This project implements Monte Carlo methods to simulate the performance of a portfolio of investments. Yahoo! Finance is queried for monthly prices of stocks, bonds, funds, and other securities. The implementation of the simulations is parallelized for better performance using OpenMP's multithreading model. To read in-depth about the finance, math, and computer science concepts utilized in the project see my poster. For even more details, see my research paper.
Before attempting to build this project, first be sure to install the following dependencies:
The GNU gcc compiler comes pre-equipped with OpenMP. On Mac OS X, gcc is a symlink to clang by default. clang does not come-prequipped with OpenMP. You can download clang's OpenMP implementation through the clang-omp Homebrew formula. However, please be aware that this project has only been tested using GNU gcc. GNU gcc can be installed on Mac OS X using the gcc Homebrew formula and the --without-multilib option on install like so: brew install gcc --without-multilib. Without that option, OpenMP support will not be installed. In order to use my Makefile without any modifications on Mac OS X with GNU gcc, you must create a symlink to run the Homebrew-installed gcc. See this entry on StackOverflow for more details.
Before compiling, create two subdirectories called data and objs. The simulations and compilation process needs to create files within these directories. You can create these subdirectories by running mkdir data and mkdir objs
To compile both parallel and serial implementations, execute this command: make compile
Not all omp parallel pragmas specify a number of threads to use. For those pragmas, thenumber of threads specified by the OMP_NUM_THREADS environment variable. Be sure to set this environment variable before running the parallel executable so that the desired number of parallel threads will be used. To set this environment variable to 4, for example, use the following command: export OMP_NUM_THREADS=4
To run the serial code, execute this command: ./ser_portfolio.out <num_months> <num_runs> <portfolio_name> [<initial_seed>]
To run the parallel code, execute this command: ./par_portfolio <num_months> <num_runs> <portfolio_name> [<initial_seed>]
To see the histogram of the most recently run set of simulations, execute this command: python src/plot.py <chart-title>
NOTE: Be sure to be connected to the Internet when running. Stock data is retrieved from Yahoo! Finance. A command line option to skip the remote retrieval and read files locally is coming soon.
Once you have compiled, you can run 10000 simulations in parallel for 2 years with the MKT portfolio (details given by the data/tickersMKT.csv and weightsMKT.csv files) with an initial random number generator seed of 4 using this command ./par_portfolio.out 24 10000 MKT 4. To perform this simulation and plot the results when finished, run this command ./par_portfolio.out 24 10000 MKT 4 && python src/plot.py