đ The beginners guide to professional backtesting
Use Zipline and PyFolio for institutional grade backtesting
Each week, I send out one Python tutorial to help you get started with algorithmic trading, market data analysis, and quant finance. Upgrade to a paid plan to access the Python code notebooks, Discord community, and code support.
Most trading strategies die before they ever touch real money.
The usual cause isnât a bad idea. Itâs a bad test. Beginners build strategies that look profitable on paper, then watch them fall apart the moment real dollars are on the line. The gap between a backtest (testing a strategy on historical data) and live trading is where most accounts go to zero.
That gap costs people months of work and, often, real money they canât afford to lose.
The hidden cost of toy backtests
Early on, I built a moving average strategy that returned 40% a year in my backtest. I was thrilled until I realized my test didnât account for trading costs or the delay between placing an order and getting it filled. Once I added those, the 40% turned into a loss.
Most beginners skip these details because their tools donât include them by default.
Professional traders donât use stripped-down testing tools. They use frameworks that simulate commissions (the fee you pay per trade), slippage (the difference between the price you expected and the price you actually got), and realistic order handling from day one.
What youâll build today
In todayâs post, youâll set up Zipline and PyFolio, two professional-grade Python tools, and use them to backtest a simple strategy on real historical data with realistic trading costs built in.
Letâs go.
