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stock-market-probabilities-deep-learning's Introduction

Stock-Market-Probabilities-Deep-Learning

Tries to predict if a stock will rise or fall with a certain percentage through giving probabilities of what events it thinks will happen.

Requirements

  • Python 3.*
  • Tensorflow 1.3
  • Pandas
  • tqdm
  • Numpy
  • Six

Adjusting parameters

Before you create your dataset file and train your model it's adviseable that you adjust the parameters first. This you can do by editing the file config.py. If you don't understand a parameter it's best to leave it alone.

When running a script you can always pass the parameters directly and override the config.py parameters. To get to know what the parameters are you can type python3 script_name.py -h.

Creating dataset file

As a default I use a csv file from Yahoo Finance of NASDAQ, you can see the format of the file and use the same format if you want to use your own dataset.

Run python3 create_data_set.py. This will create a file called data_set.pkl. This script does everything that you'll need.

Training

To train a model you firstly have to create a dataset file. After that it's as easy as running the script python3 train.py

Todos

  • Evaluation script
  • Fix a better dataset

Other

Made by Oliver Edholm, 15 years old.

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stock-market-probabilities-deep-learning's Issues

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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.

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