A Monte Carlo Simulation For Stocks

Languages Used

App objectives

• get user stock ticker input
• fetch ALL stock historical prices
• calculate price volatility
• Use rust to generate sims based on stock historical prices
• tally avg final price for all sims
• use python to plot results

Setup

• Inside root directory cd into scripts to fetch stock's historical prices, run python3 get_data.py
• enter the stock ticker you want to get prices for
• a csv output file will be created inside the src directory containing the prices.
• python will also print the initial price and volatility
• update main.rs variables line 70 - 75
• now run monte carlo engine (mc)
• cargo build
• after your rust app finishes building, run your app cargo run
• the rust app will ask for the stock ticker, initial price, and volatility, number of days to speculate (252 trading days per year)
• after entering these inputs the mc engine will and run print results

Plot results

• inside scripts directory, run python3 plot.py to see the price distribution histogram from all the simulations.

Upcoming implementation

• automate the workflow with one command
• find way to make python execute rust code
• try new rust web framework to run client

Demo

Monte Carlo results

results of a 10 Million simulation for HUT stock price

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Price Distribution from Simulations