Ever wonder what the expectation value of FAP Turbo is over the long run? I’ve recently added a new section to the members area titled FAP Turbo Expectation Value Examples where I’ve walked thorugh some example calculations ofof expectancy using FAP Turbo trading data. There’s also an example with nearly 500 real trades, more than enough to get a fairly accurate expectation value.

If you’re not familiar with concept of the expectation value it’s basically a statistical measurement used to measure the performance of a trading system based on real historical data.

Expectancy Value

Expectancy = (% Win X Avg_Win) - (% Loss X Avg_Loss)

Where

% Win = percentage of trades that are winners
% Loss = percentage of trades that are losers
Avg_Win = average size of a win
Avg_ Loss = average size of a loss

The expectancy value tells us how much we can expect to make on average over the long run for every $1 risked. It does NOT tell us how much we will make on the next trade or the one after that.

Of course for this value becomes more accurate as a future predictor the more trades we have to base it on.

It’s important to understand that the expectation value of a system takes into account all of the factors involved in trading the system because it is based on real trading history. It accounts for the impact of your broker, slippage, re-quotes, off-quotes disconnects etc …

It’s also specific to the FAP Turbo settings you use when trading because the settings essentially constitute a unique configuration of the trading system. It’s therefore imperative that you calculate your own expectation value for your trading so you can better understand the performance you can expect over the long run.

To calculate the expectation value of FAP Turbo for your FAP Turbo settings as well as understand its predictive value and its limitations  be sure to read the section Expectation Value of Your System.

In the section  FAP Turbo Expectation Value Examples I show a calculation with nearly 500 trades where the expectation value is calculated to be about 1.7, meaning that for every $1 risked you can expect to make $1.70 over the long run. In this section I’ve also included the spreadsheets with the data and formulas used in the calculations which you can download for your own use.