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.
I've been using Random Forests to predict stock price movements with decent success. They handle non-linearity quite well and are relatively easy to implement. One tip is to ensure you have a large and diverse dataset to prevent overfitting. Anyone else using ensemble methods?
I'm leveraging Support Vector Machines (SVM) for classification tasks in my trading algorithms. They've been quite effective in identifying bullish and bearish patterns. However, they do require careful tuning of hyperparameters.
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'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. :)
ChatGPT doesn't have real time knowledge. Beware of overfitting and front-running problem when backtest your strategy!
@Tony Lam is expert in using Bayesian Networks for modeling the probabilistic relationships between different market factors. I did some research about this topic recently. This model seems can provide a good balance between interpretability and predictive power. May be Tony can give more insights on this?
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.
Can't agree any more. Data is the most important part in building an ML model
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.
it will be helpful if you can provide sample codes for different strategies using these ML models
