def create_objects(cls, securities):
ranks = utils.get_pumps_rank()
quotient_metrics = utils.get_quotient_metrics()
ranks['quotient'] = quotient_metrics['quotient']
foo = lambda x: utils.spam_counts.get(x, 0)
ranks['spams'] = map(foo, ranks['symbol'])
ranks = ranks.sort('quotient', ascending=False)
cls._pre_filtered_ranks = {
'All': {k: ranks[k]
for k in ranks.columns},
'Stocks with Spam':
dict(ranks[ranks['symbol'].isin(plugins.spam_counts.keys())].sort(
'quotient', ascending=False)),
'Stocks without Spam':
dict(ranks[~ranks['symbol'].isin(plugins.spam_counts.keys())].sort(
'quotient', ascending=False)),
}
source_stocks_rank = ColumnDataSource(
cls._pre_filtered_ranks['Stocks with Spam'])
table_stocks_rank = DataTable(source=source_stocks_rank,
width=785,
height=400,
selectable=True,
editable=True,
columns=[
TableColumn(field='symbol',
title='symbol',
editor=StringEditor()),
TableColumn(field='quotient',
title='quotient',
editor=StringEditor()),
TableColumn(field='rank',
title='rank',
editor=StringEditor()),
TableColumn(field='spams',
title='spams',
editor=StringEditor()),
])
callback = Callback(args={
'tr': table_stocks_rank,
'sr': source_stocks_rank
},
code=callbacks.source_stocks_rank)
source_stocks_rank.callback = callback
return locals()
def create_objects(cls, securities):
ranks = utils.get_pumps_rank()
quotient_metrics = utils.get_quotient_metrics()
ranks['quotient'] = quotient_metrics['quotient']
foo = lambda x: utils.spam_counts.get(x, 0)
ranks['spams'] = map(foo, ranks['symbol'])
ranks = ranks.sort('quotient', ascending=False)
cls._pre_filtered_ranks = {
'All': {k: ranks[k] for k in ranks.columns},
'Stocks with Spam': dict(ranks[ranks['symbol'].
isin(plugins.spam_counts.keys())].
sort('quotient', ascending=False)),
'Stocks without Spam': dict(ranks[~ranks['symbol'].
isin(plugins.spam_counts.keys())].
sort('quotient', ascending=False)),
}
source_stocks_rank = ColumnDataSource(cls._pre_filtered_ranks['Stocks with Spam'])
table_stocks_rank = DataTable(
source=source_stocks_rank, width=785, height=400,
selectable=True, editable=True,
columns=[
TableColumn(field='symbol', title='symbol', editor=StringEditor()),
TableColumn(field='quotient', title='quotient', editor=StringEditor()),
TableColumn(field='rank', title='rank', editor=StringEditor()),
TableColumn(field='spams', title='spams', editor=StringEditor()),
])
callback = Callback(
args={'tr': table_stocks_rank, 'sr': source_stocks_rank},
code=callbacks.source_stocks_rank
)
source_stocks_rank.callback = callback
return locals()
def create_objects(cls, symbol, df, securities):
descr_box = Paragraph(text='content loading...')
btn_close_loading = Button(label='Close Loading')
dialog_loading = Dialog(
title='loading', content=vplot(descr_box), name='loading_dialog',
buttons=[btn_close_loading], visible=False)
source_data = dict(df)
main_source = ColumnDataSource(dict(df))
source = ColumnDataSource(source_data)
# TODO: REMOVE THIS COMMENTED CODE! IT'S JUST THE PREVIOUS
# VERSION USED BEFORE NEW P&D Cached results and algorithm
# get the cached results of the P&D algorithm computed with the
# "default" configuration
# intervals = utils.cached_pumps.get(symbol, pumps.to_dicts(((),(),(),(),(),())))
# intervals['bottom'] = [0] * len(intervals['start'])
# intervals['values'] = [max(df['price'])] * len(intervals['start'])
#
# intervals = pd.DataFrame(intervals)
# new version
stats = utils.get_symbols_cached_stats()[symbol]
intervals = pd.DataFrame(stats)
intervals['bottom'] = [0] * len(intervals['start'])
intervals['values'] = [max(df['price'])] * len(intervals['start'])
conv = lambda x: utils.to_seconds(pd.to_datetime(x))
intervals = intervals[
(pd.to_datetime(intervals['start']) > conv(config.date_range[0])) &
(pd.to_datetime(intervals['start']) < conv(config.date_range[1]))
]
# Create P&Ds intervals DataSource
intervals_source = ColumnDataSource(intervals)
source.tags = ['main_source']
trends = utils.load_trends_data(symbol, start_date=min(df['dt']))
trends_source = ColumnDataSource(trends)
trades = Slider(
title="trades", name='trades',
value=0, start=0, end=124, step=1
)
# Selectors
symbol = Select.create(
options=securities, value=symbol, name='symbol', title=""
)
window_selector = Select.create(
options=['---'], name='period_selector', title="Search intervals with:"
)
symbol_filter = Select.create(
options=['All', 'Stocks with Spam', 'Stocks without Spam'],
name='symbol_filter', title="Filter Symbols:",
value='Stocks with Spam'
)
callback = Callback(
args={'symbol_filter': symbol_filter,
'dialog_loading': dialog_loading},
code=callbacks.symbol_filter
)
symbol_filter.callback = callback
btn_detect_pumps = Button(label='Configure P&D Detection', name='config_pumps')
main_tab = Panel(title="Main")
tabs = Tabs()
# Create STOCKS TABLE
ranks = utils.get_pumps_rank()
# quotient_metrics = utils.get_quotient_metrics()
# ranks['quotient'] = quotient_metrics['quotient']
foo = lambda x: utils.spams_count.get(x, 0)
ranks['spams'] = map(foo, ranks['symbol'])
ranks = ranks.sort(['spams', 'vol_quotient'], ascending=False)
cls._pre_filtered_ranks = {
'All': {k: ranks[k] for k in ranks.columns},
'Stocks with Spam': dict(ranks[ranks['spams'] > 0].
