Learn The Code Behind The Crypto Market With These Expert Led Trading Classes

If you know how to code, you can learn how to apply that knowledge to markets in this bundle.

Mar 16 by Futurism Creative
Image by StackCommerce

As technology and finance have intertwined, it’s wrought some complex changes in how we buy and sell securities. Core to this change has been the arrival of quantitative finance, which uses complex models and large data sets to automate investing. This is seen as the province of major financial institutions, but anyone with enough coding knowledge can do it themselves, as the Quantitative Crypto Trading Strategies for Intermediate to Advanced Learners Bundle, currently $144.99, shows us.

The bundle includes three courses from Quantra, which develops courses with input from leading traders and exchanges, using the crypto market as a demonstration tool and aimed at those already knowledgeable about coding and finance:

Quantitative Trading Strategies & Models discusses quantitative finance in detail to ground you in the overall theory. Over seven lectures, you’ll learn about time series analysis, options, and derivatives. The course also covers autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH) models, and how to use them; options and derivatives.  Finally, you’ll discover how to implement this in Python.

Then the bundle narrows in on cryptocurrencies. Crypto Trading Strategies: Intermediate discusses three different intraday trading approaches including how to code them in Python; backtesting; and how to assess risk:


  • Ichimoku Cloud strategy: Analyzing moving averages over time to better determine overall price trends.
  • Calendar Anomalies: Studying market history to spot specific anomalies at given times of the year.
  • Divergence Strategy: Using data sets to determine the overall momentum, and shifts in momentum, of the market.

Finally, Crypto Trading Strategies: Advanced looks at using algorithms in Python to automate your trading. It delves into machine learning, statistical arbitrage, long-only moment, K-means clustering, Hurst exponents, and other techniques to craft your own specific trading strategy, built around your needs. You’ll also learn how to analyze the performance of your trading as you take it into live markets, and how to refine your code and approach as you collect more data.

The cryptocurrency market is only used as an example. All of these strategies were developed for, and can be applied to, other markets, from foreign exchange (forex) to the stock market.

The course bundle is ideal both for those looking to begin using quantitative finance for their personal investing, and those who want to better understand how it works and its impacts on the market. Usually $577, currently the Quantitative Crypto Trading Strategies for Intermediate to Advanced Learners Bundle is $144.99, or 72% off, and perfect for better understanding the changing world of finance.

Prices subject to change.


Futurism fans: To create this content, a non-editorial team worked with an affiliate partner. We may collect a small commission on items purchased through this page. This post does not necessarily reflect the views or the endorsement of the Futurism.com editorial staff.

As a Futurism reader, we invite you join the Singularity Global Community, our parent company’s forum to discuss futuristic science & technology with like-minded people from all over the world. It’s free to join, sign up now!

Share This Article

Keep up.
Subscribe to our daily newsletter to keep in touch with the subjects shaping our future.
I understand and agree that registration on or use of this site constitutes agreement to its User Agreement and Privacy Policy


Copyright ©, Singularity Education Group All Rights Reserved. See our User Agreement, Privacy Policy and Cookie Statement. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with prior written permission of Futurism. Fonts by Typekit and Monotype.