This screen shows the trading statistics for the selected optimal net, paper trading net or actual trading net. Using this information, you can evaluate how well you have setup your prediction.

If you have chosen to display the detailed analysis of a walk-forward or an average you will be given a choice of displaying a list of the training or evaluation trade by trade results for the trades that occurred over the selected periods. To do this, select the Show Training Statistics option or the Show Evaluation Statistics option.

The following is a list of items that you will receive for each entry and exit signal in the backtest:
Date – Date on which the entry or exit was signaled.
Signal – The type of order (Long Entry, Long Exit, Short Entry, or Short Exit). On the graph, a Long Entry is annotated with a solid blue upward triangle, a Long Exit is annotated with a blue outlined downward triangle, a Short Entry is annotated with a solid red downward triangle, and a Short Exit is annotated with a red outlined upward triangle.
Shares – The number of shares (or contracts) bought or sold with the order.
Fill Date – Date on which the entry or exit was actually filled. For the simple case of a market order, the date will be the next trading day after the signal.
Price – The price at which the order was filled. On the graph, this price is annotated with an X on the Fill Date. For the simple case of a market order with no slippage the price is the open on the Fill Date. If no open is included in your data, then the previous days close is used and adjusted for gaps if a high and low is included in your data (i.e. if today’s low is higher than yesterday’s close then the low will be used as today’s open).
Commission – The commission paid for the order. If you have not specified any entry or exit commissions in the trading parameters, this value will be zero.
%Return (exit only) – % return for the position that was exited.

%Return is calculated as follows:

Long Percent Return = 100 * ((entry price – exit price)*shares – entry commission – exit commission) / (entry price*shares + entry commissions).

Short Percent Return = 100 * ((exit price – entry price)*shares – entry commission – exit commission) / (entry price*shares + entry commissions).

If using margin $ per share/contract, then % return is calculated as follows:

Long Percent Return = 100 * ((entry price – exit price)*shares – entry commission – exit commission) / (margin*contracts + entry commissions)

Short Percent Return = 100 * ((entry price – exit price)*shares – entry commission – exit commission) / (margin* contracts + entry commissions)

Note that the entry and exit price incorporate any specified slippage and/or point value.

Profit/Loss (exit only) – The dollar amount made (or lost) by the position that was exited.
Cum %Ret (exit only) – The cumulative percent return for all positions up to and including the position just exited.
Cum Profit/Loss (exit only) – The cumulative profit (or loss) for all positions up to and including the position just exited.
Notes:

• If missing data prevents the prediction from being made (i.e. you’re using the Japanese Nikki Index as an input, but it’s a Japanese Holiday, so there is no prediction made) the prediction will not generate any trading signals. Therefore, a trading system based upon the prediction will remain in the same position it was prior to the missing data. Hence, the prediction’s profit statistics are based on not trading on days with missing data.
• Note that predictions based upon data in the Mutual Fund or Money Market Fund category will have fill prices on the next close instead of the next open. This is done to better simulate actual Mutual Fund orders which are filled at the funds posted closing price the next day.
• It should be understood that trading can result in losses, and that past performance of a trading system is no guarantee of future performance.
• If the underlying data is categorized as Mutual Fund or Market Fund data, then Market Orders will be filled at the closing price to more accurately represent actual closing price fills that occur when trading funds in the real world.

 
Topic of Interest:
What are Neural Networks?