def _find(cls, data, stocks, num_portfolios, risk_free_rate, period):
data.sort_index(inplace=True)
returns = FINANCE.findDailyReturns(data)
#set up array to hold results
#We have increased the size of the array to hold the weight values for each stock
size = len(stocks)
ret = np.zeros((3 + size, num_portfolios))
#calculate mean daily return and covariance of daily returns
mean = returns.mean()
cov_matrix = returns.cov()
for weights, i in cls._weights(size, num_portfolios):
returns, risk, sharpe = cls._sharpe(cov_matrix, mean, period,
risk_free_rate, weights)
#store results in results array
ret[0, i] = returns
ret[1, i] = risk
ret[2, i] = sharpe
for j in range(len(weights)):
ret[j + 3, i] = weights[j]
#convert results array to Pandas DataFrame
columns = cls.columns + stocks
ret = pd.DataFrame(ret.T, columns=columns)
logging.info(ret.head(3))
logging.info(ret.tail(3))
return ret
def transformReturns(cls, returns) :
ret = FINANCE.findDailyReturns(returns)
mean = ret.mean()
cov_matrix = ret.cov()
#logging.info(cov_matrix)
#logging.info(ret)
#logging.info(mean)
return ret, mean, cov_matrix
def t01(cls, baseline, test):
baseline = baseline.reindex(test.index)
test = FINANCE.findDailyReturns(baseline)
ret = test / baseline
ret = ret.replace([np.inf, -np.inf], np.nan)
ret = ret.fillna(0)
ret = ret.dropna(how='all')
return ret
def test_04_(self) :
msg = {}
for stock, ret in T.extract() :
daily = HELPER.findDailyReturns(ret)
_risk, _returns = HELPER.findRiskAndReturn(daily, period=HELPER.YEAR)
value = [_returns,_risk,_returns/_risk]
value = dict(zip(['returns','risk','sharpe'],value))
msg[stock] = value
msg = pd.DataFrame(msg).T
logging.info(msg)
def main(file_list, portfolio_ini, ini_list):
local_enrich = enrich(*ini_list)
bench_list = benchmark(*ini_list)
SNP = 'SNP500'
snp = bench_list[SNP]
gcps = snp[0]
name_list, snp_returns, snp_sharpe = prototype(file_list, snp)
snp_returns.rename(columns={gcps: SNP}, inplace=True)
FUNDS = 'NASDAQMUTFUND'
funds = bench_list[FUNDS]
fund_name_list, fund_returns, fund_sharpe = prototype(file_list, funds)
portfolio_list = prep(*portfolio_ini)
portfolio_list = pd.DataFrame(portfolio_list.values())
portfolio_list = portfolio_list.sort_values(['risk'])
logging.info(portfolio_list)
ret_detail_list = []
ret_name_return_list = []
ret_summary_list = {}
ret_diversified_list = []
ret_name_diversified_list = []
ret_sharpe_list = {}
for weights, sharpe, diversified, name_diversified, name_returns in find(
local_enrich, portfolio_list):
stock_list = sorted(weights.index)
name_list, ret = load(file_list, stock_list)
ret = FINANCE.findDailyReturns(ret)
logging.info(weights)
logging.info(stock_list)
logging.info(sharpe)
portfolio_return = weights.dot(ret.T).dropna(how="all")
ret[name_returns] = portfolio_return
ret = 1 + ret
ret.iloc[0] = 1 # set first day pseudo-price
ret = ret.cumprod()
print ret.head(2)
print ret.tail(2)
print name_diversified, name_returns
ret_detail_list.append(ret)
ret_summary_list[name_returns] = ret[name_returns]
ret_sharpe_list[name_returns] = sharpe
ret_diversified_list.append(diversified)
ret_name_return_list.append(name_returns)
ret_name_diversified_list.append(name_diversified)
ret_summary_list[SNP] = snp_returns
ret_sharpe_list[SNP] = snp_sharpe[gcps]
for name in fund_name_list:
ret_summary_list[name] = fund_returns[name]
ret_sharpe_list[name] = fund_sharpe[name]
return ret_detail_list, ret_name_return_list, ret_summary_list, ret_diversified_list, ret_name_diversified_list, ret_sharpe_list
def by_month(cls, name, data):
year_to_year = {}
many_years = {}
for month, year, dataSeries in cls.group_by_month(data):
percent = FINANCE.findDailyReturns(dataSeries)
if len(percent) == 0: continue
if month not in year_to_year:
year_to_year[month] = pd.DataFrame(percent)
else:
init = len(year_to_year[month])
year_to_year[month] = year_to_year[month].append(percent)
if init == len(year_to_year[month]):
raise ValueError("dataframe did not append")
label = '{},{}-{}'.format(month, year, name)
many_years[label] = cls.many_years(month, year, percent)
return year_to_year, many_years
def find(cls, data, **kwargs):
target = "period"
period = kwargs.get(target, 0)
target = "risk_free_rate"
risk_free_rate = kwargs.get(target, 0.02)
target = "span"
span = kwargs.get(target, 2 * FINANCE.YEAR)
data, risk_free_rate, period, span = cls.validate(
data, risk_free_rate, period, span)
data = FINANCE.findDailyReturns(data, period)
if data is None:
ret = dict(zip(cls.key_list, [0, 0, 0, 0]))
return ret
risk, returns = FINANCE.findRiskAndReturn(data, span)
sharpe = 0
if risk != 0:
sharpe = (returns - risk_free_rate) / risk
ret = dict(zip(cls.key_list, [returns, risk, sharpe, len(data)]))
return ret
def summarizeReturns(cls, data):
stock_list = cls.parseStockList(data)
ret = map(lambda stock: FINANCE.findDailyReturns(data[stock]),
stock_list)
ret = dict(zip(stock_list, ret))
return ret
def test_01_(self) :
for stock, ret in T.extract() :
logging.info (stock)
logging.info (ret.describe().round(2))
daily = HELPER.findDailyReturns(ret)
logging.info (daily.describe().round(2))
