On the Development of Systematic Crypto Trading Strategies: Lessons from Bayesian Optimization and Game Theory


A principal cause of market inefficiency is the lack of liquidity available in altcoin markets. Higher liquidity allows for more price stability and a greater ability to purchase or sell quantities of a token. Individual price levels in altcoin order books often have under a tenth of a Bitcoin, creating extreme levels of price impact from any sizable trade, effectively minimizing gains and preventing proper price formation. Due to the low trading volume, there is a decreased benefit for adding liquidity to the books. This leads to a low capacity for market makers, which further prevents proper price formation.⁷ These markets present alpha-generating opportunities due to their large bid-ask spreads and price fluctuation, and projects such as Hummingbot and Freqtrade have developed open-source frameworks to increase the accessibility of quantitative trading. The developers contributing to the codebase of these frameworks continually amend bugs and feature updates, allowing systematic traders more time to be spent on development, training, testing, and ultimately deployment of trading systems.

The above graph illustrates the liquidity premium in Bitcoin during a portion of November 2012 when it was significantly more illiquid than the present-day. After WordPress announced its acceptance of BTC, the illiquidity discount decreased as there was an additional widely used outlet to spend Bitcoin.


Volume also plays a significant role in determining market efficiency. The volume to liquidity ratio, a measure of market efficiency fluctuates widely among tokens. Cryptocurrencies and altcoins in particular trade at values multiples above traditional equities, which allows for greater slippage and price impact when trades are executed.

Units of Precision

Units of precision are the number of decimal places in which the base asset, usually quoted in Bitcoin, is denominated. Asset denomination plays a large role in the price action and formation in markets. Too few units of precision lead to instances where markets will be rendered stale or have inactivity because the gap in price levels will discourage institutional volume as price action is minimal.

State of Cryptocurrency Markets

Bitcoin has a market capitalization larger than all other altcoins combined and has a much greater impact on altcoins than vice versa. Even then, correlations between individual assets have been relatively minimal, and Brauneis and Mestel find that when looking at 500 cryptocurrencies between 2015 and 2017, 98% of the tokens had correlation coefficients between -0.2 to 0.1 with Bitcoin. While comparisons with the indices Bitwise 100, 70, and 20 tend to show similar price movement, the scope of returns between indices and Bitcoin varied by thousands of percent, especially during the ICO craze of 2017 and 2018.

Modeling Markets with Competitive Coevolution

Modeling market behavior gives us a macro-lens from which to view the development of strategies and changing market conditions. In our view, an evolutionary game-theoretic vantage point, specifically competitive coevolution allows us to develop resilient trading strategies and maintain portfolio diversification.

The above graph illustrates the return of strategies over a period of time. As the strategy is deployed for longer, it becomes less effective due to changing market conditions and the interactions of other firms in the space. For this reason, improvements or new strategies need to be deployed when return hits an inflection point in order to maintain consistent returns over a period of time. The labels given to the three S-curves only represent one of many possible scenarios in regard to our strategy development.


To generate alpha from these markets, Pareto Technologies employs algorithmic trading. We start by taking technical indicators that we have time tested in our discretionary trading activity. These indicators inform the buy and sell signals for our algorithmic strategies. In addition to these, we implement a number of variables in our parameter space including a dynamic stop-loss and time-based exit signals for certain return thresholds. Our strategies are also developed and optimized through a process of backtesting to assess for market condition resilience and slippage before they are deployed on investor capital.

This image is a sample plot of our portfolio allocation system which dynamically allocates AUM to various strategies during changing market conditions. Short strategies will perform best during downturns while volatility trading can squeeze out gains from resistance levels. During an upswing, long-focused strategies will generate the most return.

Works Cited

[1] Beguvsi’c, Stjepan and Zvonko Kostanjvcar. (2019). Momentum and liquidity in cryptocurrencies.



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