# # Time Series

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

# ## Date and Time Data Types and Tools

from dateutil.parser import parse

parse('2011-01-03')

parse('Jan 31, 1997 10:45 PM')

parse('6/12/2011', dayfirst=True)

parse('6/12/2011')

#%%

datestrs = ['2011-07-06 12:00:00', '2011-08-06 00:00:00']

pd.to_datetime(datestrs)

idx = pd.to_datetime(datestrs + [None])

idx

idx[2]

pd.isnull(idx)

# ## Time Series Basics

dates = pd.date_range('1/1/2000', periods=100, freq='W-WED')

long_df = pd.DataFrame(np.random.randn(100, 4),

index=dates,

columns=['Colorado', 'Texas',

'New York', 'Ohio'])

long_df.loc['5-2001']

# ## Date Ranges, Frequencies, and Shifting

resampler = long_df.resample('D')

# ### Generating Date Ranges

#%%

index = pd.date_range('2012-04-01', '2012-06-01')

index

pd.date_range(start='2012-04-01', periods=20)

pd.date_range(end='2012-06-01', periods=20)

# ### Frequencies and Date Offsets

#%%

pd.date_range('2000-01-01', '2000-01-03 23:59', freq='4h')

pd.date_range('2000-01-01', periods=10, freq='1h30min')

# #### Week of month dates

rng = pd.date_range('2012-01-01', '2012-09-01', freq='WOM-3FRI')

list(rng)

# ### Shifting (Leading and Lagging) Data

#%%

ts = pd.Series(np.random.randn(4),

index=pd.date_range('1/1/2000', periods=4, freq='M'))

ts

ts.shift(2)

ts.shift(-2)

# ## Periods and Period Arithmetic

#%%

p = pd.Period(2007, freq='A-DEC')

p

rng = pd.period_range('2000-01-01', '2000-06-30', freq='M')

rng

pd.Series(np.random.randn(6), index=rng)

values = ['2001Q3', '2002Q2', '2003Q1']

index = pd.PeriodIndex(values, freq='Q-DEC')

index

# ### Converting Timestamps to Periods (and Back)

rng = pd.date_range('2000-01-01', periods=3, freq='M')

ts = pd.Series(np.random.randn(3), index=rng)

ts

pts = ts.to_period()

pts

rng = pd.date_range('1/29/2000', periods=6, freq='D')

ts2 = pd.Series(np.random.randn(6), index=rng)

ts2

ts2.to_period('M')

# ### Creating a PeriodIndex from Arrays

#%%

data = pd.read_csv('examples/macrodata.csv')

data.head(5)

data.year

data.quarter

index = pd.PeriodIndex(year=data.year, quarter=data.quarter,

freq='Q-DEC')

index

data.index = index

data.infl

# ## Resampling and Frequency Conversion

#%%

rng = pd.date_range('2000-01-01', periods=100, freq='D')

ts = pd.Series(np.random.randn(len(rng)), index=rng)

ts

ts.resample('M').mean()

ts.resample('M', kind='period').mean()

# ### Downsampling

rng = pd.date_range('2000-01-01', periods=12, freq='T')

ts = pd.Series(np.arange(12), index=rng)

ts

ts.resample('5min', closed='left').sum()

ts.resample('5min', closed='right').sum()

ts.resample('5min', closed='right', label='right').sum()

ts.resample('5min', closed='right',

label='right', loffset='-1s').sum()

# #### Open-High-Low-Close (OHLC) resampling

#%%

ts.resample('5min').ohlc()

# ### Upsampling and Interpolation

#%%

frame = pd.DataFrame(np.random.randn(2, 4),

index=pd.date_range('1/1/2000', periods=2, freq='W-WED'),

columns=['Colorado', 'Texas', 'New York', 'Ohio'])

frame

df_daily = frame.resample('D').asfreq()

df_daily

frame.resample('D').ffill()

frame.resample('D').ffill(limit=2)

frame.resample('W-THU').ffill()

# ### Resampling with Periods

frame = pd.DataFrame(np.random.randn(24, 4),

index=pd.period_range('1-2000', '12-2001', freq='M'),

columns=['Colorado', 'Texas', 'New York', 'Ohio'])

frame[:5]

annual_frame = frame.resample('A-DEC').mean()

annual_frame

# Q-DEC: Quarterly, year ending in December

annual_frame.resample('Q-DEC').ffill()

# ## Moving Window Functions

#%%

close_px_all = pd.read_csv('examples/stock_px_2.csv',

parse_dates=True, index_col=0)

close_px = close_px_all[['AAPL', 'MSFT', 'XOM']]

close_px = close_px.resample('B').ffill()

close_px.AAPL.plot()

close_px.AAPL.rolling(250).mean().plot()

appl_std250 = close_px.AAPL.rolling(250, min_periods=10).std()

appl_std250

appl_std250.plot()

close_px.rolling('20D').mean()

aapl_px = close_px.AAPL['2006':'2007']

ma60 = aapl_px.rolling(30, min_periods=20).mean()

ewma60 = aapl_px.ewm(alpha=0.1).mean()

aapl_px.plot()

ma60.plot(style='k--', label='Simple MA')

ewma60.plot(style='k-', label='EW MA')

plt.legend()

# ### Binary Moving Window Functions

#%%

spx_px = close_px_all['SPX']

spx_rets = spx_px.pct_change()

returns = close_px.pct_change()

corr = returns.AAPL.rolling(125, min_periods=100).corr(spx_rets)

corr

corr = returns.rolling(125, min_periods=100).corr(spx_rets)

corr.plot()

corr

# ### User-Defined Moving Window Functions

#%%

from scipy.stats import percentileofscore

score_at_2percent = lambda x: percentileofscore(x, 0.02)

result = returns.AAPL.rolling(250).apply(score_at_2percent)

result.plot()