Inferring the Aggressor using Options Data

We will be implementing the bulk volume classification algorithm to attempt to discern information from tick by tick trade data. We will be using ThetaData's API which provides both Historical and Real-time Streaming of Options Tick Level Data! We first explore what algorithms have been used previously to attempt to infer the aggressor (the trader who initiates the trade), which would classify every trade as either a buy or sell initiated trade. In todays world of high frequency and complex execution algorithms that can split orders up into multiple child order and distribute across exchanges, the two papers we discuss argue that these traditional classification algorithms are not so relevant. Therefor we implement the Bulk Volume Classification algorithm that looks at aggregated trades, and therefore captures market makers response to trade flow over trade periods. We have completed this analysis using only Historical trades data, however in the next video we will implement this algorithm with Real-time Streaming. Online written tutorial: https://quantpy.com.au/options-data/i... ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube... ★ A data driven path to getting a job in Quant Finance https://www.quantpykit.com/ ★ QuantPy GitHub Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise.