def one_filter(plot, filter_col, filter_vals, filters_state, max_items):
# Remove (FX) from column name; probaby temporary
title = re.sub(r'\s*[(]F[0-9]+[)]\s*', '', filter_col)
# Deduplicate and turn into name-value pairs, as required by MultiSelect.
options = [(opt,) * 2 for opt in sorted(filter_vals)]
multi_select = MultiChoice(
title=title,
width=int(plot.plot_width / 1.5),
height=int(plot.plot_height / 8),
max_items=max_items,
options=options
)
multi_select.js_on_change('value', CustomJS(
args=dict(filter_col=filter_col,
filters_state=filters_state),
code="""
let select_vals = cb_obj.value;
let state_col = filters_state.data[filter_col];
for (let i = 0; i < state_col.length; i++) {
if (i < select_vals.length) {
state_col[i] = select_vals[i];
} else {
state_col[i] = '';
}
}
filters_state.properties.data.change.emit();
""")
)
return multi_select
def generate_selection(self):
devices = self.network.registered_devices
selectors = []
_cache = []
for device in devices:
try:
_cache = self._peristent_widget_choices[device.properties.name]
except KeyError:
pass
options = list(device.points_name)
options.extend(list(device.trendlogs_names))
mc = MultiChoice(value=_cache,
options=options,
title=device.properties.name)
mc.js_on_change(
"value",
CustomJS(code="""
console.log('multi_choice: value=' + this.value, this.toString())
"""),
)
selectors.append(mc)
return selectors
"menu_item_click",
CustomJS(code="console.log('dropdown: ' + this.item, this.toString())"))
# Toggle button GUI
toggle = Toggle(label="Button", button_type="success")
toggle.js_on_click(
CustomJS(code="""
console.log('toggle: active=' + this.active, this.toString())
"""))
# choice menu GUI
OPTIONS = [str(i) for i in range(20)]
multi_choice = MultiChoice(value=["foo", "baz"], options=OPTIONS)
multi_choice.js_on_change(
"value",
CustomJS(code="""
console.log('multi_choice: value=' + this.value, this.toString())
"""))
# # SELECT menu GUI
# selectoptions = ["Postive tested on Covid-19 virus", "Negative tested on Covid-19 virus", "Show both"]
# resultSelect = Select(title="What to show", options=selectoptions)
# TOOL BUTTON CALLBACKS -----------------------------------------------------------------------------------------------
# Test button GUI
lab = "Click me!"
but = Button(label=lab)
def callback_button1(
): # simple test callback -> changes lable on button and reports a click to the console
def Persistence_EM_Matrix():
### ------------ read files ------------
final = pd.read_csv('./data/App_data/AQ_EM_tse_final.csv',encoding='utf-8')
profit = pd.read_csv('./data/App_data/AQ_EM_tse_final_profibility_1.csv',encoding='utf-8')
profit['company_name'] = profit.company.astype(str)+" "+profit.company_abbreviation
#此檔案中放的為dict, key為用途及名稱，value為list
with open('app_lists.json','r')as f:
app_lists = json.load(f)
with open('smallco.json','r')as f:
smallco_index = json.load(f)
### ------------ 左側的選項 ------------
year = Select(title='Year：',value='200812',
options=list(final.yyyymm.drop_duplicates().sort_values().astype(str)))
industry = Select(title='Industry:',value='水泥工業',
options = list(profit.tse_industry_name.drop_duplicates())+['All Sectors'])
index_factor = Select(title='Compared Market Index:',value='TWN50',options=["TWN50", "TM100","TF001"])
company_list = list((profit.query("yyyymm==200812 & tse_industry_name=='水泥工業'").company_name.astype(str)))
company_code = Select(title='Company Code: ',value='',options=['']+company_list)
persistence = Select(title='Persistence:',value='ebit_slope_standard',
options= app_lists['options_persistence'])
EM = Select(title='EM:',value='Jones_model_measure',options=app_lists['options_EM'])
