In one of our older posts titled ‘Case Study: Quantpedia’s Composite Seasonal / Calendar Strategy,’ we offer insights into seasonal trading strategies such as the Turn of the Month, FOMC Meeting Effect, and Option-Expiration Week Effect. These strategies, freely available in our database, are not only examined one by one, but are also combined and explored as a cohesive composite strategy. In partnership with Deltaray, using MesoSim — an options strategy simulator known for its unique flexibility and performance — we decided to explore and quantify how our Seasonal Strategy performs when applied to options trading. Our motivation is to investigate whether this strategy can be improved in terms of risk and return. We aim to systematically harvest the VRP (volatility risk premium) timing the entries using calendar strategy to avoid historically negative trading days.

Hello all,

What have we accomplished in the last month?

– A Quantpedia Awards 2024 competition – winners announcement
– Automatic detection of equity-like assets
– 12 new Quantpedia Premium strategies have been added to our database
– 10 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 9 new backtests written in QuantConnect code
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

Six new strategies have been added. Five new related research papers have been included into existing strategy reviews and three short free blog posts have been published during last few weeks. Plus, five trading strategies have been backtested in QuantConnect in the previous two weeks.

The main goal of our new article is to explore the efficacy of passive versus active management strategies in the context of savings for long-term financial goals. By analyzing the performance of nine distinct asset classes, including Double Leveraged ETFs and an implementation of the Pragmatic Asset Allocation (PAA) strategy, over an almost-century-long horizon, we simulate and compare the outcomes of three passive and three active strategies. This comparative analysis focuses on their influence on key investment characteristics, including Final Portfolio Size, Maximum Drawdown, and Maximum Loss, to determine their potential in enhancing long-term investment results.

Convolutional neural networks (CNNs), inspired by the human brain’s ability to recognize visual patterns, excel in tasks like object detection, facial recognition, and image classification, making them powerful tools for extracting insights from visual data. However, we are traders, so a natural question arises: Can we use that in trading? A recent paper shows that we can actually do it. Utilizing CNNs, Niklas Paluszkiewicz introduces a novel approach to pairs trading by visually analyzing historical price movements while converting traditional time series data into image representations. 

This is the moment we all have been waiting for, and today, we would like to acknowledge the accomplishments of the researchers behind innovative studies in quantitative trading. So, what do the top five look like, and what will the authors of the papers receive?

Let’s find out …