def on_stock_update(n_clicks,
symbol):
stock = stock_cache.get(symbol, Stock(symbol))
stock_projection_input_table_output = stock_projection_input_table_on_stock_update(stock)
stock_ratios_table_output = update_stock_ratios_table(stock)
stock_company_name_output = onsumbit_stock_company_name_label(stock)
return stock_ratios_table_output, stock_company_name_output
def update_stock_ratios_table(n_clicks, symbol):
print('update_stock_ratios_table')
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]) + [
html.Tr([html.Th("Error"), html.Td(e)]) for e in stock.error_log
]
def lambda_handler(event, context):
watch_list = get_watch_list()
for item in watch_list:
print(item['user_name'] + ' ' + item['company'])
stock = stock_cache.get(item['company'], Stock(item['company']))
watch_list_item = {
'projection_years':
stock.n_projection_years,
'excpected_eps':
round(stock.estimated_growth * 100., 2),
'target_yield':
round(stock.target_yield * 100., 2),
'target_p_e':
round(stock.target_pe, 1),
'expected_dividends':
round(stock.expected_dividends, 2),
'expected_dividends_growth':
round(stock.projected_dividends_growth * 100., 2),
#'projected_price': projected_price,
#'projected_total_dividends': projected_total_dividends,
#'target_price': target_price,
#'current_price': current_target_price,
'alert_price':
round(stock.target_price, 2)
}
try:
userInfo = UserInfo(item['user_name'])
if 'alert_price' in item and not pd.isna(
stock.current_price) and stock.current_price < float(
item['alert_price']):
if not 'last_mail_sent' in item or datetime.utcnow(
) - timedelta(days=14) > datetime.fromisoformat(
item['last_mail_sent']):
#datetime.datetime.now()-datetime.timedelta(seconds=20)
email = getUserEmail(userInfo, item['user_name'])
send_mail(email, item['company'], item['company_name'])
item['last_mail_sent'] = datetime.utcnow().isoformat()
#item['target_price'] = Decimal(str(round(stock.target_price,2)))
#item['projected_price'] = Decimal(str(round(stock.projected_price,2)))
if not pd.isna(stock.current_price):
item['current_price'] = Decimal(
str(round(stock.current_price, 2)))
new_epsTTM = Decimal(
str(
round(
stock.ratios["earnings-per-share-%s" %
stock.currency].iloc[-1], 2)))
if not pd.isna(new_epsTTM) and item['epsTTM'] != new_epsTTM:
email = getUserEmail(userInfo, item['user_name'])
send_change_eps_mail(email, item['company'],
item['company_name'], item['epsTTM'],
new_epsTTM)
item['epsTTM'] = new_epsTTM
userInfo.put_company_to_watch_list(item)
except Exception as e:
print(item)
print(e)
print("TaskScheduler")
return 200
def stock_clear_cache_on_click(_nclicks, symbol):
print(symbol + '- stock_clear_cache_on_click')
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 onsumbit_stock_company_name_label(n_clicks, symbol):
print('onsumbit_stock_company_name_label')
print(symbol)
stock = stock_cache.get(symbol, Stock(symbol))
#sprint(stock.company_name())
if symbol not in stock_cache:
stock_cache[symbol] = stock
return u'{name}'.format(name=stock.company_name)
def onsumbit_stock_rule1_summary_table(n_clicks, symbol):
print('onsumbit_stock_rule1_summary_table')
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 get_layout(company):
stock = stock_cache.get(company, Stock(company))
stock_company_name_output = onsumbit_stock_company_name_label_raw(stock)
ratios_df = stock.load_report_csv_to_original_df('ratios')
ratios_table = dbc.Table.from_dataframe(ratios_df)
balancesheet_df = stock.load_report_csv_to_original_df('balancesheet')
balancesheet_table = dbc.Table.from_dataframe(balancesheet_df)
cashflow_df = stock.load_report_csv_to_original_df('cashflow')
cashflow_table = dbc.Table.from_dataframe(cashflow_df)
income_df = stock.load_report_csv_to_original_df('income')
income_table = dbc.Table.from_dataframe(income_df)
layout = html.Div([
html.Div([
html.Div([
dcc.Input(
id='stock-symbol-input-raw', type="text", value=company),
html.Button(id='submit-button-raw',
