matheusbfernandes / stock-market-prediction Goto Github PK

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.

Hello, now I want to use CNN+LSTM to achieve stock prediction. My LSTM is completely handwritten, so I encountered a problem. That is, I designed a three-layer CNN. The dimensions of the CNN input are (1, 1, N) (N represents uncertainty). The CNN also has a flaten at the end, and finally we get (1, M). After that, I take this and enter the LSTM layer. Since M is uncertain, I want to input it into the LSTM layer one by one. In each prediction, LSTM backpropagation needs to be performed. Do I use the predicted value of LSTM and the value output by the CNN input to the LSTM layer to calculate the loss? Also, since the LSTM layer is handwritten, the backpropagation is also handwritten. So, can I still use pytorch’s automatic derivation mechanism for CNN backpropagation?