  Financial market forecasting remains a formidable challenge despite the surge
in computational capabilities and machine learning advancements. While numerous
studies have underscored the precision of computer-generated market
predictions, many of these forecasts fail to yield profitable trading outcomes.
This discrepancy often arises from the unpredictable nature of profit and loss
ratios in the event of successful and unsuccessful predictions. In this study,
we introduce a novel algorithm specifically designed for forecasting the profit
and loss outcomes of trading activities. This is further augmented by an
innovative approach for integrating these forecasts with previous predictions
of market trends. This approach is designed for algorithmic trading, enabling
traders to assess the profitability of each trade and calibrate the optimal
trade size. Our findings indicate that this method significantly improves the
performance of traditional trading strategies as well as algorithmic trading
systems, offering a promising avenue for enhancing trading decisions.
