def run_optimization(self, end_date, start_date, mu_ml):
rebalance_date = (datetime.strptime(end_date, "%Y-%m-%d") +
relativedelta(months=6, days=1)).strftime("%Y-%m-%d")
rebalance_date = datetime.strftime(
pd.bdate_range(end_date, rebalance_date)[-1], "%Y-%m-%d")
prices = get_data(self.tickers,
'adjClose',
start_date,
end_date,
save=False)
factors = fama_french(start_date, end_date, save=False)
returns = (prices / prices.shift(1) - 1).dropna()[:len(factors)]
R = returns.values
## *********************************************************************************************************************
# factor model
## *********************************************************************************************************************
factors.drop('RF', axis=1, inplace=True)
F = np.hstack((np.atleast_2d(np.ones(factors.shape[0])).T, factors))
transmat, loadings, covarainces = regime_switch(R, F, self.tickers)
baseline = 30
regime = current_regime(R, F, loadings, baseline)
# get the number of days until the next scheduled rebalance
days = business_days((datetime.strptime(end_date, "%Y-%m-%d") +
relativedelta(days=1)).strftime("%Y-%m-%d"),
rebalance_date)
# get the estimate returns and covariances from the factor model
mu_rsfm = pd.DataFrame(days *
expected_returns(F, transmat, loadings, regime),
index=self.tickers,
columns=['returns'])
cov_rsfm = pd.DataFrame(
days * covariance(R, F, transmat, loadings, covarainces, regime),
index=self.tickers,
columns=self.tickers)
# write estimates to a csv file
mu_rsfm.to_csv(os.getcwd() + r'/data/mu_rsfm.csv')
cov_rsfm.to_csv(os.getcwd() + r'/data/cov_rsfm.csv')
mktcap = get_mkt_cap(self.tickers, save=True)
# calculate the market coefficient
l = (gmean(factors.iloc[-days:, :]['MKT'] + 1, axis=0) -
1) / factors.iloc[-days:, :]['MKT'].var()
mu_bl1, cov_bl1 = bl(tickers=self.tickers,
l=l,
tau=1,
mktcap=mktcap,
Sigma=returns.iloc[-days:, :].cov().values * days,
P=np.identity(len(self.tickers)),
Omega=np.diag(np.diag(cov_rsfm)),
q=mu_rsfm.values,
adjust=False)
#mu_ml = mu_bl1.mul(pd.DataFrame(1 + np.random.uniform(-0.05, 0.1, len(tickers)), index=mu_bl1.index, columns=mu_bl1.columns))
mu_ml = pd.DataFrame(mu_ml, columns=['returns'])
mu_bl2, cov_bl2 = bl(tickers=self.tickers,
l=l,
tau=1,
mktcap=mktcap,
Sigma=returns.iloc[-days:, :].cov().values * days,
P=np.identity(len(self.tickers)),
Omega=np.diag(np.diag(cov_rsfm)),
q=mu_ml.values,
adjust=True)
cost = costs(
tickers=self.tickers,
cov=cov_rsfm,
prices=prices.iloc[-2, :] if
prices.iloc[-1, :].isnull().values.any() else prices.iloc[-1, :],
start_date=(datetime.strptime(end_date, "%Y-%m-%d") -
relativedelta(years=1)).strftime("%Y-%m-%d"),
end_date=end_date,
alpha=5)
risk_tolerance = [((1, 10), (0, 0.10)), ((5, 5), (0, 0.20)),
((10, 1), (-0.05, 0.30))]
soln = optimize(mu=(mu_bl1.values.ravel(), mu_bl2.values.ravel()),
sigma=(cov_bl1.values, cov_bl2.values),
alpha=(0.05, 0.10),
return_target=(0.05, 0.05),
costs=cost,
prices=prices.iloc[-2, :].values
if prices.iloc[-1, :].isnull().values.any() else
prices.iloc[-1, :].values,
gamma=risk_tolerance[2])
x1 = pd.DataFrame(soln.x[:int(len(mu_bl1))],
index=mu_bl1.index,
columns=['weight'])
x2 = pd.DataFrame(soln.x[int(len(mu_bl2)):],
index=mu_bl2.index,
columns=['weight'])
print(
'\n\n********************************************************************'
)
print('\tperiod one results')
print(
'********************************************************************\n'
)
#print(x1)
print("\nportfolio return: %f" % (ret(mu_bl1, x1) * 100))
print("portfolio volatility: %f" % (vol(cov_bl1, x1) * 100))
print("portfolio var%f: %f" %
(1 - 0.05, var(mu_bl1, cov_bl1, 0.05, x1)))
print("portfolio cvar%f: %f" %
(1 - 0.05, cvar(mu_bl1, cov_bl1, 0.05, x1)))
print(
'\n\n********************************************************************'
)
print('\tperiod two results')
