Abstract
Algorithmic trading, also known as “algo trading”, is a trading paradigm that follows a set of algorithms to place trades which can generate profits at high frequency and volume, theoretically impossible for a human trader. These trading algorithms are derived from strategies which encompass mathematical models. The practice of “algo trading” is quickly gaining traction in the world of fin-tech due to its automated approach toward trading and provision of increased liquidity in the market. We believe retail intraday traders across India should have tools to automate their daily trades based on the strategies they employ, seamlessly and effectively. Intraday trading requires the constant presence of a trader throughout the hours of a trading day, incapacitating them from focusing on other responsibilities such as their day jobs. Furthermore, currently, the few present algorithmic trading software consist of expensive fees and/or a confusing UI/UX experience for novice traders. Furthermore, problems faced by intraday traders worldwide, due to usage of algorithmic trading software include complex and incomprehensible user interfaces, trading of illiquid securities, handling of special days, exorbitant trading and brokerage fees, and multiple risks including regulatory, financial, reputational, operational and technological risks. We propose a clean, lightweight and object oriented approach toward algorithmic trading, eliminating the need for commercial algorithmic trading software. This proposed novel system aims to allow high frequency, multi-threaded trading strategies based on technical indicators such as MACD, Moving Averages, Relative Strength Index, Candlestick patterns, to be run parallely, reducing latency in high volume trades.
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Mittur, A., Kenish, R., Vijayshankar, K., Gottumukkala, A., Nayak, J.S. (2023). MultiStrategy Algorithmic Trader. In: Bhattacharyya, S., Banerjee, J.S., Köppen, M. (eds) Human-Centric Smart Computing. Smart Innovation, Systems and Technologies, vol 316. Springer, Singapore. https://doi.org/10.1007/978-981-19-5403-0_32
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DOI: https://doi.org/10.1007/978-981-19-5403-0_32
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