def make_document(doc):
source = ColumnDataSource()
source.data = make_cds(df, None).data
table = make_table_tab(source)
plots = make_plots_tab(source)
about = make_about_tab()
tabs = Tabs(tabs=[plots, table, about])
doc.add_root(tabs)
doc.title = "iPlot"
#color_bar.click_policy="hide"
p.add_layout(color_bar, 'right')
from bokeh.io import output_file, show
#output_file("hex_tile.html")
#VERIFICATION STUFF
dictionary = {}
for col_name in df.columns:
# if col_name not in [X,Y]:
dictionary[col_name] = df[col_name]
datapoints_source = ColumnDataSource()
datapoints_source.data = dictionary
circle_plot = p.circle(
x='mrc_start_long',
y='mrc_start_lat',
#size=cluster_point_size,
fill_alpha=0.2,
source=datapoints_source,
color="firebrick",
line_alpha=0
#line_color='black'
)
#VERIFICATION STUFF
#df['x']=df['mrc_start_long']
#df['y']=df['mrc_start_lat']
resample_df = df.resample(sample_peorid).sum()
materals = get_materal_cluster(resample_df)
slider_smoothing = Slider(start=2,
end=9,
step=1,
value=smoothing_window,
title="Smoothing Window")
p = Figure(title="Clustered Materals", plot_height=500, plot_width=650)
df_source = ColumnDataSource(
data=dict(x=resample_df[materals[0]], y=resample_df.index))
p.line(x='x', y='y', source=df_source)
df_source.data = dict(x=resample_df[materals[0]], y=resample_df.index)
grid = girdplot()
def get_materal_cluster(df):
rolling_df = df.rolling(window=smoothing_window, center=True)
rolling_df = rolling_df.mean()
rm = round(smoothing_window / 2)
rolling_df = c.iloc[rm:-rm]
clusters = hac.linkage(rolling_df.T, 'complete', 'correlation')
clusters = hac.fcluster(cluster, .5, criterion='distance')
clst = pd.value_counts(clusters).index[0]
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ["price"]
# Creating tooltips
price_hover.tooltips = [("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{$0,0.00}"),
("Close", "@Close{$0,0.00}"),
("Volume", "@Volume{($ 0.00 a)}")]
price_hover.formatters = {"Date": 'datetime'}
return p
stock = ColumnDataSource(
data=dict(Date=[], Open=[], Close=[], High=[], Low=[], index=[]))
myPortfolio = Portfolio(['CSCO', 'DTE.DE', 'NOK'], [1, 1, 2], [1, 1, 1],
[1, 1, 1])
sample_df = pd.DataFrame(data=myPortfolio.share_data['CSCO']).dropna()
sample_df.reset_index(inplace=True)
stock.data = stock.from_df(sample_df)
elements = list()
p_stock = plot_stock_price(stock)
elements.append(p_stock)
curdoc().add_root(column(elements))
curdoc().title = 'CSCO Data'
def modify_doc(doc):
""" Contains the application, including all callbacks
TODO: could the callbacks be outsourced?
