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 fail because they’re built on math instead of market behavior.
Beginners usually start by searching for the perfect combination of settings. They test hundreds of variations on historical data, find one that looks amazing, and assume it will keep working. The strategy looks profitable on paper because it was designed to fit the past, not predict the future.
That approach burns through time and money, and it’s the main reason most people give up on algorithmic trading within a few years.
An old options trader in Chicago once told me something that changed how I think about markets. He said he made money when other people panicked, because panic pushes prices further than they should go.
That’s not a math trick. It’s a pattern in human behavior.
Most beginners skip this step entirely. They jump straight to testing without asking why a strategy should work in the first place.
Professional traders don’t start with optimization. They start by identifying a specific behavior in the market that creates a repeatable opportunity, then they build a strategy around it.
In this post, you’ll use Python to build a momentum trading strategy that buys stocks trending upward and rebalances (adjusts the holdings) every week.
Let’s go.
Comments
Nothing yet. Say the first thing.
Sign in to join the conversation.