We’re taking a break from our portfolio series and million sample simulations to return to a subject that we haven’t discussed of late despite its featured spot in this blog’s name—options. In this post, we’ll look at the buy-write (BXM) and put-write (PUT) indices on ...

We’re taking a break from our portfolio series and million sample simulations to return to a subject that we haven’t discussed of late despite its featured spot in this blog’s name—options. In this post, we’ll look at the buy-write (BXM) and put-write (PUT) indices on ...

DataCamp’s unguided projects are a new way to get real-world experience and learn how to solve data science problems—all on your own and without leaving your browser. Designed for our intermediate and advanced learners, you’ll challenge your Python an...

In the past few years, and especially during the COVID-19 crisis, the oil and gas industry has been significantly challenged, subject to wildly fluctuating oil prices caused by unprecedented world events, limited market access, and uncertain governme...

The pypyodbc is the library you need to drive the connection to a SQL server database. If, like me, you struggle with connection strings, then please navigate to this site for help with all types of connection string: https://www.connectionstrings.com/. Installing with pip install To install the relevant ...

In our last post, we ran simulations on our 1,000 randomly generated return scenarios to compare the average and risk-adjusted return for satisfactory, naive, and mean-variance optimized (MVO) maximum return and maximum Sharpe ratio portfolios.1 We ...

In our last post, we ran simulations on our 1,000 randomly generated return scenarios to compare the average and risk-adjusted return for satisfactory, naive, and mean-variance optimized (MVO) maximum return and maximum Sharpe ratio portfolios.1 We ...

Technical documentation.

In our last post, we ran simulations on our 1,000 randomly generated return scenarios to compare the average and risk-adjusted return for satisfactory, naive, and mean-variance optimized (MVO) maximum return and maximum Sharpe ratio portfolios.1 We ...

Introduction to PP-YOLO PP-YOLO (or PaddlePaddle YOLO) is a machine learning object detection framework based on the YOLO (You Only Look Once) object detection algorithm. PP-YOLO is not a new kind of object detection framework. Rather, PP-YOLO is a modified version of YOLOv4 with an improved inference speed and mAP ...