:param doc:
:type doc:
"""
logger.debug('modify_doc has been called')
def get_data_frames(ie,):
""" Called one time initially, and then every time the experiment number is changed by the slider
:param ie: experiment number
:type ie: int
:returns: dataframe from stella datafile and dataframe with tau and phi and fitted values
:rtype: list of 2 pandas dataframes
"""
logger.debug('get_dataframe with ie={}'.format(ie))
fid = polymer.getfid(ie) #read FID or series of FIDs for selected experiment
try:
tau = polymer.get_tau_axis(ie) #numpy array containing the taus for experiment ie
try:
startpoint=fid_slider.range[0] #lower integration bound
endpoint = fid_slider.range[1] #upper integration bound
except NameError:
# fid_slider not initialized for first plot. Use default values:
startpoint=int(0.05*polymer.getparvalue(ie,'BS'))
endpoint = int(0.1*polymer.getparvalue(ie,'BS'))
logger.debug('fid_slider not initialized for first plot. Use default values {} and {}.'.format(startpoint, endpoint))
polymer.addparameter(ie,'fid_range',(startpoint,endpoint)) #add integration range to parameters to make it accesible
phi = get_mag_amplitude(fid, startpoint, endpoint,
polymer.getparvalue(ie,'NBLK'),
polymer.getparvalue(ie,'BS')) # list containing averaged fid amplitudes (which is proportional to a magnetization phi)
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi']) # DataFrames are nice
df['phi_normalized'] = (df['phi'] - df['phi'].iloc[0] ) / (df['phi'].iloc[-1] - df['phi'].iloc[1] ) #Normalize magnetization,
#Note: in the normalized magnetization the magnetization build-up curves and magnetization decay curves look alike
#Note: this makes it easier for fitting as everything looks like 1 * exp(-R/time) in first order
polymer.addparameter(ie,'df_magnetization',df) # make the magnetization dataframe accesible as parameter
fit_option = 2 #mono exponential, 3 parameter fit
p0=[1.0, polymer.getparvalue(ie,'T1MX')**-1*2, 0] #choose startparameters for fitting an exponential decay
df, popt = magnetization_fit(df, p0, fit_option) # use leastsq to find optimal parameters
polymer.addparameter(ie,'popt(mono_exp)',popt) # add fitting parameters for later access
logger.info('fitfunction(t) = {} * exp(- {} * t) + {}'.format(*popt)) # print the fitting parameters to console (for convenience)
except KeyError:
logger.warning('no relaxation experiment found')
tau=np.zeros(1)
phi=np.zeros(1)
df = pd.DataFrame(data=np.c_[tau, phi], columns=['tau', 'phi'])
df['phi_normalized'] = np.zeros(1)
df['fit_phi'] = np.zeros(1)
return fid, df
def calculate_mag_dec(attr, old, new, start_ie=None):
''' Is being call from the callback for the experiment chooser
loads selected experiment visualize in plot p1 and p2
gets experiment number from the slider
writes source_fid.data from the fid from the polymer object
writes source_mag_dec.data from the dataframe
'''
ie = experiment_slider.value #get expermient number from the slider
logger.debug('calculate mag_dec for ie={}'.format(ie))
fid, df = get_data_frames(ie)
source_fid.data=ColumnDataSource.from_df(fid) #convert fid to bokeh format
source_mag_dec.data = ColumnDataSource.from_df(df)
def plot_par():
''' Creates plot for the parameters
Called with every update from the callback'''
logger.debug('creating plot for the parameters')
# read data due to selection of select_x/y
xs = par_df[select_xaxis.value ].values
ys = par_df[select_yaxis.value].values
# read titles due to name of select_x/y
x_title = select_xaxis.value.title()
y_title = select_yaxis.value.title()
# remark: many attributes in a bokeh plot cannot be modified after initialization
# for example p4.x_axis_type='datetime' does not work. keywords are a
# workaround to pass all optional arguments initially
# set optional keyword arguments, kw, for figure()
kw = dict() #initialize
if select_xaxis.value in discrete:
kw['x_range'] = sorted(set(xs))
if select_yaxis.value in discrete:
kw['y_range'] = sorted(set(ys))
if select_yaxis.value in time:
kw['y_axis_type'] = 'datetime'
if select_xaxis.value in time:
kw['x_axis_type'] = 'datetime'
kw['title']="%s vs %s" % (x_title, y_title)
# create figure using optional keywords kw
p4 = figure(plot_height=300, plot_width=600, tools='pan,box_zoom,reset',
**kw)
# set axis label
p4.xaxis.axis_label = x_title
p4.yaxis.axis_label = y_title
# strings at x axis ticks need a lot of space. solution: rotate label orientation
if select_xaxis.value in discrete:
p4.xaxis.major_label_orientation = pd.np.pi / 4 # rotates labels...
