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Utility to create a monthly returns heatmap from Pandas series

Project description

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monthly-returns-heatmap is a simple Python library for creating Monthly Returns Heatmap from Pandas series with ease.

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Quick Start

Let’s create a returns heatmap for SPY (S&P 500 Spider ETF).

First, let’s download SPY’s daily close prices from Google finance.

from pandas_datareader import data
prices = data.get_data_google("SPY")['Close']
returns = prices.pct_change()

Next, we’ll import monthly_returns_heatmap and plot the monthly return heatmap:

import monthly_returns_heatmap as mrh

returns.plot_monthly_returns_heatmap()
# mrh.plot(returns) # <== or using direct call
demo

Getting heatmap data only (no plotting)

heatmap = prices.get_monthly_returns_heatmap()
# heatmap = mrh.get(returns) # <== or using direct call

print(heatmap)

# prints:

Month       Jan        Feb        Mar        Apr  ...        Dec
Year
2010   0.000000   0.031195   0.056529   0.015470  ...   0.061271
2011   0.023300   0.034737  -0.004807   0.030413  ...   0.003117
2012   0.045498   0.043137   0.028129  -0.006751  ...   0.001759
2013   0.051190   0.012759   0.033375   0.019212  ...   0.020387
2014  -0.035248   0.045516   0.003865   0.006951  ...  -0.008012
2015  -0.029629   0.056205  -0.020080   0.009834  ...  -0.023096
2016  -0.049787  -0.001910   0.062943   0.003941  ...   0.014293
2017   0.017895   0.039292  -0.003087   0.009926  ...   0.000000

Get Parameters (optional)

  • is_prices - set to True if the data used is price data instead of returns data
  • compounded - set to False if the you don’t want the calculation to use compounded returns
  • eoy - set to True to add a End Of Year column with total yearly returns

Plot Parameters (optional)

  • title - Heatmap title (defaults to "Monthly Returns (%)")
  • title_color - Heatmap title color (defaults to "black")
  • title_size - Heatmap title font size (defaults to 12)
  • annot_size - Returns boxes font size (defaults to 10)
  • figsize - Heatmap figure size (defaults to None)
  • cmap - Color map (defaults to "RdYlGn")
  • cbar - Show color bar? (defaults to True)
  • square - Force squere returns boxes? (defaults to False)
  • is_prices - set to True if the data used is price data instead of returns data
  • compounded - set to False if the you don’t want the calculation to use compounded returns
  • eoy - set to True to add a End Of Year column with total yearly returns

Installation

Install monthly_returns_heatmap using pip:

$ pip install monthly_returns_heatmap --upgrade --no-cache-dir

Requirements

P.S.

Please drop me an note with any feedback you have.

Ran Aroussi

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