def calculateKeypoint(dog1, dog2, dog3, octaveNo):
stopJLoop = len(dog2[0])
stopILoop = len(dog2)
# Calculate terminating conditions
if len(dog2) % 3 == 0:
stopILoop = len(dog2)
else:
stopILoop = stopILoop - (len(dog2) % 3)
if len(dog2[0]) % 3 == 0:
stopJLoop = len(dog2[0])
else:
stopJLoop = stopJLoop - (len(dog2[0]) % 3)
# start calculating keypoints
detectedPoints = []
for imrow in range(3, len(dog1) - 3, 1):
# if imrow == stopILoop:
# return detectedPoints
for imcol in range(3, (len(dog1[0])) - 3, 1):
# if imcol == stopJLoop:
# imrow += 1
# imcol = 0
# continue
mid = dog2[imrow + 1][imcol + 1]
maxDOG2 = findMax(dog2, imrow, imcol)
minDOG2 = findMin(dog2, imrow, imcol)
if mid >= maxDOG2:
maxDOG1 = findMax(dog1, imrow, imcol)
maxDOG3 = findMax(dog3, imrow, imcol)
if mid > maxDOG1 and mid > maxDOG3:
detectedPoints.append([(2**octaveNo) * imrow + 1,
(2**octaveNo) * imcol + 1])
elif mid <= minDOG2:
minDOG1 = findMin(dog1, imrow, imcol)
minDOG3 = findMin(dog3, imrow, imcol)
if mid < minDOG1 and mid < minDOG3:
detectedPoints.append([(2**octaveNo) * imrow + 1,
(2**octaveNo) * imcol + 1])
return detectedPoints
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)
(StockMax, highMax, lowMax) = functions.findMax(StockReturns, START, tickers)
decay = 0.003
Coef = pd.read_csv(r'%s/Coef_%s_%s_%.4f.csv' %
(DataFolder, START_TRAIN.date(), END_TRAIN.date(), decay))
betas = Coef[['ticker', 'Mkt-RF']].merge(tickers).rename(columns={
'Mkt-RF': 'beta'
}).sort('beta')
maxes = StockMax[['ticker', 'MAX']].merge(tickers).sort('MAX')
N = betas.shape[0] / 10
def groupPerformance(StockPrices, StockReturns, SP500Prices, StockBeta, betas,
START):
factor = betas.columns.values[1]
betas = betas.sort(factor)
from scipy.optimize import minimize
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)
(StockMax, highMax, lowMax) = functions.findMax(StockReturns, START, tickers)
decay = 0.003
Coef = pd.read_csv(r'%s/Coef_%s_%s_%.4f.csv' % (DataFolder, START_TRAIN.date(), END_TRAIN.date(), decay))
betas = Coef[['ticker', 'Mkt-RF']].merge(tickers).rename(columns={'Mkt-RF': 'beta'}).sort('beta')
maxes = StockMax[['ticker', 'MAX']].merge(tickers).sort('MAX')
N = betas.shape[0]/10
def groupPerformance(StockPrices, StockReturns, SP500Prices, StockBeta, betas, START):
factor = betas.columns.values[1]
betas = betas.sort(factor)
pfValuesAll = pd.DataFrame()
for n in range(10):
wTickers = betas.head(N * (n+1)).tail(N)[['ticker']].reset_index(drop=True)
wTickers['weight'] = 1/np.float64(N)
(pfValues, pfRecord) = functions.testPortfolio(StockPrices, StockReturns, SP500Prices, StockBeta, wTickers, START)
print(stock)
list_price = price[:, i + 1]
# Change all the values to float
list_price = np.asfarray(list_price, float)
# Set the directory
directory = "Optimization/"
# Import Optimization Result
with open(directory + stock + ' optimization.csv') as f:
reader = csv.reader(f)
results = [rows for rows in reader]
results = results[1:optimization_count] # Remove headers
results = np.array(results)
results = np.asfarray(results, float) # Change all the values to float
annual_return = results[:, -1] # Last Column = Annual Return (%)
maximum_return = functions.findMax(annual_return)
# Find the index of maximum return
index = functions.findMaxIndex(annual_return)
# Technical Variables
RSI_period = int(results[:, 0][index])
RSI_Low = int(results[:, 1][index])
RSI_High = int(results[:, 2][index])
MACD_Fast = int(results[:, 3][index])
MACD_Slow = int(results[:, 4][index])
MACD_Sig = int(results[:, 5][index])
EMA_Slow = int(results[:, 6][index])
EMA_Fast = int(results[:, 7][index])
#market_timing(stock, list_price, RSI_period, MACD_Fast, MACD_Slow, EMA_Slow, RSI_Low, RSI_High, MACD_Sig, EMA_Fast)