# standard size of symbols
sz = 9
# custom size of symbols due to select_size
if select_size.value != 'None':
groups = pd.qcut(pd.to_numeric(par_df[select_size.value].values), len(SIZES))
sz = [SIZES[xx] for xx in groups.codes]
# standard color
c = "#31AADE"
# custom color due to select_color
if select_color.value != 'None':
groups = pd.qcut(pd.to_numeric(par_df[select_color.value]).values, len(COLORS))
c = [COLORS[xx] for xx in groups.codes]
# create the plot using circles
p4.circle(x=xs, y=ys, color=c, size=sz, line_color="white", alpha=0.6, hover_color='white', hover_alpha=0.5)
return p4 #return the plot
def callback_update_plot_1(attr, old, new):
''' Callback for update of figure 1 in parameters tab '''
tabs.tabs[1].child.children[1] = plot_par()
print(tabs.tabs[1].child.children[1])
logger.debug('Parameter plot updated')
# p4 = plot_par()
def callback_update_p3():
logger.debug('update plot 3')
p3 = fit_mag_decay_all(polymer,par_df)
return p3
def callback_update_experiment(attr, old, new):
""" Callback for the experiment chooser
"""
ie = experiment_slider.value
logger.debug('Callback experiment update, ie={}'.format(ie))
fid_slider.end = polymer.getparvalue(ie,'BS')
try:
fid_slider.range=polymer.getparvalue(ie,'fid_range')
except:
startpoint = int(0.05 * polymer.getparvalue(ie,'BS'))
endpoint = int(0.1 * polymer.getparvalue(ie,'BS'))
fid_slider.range=(startpoint,endpoint)
calculate_mag_dec(attr,old,new)
def callback_load_more_data(attr,old,new):
''' callback for loading of data '''
# TODO: implement
logger.debug('callback for loading of data ')
logger.error('Not implemented!')
path=pathbox.value.strip()
file=filebox.value.strip()
if file=="*.sdf":
logger.info('callback for loading data. filename: {}'.format(file))
allsdf=filter(lambda x: x.endswith('.sdf'),os.listdir(path))
for f in allsdf:
sdf_list.append(sdf.StelarDataFile(f,path))
else:
sdf_list.append(sdf.StelarDataFile(file,path))
filenames=[x.file() for x in sdf_list]
filenames_df=pd.DataFrame(data=filenames,columns=['file'])
table_source.data=ColumnDataSource.from_df(filenames_df)
def callback_export_data(attr,old,new):
logger.debug('callback_export_data has been called ')
logger.error('Not implemented!')
pass
def callback_write_table_to_file(attr,old,new): ##FIXME
logger.debug('callback_write_table_to_file has been called ')
logger.error('Not implemented!')
pass
# path=export_text.value.strip()
# exportdata=export_source.data
# CustomJS(args=dict(source=export_source),
# code=open(join(dirname(__file__), "export_csv.js")).read())
def callback_update_parameters():
''' callback for button
function to call when button is clicked
for updates parameters of polymer, since they can change during evaluation '''
logger.debug('callback for button (update parameter).')
par_df, columns, discrete, continuous, time, quantileable = polymer.scan_parameters()
select_xaxis.options=columns
select_yaxis.options=columns
select_size.options=['None']+quantileable
select_color.options=['None']+quantileable
logger.info('Starting the script')
### This is the start of the script ###
### The callbacks are above ###
#load data:
# TODO: how to handle multiple datafiles?
# New Tab for each datafile?
# dropdown selection to choose datafile
# complete new start of process? (probably not prefered)
polymer = load_data('glyzerin_d3_300K.sdf')
nr_experiments = polymer.get_number_of_experiments()
start_ie = 1 # initially set ie = 1
par_df, columns, discrete, continuous, time, quantileable = polymer.scan_parameters(20)
# for the initial call get the dataframes without callback
# they are being updated in following callbacks
fid, df = get_data_frames(start_ie)
source_fid = ColumnDataSource(data=ColumnDataSource.from_df(fid))
source_mag_dec = ColumnDataSource(data=ColumnDataSource.from_df(df))
# initialy creates the plots p1 and p2
p1, p2 = create_plot_1_and_2(source_fid, source_mag_dec)
