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https://github.com/martincastroalvarez/python-monte-carlo-simulator
Monte Carlo simulator in Python.
https://github.com/martincastroalvarez/python-monte-carlo-simulator
monte-carlo-simulation montecarlo normal-distribution numpy pandas python simulation
Last synced: 10 days ago
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Monte Carlo simulator in Python.
- Host: GitHub
- URL: https://github.com/martincastroalvarez/python-monte-carlo-simulator
- Owner: MartinCastroAlvarez
- Created: 2019-05-25T21:39:59.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-06-21T22:01:10.000Z (over 2 years ago)
- Last Synced: 2023-03-04T12:42:01.164Z (almost 2 years ago)
- Topics: monte-carlo-simulation, montecarlo, normal-distribution, numpy, pandas, python, simulation
- Language: Python
- Homepage: https://martincastroalvarez.com
- Size: 35.2 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# MontePy
*Monte Carlo simulation using Numpy and Pandas*
## References
- [Monte Carlo Simulations with Python](https://towardsdatascience.com/monte-carlo-simulations-with-python-part-1-f5627b7d60b0)
- [Numpy Random Distribution](https://docs.scipy.org/doc/numpy/reference/routines.random.html)
- [Python Monte Carlo](https://pbpython.com/monte-carlo.html)
- [Numpy Binomial Distribution](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.binomial.html#numpy.random.binomial)
- [Numpy Normal Distribution](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html#numpy.random.normal)
- [Numpy Exponential Distribution](https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.exponential.html#numpy.random.exponential)
- [Pandas Eval](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.eval.html)
## Installation
```
git clone ssh://[email protected]/MartinCastroAlvarez/monte-py
cd monte-py
virtualenv -p python3 env
source env/bin/activate
pip install -r requirements.txt
```
## Usage
Create a file called `dataset.json` and put this config inside:
```
{
"features": {
"Sales": {
"distribution": "normal",
"avg": 100,
"std": 0.8,
"min": 0
},
"Price": {
"distribution": "normal",
"avg": 10,
"std": 0.2,
"min": 0
},
"FixedCost": {
"distribution": "normal",
"avg": 100,
"std": 0.5,
"min": 0
},
"VariableCost": {
"distribution": "normal",
"avg": 8,
"std": 2,
"min": 0
}
},
"targets": [{
"Revenue": "Price * Sales",
"Cost": "FixedCost + VariableCost * Sales"
}, {
"Profit": "Revenue - Cost"
}]
}
```
Execute the following command to run the simulation:
```
python3 simulate.py --simulations 100 --path dataset.json
```
Look at the output:
```
Sales Price FixedCost VariableCost Revenue Cost Profit
count 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000
mean 100.016270 9.985491 99.940654 7.858189 998.716079 886.028664 112.687415
std 0.823051 0.197306 0.485513 1.887991 21.583492 189.225182 189.462751
min 97.984897 9.473178 98.542858 3.344354 939.624517 434.246999 -438.177117
25% 99.490436 9.845898 99.620062 6.867120 983.393390 787.918456 -12.257061
50% 100.028057 9.989368 99.908596 8.057500 999.504628 906.059768 98.668392
75% 100.542664 10.142087 100.307828 9.117511 1014.255521 1010.227241 225.900207
max 102.427096 10.404904 100.937590 13.529123 1044.997147 1435.870633 526.558347
```
In this example, the expected profit is `$112.687415` after simulating the scenario 100 times.