#probability

Test enough strategies and one will always look brilliant.

Multiple testing bias in trading strategies makes the best backtest look better than it is. Learn how it arises and how darwintIQ re-evaluates models.

11/4/2026

Ten winning trades prove nothing. The hard question is how many would.

Statistical significance in trading decides whether a track record is skill or luck. Learn how many trades you need before a model’s edge means anything at all.

6/18/2026

Win rate alone tells you nothing. Risk/reward alone tells you nothing. Expectancy combines both into a single answer.

Trading expectancy is the average profit per trade once win rate and risk/reward are combined. Here's the formula, how to read it, and where it fits in.

5/12/2026

Correlation tells you two things move together. Mutual information asks whether knowing one actually tells you something useful about the other.

Mutual information measures whether a model's entry signals genuinely predict outcomes. Learn what it detects and how darwintIQ uses it to assess model quality.

4/30/2026

Winning more than you lose sounds like the right goal. In systematic trading, it rarely is

A 70% win rate sounds impressive — until you check the average loss. Learn why win rate alone misleads traders, and which metric combinations tell the real story about a strategy's edge.

4/1/2026

The Statistical Foundation of a Trading Edge

Learn what Expected Value means in quantitative trading and how darwintIQ uses it to identify trading models with stable statistical edge under changing market conditions.

3/2/2026

From Data and Statistics to Adaptive Trading Models

What is quantitative analysis in trading? A beginner-friendly guide to data-driven market analysis and how darwintIQ evaluates adaptive trading models.

2/25/2026