In Python, the most common model for simulating stock prices is geometric Brownian motion(GBM), which assumes that random shocks accompany a constant drift. At the same time, the period of returns under GBM is normally distributed, and the consequent multi-period price levels are lognormally distributed.
We can predict stock prices in Python. An accurate stock price prediction is extremely challenging because of multiple factors such as politics, global economic conditions, a company's financial performance, unexpected events, etc. Python can be used for predictive analytics, and it is one of the popular programming languages now. Python is rich with powerful libraries that make building predictive models a straightforward process, and some of the popular ones include pandas, NumPy, matplotlib, seaborn, and sci-kit-learn.
- Pandas: It is used to work with data sets and has functions for exploring, analyzing, manipulating data, and cleaning.
- NumPy: It is used to work with arrays and has functions for working in the domain of linear algebra and matrices.
- Matplotlib: It is a comprehensive library in Python for creating animated, interactive visuals, and static, and it makes things easy and hard things possible. It also makes interactive figures that can zoom, pan, and update.
- sci-kit-learn: It is an open-source data analysis python library. The gold standard for Machine Learning(ML) in the python ecosystem.
Here is an example of how to make a simple stock price simulation process efficient in Python:
Fig : Preview of the output that you will get on running this code from your IDE.
In this solution we're using Pandas and NumPy libraries.
import numpy as np for x in range(1, days-1): mainframe.iloc[x] = mainframe.iloc[x-1] * np.random.uniform(0.98, 1.02, nsims-1) import pandas as pd import numpy as np nsims = 100 stpx = 100 days = 256 mainframe = pd.DataFrame(stpx, index=list(range(1, days)), columns=list(range(1, nsims))) rand_matrix = np.random.uniform(0.98, 1.02, (days-2, nsims-1)).cumprod(axis=0) mainframe.iloc[1:] *= rand_matrix
Follow the steps carefully to get the output easily.
- Install pandas on your IDE(Any of your favorite IDE).
- Copy the snippet using the 'copy' and paste it in your IDE.
- Add required dependencies and import them in Python file.
- Run the file to generate the output.
I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.
I found this code snippet by searching for 'How to make a simple stock price simulation process more efficient in Python' in kandi. You can try any such use case!
I tested this solution in the following versions. Be mindful of changes when working with other versions.
- The solution is created in PyCharm 2021.3.
- The solution is tested on Python 3.9.7.
- Pandas version-v1.5.2.
- NumPy version-v1.24.0.
Using this solution, we are able to make a simple stock price simulation process more efficient in python with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to make a simple stock price simulation process more efficient in python.
Python 38689 Version:v2.0.2 License: Permissive (BSD-3-Clause)