Artificial Intelligence in Trading

Trading

Artificial intelligence may become the cornerstone instrument for developing financial strategies that were previously considered hard-to-predict. It is because not only can the machine monitor multiple data points and revise calculations in real time but also it can emulate/learn the successful traders' behaviour.

Problem Statement

The objective was to build a complete end to end trading system that connected to multiple data sources and execution engines, would allow users to create own strategy as well as have suggested strategies. The app is on a pay per trade model. The use case dealt with stocks and options as the instruments under consideration.
120% improvement in the current return/risk ratio for client

there was 79% decline in the latency achieved through programming efficiency, hardware and data source provider change

streamline front end platform and allow user configured algos buy using FIX protocol

Designed cuda powered algos that picks up desired patterns on stocks on real time while monitoring multiple data series simultaneously

Backtesting of algorithms validated across multiple economic scenarios to rest for stability and reliability

Self learning algorithms trained to change their parameters based on market dynamics to control risk - reward ratio

Artificial Intelligence in Trading
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