Discussing the article: "Neural Networks in Trading: The Temporal Query Model (Conclusion)"
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Check out the new article: Neural Networks in Trading: The Temporal Query Model (Conclusion).
In the first, offline stage, we performed thorough training on historical data for the EURUSD pair on the H1 timeframe for the entire year of 2024. This year included a full range of market regimes — calm sideways markets, steady trends, sudden spikes in volatility, and periods of increased noise — making it an excellent training ground for the model.
The second stage is online fine-tuning. Training was implemented in an environment as close as possible to live trading: in the MetaTrader 5 Strategy Tester, the model processed the stream of candlesticks sequentially, just as it would in real time. Online mode reveals completely different properties than batch training: the ability to withstand noise, respond to changes in liquidity, and correctly account for delays and the effect of end-to-end slippage. During fine-tuning, we simulated real execution conditions so that the model's behavior would be predictable when transferred to live trading.
The final — and perhaps the most rigorous — stage was testing on completely out-of-sample data: market quotes from January to March 2025. All model parameters and hyperparameters remained frozen; no additional adjustment to these data was performed. This type of validation provides an objective picture of practical effectiveness: it reflects the algorithm's ability to remain predictable and robust under new conditions.
The test results are shown below.
Author: Dmitriy Gizlyk