About

This is our final project for W209: Data Visualization with Professor Mak Ahmad for the Summer 2022 term.

Intended Audience

Our intended audience for this project are users with entry-level to intermediate knowledge of financial trading concepts. They can be novices who are interested in learning about the trading world or retail investors/nonprofessional investors who would like to use our visualization to supplement their trading toolbelt.

Our Visualization's Goals

The goal of our visualization is to give users exposure to basic trading tools via a technical indicators dashboard. More specifically, the goal is for users to accomplish these tasks:

• Make an informed investment decision based on technical indicators
• Explore the relationship between a chosen stock’s relative strength index (RSI) and forward return
• Decide if a stock is worth purchasing
• Identify trends in stock price, RSI, and forward returns

Data Source

We are using a dataset of historical daily stock prices (2014-2017) of companies within the S&P 500 Stock Market Index. This dataset is freely available on Kaggle here. Note that the visualizations can be extended to other periods; there isn’t anything particularly special about this time period.

The dataset contains the following columns for each stock and each date:

• Open - the price of the stock at the opening of the stock market
• High - the highest price the stock traded at during trading hours
• Low - lowest price the stock traded at during trading hours
• Close - the last price the stock traded at for that day
• Volume - the total number of shares traded during that day

We have data for each trading day over the observed period, which excludes weekends and holidays in which the stock market is closed.

Data Transformations

A large component of the visualization is the indicators that are derived from the stock price data. Indicators can be a single time series ranging between 0-100 or they can be made of multiple time series without strict ranges. Indicators can then be interpreted individually or in combination to create trading signals (another time series), which can be as simple as “buy” or “sell”. For this project, we derived two indicators, the relative strength index (RSI) and forward return, from the original dataset and added them as extra data columns to the dataset.

Our Team