#Using the Minority Game Model to Understand Financial Markets#
I built the Minority Game model from game theory using an Agent-Based Simulation via Python. I converted the output into a series of financial returns and tested them for statistical properties that are present in real world financial returns.
You can read the project report here: https://www.academia.edu/30952084/Using_the_Minority_Game_Model_to_Understand_Financial_Markets
ABM.py = agent based model.
MSA.py = tests for stylized facts.
ABM.py and MSA.py are imported into '100 companies from NASDAQ.ipynb' and '100 simulated data sets.ipynb'.
'company vs Sim.ipynb' is the anaylsis of indiviudal companies.
the '100 daily prices.csv' etc. files are used in '100 companies from NASDAQ.ipynb' and '100 simulated data sets.ipynb'.
these are data sets were all 100 company data sets have been amalgamated into one csv file.
the data sets used in 'company vs Sim.ipynb' are the stand alone data sets, pre-amalgamation, which have not been uploaded.