# coding: utf-8

# In[1]:

from datetime import datetime

from pandas import DataFrame,Series

import numpy as np

import pandas as pd

# In[2]:

import pytz

pytz.common_timezones[60:65]

# In[3]:

pytz.timezone('US/Eastern')

# In[4]:

rng = pd.date_range('1/2/2011 9:30',periods=6,freq='D')

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

ts

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print(ts.index.tz)

# In[6]:

pd.date_range('1/2/2011 9:30',periods=6,freq='D',tz = 'UTC')

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ts_utc = ts.tz_localize('UTC')

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ts_utc

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ts_utc.tz_convert('US/Eastern')

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ts_eastern = ts.tz_localize('US/Eastern')

ts_eastern.tz_convert('Europe/Berlin')

# ### 操作时区意识型TimeStamp对象

# In[11]:

stamp = pd.Timestamp('2018-3-2 04:00')

stamp_utc = stamp.tz_localize('UTC')

stamp_utc.tz_convert('Asia/Shanghai')

# stamp_utc

# In[12]:

stamp_moscow = pd.Timestamp('2018-4-5 9:00',tz = 'Europe/Moscow')

stamp_moscow

# In[13]:

now = datetime.now()

stamp_utc.value

# In[14]:

rng = pd.date_range('3-18-2018',periods = 6,freq = 'B')

ts = Series(np.random.rand(6),index = rng)

# In[15]:

ts

# In[16]:

ts1 = ts[:3].tz_localize('Asia/Shanghai')

ts2 = ts[4:].tz_localize('US/Eastern')

ts_re = ts1 + ts2

ts_re

# In[17]:

ts_re.index

# ### 时期及其算术运算

# In[18]:

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

p

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pd.Period('2014', freq='A-DEC') - p

# In[21]:

rng = pd.date_range('2-1-2013','3-2-2015',freq = 'MS')

rng

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Series(np.random.rand(26),index = rng)

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

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

index

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

p.asfreq('M',how = 'end')

# In[28]:

p = pd.Period('2007-08', 'M')

p.asfreq('A-JUN')

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rng = pd.date_range('2007','2010',freq = 'A-DEC')

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

ts

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ts.asfreq('M', how='start')

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p = pd.Period('2012Q4', freq='Q-JAN')

p

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p.asfreq('D', 'start')

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p4pm = (p.asfreq('B', 'e') - 1).asfreq('T', 's') + 16 * 60

p4pm

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p4pm.to_timestamp()

# In[41]:

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

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

ts2.to_period('M')

# In[42]:

macrodata_file = open('F:/电影/数据分析/pydata-book-master/ch08/macrodata.csv')

data = pd.read_csv(macrodata_file)

# In[44]:

data.head()

# In[45]:

index = pd.PeriodIndex(year=data.year, quarter=data.quarter, freq='Q-DEC')

data.index = index

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data.head()

# In[48]:

data.infl.plot()

# ### 重采样和频率转换

# In[2]:

rng = pd.date_range('3/4/2018',periods=100,freq = 'D')

ts = Series(np.random.randn(100),index = rng)

ts.head()

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

# In[7]:

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

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

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rng = pd.date_range('1/1/2000', periods=12, freq='T')

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

ts

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

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

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ts.resample('5min',closed='left',label = 'right').sum() # closed控制闭区间的位置，label控制结果的区间的位置

# In[20]:

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

# In[24]:

# 金融领域开盘收盘最高最低

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

# In[25]:

rng = pd.date_range('1/1/2000', periods=100, freq='D')

ts = Series(np.arange(100), index=rng)

ts.groupby(lambda x: x.month).mean()

# In[26]:

ts.groupby(lambda x : x.weekday).mean()

# In[30]:

# 升采样

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

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

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

df_daily = frame.resample('D')

df_daily.mean()

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

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

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

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

frame[:4]

# In[36]:

anual_frame = frame.resample('A-DEC').ohlc()

anual_frame

# In[39]:

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

# ## pandas 时间序列函数

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close_px_file = open('F:/电影/数据分析/pydata-book-master/ch09/stock_px.csv')

close_px_all = pd.read_csv(close_px_file,parse_dates=True,index_col=0)

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

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

close_px[:6]

# In[42]:

close_px['AAPL'].plot()

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close_px.loc['2009'].plot()

# In[47]:

close_px['AAPL'].loc['2011-1':'2011-3'].plot(fontsize = 'larger')

# In[49]:

aapl_q = close_px['AAPL'].resample('Q-DEC').ffill()

# In[51]:

aapl_q.loc['2009':].plot()

# ### 移动窗口函数

# In[2]:

close_px_file = open('F:/电影/数据分析/pydata-book-master/ch09/stock_px.csv')

close_px_all = pd.read_csv(close_px_file,parse_dates=True,index_col=0)

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

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

close_px[:6]

# In[7]:

pd.rolling_mean(close_px.AAPL,250).plot()

# In[10]:

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

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appl_std250[7:12]

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appl_std250.plot()

# In[16]:

close_px.rolling(250,min_periods=60).mean().plot()

# ### 指数加权函数

# In[23]:

import matplotlib.pyplot as plt

fig,axes = plt.subplots(nrows = 2,ncols = 1,sharex = True,sharey = True,figsize = (12,7))

aapl_px = close_px.AAPL.loc['2005':'2009']

ma60 = aapl_px.rolling(60,min_periods = 50).mean()

ewma60 =aapl_px.ewm(span = 60).mean()

aapl_px.plot(style = 'k-',ax = axes[0])

ma60.plot(style = 'k--',ax = axes[0])

aapl_px.plot(style = 'k-',ax = axes[1])

ewma60.plot(style = 'k--',ax = axes[1])

axes[0].set_title('Simple MA')

axes[1].set_title('Exponentially-weighted MA')

# In[28]:

spx = close_px_all['SPX']

spx_pct = spx / spx.shift(1) -1

returns = close_px.pct_change()

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

# In[29]:

corr.plot()

# In[30]:

returns.rolling(125,min_periods=100).corr(other = spx_pct).plot()

# In[31]:

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()