def update_graph(n, df=dfresample.copy(), dfcontext=dfcontext):
"""
main loop for resfreshing the data and to display the chart. It gets triggered every n second as per our
settings.
"""
dfmp_list = dfcontext[0]
df_distribution = dfcontext[1]
distribution_hist = df_distribution.copy()
dflive = pd.read_csv('live.txt')
dflive.iloc[:, 2:] = dflive.iloc[:, 2:].apply(pd.to_numeric)
dflive = datetime(dflive)
dflive = get_rf(dflive.copy())
dflive = dflive.resample(str(freq) + 'min').agg({
'symbol': 'last',
'datetime': 'last',
'Open': 'first',
'High': 'max',
'Low': 'min',
'Close': 'last',
'Volume': 'sum',
'rf': 'sum'
})
dflive = dflive.dropna()
dfcontext_live = get_context(dflive.copy(),
freq=freq,
ticksize=ticksz,
style=mode,
session_hr=trading_hr)
dfmp_live = dfcontext_live[
0] # it will be in list format so take [0] slice for current day MP data frame
df_distribution_live = dfcontext_live[1]
df_distribution_concat = pd.concat(
[distribution_hist, df_distribution_live])
df_distribution_concat = df_distribution_concat.reset_index(inplace=False,
drop=True)
df_updated_rank = get_dayrank(df_distribution_concat, mean_val)
ranking = df_updated_rank[0]
power1 = ranking.power1 # Non-normalised IB strength
power = ranking.power # Normalised IB strength for dynamic shape size for markers at bottom
breakdown = df_updated_rank[1]
dh_list = ranking.highd
dl_list = ranking.lowd
# !!! get context based on IB It is predictive value caculated by using various IB stats and previous day's value area
# IB is 1st 1 hour of the session. Not useful for scrips with global 24 x 7 session
context_ibdf = get_ibrank(mean_val, ranking)
ibpower1 = context_ibdf[0].ibpower1 # Non-normalised IB strength
ibpower = context_ibdf[
0].IB_power # Normalised IB strength for dynamic shape size for markers at bottom
ibbreakdown = context_ibdf[1]
ib_high_list = context_ibdf[0].ibh
ib_low_list = context_ibdf[0].ibl
dfmp_list = dfmp_list + dfmp_live
df_merge = pd.concat([df, dflive])
df_merge2 = df_merge.drop_duplicates('datetime')
df = df_merge2.copy()
fig = go.Figure(data=[
go.Candlestick(x=df['datetime'],
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'],
showlegend=True,
name=symbol,
opacity=0.3)
]) # To make candlesticks more prominent increase the opacity
# !!! get TPO for each day
DFList = [group[1] for group in df.groupby(df.index.date)]
for i in range(len(dfmp_list)): # test the loop with i=1
# df1 is used for datetime axis, other dataframe we have is df_mp but it is not a timeseries
df1 = DFList[i].copy()
df_mp = dfmp_list[i]
irank = ranking.iloc[i]
# df_mp['i_date'] = df1['datetime'][0]
df_mp['i_date'] = irank.date
# # @todo: background color for text
df_mp['color'] = np.where(
np.logical_and(df_mp['close'] > irank.vallist,
df_mp['close'] < irank.vahlist), 'green', 'white')
df_mp = df_mp.set_index('i_date', inplace=False)
fig.add_trace(
go.Scattergl(x=df_mp.index,
y=df_mp.close,
mode="text",
name=str(df_mp.index[0]),
text=df_mp.alphabets,
showlegend=False,
textposition="top right",
textfont=dict(family="verdana",
size=6,
color=df_mp.color)))
if power1[i] < 0:
my_rgb = 'rgba({power}, 3, 252, 0.5)'.format(power=abs(165))
else:
my_rgb = 'rgba(23, {power}, 3, 0.5)'.format(power=abs(252))
brk_f_list_maj = []
f = 0
for f in range(len(breakdown.columns)):
brk_f_list_min = []
for index, rows in breakdown.iterrows():
brk_f_list_min.append(index + str(': ') + str(rows[f]) +
'<br />')
brk_f_list_maj.append(brk_f_list_min)
breakdown_values = '' # for bubbles
for st in brk_f_list_maj[i]:
