def on_button_click(n):
if n is not None:
positions = Account_Positions()
df_puts = positions.get_put_positions()
df_calls = positions.get_call_positions()
df_stocks = positions.get_stock_positions()
cash_required = formatter_currency(df_puts["COST"].sum())
calls_dt = (dash_tabulator.DashTabulator(
id="call-table",
columns=[{
"id": i,
"title": i,
"field": i
} for i in df_calls.columns],
data=df_calls.to_dict("records"),
), )
stocks_dt = (dash_tabulator.DashTabulator(
id="put-table",
columns=[{
"id": i,
"title": i,
"field": i
} for i in df_stocks.columns],
data=df_stocks.to_dict("records"),
), )
puts_dt = (dash_tabulator.DashTabulator(
id="put-table",
columns=[{
"id": i,
"title": i,
"field": i
} for i in df_puts.columns],
data=df_puts.to_dict("records"),
), )
return (
puts_dt,
calls_dt,
stocks_dt,
True,
f" Cash Required : {cash_required}",
)
else:
return None, None, None, None, None
def on_button_click(n, start_date, end_date, ticker, instrument_type, tran_type, is_group):
if n is None:
return None, False, ""
else:
df = retrive_transactions(start_date, end_date, ticker, instrument_type, tran_type)
logging.info("instrument_type is %s ", instrument_type)
if not df.empty:
sumText = 'Grand Total = "{}"'.format(round(df["TOTAL_PRICE"].sum(), 2))
options = {"selectable": 1}
# Padd groupBy option to the Tabulator component to group at Ticker level
if is_group:
options["groupBy"] = "TICKER"
columns = [
{"title": "DATE", "field": "DATE"},
{"title": "TOTAL PRICE", "field": "TOTAL_PRICE", "topCalc":"sum", "topCalcParams":{"precision":2,}},
{"title": "SYMBOL", "field": "SYMBOL"},
{"title": "TRAN TYPE", "field": "TRAN_TYPE"},
{"title": "POSITION", "field": "POSITION"},
{"title": "QTY", "field": "QTY"},
{"title": "PRICE", "field": "PRICE"},
{"title": "TICKER", "field": "TICKER"},
{"title": "TYPE", "field": "TYPE"},
{"title": "OPTION TYPE", "field": "OPTION_TYPE"},
]
dt = (
dash_tabulator.DashTabulator(
id="screener-table",
columns=columns,
data=df.to_dict("records"),
options=options,
),
)
return dt, True, sumText
else:
return None, True, "No Records found"
{"id":2, "name":"Mary May", "age":"1", "col":"blue", "dob":"14/05/1982"},
{"id":3, "name":"Christine Lobowski", "age":"42", "col":"green", "dob":"22/05/1982"},
{"id":4, "name":"Brendon Philips", "age":"125", "col":"orange", "dob":"01/08/1980"},
{"id":5, "name":"Margret Marmajuke", "age":"16", "col":"yellow", "dob":"31/01/1999"},
{"id":6, "name":"Fred Savage", "age":"16", "col":"yellow", "rating":"1", "dob":"31/01/1999"},
{"id":7, "name":"Brie Larson", "age":"30", "col":"blue", "rating":"1", "dob":"31/01/1999"},
]
options = { "groupBy": "col", "selectable":1}
downloadButtonType = {"css": "btn btn-primary", "text":"Export", "type":"xlsx"}
app.layout = html.Div([
dash_tabulator.DashTabulator(
id='input',
columns=[],
data=[],
options=options,
downloadButtonType=downloadButtonType,
),
html.Div(id='output'),
dcc.Interval(
id='interval-component-iu',
interval=1*10, # in milliseconds
n_intervals=0,
max_intervals=0
)
])
@app.callback([ Output('input', 'columns'),
Output('input', 'data')],
downloadButtonType = {
"css": "btn btn-primary",
"text": "Export",
"type": "xlsx"
}
clearFilterButtonType = {
"css": "btn btn-outline-dark",
"text": "Clear Filters"
}
initialHeaderFilter = [{"field": "col", "value": "blue"}]
app.layout = html.Div([
dash_tabulator.DashTabulator(
