| Name | pyfmc JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | https://github.com/ethanlee928/pyfmc |
| Summary | Finance Monte-Carlo Simulation using PyTorch |
| upload_time | 2023-10-20 15:24:22 |
| maintainer | |
| docs_url | None |
| author | Ethan Lee |
| requires_python | |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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|
# Finance Monte-Carlo Simulation using PyTorch
- An easy-to-use python package to do Monte-Carlo Simulation on stock prices
- GPU accelerated Monte-Carlo simulation, that could allow simulation more random walkers without a large time penalty
## Installation
```bash
pip install pyfmc
```
## Geometric Brownian Motion Simulation
### Configure the simulation
```python
import pandas as pd
import matplotlib.pyplot as plt
from pyfmc.simulations.gbm import GBM
data_path = "./tests/data/AAPL.csv" # Replace with one's desired data
simulation = GBM(
df=pd.read_csv(data_path),
n_walkers=500_000,
n_steps=100,
n_trajectories=50,
open_index="Open", # Make sure the DataFrame has column index specified here
close_index="Close", # Make sure the DataFrame has column index specified here
)
result = simulation.simulate()
```
### Simulation Results
#### Price Distribution
```python
price_dist = result.price_distribution()
price_dist.plot(bins=500)
plt.show()
```
#### Return Distribution
```python
return_dist = result.return_distribution()
return_dist.plot(kde=True)
plt.show()
```
### Walkers Trajectories
```python
trajectories = result.trajectories()
trajectories.plot()
plt.show()
```
### Value at Risk (VaR)
```python
var = result.VaR(alpha=5)
# output: -0.2515...
# The worst 5% chance -> -25% return
```
## For Development
Python virtual environment:
```bash
python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.txt
```
## Reference
- [How to Use Monte Carlo Simulation With GBM (Investopedia)](https://www.investopedia.com/articles/07/montecarlo.asp)
- [Understanding Value at Risk (VaR) and How It’s Computed (Investopedia)](https://www.investopedia.com/terms/v/var.asp)
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