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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.
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# Using the latest advancements in AI to predict stock market movements
In this notebook 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.
We will go into greater details for each step, of course, but the most difficult part is the GAN: very tricky part of successfully training a GAN is getting the right set of hyperparameters. For that reason we will use **Bayesian optimisation** (along with Gaussian processes) and **Reinforcement learning** (RL) for deciding when and how to change the GAN's hyperparameters (the exploration vs. exploitation dilemma). In creating the reinforcement learning we will use the most recent advancements in the field, such as **Rainbow** and **PPO**.
We will use a lot of different types of input data. Along with the stock's historical trading data and technical indicators, we will use the newest advancements in **NLP** (using 'Bidirectional Embedding Representations from Transformers', **BERT**, sort of a transfer learning for NLP) to create sentiment analysis (as a source for fundamental analysis), **Fourier transforms** for extracting overall trend directions, **Stacked autoencoders** for identifying other high-level features, **Eigen portfolios** for finding correlated assets, autoregressive integrated moving average (**ARIMA**) for the stock function approximation, and many more, in order to capture as much information, patterns, dependencies, etc, as possible about the stock. As we all know, the more (data) the merrier. Predicting stock price movements is an extremely complex task, so the more we know about the stock (from different perspectives) the higher our changes are.
For the purpose of creating all neural nets we will use MXNet and its high-level API - Gluon, and train them on multiple GPUs.
**Note:** _Although I try to get into details of the math and the mechanisms behind almost all algorithms and techniques, this notebook is not explicitly intended to explain how machine/deep learning, or the stock markets, work. The purpose is rather to show how we can use different techniques and algorithms for the purpose of accurately predicting stock price movements, and to also give rationale behind the reason and usefulness of using each technique at each step._
_Notebook created: January 9, 2019_.
**Figure 1 - The overall architecture of our work**
<center><img src='imgs/main.jpg' width=1060></img></center>
## Table of content
* [Introduction](#overview)
* [Acknowledgement](#acknowledgement)
* [The data](#thedata)
* [Correlated assets](#corrassets)
* [Technical indicators](#technicalind)
* [Fundamental analysis](#fundamental)
- [Bidirectional Embedding Representations from Transformers - BERT](#bidirnlp)
* [Fourier transforms for trend analysis](#fouriertransform)
* [ARIMA as a feature](#arimafeature)
* [Statistical checks](#statchecks)
- [Heteroskedasticity, multicollinearity, serial correlation](#hetemultiser)
* [Feature Engineering](#featureeng)
* [Feature importance with XGBoost](#xgboost)
* [Extracting high-level features with Stacked Autoencoders](#stacked_ae)
* [Activation function - GELU (Gaussian Error)](#gelu)
* [Eigen portfolio with PCA](#pca)
* [Deep Unsupervised Learning for anomaly detection in derivatives pricing](#dulfaddp)
* [Generative AdversariExcerpt of 83,603 characters
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matched fp:56eb427f119754ce, llm:Description: 'complete process for predicting stock price movements... use a Generative Adversarial Network (GAN) with LSTM ... and a Convolutional Neural Network (CNN) as a discriminator' — time series forecasting/financial modeling using ML.
matched fp:56eb427f119754ce, llm:Description: 'complete process for predicting stock price movements... use a Generative Adversarial Network (GAN) with LSTM ... and a Convolutional Neural Network (CNN) as a discriminator' — time series forecasting/financial modeling using ML.