The statistics that matter: R, expectancy and drawdown

Measuring in R

R is the initial risk of a trade. If you risk 50 euros, a 100-euro win is +2R and a 50-euro loss is -1R. Measuring in R makes trades of different sizes, and different systems, comparable.

Expectancy

Expectancy = (win rate x average win in R) - (loss rate x average loss in R). If it is positive, on average each trade earns that many R. Example: 40% wins with an average win of 2.5R and an average loss of 1R gives 0.4 x 2.5 - 0.6 x 1 = +0.4R per trade.

Other measures

• Profit factor: sum of gains divided by the sum of losses (in absolute value). Above 1 it means it makes money, but very high values on little data are suspicious
• Maximum drawdown: the largest drop from the equity peak. It helps you understand whether you could endure it
• Losing streaks: longer than you imagine. With a 40% win rate, a run of 8-10 losses in a row is possible

Sample uncertainty

On 100 trades with an observed win rate of 50%, the standard error is about 5 points: the true rate could lie, with good probability, between 40% and 60%. Little data produces unstable numbers: don't celebrate or despair after 20 trades.

Monte Carlo simulation (the idea)

Take your trades in R, shuffle their order many times and observe how many different drawdowns can arise from the same results. It shows the realistic range of what you might experience, not just the lucky or unlucky historical sequence.

Exercise

• Calculate in a spreadsheet win rate, average R of wins, average R of losses and expectancy of your last 100 trades (even on demo)
• Calculate the maximum drawdown in R and the longest losing streak