I am learning about monte carlo simulations and I have found many blogs explaining its implementation in python. Because its a widely known and an important technique for structuring asset prices. I want to know if there are any good libraries in python for monte carlo simulations on financal instruments.
Try Quantlib https://www.quantlib.org, it comes with everything you need.
We recently released qmcpy which does both Monte Carlo and quasi-Monte Carlo with guaranteed accuracy.
For a MC/qMC problem in our framework you need to define your function, measure, discrete distribution (iid standard uniform, iid standard Gaussian, ...), and an algorithm to determine the number of points needed to meet your error tolerance. Lots of examples and components are already implemented so most problems shouldn't take more than a few lines.
If you get a chance check it out and let me know what you think!
You can directly use pandas-montecarlo to perform a Monte-Carlo simulation.
Code for the same:
# Import data import pandas_montecarlo from pandas_datareader import data data = data.get_data_yahoo('AAPL', '2017-01-01', '2018-01-01') # Calculate Returns data['return'] = data.Close.pct_change() # Perform Monte-Carlo Simulation data['return'].montecarlo(sims=5).plot()
For more detail, you can read the pandas-montecarlo documentation here.
That's very vague question. You don't need libraries, as first step you need to define what you want to do. E.g. if you want to use GBM. You can take a look code I have written. I have solutions for few exotic options.