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!

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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.

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Mad Libs and Python

It’s been a long time since I’ve written a blog post. As I have written previously, I intentionally scaled back on my blogging. I didn’t want to scale back to the point that I have not written a post since July. But my life has been busy lately, a topic that may be the subject of a future post (for those who care about what goes on in my life). Continue reading

Let’s Create Our Own Cryptocurrency

Today I’m sharing my favorite blog post of the week, written by a blogger with username cranklin.

Have whatever opinion you want about crypto-currencies, Bitcoin, and so on, but many in the business world take blockchain technology very seriously. (I read about it regularly in The Economist, and a new book by David Birch entitled Before Bablyon, Beyond Bitcoin, imagines a new crypto-currency world order with money entering a new evolutionary state, with everything from governments to community churches issuing their own coins; the book is on my reading list.) Perhaps the best (and most-fun) way to lean about the technology is to create your own crypto-currency. Then share it with your friends and family because why not.

Perhaps at some point in the future I will create my own coin and write about it as well. If I do, I will be using this post as a reference.

cranklin.com

I’ve been itching to build my own cryptocurrency… and I shall give it an unoriginal & narcissistic name: Cranky Coin.

After giving it a lot of thought, I decided to use Python. GIL thread concurrency is sufficient. Mining might suffer, but can be replaced with a C mining module. Most importantly, code will be easier to read for open source contributors and will be heavily unit tested. Using frozen pip dependencies, virtualenv, and vagrant or docker, we can fire this up fairly easily under any operating system.

I decided to make Cranky coin a VERY simple, but complete cryptocurrency/blockchain/wallet system. This implementation will not include smart contracts, transaction rewards, nor utilize Merkel trees. Its only purpose is to act as a decentralized ledger. It is rudimentary, but I will eventually fork a few experimental blockchains with advanced features from this one.

The Wallet
This currency will only be compatible with…

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

I have exciting news!

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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.

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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!

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