#combined_signal(stock, 'weight 1', i)
#combined_signal(stock, 'weight 2', i)
#combined_signal(stock, 'weight optimized', i)
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)
BETA_BOUND = 0.5
MAX_POSITION = 0.05
(StockPrices, SP500Prices, CarhartDaily, StockBeta) = ReadData.ReadAll(DataFolder)
StockBeta = ReadData.ReadBeta(DataFolder)
StockReturns = ReadData.ReadReturn(DataFolder)
# tickers = ReadData.ReadTickers(DataFolder)
tickers = ReadData.GetTickers(StockPrices, StockBeta, DataFolder)
# Coef = ReadData.ReadCoef(START_TRAIN, END_TRAIN, DataFolder)
# mCoef = Coef[['Mkt-RF', 'SMB', 'HML', 'UMD']].astype(np.float64).as_matrix()
# alphas = Coef[['alphaFF', 'alphaCAPM', 'alphaSP500']].astype(np.float64).as_matrix()
(longTickers, shortTickers, wTickers) = functions.findMax(StockReturns, BUILD_DATE, tickers)
mCqaBeta = pd.merge(StockBeta, tickers).cqaBeta.astype(np.float64).as_matrix()
CAPITAL = 1e7
def intPart(x):
return math.modf(x)[1]
iniPrices = pd.merge(StockPrices[StockPrices.date == BUILD_DATE][['ticker', 'date', 'adj_close']],
tickers, on = 'ticker')
##tickers = iniPrices[['ticker']]
portfolio = pd.concat((tickers, iniPrices[['date']], wTickers[['weight']], iniPrices[['adj_close']]), axis = 1)
portfolio['nShare'] = ((portfolio['weight'] * CAPITAL / portfolio['adj_close']).map(intPart))
portfolio['value'] = portfolio['nShare'] * portfolio['adj_close']
## Recording the initial portfolio
iniPortfolio = portfolio
resCapital = CAPITAL - sum(portfolio['value'])
pfRecord = iniPortfolio
pfValues = pd.DataFrame()
for group in range(10):
pfValues = pfValues.append(pd.DataFrame([
{'date': startDate.date(),
'value': CAPITAL,
'MAX_group': group,
'MAX_mean': 0}
]))
lastValues = np.ones(10) * CAPITAL
# lastValues.fill(CAPITAL)
while True:
# print endDate.date()
StockPricesSample = StockPrices[(StockPrices['date'] >= backDate) &
(StockPrices['date'] <= endDate)]
(StockMax, highMax, lowMax) = functions.findMax(StockReturns, startDate, tickers)
# print 'backDate = %s' % backDate
# print 'startDate = %s' % startDate
print 'endDate = %s' % endDate
for group in range(10):
# print 'endDate: %s, group: %d' % (endDate, group)
tmpGroup = StockMax[StockMax['MAX_group'] == group]
MAX_mean = tmpGroup['MAX'].mean(axis=1)
tmpTickers = tmpGroup[['ticker']]
StockPricesSample2 = StockPricesSample.merge(tmpTickers)
tmpGroup = tmpGroup.merge(
StockPricesSample2[StockPricesSample2['date'] == startDate][['ticker', 'adj_close']]
).rename( columns={'adj_close': 'iniPrice'})
N = tmpGroup.shape[0]
# print 'N = %d' % N
# tmpGroup['nShare'] = CAPITAL / N / tmpGroup['iniPrice']
START_TRAIN = datetime.datetime(2010, 11, 1)
END_TRAIN = datetime.datetime(2013, 10, 31)
BETA_BOUND = 0.5
MAX_POSITION = 0.05
(StockPrices, SP500Prices, CarhartDaily,
StockBeta) = ReadData.ReadAll(DataFolder)
StockBeta = ReadData.ReadBeta(DataFolder)
StockReturns = ReadData.ReadReturn(DataFolder)
# tickers = ReadData.ReadTickers(DataFolder)
tickers = ReadData.GetTickers(StockPrices, StockBeta, DataFolder)
# Coef = ReadData.ReadCoef(START_TRAIN, END_TRAIN, DataFolder)
# mCoef = Coef[['Mkt-RF', 'SMB', 'HML', 'UMD']].astype(np.float64).as_matrix()
# alphas = Coef[['alphaFF', 'alphaCAPM', 'alphaSP500']].astype(np.float64).as_matrix()
(longTickers, shortTickers,
wTickers) = functions.findMax(StockReturns, BUILD_DATE, tickers)
mCqaBeta = pd.merge(StockBeta, tickers).cqaBeta.astype(np.float64).as_matrix()
CAPITAL = 1e7
def intPart(x):
return math.modf(x)[1]
iniPrices = pd.merge(StockPrices[StockPrices.date == BUILD_DATE][[
'ticker', 'date', 'adj_close'
]],
tickers,
on='ticker')
##tickers = iniPrices[['ticker']]