sort('vol_quotient', ascending=False)),
'Stocks without Spam': dict(ranks[ranks['spams'] == 0].
sort('vol_quotient', ascending=False)),
}
source_stocks_rank = ColumnDataSource(cls._pre_filtered_ranks['All'])
table_stocks_rank = DataTable(
source=source_stocks_rank, width=560, height=450,
selectable=True, editable=True,
columns=[
TableColumn(field='symbol', title='symbol', width=130, editor=StringEditor()),
TableColumn(field='vol_quotient', title='volume ratio', editor=StringEditor(),
default_sort='descending'),
TableColumn(field='risk_score', title='risk', width=100, editor=StringEditor(),
default_sort='descending'),
TableColumn(field='spams', title='spams', width=130, editor=StringEditor(),
default_sort='descending'),
])
callback = Callback(args={'tr': table_stocks_rank, 'sr': source_stocks_rank, 'symb': symbol,
'dialog_loading': dialog_loading},
code=callbacks.source_stocks_rank)
source_stocks_rank.callback = callback
return locals()
def create_objects(cls, symbol, df, securities):
descr_box = Paragraph(text='content loading...')
btn_close_loading = Button(label='Close Loading')
dialog_loading = Dialog(title='loading',
content=vplot(descr_box),
name='loading_dialog',
buttons=[btn_close_loading],
visible=False)
source_data = dict(df)
main_source = ColumnDataSource(dict(df))
source = ColumnDataSource(source_data)
# TODO: REMOVE THIS COMMENTED CODE! IT'S JUST THE PREVIOUS
# VERSION USED BEFORE NEW P&D Cached results and algorithm
# get the cached results of the P&D algorithm computed with the
# "default" configuration
# intervals = utils.cached_pumps.get(symbol, pumps.to_dicts(((),(),(),(),(),())))
# intervals['bottom'] = [0] * len(intervals['start'])
# intervals['values'] = [max(df['price'])] * len(intervals['start'])
#
# intervals = pd.DataFrame(intervals)
# new version
stats = utils.get_symbols_cached_stats()[symbol]
intervals = pd.DataFrame(stats)
intervals['bottom'] = [0] * len(intervals['start'])
intervals['values'] = [max(df['price'])] * len(intervals['start'])
conv = lambda x: utils.to_seconds(pd.to_datetime(x))
intervals = intervals[
(pd.to_datetime(intervals['start']) > conv(config.date_range[0])) &
(pd.to_datetime(intervals['start']) < conv(config.date_range[1]))]
# Create P&Ds intervals DataSource
intervals_source = ColumnDataSource(intervals)
source.tags = ['main_source']
trends = utils.load_trends_data(symbol, start_date=min(df['dt']))
trends_source = ColumnDataSource(trends)
trades = Slider(title="trades",
name='trades',
value=0,
start=0,
end=124,
step=1)
# Selectors
symbol = Select.create(options=securities,
value=symbol,
name='symbol',
title="")
window_selector = Select.create(options=['---'],
name='period_selector',
title="Search intervals with:")
symbol_filter = Select.create(
options=['All', 'Stocks with Spam', 'Stocks without Spam'],
name='symbol_filter',
title="Filter Symbols:",
value='Stocks with Spam')
callback = Callback(args={
'symbol_filter': symbol_filter,
'dialog_loading': dialog_loading
},
code=callbacks.symbol_filter)
symbol_filter.callback = callback
btn_detect_pumps = Button(label='Configure P&D Detection',
name='config_pumps')
main_tab = Panel(title="Main")
tabs = Tabs()
# Create STOCKS TABLE
ranks = utils.get_pumps_rank()
# quotient_metrics = utils.get_quotient_metrics()
# ranks['quotient'] = quotient_metrics['quotient']
foo = lambda x: utils.spams_count.get(x, 0)
ranks['spams'] = map(foo, ranks['symbol'])
ranks = ranks.sort(['spams', 'vol_quotient'], ascending=False)
cls._pre_filtered_ranks = {
'All': {k: ranks[k]
for k in ranks.columns},
'Stocks with Spam':
dict(ranks[ranks['spams'] > 0].sort('vol_quotient',
ascending=False)),
'Stocks without Spam':
dict(ranks[ranks['spams'] == 0].sort('vol_quotient',
ascending=False)),
}
source_stocks_rank = ColumnDataSource(cls._pre_filtered_ranks['All'])
table_stocks_rank = DataTable(
source=source_stocks_rank,
width=560,
height=450,
selectable=True,
editable=True,
columns=[
TableColumn(field='symbol',
title='symbol',
width=130,
editor=StringEditor()),
TableColumn(field='vol_quotient',
title='volume ratio',
editor=StringEditor(),
default_sort='descending'),
TableColumn(field='risk_score',
title='risk',
width=100,
editor=StringEditor(),
default_sort='descending'),
TableColumn(field='spams',
title='spams',
width=130,
editor=StringEditor(),
default_sort='descending'),
])
callback = Callback(args={
'tr': table_stocks_rank,
'sr': source_stocks_rank,
'symb': symbol,
'dialog_loading': dialog_loading
},
code=callbacks.source_stocks_rank)
source_stocks_rank.callback = callback
return locals()
