Algorithmic trading systems have garnered immense interest in recent years, fuelled by the potential for increased efficiency and profits in the financial markets. Building an effective algorithmic trading system, however, is no easy task. In this blog post, We explore the main challenges faced when developing such a system, with a focus on understanding the markets, selecting the right strategy, and managing the technical aspects.
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Data engineering is the practice of designing, building, and maintaining the infrastructure and systems that are used to collect, process, and store data. Data engineers are responsible for designing and implementing scalable, high-performance data pipelines and systems that can handle large volumes of data and support complex data processing tasks.
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Trading is and will become more algorithmic and there are plenty of reasons that can explain it. The amount of money flowing into quant-based trading strategies has grown tremendously recently.
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Absurdia is on a mission to make algorithmic trading accessible to the new generations of modellers, brilliant minds, and ecosystems worldwide. It is effectively about helping traders succeed with automated operations without hiring as many data scientists, engineers and lawyers as larger quantitative funds do. In other words: arming smaller players with the arms of the giant whales, and even more.
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