print(
'********************************************************************\n'
)
#print(x2)
print("\nportfolio return: %f" % (ret(mu_bl2, x1) * 100))
print("portfolio volatility: %f" % (vol(cov_bl2, x1) * 100))
print("portfolio var%f: %f" %
(1 - 0.05, var(mu_bl2, cov_bl2, 0.05, x1)))
print("portfolio cvar%f: %f" %
(1 - 0.05, cvar(mu_bl2, cov_bl2, 0.05, x1)))
return (ret(mu_bl2, x1) * 100)
print('\n\ntransition matrix')
print(transmat)
for i in range(2):
print('\n\nregime %d' % (i + 1))
print('\nfactor loadings matrix')
print(loadings[i])
print("\ncovariance matrix")
print(covarainces[i])
# see what regime best fits the most recent data (# days = baseline)
baseline = 30
regime = current_regime(R, F, loadings, baseline)
print('\nbased on the last %d trading days, the best fitted regime is %d' % (baseline, regime))
## *********************************************************************************************************************
# expeceted mean and variances from the factor model
## *********************************************************************************************************************
print('\n\n********************************************************************')
print('\tcalculating period one estimates')
print('********************************************************************')
# get the number of days until the next scheduled rebalance
days = business_days((datetime.strptime(end_date, "%Y-%m-%d") + relativedelta(days=1)).strftime("%Y-%m-%d"), rebalance_date)
# get the estimate returns and covariances from the factor model
def prepare():
#tickers = list(pd.read_csv(os.getcwd() + r'/data/tickers.csv')['Tickers'])
tickers = SYMBOLS
end_date = datetime.now().strftime("%Y-%m-%d")
start_date = (datetime.strptime(end_date, "%Y-%m-%d") -
relativedelta(years=6)).strftime("%Y-%m-%d")
# target rebalance date ... based on calendar days ... need to adjust for trading days
rebalance_date = (datetime.strptime(end_date, "%Y-%m-%d") +
relativedelta(months=6, days=1)).strftime("%Y-%m-%d")
rebalance_date = datetime.strftime(
pd.bdate_range(end_date, rebalance_date)[-1], "%Y-%m-%d")
np.set_printoptions(precision=4)
## *********************************************************************************************************************
# pull the factors and asset prices
## *********************************************************************************************************************
print(
'\n\n********************************************************************'
)
print('\tretrieving asset prices')
print(
'********************************************************************')
prices = get_data(tickers,
'adjClose',
start_date,
end_date,
save=False,
fail_safe=True)
print(prices.tail(10))
print(
'\n\n********************************************************************'
)
print('\tretrieving factor data')
print(
'********************************************************************')
factors = fama_french(start_date, end_date, save=True)
print(factors.tail(10))
print(
'\n\n********************************************************************'
)
print('risk adjusted returns')
print(
'********************************************************************')
returns = (prices / prices.shift(1) - 1).dropna()[:len(factors)]
R = returns.values
print(
pd.DataFrame(R, index=factors.index, columns=prices.columns).tail(10))
## *********************************************************************************************************************
# factor model
## *********************************************************************************************************************
factors.drop('RF', axis=1, inplace=True)
F = np.hstack((np.atleast_2d(np.ones(factors.shape[0])).T, factors))
print(
'\n\n********************************************************************'
