Webinar Date and Time
Tuesday, January 10, 2017
8:30 PM IST | 9.00 AM CST
Asset returns based on low frequency prices (e.g. end-of-day quotes) are still dominating modern portfolio analysis. To make portfolio metrics more relevant intraday and improve the precision of estimates, new data frequency needs to be explored.
In this presentation we demonstrate how using high frequency market data for portfolio risk management and optimization could improve the classic variance-bias trade-off and bring new insights to strategy backtesting.
Since high frequency prices require special handling, we discuss key components of an automatic model pipeline for microstructure noise, price jumps, outliers, fat tails and long-memory.
We conclude our presentation with an introduction to high frequency portfolio optimization built on top of intraday portfolio metrics. Examples will be shown in Python.
PortfolioEffect service offers portfolio optimization, portfolio backtesting, metrics forecasting and intraday risk metrics through 4 APIs: Python, R, Matlab and Java. The uniqueness of our service is that all calculation are done using high frequency market data which benefits low and high frequency traders. We cover 8,000+ US Equities (stocks, indices, ETFs). Clients can also upload their own market data. PortfolioEffect service employs latest advances in high frequency market microstructure theory to make classic portfolio risk and optimization results available intraday at tick-level resolution. It uses automated model pipeline to process high frequency price returns in a streaming fashion.
Who should attend?
This webinar will be very beneficial for those who need intraday risk metrics at any frequency, portfolio optimization, portfolio backtesting and metrics forecasting. Example will be shown in Python. The session will be ideal for:
- Quant Analysts
- Traders on Equities, ETF and Indices
- Those who are looking for backtesting strategies
- Python coders interested in financial markets
To register click here.