Artificial intelligence-based indicators for ALGO trading strategy on the NSE derivatives

Joyjit Patra, Mimo Patra, Subir Gupta
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Abstract

Machine learning and ALGO trading processes have resulted in a worldwide change in corporate practices. As a result of these technical advancements, traders may now lower their susceptibility to loss while improving their possibilities of earning from their investments. The buy-or-write method is often used for a single asset or a marketable product. A buy-and-sell maximization study design is a type of research design used to form an opinion. This research design establishes a position by optimizing an exponential moving average (EMA), a relative strength index (RSI), and an average true range (ATR). When the buy and sell signals of the proposed approach are engaged, it also generates signals for a stop-loss order and target price for the position. Additionally, gains and losses for a specific period are represented.
基于人工智能指标的NSE衍生品ALGO交易策略
机器学习和人工智能交易流程已经导致了全球范围内企业实践的变化。由于这些技术的进步,交易者现在可以降低他们对损失的敏感性,同时提高他们从投资中获利的可能性。买或写方法通常用于单一资产或可销售的产品。买卖最大化研究设计是一种用于形成意见的研究设计。本研究设计通过优化指数移动平均线(EMA)、相对强弱指数(RSI)和平均真实范围(ATR)来建立头寸。当所建议的方法的买入和卖出信号被使用时,它也会产生止损订单和目标价格的信号。此外,还列出了某一特定时期的损益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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