Papers about algorithmic trading#

An Investor’s Guide to Crypto#

We provide practical insights for investors seeking exposure to the growing cryptocurrency space. Today, crypto is much more than just bitcoin, which historically dominated the space but accounted for just a 21% share of total crypto trading volume in 2021. We discuss a wide variety of tokens, highlighting both their functionality and their investment properties. We critically compare popular valuation methods. We contrast buy-and-hold investing with more active styles. We only deem return data from 2017 representative, but the use of intraday data boosts statistical power. Underlying crypto performance has been notoriously volatile, but volatility-targeting methods are effective at controlling risk, and trend-following strategies have performed well. Crypto assets display a low correlation with traditional risky assets in normal times, but the correlation also rises in the left tail of these risky assets. Finally, we detail important custody and regulatory considerations for institutional investors.

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Low-volatility strategies for highly liquid cryptocurrencies#

Managing extreme price fluctuations in cryptocurrency markets are of central importance for investors in this market segment. Using a sample of highly liquid cryptocurrencies from January 2017 to June 2021, this paper proposes a dynamic investment strategy that selects cryptocurrencies based on their historical volatility and is complemented by a simple stop-loss rule. Our results reveal that investing in highly concentrated low volatility cryptocurrency portfolios with six to twelve months volatility look-back and holding period generate statistically significant excess returns. By including a simple stop-loss rule, the downside risk of cryptocurrency portfolios is reduced markedly, and the Sharpe ratios are improved significantly.

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How to avoid overfitting trading strategies#

Running a lossy trading strategy would be a very costly mistake, so we spend a lot of effort on assessing the expected performance of our strategies. This task gets harder when we have limited data for this evaluation or when we experiment with the strategy for a longer time and risk manually overfitting the strategy on the same out-of-sample data.

Read the Quantlane blog post.

An Efficient Algorithm for Optimal Routing Through Constant Function Market Makers#

Constant function market makers (CFMMs) such as Uniswap have facilitated trillions of dollars of digital asset trades and have billions of dollars of liquidity. One natural question is how to optimally route trades across a network of CFMMs in order to ensure the largest possible utility (as specified by a user). We present an efficient algorithm, based on a decomposition method, to solve the problem of optimally executing an order across a network of decentralized exchanges

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Can machines learn finance?#

Machine learning for asset management faces a unique set of challenges that differ markedly from other domains where machine learning has excelled. Understanding these differences is critical for developing impactful approaches and realistic expectations for machine learning in asset management. We discuss a variety of beneficial use cases and potential pitfalls, and emphasize the importance of economic theory and human expertise for achieving success through financial machine learning.

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Automated Market Making and Arbitrage Profits in the Presence of Fees#

We consider the impact of trading fees on the profits of arbitrageurs trading against an automated marker marker (AMM) or, equivalently, on the adverse selection incurred by liquidity providers due to arbitrage. We extend the model of Milionis et al. [2022] for a general class of two asset AMMs to both introduce fees and discrete Poisson block generation times. In our setting, we are able to compute the expected instantaneous rate of arbitrage profit in closed form. When the fees are low, in the fast block asymptotic regime, the impact of fees takes a particularly simple form: fees simply scale down arbitrage profits by the fraction of time that an arriving arbitrageur finds a profitable trade.

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