https://github.com/ranaroussi/pandas-montecarlo
A lightweight Python library for running simple Monte Carlo Simulations on Pandas Series data
https://github.com/ranaroussi/pandas-montecarlo
monte-carlo montecarlo pandas python
Last synced: 2 months ago
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A lightweight Python library for running simple Monte Carlo Simulations on Pandas Series data
- Host: GitHub
- URL: https://github.com/ranaroussi/pandas-montecarlo
- Owner: ranaroussi
- License: lgpl-3.0
- Created: 2017-05-07T13:21:12.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-10-01T11:41:11.000Z (about 7 years ago)
- Last Synced: 2025-07-23T15:46:58.330Z (3 months ago)
- Topics: monte-carlo, montecarlo, pandas, python
- Language: Python
- Size: 363 KB
- Stars: 227
- Watchers: 9
- Forks: 61
- Open Issues: 5
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOG.rst
- License: LICENSE.txt
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README
Monte Carlo Simulator for Pandas Series
=======================================
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\
**pandas-montecarlo** is a lightweight Python library for running simple
`Monte Carlo Simulations `_ on Pandas Series data.
`Changelog » <./CHANGELOG.rst>`__
-----
Quick Start
-----------
Let's run a monte carlo simulation on the returns of `SPY `_ (S&P 500 Spider ETF).
First, let's download SPY's data and calculate the daily returns.
.. code:: python
from pandas_datareader import data
df = data.get_data_yahoo("SPY")
df['return'] = df['Adj Close'].pct_change().fillna(0)
Next, we'll import ``pandas_montecarlo`` and run monte carlo simulation
with 10 simulations (for demo simplifications) and bust/max drawdown set to ``-10.0%``
and goal threshhold set to ``+100.0%`` (defaults is ``>=0%``):
.. code:: python
import pandas_montecarlo
mc = df['return'].montecarlo(sims=10, bust=-0.1, goal=1)
**Plot simulations**
.. code:: python
mc.plot(title="SPY Returns Monte Carlo Simulations") # optional: , figsize=(x, y)
.. image:: https://raw.githubusercontent.com/ranaroussi/pandas-montecarlo/master/demo.png
:width: 640 px
:height: 360 px
:alt: demo
**Show test stats**
.. code:: python
print(mc.stats)
# prints
{
'min': 0.98088401987146789,
'max': 0.98088401987146934,
'mean': 0.98088401987146911,
'median': 0.98088401987146911,
'std': 4.0792198665315552e-16,
'maxdd': -0.17221175099828012, # max drawdown
'bust': 0.2, # probability of going bust
'goal': 0.0 # probability of reaching 100% goal
}
**Show bust / max drawdown stats**
.. code:: python
print(mc.maxdd)
# prints
{
'min': -0.27743285515585991,
'max': -0.00031922711279186444,
'mean': -0.07888087155686732,
'median': -0.06010335858432081,
'std': 0.062172124557467685
}
**Access raw simulations' DataFrame**
.. code:: python
print(mc.data.head())
.. code:: text
original 1 2 3 4 ... 10
0 0.000000 0.017745 -0.002586 -0.005346 -0.042107 ... 0.00139
1 0.002647 0.000050 0.000188 0.010141 0.007443 ... 0.00108
2 0.000704 0.002916 0.005324 0.000073 -0.003238 ... 0.00071
3 0.004221 0.008564 0.001397 0.007950 -0.006392 ... 0.00902
4 0.003328 -0.000511 0.005123 0.013491 -0.005105 ... 0.00252
Installation
------------
Install ``pandas_montecarlo`` using ``pip``:
.. code:: bash
$ pip install pandas_montecarlo --upgrade --no-cache-dir
Requirements
------------
* `Python `_ >=3.4
* `Pandas `_ (tested to work with >=0.18.1)
* `Matplotlib `_ (tested to work with >=1.5.3)
Legal Stuff
------------
**pandas-montecarlo** is distributed under the **GNU Lesser General Public License v3.0**. See the `LICENSE.txt <./LICENSE.txt>`_ file in the release for details.
P.S.
------------
Please drop me an note with any feedback you have.
**Ran Aroussi**