### initiates widgets, which will call the callback on change ###
# initiate slider to choose experiment
experiment_slider = Slider(start=1, end=nr_experiments, value=1, step=1,callback_policy='mouseup', width=800) #select experiment by value
# initiate slider for the range in which fid shall be calculated
# select the intervall from which magneitization is calculated from fid
fid_slider = RangeSlider(start=1,end=polymer.getparvalue(start_ie,'BS'),
range=polymer.getparvalue(start_ie,'fid_range'),
step=1,callback_policy='mouseup', width=400)
# fit magnetization decay for all experiments
p3 = fit_mag_decay_all(polymer, par_df)
# refit mag dec with updated ranges after button push
button_refit = Button(label='Update',button_type="success")
button_refit.on_click(callback_update_p3)
# initialize empty source for experiment slider
source = ColumnDataSource(data=dict(value=[]))
# 'data' is the attribute. it's a field in source, which is a ColumnDataSource
# initiate callback_update_experiment which is the callback
source.on_change('data',callback_update_experiment) #source for experiment_slider
experiment_slider.callback = CustomJS(args=dict(source=source),code="""
source.data = { value: [cb_obj.value] }
""")#unfortunately this customjs is needed to throttle the callback in current version of bokeh
# initialize empty source for fid slider, same as above
source2 = ColumnDataSource(data=dict(range=[], ie=[]))
source2.on_change('data',calculate_mag_dec)
fid_slider.callback=CustomJS(args=dict(source=source2),code="""
source.data = { range: cb_obj.range }
""")#unfortunately this customjs is needed to throttle the callback in current version of bokeh
# same for the update button
button_scan = Button(label='Scan Parameters',button_type="success")
button_scan.on_click(callback_update_parameters)
# here comes for callbacks for x, y, size, color
select_xaxis = Select(title='X-Axis', value='ZONE', options=columns)
select_xaxis.on_change('value', callback_update_plot_1)
select_yaxis = Select(title='Y-Axis', value='TIME', options=columns)
select_yaxis.on_change('value', callback_update_plot_1)
select_size = Select(title='Size', value='None', options=['None'] + quantileable)
select_size.on_change('value', callback_update_plot_1)
select_color = Select(title='Color', value='None', options=['None'] + quantileable)
select_color.on_change('value', callback_update_plot_1)
controls_p4 = widgetbox([button_scan, select_xaxis,select_yaxis,select_color,select_size], width=150)
#p4 = plot_par()
layout_p4 = row(controls_p4,plot_par())
logger.debug('layout for parameter plot created')
####
#### TODO: write file input
#### TODO: select files to import
#### TODO: discard imported files
####
# load more data:
table_source=ColumnDataSource(data=dict())
sdf_list=[polymer]
# TODO: This is current plan, to save the different dataframes in a list, right?
filenames=[x.file() for x in sdf_list]
files_df=pd.DataFrame(data=filenames,columns=['file'])
table_source.data=ColumnDataSource.from_df(files_df)
t_columns = [
TableColumn(field='file', title='Path / Filename'),
#TableColumn(field='file', title='Filename'),
]
table=DataTable(source=table_source,columns=t_columns)
pathbox=TextInput(title="Path",value=os.path.curdir)
filebox=TextInput(title="Filename",value="*.sdf")
pathbox.on_change('value',callback_load_more_data)
filebox.on_change('value',callback_load_more_data)
layout_input=column(pathbox,filebox,table)
# Data Out: export data from figures
# & export parameters
export_source=ColumnDataSource(data=dict())
export_columns=[]
output_table=DataTable(source=export_source,columns=export_columns)
export_slider = Slider(start=1, end=4, value=3, step=1,callback_policy='mouseup', width=200) #do we need mouseup on this?
export_slider.on_change('value',callback_export_data)
export_text = TextInput(title="Path",value=os.path.curdir)
