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stockpredictionai icon stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

stumpy icon stumpy

STUMPY is a powerful and scalable Python library that can be used for a variety of time series data mining tasks

tablib icon tablib

Python Module for Tabular Datasets in XLS, CSV, JSON, YAML, &c.

timeseriescv icon timeseriescv

Scikit-learn style cross-validation classes for time series data

tpot icon tpot

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

ultrafinance icon ultrafinance

Python project for real-time financial data collection, analyzing && backtesting trading strategies

wtfpython icon wtfpython

A collection of surprising Python snippets and lesser-known features.

xmltodict icon xmltodict

Python module that makes working with XML feel like you are working with JSON

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