# A MATLAB and Python implementation of Finite Difference method for Heat and Black-Scholes Partial Differential Equation
These code implements the numerical method of Finite Difference method to solve Heat PDE and Black-Scholes PDE. Specificly, the code for Black-Scholes PDE aims to price vanilla options such as European and American call and put.
The algorithm is implemented in Python and MATLAB, and the Python code is in Object Oriented discipline and used Numpy to handle matrices. Also, both Python and MATLAB code allow users to write their own function to put into the code to set boundary conditions for the Finite difference grid. The code provided sample user-generated function for setting boundary condition for both Python code and MATLAB code. The Python objects also implemented special methods in Python classes so as to make it sliceable (`__getitem__()`)and printable(`__repr__()`)
Please find detailed mathematical explanation in the README.pdf file as Github does not support Latex formulas.
Author: Ruinan Lu
*References:*
*[1]Brandimarte P. Numerical methods in finance and economics: a MATLAB-based introduction[M]. John Wiley & Sons, 2013.*
*[2]Seydel R, Seydel R. Tools for computational finance[M]. Berlin: Springer, 2006.*
*[3]Ramalho L. Fluent python: Clear, concise, and effective programming[M]. " O'Reilly Media, Inc.", 2015.*
Finite-Difference-master.zip （22个子文件） 
Finite-Difference-master 
README.pdf 1.54MB
LICENSE 1KB .DS_Store 6KB README.md 1KB README_OriginalMD.md 10KB Python code .DS_Store 6KB HeatFDGrid.py 8KB BSPDE_FDGrid.py 12KB MATLAB code Script_testing_OptCrankNicolson.m 876B script_testing_EuOpt.m 757B script_testing_HeatExplict.m 318B AmOptCrankNicolson_optimizer_method.m 2KB EuOptImplicit.m 1KB script_testing_HeatImplict.m 318B EuOptExplicit.m 1KB script_testing_Heat_CrankNicolson.m 324B AmOptCrankNicolson.m 2KB Heat_equation_Implicit.m 1KB demo_fun_explict.m 653B EuOptCrankNicolson.m 2KB Heat_equation_Crank_Nicolson.m 2KB Heat_equation_Explicit.m 2KB
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