def stock_clear_cache_on_click(_nclicks, symbol):
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
stock.clear_cached_files()
del stock_cache[symbol]
return 1
def stock_projection_input_table_on_stock_update(
n_clicks, n_years, estimated_growth, target_yield, target_pe,
expected_dividends, projected_dividends_growth, symbol):
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
stock.n_projection_years = int(n_years)
stock.estimated_growth = float(estimated_growth) / 100.
stock.target_yield = float(target_yield) / 100.
stock.target_pe = float(target_pe)
stock.expected_dividends = float(expected_dividends)
stock.projected_dividends_growth = float(projected_dividends_growth) / 100.
rows = []
rows.append(
html.Tr([
html.Th("Projected price"),
html.Td(round(stock.price_projection, 2))
]))
rows.append(
html.Tr([
html.Th("Projected Total Dividends"),
html.Td(round(stock.projected_dividend_earnings, 2))
]))
rows.append(
html.Tr(
[html.Th("Target Price"),
html.Td(round(stock.target_price, 2))]))
rows.append(
html.Tr(
[html.Th("Current Price"),
html.Td(round(stock.current_price, 2))]))
# rows.append(html.Tr([html.Th("dividend"), html.Td(stock.projected_dividends_growth)]))
return rows
def update_stock_ratios_table(n_clicks, symbol):
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
desired_keys = [
'earnings-per-share-%s' % stock.currency,
'dividends-%s' % stock.currency,
'book-value-per-share-%s' % stock.currency,
'free-cash-flow-per-share-%s' % stock.currency,
'operating-cash-flow-growth-yoy',
'free-cash-flow-growth-yoy',
'shares',
'operating-cash-flow-%s' % stock.currency,
'free-cash-flow-%s' % stock.currency,
'working-capital-%s' % stock.currency,
'net-income-%s' % stock.currency,
'revenue-%s' % stock.currency,
'return-on-invested-capital',
'return-on-equity',
'debt-equity',
'long-term-debt',
'short-term-debt',
'total-debt-%s' % stock.currency,
'debt-per-earnings',
'debt-per-bookvalue',
]
existing_keys = set(list(stock.ratios))
keys = [k for k in desired_keys if k in existing_keys]
return fundamentals_to_table(stock.ratios[keys])
def onsumbit_stock_rule1_summary_table(n_clicks, symbol):
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
return [
html.Tr([html.Th(s[0]), html.Td(s[1])])
for s in stock.get_summary_info()
]
def stock_projection_input_table_on_stock_update(n_clicks, symbol):
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
rows = []
default_input_kwargs = {
"type": "number",
"inputmode": "numeric",
}
n_years_input = dcc.Input(id='stock-projection-table-nyear-input',
value=stock.n_projection_years,
**default_input_kwargs)
rows.append(html.Tr([html.Th("Projection years"), html.Td(n_years_input)]))
eps_growth_input = dcc.Input(
id='stock-projection-table-eps-growth-rate-input',
value=round(stock.estimated_growth * 100., 2),
**default_input_kwargs)
rows.append(
html.Tr([html.Th("Expected EPS growth %"),
html.Td(eps_growth_input)]))
target_yield = dcc.Input(id='stock-projection-table-target-yield-input',
value=round(stock.target_yield * 100., 2),
**default_input_kwargs)
rows.append(html.Tr([html.Th("Target yield %"), html.Td(target_yield)]))
target_pe = dcc.Input(id='stock-projection-table-target-pe-input',
value=round(stock.target_pe, 1),
**default_input_kwargs)
rows.append(html.Tr([html.Th("Target p/e"), html.Td(target_pe)]))
expected_dividends = dcc.Input(
id='stock-projection-table-expected-dividends-input',
value=round(stock.expected_dividends, 2),
**default_input_kwargs)
rows.append(
html.Tr([
html.Th("Expected dividends %s" % stock.currency),
html.Td(expected_dividends)
]))
projected_dividends_growth = dcc.Input(
id='stock-projection-table-projected-dividends-growth-input',
value=round(stock.projected_dividends_growth * 100., 2),
**default_input_kwargs)
rows.append(
html.Tr([
html.Th("Expected dividends growth %"),
html.Td(projected_dividends_growth)
]))
return rows
def update_price_graph(n_clicks, symbol):
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
data = []
try:
data.append(
go.Scatter(
x=list(stock.historic_prices.index),
y=stock.historic_prices['Close'],
# text=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'],
mode='lines',
name="closing",
marker={
'size': 5,
'opacity': 0.5,
'line': {
'width': 0.5,
'color': 'white'
}
}))
except:
pass
return {
'data':
data,
'layout':
go.Layout(xaxis={
'title': "Date",
'type': 'date'
},
yaxis={
'title': "closing price (%s)" % stock.currency,
