Discussing the article: "Neural Networks in Trading: Iterative Forecast Refinement Models (Conclusion)"

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Check out the new article: Neural Networks in Trading: Iterative Forecast Refinement Models (Conclusion).

Introducing the RAFT framework — a powerful tool for analyzing and forecasting financial time series. Its flexible and optimized architecture ensures accurate forecasts, stable performance, and faster data processing. RAFT reduces the risk of errors and makes it easier to create effective trading strategies.

The first stage of model training can be thought of as a novice rehearsing in an arena of historical market data. We use data for the EURUSD currency pair on the H1 timeframe for the period from January 2024 to June 2025. During this time period, the model receives a historical map of the market and learns to recognize patterns: price dynamics, trading volumes, and hidden relationships between key features. RAFT acts as an experienced mentor here: it forms an informative state representation for the Actor and the Critic, helping the model develop its own trader’s intuition and gradually accumulate strategic experience, enabling it to forecast market movements and assess the risk of each potential trade.

The next step is online fine-tuning in the MetaTrader 5 Strategy Tester. We move training into a dynamic, almost lifelike environment. The model processes data one candlestick at a time, reacting to every price movement. RAFT allows it to remain stable amid noisy fluctuations, adapt to sharp spikes, and adjust its actions during periods of low liquidity. The underlying structure learned from historical data remains the foundation, but the model learns to respond flexibly to current market conditions, avoiding overfitting and improving forecast accuracy even in an unstable environment.

The final stage — testing — is conducted using entirely new data from July through September 2025. All parameters obtained in the previous stages are loaded without modification, ensuring a fair assessment of the model's generalization ability. The test results are presented below.


Author: Dmitriy Gizlyk

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