← Back to Curriculum
Beginner

Practice Drills: Backtesting a Moving Average Crossover Strategy

A hands-on practice drill for building and honestly evaluating a moving average crossover backtest in Python, using real NSE price data. You will code the strategy, compute performance metrics, and learn where backtests quietly lie to you.

BacktestingMoving AveragesPython for TradingNSE DataAlgorithmic Trading
MODULES
4
DURATION
4 Hours
TRACK
Algorithmic Trading

What You'll Master

How a moving average crossover strategy generates buy and sell signals
How to source and clean NSE price data for a backtest
How to code a crossover backtest in Python from scratch
How to read CAGR, Sharpe ratio, and max drawdown correctly
How lookahead bias, survivorship bias, and overfitting quietly wreck backtests
How transaction costs, slippage, and taxes change your real returns
Access Level
LEARNER
Start Learning Now →Sign in to resume
Everything included
Full Text Playbooks
Actionable Exercises
Mobile Reading Mode
Lifetime Updates

Curriculum Breakdown

Understanding the Strategy Before You Code It

2 Lessons
▶
What a Moving Average Crossover Strategy Actually Does8 min read
Preview
▶
Why Backtest, and Where It Lies to You7 min read

Building the Backtest in Python

3 Lessons
▶
Sourcing Clean NSE Price Data9 min read
▶
Coding the Crossover Signal and Position Logic10 min read
▶
Running the Backtest and Computing Returns9 min read

Judging Your Results Honestly

2 Lessons
▶
Performance Metrics That Matter: CAGR, Sharpe, Max Drawdown9 min read
▶
The Three Backtest Traps: Lookahead Bias, Survivorship Bias, Overfitting9 min read

From Backtest to Reality

2 Lessons
▶
Transaction Costs, Slippage, and Taxes in the Indian Context8 min read
▶
What a Backtest Still Can't Tell You7 min read