profit_measure = Select(title='Profit Measure:',value='ROA_ebi',
options=app_lists['options_profit_measure'])
Persistence_percent = RangeSlider(start=0, end=100,value=(20,40), step=1, title="Persistence % :")
EM_percent = RangeSlider(start=0, end=100,value=(20,40), step=1, title="EM %:")
#根據選擇日期、產業更新公司列表選項
###############################################################################
def update_company_list(attr,old,new):
selected_year = year.value
selected_industry = industry.value
if selected_industry !='All Sectors':
company_list = list((profit.query("yyyymm==@selected_year & tse_industry_name==@selected_industry").\
company_name.sort_values().astype(str)))
#前面加入空値，代表沒有選公司
company_code.options = ['']+company_list
#default 為空値
company_code.value = ''
else:
company_list = list((profit.query("yyyymm==@selected_year").\
company_name.sort_values().astype(str)))
#前面加入空値，代表沒有選公司
company_code.options = ['']+company_list
#default 為空値
company_code.value = ''
#選出畫圖的資料
###############################################################################
def get_plot_data():
selected_year=year.value
selected_industry = industry.value
selected_index_factor = index_factor.value
selected_Persistence = persistence.value
selected_EM = EM.value
selected_profit_measure = profit_measure.value
if selected_industry !='All Sectors':
data = profit.query('yyyymm == @selected_year & tse_industry_name == @selected_industry')
else :
data = profit.query('yyyymm == @selected_year')
#依據日期、產業選擇
data = data[(data[selected_Persistence].notna()) & (data[selected_EM].notna())]
#因為有可能選擇的資料也是之後要保留的資料，因此先備份，以免在rename後找不到資料
origin_data = [selected_Persistence,selected_EM,selected_profit_measure]
origin = data[origin_data]
#重新命名，在ColumnDataSource中較好使用
data.rename(columns={selected_Persistence:'Persistence',selected_EM:'EM',selected_index_factor:'index_factor',
selected_profit_measure:'profit_measure'} , inplace=True)
for i in origin_data:
data[i] = origin[i]
data['Persistence'] = data.Persistence.apply(lambda x:value_transfrom(x,selected_Persistence))
data['EM'] = data.EM.apply(lambda x:value_transfrom(x,selected_EM))
data['color'] = data['index_factor'].apply(lambda x:'green' if x=='Y' else 'blue')
data['color'] = data.apply(lambda x:'red' if str(x.company) in smallco_index['2020'] else x.color, 1)
profit_min = data['profit_measure'].min()
profit_range = data['profit_measure'].max()-data['profit_measure'].min()
data['profit_score'] = data['profit_measure'].apply(lambda x:((x-profit_min)/profit_range)*25+5\
if profit_range!=0 else 30 if x==1 else 5)
table_data = data[app_lists['select_stock_picking_table_column']]
data_for_source = data.fillna('--')
if company_code.value!='':
data_for_source['text'] = data_for_source['company'].apply(lambda x:'.Here' if x==int(company_code.value[:4])else '')
else :
data_for_source['text']=''
data_for_source = data_for_source[~data_for_source.isin([np.nan, np.inf, -np.inf]).any(1)]
if company_code.value!='':
select_co = int(company_code.value[:4])
data_for_source['select_p'] = data.query('company==@select_co')['Persistence'].to_list()[0]
data_for_source['select_e'] = data.query('company==@select_co')['EM'].to_list()[0]
else :
data_for_source['select_p'] = np.nan
data_for_source['select_e'] = np.nan
plot_source = ColumnDataSource(data_for_source)
return (plot_source,table_data)
def get_stock_picking_table_data(table_data):
df = table_data
Persistence_top = df.Persistence.quantile(Persistence_percent.value[1]/100)
Persistence_low = df.Persistence.quantile(Persistence_percent.value[0]/100)
EM_top = df.EM.quantile(EM_percent.value[1]/100)
EM_low = df.EM.quantile(EM_percent.value[0]/100)
