Plot Multicolored line based on conditional in python

Question

I have a pandas dataframe with three columns and a datetime index

date        px_last  200dma     50dma           
2014-12-24  2081.88 1953.16760  2019.2726
2014-12-26  2088.77 1954.37975  2023.7982
2014-12-29  2090.57 1955.62695  2028.3544
2014-12-30  2080.35 1956.73455  2032.2262
2014-12-31  2058.90 1957.66780  2035.3240

I would like to make a time series plot of the 'px_last' column that is colored green if on the given day the 50dma is above the 200dma value and colored red if the 50dma value is below the 200dma value. I have seen this example, but can't seem to make it work for my case http://matplotlib.org/examples/pylab_examples/multicolored_line.html

Answer 1

Here is an example to do it without matplotlib.collections.LineCollection. The idea is to first identify the cross-over point and then using a plot function via groupby.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# simulate data
# =============================
np.random.seed(1234)
df = pd.DataFrame({'px_last': 100 + np.random.randn(1000).cumsum()}, index=pd.date_range('2010-01-01', periods=1000, freq='B'))
df['50dma'] = pd.rolling_mean(df['px_last'], window=50)
df['200dma'] = pd.rolling_mean(df['px_last'], window=200)
df['label'] = np.where(df['50dma'] > df['200dma'], 1, -1)


# plot
# =============================
df = df.dropna(axis=0, how='any')

fig, ax = plt.subplots()

def plot_func(group):
    global ax
    color = 'r' if (group['label'] < 0).all() else 'g'
    lw = 2.0
    ax.plot(group.index, group.px_last, c=color, linewidth=lw)

df.groupby((df['label'].shift() * df['label'] < 0).cumsum()).apply(plot_func)

# add ma lines
ax.plot(df.index, df['50dma'], 'k--', label='MA-50')
ax.plot(df.index, df['200dma'], 'b--', label='MA-200')
ax.legend(loc='best')