export_button = Button(label='Export to csv',button_type="success") # FIXME Callback doesn't work yet
export_button.on_click(callback_write_table_to_file)
layout_output=row(column(export_slider,export_text,export_button),output_table)
print('after layout_output')
# set the layout of the tabs
layout_p1 = column(experiment_slider, p1,
row(
column(fid_slider,p2),
column(button_refit, p3)
),
)
tab_relaxation = Panel(child = layout_p1, title = 'Relaxation')
tab_parameters = Panel(child = layout_p4, title = 'Parameters')
tab_input = Panel(child = layout_input, title = 'Data In')
tab_output = Panel(child = layout_output, title = 'Data Out')
# initialize tabs object with 3 tabs
tabs = Tabs(tabs = [tab_relaxation, tab_parameters,
tab_input, tab_output])
print('tabs')
doc.add_root(tabs)
doc.add_root(source) # i need to add source to detect changes
doc.add_root(source2)
print('tab tab')
def dashboard(request):
current_user = request.user
user = Profile.objects.get(user = current_user)
if request.user.is_authenticated:
all_stocks = []
all_portfolios_of_current_users = Portfolio.objects.filter(user = user)
for indv in all_portfolios_of_current_users:
all_stocks.append(indv.stock)
else:
return redirect(landingpage)
if not all_stocks:
all_stocks = ["MMM", "ABT", "ABBV", "ABMD", "ACN", "ATVI", "AMZN", "FB", "GOOGL", "GOOG", "JNJ" ]
if request.method == 'POST':
symbol = request.POST.get('symbol')
# Get Stock DataFrame
# msft = yf.Ticker("MSFT")
msft = yf.Ticker(symbol)
hist = msft.history(period='max')
# Define constants
W_PLOT = 1000
H_PLOT = 400
TOOLS = 'pan,wheel_zoom,hover,reset'
VBAR_WIDTH = 0.2
RED = Category20[7][6]
GREEN = Category20[5][4]
BLUE = Category20[3][0]
BLUE_LIGHT = Category20[3][1]
ORANGE = Category20[3][2]
PURPLE = Category20[9][8]
BROWN = Category20[11][10]
def get_symbol_df(symbol=None):
df = pd.DataFrame(hist)[-50:]
df.reset_index(inplace=True)
df["Date"] = pd.to_datetime(df["Date"])
return df
def plot_stock_price(stock):
p = figure(plot_width=W_PLOT, plot_height=H_PLOT, tools=TOOLS,
title="Stock price", toolbar_location='above')
inc = stock.data['Close'] > stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
# map dataframe indices to date strings and use as label overrides
p.xaxis.major_label_overrides = {i+int(stock.data['index'][0]): date.strftime('%b %d') for i, date in enumerate(pd.to_datetime(stock.data["Date"]))}
p.xaxis.bounds = (stock.data['index'][0], stock.data['index'][-1])
p.segment(x0='index', x1='index', y0='Low', y1='High', color=RED, source=stock, view=view_inc)
p.segment(x0='index', x1='index', y0='Low', y1='High', color=GREEN, source=stock, view=view_dec)
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=BLUE, line_color=BLUE,source=stock,view=view_inc, name="price")
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=RED, line_color=RED,source=stock,view=view_dec, name="price")
p.legend.location = "top_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
p.yaxis.formatter = NumeralTickFormatter(format='$ 0,0[.]000')
p.x_range.range_padding = 0.05
p.xaxis.ticker.desired_num_ticks = 40
p.xaxis.major_label_orientation = 3.14/4
# Select specific tool for the plot
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ["price"]
# Creating tooltips
price_hover.tooltips = [("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{$0,0.00}"),
("Close", "@Close{$0,0.00}"),("Volume", "@Volume{($ 0.00 a)}")]
price_hover.formatters={"Date": 'datetime'}
return p
stock = ColumnDataSource(data=dict(Date=[], Open=[], Close=[], High=[], Low=[],index=[]))
df = get_symbol_df(symbol)
stock.data = stock.from_df(df)
elements = list()
# update_plot()
p_stock = plot_stock_price(stock)
elements.append(p_stock)
curdoc().add_root(column(elements))
curdoc().title = 'Bokeh stocks historical prices'
#show(p_stock)
script, div = components(p_stock)
kwargs = {'script': script, 'div': div}
