Quantitative Trading Strategy: Introduction To Developing Quantitative Models

Technological advances have been taking place in several industries, and the financial one is not an exemption. Cryptocurrencies are an example themselves. Quantitative trading is one of the advancements that have been developed to help traders improve their performance by focusing not precisely, on trading a chart pattern, a candle, a price action, or any other strategic concept, but aggregating data and developing easier and more complex algorithmic models that will execute automated trades according to a set of pre-defined rules.

In this sense, quant models help traders overcome several pitfalls related to emotional biases, consistency, and human limitations. Throughout the following sections, all of these matters will be disclosed trying to deliver an inside view of how quant models work and how to develop streamlined crypto trading strategies.

What is Quantitative Trading

Highly correlated to machine learning, data analysis and aggregation, mathematical models, algorithms, and statistical implementation, the quant trading method is an advanced step toward financial markets that employ traditional market strategies to develop new and refined technological models streamlined to identify trading opportunities and execute automated trades.

It involves using computers and demands analyzing plenty of data points such as:

• Historical prices.
• Market news.
• Economic Indicators.
• Assets technical data.

Technological resources play a significant role in achieving a higher standard in those matters, guaranteeing high-speed performances. Quant traders and developers may use mechanisms such as:

• Software programming.
• HPC: High-performance computing servers for complex calculations and extensive datasets.
• Graphic and Tensor Processing Units streamlined for machine learning tasks.
• Cloud platforms.

Benefits of Quantitative Trading

Concerning the benefits of quant trading, we can highlight the automation approach, which can help traders sidestep stressful times and emotional biases that may lead them to irrational behaviors and, therefore, erroneous decision-making.

In this sense, we can classify the quant trading benefits into the following points:

• Reduce emotional biases: Eliminates irrational responses by letting the trading process rely on computational performances.
• Consistency: Strategies based on quant trading stand out by their systematic implementation, reducing risks associated with inconsistent behaviors.
• Speed: Quant models process data and execute high-speed trades, allowing immediate adaptation to market changes.
• Efficiency: Quantitative strategies can identify patterns and opportunities ignored due to human capacity limitations.
• Scalability: Managing and scaling large portfolios becomes easier by employing quantitative models.
• Backtesting: The collected market data will serve as a basis to test models against the historical price action of assets, refining the performance and reliability of a strategy.

Strategies For Quantitative Trading Models

The efficiency and versatile capabilities of quantitative models enable developers and traders to tailor outstanding standards for different trading frameworks.

Strategies implemented on top of quant models can meet the unique characteristics of the cryptocurrency market, so let's proceed to classify those belonging to most ordinary approaches from those streamlined for cryptocurrencies, remarking on how they work specifically.

Typical strategies:

• Mean reversion: This concept is based on the theory that asset prices will reverse to their average point due to mathematical probabilities. Developers can leverage this idea to build a mean reversion quant model.

• Trend following: Involves trading the directional move of an asset price. Traders can use quant models to identify and enter trend trades algorithmically.

• Statistical arbitrage: It consists of statistical data models that take advantage of assets quoting undervalued levels.
• High-frequency trading (HFT): It refers to using algorithms to execute trades at high speed and repetitively.
• Pairs trading: By trading two related assets at the same time, it awaits to profit from transient disparities in their prices.

Crypto strategies:

• Crypto arbitrage: Quant traders can exploit the price discrepancies between digital assets across different exchanges. This strategy can be more profitable due to the decentralized background of cryptocurrencies. Decentralized exchanges can be an option to exploit high returns.
• Market making: Involves using crypto assets that lack liquidity and can be affected by not-so-large margin capital. Quant traders with a substantial amount of margin or leveraged capital can act as liquidity providers by selling and buying consistently at slightly distinct prices.
• Blockchain Analysis: By examining the blockchain data, quantitative models can identify transactional patterns between networks and wallets, offering insights into future assets that investors are likely to hold or trade according to the volume present in exchanges. This encourages traders to predict future trends in the market.

Concepts Of Quantitative Trading

Understanding quant trading requires overviewing noteworthy concepts that serve as the ground for developing curated strategic models. We must then make a mention of algorithmic trading: also known as algo trading, it establishes a base for quant models by offering automated techniques for executing trades.

Market microstructure is also a powerful notion riding model developments. This concept studies the markets from a granular level viewpoint by focusing on deeply understanding order flow, order books, market depth, and technical factors influencing price formation.

High-frequency data plays a key role here since the market has plenty of rapidly flowing information. By collecting data at a high frequency, traders will ensure they do not miss substantial details. For example, this concept can collect tick-by-tick data.

Last but not least, integrating several data points from different timeframes is a time and event aggregation that results in an advanced notion to apprehend order book dynamics. In this sense, it is possible to analyze order book dynamics, obtaining inside data on the behavior of sell and buy orders to craft more informed decisions.

Conclusion

Quantitative models offer a solution for approaching the markets in an automated procedure, but beyond this algorithmic viewpoint, quantitative trading focuses on the data riding these models and shaping their performance. Quantitative datasets provide developers with insightful details on the causes behind the fluctuations of financial assets.

In the case of cryptocurrency, we learned specific strategies that can be streamlined to accurately develop models according to the nature of this market, such as blockchain analysis.

In this article, we covered fundamental concepts in the development of quantitative models, exploring the deeper details behind market dynamics.

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