df = df.query('Persistence <= @Persistence_top & Persistence >= @Persistence_low & EM <= @EM_top & EM >= @EM_low')
df = df.applymap(lambda x:round(x,2) if type(x)==float else x)
stock_picking_table_co_choice.options = (df.company.astype(str)+' '+df.company_abbreviation).sort_values().to_list()
stock_picking_table_co_num.text = f'Total: {df.shape[0]} company'
return ColumnDataSource(df)
def get_stock_return_table_2_data():
selected_year=year.value
selected_index_factor = index_factor.value
df = profit.rename(columns={selected_index_factor:'index_factor'})
df = df.query('yyyymm == @selected_year & index_factor=="Y"')
return ColumnDataSource(df)
def get_stock_return_table_3_data(stock_picking_table_source,stock_return_table_2_source):
if stock_picking_table_source.data['yearly_return'].size ==0 :
stock_average = [' ']
else : stock_average = [round(np.nanmean(stock_picking_table_source.data['yearly_return']),4)]
if stock_return_table_2_source.data['yearly_return'].size ==0 :
etf_average = [' ']
else : etf_average = [round(np.nanmean(stock_return_table_2_source.data['yearly_return']),4)]
return ColumnDataSource(data={'Stock Picking Return (Equally Weighted)':stock_average,
"ETF Return (Equally Weighted)" :etf_average
})
def get_matrix_plot_data():
selected_year=year.value
df = profit.query('yyyymm == @selected_year')
df = df[app_lists['options_persistence']+app_lists['options_EM']].corr()
df = df.apply(lambda x:round(x,2))
return ColumnDataSource(df)
###################################################
# 製作圖、表
def make_scatter_plot(plot_source):
hover = HoverTool( names=['circle'],
tooltips=[('Company Abbreviation :','@company_abbreviation'),
('Company Code :','@company'),
('Persistence','@Persistence'),
('EM :','@EM'),('ROA (EBI) :','@ROA_ebi'),
('EPS :','@eps'),('ROE_b :','@ROE_b'),
('Diluted EPS :','@eps_diluted'),('Yearly Return','@yearly_return')]
)
plot = figure(plot_height=500, plot_width=800,
tools = ['box_zoom','reset',hover],
x_axis_label='Persistence (Log Transformed)',
y_axis_label='EM (Log Transformed)',
toolbar_location="right"
)
plot.circle(x="Persistence", y="EM", source=plot_source,color= 'color',size='profit_score', name='circle',
line_color=None,alpha=0.5)
# plot.text('Persistence','EM','text',source=plot_source,color='red',text_font_style='bold',text_font_size='20pt')
plot.asterisk('select_p','select_e',source=plot_source,color='red',size=20)
plot.toolbar.active_drag = None
return plot
def make_stock_picking_table(stock_picking_table_source):
columns = []
for colnames in stock_picking_table_source.data.keys():
if colnames !='index':
columns.append(TableColumn(field=colnames, title=colnames, width=6*len(colnames)))
stock_picking_table = DataTable(source=stock_picking_table_source, columns=columns, width=4000, height = 500)
return (stock_picking_table)
def make_stock_return_table_1(stock_picking_table_source):
columns = []
for colnames in ['tse_industry_name','company','company_abbreviation','index_factor','yearly_return']:
columns.append(TableColumn(field=colnames, title=colnames, width=6*len(colnames)))
stock_return_table_1 = DataTable(source=stock_picking_table_source, columns=columns, height = 500)
return (stock_return_table_1)
def make_stock_return_table_2(stock_return_table_2_source):
columns = []
for colnames in ['tse_industry_name','company','company_abbreviation','index_factor','yearly_return']:
columns.append(TableColumn(field=colnames, title=colnames, width=6*len(colnames)))
stock_return_table_2 = DataTable(source=stock_return_table_2_source, columns=columns, height = 500)
return (stock_return_table_2)
def make_stock_return_table_3(stock_return_table_3_source):
