算法交易
技术分析
交易策略
高频交易
另类交易系统
结对贸易
交易炮塔
金融市场
计算机科学
资产(计算机安全)
利润(经济学)
电子交易
金融经济学
计量经济学
财务
公开抗议
经济
微观经济学
算法
计算机安全
作者
Tanishq Salkar,Aditya Shinde,Neelaya Tamhankar,Narendra Bhagat
标识
DOI:10.1109/iccict50803.2021.9510135
摘要
Financial markets are volatile and dynamic. The uncertainties involved in the market and various economic factors affect the asset price. Predicting trends in asset prices and calculating future value of an asset is a very challenging task. This is responsible for increased use of algorithmic trading amongst traders in financial markets. Algorithmic trading is a method of executing orders using pre-programmed automated trading instructions that consider asset variables including price and volume. Algorithmic trading is widely used in financial firms where large orders are executed and where humans take more time to respond. Algorithmic trading is also called black-box trading, automated trading, or Algo-trading. Algorithmic Trading uses the calculating powers of the computer. News or quotes are not sufficient to trade in financial markets. The challenges in trading can be reduced by proper analysis of data. Technical indicators consider the price and volume data of stock. These technical indicators together can be used to build trading strategies with calculated risks. This paper proposes trading strategies based on quantitative analysis of time series data. These strategies were developed for intraday high-profit trading. The strategy with RSI and MACD technical indicator gives the highest returns up to 12%.
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