def pfOptimizer(longTickers, shortTickers, Coef, StockBeta, BETA_BOUND, WEIGHT_BOUND):
longTickers["bWeight"] = 1 # binary weights
shortTickers["bWeight"] = -1
pfTickers = (
pd.concat((shortTickers[["ticker"]], longTickers[["ticker"]]), axis=0).sort("ticker").reset_index(drop=True)
)
# sens = Coef[['ticker', 'Mkt-RF', 'SMB', 'HML', 'UMD']].merge(pfTickers).rename(columns={'Mkt-RF': 'beta'})
# control = matrix([1, 0.2, 0.2, 0.5])
# scores = matrix(sens[['beta', 'SMB', 'HML', 'UMD']].as_matrix()) * control
betas = Coef[["ticker", "Mkt-RF"]].merge(pfTickers).rename(columns={"Mkt-RF": "beta"}).reset_index(drop=True)
mBeta = matrix(betas["beta"])
mCqaBeta = matrix(StockBeta.merge(pfTickers)["cqaBeta"])
longIndex = matrix(pfTickers.merge(longTickers, how="left").fillna(0)["bWeight"])
mLongIndex = np.diag(pfTickers.merge(longTickers, how="left").fillna(0)["bWeight"])
mLongIndex = matrix(mLongIndex[np.logical_or.reduce([np.sum(mLongIndex, 1) > 0.5])]).trans()
# mLongIndex = matrix(np.diag(tickers.merge(longTickers, how='left').fillna(0)['bWeight']))
shortIndex = -matrix(pfTickers.merge(shortTickers, how="left").fillna(0)["bWeight"])
mShortIndex = -np.diag(pfTickers.merge(shortTickers, how="left").fillna(0)["bWeight"])
mShortIndex = matrix(mShortIndex[np.logical_or.reduce([np.sum(mShortIndex, 1) > 0.5])]).trans()
# mShortIndex = matrix(np.diag(pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight']))
# wTickers = functions.iniWeights(pfTickers, shortTickers, longTickers) # initial weights
wStart = matrix(functions.iniWeights(pfTickers, longTickers, shortTickers)["weight"])
N = pfTickers.shape[0]
id = spmatrix(1.0, range(N), range(N))
wBounds = matrix(np.ones((N, 1)) * WEIGHT_BOUND)
longBounds = matrix(np.zeros((shortTickers.shape[0], 1)))
# longBounds = matrix(np.ones((shortTickers.shape[0], 1)) * 0.002)
shortBounds = matrix(np.zeros((longTickers.shape[0], 1)))
# shortBounds = matrix(np.ones((longTickers.shape[0], 1)) * (-0.005))
A = matrix(
[
[mCqaBeta],
[-mCqaBeta],
[longIndex],
[-longIndex],
[shortIndex],
[-shortIndex],
[id],
[-id],
[-mLongIndex],
[mShortIndex],
]
).trans()
b = matrix([BETA_BOUND, BETA_BOUND, 1, -0.98, -0.98, 1, wBounds, wBounds, longBounds, shortBounds])
# A = matrix([ [longIndex], [-longIndex],
# [shortIndex], [-shortIndex],
# [id], [-id],
# [-mLongIndex], [mShortIndex]]).trans()
# b = matrix([ 1, -0.98, -0.98, 1, wBounds, wBounds, longBounds, shortBounds])
# scores = mBeta
# sol = solvers.lp(-scores, A, b)
sol = solvers.lp(-mBeta, A, b)
w_res = sol["x"]
wTickers = pfTickers
wTickers["weight"] = w_res
return wTickers
def pfOptimizer_sector(longTickers, shortTickers,
Coef, StockBeta, industry_data,
BETA_BOUND, WEIGHT_BOUND, SECTOR_WEIGHT):
longTickers['bWeight'] = 1 # binary weights
shortTickers['bWeight'] = -1
pfTickers = pd.concat((shortTickers[['ticker']], longTickers[['ticker']]), axis=0).sort('ticker').reset_index(drop=True)
pfTickers = pfTickers.merge(industry_data[['ticker', 'sector']])
sector_list = pfTickers['sector'].unique()
betas = Coef[['ticker', 'Mkt-RF']].merge(pfTickers).rename(columns={'Mkt-RF': 'beta'}).reset_index(drop=True)
mBeta = matrix(betas['beta'])
mCqaBeta = matrix(StockBeta.merge(pfTickers)['cqaBeta'])
longIndex = matrix(pfTickers.merge(longTickers, how='left').fillna(0)['bWeight'])
mLongIndex = np.diag(pfTickers.merge(longTickers, how='left').fillna(0)['bWeight'])
mLongIndex = matrix(mLongIndex[np.logical_or.reduce([np.sum(mLongIndex,1) > 0.5])]).trans()
# mLongIndex = matrix(np.diag(tickers.merge(longTickers, how='left').fillna(0)['bWeight']))
shortIndex = -matrix(pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight'])