stock_list = all_stocks
er = EventRegistry(apiKey = '3a7a023b-6280-4476-bec0-5c9ff41770bd')
q = QueryArticlesIter(
keywords = QueryItems.OR(stock_list),
dataType = ["news"],
lang = 'eng'
)
news_list = q.execQuery(er, sortBy = ["rel","date","sourceImportance"], maxItems = 5, )
url = "https://apidojo-yahoo-finance-v1.p.rapidapi.com/stock/get-detail"
querystring = {"region":"US","lang":"en","symbol":symbol}
headers = {
'x-rapidapi-host': "apidojo-yahoo-finance-v1.p.rapidapi.com",
'x-rapidapi-key': "ff4cff81d5msh596a92309f2f43ap13412cjsn002bc2a74b4d"
}
response = requests.request("GET", url, headers=headers, params=querystring)
json_dict = json.loads(response.text)
for key in json_dict.keys():
if key == "summaryDetail":
try:
stock_close = json_dict[key]['previousClose']['raw']
except KeyError:
stock_close = ''
try:
stock_open = json_dict[key]['open']['raw']
except KeyError:
stock_open = ''
try:
stock_bid = json_dict[key]['bid']['raw']
except KeyError:
stock_bid = ''
try:
stock_ask = json_dict[key]['ask']['raw']
except KeyError:
stock_ask = ''
try:
stock_volume = json_dict[key]['volume']['fmt']
except KeyError:
stock_volume = ''
try:
stock_averageVolume = json_dict[key]['averageVolume']['fmt']
except KeyError:
stock_averageVolume = ''
try:
stock_marketCap = json_dict[key]['marketCap']['fmt']
except KeyError:
stock_marketCap = ''
if key == "defaultKeyStatistics":
try:
stock_weekChange = json_dict[key]['52WeekChange']['fmt']
except KeyError:
stock_weekChange = ''
try:
stock_beta = json_dict[key]['beta']['fmt']
except KeyError:
stock_beta = ''
try:
stock_EPS = json_dict[key]['trailingEps']['raw']
except KeyError:
stock_EPS = ''
try:
stock_PE = json_dict[key]['forwardPE']['fmt']
except KeyError:
stock_PE = ''
return render(request, 'dashboard.html', {**kwargs, 'news_list' : news_list,'stock_close':stock_close,'stock_open':stock_open,'stock_bid':stock_bid,'stock_ask':stock_ask,'stock_volume':stock_volume,'stock_averageVolume':stock_averageVolume,'stock_marketCap':stock_marketCap,'stock_weekChange':stock_weekChange,'stock_beta':stock_beta,'stock_EPS':stock_EPS,'stock_PE':stock_PE,'stock_list':stock_list,'symbol':symbol})
else:
symbol = all_stocks[0]
stock_list = all_stocks
msft = yf.Ticker(symbol)
hist = msft.history(period='max')
# Define constants
W_PLOT = 1000
H_PLOT = 400
TOOLS = 'pan,wheel_zoom,hover,reset'
VBAR_WIDTH = 0.2
RED = Category20[7][6]
GREEN = Category20[5][4]
BLUE = Category20[3][0]
BLUE_LIGHT = Category20[3][1]
ORANGE = Category20[3][2]
PURPLE = Category20[9][8]
BROWN = Category20[11][10]
def get_symbol_df(symbol=None):
df = pd.DataFrame(hist)[-50:]
df.reset_index(inplace=True)
df["Date"] = pd.to_datetime(df["Date"])
return df
def plot_stock_price(stock):
p = figure(plot_width=W_PLOT, plot_height=H_PLOT, tools=TOOLS,
title="Stock price", toolbar_location='above')
inc = stock.data['Close'] > stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
# map dataframe indices to date strings and use as label overrides
p.xaxis.major_label_overrides = {i+int(stock.data['index'][0]): date.strftime('%b %d') for i, date in enumerate(pd.to_datetime(stock.data["Date"]))}
p.xaxis.bounds = (stock.data['index'][0], stock.data['index'][-1])
p.segment(x0='index', x1='index', y0='Low', y1='High', color=RED, source=stock, view=view_inc)
p.segment(x0='index', x1='index', y0='Low', y1='High', color=GREEN, source=stock, view=view_dec)
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=BLUE, line_color=BLUE,source=stock,view=view_inc, name="price")
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=RED, line_color=RED,source=stock,view=view_dec, name="price")
p.legend.location = "top_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
p.yaxis.formatter = NumeralTickFormatter(format='$ 0,0[.]000')
p.x_range.range_padding = 0.05
p.xaxis.ticker.desired_num_ticks = 40
p.xaxis.major_label_orientation = 3.14/4