)
print('\tfitting the factor model')
print(
'********************************************************************')
transmat, loadings, covarainces = regime_switch(R, F, tickers)
print('\n\ntransition matrix')
print(transmat)
for i in range(2):
print('\n\nregime %d' % (i + 1))
print('\nfactor loadings matrix')
print(loadings[i])
print("\ncovariance matrix")
print(covarainces[i])
# see what regime best fits the most recent data (# days = baseline)
baseline = 30
regime = current_regime(R, F, loadings, baseline)
print('\nbased on the last %d trading days, the best fitted regime is %d' %
(baseline, regime))
## *********************************************************************************************************************
# expeceted mean and variances from the factor model
## *********************************************************************************************************************
print(
'\n\n********************************************************************'
)
print('\tcalculating period one estimates')
print(
'********************************************************************')
# get the number of days until the next scheduled rebalance
days = business_days((datetime.strptime(end_date, "%Y-%m-%d") +
relativedelta(days=1)).strftime("%Y-%m-%d"),
rebalance_date)
# get the estimate returns and covariances from the factor model
mu_rsfm = pd.DataFrame(days *
expected_returns(F, transmat, loadings, regime),
index=tickers,
columns=['returns'])
cov_rsfm = pd.DataFrame(
days * covariance(R, F, transmat, loadings, covarainces, regime),
index=tickers,
columns=tickers)
# write estimates to a csv file
#mu_rsfm.to_csv(os.getcwd() + r'/data/mu_rsfm.csv')
#cov_rsfm.to_csv(os.getcwd() + r'/data/cov_rsfm.csv')
print('\nexpected returns from the factor model')
print(mu_rsfm)
print('\nexpected covariance from the factor model')
print(cov_rsfm)
## *********************************************************************************************************************
# black litterman for period one returns
## *********************************************************************************************************************
mktcap = get_mkt_cap(tickers, save=True)
print("\nmarket cap data")
print(mktcap)
# calculate the market coefficient
l = (gmean(factors.iloc[-days:, :]['MKT'] + 1, axis=0) -
1) / factors.iloc[-days:, :]['MKT'].var()
mu_bl1, cov_bl1 = bl(tickers=tickers,
l=l,
tau=1,
mktcap=mktcap,
Sigma=returns.iloc[-days:, :].cov().values * days,
P=np.identity(len(tickers)),
Omega=np.diag(np.diag(cov_rsfm)),
q=mu_rsfm.values,
adjust=False)
print('\nperiod one returns')
print(mu_bl1)
print('\nperiod one covariances')
print(cov_bl1)
## *********************************************************************************************************************
# AIDAN ML ... pass along a new returns dataframe, mu_ml, with the same format as mu_rsfm
## *********************************************************************************************************************
print(
'\n\n********************************************************************'
)
print('\tcalculating period two estimates')
print(
'********************************************************************')
# temp mu_ml
#mu_ml = mu_bl1.mul(
# pd.DataFrame(1 + np.random.uniform(-0.05, 0.1, len(tickers)), index=mu_bl1.index, columns=mu_bl1.columns))
df = pd.DataFrame()
df['prices'] = prices.apply(lambda x: ','.join(x.astype(str)), axis=1)
df['prices'] = df.prices.apply(lambda x: [float(y) for y in x.split(',')])
#df.prices = df.prices.apply(lambda x: ast.literal_eval(x))
mu_ml = predict(torch.FloatTensor(
df['prices'].iloc[-1:].values.tolist()).transpose(0, 1),
check_ml=mu_bl1,
tickers=tickers)
