# Average Trade Net Profit

Performance ReportHere you can find detailed information about the Average Trade key figures in the

'''Tradesignal Performance Report'''.

In the Performance Report, all figures are displayed as cumulated valued for all trades and also for long and short trades separately.

### Average Trade Netto Profit

This value gives the average profit of all trades for the data. For this, the

**Total Netto Profit** is divided by the

**Total Number of Trades**. The Average Trade Netto Profit is a key figure of the trading system statistics. With its help, different results for one or several systems can be compared. The value should also be compared to the commission and slippage per trade. It helps you estimate the probability that after deducting all costs, the trading system will still be profitable.

Average Trade Netto Profit = Total Netto Profit / Total Number of Trades

### Average Winning Trade

This key figure gives the average of all profitable trades. Compare this value with the average of all losing trades to get an impression of the trading system performance.

Average Winning Trade = Gross Profit / Number of Winning Trades

### Average Losing Trade

This key figure gives the average of all losing trades. Compare this value with the average of all winning trades to get an impression of the trading system performance.

Average LosingTrade = Gross Loss / Number of Losing Trades

### Ratio Average Winning Trade / Average Losing Trade

This key figure gives the ratio of the two key figures above. It is important for judging the system performance. By combining it with further figures, e.g. for the number of loss and win trades, the potential of the system can be estimated.

### Largest Winning Trade

This key figures gives the largest single profit of the data set. It is often preferable to ignore this value and to remove it from the backtest result. This way, random results are less likely to influence your system.

### Largest Losing Trade

This key figures gives the largest single loss of the data set. The explanation given above applies. It is often preferable to ignore this value.