Distributed Computing or the process of offloading data across a panel of interconnected computers or clusters, is the future of Quant trading. Not only have people been doing this for a while, but people have found ways to multi-thread processes on their own machine utilize distributed computing-like mechanisms. In today’s age of high frequency trading, algorithmic capabilities and the proficiency of speed can be greatly impacted by utilizing distributed computing in order to optimize the performance of a trader’s algorithm.
This can especially be critical when looking at reinforcement learning or helping offloading more financial data for trading models. Live trading can also optimize speed, learning capabilities, and lower the processing impact while improving latency for a trader’s computational systems utilizing custom distributed computing architecture.
Visit QuantPortal LLC website to learn how to get started with methods such as Decentralized-Internet SDK, dispy for Python, EECluster, or migrid.
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