import datetime
import ReadData
import math
import matplotlib.pyplot as plt
DataFolder = 'E:\Dropbox\CQA 2014\Data'
# BUILD_DATE = datetime.datetime(2013, 11, 1)
START_TRAIN = datetime.datetime(2010, 11, 1)
END_TRAIN = datetime.datetime(2013, 10, 31)
START = datetime.datetime(2013, 11, 1)
END = datetime.datetime(2014, 11, 5)
BETA_BOUND = 0.45
WEIGHT_BOUND = 0.05
CAPITAL = 1e7
(StockPrices, SP500Prices, CarhartDaily, StockBeta) = ReadData.ReadAll(DataFolder)
StockReturns = ReadData.ReadReturn(DataFolder)
tickers = ReadData.ReadTickers(DataFolder)
decay = 0.003
Coef = pd.read_csv(r'%s/Coef_%s_%s_%.4f.csv' % (DataFolder, START_TRAIN.date(), END_TRAIN.date(), decay))
Res = pd.read_csv(r'%s/Res_%s_%s_%.4f.csv' % (DataFolder, START_TRAIN.date(), END_TRAIN.date(), decay))
betas = Coef.merge(tickers).rename(columns={'Mkt-RF': 'beta'})[['ticker', 'beta']].reset_index(drop=True)
sortCoef = betas.sort('beta').reset_index(drop=True)
N = sortCoef.shape[0] / 10
longTickers = sortCoef.head(4*N).tail(3*N)[['ticker']].sort('ticker').reset_index(drop=True)
shortTickers = sortCoef.tail(3*N)[['ticker']].sort('ticker').reset_index(drop=True)
pfTickers = pd.concat((shortTickers[['ticker']], longTickers[['ticker']]), axis=0).sort('ticker').reset_index(drop=True)
# def getCovMatrix(tickers):
plt.title('Long/Shoart Market Value for each Sector', size=font_size)
plt.xticks(index + bar_width, sector_list, rotation=20)
plt.legend()
plt.tight_layout()
plt.grid()
plt.show()
sector_bar_chart()
print sector_summary[['sector', 'long_weight', 'short_weight', 'net_weight']]
# Now let's look at sector returns since 11/10
(StockPrices, SP500Prices, CarhartDaily,
StockBeta) = ReadData.ReadAll(data_folder)
StockReturns = ReadData.ReadReturn(data_folder)
build_date = datetime.datetime(2014, 11, 10)
end_date = StockPrices['date'].max()
end_tickers = pd.DataFrame({
'ticker':
StockPrices[StockPrices['date'] == end_date]['ticker'].unique()
})
sector_values = pd.DataFrame()
for sector in sector_list:
# sector = sector_list[0]
wTickers = ticker_sector[ticker_sector['sector'] == sector].merge(
end_tickers)
wTickers['weight'] = 1 / float(wTickers.shape[0])
(pfValues, pfRecord) = functions.testPortfolio(StockPrices, StockReturns,
SP500Prices, StockBeta,
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
DataFolder = 'E:\Dropbox\CQA 2014\Data'
CAPITAL = 1e7
# BUILD_DATE = datetime.datetime(2013, 11, 1)
START_TRAIN = datetime.datetime(2010, 11, 1)
END_TRAIN = datetime.datetime(2013, 10, 31)
START = datetime.datetime(2013, 11, 1)
END = datetime.datetime(2014, 11, 5)
BETA_BOUND = 0.45
WEIGHT_BOUND = 0.05
(StockPrices, SP500Prices, CarhartDaily,
StockBeta) = ReadData.ReadAll(DataFolder)
StockReturns = ReadData.ReadReturn(DataFolder)
tickers = ReadData.ReadTickers(DataFolder)
wTickers = tickers
wTickers['weight'] = 1 / np.float64(wTickers.shape[0])
def testPortfolio2Test():
pfValues = functions.testPortfolio2(StockPrices, SP500Prices, StockBeta,
wTickers, CAPITAL, START, END)
plt.subplot(121)
plt.title('Portfolio Value vs Date')
plt.plot(pfValues['date'],
pfValues['value'],
label='Portfolio Value',
color='b')
# import cvxopt
# import pymatlab
##import pylab
##from statsmodels.sandbox.descstats import descstats
##from pylab import figure, show
from scipy.optimize import minimize
DISK = 'E'
## We need to register datetime64 as a recognizable format in matplotlib first
matplotlib.units.registry[
np.datetime64] = pd.tseries.converter.DatetimeConverter()
## Read data
(StockPrices, StockReturns, SP500Prices, CarhartDaily,
StockBeta) = ReadData.ReadAll(DISK)
print "Finished reading data..."
SP500Returns = ReadData.GetSP500Return(SP500Prices)
## For training purpose we only use the data before 2013/11/01 (start date of testing period)
START_TRAIN = datetime.datetime(2010, 11, 1)
END_TRAIN = datetime.datetime(2013, 10, 31)
START = datetime.datetime(2013, 11, 1)
END = datetime.datetime(2014, 10, 31)
CarhartDailyTrain = CarhartDaily[(CarhartDaily.date >= START_TRAIN)
& (CarhartDaily.date < START)]
## Delete stock tickers without beta value
## We need to make sure all stocks exist on the whole testing period
tickers = StockBeta[np.isnan(StockBeta.cqaBeta) == False][['ticker']]
avaTickers = pd.merge(tickers,
