Having effective intraday forecast for the level of trading volume is of vital importance to algorithmic trading and portfolio management since it attempts to minimize transaction costs by optimally scheduling and placing. The purpose of this project is to create dynamic statistical models of intraday trading volume prediction (in Python). By assuming the stable U shape distribution of intraday trading volume, we apply Deterministic blend, Lognormal Bayesian, Kalman filter and ARIMA model to estimate and generate out of sample forecast on 12 US equity sector ETFs. Results show that some of the proposed methods are able to obviously outperform common volume forecasting methods.

Mini project on stock market intra day prediction in python - 1) Top 10 companies for trading are found out using the stock screener from investing.com. 2) The details of a particular company selected from this list by the user are scraped from screener.in. Using the values scraped here like ROCE, ROE etc., we can figure out whether the stock is good for intraday trading and/or investment. 3) Open, close, high, low, adj. close, volume details are scraped for the particular stock from yahoo finance. 4) A basic linear regression model is used to predict the open price for next day.