Own-research

Delta hedging is a trading strategy that aims to reduce the directional risk of short option strategy and reach a so-called delta-neutral position. It does so by buying or selling small increments of the underlying asset. Similarly, grid trading is a trading strategy that buys/sells an asset depending on its price moves. When the price falls, it buys and sells when the price rises a certain amount above the buying price. This article examines the similarities between delta hedging and grid trading. Additionally, it analyzes numerous versions of grid trading strategies and compares their advantages and disadvantages.

At Quantpedia, we love seasonality effects, and our screener includes several strategies that exploit them. These anomalies are fascinating since they usually offer a favorable risk and reward ratio and are commonly invested only during short periods. Frequently, these strategies are valuable additions to portfolios because they are not that sensitive to overall market performance. This short article presents a brief examination of some possible Bitcoin seasonalities.

Grid trading is an automated currency trading strategy where an investor creates a so-called “price grid”. The basic idea of the strategy is to repeatedly buy at the pre-specified price and then wait for the price to rise above that level and then sell the position (and vice versa with shorting and covering). We will explore the basics and show favorable and unfavorable scenarios in the first article about this trading style. Later articles will dig deeper and investigate how Grid trading is related to other systematic trading strategies.

Our new article investigates “rebalancing premium” or “diversification return” in cryptocurrencies which can be achieved by periodically rebalancing portfolios. We analyze whether the daily/ monthly rebalanced portfolios outperform a simple buy-and-hold portfolio of cryptocurrencies and under which conditions. Additionally, we also look at the various combinations of volatile cryptocurrency portfolios with low-risk bonds.

It is indisputable that the data are changing financial markets – computing power has increased, allowing to rise the trends of ML/AI and big data (number of possible predictors or granularity) or HFT strategies. Indeed, not all the datasets are worth the time of academics, investors or traders, but we are always keen to analyze the novel and unique datasets. Of course, if we believe that the analysis is worthy of sharing, we are happy to do so. This post offers a shorter version of our newest research about Synthetic lending rates and subsequent market return. We hope that you find it enriching; enjoy the reading!