def action(file_list, ini_list):
for sector, portfolio_list in prep(*ini_list):
portfolio_list = pd.DataFrame(portfolio_list)
logging.debug(portfolio_list)
logging.info(sector)
ret = {}
for key, data in HELPER.partition(portfolio_list):
results = HELPER.transform(key, data)
logging.info(results)
key_list = sorted(results)
value_list = map(lambda x: results[x], key_list)
key_list = map(lambda x: "{}_{}".format(sector, x), key_list)
results = dict(zip(key_list, value_list))
ret.update(results)
yield sector, ret
def load(file_list, value_list):
ret = {}
for name, data in STOCK_TIMESERIES.read(file_list, value_list):
if len(data) < 7 * FINANCE.YEAR:
logging.info(
"{} of length {} rejected for being less than {}".format(
name, len(data), 7 * FINANCE.YEAR))
continue
data = MONTECARLO.find(data['Adj Close'], period=FINANCE.YEAR)
#filter stocks that have less than a year
sharpe = data.get('sharpe', 0)
if sharpe == 0:
continue
#filter stocks that have negative returns
returns = data.get('returns', 0)
if returns <= 0:
logging.info(
"{} of returns {} rejected for being unprofitable".format(
name, returns))
continue
key_list = data.keys()
value_list = map(lambda x: data[x], key_list)
value_list = map(lambda x: round(x, 2), value_list)
msg = dict(zip(key_list, value_list))
logging.info((name, msg))
ret[name] = data
return ret
def getSharpe(cls, stock_list, data):
ret = map(lambda d: SHARPE.find(data[d], period=FINANCE.YEAR),
stock_list)
ret = dict(zip(stock_list, ret))
ret = pd.DataFrame(ret).T
logging.info(ret.head(3))
logging.info(ret.tail(3))
return ret
def prototype(file_list, stock_list):
name_list, _ret = load(file_list, stock_list)
ret = FINANCE.graphDailyReturns(_ret)
value_list = map(lambda x: _ret[x], name_list)
value_list = map(lambda data: data.sort_index(inplace=True), value_list)
value_list = map(
lambda data: MONTECARLO.find(
data, risk_free_rate=0.02, period=FINANCE.YEAR, span=0),
value_list)
sharpe = dict(zip(name_list, value_list))
return name_list, ret, sharpe
def is_stock_invalid(cls, name, data) :
ret = MONTECARLO.find(data['Adj Close'], period=FINANCE.YEAR)
sharpe = ret.get('sharpe',0)
if sharpe <= 1.01 :
logging.info("{} of sharpe ratio {} rejected for being less stable than SPY".format(name,sharpe))
return True, None
returns = ret.get('returns',0)
if returns <= 0.04 :
logging.info("{} of returns {} rejected for being less profitable than bonds".format(name,returns))
return True, None
return False, ret
def process(file_list, value_list):
ret = {}
for name, data in STOCK_TIMESERIES.read(file_list, value_list):
data.sort_index(inplace=True)
data = data['Adj Close']
data = MONTECARLO.find(data,
risk_free_rate=0.02,
period=FINANCE.YEAR,
span=0)
# filter stocks that have less than a year
sharpe = data.get('sharpe', 0)
if sharpe == 0: continue
ret[name] = data
return ret
def load(file_list, value_list):
ret = {}
for name, data in STOCK_TIMESERIES.read(file_list, value_list):
data = MONTECARLO.find(data['Adj Close'], period=FINANCE.YEAR)
#filter stocks that have less than a year
sharpe = data.get('sharpe', 0)
if sharpe == 0: continue
#filter stocks that have negative returns
returns = data.get('returns', 0)
if returns <= 0: continue
key_list = data.keys()
value_list = map(lambda x: data[x], key_list)
value_list = map(lambda x: round(x, 2), value_list)
msg = dict(zip(key_list, value_list))
logging.info((name, msg))
ret[name] = data
return ret
def main(stock_list):
name, spy_data, file_list = prep()
annual = MONTECARLO.YEAR()
_ret, _dev, _sharpe, _length = annual.single(spy_data['Adj Close'])
logging.info("{} return {}, dev {}, sharpe {} length {}".format(
name, _ret, _dev, _sharpe, _length))
perf_list = init(*ini_list)
for name, data in action(perf_list):
ret, dev, sharpe, length = annual.single(data['Adj Close'])
flag_long = length > 250
flag_stable = dev < 3 * _dev
flag_profit = ret > _ret
flag_sharpe = sharpe > _sharpe
if flag_long and flag_stable and flag_profit and flag_sharpe:
logging.info("{} return {}, dev {}, sharpe {}, length {}".format(
name, ret, dev, sharpe, length))
del data
def demo_sharpe(data, path):
pt_list = map(lambda name: "legend_" + name, ticker_list)
pt_list = list(pt_list)
labels = dict(zip(ticker_list, pt_list))
labels['^GSPC'] = 'SNP500'
logging.info(type(data))
_data = map(
lambda stock: MONTECARLO.find(
data[stock], span=2 * FINANCE.YEAR, period=FINANCE.YEAR), data)
_data = list(_data)
data = dict(zip(ticker_list, _data))
data = pd.DataFrame(data, columns=ticker_list)
logging.debug(data)
POINT.plot(data,
labels=labels,
x='risk',
y='returns',
ylabel="Returns",
xlabel="Risk",
title="Sharpe Ratio",
alpha=0.3)
save(path, loc="lower right")