felixpatzelt / priceprop

Calibrate and simulate linear propagator models for the price impact of an extrinsic order flow.

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.

Function	Synopsis
G_pow	Return power law Propagator kernel
beta_from_gamma	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
response_grouped_df	Calculate response for pandas groups and average
smooth_tail_rbf	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