'type': 'linear'
},
margin={
'l': 40,
'b': 40,
't': 10,
'r': 40
},
hovermode='closest',
legend=dict(x=0.1,
y=1,
traceorder='normal',
font=dict(family='sans-serif',
size=12,
color='#000'),
bgcolor='rgba(0,0,0,0)')),
}
def main():
parser = argparse.ArgumentParser(
description='Create xls reports based on stock symbol')
parser.add_argument('stock_symbols', nargs="+")
args = parser.parse_args()
out = []
wb = xls.Workbook(write_only=True)
ws = wb.create_sheet("summary")
ws.append(Stock.get_summary_header_row())
for symbol in args.stock_symbols:
stock = Stock(symbol)
stock.write_xls_report()
stock.plot_growth_ratios()
ws.append(stock.get_summary_row())
print(list(stock.ratios.keys()))
print(stock.ratios['short-term-debt'])
def onadd_stock_company_name_label(n_clicks, symbol, selected_stocks):
symbols = []
if len(symbol.split(" ")) > 1:
symbols = symbol.split(" ")
if len(symbol.split(";")) > 1:
symbols = symbol.split(";")
if not symbols:
symbols = [symbol]
symbols = [s for s in symbols if symbol]
for symbol in symbols:
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
return dcc.Dropdown(id='screener-stock-selection',
options=get_available_stock_options(),
multi=True,
value=list(set(list(selected_stocks) + symbols)))
def update_stock_ratios_summary_table(n_clicks, symbol):
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
desired_keys = [
'book-value-per-share-%s' % stock.currency,
'earnings-per-share-%s' % stock.currency,
'revenue-per-share-%s' % stock.currency,
'operating-cashflow-per-share-%s' % stock.currency,
'shares',
'return-on-invested-capital',
'return-on-equity',
'long-term-debt',
'debt-equity',
]
existing_keys = set(list(stock.ratios))
keys = [k for k in desired_keys if k in existing_keys]
return fundamentals_to_table(stock.ratios[keys], n_years=8)
def update_2d_graph(symbols, xkey, ykey, sizekey):
data = []
stocks = [stock_cache.get(symbol, Stock(symbol)) for symbol in symbols]
if not stocks:
max_area = 1
elif len(stocks) > 1:
max_area = max(*[stock.ratios[sizekey].iloc[-1] for stock in stocks])
else:
max_area = stocks[0].ratios[sizekey].iloc[-1]
# max_area = 20000000
for stock in stocks:
if not xkey:
x = []
else:
x = [stock.ratios[xkey].iloc[-1]]
if not ykey:
y = []
else:
y = [stock.ratios[ykey].iloc[-1]]
data.append(
go.Scatter(
x=x,
y=y,
text=[stock.company_name],
mode='line',
name=stock.symbol,
marker={
'sizemode': 'area', #stock.ratios[sizekey].iloc[-1],
'sizeref': 2. * max_area / (40.**2),
'size': [stock.ratios[sizekey].iloc[-1]],
'sizemin': 4,
'opacity': 0.5,
'line': {
'width': 0.5,
'color': 'blue'
}
}))
return {
'data':
data,
'layout':
go.Layout(
xaxis={
'title': xkey,
'type': 'linear'
},
yaxis={
'title': ykey,
'type': 'linear'
},
margin={
'l': 40,
'b': 40,
't': 10,
'r': 40
},
hovermode='closest',
showlegend=False,
),
}
def onadd_stock_yaxis_options(symbols):
stocks = [stock_cache.get(symbol, Stock(symbol)) for symbol in symbols]
return get_ratio_axis_options()
def onadd_stock_xaxis_options(symbols):
print("updating x axis")
stocks = [stock_cache.get(symbol, Stock(symbol)) for symbol in symbols]
return get_ratio_axis_options()
def update_growth_rate_graph(n_clicks, symbol):
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
label_map = {
'earnings-ps-growth': "Earnings per share",
'book-value-ps-growth': "Book value per share",
'revenue-ps-growth': "Revenue per share",
'operating-cashflow-ps-growth': "Operating Cashflow per share"
}
growth_keys = [
'earnings-ps-growth', 'book-value-ps-growth', 'revenue-ps-growth',
'operating-cashflow-ps-growth'
]
data = []
for key in growth_keys:
data.append(
go.Scatter(
x=list(stock.ratios[key].index),
y=stock.ratios[key] * 100,
# text=dff[dff['Indicator Name'] == yaxis_column_name]['Country Name'],
mode='lines',
name=label_map[key],
marker={
'size': 15,
'opacity': 0.5,
'line': {
'width': 0.5,
'color': 'white'
}
}))
return {
'data':
data,
'layout':
go.Layout(xaxis={
'title': "Date",
'type': 'date'
},
yaxis={
'title': "relative change (%)",
'type': 'linear'
},
margin={
'l': 40,
'b': 40,
't': 10,
'r': 40
},
hovermode='closest',
legend=dict(x=0.1,
y=1,
traceorder='normal',
font=dict(family='sans-serif',
size=12,
color='#000'),
bgcolor='rgba(0,0,0,0)')),
}
def onsumbit_stock_company_name_label(n_clicks, symbol):
stock = stock_cache.get(symbol, Stock(symbol))
if symbol not in stock_cache:
stock_cache[symbol] = stock
return u'{name}'.format(name=stock.company_name)