columns = []
for colnames in stock_return_table_3_source.data.keys():
if colnames !='index':
columns.append(TableColumn(field=colnames, title=colnames, width=6*len(colnames)))
stock_return_table_3 = DataTable(source=stock_return_table_3_source, columns=columns)
return (stock_return_table_3)
def make_matrix_plot(matrix_plot_source):
columns = []
for colnames in matrix_plot_source.data.keys():
if colnames =='index':
columns.append(TableColumn(field=colnames, title=' ', width=200))
else:
columns.append(TableColumn(field=colnames, title=colnames, width=6*len(colnames)))
matrix_plot = DataTable(source=matrix_plot_source, columns=columns, index_position=None, width = 2500, height=300)
return (matrix_plot)
###################################################
# 更新
def update(attr,old,new):
stock_picking_table_co_choice.value = []
new_plot_source,new_table_data=get_plot_data()
plot_source.data.update(new_plot_source.data)
new_stock_picking_table_source = get_stock_picking_table_data(new_table_data)
stock_picking_table_source.data.update(new_stock_picking_table_source.data)
new_stock_return_table_2_source = get_stock_return_table_2_data()
stock_return_table_2_source.data.update(new_stock_return_table_2_source.data)
new_stock_return_table_3_source = get_stock_return_table_3_data(new_stock_picking_table_source,new_stock_return_table_2_source)
stock_return_table_3_source.data.update(new_stock_return_table_3_source.data)
new_matrix_plot_source = get_matrix_plot_data()
matrix_plot_source.data.update(new_matrix_plot_source.data)
def update_stock_picking(attr,old,new):
pick_list = list(map(lambda x:x[:4],stock_picking_table_co_choice.value))
new_plot_source,new_table_data=get_plot_data()
new_stock_picking_table_source = get_stock_picking_table_data(new_table_data)
df = pd.DataFrame(new_stock_picking_table_source.data).iloc[:,1:]
if len(pick_list)==0:
df = df
else:
df = df.query('company in @pick_list')
stock_picking_table_source.data.update(ColumnDataSource(df).data)
stock_picking_table_co_num.text = f'Total: {df.shape[0]} company'
###################################################
# initial
plot_source,table_data = get_plot_data()
plot = make_scatter_plot(plot_source)
plot_explain = Div(text =
'''
<span style="padding-left:20px">顏色(綠色): 該公司在該年，有被列在所選的Compared Market Index中 <br/>
<span style="padding-left:20px">顏色(紅色): 該公司在該年，有被列在中小型成分股中 <br/>
<span style="padding-left:20px">大小: 圈圈越大，代表該公司Profit Measure越大
''')
tab1 = Panel(child=column(Spacer(height=35), plot, Spacer(height=20), plot_explain), title='Persistence EM Matrix')
stock_picking_table_co_choice = MultiChoice(title = 'select_company:', value=[], options=[], placeholder = '選擇想看的公司')
stock_picking_table_co_choice.js_on_change("value", CustomJS(code="""
console.log('multi_choice: value=' + this.value, this.toString())
"""))
stock_picking_table_co_num = Div(text ='Total: company')
stock_picking_table_source = get_stock_picking_table_data(table_data)
stock_picking_table = make_stock_picking_table(stock_picking_table_source)
tab2 = Panel(child=column(stock_picking_table_co_num, stock_picking_table, stock_picking_table_co_choice), title='Stock Picking Table')
div1 = Div(text ='Table 1: The next year return of stocks from the matrix')
stock_return_table_1 = make_stock_return_table_1(stock_picking_table_source)
div2 = Div(text ='Table 2: The next year return of stocks in ETF')
stock_return_table_2_source = get_stock_return_table_2_data()
stock_return_table_2 = make_stock_return_table_2(stock_return_table_2_source)
div3 = Div(text ='Table 3: The next year return of equally weighted portfolios')
