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 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 <//github.com/felixpatzelt/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