Stock Trend Analysis

This project contains a stock-recommender system that uses quarterly reports, news information pieces and stock prices to recommend relevant stocks for further (manual) analysis based on user interest (e.g. resources or tech-companies). The system is designed for relevance, novelty and serendipity (with configurable parameters) to allow exploration of potential n-bagger stocks.

Getting Started

1. Data Access:

First you will need to create a keys.csv file in the root directory that contains the API keys for the various serivces used. You can find the available keys in the keys.tmp.csv template.

2. Training:

Next we need to train the machine learning models. This is currently done in the regarding notebook (03-1_stock-prediction.ipynb), but will be outsourced into a separate training file in the future.

3. Deploy:

The simplest way to execute the project, is using the streamlit report. Simply install streamlit (pip install streamlit) and execute the report file:

$ cd notebooks
$ streamlit run 09-1_project-report.py

Note: As no data is provided in this repo, the first start might take a few minutes to download the relevant profile data from the API

Note: The web-app is currently not functional, but will come soon.

The system deploys as a flask web services. The easiest way to run it is through docker (recommended nvidia docker for TensorFlow components):

$ docker build -t felixnext/stocks .
$ docker run -d -p 8000:3001 --name stocks -v <PATH>:/storage felixnext/stocks

The service should now be available under http://localhost:8000/

You might also run the system locally through the command line:

$ cd frontend
$ python run.py

The service should now be available under http://localhost:3001/

Code documentation

You can find the documentation of the recommender library here

Architecture

The goal of the system should be to provide recommendations of stocks for a specific user. Therefore the system should leverage the following information:

• User Interest - Which economic field the user wants to invest in (KB Filtering)
• Specific Stocks - Which stocks liked the user
• Stock Forecast - Using various sources of information (including news, balance sheet statements and historic stock prices among others) to create a ranking for stocks to suggest potentially profitable stocks to the user

General Design

You can find the data analysis and test of single algorithms in the jupyter notebooks (notebooks folder). Based on the results, I have created the actual Machine Learning Pipeline as a separate package in the recommender folder. This in turn is used by the frontend to be integrated into a flask webapp.

Recommender

The recommender consists of the following parts:

1. ETL Pipeline - This pipeline uses various APIs (e.g. RSS Feeds, Stock APIs) to gather relevant information and create a list of available stocks with categories to recommend
2. Higher Order Features - Machine learning pipeline that uses various approaches to generate higher-order features based on the data coming from ETL (e.g. a rating for stock profitability)
3. User Recommendation - A recommendation system that compares user interest to relevant stocks and computes the higher-order features for these stocks to generate a basic understanding of the data

All pipelines are implemented into a Spark Process, allowing them to easily scale out.

Frontend

The frontend consists of a simple flask web-app that has access to the spark pipeline. From there it can retrieve information and render general stock information to the user.

Data Sources

The system uses various sources of data. However, as financial apis appear incomplete and volatile, the system pacakges each of these API behind an abstraction interface, that will make it easier to change or add new APIs down the road.

Stock Data

• Alpha Vantage Data (through alpha-vantage) - Allows to retrieve daily stock data (including long range historic data). It also allows to retrieve intra-day data (in 15min intervals)
• Quandl Data (through quandl) - Allows to retrieve intraday trading data (however does not have long term historic data in free plans)

Training Data

• For training an additional stock market dataset is used to account for historic data

Note: There is a download script to retrieve the data in data folder. Before you run it, make sure you have the kaggle-cli installed and setup

Quarterly Reports

• IEX Cloud (using iexfinance)
• Financial Modeling Prep (using the API directly)

News Ticker

• Twitter Data - (using tweepy)
• RSS Feeds - This allows us to basically read in any news source (using feedparser)

Sources of RSS Data:

• Google Alerts - Allow to create a RSS reader based on any topic (using python library)
• Financial Times RSS Feed
• CNN Money RSS Feed

Economic Data

• World Bank - (using wbdata)

Data Insights

A current report on the data insights is in this markdown.

ML Pipeline Design

The pipeline has two core components: The stock classifier (higher order features) and the actual recommender system.

Stock Classifier

There are multiple approaches for classification that

• MutliOutput - Logistic Regression and SVMs
• Feedforward Network

The stock classifier is tested on historic stock data (test-set that is hold-out from the training set). The categories and time-frames are clearly defined.

Experiments

The performance of the classifier is measured through accuracy and a custom metric (as the result is a ordered scale, we can penalize close categories).

Recommender

The recommender is based on NLP parsing of user queries to identify relevant stocks and using the prediction system to rank the stocks. The system can be tested through streamlit by running: streamlit run notebooks/09-1_project-report.py

Dependencies

I am using the following packages for the system:

• sklearn-recommender (note: written for this project, but decoupled into a separate repository)
• DS Python Toolstack (Pandas, Numpy, Sklearn, Seaborn, Matplotlib, etc.)
• TensorFlow

Future Work

• Integration of Spark to handle online learning and real-time data processing (continuous prediction)
• Create Recommenders for different time frames
• Integrate multiple higher order features
• Create additional higher order features (e.g. RNN predictions)
• Integrate Rule Based approaches (e.g. implement Ben Graham Strategies)
• Implement better error handling for financialmodelingprep
• Balance Dataset for prediction
• Test additional NLP approaches (LSTM embeddings through character prediction)
• Bayesian Networks to measure confidence in stock predictions

License

The code is published under MIT License.