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lamres / hmm_market_behavior

Unsupervised Learning to Market Behavior Forecasting Example
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new #2

Open Leci37 opened 1 year ago

Leci37 commented 1 year ago

My name is Luis, I'm a big-data machine-learning developer, I'm a fan of your work, and I usually check your updates.

I was afraid that my savings would be eaten by inflation. I have created a powerful tool that based on past technical patterns (volatility, moving averages, statistics, trends, candlesticks, support and resistance, stock index indicators). All the ones you know (RSI, MACD, STOCH, Bolinger Bands, SMA, DEMARK, Japanese candlesticks, ichimoku, fibonacci, williansR, balance of power, murrey math, etc) and more than 200 others.

The tool creates prediction models of correct trading points (buy signal and sell signal, every stock is good traded in time and direction). For this I have used big data tools like pandas python, stock market libraries like: tablib, TAcharts ,pandas_ta... For data collection and calculation. And powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM.

With the models trained with the selection of the best technical indicators, the tool is able to predict trading points (where to buy, where to sell) and send real-time alerts to Telegram or Mail. The points are calculated based on the learning of the correct trading points of the last 2 years (including the change to bear market after the rate hike).

I think it could be useful to you, to improve, I would like to share it with you, and if you are interested in improving and collaborating I am also willing, and if not file it in the box.

lamres commented 1 year ago

@Leci37 hey, I'm glad to hear about your initiatives. Also, I really appreciate your feedback! As for the description about your research, that's quite interesting.

First of all, I'd recommend you to conduct a research with these components at least:

  1. Implement a walk-forward or similar analysis to prevent an overfitting.
  2. Test a hypothesis that your signals work on a bunch of assets.
  3. Test a hypothesis on a different market states (bearish, bullish, sideways). The last 4-5 years are acceptable for that.
  4. Compare the result with a buy and hold strategy and/or simple trading strategies (e.g. MA crossing, RSI, and etc.). Provide statistically tests for that.

The second one is to share your research on the Internet in a community with algotraders, data scientists, investors. It will allow you to get diversified feedback, connect with a potential audience of your library, and so on.

Leci37 commented 1 year ago

Please read the readme , and in case of any problem you can contact me , If you are convinced try to install it with the documentation. https://github.com/Leci37/LecTrade/tree/develop