pandas-montecarlo 0.0.1

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.

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%:

import pandas_montecarlo
mc = df['return'].montecarlo(sims=10, bust=-0.1)

Plot simulations

mc.plot("SPY Returns Monte Carlo Simulations")

Show test stats

print(mc.stats)

# prints
{
    'min':    0.98088401987146789,
    'max':    0.98088401987146934,
    'mean':   0.98088401987146911,
    'median': 0.98088401987146911,
    'std':    4.0792198665315552e-16,
    'maxdd': -0.17221175099828012,
    'bust':   0.0005411255411255411}
}

Show bust / max drawdown stats

print(mc.maxdd)

# prints
{
    'min':    -0.27743285515585991,
    'max':    -0.00031922711279186444,
    'mean':   -0.07888087155686732,
    'median': -0.06010335858432081,
    'std':     0.062172124557467685
}

Access raw simulations data

print(mc.data.head())

    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:

$ 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 file in the release for details.

P.S.

Please drop me an note with any feedback you have.

Ran Aroussi