MoonAI Chaos
Category: Other Version: 1.00
Our product focuses on forecasting Forex market symbols with flexible training options and user-friendly project management. It is designed for traders and researchers who want to apply machine learning to financial time series without needing deep programming knowledge.
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Our product focuses on forecasting Forex market symbols with flexible training options and user-friendly project management. It is designed for traders and researchers who want to apply machine learning to financial time series without needing deep programming knowledge.
Support data from multiple sources:
MoonTheTrain website (our platform, also where the product is published).
MetaTrader (direct integration).
Manual import of price data files.
Create and manage projects.
Train models and evaluate training results through test datasets.
Save trained models for reuse in future sessions.
Users can configure training with a variety of options:
Select the training architecture: Sequential or other supported structures.
Define the number of LSTM layers and neurons.
Train using a window size (lookback) approach.
Choose different loss functions.
Set the number of epochs for training.
Test the trained model with unseen data.
Compare predictions with actual values.
Display evaluation metrics and visualization charts for clarity.
Simple interface, easy to use even for non-programmers.
Multiple data sources supported.
High flexibility in selecting model architecture and parameters.
Ability to save and reuse trained models.
While the system is designed to handle Forex data efficiently, there are some points to keep in mind regarding training performance:
Forex historical data is not very large in practice:
At D1 (daily) timeframe, one year contains only about 300 rows of data.
Even 10 years of history amounts to only ~3000 rows, which is relatively small and manageable for most personal computers.
At H4 (4-hour) timeframe, the data increases about 6×, but still only reaches a few tens of thousands of rows – not too heavy for common machines.
Cases where performance demands increase:
Using a very large lookback (window size) → more memory usage.
Choosing multiple LSTM layers and high neuron counts → increases model complexity and training time.
Running training with very high epoch values (e.g., thousands) → requires more time and computing power.
👉 Best practice suggestions:
Beginners should start with lightweight configurations (small lookback, 1–2 layers, moderate epochs).
A typical personal computer is sufficient for most Forex training tasks.
For users with very large datasets or highly complex models, upgrading hardware (e.g., more RAM, multi-core CPU, or GPU with CUDA support) will speed up training significantly.
Category: Version: 1.02
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Category: Version: 1.00
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Category: Version: 1.01
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Category: Version: 1.00
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Category: Version: 1.00
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Category: Version: 1.02
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