Systematic Trading & Backtesting
Learn to turn trading ideas into precise rules, test them on clean historical data, measure the results with standard metrics and judge honestly whether a backtest is robust or overfitted.
Self-study course Self-study course: written lessons, videos and downloadable materials for independent learning. There is no supervision, assessment, individual feedback or tutoring. Self-check quizzes are evaluated automatically in your browser — we do not see your results.
All 12 lessons free
Before you start
- Basic risk management: R-multiples, expectancy and position sizing
- Reading price charts and simple indicators such as moving averages
- Working with percentages and a spreadsheet
What you will be able to do
- Write a complete, unambiguous rule set for a trading idea, including edge cases and signal timing
- Identify and correct data problems such as survivorship bias, split and dividend adjustments and futures rolls
- Build a spreadsheet backtest with signal, position, equity curve and drawdown columns
- Calculate CAGR, maximum drawdown, Sharpe, Calmar and profit factor and explain what each one hides
- Assess overfitting with parameter grids, test counts and out-of-sample and walk-forward tests
- Plan realistic costs, position sizing and live monitoring rules, including when to stop a system
Lessons in this course
What systematic trading is and why rules help
Understand what makes a trading approach systematic, which parts every system needs, and why fixed rules make results measurable.
Turning an idea into precise, testable rules
Translate a trading idea into a written hypothesis and an unambiguous rule set that a spreadsheet can test.
Data quality: survivorship bias, adjustments and gaps
Recognise the data problems that silently distort backtests and correct for splits, dividends, delistings, futures rolls and gaps.
Building a simple backtest in a spreadsheet
Build a complete, column-by-column backtest of a moving-average rule in a spreadsheet and check it for errors.
Performance metrics: CAGR, drawdown, Sharpe and profit factor
Calculate and interpret CAGR, maximum drawdown, the Sharpe ratio, the Calmar ratio and profit factor from a backtest.
Overfitting and how to avoid it
Understand why optimised backtests overstate future results and use parameter plateaus, test counts and simplicity rules to reduce overfitting.
Out-of-sample and walk-forward testing
Split your data into training and test periods, run a walk-forward analysis and judge how much of a backtest's performance survives on unseen data.
Costs, slippage and realistic assumptions
Build a per-trade cost model for a backtest, calculate cost drag and break-even costs, and stress-test a system with higher slippage.
Position sizing rules inside a system
Choose and code a sizing rule for a trading system, apply volatility-based sizing with caps and rounding, and see how the rule changes backtest results.
Combining systems and diversification
Combine two trading systems, calculate portfolio volatility with correlation, and compare equal-capital and inverse-volatility weights.
From backtest to live: monitoring and when to stop a system
Move a tested system into live trading in stages, measure implementation shortfall, and use pre-defined statistical and drawdown rules to decide when to pause or stop.
A complete systematic-trading project on paper
Run a full project from hypothesis to live plan: rules, data, out-of-sample test, costs, sizing and a written go or no-go decision.
Order Books, Liquidity & How Orders Are Filled
To understand how the orders your system sends are actually filled, continue with the course "Order Books, Liquidity & How Orders Are Filled".
View course outline