Finance, Data Science, and Stoicism
I haven’t had a chance to update this site because I am working on Udacity’s Data Engineer Nanodegree. It had been very time consuming, but worth it to learn databases and a slew of different technologies. Going forward, I’ll be writing a
KPIs Goal of post is to get a good understanding of what KPIs are and why they are important to a company. What are the most common KPIs? How to interpret them and benchmark? “If you know the enemy and know yourself,
The decision to go to a coding bootcamp is a very difficult one. I’ve had this conversation with several friends and I wanted to have a quick guide on what to expect and how to preaper. I went to an immersive coding
SELECT {column} FROM {table} WHERE {other_column} >= {number}. For those with a working knowledge of Excel, SQL seems straight-forward. Its usually when a very specific question is asked regarding data that one will need to learn and understand the concepts below. This
Background Reason for creating the algorithm Future projects My background has been in financial services and there’s a lot of places that machine learning would be applicable. I was surprised that there hasn’t been much in terms of recommendation systems and mutual
At first, I had very little interest in time series. Within finance, predicting future prices has made many managers underperform a simple basket of stocks like the SPY, which tracks the S&P 500 index. The chart below shows the performances of active
Difference between targets and no targets. Unsupervised: 10 items and we separate by similarities. Supervised has a y label. Goal of supervised learning discover subgroups is there a better way to view data? Is there a way to visualize to show underlying
I just finished working on a stock market prediction model for a fintech company and before I can kick my feet back and relax, I started reading Advances in Financial Machine Learning by Marcos Lopez de Prado. As I read, I realized
Plan: To predict prices of stocks based sentiment and previous prices 5 models: Linear Regression Decision Trees (bagging and boosting) Random Forrests Support Vector Regressor Time Series Large dataframe with each row being a stock and model. Once you add the information,
There are several approaches to regression and classification problems that can boost performance over linear regression. Classification and regression trees (CART) involve segmenting the predictor space into a number of simple regions and our prediction involves using the mean of the training