Swoopr Academy
Strategy Research & Backtesting
How to test a trading idea without fooling yourself: the five questions a complete backtest answers, point-in-time data and its biases, in-sample versus out-of-sample discipline, walk-forward validation, overfitting detection, and realistic execution modeling.
Advanced
You will be able to
- Specify the five components every complete backtest must answer, from entry logic to result stability
- Identify look-ahead and survivorship bias and build datasets with point-in-time accuracy
- Run a disciplined in-sample/out-of-sample split and extend it into walk-forward validation
- Detect overfitting from search size and parameter instability, and model execution costs honestly
Strategy Research & Backtesting is a 5-module Swoopr Academy course. How to test a trading idea without fooling yourself: the five questions a complete backtest answers, point-in-time data and its biases, in-sample versus out-of-sample discipline, walk-forward validation, overfitting detection, and realistic execution modeling. Complete every module's knowledge check to finish the course.
This course is an educational guide only, not personalized investment or trading advice. See our Risk Disclosure.
Recommended before this course: Investing Foundations, Trading Risk Management.
Your progress
Course Outline
Modules and knowledge checks
Module 1: What a Backtest Actually Tests
The five questions a complete backtest answers, and what a historical result can and cannot prove.
Knowledge Check
Module 2: Data Quality, Survivorship, and Look-Ahead Bias
Point-in-time accuracy, the current-constituent trap, corporate actions, and the timing of information.
Knowledge Check
Module 3: In-Sample, Out-of-Sample, and Walk-Forward
Splitting history chronologically, freezing rules before touching held-out data, and extending the split into walk-forward validation.
- In-Sample vs. Out-of-Sample Testing, and Walk-Forward Analysis
- Walk-Forward Testing and Out-of-Sample Validation
Knowledge Check
Module 4: Detecting and Avoiding Overfitting
Why more testing increases overfitting risk, and the habits that keep a search honest.
Knowledge Check
Module 5: Modeling Realistic Execution
Commissions, spread, slippage, fill assumptions, and capacity: turning signals into honest fill estimates.