The Probability Secret Most Traders Never Learn | Tom Preston

Central Limit Theorem in Trading

The central limit theorem (CLT) states that as the sample size increases, the distribution of data tends to become normal, regardless of the original distribution. In trading, this means that increasing the number of trades with defined risk and high probability of profit can lead to a portfolio distribution that converges toward the probability of the individual trades. This is applicable when traders use small, frequent trades with a high probability of success, and it implies that the portfolio's performance becomes more predictable as the number of trades increases. However, this theory assumes that each trade is independent and has a consistent probability of success, which may not always hold in real-world trading conditions.

What is the central limit theorem and how does it apply to trading?

The central limit theorem (CLT) states that as the sample size increases, the distribution of data tends to become normal, regardless of the original distribution. In trading, this means that increasing the number of trades with defined risk and high probability of profit can lead to a portfolio distribution that converges toward the probability of the individual trades. This is applicable when traders use small, frequent trades with a high probability of success, and it implies that the portfolio's performance becomes more predictable as the number of trades increases.

TakeawayThe CLT supports the idea that frequent, small trades with a high probability of success can lead to a more predictable portfolio performance.