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Show HN: My Machine Learning Algorithms for Stock Market Predictions(example.com)

45 points by ml_stockpred 6 months ago flag hide 20 comments

john_doe 6 months ago next
Interesting work! I've been trying to build something similar for a while now. Can you tell us more about the types of ML algorithms and models you're using for stock predictions?
- machinelearner 6 months ago next
  Sure, I'm mainly using recurrent neural networks (RNNs) with long short-term memory (LSTM) layers, as well as some gradient boosting techniques. I've found these methods to be quite effective in predicting stock market trends and patterns.
techie_nerd 6 months ago prev next
Thanks for sharing! Are you using any specific tools or libraries for implementing these algorithms, like TensorFlow or Scikit-learn?
- machinelearner 6 months ago next
  Yes, I'm using TensorFlow and Keras for implementing my RNNs, and I'm using Scikit-learn for some preprocessing and data cleaning. I've found these tools to be quite helpful in streamlining my workflow.
codecruncher 6 months ago prev next
What kind of data are you using as input for your models? Historical prices, volume, or other indicators?
- machinelearner 6 months ago next
  I'm using a combination of historical prices and some technical indicators, like moving averages, MACD (Moving Average Convergence Divergence), andRelative Strength Index (RSI). I've found that this approach yields the most accurate predictions for me.
ai_enthusiast 6 months ago prev next
Are you taking into account any exogenous factors, like economic news or fundamental data?
- machinelearner 6 months ago next
  Yes, I'm experimenting with using natural language processing (NLP) techniques to extract useful information from news articles and social media posts. I'm still in the early stages of this, but so far it seems promising.
quant_trader 6 months ago prev next
What's your backtesting approach? How do you evaluate the performance of your models?
- machinelearner 6 months ago next
  I'm mainly using walk-forward optimization and cross-validation techniques for backtesting. I'm also using various performance metrics, like the Sharpe ratio, Sortino ratio, and maximum drawdown, to evaluate the quality of my models' predictions.
deeplearning_hacker 6 months ago prev next
How are you handling the risk of overfitting with your models?
- machinelearner 6 months ago next
  I'm using various techniques to prevent overfitting, like early stopping, regularization, dropout, and model ensembling. I'm also using a lot of data for training and validation to make sure my models are robust and generalize well.
algo_trader 6 months ago prev next
How often are you re-training your models, and are you using any real-time data?
- machinelearner 6 months ago next
  I'm re-training my models on a daily basis using the latest available data. I'm also using some real-time data streams, like tick-by-tick prices and order book data, to get a more accurate and up-to-date picture of the market.
reinforcement_learner 6 months ago prev next
Have you considered using reinforcement learning techniques for stock market predictions?
- machinelearner 6 months ago next
  Yes, I've been looking into reinforcement learning methods, like Q-learning and deep reinforcement learning, as a potential way to improve my models' performance. I'm still in the early stages of this, but so far it seems promising.
stochastic_process 6 months ago prev next
What's your take on using fundamental analysis and qualitative factors for stock market predictions?
- machinelearner 6 months ago next
  While I focus mainly on quantitative and technical approaches for my models, I do see the value in using fundamental analysis and qualitative factors for investment decisions. However, I think it's important to find a way to integrate these factors in a more systematic and structured way for better predictions.
game_theorist 6 months ago prev next
How do you make sure that your models are robust to adversarial attacks or unusual market conditions?
- machinelearner 6 months ago next
  I'm using various stress-testing and scenario analysis techniques to make sure my models are robust to different market conditions and strategies. I'm also monitoring the market for any unusual activity or trends, and I'm updating my models accordingly. However, I do acknowledge that no model is perfect and that there's always a certain level of risk involved in any investment strategy.

john_doe 6 months ago next
Interesting work! I've been trying to build something similar for a while now. Can you tell us more about the types of ML algorithms and models you're using for stock predictions?
- machinelearner 6 months ago next
  Sure, I'm mainly using recurrent neural networks (RNNs) with long short-term memory (LSTM) layers, as well as some gradient boosting techniques. I've found these methods to be quite effective in predicting stock market trends and patterns.
techie_nerd 6 months ago prev next
Thanks for sharing! Are you using any specific tools or libraries for implementing these algorithms, like TensorFlow or Scikit-learn?
- machinelearner 6 months ago next
  Yes, I'm using TensorFlow and Keras for implementing my RNNs, and I'm using Scikit-learn for some preprocessing and data cleaning. I've found these tools to be quite helpful in streamlining my workflow.
codecruncher 6 months ago prev next
What kind of data are you using as input for your models? Historical prices, volume, or other indicators?
- machinelearner 6 months ago next
  I'm using a combination of historical prices and some technical indicators, like moving averages, MACD (Moving Average Convergence Divergence), andRelative Strength Index (RSI). I've found that this approach yields the most accurate predictions for me.
ai_enthusiast 6 months ago prev next
Are you taking into account any exogenous factors, like economic news or fundamental data?
- machinelearner 6 months ago next
  Yes, I'm experimenting with using natural language processing (NLP) techniques to extract useful information from news articles and social media posts. I'm still in the early stages of this, but so far it seems promising.
quant_trader 6 months ago prev next
What's your backtesting approach? How do you evaluate the performance of your models?
- machinelearner 6 months ago next
  I'm mainly using walk-forward optimization and cross-validation techniques for backtesting. I'm also using various performance metrics, like the Sharpe ratio, Sortino ratio, and maximum drawdown, to evaluate the quality of my models' predictions.
deeplearning_hacker 6 months ago prev next
How are you handling the risk of overfitting with your models?
- machinelearner 6 months ago next
  I'm using various techniques to prevent overfitting, like early stopping, regularization, dropout, and model ensembling. I'm also using a lot of data for training and validation to make sure my models are robust and generalize well.
algo_trader 6 months ago prev next
How often are you re-training your models, and are you using any real-time data?
- machinelearner 6 months ago next
  I'm re-training my models on a daily basis using the latest available data. I'm also using some real-time data streams, like tick-by-tick prices and order book data, to get a more accurate and up-to-date picture of the market.
reinforcement_learner 6 months ago prev next
Have you considered using reinforcement learning techniques for stock market predictions?
- machinelearner 6 months ago next
  Yes, I've been looking into reinforcement learning methods, like Q-learning and deep reinforcement learning, as a potential way to improve my models' performance. I'm still in the early stages of this, but so far it seems promising.
stochastic_process 6 months ago prev next
What's your take on using fundamental analysis and qualitative factors for stock market predictions?
- machinelearner 6 months ago next
  While I focus mainly on quantitative and technical approaches for my models, I do see the value in using fundamental analysis and qualitative factors for investment decisions. However, I think it's important to find a way to integrate these factors in a more systematic and structured way for better predictions.
game_theorist 6 months ago prev next
How do you make sure that your models are robust to adversarial attacks or unusual market conditions?
- machinelearner 6 months ago next
  I'm using various stress-testing and scenario analysis techniques to make sure my models are robust to different market conditions and strategies. I'm also monitoring the market for any unusual activity or trends, and I'm updating my models accordingly. However, I do acknowledge that no model is perfect and that there's always a certain level of risk involved in any investment strategy.