stock_return_table_3_source = get_stock_return_table_3_data(stock_picking_table_source,stock_return_table_2_source)
stock_return_table_3 = make_stock_return_table_3(stock_return_table_3_source)
tab3 = Panel(child=row([column(div1,stock_return_table_1),
column(div2,stock_return_table_2),
column(div3,stock_return_table_3)]),
title='Stock Return Table')
matrix_plot_source = get_matrix_plot_data()
matrix_plot = make_matrix_plot(matrix_plot_source)
matrix_plot_explain = Div(text =
'''
Persistence: <br/>
<span style="padding-left:50px">ebit_slope_standard <br/>
<span style="padding-left:50px">operating_slope_standard <br/>
<span style="padding-left:50px">yoy_ebit_standard <br/>
<span style="padding-left:50px">yoy_operating_standard <br/>
<br/><br/>
EM: <br/>
<span style="padding-left:50px">Jones_model_measure <br/>
<span style="padding-left:50px">Modified_Jones_model_measure <br/>
<span style="padding-left:50px">Performance_matching_measure <br/>
<span style="padding-left:50px">opacity_Jones_model_measure <br/>
<span style="padding-left:50px">opacity_modified_Jones_model_measure <br/>
<span style="padding-left:50px">opacity_performance_matching <br/>
''')
tab4 = Panel(child=column(matrix_plot,row(Spacer(width=20), matrix_plot_explain)), title='Correlation Matrix of Persistence & EM')
tabs = Tabs(tabs=[tab1,tab2,tab3,tab4])
###################################################
# input change
year.on_change('value', update, update_company_list)
industry.on_change('value', update, update_company_list)
index_factor.on_change('value', update)
company_code.on_change('value', update)
persistence.on_change('value', update)
EM.on_change('value', update)
profit_measure.on_change('value', update)
Persistence_percent.on_change('value', update)
EM_percent.on_change('value', update)
stock_picking_table_co_choice.on_change('value', update_stock_picking)
###################################################
# layout
div_title = Div(text ='Persistence & EM Matrix',style={'font-size': '200%', 'color': 'blue'})
inputs = column(div_title,year, industry, index_factor, company_code,persistence,EM,profit_measure,
Persistence_percent,EM_percent, background='gainsboro')
final_layout = row(inputs, tabs, width=1200)
return Panel(child = column(Spacer(height = 35), final_layout), title = 'Persistence & EM 概況')
def multilinePlot(dataset_id, columnName):
log = DatasetManager.query.get_or_404(dataset_id)
datasetName = log.datasetName
datasetSqlName = log.datasetSqlName
df = pd.read_sql_table(datasetSqlName, db.engine)
# prepare some data
x = df["index"]
# create a new plot with a title and axis labels
p = figure(
title="Dataset Name: " + datasetName,
x_axis_label="Index",
y_axis_label="y",
sizing_mode="fixed",
plot_width=1100,
plot_height=400,
)
# Initialize source to x=[x] and y=[x]
# Note:
# Multi-line plots take an array as x's and y's
# The x and y arrays must be the same length
initialColumnName = columnName
y = df[columnName]
source = ColumnDataSource(data=dict(x=[x], y=[y]))
df_source = ColumnDataSource(data=dict(df))
p.multi_line("x", "y", source=source)
# Create menu and options with name of data columns with numerical data
columns = df.columns.to_list()
menu = []
options = []
for columnName in columns:
# if df[columnName].dtype == np.float64 or df[columnName].dtype == np.int64:
if is_numeric_dtype(df[columnName]):
# Menu is a list of tuples of name/value pairs for dropdown widget
menu.append((columnName, columnName))
# Option is a list of column names for multi-select widget
options.append(columnName)
# Create dropdown widget
dropdown = Dropdown(label="Select Column",
button_type="primary",
menu=menu,
width_policy="min")
