from pandas_datareader.data import DataReader
import pandas as pd
import matplotlib.pyplot as plt
from datetime import date
series_code = 'DEXJPUS'
data_source = 'fred'
startDate = date(1979, 1, 1)
jpy = DataReader(series_code, data_source=data_source, start=startDate)
jpy.index = pd.to_datetime(jpy.index)
jpy['usdjpy'] = jpy['DEXJPUS']
del jpy['DEXJPUS']
jpy['returns'] = jpy['usdjpy'].pct_change()
autocorrelation_daily = jpy['returns'].autocorr()
print("The daily autocorrelation is: " + str(autocorrelation_daily))
jpyWeekly = jpy.resample('W').last()
jpyWeekly['weekly_returns'] = jpyWeekly['usdjpy'].pct_change()
atuocorrelation_weekly = jpyWeekly['weekly_returns'].autocorr()
print("The weekly autocorrelation is: " + str(atuocorrelation_weekly))
jpyMonthly = jpy.resample('M').last()
jpyMonthly['returns'] = jpyMonthly['usdjpy'].pct_change()
autocorrelation_monthly = jpyMonthly['returns'].autocorr()
print("The monthly autocorrelation is: " + str(autocorrelation_monthly))
self['transition'] = transition
self['state_cov', 0, 0] = structural_params[self.idx_tech_var]
if __name__ == '__main__':
start = '1984-01'
end = datetime.today().strftime('%Y-%m-%d')
labor = DataReader('HOANBS', 'fred', start=start,
end=end).resample('QS').first()
cons = DataReader('PCECC96', 'fred', start=start,
end=end).resample('QS').first()
inv = DataReader('GPDIC1', 'fred', start=start,
end=end).resample('QS').first()
pop = DataReader('CNP16OV', 'fred', start=start, end=end)
pop = pop.resample(
'QS').mean() # Convert pop from monthly to quarterly observations
recessions = DataReader('USRECQ', 'fred', start=start, end=end)
recessions = recessions.resample('QS').last()['USRECQ'].iloc[1:]
# Get in per-capita terms
N = labor['HOANBS'] * 6e4 / pop['CNP16OV']
C = (cons['PCECC96'] * 1e6 / pop['CNP16OV']) / 4
I = (inv['GPDIC1'] * 1e6 / pop['CNP16OV']) / 4
Y = C + I
# Log, detrend
y = np.log(Y).diff()[1:]
c = np.log(C).diff()[1:]
n = np.log(N).diff()[1:]
i = np.log(I).diff()[1:]
rbc_data = pd.concat((y, n, c), axis=1)
from pandas_datareader.data import DataReader
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
# %%
amazon = DataReader('AMZN', 'stooq')
amazon.to_csv('data.csv')
# %%
amazon['Close'].plot()
amazon['Close'].plot(alpha=0.5)
# %%
amazon['Close'].plot(alpha=0.7)
amazon.resample('BQ').mean()['Close'].\
plot(color='green', style='--', alpha=0.7)
plt.legend(['price', 'quarter average'])
# %% shifting
fig, ax = plt.subplots(3, sharex=True)
amazon['Close'].plot(ax=ax[0])
amazon['Close'].shift(365).plot(ax=ax[1])
amazon['Close'].shift(-365).plot(ax=ax[2])
ax[0].legend(['input'])
ax[1].legend(['shift by 365'])
ax[2].legend(['shift by -365'])
# %% ROI
from pandas_datareader.data import DataReader
import pandas as pd
import matplotlib.pyplot as plt
from datetime import date
series_code = 'msft'
data_source = 'yahoo'
startDate = date(2012,1,1)
endDate = date(2019,11,1)
msft = DataReader(series_code,data_source,startDate,endDate)
msft = msft.resample('W').last()
returns = msft['Adj Close'].pct_change().dropna()
autocorrelation = returns.autocorr()
print("The autocorrelation of weekly returns is %4.2f" %(autocorrelation))
###########################################################################################
from statsmodels.graphics.tsaplots import plot_acf
from math import sqrt
#find number of observations
nobs = len(returns)
#compute approximate confidence interval
conf = 1.96/sqrt(nobs)
print("The approximate confidence interval is +/- %4.2f" %(conf))
#plot the autocorrelation function with 95% confidence interval and 20 lags using plot_acf
plot_acf(returns,alpha=0.05,lags=20,title="MSFT Autocorrelation")
plt.show()
#compare to white noise
import numpy as np
whiteNoise = np.random.normal(loc=0.0025, scale= 0.05, size=1000)
#print white noise mean/std
wnMean = np.mean(whiteNoise)
from dateutil.relativedelta import relativedelta
start_data = datetime.now() - relativedelta(years=66)
today = datetime.now()
from pandas_datareader.data import DataReader
consumer_df = DataReader([
'PCE', 'UMCSENT', 'UNRATE', 'LCEAMN01USM189S', 'TOTALSL', 'MRTSSM44X72USS',
'HOUST'
], 'fred', start_data, today)
consumer_df = consumer_df.dropna()
consumer_df.columns = [
'PCE', 'ConConf', 'Unempl', 'HourlyEarning', 'CCredit', 'RetSales',
'HouseStarts'
]
consumer_df = consumer_df.resample('1M').mean()
type(consumer_df)
# lag order selection
lag_order = select_order(data=consumer_df,
maxlags=10,
deterministic="ci",
seasons=12)
print(lag_order.summary())
print(lag_order)
# Cointegration rank
rank_test = select_coint_rank(consumer_df, 0, 2, method="trace", signif=0.05)
rank_test.rank
print(rank_test.summary())
print(rank_test)
from pandas_datareader.data import DataReader
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
#%% Import data to dataframe
raw_uber = DataReader('UBER', 'stooq')
#%% Export imported data to csv
raw_uber.to_csv('./source_data/Uber.csv')
#%% Plot close Uber values
raw_uber['Close'].plot()
#%% Create new sample with monthly periods and calculated mean
resampled_uber = raw_uber.resample('BM').mean()
#%% Plot resampled and raw close data on one plot
raw_uber['Close'].plot()
resampled_uber['Close'].plot(style='--', color='green')
#%% Plot raw close values with shift on one plot
fig, ax = plt.subplots(3, sharex=True)
raw_uber['Close'].plot(ax=ax[0])
raw_uber['Close'].shift(100).plot(ax=ax[1])
raw_uber['Close'].shift(-100).plot(ax=ax[2])
ax[0].legend(['Input'])
ax[1].legend(['Shift by 100 days'])
ax[2].legend(['Shift by -100 days'])
