Algorithmic Trading (AT) - Framework for Futuristic Intelligent Human Interaction with Small Investors

Bansal, Archit; Mishra, Kaushik; Pachouri, Anshul

doi:10.5120/71-660

Cited by 3 publications

(1 citation statement)

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“…Technical indicators and neural networks were used in [6] to predict the US Dollar Vs British Pound exchange rates. In [7] a framework for intelligent interaction of automatic trading algorithms with the user was presented. In [8] a back propagation neural network was employed to predict the buy/sell points for a stock and then applied a case based dynamic window to further improve the forecast accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

A Decision tree- Rough set Hybrid System for Stock Market Trend Prediction

Nair¹,

Mohandas²,

Sakthivel³

2010

IJCA

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Prediction of stock market trends has been an area of great interest both to those who wish to profit by trading stocks in the stock market and for researchers attempting to uncover the information hidden in the stock market data. Applications of data mining techniques for stock market prediction, is an area of research which has been receiving a lot of attention recently. This work presents the design and performance evaluation of a hybrid decision tree-rough set based system for predicting the next days" trend in the Bombay Stock Exchange (BSE-SENSEX). Technical indicators are used in the present study to extract features from the historical SENSEX data. C4.5 decision tree is then used to select the relevant features and a rough set based system is then used to induce rules from the extracted features. Performance of the hybrid rough set based system is compared to that of an artificial neural network based trend prediction system and a naive bayes based trend predictor. It is observed from the results that the proposed system outperforms both the neural network based system and the naive bayes based trend prediction system.

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Section: Literature Surveymentioning

confidence: 99%

A Decision tree- Rough set Hybrid System for Stock Market Trend Prediction

Nair¹,

Mohandas²,

Sakthivel³

2010

IJCA

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A few useful considerations in the development of intra-day trading software

Sharma¹,

Gupta²

2011

SIGSOFT Softw. Eng. Notes

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Trading has evolved from being merely an "open outcry" to becoming tech-savvy and computer intensive. With the stock exchanges computerizing the trading procedures and the traders following suit there was a need felt to upgrade the trading systems in sync with the innovational changes in technology like ECN (Electronic Communication Network) and SOES (Short Order Execution System). In their quest for maximizing gains while reducing risk and brokerage, trading companies fell over the idea of using computers for latency based arbitrage in an intra-day trading setup. It became quite empirical that better machinery meant more profits. The present paper realizes that for a vendor to develop the most efficient intra-day trading systems, it is necessary to incorporate various software standards that are otherwise of very little concern in other domains. This paper focuses on how the Indian software industry can put their clients at an advantage by adopting certain strategies which are a general feature of their western counterparts. Hence it is attempted to compare how the Indian trading software lag behind the internationally accepted software and how these shortcomings can be overcome.

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