children='Submit',
style={'backround': 'white'})
],
style={
'width': '49%',
'display': 'inline-block'
}),
html.Div([
html.Div(id='stock-company-name-label_raw',
children=stock_company_name_output,
style={'fontSize': 14})
],
style={
'width': '49%',
'display': 'inline-block',
}),
],
style={
'position': 'sticky',
'top': '90px',
'background': 'white'
}),
html.Div(id='fundamental-redirect-raw'),
html.H1("Income:"), income_table,
html.H1("Balancesheet:"), balancesheet_table,
html.H1("Cashflow:"), cashflow_table,
html.H1("Ratios:"), ratios_table
],
style={
'padding': '10px',
'margin-right': '10px'
})
return layout
def update_price_graph(n_clicks, symbol):
print('update_price_graph')
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 stock_projection_output_table_on_stock_update(
n_clicks,
# n_years,
# estimated_growth,
# target_yield,
# target_pe,
# expected_dividends,
#projected_dividends_growth,
symbol):
print('stock_projection_output_table_on_stock_update')
stock = stock_cache.get(symbol, Stock(symbol))
try:
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))
]))
except Exception as e:
print("Error" + str(e))
#raise
return []
return rows
def update_stock_ratios_summary_table(n_clicks, symbol):
print('update_stock_ratios_summary_table')
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',
'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], n_years=8)
def 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))
stock_projection_input_table_output = stock_projection_input_table_on_stock_update(
n_clicks, symbol)
stock_ratios_summary_table_output = update_stock_ratios_summary_table(
n_clicks, symbol)
stock_ratios_table_output = update_stock_ratios_table(n_clicks, symbol)
stock_company_name_output = onsumbit_stock_company_name_label(
n_clicks, symbol)
stock_rule1_summary_table_output = onsumbit_stock_rule1_summary_table(
n_clicks, symbol)
growth_rate_graph = update_growth_rate_graph(n_clicks, symbol)
price_graph_output = update_price_graph(n_clicks, symbol)
return stock_projection_input_table_output, stock_ratios_summary_table_output, stock_ratios_table_output, stock_company_name_output, stock_rule1_summary_table_output, growth_rate_graph, price_graph_output
def update_growth_rate_graph(n_clicks, symbol):
print('update_growth_rate_graph')
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:
try:
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'
}
}))
except:
pass
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 stock_projection_input_table_on_stock_update(n_clicks, symbol):
print('stock_projection_input_table_on_stock_update')
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)]))
try:
value = round(stock.estimated_growth * 100., 2)
except Exception as e:
print("Error:" + str(e))
raise
value = 0
eps_growth_input = dcc.Input(
id='stock-projection-table-eps-growth-rate-input',
value=value,
**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)]))
try:
value = round(stock.target_pe, 1)
except Exception as e:
print("Error" + str(e))
raise
value = 0
target_pe = dcc.Input(id='stock-projection-table-target-pe-input',
value=value,
**default_input_kwargs)
rows.append(html.Tr([html.Th("Target p/e"), html.Td(target_pe)]))
try:
value = round(stock.expected_dividends, 2)
except Exception as e:
print("Error:" + str(e))
raise
value = 0
expected_dividends = dcc.Input(
id='stock-projection-table-expected-dividends-input',
value=value,
**default_input_kwargs)
rows.append(
html.Tr([
html.Th("Expected dividends %s" % stock.currency),
html.Td(expected_dividends)
]))
try:
value = round(stock.projected_dividends_growth * 100., 2)
except Exception as e:
print("Error:" + str(e))
raise
value = 0
projected_dividends_growth = dcc.Input(
id='stock-projection-table-projected-dividends-growth-input',
value=value,
**default_input_kwargs)
rows.append(
html.Tr([
html.Th("Expected dividends growth %"),
html.Td(projected_dividends_growth)
]))
return rows