• Mamatha T
• Danish Jamal1
• Manav Jain3
• Pooja I
• Rajarajeshwari HS

Stock market is highly fluctuating and there are a lot of uncertainties involved which makes intraday trading a challenging task for the traders. Analyzing the data with the help of technical indicators becomes tedious and in such scenario machine learning can play a vital role in identifying the patterns of stock market. The prediction problem has been mapped to being a classification problem and a unique strategic method has been proposed in this paper in order to solve the same. Various combinations of technical indicators have been used as the input variables and objective functions of various types serve as target variable. Standard classification algorithms have been used such as Random Forest and Support Vector Machine. Various preprocessing techniques have been applied to the input vectors and optimal hyper parameter search techniques have also been used. Some heuristics have also been implied in order to get better results. An accuracy of 64 percent in Random Forest and 60 percent in Support Vector Machine has been obtained.

OHLC, RF, SVM, technical indicators

"Strategized Intraday Stock Market prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 8, page no.312-314, August-2018, Available :http://www.jetir.org/papers/JETIR1808342.pdf

"Strategized Intraday Stock Market prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 8, page no. pp312-314, August-2018, Available at : http://www.jetir.org/papers/JETIR1808342.pdf