Data Science and Economics

Bollinger Bands are a type of technical analysis indicator created by John Bollinger. The bands serve as a trading envelope that provide a feel for a relative measurement for high and low points that can be used as overbought and oversold levels.

Bollinger Bands typically include two boundary lines (upper and lower bands) and one moving average line (simple moving average).

Using an “overbought and oversold” strategy, Bollinger Bands can be viewed as creating a ceiling and a floor that a stock price would bounce between. When the stock price hits the floor (lower band), it may indicate that the…

Investing in assets that have low correlations between them is a great way to start diversifying your portfolio. Diversifying with asset correlation is known to be helpful in reducing your portfolio’s volatility, which could mean more consistent and reliable long-term returns while potentially limiting risk.

In this article, calculating asset correlations in a portfolio will be demonstrated using the Python programming language.

The measure of the correlation between two or more assets is called the correlation coefficient. The correlation coefficient ranges from -1 to 1, where numbers closer to -1 signal a negative correlation (inverse relationship), while numbers closer to…

**Summary:** In this post I will discuss the details of **unsupervised** machine learning and its applications. Code examples will be shown to demonstrate certain techniques.

Unsupervised learning is a branch of machine learning that is used to find underlying patterns in data and is often used in exploratory data analysis. Unsupervised learning does not use labeled data like supervised learning, but instead focuses on the data’s features. Labeled training data has a corresponding output for each input. When using unsupervised learning, we are not concerned with the targeted outputs because the goal of the algorithm is to find relationships within…

Does it work?

I will briefly touch on simple linear regression in this post, but I do have an article specifically about simple linear regression using Python that can be found here and it may be a bit more detailed and helpful.

Linear regression can be used to find a relationship between two or more variables of interest and allows us to make predictions once these relationships are found. In simple linear regression, there are only two variables: one dependent variable and one independent variable.

Simple linear regression will provide a **line of best fit**, or the regression line. …

A moving average is one of the most basic technical indicators used to analyze stocks. “Moving average” is a broad term and there are many variations used by analysts to smooth out price data and analyze trends.

Moving averages will require a time period for calculations. For example, an investor may choose a 50-day moving average, where the past 50 days in the data will be used to calculate the average. …

Simple linear regression is a concept that you may be familiar with already from middle school or high school. If you have ever heard of a slope and an intercept, or ** y = mx + b**, then you have already learned about simple linear regression!

Simple linear regression is a statistical method that we can use to find a relationship between two variables and make predictions. The two variables used are typically denoted as ** y** and

I recently finished up an introductory course for data science at my university and for my final project, I decided I wanted to work with stock market data. I wanted to place my focus on the algorithmic trading and needed a quick and easy way to gather stock data that was easily useable.

I came across a library called **yfinance** and it made my project a lot easier!

I would highly recommend reading through the documentation. Its a quick read that will aid you with any projects involving stock data.

Before we begin, we will need to install the **yfinance**…

I recently decided to read the popular book by Peter Thiel and Blake Masters titled, *Zero to One: Notes on Startups, or How to Build the Future*. While reading, I took some notes that I felt were important and needed to be shared.

All quotes are taken straight from the book *Zero to One: Notes on Startups, or How to Build the Future.*

The next Bill Gates will not build an operating system. The next Larry Page or Sergey Brin will won’t make a search engine. And the next Mark Zuckerberg wont create a social network.

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This blog post will start with a brief introduction and overview of convolutional neural networks and will then transition over to applying this new knowledge by predicting pneumonia from x-ray images with an accuracy of over 92%. While such an accuracy is nothing to get too excited about, it is a respectable result for such a simple convolutional neural network.

When it comes time to show the code examples, the code will be shown first, then below each code example there will be an explanation about the code.

**Dataset**: Pneumonia X-Ray Dataset

Convolutional Neural Networks (CNNs), or ConvNets, are neural…

A neural network is loosely based on how the human brain works: many neurons connected to other neurons, passing information through their connections and firing when the input to a neuron surpasses a certain threshold. Our artificial neural network will consist of artificial neurons and synapses with information being passed between them. The synapses, or connections, will be weighted according to the neuron’s strength of influence on determining the output. These synaptic weights will go through an optimization process called **backpropagation**. …