Most traders use the same strategy no matter what the market is doing (here's how to know when to switch)
You've probably found a strategy that looked great on a chart, then watched it bleed money for weeks.
The frustrating part isn't the loss itself.
It's not knowing why it stopped working, or what you should do differently. Without a way to measure what the market is actually doing right now, you're stuck guessing.
Markets shift between periods where prices trend steadily in one direction and periods where prices bounce back and forth around an average.
Research from the CFA Institute shows that most retail traders lose money not because their strategies are bad, but because they apply them at the wrong time.
A trend-following strategy in a sideways market will slowly drain your account, and a mean-reversion strategy (one that bets prices will snap back to their average) during a strong rally will do the same.
There's a structured way to measure this before you put money on the line.
Instead of eyeballing a chart and hoping, you can calculate a single number that tells you whether the market is trending or bouncing sideways.
That number helps you pick the right approach for the current conditions, which is exactly what professional trading teams do before they commit capital.
Here's a Python example you can use now.
Assumes prices is a pandas Series of daily closing prices (for example, S&P 500 adjusted close).
This code computes the Hurst exponent, a number between 0 and 1 that tells you the market's current behavior.
A value above 0.5 means prices are trending, so momentum strategies (buying what's going up, selling what's going down) tend to work.
Below 0.5 means prices are bouncing back toward their average, which favors strategies that bet on reversals.
Near 0.5 means the market is moving randomly, and directional strategies will struggle.
Checking this number at several time horizons lets you see whether the result holds up or only appears at one specific scale.
Professionals recalculate this regularly and switch strategies based on what it tells them.
Getting Started With Python for Quant Finance walks you through this exact kind of workflow, from pulling market data to measuring market behavior to testing whether a strategy actually works on data it hasn't seen before.
The course includes 40 code templates you can modify instead of writing everything from scratch, so you spend less time on setup and more time on analysis.
Inside Getting Started With Python for Quant Finance
- 13 modules with 134 lessons. Each module builds on the last, so you follow a clear sequence from loading data to running live trades instead of jumping between disconnected tutorials.
- 40 code templates. Ready-to-use starting points for tasks like this one, so you can measure market behavior, test strategies, and analyze risk without building everything from zero.
- Private community of 1,700+ Python traders. When you get stuck on a calculation or aren't sure how to interpret a result, you can ask people who are working through the same problems.
These are the building blocks for going from "I found a strategy online" to "I tested it, measured the market conditions, and know when it works."
What students are saying
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A top-tier introduction to Python and quant finance.
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Your next steps
Start by measuring the market before you trade it.
Use the code above on any stock or index you're interested in, check the Hurst exponent across several time windows, and see whether the number matches the strategy you're planning to use.
From there, the natural next step is to test that strategy on historical data and confirm it actually makes money before you risk real capital.
A structured program can take you through that entire process in order, so you're not piecing together random tutorials and hoping they fit.
Know what the market is doing before you trade it 👉
One number can tell you whether your strategy fits the current market. Learn the full workflow for measuring, testing, and running strategies with real data.