Python Real data Finance Stock Graph in HTML (2017 version!)

Here I publish Real World Python application. This Python video tutorial focuses on Stock Finance market. This Python application will render a Real Data Finance Stock Graph on HTML file which is suitable for publishing and sharing. In this example I demonstrate how you can define a frame where stock graph should be built, how to import real stock data from Internet (I use Google API stocks in this example), how to set period for a stock graph, how to work with Pandas data structures in order to identify Increase and Decrease values of stocks, how to set graphical parameters for a candle of stock (height, width, colors, etc.), how to customize Bokeh plotting window area and finally how to build very nice finance graph. There is no sound in this video tutorial but I put many comments and notes inside the video. I hope it will be clear for you. In this video I also little explain about how to understand real finance market candles and identify: - High price, - Low price, - Open price, - Close price. I think that it will be interesting to show Google Stock graph by Python for selected period. The content of this video is: 0:18 - Import Python modules and libraries 1:10 - Select a Stock by choosing Stock symbol from the Internet 1:35 - Set a source of data (in this example I set Google) 1:59 - Set a period for a finance graph 3:18 - Define a window (frame) for a graph (variable p) 3:56 - Set a destination HTML file where result Finance graph will be published 4:40 - Checking official Bokeh documentation for rect method 4:56 - Define graphical parameters for a stock (candle) 10:44 - Result 1: real finance stock graph on WEB 11:04 - Create a stock candle segment 11:47 - Result 1: real finance stock graph with segments on WEB 12:34 - Define a transparent gridlines 13:23 - The final result: Finance stock Real Data graph in HTML Used resources: - bokeh.plotting Interface: http://bokeh.pydata.org/en/0.10.0/docs/reference/plotting.html - Real Stock Finance Data for download to Panda dataframe: https://www.google.com/finance?cid=694653 Used Python libraries: Pandas, Datetime, Bokeh.plotting. Used Python Framework: PyCharm (https://www.jetbrains.com/pycharm/) If you have any questions, just write below in comments! Do not forget to subscribe me for follow more new Python learning videos on my channel! Vytautas [email protected]