# Select specific tool for the plot
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ["price"]
# Creating tooltips
price_hover.tooltips = [("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{$0,0.00}"),
("Close", "@Close{$0,0.00}"),("Volume", "@Volume{($ 0.00 a)}")]
price_hover.formatters={"Date": 'datetime'}
return p
stock = ColumnDataSource(data=dict(Date=[], Open=[], Close=[], High=[], Low=[],index=[]))
df = get_symbol_df(symbol)
stock.data = stock.from_df(df)
elements = list()
# update_plot()
p_stock = plot_stock_price(stock)
elements.append(p_stock)
curdoc().add_root(column(elements))
curdoc().title = 'Bokeh stocks historical prices'
#show(p_stock)
script, div = components(p_stock)
kwargs = {'script': script, 'div': div}
er = EventRegistry(apiKey = '3a7a023b-6280-4476-bec0-5c9ff41770bd')
q = QueryArticlesIter(
keywords = QueryItems.OR(stock_list),
dataType = ["news"],
lang = 'eng'
)
news_list = q.execQuery(er, sortBy = ["rel","date","sourceImportance"], maxItems = 5, )
url = "https://apidojo-yahoo-finance-v1.p.rapidapi.com/stock/get-detail"
querystring = {"region":"US","lang":"en","symbol":symbol}
headers = {
'x-rapidapi-host': "apidojo-yahoo-finance-v1.p.rapidapi.com",
'x-rapidapi-key': "ff4cff81d5msh596a92309f2f43ap13412cjsn002bc2a74b4d"
}
response = requests.request("GET", url, headers=headers, params=querystring)
json_dict = json.loads(response.text)
for key in json_dict.keys():
if key == "summaryDetail":
try:
stock_close = json_dict[key]['previousClose']['raw']
except KeyError:
stock_close = ''
try:
stock_open = json_dict[key]['open']['raw']
except KeyError:
stock_open = ''
try:
stock_bid = json_dict[key]['bid']['raw']
except KeyError:
stock_bid = ''
try:
stock_ask = json_dict[key]['ask']['raw']
except KeyError:
stock_ask = ''
try:
stock_volume = json_dict[key]['volume']['fmt']
except KeyError:
stock_volume = ''
try:
stock_averageVolume = json_dict[key]['averageVolume']['fmt']
except KeyError:
stock_averageVolume = ''
try:
stock_marketCap = json_dict[key]['marketCap']['fmt']
except KeyError:
stock_marketCap = ''
if key == "defaultKeyStatistics":
try:
stock_weekChange = json_dict[key]['52WeekChange']['fmt']
except KeyError:
stock_weekChange = ''
try:
stock_beta = json_dict[key]['beta']['fmt']
except KeyError:
stock_beta = ''
try:
stock_EPS = json_dict[key]['trailingEps']['raw']
except KeyError:
stock_EPS = ''
try:
stock_PE = json_dict[key]['forwardPE']['fmt']
except KeyError:
stock_PE = ''
return render(request, 'dashboard.html', {**kwargs, 'news_list' : news_list,'stock_close':stock_close,'stock_open':stock_open,'stock_bid':stock_bid,'stock_ask':stock_ask,'stock_volume':stock_volume,'stock_averageVolume':stock_averageVolume,'stock_marketCap':stock_marketCap,'stock_weekChange':stock_weekChange,'stock_beta':stock_beta,'stock_EPS':stock_EPS,'stock_PE':stock_PE,'stock_list':stock_list,'symbol':symbol})
elif symbol == 'EBAY':
lengthOfTime = '1y'
break
else:
print("Oops you typed it in wrong, try again... \n")
df = get_symbol_df(symbol, lengthOfTime)
stock = ColumnDataSource(data=dict(date=[],
Open=[],
Close=[],
high=[],
low=[],
volume=[],
volHeight=[],
index=[]))
stock.data = stock.from_df(df)
p_stock = plot_stock_price(stock, symbol, lengthOfTime)
while True:
fifty = input("An orange 50 period moving average? Y/N: ").upper()
if fifty == 'Y':
plot_fifty_ma(df, p_stock)
break
elif fifty == 'N':
break
else:
print("Oops you typed it in wrong, try again... \n")
while True:
bollinger = input(
def plot():
if request.method == 'POST':
symbol = request.form['symbol']
# Get Stock DataFrame
# msft = yf.Ticker("MSFT")
msft = yf.Ticker(symbol)
hist = msft.history(period='max')
# Define constants
W_PLOT = 1000
H_PLOT = 400
TOOLS = 'pan,wheel_zoom,hover,reset'
VBAR_WIDTH = 0.2
RED = Category20[7][6]
GREEN = Category20[5][4]
BLUE = Category20[3][0]
BLUE_LIGHT = Category20[3][1]