mShortIndex = -np.diag(pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight'])
mShortIndex = matrix(mShortIndex[np.logical_or.reduce([np.sum(mShortIndex,1) > 0.5])]).trans()
# mShortIndex = matrix(np.diag(pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight']))
sector_index = pfTickers[['ticker', 'sector']]
for sector in sector_list:
sector_index.loc[:,sector] = 0.0
sector_index.ix[sector_index['sector'] == sector, sector] = 1.0
mSector_index = matrix(sector_index.iloc[:, 2:].as_matrix())
# wTickers = functions.iniWeights(pfTickers, shortTickers, longTickers) # initial weights
wStart = matrix(functions.iniWeights(pfTickers, longTickers, shortTickers)['weight'])
N = pfTickers.shape[0]
id = spmatrix(1.0, range(N), range(N))
wBounds = matrix(np.ones((N,1)) * WEIGHT_BOUND)
longBounds = matrix(np.zeros((shortTickers.shape[0], 1)))
# longBounds = matrix(np.ones((shortTickers.shape[0], 1)) * 0.002)
shortBounds = matrix(np.zeros((longTickers.shape[0], 1)))
# shortBounds = matrix(np.ones((longTickers.shape[0], 1)) * (-0.005))
A = matrix([[mSector_index], [-mSector_index],
[mCqaBeta], [-mCqaBeta],
[longIndex], [-longIndex],
[shortIndex], [-shortIndex],
[id], [-id],
[-mLongIndex], [mShortIndex]]).trans()
b = matrix([SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT,
SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT,
SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT,
SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT,
BETA_BOUND, BETA_BOUND,
1, -0.98,
-0.98, 1,
wBounds, wBounds,
longBounds, shortBounds])
sol = solvers.lp(-mBeta, A, b)
w_res = sol['x']
print 'cqaBeta = %.4f' % np.float64(w_res.trans() * mCqaBeta)[0,0]
print 'beta = %.4f' % np.float64(w_res.trans() * mBeta)[0,0]
wTickers = pfTickers
wTickers['weight'] = w_res
return wTickers
def pfOptimizer(longTickers, shortTickers, Coef, StockBeta, BETA_BOUND,
WEIGHT_BOUND):
longTickers['bWeight'] = 1 # binary weights
shortTickers['bWeight'] = -1
pfTickers = pd.concat((shortTickers[['ticker']], longTickers[['ticker']]),
axis=0).sort('ticker').reset_index(drop=True)
# sens = Coef[['ticker', 'Mkt-RF', 'SMB', 'HML', 'UMD']].merge(pfTickers).rename(columns={'Mkt-RF': 'beta'})
# control = matrix([1, 0.2, 0.2, 0.5])
# scores = matrix(sens[['beta', 'SMB', 'HML', 'UMD']].as_matrix()) * control
betas = Coef[['ticker', 'Mkt-RF']].merge(pfTickers).rename(columns={
'Mkt-RF': 'beta'
}).reset_index(drop=True)
mBeta = matrix(betas['beta'])
mCqaBeta = matrix(StockBeta.merge(pfTickers)['cqaBeta'])
longIndex = matrix(
pfTickers.merge(longTickers, how='left').fillna(0)['bWeight'])
mLongIndex = np.diag(
pfTickers.merge(longTickers, how='left').fillna(0)['bWeight'])
mLongIndex = matrix(mLongIndex[np.logical_or.reduce(
[np.sum(mLongIndex, 1) > 0.5])]).trans()
# mLongIndex = matrix(np.diag(tickers.merge(longTickers, how='left').fillna(0)['bWeight']))
shortIndex = -matrix(
pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight'])
mShortIndex = -np.diag(
pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight'])
mShortIndex = matrix(mShortIndex[np.logical_or.reduce(
[np.sum(mShortIndex, 1) > 0.5])]).trans()
# mShortIndex = matrix(np.diag(pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight']))
# wTickers = functions.iniWeights(pfTickers, shortTickers, longTickers) # initial weights
wStart = matrix(
functions.iniWeights(pfTickers, longTickers, shortTickers)['weight'])
N = pfTickers.shape[0]
id = spmatrix(1.0, range(N), range(N))
wBounds = matrix(np.ones((N, 1)) * WEIGHT_BOUND)
longBounds = matrix(np.zeros((shortTickers.shape[0], 1)))
# longBounds = matrix(np.ones((shortTickers.shape[0], 1)) * 0.002)
shortBounds = matrix(np.zeros((longTickers.shape[0], 1)))
