In this challenge, I used API calls to pull price information for a retirement portfolio, applied statistical concepts, and used Monte Carlo simulations to forecast its performance over time.
This project uses Python 3.7 and imports the following packages:
pandas - For data structuring and analysis.
dotnev - For API credentials in environment variable.
os - For OS functionality to get API keys in .env
requests - For API calls parsing
json - For API data readability
MCForecastTools - For MonteCarlo simulations
alpaca_trade_api - For price information on stocks and bonds
alternative.me - For price information on crypto assets
1- Made API calls using the requests library and SDK to get current data.
2- Used environment variables to protect private API credentials
3- Applied statistical concepts, like probability distributions and confidence intervals, to measure the likelihood of future events
4- Built and ran Monte Carlo simulations to evaluate the long-term future performance outcomes for portfolios.
Aquiba Benarroch, CFA aquiba.me