Monte Carlo Simulator for Pandas
Project description
pandas-montecarlo is a lightweight Python library for running simple Monte Carlo Simulations on Pandas Series data.
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% and goal threshhold set to +100.0% (defaults is >=0%):
import pandas_montecarlo
mc = df['return'].montecarlo(sims=10, bust=-0.1, goal=1)
Plot simulations
mc.plot(title="SPY Returns Monte Carlo Simulations") # optional: , figsize=(x, y)
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, # max drawdown
'bust': 0.2, # probability of going bust
'goal': 0.0 # probability of reaching 100% goal
}
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’ DataFrame
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
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