
Check causality, completeness, assumptions, and acceptance criteria.
Version and report
Export immutable inputs beside complete results and diagnostics.
Knowledge check
Check your understanding
Select one answer for each question. Feedback appears immediately.
1. What makes a trading-system specification reproducible?
Another implementation needs exact data, timing, rules, and assumptions.
2. Acceptance criteria should be defined:
Predefined criteria reduce outcome-driven decision changes.
3. What should be included with performance metrics?
Metrics require context and complete assumptions to be interpretable.
4. A specification sets risk per trade at 3.5% and maximum open risk at 3%. The builder should:
The first position alone would carry 3.5% of open risk against a 3% cap, so the cap can never bind: the lab marks this a hard failure and refuses to export it.
Practical rules
- Version the specification and source data identifiers.
- Export assumptions beside results.
- Reject incomplete or contradictory systems before automation.
Common mistakes
- Keeping critical execution logic outside the specification.
- Reporting return without dates or costs.
- Automating a rule that has not passed validation and robustness testing.
References
Official reading
The labs are simplified simulations. These sources cover historical performance, data, costs, or execution limitations.
FINRA · Smart Beta—What You Need to Know
Explains that historical backtesting is hypothetical and does not predict future performance.
FINRA Rule 2210 · Communications with the Public
Includes restrictions on performance projections and misleading performance claims.