Quantpedia’s Research in 2022

Dear readers & clients,

The beginning of the new year is usually a traditional time for recapitulation. The year 2022 was a really challenging one on the financial markets, filled with unexpected events (Russia’s invasion of Ukraine or surge in inflation etc.). But I am again really proud of my whole team for their work as we continue fulfilling our primary mission to process academic research related to quant&algo trading to a more user-friendly form.

Last year was again really productive:

– We have enlarged our database to over 800 trading strategies, produced over 130 out-of-sample backtests (and now we have over 630 of them, with over 400 of them periodically updated)

– Other public ventures included our new Youtube series Quantpedia Explain Trading Strategies, an additional bunch of explanatory videos for Quantpedia Pro, new partners in our section for algo trading discounts for our readers, a new series of short instructive articles to solve common portfolio management problems, a lot of public blogs and countless of our own research articles

– We have also not stopped to built also our Quantpedia Pro offering. Over the year, we expanded its capabilities to over 30 reports, with new additions like Monte Carlo Analysis, Dollar Cost Averaging, Kelly & Optimal F, ETF Replication, Trading Edge Analysis and multiple reports based on our multi-factor method that allows analyzing long-term periods and impact of historical events. December’s addition is also a report based on this methodology. The 100yrs Market States Analysis report aims to show how sensitive your Model Portfolio is to various market states, be it periods of bull/bear markets, rising/falling interest rates, or high/low inflation. We replicate your selected Model Portfolio, show you the underlying explanative factors, extend the portfolio’s history to 100 years, and then show how it performs in all market states.

The report allows you to investigate which period is the best and which one is the worst for your investment, plus you may compare the performance of your portfolio to all important global investment factors. This way, you can consider whether to tilt the composition of your portfolio to better sync with the current state of the economic cycle. The report uses the out-of-sample methodology for the definition and calculation of the performance of the model portfolio and all investment factors described in the following article.


Happy New Year 2023 and profitable trading …

Yours,

Radovan Vojtko & Team of Quantpedia.com

PS: If you are looking for a few more articles to read, as usual, here is the list of previous month’s blog posts, that you may find interesting:

Quantum Computing as the Means to Algorithmic Trading
Author: Ivana Dragonova
Title: Quantum Computing as the Means to Algorithmic Trading

Stock Returns vs Inflation Expectations
Author: Manav Chaudhary and Benjamin Marrow
Title: Inflation Expectations and Stock Returns

100 Years of Historical Market Cycles
Author: Daniela Hanicova
Title: 100 Years of Historical Market Cycles

A Balanced Portfolio and Trend-Following During Different Market States
Author: Daniela Hanicova
Title: A Balanced Portfolio and Trend-Following During Different Market States

Factor’s Performance During Various Market Cycles
Author: Daniela Hanicova
Title: Factor’s Performance During Various Market Cycles

Defining Market Cycles Out of Sample
Author: Daniela Hanicova
Title: Defining Market Cycles Out of Sample


Are you looking for more strategies to read about? Visit our Blog or Screener.

Do you want to learn more about Quantpedia Pro service? Check its description, watch videos, review reporting capabilities and visit our pricing offer.

Do you want to know more about us? Check how Quantpedia works and our mission.

Are you looking for historical data or backtesting platforms? Check our list of Algo Trading Discounts.


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