# Create multi-select widget
multi_choice = MultiChoice(value=[initialColumnName],
options=options,
title="Select Columns")
# Define JS callback to change column to plot
callback_dropdown = CustomJS(
args=dict(source=source, df_source=df_source),
code="""
// Initialize x and y arrays
var data = source.data;
var x = data['x'];
// console.log(' x=' + x);
var y = data['y'];
// console.log('y=' + y);
// Get dataframe
var df_data = df_source.data;
// Set y array to selected value
y[0] = df_data[this.item];
// Update plot with new x,y source data
source.change.emit();
""",
)
callback_multi_select = CustomJS(
args=dict(source=source, df_source=df_source),
code="""
// Initialize x and y arrays
var data = source.data;
var x = data['x'];
var y = data['y'];
x.length = 0;
y.length = 0;
// console.log(' x=' + x);
// console.log('x.length=' + x.length);
// console.log('y=' + y);
// console.log('y.length=' + y.length);
// Get dataframe
var df_data = df_source.data;
// Get value of multi-select widget
// console.log('multi_select: ' + this.value, this.value.toString());
var array = this.value;
// Iterate through multi-select values and update x,y arrays with selected values
var array_iterator = array.values();
let next_value = array_iterator.next();
while (!next_value.done) {
// console.log(next_value.value);
x.push(df_data['index']);
y.push(df_data[next_value.value]);
next_value = array_iterator.next();
}
// console.log('y=' + y);
// console.log('y.length=' + y.length);
// console.log('x.length=' + x.length);
// Update plot with new x,y source data
source.change.emit();
""",
)
# Define javascript events to trigger callbacks
dropdown.js_on_event(
"menu_item_click",
callback_dropdown,
)
multi_choice.js_on_change(
"value",
callback_multi_select,
)
layout = column(multi_choice, p)
return layout
def one_filter(plot, point_source, filter_col):
# Remove (FX) from column name; probaby temporary
title = re.sub(r'\s*[(]F[0-9]+[)]\s*', '', filter_col)
full_source = GeoJSONDataSource(geojson=point_source.geojson)
# Extract options from values in the data:
options = [
f['properties'][filter_col]
for f in json.loads(point_source.geojson)['features']
]
# Some values are comma-separated lists; split and unpack, dropping None.
options = [
opt.strip() for opt_list in options if opt_list is not None
for opt in opt_list.split(',')
]
# Deduplicate and turn into name-value pairs, as required by MultiSelect.
options = [(opt, ) * 2 for opt in sorted(set(options))]
multi_select = MultiChoice(title=title,
width=int(plot.plot_width / 1.5),
height=int(plot.plot_height / 8),
max_items=4,
options=options)
callback = CustomJS(args=dict(source=point_source,
multi_select=multi_select,
full_source=full_source,
filter_col=filter_col),
code="""
function filter(select_vals, source, filter, full_source) {
for (const [key, value] of Object.entries(source.data)) {
while (value.length > 0) {
value.pop();
}
}
for (const [key, value] of Object.entries(full_source.data)) {
for (let i = 0; i < value.length; i++) {
if (isIncluded(filter, select_vals, i, full_source)) {
source.data[key].push(value[i]);
}
}
}
}
function isIncluded(filter, select_vals, index, full_source) {
// Slow -- O(i * j) -- but i and j never get much larger than 10
for (var i = 0; i < select_vals.length; i++) {
let vals = full_source.data[filter][index];
vals = vals ? vals.split(',').map(s => s.trim()) : [];
for (let j = 0; j < vals.length; j++) {
if (vals[j] == select_vals[i]) {
return true;
}
}
}
return false;
}
var select_vals = cb_obj.value;
filter(select_vals, source, filter_col, full_source);
source.change.emit();
""")
multi_select.js_on_change('value', callback)
return multi_select