Multi-ETF machine-learning stockmarket sentiment engine

Aurum·AI

A Python research engine that trains LSTM models to forecast forward returns across a basket of ETFs (gold miners, energy and the Nasdaq-100), backtests each with realistic transaction costs, and runs a confidence-ranked rotation strategy that shifts capital toward the strongest signals — falling back to cash when none are convincing. Built with PyTorch, walk-forward cross-validation, and a fully reproducible train → backtest → rotate → live-signal pipeline. Best used from a Claude Code interactive window — then you can speak to the engine in English rather than computer-ese.

Disclaimer: a personal research project — not investment advice. Backtested results are historical simulations and do not guarantee future performance. Never risk real money based on scenario back-tests. Always trial out trading strategies in real time using paper, not real money. What may work in one trading regime/time frame may not work in another. In my humble opinion it is extremely difficult for amateur investors to get ahead of the professionals, even using AI and neural networks. It is extremely easy to lose all your money. Slippage and friction don't help. Use absolutely at your own risk.

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