Trading results were originally executed in Beta test mode over a 6 month period during 2017. Below is a 1 month segment from the test dataset. The selected matches are what the trading algorithm identified as high probability trading opportunities based on specific criteria required to ensure the system maintains a positive expectancy balance between the win rate and the reward to risk ratio.

The initial strategy was focused on identifying football matches with a strong probability of goals being scored in the latter stages of the match.

The 7th column in the report below confirms whether a goal was scored late (and what time it was scored) or whether the trade was a loss. If price drops below 1.10 I normally trade out for the loss depending on specific metrics I am observing in regards to the in play statistics during the closing stages. The file also highlights the potential profit and potential loss level which I would be looking to attain.

Obviously in a real trading live environment there will be variation in profit and loss by up to 10% as different games present variation in price action but the idea is to show the strength of the trading algorithm and its associated positive expectancy.


The key to success in trading is the ability to accept losing a battle whilst knowing you are winning the war

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