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Show HN: Personal ML-based Stock Trading Bot - Achieved 30% Returns in 2021(github.com)

850 points by datasciencenewb 2 years ago ago | | | 12 comments

financeguru 4 minutes ago | prev | next
Impressive returns! Can you share more details about the model training process and which ML algorithms you used?
- traderbotwhiz 4 minutes ago | prev | next
  We trained the model using historical data and sophisticated feature engineering, focusing on tree-based algorithms like Random Forest and AdaBoost. This helped merge multiple insights to make informed decisions.
  traderbotwhiz 4 minutes ago | prev | next
  @newToML, we used regularization techniques and aggressive feature selection to avoid overfitting. Regarding survivorship bias, we made sure to use complete datasets, including delisted and bankrupt companies, to get a well-rounded view of long-term returns.
  newtoml 4 minutes ago | prev | next
  @traderBotWhiz, thanks for the insight! Would you recommend any resources for understanding long-term returns and the concept of avoiding survivorship bias in datasets?
  statsguru 4 minutes ago | prev | next
  @newToML, a resource I'd recommend is 'The Little Book of Common Sense Investing' by John C. Bogle, a fundamental guide to long-term investing strategies and dealing with distorted statistics in financial datasets.
aiwanderer 4 minutes ago | prev | next
I wonder how much of this is simply a result of market trends and how much is due to the model itself. Can you elaborate on testing the model's performance during various market conditions?
- financeguru 4 minutes ago | prev | next
  @AIWanderer, we tested the model in various market conditions by employing k-fold cross-validation, allowing us to assess its performance in multiple subsets of the data.
newtoml 4 minutes ago | prev | next
How did you avoid overfitting, and can you comment on the challenge of dealing with survivorship bias in financial data?
dataexpert 4 minutes ago | prev | next
Have you explored the possibility of introducing Generative Adversarial Nets (GANs) or Recurrent Neural Networks (RNNs) to capture time dependencies?
- aiwanderer 4 minutes ago | prev | next
  @dataExpert, yes, we attempted to leverage LSTMs and GRUs as part of our RNN exploration, but decided to postpone these for future iterations. Time dependency was critical, but tree-based models sufficed in initial feasibility tests.
thinkingquant 4 minutes ago | prev | next
Were there specific sectors or stock categories where your model consistently outperformed market indices?
traderbotwhiz 4 minutes ago | prev | next
@thinkingQuant, the model seemed to favor growth, technology, and healthcare stocks, which were within the S&P 500's top-performing sectors. However, we did notice slightly better risk-adjusted returns compared to the index itself.