Increase the quality of your backtesting by applying real market conditions.

By Valerio Pagano, CEO of Sparkling Rocks (an ATS-BDGroup subsidiary specializing in quantitative investing)
In today’s increasingly competitive financial landscape, quantitative trading has become a cornerstone for financial institutions seeking a competitive edge.
A key element in the development and evaluation of quantitative strategies is backtesting. This technique consists of testing a strategy on historical data to assess its performance and identify potential weaknesses.
Backtesting is essential for mitigating the risks associated with implementing new strategies and for increasing the likelihood of achieving positive results.
However, performing effective backtesting is not a simple task. Numerous challenges confront industry operators, including the complexity of models, the management of large volumes of data, and the need to ensure consistency between development and production phases.
Often, simplified models are used in backtesting processes for performance reasons, introducing distortions compared to the real model. Conversely, having a tool capable of using the same model for both backtesting and production ensures consistency and reduces operational risks.
Another important aspect is the effective management of the data used, which differs in frequency and nature. Traditional backtesting, based on periodic data and simplified calculations, can introduce distortions in the results. For example, data that is updated at regular intervals (daily, hourly, etc.) is often used. This deterministic methodology does not capture the dynamics of real markets, where prices move continuously and at different speeds (asynchronous mode). Consequently, strategies developed and tested with this approach may not behave as expected in real market conditions, especially in the case of high-frequency operations such as intraday trading.
Even in this case, it is necessary to manage different types of information efficiently, high-frequency data, and complex and heterogeneous data, including macroeconomic, microeconomic, and news data, replicating the market flow in a realistic manner and to be able to cover the different needs related to business types: high frequency such as intraday trading or statistical arbitrage, and heterogeneity of information (micro macro and news) for fund management or wealth management strategies.

In this multifaceted scenario, the Jagged Island platform, natively designed for quantitative investing, is the right tool to perform advanced backtesting on the most complex strategies.

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