Algo-Trading using Statistical learning and optimizing Sharpe ratio and drawdown 
Penumatcha Bharath varma1, Neeraj Kasheety2, Hanumanula Sravya3, Chinthapalli Amarnath Reddy4, Jaypal Medida5
1Penumatcha Bharath Varma*, Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India.
2Dr. Jaypal Medida, Professor Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India.
3Neeraj Kasheety, Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India.
4Hanumanula Sravya, Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India.
5Chinthapalli Amarnath Reddy, Department of Computer Science Mallareddy College of Engineering and Technology, MRCET Campus Hyderabad, India.
Manuscript received on October 04, 2021. | Revised Manuscript received on October 18, 2021. | Manuscript published on November 30, 2021. | PP: 95-100 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D65851110421 | DOI: 10.35940/ijrte.D6585.1110421
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Algorithmic Trading, high-frequency trading, Machine learning, Statistical Learning.