Master thesis: Deep Structural estimation: with an application to market microstructure modelling
This package proposes an easy application of the master thesis: "Deep Structural estimation: with an application to market microstructure modelling"
Installation
pip install -i https://test.pypi.org/simple/ DeepSurrogate-pin
link of the pypl library: https://test.pypi.org/project/DeepSurrogate-pin/
Authors
- Guillaume Pavé (guillaumepave@gmail.com)
Supervisors
- Simon Scheidegger (Department of Economics, HEC Lausanne, simon.scheidegger@unil.ch)
- Antoine Didisheim (Swiss Finance Institute, antoine.didisheim@unil.ch)
Deep surrogate (architecture)
| Hyparameter | Value |
|---|---|
| architecture | [400,400,200,100] |
| activation function | Swish |
| optimizer | ADAM |
| loss function | MSE |
| learning rate | 0.5e-3 |
| # of epoch | 15 |
Instruction
- Clone project
git clone https://github.com/GuillaumePv/pin_surrogate_model.git- Go into project folder
cd pin_surrogate_model- Create your virtual environment (optional)
python3 -m venv venv- Enter in your virtual environment (optional)
- Mac OS / linux
source venv/bin/activate venv venv- Windows
.\venv\Scripts\activate- Install libraries
- Python 3
pip3 install -r requirements.txtParameter range
Surrogate model are defined inside some specific range of parameter. PIN model in this surrogate library have been trained inside the range defined the table below. The surroate can not estimate PIN probability with parameters outside of this range of parameters.
| Parameter | Min | Max |
|---|---|---|
| a | 0 | 0.99 |
| δ | 0 | 0.99 |
| μ | 100 | 300 |
| ε_buy | 100 | 300 |
| ε_sell | 100 | 300 |
| # of buy trades | 55 | 700 |
| # of sell trades | 55 | 700 |
Demo
- To see demo of inverse modelling: see estimate_par_lbfgs.py
- to see how to determine the PIN value: demo.ipynb
