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How are you integrating machine learning into your trading algorithms? What models or techniques have proven most effective for predictive analysis in your experience? Let's share insights and tips on leveraging ML for better trading performance.
Original Posted by - b'Davis': Recently I’ve been focusing on sentiment analysis using Natural Language Processing (NLP) techniques. Tools like BERT and ChatGPT have been invaluable for extracting sentiment from news and social media. :)
Original Posted by - b'tony lam': There are many different ML models you can explore
- Long Short-Term Memory (LSTM)
- Reinforcement Learning
- Gradient Boosting Machines (GBM)
- Bayesian Networks
- Markov Decision Process
- Convolutional Neural Networks (CNNs) to analyze technical indicators and candlestick patterns visually
- Autoencoders for anomaly detection
- Ensemble learning
- Decision Trees
- Support Vector Machine (SVM)
- Neural Networks
- Quantum Machine Learning
- ...
The data inputs/features are more important than whichi ML model you use. If you can find a good factor, you can already get a good performance with a simple model. Some data processing techniques like normalization, transformation, outlier filtering could be helpful to study whether the factor is useful or not.
Original Posted by - b'tony lam': There are many different ML models you can explore
- Long Short-Term Memory (LSTM)
- Reinforcement Learning
- Gradient Boosting Machines (GBM)
- Bayesian Networks
- Markov Decision Process
- Convolutional Neural Networks (CNNs) to analyze technical indicators and candlestick patterns visually
- Autoencoders for anomaly detection
- Ensemble learning
- Decision Trees
- Support Vector Machine (SVM)
- Neural Networks
- Quantum Machine Learning
- ...
The data inputs/features are more important than whichi ML model you use. If you can find a good factor, you can already get a good performance with a simple model. Some data processing techniques like normalization, transformation, outlier filtering could be helpful to study whether the factor is useful or not.