Difference between revisions of "Stock Analysis"
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[https://campus.datacamp.com/courses/supervised-learning-with-scikit-learn/classification?ex=1 Data Camp] | [https://campus.datacamp.com/courses/supervised-learning-with-scikit-learn/classification?ex=1 Data Camp] | ||
| + | [https://machinelearningmastery.com/backtest-machine-learning-models-time-series-forecasting/ MachineLearningMastery] | ||
[[Category:Projects]] | [[Category:Projects]] | ||
[[Category:Fall 2018 Projects]] | [[Category:Fall 2018 Projects]] | ||
Revision as of 16:36, 24 October 2018
Link to Project Log: Stock Analysis Log
Contents
Project Overview
The financial market is an extremely complicated place. While there are many metrics used to measure the health of markets, market values such as the S&P 500, Russell 2000, and the Dow Jones Industrial average are the ones that we will focus on. We will build models of the market so that users can input how they think the interest rate, unemployment rate, price of gold, and other factors will change, and our models will predict how the market will react to those changes. The user will enter their predictions into a user interface, and the product will output visual aid as well as estimated prices and behavior. Our product will help users make more informed investment decisions.
Group Members
- Brandt Lawson
- Keith Kamons
- Jessica
- Chang Xue (TA)
Objectives
Produce a user interface which, given a user specified value for some factors, can predict what impact this change will have on future index values. The accuracy and precision of the model can be determine through back testing and measure the success of the project. We will demonstrate our project by allowing users to predict changes in many factors and to see how our model does at predicting the markets reaction.
Challenges
- Learning how to use MySQL.
- The web security behind sharing data online and how to accomplish this in a safe and secure way.
- Learning how to use Python's data analysis toolboxes.
- Nobody in the group has taken a course or studied how many of the analysis tools and underlying math and computer science works, so it will be incredibly challenging to figure it all out along the way.
- Integrating this software across different platforms and allowing everyone in the group to have access to this data.
- Data collection and storage in an efficient and complete way
- Collecting data in an autonomous and dynamic way will be a challenge due to the ways that resources such as Google Finance, Yahoo Finance and Quandl change their policies on data collection.
- Manual data collection and cleaning while the code for autonomous collection and analysis is setup.
Gantt Chart
Budget
Our goal is to exclusively use open source data and software, which makes our budget $0.00.
Data
Data Scraping
In order to enable our data set to dynamically update, we must create code that will "scrape" the newest information from the specified website.
Database
Once this new data is scraped, we will add it to our relational database using SQL. This will make it more organized and easily searchable.
App Prototype
