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Stock-Market-Analysis

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Analyzing stock market trends using several different indicators in quantum finance. I explore machine learning and standard crossovers to predict future short term stock trends.

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- AWS_Lambda.py
- Data_Export.py
- Get_Data.py
- Get_Todays_Picks.py
- Main.py
- Stock_List.py
- Stock_ML.ipynb
- Stock_ML.pdf
- Back_Test.py
- Moving_Average.py
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/mhsmathew/Stock-Market-Analysis

Downloads the entire project code from GitHub to your computer.

cd Stock-Market-Analysis

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# Stock Market Analysis
Analyzing stock market trends using several different indicators in quantum finance. I explore machine learning and standard crossovers to predict future short term stock trends.

An article write-up on this project can be found [here](https://towardsdatascience.com/predicting-short-term-stock-movements-with-quantitative-finance-and-machine-learning-in-python-e6e04e3e0337?source=friends_link&sk=1f1bfd0f3af45158a7a0f7e9731987d6) and I highly suggest checking that out.

## Simple Analysis

In quantum finance, the simplest of trading indicators is a crossover. A crossover is defined as when the short moving average crosses the long moving average, and a moving average is the average closing price over a set period. In this analysis, I utilize the Simple Moving Average and the Exponential Moving Average (which weighs newer averages greater). When our script detects a crossover in favor of the stock going up, a buy is triggered, and vice-versa for a sell. Using a custom backtesting analysis, we can test our strategy with historical data and even plot the trades. 
## Machine Learning

Our machine learning approach utilizes a vast set of indicators from the finta.py library. I utilize several complex moving averages, oscillators, strength indexes, and more. Other than that, our main data was aggregated using yahoo finance for every stock in the S&P500 for the past 30 years. In the Machine Learning folder, you can view a generated Jupyer Notebook pdf which goes into several different classification machine learning approaches. The variable I am trying to predict is called the short_result and is determined by the combined percent increase over 30 days over the S&P500. This is because a stock gaining 20% over 30 days is not impressive if the S&P500 increased by 30%. For example, if Apple's stock price increased 8% and the S&P500 dropped 2%, the short_result will be 10% and classified as a strong buy.

For the model and predictions on my [personal website](https://mathewsteininger.com/#stock), I utilized several AWS services, particularly SageMaker and Lambda do create an API for my models. SageMaker allowed me to easily tune and train multiple regression models with the optimal hyperparameters. Overall, the best model had a Mean Squared Error of around 28.5 and a Root Mean Squared Error of 5.3. This means that our validation tests differed on average from our model around 5%. The model on my website updates daily on the most current trading day's stocks for every S&P500 company.

### Analysis

Regardless if you use a regression-based algorithm or a classification one, the root issue of whether to buy or sell a stock is a classification problem. However, it is effective to set bins and upper/lower limits of a regression model to predict whether to buy or sell. From our model, we use the labels `Strongly Buy` and `Strongly Sell` associated with prediction ratings of +- 10. `Buy/Sell` classifications are associated with a rating of either [5, 10) or [-5, -10) respectively, and `Hold` is values (-5, 5). Moreover, after doing extensive hyperparameter optimization using gradient descent, our algorithm performed as followed:
* 67.6% accuracy when predicting short-term stock movements relative to the S&P 500 using the `Strongly Buy/Sell` classification
* 55.8% accuracy when predicting short-term stock movements relative to the S&P 500 using any `Buy/Sell` classification

Here are two confusion matrixes. The first represents the model testing our `Strongly Buy/Sell` classifications and the second is any `Buy/Sell` Classification.

#### `Strongly Buy/Sell` Confusion Matrix

| n = 1853      | Predicted Sell | Predicted Buy | 
| ------------- | -------------- |-------------- |
| **Actual Sell**   | 0.416082029    | 0.104695089   |
| **Actual Buy**    | 0.286562331    | 0.19266055    |

#### `Buy/Sell` Confusion Matrix

| n = 47807      | Predicted Sell | Predicted Buy | 
| ------------- | -------------- |-------------- |
| **Actual Sell**   | 0.247557889    | 0.2370155   |
| **Actual Buy**    |  0.20352668   | 0.310582132    |

The following analyis was conducted by testing over 3 years of data from the entire S&P 500.

## Installation

Stock Market Analysis requires python3 and pip

Install the requirements here
```
pip install -r requirements.txt
```

## Built With

* [Pandas](https://pandas.pydata.org) - Data Analysis Library
* [scikit-learn](https://scikit-learn.org/stable/) - Machine Learning Library

## Authors

* **Mat Steininger** - [Personal Site](https://mathewsteininger.com)

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details