Neural LSTM Trading Model — PyTorch Implementation

A production-quality neural network for time-series analysis and prediction. Combines LSTM and Transformer architectures for robust sequence modeling. Built with PyTorch, includes full source code, working examples, and documentation.

WHAT YOU GET

• Full source code — clean, documented, ready to integrate
• Learnable Positional Encoding with mask support
• LSTM encoder (uni- or bidirectional, multi-layer)
• Transformer Encoder layer with Multi-Head Self-Attention (Pre-LN)
• Flexible pooling — max, mean, attention, or both
• MLP classifier with configurable hidden layers
• Complete configuration file (config.yaml)
• Training example script with sample data
• Inference example script
• Sample time-series CSV (40 rows, ready to run)
• README with setup, usage, and configuration reference
• Commercial license
• Risk disclaimer

TECHNICAL DETAILS

• Framework: PyTorch 2.0+
• Architecture: LSTM + Transformer Encoder
• Input: (Batch, Seq_Len, Features) Tensor + optional mask
• Output: konfigurierbar (Regression oder Classification)
• Python: 3.9+

KEY FEATURES

• Masked operations — handles variable-length sequences cleanly
• Packed sequences — efficient LSTM processing for padded batches
• Gradient checkpointing — trade compute for memory on large models
• Attention pooling — learnable weighted pooling with temperature
• NaN protection — output sanitization for numerical stability
• Dimension validation — catches shape errors early

WHO THIS IS FOR

• Quantitative Researchers
• Algorithmic Traders
• Data Scientists working with time-series data
• Developers building custom trading systems

NOTE: This is a model architecture, not a complete trading system.
It includes training and inference examples, but you are responsible
for preparing your own data, features, and backtesting pipeline.
The model works with any time-series data — finance, sensor data,
logs, metrics — not just trading.

HOW TO RUN

1. Install dependencies: pip install -r requirements.txt
2. Train: python examples/example_train.py
3. Predict: python examples/example_predict.py

The included config and sample data run out of the box — you get a working pipeline in under a minute.

IMPORTANT — RISK DISCLAIMER

This software is provided for educational and research purposes only. It does not constitute financial, investment, or trading advice. Trading involves substantial risk of loss and is not suitable for every investor. Past performance does not guarantee future results. You are solely responsible for your trading decisions and outcomes.

WHAT YOU RECEIVE

• ZIP archive with full source code
• Training and inference examples
• Sample data and config
• README with setup instructions
• Commercial license for your own projects