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stockprediction_transformer's Introduction

StockPrediction_Transformer

Used Transformer Architecture to predict stock price ten minutes into the future given historical prices. In future, planing on using the evolved transformer architecture to make predictions.

This code is trained on IBM 1 minute bar stock prices and S&P 500 bid/ask data as additional features.

Run StockPredTest to test on live data.

stockprediction_transformer's People

Contributors

makaveli10 avatar

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stockprediction_transformer's Issues

google drive cannot work

Hi, your work is really amazing, but the google drive cannot work, could you share the dataset to us? Thank you very much.

Could you share the dataset?

Hi, I'm very interested in your fantastic work! I want to run this code locally, but I cannot find the datasets. Could you share it with me? Very thank!

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.

recommended

hi this was recommended to me by someone in the microprediction slack (here btw). Have you tried it out? Any interest in giving it a spin in the M6 competition?

StockPrediction_Transformer

When run this code
from google.colab import drive
drive.mount('/gdrive')
%cd /gdrive
It appears:
ModuleNotFoundError: No module named 'termios'
{%8A59ZEIUT2S71{HE1ZF

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