I tiakina i:
| Kaituhi matua: | |
|---|---|
| Hōputu: | Recurso digital |
| Reo: | Ingarihi |
| I whakaputaina: |
Zenodo
2025
|
| Ngā marau: | |
| Urunga tuihono: | https://doi.org/10.5281/zenodo.16966978 |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
Rārangi ihirangi:
- <p>This paper introduces a <strong>hybrid machine learning framework for algorithmic trading</strong> in highly volatile <strong>cryptocurrency</strong> and <strong>foreign exchange (FX) markets</strong>. The framework integrates <strong>high-frequency market data (OHLCV)</strong>, <strong>order book microstructure signals</strong>, and <strong>sentiment analysis powered by Large Language Models (LLMs)</strong> to generate systematic trading strategies.</p> <p>The approach leverages a multi-model architecture: <strong>LightGBM</strong> for efficient feature learning, <strong>LSTM and Transformer neural networks</strong> for capturing temporal dependencies, and a <strong>regime-switching mechanism</strong> to adapt across different market conditions. Final trading signals are combined through a <strong>stacking ensemble method</strong> to maximize robustness and predictive accuracy.</p> <p>To ensure practical application, the framework incorporates <strong>walk-forward validation</strong>, <strong>realistic transaction cost and slippage modeling</strong>, and a <strong>risk management overlay</strong> based on ATR stops, volatility targeting, and maximum drawdown controls. Backtests using data from <strong>2019–2024</strong> show significant improvements in <strong>Sharpe ratio, drawdown reduction, and profit factor</strong>, outperforming individual models and benchmark strategies.</p> <p>Results highlight the <strong>importance of LLM-driven sentiment features</strong>, which provide measurable improvements in predictive power and trading performance. This work contributes to the growing literature on <strong>machine learning in finance, quantitative trading, and AI-driven systematic strategies</strong>, offering a <strong>scalable and adaptable solution for crypto trading, forex trading, and algorithmic portfolio management</strong>.</p>