## *********************************************************************************************************************
# black litterman for period two returns
## *********************************************************************************************************************
mu_bl2, cov_bl2 = bl(tickers=tickers,
l=l,
tau=1,
mktcap=mktcap,
Sigma=returns.iloc[-days:, :].cov().values * days,
P=np.identity(len(tickers)),
Omega=np.diag(np.diag(cov_rsfm)),
q=mu_ml.values,
adjust=True)
print('\nperiod two returns')
print(mu_bl2)
print('\nperiod two covariances')
print(cov_bl2)
## *********************************************************************************************************************
# calculate transaction cost coefficients
## *********************************************************************************************************************
print(
'\n\n********************************************************************'
)
print('\tcalculating cost coefficients')
print(
'********************************************************************')
cost = costs(
tickers=tickers,
cov=cov_rsfm,
prices=prices.iloc[-2, :]
if prices.iloc[-1, :].isnull().values.any() else prices.iloc[-1, :],
start_date=(datetime.strptime(end_date, "%Y-%m-%d") -
relativedelta(years=1)).strftime("%Y-%m-%d"),
end_date=end_date,
alpha=5)
print('\ncost coefficients')
print(cost)
return (mu_bl1.values.ravel(), mu_bl2.values.ravel()), (
cov_bl1.values,
cov_bl2.values), cost, prices.iloc[-2, :].values if prices.iloc[
-1, :].isnull().values.any() else prices.iloc[-1, :].values
def get_params_for_optimization():
tickers = SYMBOLS
end_date = datetime.now().strftime("%Y-%m-%d")
start_date = (datetime.strptime(end_date, "%Y-%m-%d") -
relativedelta(years=10)).strftime("%Y-%m-%d")
# target rebalance date ... based on calendar days ... need to adjust for trading days
rebalance_date = (datetime.strptime(end_date, "%Y-%m-%d") +
relativedelta(months=6, days=1)).strftime("%Y-%m-%d")
rebalance_date = datetime.strftime(
pd.bdate_range(end_date, rebalance_date)[-1], "%Y-%m-%d")
np.set_printoptions(precision=4)
## *********************************************************************************************************************
# pull the factors and asset prices
## *********************************************************************************************************************
print(
'\n\n********************************************************************'
)
print('\tretrieving asset prices')
print(
'********************************************************************')
prices = get_data(tickers, 'adjClose', start_date, end_date,
save=True).dropna() # Stored
print(prices.tail(10))
print(
'\n\n********************************************************************'
)
print('\tretrieving factor data')
print(
'********************************************************************')
factors = fama_french(start_date, end_date, save=True)
print(factors.tail(10))
print(
'\n\n********************************************************************'
)
print('risk adjusted returns')
print(
'********************************************************************')
returns = (prices / prices.shift(1) - 1).dropna()[:len(factors)] # Stored
R = returns.values # Stored
print(
pd.DataFrame(R, index=factors.index, columns=prices.columns).tail(10))
## *********************************************************************************************************************
# factor model
## *********************************************************************************************************************
factors.drop('RF', axis=1, inplace=True)
factors = factors[:len(returns)]
F = np.hstack((np.atleast_2d(np.ones(factors.shape[0])).T, factors))
print(
'\n\n********************************************************************'
)
print('\tfitting the factor model')
print(
'********************************************************************')
