PriceProp =========

Simulate and calibrate linear propagator models for price responses to an external order flow. The models and methods are explained and applied to real high-frequency trading data in: Patzelt, F. and Bouchaud, J-P. (2017): Nonlinear price impact from linear models. Journal of Statistical Mechanics: Theory and Experiment, 12, 123404. Preprint at arXiv:1708.02411 <//arxiv.org/abs/1708.02411>_. ===================== ====================================================== Function Synopsis ===================== ====================================================== G_pow Return power law Propagator kernel betafromgamma Return exponent beta for a power law propagator kernel that decorrelates an input with a pure power law autocorrelation with exponent gamma calibrate_hdim2 Calibrate two-kernel History Dependent Impact Model calibrate_tim1 Calibrate original Transient Impact Model calibrate_tim2 Calibrate two-kernel Transient Impact Model hdim2 Simulate two-kernel History Dependent Impact Model integrate Return lag 1 sum, i.e. convert a differential kernel to a "bare response". k_pow Return differential form of power law propagator kernel propagate Apply propagator kernel to a time series (FFT conv.) response Calculate e.g. a price response responsegroupeddf Calculate response for pandas groups and average smoothtailrbf Smooth the tail of a long kernel using logarithmically spaced Radial Basis Functions tim1 Simulate original Transient Impact Model tim2 Simulate two-kernel Transient Impact Model ===================== ======================================================

The submodule `batch automates model calibration and simulation. Please find further explanations in the docstrings and in the examples directory.

The required methods to efficiently estimate two- and three-point correlation matrices were released in the separate package scorr `_.

Installation

pip install priceprop Dependencies (automatically installed)

- Python 2.7 - NumPy - SciPy - Pandas - scorr Optional Dependencies required only for the examples (pip installable)

- Jupyter - Matplotlib - colorednoise