Introduction to timeseries plots in Python

Imports

1
2
3
4
5
6
7
8

%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math

from datetime import date
from nsepy import get_history

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

# NIFTY 50 index
nifty_50 = get_history(symbol="NIFTY 50",
                            start=date(2001,1,1),
                            end=date(2020,5,3),
                            index=True)

# NIFTY Next 50 index
nifty_next50 = get_history(symbol="NIFTY NEXT 50",
                            start=date(2001,1,1),
                            end=date(2020,5,3),
                            index=True)

# India VIX index
india_vix = get_history(symbol="INDIAVIX",
                            start=date(2001,1,1),
                            end=date(2020,5,3),
                            index=True)

1

nifty_50.shape, nifty_next50.shape, india_vix.shape

1

((4806, 6), (4806, 6), (2796, 7))

1

MKT = nifty_next50.join(nifty_50['Close'], how='outer', rsuffix='_N50').dropna()

1
2
3

data = MKT.rename(columns={'Close':'NN50','Close_N50':'N50'})\
            .assign(NN50_returns=lambda x: np.log(x['NN50']/x['NN50'].shift(1)))\
            .assign(N50_returns=lambda x: np.log(x['N50']/x['N50'].shift(1)))

1

data = data[['NN50', 'N50', 'NN50_returns', 'N50_returns']].dropna()

1

data.index = pd.DatetimeIndex(data.index)

1
2
3
4
5
6
7

# Let's plot SPX and VIX cumulative returns with recession overlay
plot_cols = ['NN50', 'N50']

# 2 axes for 2 subplots

fig, axes = plt.subplots(2,1, figsize=(10,7), sharex=True)
data[plot_cols].plot(subplots=True, ax=axes)

1
2
3

array([<matplotlib.axes._subplots.AxesSubplot object at 0x11b5f6610>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x11a0cf8d0>],
      dtype=object)

png

1
2
3
4
5
6
7

# Let's plot SPX and VIX cumulative returns with recession overlay
plot_cols = ['NN50_returns', 'N50_returns']

# 2 axes for 2 subplots

fig, axes = plt.subplots(2,1, figsize=(10,7), sharex=True)
data[plot_cols].plot(subplots=True, ax=axes)

1
2
3

array([<matplotlib.axes._subplots.AxesSubplot object at 0x11669c550>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x115ccc090>],
      dtype=object)

png

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

# ================================================================== #
# resample log returns weekly starting monday
lrets_resampled = data[['NN50_returns', 'N50_returns']].resample('W-MON').sum()

# ================================================================== #
# rolling mean returns
n = 52
roll_mean = lrets_resampled.rolling(window=n, min_periods=n ).mean().dropna(axis=0,how='all')

# ================================================================== #
# rolling sigmas
roll_sigs = lrets_resampled.rolling(window=n, min_periods=n ).std().dropna(axis=0,how='all') * math.sqrt(n)

# ================================================================== #
# rolling risk adjusted returns 
roll_risk_rets = roll_mean/roll_sigs

1
2
3
4
5
6
7

# Let's plot SPX and VIX cumulative returns with recession overlay
plot_cols = ['NN50_returns', 'N50_returns']

# 2 axes for 2 subplots

fig, axes = plt.subplots(2,1, figsize=(10,7), sharex=True)
roll_mean[plot_cols].plot(subplots=True, ax=axes)

1
2
3

array([<matplotlib.axes._subplots.AxesSubplot object at 0x1165e9b50>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x11738d190>],
      dtype=object)

png

1
2
3
4

data[['NN50', 'N50']].rolling(250).mean().head()
# \
#             .assign(NN50_returns=lambda x: np.log(x['NN50']/x['NN50'].shift(1)))\
#             .assign(N50_returns=lambda x: np.log(x['N50']/x['N50'].shift(1)))
NN50 N50
Date
2001-01-02 NaN NaN
2001-01-03 NaN NaN
2001-01-04 NaN NaN
2001-01-05 NaN NaN
2001-01-08 NaN NaN 
1
2

%load_ext watermark
%watermark

1
2
3
4
5
6
7
8
9
10
11
12
13
14

The watermark extension is already loaded. To reload it, use:
  %reload_ext watermark
2020-05-04T08:30:27+05:30

CPython 3.7.4
IPython 7.9.0

compiler   : Clang 4.0.1 (tags/RELEASE_401/final)
system     : Darwin
release    : 19.4.0
machine    : x86_64
processor  : i386
CPU cores  : 4
interpreter: 64bit

1



        
      


      

      


      
  

    


    
  


  
  
    

      
You may also enjoy