https://github.com/felixpatzelt/priceprop

Calibrate and simulate linear propagator models for the price impact of an extrinsic order flow.
https://github.com/felixpatzelt/priceprop

high-frequency-trading kernel-model linear-models modeling pandas propagators python scientific scientific-publications time-series time-series-econometrics

Last synced: 5 months ago
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Calibrate and simulate linear propagator models for the price impact of an extrinsic order flow.

• Host: GitHub
• URL: https://github.com/felixpatzelt/priceprop
• Owner: felixpatzelt
• License: mit
• Created: 2017-09-29T00:15:49.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-01-16T01:33:45.000Z (over 7 years ago)
• Last Synced: 2024-12-30T17:35:52.977Z (5 months ago)
• Topics: high-frequency-trading, kernel-model, linear-models, modeling, pandas, propagators, python, scientific, scientific-publications, time-series, time-series-econometrics
• Language: Python
• Size: 1.11 MB
• Stars: 23
• Watchers: 3
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • License: LICENSE.txt

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README

        
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