transmat, loadings, covarainces = regime_switch(R, F, tickers) # Stored
# transmat - dataframe?
# loadings - np array
# cov
print('\n\ntransition matrix')
print(transmat)
for i in range(2):
print('\n\nregime %d' % (i + 1))
print('\nfactor loadings matrix')
print(loadings[i])
print("\ncovariance matrix")
print(covarainces[i])
# see what regime best fits the most recent data (# days = baseline)
baseline = 30
regime = current_regime(R, F, loadings, baseline)
print('\nbased on the last %d trading days, the best fitted regime is %d' %
(baseline, regime))
## *********************************************************************************************************************
# expeceted mean and variances from the factor model
## *********************************************************************************************************************
print(
'\n\n********************************************************************'
)
print('\tcalculating period one estimates')
print(
'********************************************************************')
# get the number of days until the next scheduled rebalance
days = business_days((datetime.strptime(end_date, "%Y-%m-%d") +
relativedelta(days=1)).strftime("%Y-%m-%d"),
rebalance_date)
# get the estimate returns and covariances from the factor model
mu_rsfm = pd.DataFrame(days *
expected_returns(F, transmat, loadings, regime),
index=tickers,
columns=['returns'])
cov_rsfm = pd.DataFrame(
days * covariance(R, F, transmat, loadings, covarainces, regime),
index=tickers,
columns=tickers)
# write estimates to a csv file
# mu_rsfm.to_csv(os.getcwd() + BASEPATH + r'/data/mu_rsfm.csv')
# cov_rsfm.to_csv(os.getcwd() +BASEPATH + r'/data/cov_rsfm.csv')
print('\nexpected returns from the factor model')
print(mu_rsfm)
print('\nexpected covariance from the factor model')
print(cov_rsfm)
## *********************************************************************************************************************
# black litterman for period one returns
## *********************************************************************************************************************
mktcap = get_mkt_cap(tickers, save=True) # Stored Daily
print("\nmarket cap data")
print(mktcap)
# calculate the market coefficient
l = (gmean(factors.iloc[-days:, :]['MKT'] + 1, axis=0) -
1) / factors.iloc[-days:, :]['MKT'].var() # Stored
mu_bl1, cov_bl1 = bl(tickers=tickers,
l=l,
tau=1,
mktcap=mktcap,
Sigma=returns.iloc[-days:, :].cov().values * days,
P=np.identity(len(tickers)),
Omega=np.diag(np.diag(cov_rsfm)),
q=mu_rsfm.values,
adjust=False) # Stored
print('\nperiod one returns')
print(mu_bl1)
print('\nperiod one covariances')
print(cov_bl1)
## *********************************************************************************************************************
# AIDAN ML ... pass along a new returns dataframe, mu_ml, with the same format as mu_rsfm
## *********************************************************************************************************************
print(
'\n\n********************************************************************'
)
print('\tcalculating period two estimates')
print(
'********************************************************************')
# temp mu_ml
mu_ml = mu_bl1.mul(
pd.DataFrame(1 + np.random.uniform(-0.05, 0.1, len(tickers)),
index=mu_bl1.index,
columns=mu_bl1.columns)) # Stored
## *********************************************************************************************************************
# black litterman for period two returns
## *********************************************************************************************************************
mu_bl2, cov_bl2 = bl(tickers=tickers,
l=l,
tau=1,
mktcap=mktcap,
Sigma=returns.iloc[-days:, :].cov().values * days,
P=np.identity(len(tickers)),
Omega=np.diag(np.diag(cov_rsfm)),
q=mu_ml.values,
adjust=True) # Stored inputs to optimization
print('\nperiod two returns')
print(mu_bl2)
print('\nperiod two covariances')
print(cov_bl2)
## *********************************************************************************************************************
# calculate transaction cost coefficients
## *********************************************************************************************************************
print(
'\n\n********************************************************************'
)
print('\tcalculating cost coefficients')
print(
'********************************************************************')
cost = costs(
tickers=tickers,
cov=cov_rsfm,
prices=prices.iloc[-2, :]
if prices.iloc[-1, :].isnull().values.any() else prices.iloc[-1, :],
start_date=(datetime.strptime(end_date, "%Y-%m-%d") -
relativedelta(years=1)).strftime("%Y-%m-%d"),
end_date=end_date,
alpha=5) # Stored
print('\ncost coefficients')
print(cost)
return {
'mu_bl1': mu_bl1,
'mu_bl2': mu_bl2,
'cov_bl1': cov_bl1,
'cov_bl2': cov_bl2,
'cost': cost,
'prices': prices
}