gbm stocks

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Inspiration

I was inspired by my passion for math and investing. Geometric brownian motion can help us in understanding the future trends of a specific stock. I am passionate about investing in stocks and even crypto currencies.

What it does

The code presents the future trends of a stock in terms of price by a graph.

How I built it

It's built in python. I took the stochastic differential equation of GBM and plugged in values to predict its future price in 2 years.

Challenges I ran into

Theres a lot of math involved in this thats a bit above my level. I had to understand how to use stochastic differential equation which i have learnt about yet. It was challenging to do normal distribution and standard deviation with having very little background of python.

Accomplishments that I'm proud of

I am proud of how I achieved a simplified code to create GBM model to predict future stock prices for a give time period.

What I learned

I learned how to define functions, use if statements, use plot functions, make comments, do basic arithmetic (and assign values) and import functions.

What's next for gbm stocks

With more knowledge about python I will able to add some options for pricing and other functions related to the Black-Sholes model of GBM. Extensions would include constructing a local volatility model (perhaps based on some historical data such as index prices you could download from somewhere) or a stochastic volatility model. This can also help me in creating a Monte-Carlo simulation using GBM. Monte carlo helps in estimating risk for stocks and portfolios.

Built With

• python