id='tabulator',
theme="tabulator",
options=options,
downloadButtonType=downloadButtonType,
clearFilterButtonType=clearFilterButtonType,
),
html.Div(id='output'),
dcc.Interval(
id='interval-component-iu',
interval=1 * 10, # in milliseconds
n_intervals=0,
max_intervals=0)
])
@app.callback([
Output('tabulator', 'columns'),
Output('tabulator', 'data'),
config={"scrollZoom":True, "displayModeBar":False, "displaylogo":False},
figure=empty_cube_figure,
),
]),
dbc.Col(width=4, children=[
dtb.DashTabulator(
id="rubik-table-history",
data=[],
columns=[
{
"title":"Move Sequence (Click to Revert)",
"columns":[
{"title":"Index", "field":"i", "hozAlign":"center", "headerSort":False},
{"title":"Move", "field":"move", "hozAlign":"center", "headerSort":False},
]
},
],
options={
"placeholder":"None",
"layout":"fitDataStretch",
"height":"100vw",
"minHeight":"100vh",
"maxHeight":"100vh",
"selectable":"false",
},
),
]),
]),
]),
]),
dbc.Card([
dbc.Input(
type="text",
disabled=True,
),
],
),
dtb.DashTabulator(
id="tabulator-pivot-raw",
data=[],
columns=[{
"title": "",
"field": "none",
"hozAlign": "center",
"headerSort": False,
}],
options={
"placeholder": "None",
"layout": "fitDataStretch",
"layoutColumnsOnNewData": False,
"minHeight": "50vh",
"maxHeight": "50vh",
"height": "50vh",
"pagination": "local",
"selectable": False,
},
),
]),
]),
dbc.Card([
dbc.CardHeader([
dcc.Markdown("""
### The Pivot Table
{
"title": "School Enrollment",
"field": "Enrollment",
"hozAlign": "right",
"formatter": "money",
"formatterParams": {
"precision": 0
},
# "topCalc":"sum", "topCalcParams":{"precision":0}
},
]
select_city_tabulator = (dash_tabulator.DashTabulator(
id="select_city_tabulator",
columns=columns,
data=df_to_data(Fin_GID[START_YR]),
options=select_cities_options,
downloadButtonType=downloadButtonType,
), )
########## Tabulator - City summary report output
city_columns = [
{
"title": "State",
"field": "ST",
"hozAlign": "left",
"headerFilter": True
},
{
"title": "City/District",
"field": "ID name",
def on_button_click(
n,
contract_type,
min_expiration_days,
max_expiration_days,
min_delta,
max_delta,
premium,
moneyness,
ticker,
ticker_list,
is_group,
):
if n is None:
return None, False, ""
else:
global df
params = {}
func = None
if contract_type == "PUT":
func = short_put
else:
func = short_call
if min_expiration_days:
params["min_expiration_days"] = int(min_expiration_days)
if max_expiration_days:
params["max_expiration_days"] = int(max_expiration_days)
if min_delta:
params["min_delta"] = float(min_delta)
if max_delta:
params["max_delta"] = float(max_delta)
if premium:
params["premium"] = premium
if moneyness:
params["moneyness"] = moneyness
if ticker:
tickers = [ticker]
elif ticker_list:
tickers = screener_list.get(ticker_list)
else:
return None, True, "Enter Ticker or Select Watchlist"
df = watchlist_income(tickers, params, func)
if not df.empty:
options = {
"selectable": 1,
"pagination": "local",
"paginationSize": 20,
"responsiveLayout": "true",
"movableRows": "true"
}
# add groupBy option to the Tabulator component to group at Ticker level
if is_group:
options["groupBy"] = "TICKER"
dt = dash_tabulator.DashTabulator(
id='screener-table',
columns=[{
"id": i,
"title": i,
"field": i
} for i in df.columns],
data=df.to_dict("records"),
options=options,
),
return dt, False, ""
else:
return None, True, "No Results Found"
