def plot_universe(universe='SMX'):
rtn, rs, rm, v = channel.get_dataset(universe, max_spread=None)
t4 = stocks.load_google_returns(data_table=stocks.UK_ESTIMATES,
data_name='T4')
t52 = stocks.load_google_returns(data_table=stocks.UK_ESTIMATES,
data_name='T52')
t4 = t4.reindex(rtn.columns, axis=1)
t52 = t52.reindex(rtn.columns, axis=1)
make_channel_pdf(rtn[dt(2010, 1, 1):], universe, t4.iloc[-1], t52.iloc[-1])
def export_lse_spreads():
filename = get_filename('spread')
data = stocks.load_google_returns(data_name='Spread',
data_table=stocks.UK_STOCKS)
f = pd.ExcelWriter(filename)
data.to_excel(f)
f.save()
def load_lse_ids(missing=True):
u = stocks.load_uk_universe()
if missing:
existing = stocks.get_universe('LSE')
u2 = u.loc[~u.index.isin(existing.index)]
if u2.empty:
s = stocks.load_google_returns(data_name='Spread')
s = s.iloc[-1].loc[u.index]
u = u.loc[s.isnull()]
else:
u = u2
ans = pd.Series([])
i = 0
for ticker in u.index:
logger.info('Loading %s' % ticker)
tmp = load_lse_id(ticker)
if tmp is None:
i += 1
else:
i = 0
ans.loc[ticker] = tmp
if i >= 5:
break
if not ans.empty:
ans.name = 'LSE'
stocks._save_tickers(ans, 'LSE')
def export_stock_returns():
filename = get_filename('Returns')
f = pd.ExcelWriter(filename)
for data_type in ['Returns', 'Volume']:
data = stocks.load_google_returns(data_name=data_type,
data_table=stocks.UK_STOCKS)
data.to_excel(f, sheet_name=data_type)
f.save()
def export_reuters_data():
tags = ['Rating', 'C1', 'C3', 'T1', 'T4', 'T8', 'T52']
filename = get_filename('Reuters')
f = pd.ExcelWriter(filename)
for data_name in tags:
data = stocks.load_google_returns(data_name=data_name,
data_table=stocks.UK_ESTIMATES)
data.to_excel(f, sheet_name=data_name)
f.save()
def run_check():
t4 = stocks.load_google_returns(data_table=stocks.UK_ESTIMATES,
data_name='T4').iloc[-1]
t8 = stocks.load_google_returns(data_table=stocks.UK_ESTIMATES,
data_name='T8').iloc[-1]
t52 = stocks.load_google_returns(data_table=stocks.UK_ESTIMATES,
data_name='T52').iloc[-1]
rtn, rm, vol, _ = cross.get_dataset('SMX')
p = .2 / vol.iloc[-1]
table = get_week_table(rtn)
table2 = get_reversal_table(rm.loc[:, t4.reindex(rm.columns) > 0], t4, p)
table7 = get_reversal_table(rm.loc[:, t8.reindex(rm.columns) > 0], t8, p,
8)
table5 = get_momentum_table(rm.loc[:, t52.reindex(rm.columns) > 0], t4,
t52, p)
rtn, rm, vol, _ = cross.get_dataset('FTSE250')
p = .2 / vol.iloc[-1]
table3 = get_week_table(rtn)
table4 = get_reversal_table(rm.loc[:, t4.reindex(rm.columns) > 0], t4, p)
table8 = get_reversal_table(rm.loc[:, t8.reindex(rm.columns) > 0], t8, p,
8)
table6 = get_momentum_table(rm.loc[:, t52.reindex(rm.columns) > 0], t4,
t52, p)
mail = Email('*****@*****.**', ['*****@*****.**'],
'Market Watch')
mail.add_date(dt.today())
mail.add_text('SMX Week')
mail.add_table(table, width=300)
mail.add_text('SMX Reversal')
mail.add_table(table2, width=350)
mail.add_text('SMX 2M Reversal')
mail.add_table(table7, width=350)
mail.add_text('SMX Momentum')
mail.add_table(table5, width=400)
mail.add_text('FTSE250 Week')
mail.add_table(table3, width=300)
mail.add_text('FTSE250 Reversal')
mail.add_table(table4, width=350)
mail.add_text('FTSE250 2M Reversal')
mail.add_table(table8, width=350)
mail.add_text('FTSE250 Momentum')
mail.add_table(table6, width=400)
mail.send_email()
def load_stock_data(self):
logger.info('Loading stock returns')
r = stocks.load_google_returns(self.start_date - relativedelta(years=1), self.end_date, data_table=stocks.UK_STOCKS)
rx = stocks.load_google_returns(self.start_date - relativedelta(years=1), self.end_date, data_table=stocks.GLOBAL_ASSETS)
vm = stocks.load_google_returns(self.start_date - relativedelta(years=1), self.end_date, 'Volume', data_table=stocks.UK_STOCKS)
a = alpha.load_alpha(self.start_date - relativedelta(years=1), self.end_date, data_table=alpha.UK_ALPHA)
self.market_returns = rx.loc[:, 'MCX']
self.stock_returns = r.loc[:, r.columns.isin(self.u.index)]
self.asset_names = self.stock_returns.columns
self.stock_alpha = a.loc[:, a.columns.get_level_values(1).isin(self.u.index)]
alpha_returns = a.loc[:, a.columns.get_level_values(0) == 'Alpha']
alpha_returns = alpha_returns.groupby(level=1, axis=1).sum().loc[:, self.asset_names]
self.stock_volume = vm.loc[:, self.stock_returns.columns]
self._r = self.stock_returns.cumsum().ffill(limit=5).resample('W').last().diff()
self._rs = alpha_returns.cumsum().ffill(limit=5).resample('W').last().diff()
w = self.stock_returns.resample('W').sum().abs()
v = w[w > 0].rolling(52, min_periods=13).median().ffill().bfill()
self.stock_vol = tu.resample(v, self._r)
self.stock_vol[self.stock_vol < STOCK_VOL_FLOOR] = STOCK_VOL_FLOOR
self.r = self._r.divide(v)
self.rs = self._rs.divide(v)
self.rm = self.rs.subtract(self.rs.mean(axis=1), axis=0)
def get_universe_returns(universe, data_name='Returns'):
u = stocks.load_universe(universe)
r = stocks.load_google_returns(data_table=stocks.UK_STOCKS,
data_name=data_name,
tickers=u.index)
return r.reindex(u.index, axis=1)