Own-research

We started to systematically search for systematic cryptocurrency trading strategies in academic research approximately two years ago. This article is a short analysis of the performance of systematic crypto strategies in 2021. We conclude that non-price predictors offered advantages over price-based only trading strategies in the previous year-to-date and 12-months periods.

This blog post will give you a short recapitulation of the five quick market/portfolio insights built from Quantpedia Pro reporting.

– Gold displays a strong seasonal tendency in returns in days around US public holidays.

– The performance of Bitcoin is usually the worst during the same time as stock market experiences the bear market.

– Cryptocurrency market correlation slowly increases, and we can’t rule out the financialization of the crypto market (the same process that happened in commodities approximately ten years ago).

– Skewness-based trading strategies could serve as a practical hedge/diversification during stock market drawdowns.

– We show the main attribute of most of the risk parity portfolios – lower total returns but significantly lower risk measures.

As we have promised, we present a short article as an introduction into the methodology of the Quantpedia Pro CPPI reports. Quantpedia Pro clients can use the model portfolio built in the Portfolio Manager as a risky asset to test various variants – Basic CPPI, Drawdown Based CPPI and Dynamic Multiplier CPPI.

Quantitative based community is represented by the Quantconnect – Algorithmic Trading Platform, where quants can research, backtest and trade their systematic strategies. Additionally, similar to Seeking Alpha, there is a possibility to follow other quants/analysts through the open free market – Alpha Market.
To our best knowledge, the literature on community/social media alpha is scarce, and this paper aims to fill this gap. In the first part, we evaluate the benchmark strategy that consists of all strategies in the alpha market that are equally weighted. Moreover, through multidimensional scaling and clustering analysis, we examine how well can significantly lower amount of strategies track the aforementioned benchmark. This could solve the problem of costly and inconvenient following of every strategy in the market. Overall, this approach can lead to a strategy that follows the benchmark with drastically reduced costs, and these strategies can be even more profitable and less volatile.

Stay tuned for the 2nd, 3rd and 4th part of this series, where we will step on the gas and explore factor meta-strategies built on top of the QuantConnect’s Alpha Market.

We present a short article as an insight into the methodology of the Quantpedia Pro report – this time for the Markowitz Portfolio Optimization. As usually, Quantpedia Pro allows the optimization of model portfolios built from the passive market factors (commodities, equities, fixed income, etc.), systematic trading strategies and uploaded user’s equity curves. The current report helps with the calculation of the efficient frontier portfolios based on the various constraints and during various predefined historical periods. The backtests of the periodically rebalanced Minimum-Variance, Maximum Sharpe Ratio and Tangency portfolios will be available at the beginning of July.
Additionally, there is a Case Study dedicated to this Quantpedia Pro tool.

This article is a primer into the methodology we use for the Portfolio Risk Parity report, which is a part of our Quantpedia Pro offering. We explain three risk parity methodologies – Naive Risk Parity (inverse volatility weighted), Equal Risk Contribution and Maximum Diversification. Quantpedia Pro allows the design of model risk parity portfolios built not just from the passive market factors (commodities, equities, fixed income, etc.) but also from systematic trading strategies and uploaded user’s equity curves.