Category: R

Chicago Python Workshop

Alpha Generation: Controlling Intraday Risk Profile with Python Friday, February 10 2017, 10:30 AM – 5:30 PM [CST] Chicago, 10th of  Frebruary You will learn why the use of high frequency market data is necessary to be able to measure correctly the risk and rebalance your portfolio adequately. You will also learn how to build strategies to […]

Very interesting R and Python Workshops in NYC

During our 2 workshops, we covered how to compute intraday risk, backtest strategies, forecast metrics and optimize your portfolio to get more alpha. We went through classic moving average strategies to comparing high frequency strategies to low frequencies. Attendees from different background have been present from banks to hedge funds to academics. It gave a stimulating […]

New R/MATLAB Package Released: High Frequency Price Estimators & Models

We are happy to announce PortfolioEffectEstim toolbox availability for both R & MATLAB. It is designed for high frequency market microstructure analysis and contains popular estimators for price variance, quarticity and noise. For R https://cran.r-project.org/web/packages/PortfolioEffectEstim/ Or via downloads section: https://www.portfolioeffect.com/docs/platform/quant/tools/r For MATLAB http://www.mathworks.com/matlabcentral/fileexchange/55335-portfolioeffectestim-high-frequency-price-estimators—models-toolbox Or via downloads section: https://www.portfolioeffect.com/docs/platform/quant/tools/matlab Features Package features key estimators for working […]

High Frequency Market Microstructure: Part 1 (Microstructure Noise)

Market Microstructure Noise¶ Microstructure noise describes price deviation from its fundamental value induced by certain features of the market under consideration. Common sources of microstructure noise are: bid-ask bounce effect order arrival latency asymmetry of information discreteness of price changes Noise makes high frequency estimates of some parameters (e.g. realized volatility) very unstable. The situation […]