Here is a simple algorithm you can implement immediately for BTC-USD, using the Coinbase Pro API and python. This is just a jumping-off point (but includes ideas for improvement in the future). Clone repo here: https://github.com/samchaaa/simple_btc_algo Here’s what to do to get started Step 1.) git clone https://github.com/samchaaa/simple_btc_algo.git Step 2.) Open btc_algo.py and replace credentials for api_key…

Disclaimer: This article is for entertainment purposes only. It discusses risk in terms of distance and size, while purposely ignoring percentage risk (to be discussed later). By reading this article, you take full responsibility for all your decisions as a trader. This article shows how you can make money more…

Setting up AWS with Python with Alpaca Trade API It is always challenging for a new quant trader to get an algorithm up and running live in the cloud. In my last post, I wrote an instruction for using PythonAnywhere. This walkthrough focuses on the basics of setting up AWS with Python, the Alpaca Trade API, and running a…

Using PythonAnywhere When Running an Algorithm Live in the Cloud After first getting into quant trading and writing algos on your local machine, the biggest area of resistance for newbie quant traders is running an algorithm live in the cloud. There are many cloud services on the market, including AWS, Google Cloud, Heroku, Digital Ocean, and PythonAnywhere. This walkthrough focuses…

Defining Technical Chart Patterns Programmatically Ever wondered how to programmatically define technical patterns in price data? At the fundamental level, technical patterns come from local minimum and maximum points in price. From there, the technical patterns may be defined by relative comparisons in these min/max points. Let’s see if we could have played this by…

In this article, I will show how you can make “roll-your-own” visualizations, using the Alpaca API with Python. See the final code on GitHub here. samchaaa/alpaca_correlation_viz Contribute to samchaaa/alpaca_correlation_viz development by creating an account on GitHub.github.com Visualizing your portfolio correlation by heatmap in Python (jupyter notebook) Step 1: Setup For this tutorial, I used Python 3 in jupyter notebook, some basic libraries, and the Alpaca trade API. Of course, you’ll need an Alpaca account…

Code adapted from https://www.quantopian.com/posts/simple-machine-learning-example-mk-ii. Ever wondered how to apply your machine learning/data science skills to algorithmic trading? If you already know Python and basic statistical principles of data science (like train-test-split, over-fitting, etc), you’re already way ahead of the curve. Translating machine learning models into trading algorithms is pretty simple…