martins6/stock_portfolio_manager

A Shiny app in R to democratise the more in-depth risk-aware quantitative study of price stocks.

I'm a Machine Learning Engineer and AI Researcher.

📚 Academic background:

• BSc in Statistics.
• Master's student in Computer Science.

🔭 I’m currently studying:

• Evolutionary Computation (EC)
• Graph Neural Networks (GNN).

📖 I'm also fascinated about the following topics:

• Autonomous Machine Intelligence
• Robot Learning / Embodied AI
  • Imitation Learning
  • Robot-at-home
• Natural Language Processing (NLP)
  • Large Language Models (LLMs)
  • Information Retrieval
  • etc.
• Metaverse (2D images to 3D models)

Connect with me and start a chat:

• LinkedIn
• Email: [email protected]

Tools:

...and many others!

stock_portfolio_manager's Issues

new

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.

If tou want, 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 I appreciate the feedback