def read_modulate_data(data):
"""
Data ingestion : Function to read and formulate the data
"""
#data = pd.read_csv(data_file)
data.fillna(data.mean(), inplace=True)
df = data.copy()
data.set_index("Date", inplace=True)
data.index = pd.to_datetime(data.index)
return data, df
def getEquityDataFromCSV(path):
data = pd.read_csv(path)
new = data.set_index('date')
new = new[::-1]
new = new.pivot(columns='ticker')
return new
def getEquityData(tickers, qopts = { 'columns': ['ticker', 'date', 'adj_close', 'adj_open', 'adj_low', 'adj_high', 'adj_volume'] }, end_date = datetime.datetime.today().strftime('%Y-%m-%d'), start_date = "2015-01-01"):
data = quandl.get_table('WIKI/PRICES',
ticker = tickers,
qopts = qopts,
date={'gte':start_date, 'lte':end_date},
api_key='KB7ZyJpPrzE1zPpBX7xu')
data = data.set_index('date')
data = new[::-1]
new = new.pivot(columns='ticker')
return new
def get_quandl_data(stocksList=['AAPL','AMZN','GOOGL','FB'], start_date='2017-01-01', end_date='2018-01-01'):
with open('quandl_api.txt', 'r') as inf:
apikey = inf.readline().strip()
print('apikey=', apikey, 'now use file input')
quandl.ApiConfig.api_key = apikey # '-dJhdMB1EcYEkxzxEGMA' # 'your_api_key_here'
# stocks = ['AAPL','AMZN','GOOGL','FB']
stocks = stocksList
data = quandl.get_table('WIKI/PRICES', ticker = stocks,
qopts = { 'columns': ['date', 'ticker', 'adj_close'] },
date = { 'gte': start_date, 'lte': end_date }, paginate=True)
df = data.set_index('date')
table = df.pivot(columns='ticker')
table.columns = [col[1] for col in table.columns]
return table
print('FF3 p-values:\n', FF3.pvalues)
print('FF5 p-values:\n', FF5.pvalues)
CAPM_coeff = CAPM.params
FF3_coeff = FF3.params
FF5_coeff = FF5.params
results = pd.DataFrame({'CAPM_coeff': CAPM_coeff,
'CAPM_tstat': CAPM_tstat,
'FF3_coeff': FF3_coeff,
'FF3_tstat': FF3_tstat,
'FF5_coeff': FF5_coeff,
'FF5_tstat': FF5_tstat},
index=['Intercept', 'MKT', 'SMB', 'HML', 'RMW', 'CMA'])
return results
ticker = 'AAPL'
tickerFile = ticker + '.csv'
data = pd.read_csv(tickerFile, parse_dates=['Date'])
data = data.sort_values(by='Date')
data.set_index('Date', inplace=True)
data['Returns'] = data['Adj Close'].pct_change() # Create daily returns column
returns = data['Returns'].dropna() # Remove values of N/A
capm = assetPrice(data)
print(capm)
import datetime
tickers = ['AAPL', 'MSFT', 'NFLX', 'AMZN', 'GOOG']
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2018, 12, 31)
df = pd.DataFrame([data.DataReader(ticker, 'yahoo', start, end)['Adj Close'] for ticker in tickers]).T
df.columns = tickers
#getting data by quandl
import quandl
quandl.ApiConfig.api_key = '-dJhdMB1EcYEkxzxEGMA' # 'your_api_key_here'
stocks = ['AAPL','AMZN','GOOGL','FB']
data = quandl.get_table('WIKI/PRICES', ticker = stocks,
qopts = { 'columns': ['date', 'ticker', 'adj_close'] },
date = { 'gte': '2016-1-1', 'lte': '2017-12-31' }, paginate=True)
df = data.set_index('date')
table = df.pivot(columns='ticker')
table.columns = [col[1] for col in table.columns]
# re-index by different date range ; but it may not work.. range from Sun - Sat..., which df has no data ..
df = pd.DataFrame()
all_weeks = pd.date_range(start='2019-01-03', end='2019-12-31', freq='W')
df = df.loc[all_weeks]
# rolling function on moving average
df['mah15'] = df['Aju Close'].rolling(15, center=True, win_type='hamming').mean()
# return
# get the table for daily stock prices and,
# filter the table for selected tickers, columns within a time range
# set paginate to True because Quandl limits tables API to 10,000 rows per call
data = quandl.get_table('WIKI/PRICES',
ticker=['AAPL', 'MSFT', 'WMT'],
qopts={'columns': ['ticker', 'date', 'adj_close']},
date={
'gte': '2015-12-31',
'lte': '2016-12-31'
},
paginate=True)
data.head()
# create a new dataframe with 'date' column as index
new = data.set_index('date')
# use pandas pivot function to sort adj_close by tickers
clean_data = new.pivot(columns='ticker')
# check the head of the output
clean_data.head()
import quandl
import pandas as pd
import datetime
from datetime import timedelta
from pandas import ExcelWriter
quandl.ApiConfig.api_key = 'NL6weZFnRNw5q_L5-Nve'
tickers = ['BA', 'MSFT', 'ACN']
