Journal article Open Access

Algo-Trading using Statistical Learning and Optimizing Sharpe Ratio and Drawdown

Penumatcha Bharath varma; Neeraj Kasheety; Hanumanula Sravya; Chinthapalli Amarnath Reddy; Jaypal Medida

Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)

Modernization in computers and Machine Learning have created new opportunities for improving the methods involved in trading, Changes have been noticed parallelly at the level of investment decisions, and at the faster executions of trades via algorithms. Nowadays 90% of the trades are placed by algorithms, to execute a transaction, algorithms that follow a trend and construct a set of instructions are used in algorithmic trading. It executes the trades more precisely by precluding the effect of human feelings on trading. It all started way back in the 20th century and nowadays it’s becoming more and more competitive, with more big players entering the market every day. Our research aims to advance the market revolution by developing an Algorithmic Trading approach that will automatically trade user strategies alongside its own algorithms for intraday trading based on different market conditions and user approach, and throughout the day invest and trade with continuous modifications to ensure the best returns for day traders and investors.

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