Build a Reliable Backtesting System
Create a research workflow for evaluating trading rules and recognising misleading results.
- Prerequisite
- Financial Markets for Python Developers or equivalent, and confident use of pandas.
- Level
- Intermediate
- Includes
- Quantfolio
- Format
- Live online · 1:1 or group
- Schedule & fee
- Agreed on your intro call
Is this for you?
Developers who can handle market data in Python and want to test strategies properly.
Quick backtests often look excellent because of hidden errors in timing, costs or data selection.
What you’ll be able to do
- Separate a backtest into signals, positions and execution.
- Model execution timing so the test only uses information available at the time.
- Include fees and slippage and see how they change results.
- Choose a fair benchmark and compare against it.
Modules
- Backtest architecture
- Execution timing
- Costs
- Benchmarks and measures
- Unseen data and validation
- Sensitivity analysis
- The evaluation report
The outline can be tailored to your goals.
A reusable backtesting project and strategy evaluation report.
- Backtesting codebase with tests
- Strategy evaluation report
Tools & data
- Python
- pandas
- NumPy
- Matplotlib
- Jupyter
- Git (recommended)
- Quantfolio
PSX and international equity data · Forex and commodity price series
Questions
Will you use a backtesting library?
The course builds a small system from first principles so you understand every step. Popular libraries are discussed for comparison.
How is it taught, and what does it cost?
Live online, one-on-one or in a small group. Schedule and fee are agreed with you on a free intro call.
Will this course guarantee trading results?
No. It builds knowledge and skills. It does not guarantee profits.
From Backtest to Paper-Trading Bot
Build the skills to research markets independently.
Talk to us for free. We'll help you choose the right path and give you a preview of Quantfolio.