ORANGE = Category20[3][2]
PURPLE = Category20[9][8]
BROWN = Category20[11][10]
def get_symbol_df(symbol=None):
df = pd.DataFrame(hist)[-50:]
df.reset_index(inplace=True)
df["Date"] = pd.to_datetime(df["Date"])
return df
def plot_stock_price(stock):
p = figure(plot_width=W_PLOT,
plot_height=H_PLOT,
tools=TOOLS,
title="Stock price",
toolbar_location='above')
inc = stock.data['Close'] > stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
# map dataframe indices to date strings and use as label overrides
p.xaxis.major_label_overrides = {
i + int(stock.data['index'][0]): date.strftime('%b %d')
for i, date in enumerate(pd.to_datetime(stock.data["Date"]))
}
p.xaxis.bounds = (stock.data['index'][0], stock.data['index'][-1])
p.segment(x0='index',
x1='index',
y0='Low',
y1='High',
color=RED,
source=stock,
view=view_inc)
p.segment(x0='index',
x1='index',
y0='Low',
y1='High',
color=GREEN,
source=stock,
view=view_dec)
p.vbar(x='index',
width=VBAR_WIDTH,
top='Open',
bottom='Close',
fill_color=BLUE,
line_color=BLUE,
source=stock,
view=view_inc,
name="price")
p.vbar(x='index',
width=VBAR_WIDTH,
top='Open',
bottom='Close',
fill_color=RED,
line_color=RED,
source=stock,
view=view_dec,
name="price")
p.legend.location = "top_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
p.yaxis.formatter = NumeralTickFormatter(format='$ 0,0[.]000')
p.x_range.range_padding = 0.05
p.xaxis.ticker.desired_num_ticks = 40
p.xaxis.major_label_orientation = 3.14 / 4
# Select specific tool for the plot
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ["price"]
# Creating tooltips
price_hover.tooltips = [("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{$0,0.00}"),
("Close", "@Close{$0,0.00}"),
("Volume", "@Volume{($ 0.00 a)}")]
price_hover.formatters = {"Date": 'datetime'}
return p
stock = ColumnDataSource(
data=dict(Date=[], Open=[], Close=[], High=[], Low=[], index=[]))
#stock = AjaxDataSource(data=dict(Date=[], Open=[], Close=[], High=[], Low=[],index=[]),data_url='http://127.0.0.1:5000/plot',polling_interval=1000,mode='append')
#symbol = 'msft'
df = get_symbol_df(symbol)
stock.data = stock.from_df(df)
elements = list()
# update_plot()
p_stock = plot_stock_price(stock)
elements.append(p_stock)
curdoc().add_root(column(elements))
curdoc().title = 'Bokeh stocks historical prices'
#show(p_stock)
script, div = components(p_stock)
kwargs = {'script': script, 'div': div}
#kwargs['title'] = 'bokeh-with-flask'
return render_template('index.html', **kwargs)
#return redirect(url_for('index', **kwargs))
#return kwargs
#return json.dumps(json_item(p_stock,"myplot"))
#return redirect(url_for('index'))
return "OK"
height=400,
width=400)
OD['thr'] = OD[Z].apply(lambda x: 1 if x > threhsold_slider.value else 0)
OD['thr'] = OD['thr'].astype('str')
OD['x'] = OD[X]
OD['y'] = OD[Y]
seg_dictionary = {}
for col_name in OD.columns:
# if col_name not in [X,Y]:
seg_dictionary[col_name] = OD[col_name]
datapoints_source = ColumnDataSource()
datapoints_source.data = seg_dictionary
th = list(set(OD['thr']))
mapper = factor_cmap('thr', 'Category10_3', th)
st = Title()
st.text = "Segmentation Plot"
seg_plot.title = st
seg_plot.scatter("x",
"y",
source=datapoints_source,
legend="thr",
alpha=0.5,
size=12,
color=mapper)
'No_of_paid_rides (Rides with (Fare - Coupon value) > 0)'
]
display_columns = df.columns
samp = 1
dfs = df.sample(int(samp * len(df) / 100), random_state=42)
dictionary = dict(x=dfs[X], y=dfs[Y], thr=dfs['threshold'])
for col_name in display_columns:
# if col_name not in [X,Y]:
dictionary[col_name] = dfs[col_name]
datapoints_source = ColumnDataSource()
datapoints_source.data = dictionary
dpal = ColumnDataSource()
dpal.data = dict(th=list(set(df['Rental_Counts']))
#time=datapoints_df['start_datetime']
)
from bokeh.models.annotations import Title
import numpy as np
def my_slider_handler():
#slider.value=new
X = Xselect.value
