Synergizing quantitative finance models and market microstructure analysis for enhanced algorithmic trading strategies

Q1 Economics, Econometrics and Finance
Om Mengshetti , Kanishk Gupta , Nilima Zade , Ketan Kotecha , Siddhanth Mutha , Gayatri Joshi
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引用次数: 0

Abstract

In today’s complex financial markets, “Algorithmic Trading” has become very important. The study delves into the amalgamation of four pivotal indicators - Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average Convergence/Divergence (MACD) to create and develop a potent trading strategy. Through intensive backtesting and parameter tuning, our study demonstrates 60.63 % profitable trades on the National Stock Exchange (NSE), India, surpassing the standalone indicators. The Weapon Candle Strategy created using the four indicators presents its efficiency as it was able to achieve a profit factor of 1.882. This suggests that when these four technical indicators combined to make a strategy, it can provide significantly more accurate and reliable trading signals compared to using a combination of two or three indicators. Algorithmic traders should use a multi-indicator approach to achieve a more comprehensive understanding of the market and make informed trading decisions.

协同量化金融模型和市场微观结构分析,增强算法交易策略
在当今复杂的金融市场中,"算法交易 "已变得非常重要。本研究深入探讨了四个关键指标--相对强弱指数 (RSI)、指数移动平均线 (EMA)、成交量加权平均价格 (VWAP) 和移动平均收敛/发散 (MACD) 的组合,以创建和开发一个强大的交易策略。通过密集的回溯测试和参数调整,我们的研究表明,在印度国家证券交易所(NSE)上,60.63% 的交易是盈利的,超过了独立指标。使用这四个指标创建的武器蜡烛图策略能够实现 1.882 的盈利系数,从而显示出其高效性。这表明,当这四个技术指标组合成一个策略时,与使用两个或三个指标组合相比,它能提供更准确、更可靠的交易信号。算法交易者应使用多指标方法来更全面地了解市场,并做出明智的交易决策。
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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来源期刊
Journal of Open Innovation: Technology, Market, and Complexity
Journal of Open Innovation: Technology, Market, and Complexity Economics, Econometrics and Finance-Economics, Econometrics and Finance (all)
CiteScore
11.00
自引率
0.00%
发文量
196
审稿时长
1 day
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