Start Getting and Working with Data with “Data Acquisition and Manipulation with Python”

This news is a few weeks late, but better late than never!

Continue reading

Advertisements

Getting S&P 500 Stock Data from Quandl/Google with Python

DISCLAIMER: Any losses incurred based on the content of this post are the responsibility of the trader, not me. I, the author, neither take responsibility for the conduct of others nor offer any guarantees. None of this should be considered as financial advice; the content of this article is only for educational/entertainment purposes.

A few months ago I wrote a blog post about getting stock data from either Quandl or Google using R, and provided a command line R script to automate the task. In this post I repeat the task but with Python. If you’re interested in the motivation and logic of the procedure, I suggest reading the post on the R version. The Python version works similarly.

Continue reading

Get Started Learning Python for Data Science with “Unpacking NumPy and Pandas”

I have exciting news!

Continue reading

Stock Trading Analytics and Optimization in Python with PyFolio, R’s PerformanceAnalytics, and backtrader

DISCLAIMER: Any losses incurred based on the content of this post are the responsibility of the trader, not me. I, the author, neither take responsibility for the conduct of others nor offer any guarantees. None of this should be considered as financial advice; the content of this article is only for educational/entertainment purposes.

Introduction

Having figured out how to perform walk-forward analysis in Python with backtrader, I want to have a look at evaluating a strategy’s performance. So far, I have cared about only one metric: the final value of the account at the end of a backtest relative. This should not be the only metric considered. Most people care not only about how much money was made but how much risk was taken on. People are risk-averse; one of finance’s leading principles is that higher risk should be compensated by higher returns. Thus many metrics exist that adjust returns for how much risk was taken on. Perhaps when optimizing only with respect to the final return of the strategy we end up choosing highly volatile strategies that lead to huge losses in out-of-sample data. Adjusting for risk may lead to better strategies being chosen.

Continue reading

Walk-Forward Analysis Demonstration with backtrader

DISCLAIMER: Any losses incurred based on the content of this post are the responsibility of the trader, not me. I, the author, neither take responsibility for the conduct of others nor offer any guarantees. None of this should be considered as financial advice; the content of this article is only for educational/entertainment purposes.

Finally I can apply a walk-forward analysis!

Continue reading

Who Survives Riddler Nation?

Introduction

Last week, I published an article on learning to fight in the Battle for Riddler Nation. Here’s a refresher of the rules:

In a distant, war-torn land, there are 10 castles. There are two warlords: you and your archenemy. Each castle has its own strategic value for a would-be conqueror. Specifically, the castles are worth 1, 2, 3, …, 9, and 10 victory points. You and your enemy each have 100 soldiers to distribute, any way you like, to fight at any of the 10 castles. Whoever sends more soldiers to a given castle conquers that castle and wins its victory points. If you each send the same number of troops, you split the points. You don’t know what distribution of forces your enemy has chosen until the battles begin. Whoever wins the most points wins the war.

Continue reading

Winning the Battle for Riddler Nation; An Agent-Based Modelling Approach to the Solution

Introduction

Oliver Roeder runs a column on FiveThirtyEight called “The Riddler,” where he proposes logical and mathematical puzzles for readers to solve. On February 3rd of this year, he posted in Riddler Classic the problem, “Can You Rule Riddler Nation?” Here is the description:

In a distant, war-torn land, there are 10 castles. There are two warlords: you and your archenemy. Each castle has its own strategic value for a would-be conqueror. Specifically, the castles are worth 1, 2, 3, …, 9, and 10 victory points. You and your enemy each have 100 soldiers to distribute, any way you like, to fight at any of the 10 castles. Whoever sends more soldiers to a given castle conquers that castle and wins its victory points. If you each send the same number of troops, you split the points. You don’t know what distribution of forces your enemy has chosen until the battles begin. Whoever wins the most points wins the war.

Continue reading