Financial Data Project

Financial, economic and alternative datasets that you can use for market analysis and backtesting.

The Financial Data Project is an invaluable resource for investors and traders looking to analyse market data and build investing models. This collection includes datasets from a variety of different sources. This is an evolving project which we will add to as time goes by.

The collection currently includes:

  • A millennium of macroeconomic data for the UK
  • Historical bitcoin data in 1-minute time interval
  • Historical daily data for 1200 cryptocurrencies
  • Intraday data for SPY, TLT and GLD
  • And lots more...

This data is provided in 'as is' format. All data is provided for informational purposes only, not intended for trading purposes or advice. Be careful when investing your money.

Marwood Research will not be liable for any informational errors, incompleteness, delays, financial losses or for any actions taken in reliance on information contained herein. Full risk warning and disclaimer applies.


Your Instructor


Joe Marwood
Joe Marwood

Joe Marwood is an independent trader and investor specialising in financial market analysis and trading systems. He worked as a professional futures trader for a trading firm in London and has a passion for building mechanical trading strategies. He has been in the market since 2008 and working with Amibroker since 2011.


Course Curriculum


  Datasets
Available in days
days after you enroll

Frequently Asked Questions


When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
Where does the data come from?
The data comes from various public domain and creative commons sources. Some data is also produced via web scraping or licensed for redistribution. Appropriate credit is given for the original source in each individual lecture accompanying the course files. With thanks to all data providers involved.

Get started now!