# shortBounds = matrix(np.ones((longTickers.shape[0], 1)) * (-0.005))
A = matrix([[mCqaBeta], [-mCqaBeta], [longIndex], [-longIndex],
[shortIndex], [-shortIndex], [id], [-id], [-mLongIndex],
[mShortIndex]]).trans()
b = matrix([
BETA_BOUND, BETA_BOUND, 1, -0.98, -0.98, 1, wBounds, wBounds,
longBounds, shortBounds
])
# A = matrix([ [longIndex], [-longIndex],
# [shortIndex], [-shortIndex],
# [id], [-id],
# [-mLongIndex], [mShortIndex]]).trans()
# b = matrix([ 1, -0.98, -0.98, 1, wBounds, wBounds, longBounds, shortBounds])
# scores = mBeta
# sol = solvers.lp(-scores, A, b)
sol = solvers.lp(-mBeta, A, b)
w_res = sol['x']
print 'cqaBeta = %.4f' % np.float64(w_res.trans() * mCqaBeta)[0, 0]
print 'beta = %.4f' % np.float64(w_res.trans() * mBeta)[0, 0]
wTickers = pfTickers
wTickers['weight'] = w_res
return wTickers
def pfOptimizer_sector(longTickers, shortTickers, Coef, StockBeta,
industry_data, BETA_BOUND, WEIGHT_BOUND, SECTOR_WEIGHT):
longTickers['bWeight'] = 1 # binary weights
shortTickers['bWeight'] = -1
pfTickers = pd.concat((shortTickers[['ticker']], longTickers[['ticker']]),
axis=0).sort('ticker').reset_index(drop=True)
pfTickers = pfTickers.merge(industry_data[['ticker', 'sector']])
sector_list = pfTickers['sector'].unique()
betas = Coef[['ticker', 'Mkt-RF']].merge(pfTickers).rename(columns={
'Mkt-RF': 'beta'
}).reset_index(drop=True)
mBeta = matrix(betas['beta'])
mCqaBeta = matrix(StockBeta.merge(pfTickers)['cqaBeta'])
longIndex = matrix(
pfTickers.merge(longTickers, how='left').fillna(0)['bWeight'])
mLongIndex = np.diag(
pfTickers.merge(longTickers, how='left').fillna(0)['bWeight'])
mLongIndex = matrix(mLongIndex[np.logical_or.reduce(
[np.sum(mLongIndex, 1) > 0.5])]).trans()
# mLongIndex = matrix(np.diag(tickers.merge(longTickers, how='left').fillna(0)['bWeight']))
shortIndex = -matrix(
pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight'])
mShortIndex = -np.diag(
pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight'])
mShortIndex = matrix(mShortIndex[np.logical_or.reduce(
[np.sum(mShortIndex, 1) > 0.5])]).trans()
# mShortIndex = matrix(np.diag(pfTickers.merge(shortTickers, how='left').fillna(0)['bWeight']))
sector_index = pfTickers[['ticker', 'sector']]
for sector in sector_list:
sector_index.loc[:, sector] = 0.0
sector_index.ix[sector_index['sector'] == sector, sector] = 1.0
mSector_index = matrix(sector_index.iloc[:, 2:].as_matrix())
# wTickers = functions.iniWeights(pfTickers, shortTickers, longTickers) # initial weights
wStart = matrix(
functions.iniWeights(pfTickers, longTickers, shortTickers)['weight'])
N = pfTickers.shape[0]
id = spmatrix(1.0, range(N), range(N))
wBounds = matrix(np.ones((N, 1)) * WEIGHT_BOUND)
longBounds = matrix(np.zeros((shortTickers.shape[0], 1)))
# longBounds = matrix(np.ones((shortTickers.shape[0], 1)) * 0.002)
shortBounds = matrix(np.zeros((longTickers.shape[0], 1)))
# shortBounds = matrix(np.ones((longTickers.shape[0], 1)) * (-0.005))
A = matrix([[mSector_index], [-mSector_index], [mCqaBeta], [-mCqaBeta],
[longIndex], [-longIndex], [shortIndex], [-shortIndex], [id],
[-id], [-mLongIndex], [mShortIndex]]).trans()
b = matrix([
SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT,
SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT,
SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT,
SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT, SECTOR_WEIGHT, BETA_BOUND,
BETA_BOUND, 1, -0.98, -0.98, 1, wBounds, wBounds, longBounds,
shortBounds
])
sol = solvers.lp(-mBeta, A, b)
w_res = sol['x']
print 'cqaBeta = %.4f' % np.float64(w_res.trans() * mCqaBeta)[0, 0]
print 'beta = %.4f' % np.float64(w_res.trans() * mBeta)[0, 0]
wTickers = pfTickers
wTickers['weight'] = w_res
return wTickers
