PyNetLogo Market for SHARKFin

The PyNetLogo (PNL) Market was the original market implementation for SHARKFin. Now it is one of several possible Market implementations. This README outlines the instalaltion of this Market, which has a number of non-Python dependencies.

NetLogo Installation

The pnl_market package, included in this repository, contains code for the MarketPNL class, which is an implementation of sharkfin.AbstractMarket that wraps a market simulation written in NetLogo.

The simulation depends on a specific NetLogo version. Be sure to have netlogo-5.3.1-64 installed. You will specify the install directory in a configuration file.

Configuration

There are now two config files:

(1) macroliquidity.ini (2) macroliquidity_local.ini

You need to have both files present on your machine.

Any parameters you specify in macroliquidity_local.ini will override the values in macroliquidity.ini.

Full paths to the directories for python, logs, NetLogo models, and NetLogo home must be specified in a macroliquidity_local.ini file.

Do NOT commit this local config file to the github repo.

The defaults are

[DEFAULT]
pythondir=./py
logdir=./out/logs
[pnl]
NLmodeldir=./nl/
NLhomedir=./netlogo-5.3.1-64/

Execution - as Market class

See the tests in the tests/ directory.

Execution - Standalone

To run the simulation:

$ python3 ./py/pnl.py

Output

The pnl.py file, when run, should output three files into ./out/logs/

 LM_90_5.log           This is the process log
 LMallorders_90_5.csv  The ticker of orders over time
 LMinventory_90_5.csv  Record of traders' inventories over time

NetLogo Simulation

This simulation builds on Mark Paddrick's OFR model. It has some additional components.

A breed in the NetLogo file order_updates is created during the simulation and instances of this breed are collected in a list: allOrders. There is then a special reporter that PyNetLogo can call to query this list: prop_allOrders.