breakdown_values += st
# .........................
ibrk_f_list_maj = []
g = 0
for g in range(len(ibbreakdown.columns)):
ibrk_f_list_min = []
for index, rows in ibbreakdown.iterrows():
ibrk_f_list_min.append(index + str(': ') + str(rows[g]) +
'<br />')
ibrk_f_list_maj.append(ibrk_f_list_min)
ibreakdown_values = '' # for squares
for ist in ibrk_f_list_maj[i]:
ibreakdown_values += ist
# irank.power1
# ..................................
fig.add_trace(
go.Scattergl(
x=[irank.date],
y=[dfresample['High'].max()],
mode="markers",
marker=dict(color=my_rgb,
size=0.90 * power[i],
line=dict(color='rgb(17, 17, 17)', width=2)),
# marker_symbol='square',
hovertext=[
'<br />Insights:<br />VAH: {}<br /> POC: {}<br /> VAL: {}<br /> Balance Target: {}<br /> Day Type: {}<br />strength: {}<br />BreakDown: {}<br />{}<br />{}'
.format(irank.vahlist, irank.poclist, irank.vallist,
irank.btlist, irank.daytype, irank.power, '',
'-------------------', breakdown_values)
],
showlegend=False))
# !!! we will use this for hover text at bottom for developing day
if ibpower1[i] < 0:
ib_rgb = 'rgba(165, 3, 252, 0.5)'
else:
ib_rgb = 'rgba(23, 252, 3, 0.5)'
fig.add_trace(
go.Scattergl(
x=[irank.date],
y=[dfresample['Low'].min()],
mode="markers",
marker=dict(color=ib_rgb,
size=0.40 * ibpower[i],
line=dict(color='rgb(17, 17, 17)', width=2)),
marker_symbol='square',
hovertext=[
'<br />Insights:<br />Vol_mean: {}<br /> Vol_Daily: {}<br /> RF_mean: {}<br /> RF_daily: {}<br /> IBvol_mean: {}<br /> IBvol_day: {}<br /> IB_RFmean: {}<br /> IB_RFday: {}<br />strength: {}<br />BreakDown: {}<br />{}<br />{}'
.format(mean_val['volume_mean'], irank.volumed,
mean_val['rf_mean'], irank.rfd,
mean_val['volib_mean'], irank.ibvol,
mean_val['ibrf_mean'], irank.ibrf, ibpower[i], '',
'......................', ibreakdown_values)
],
showlegend=False))
lvns = irank.lvnlist
for lvn in lvns:
if lvn > irank.vallist and lvn < irank.vahlist:
fig.add_shape(
type="line",
x0=df1.iloc[0]['datetime'],
y0=lvn,
x1=df1.iloc[5]['datetime'],
y1=lvn,
line=dict(
color="darksalmon",
width=2,
dash="dashdot",
),
)
# ib high and low
fig.add_shape(
type="line",
x0=df1.iloc[0]['datetime'],
y0=ib_low_list[i],
x1=df1.iloc[0]['datetime'],
y1=ib_high_list[i],
line=dict(
color="cyan",
width=3,
),
)
# day high and low
fig.add_shape(
type="line",
x0=df1.iloc[0]['datetime'],
y0=dl_list[i],
x1=df1.iloc[0]['datetime'],
y1=dh_list[i],
line=dict(
color="gray",
width=1,
dash="dashdot",
),
)
# ltp marker
ltp = df1.iloc[-1]['Close']
if ltp >= irank.poclist:
ltp_color = 'green'
else:
ltp_color = 'red'
fig.add_trace(
go.Scattergl(x=[df1.iloc[-1]['datetime']],
y=[df1.iloc[-1]['Close']],
mode="text",
name="last traded price",
text=['last ' + str(df1.iloc[-1]['Close'])],
textposition="bottom right",
textfont=dict(size=12, color=ltp_color),
showlegend=False))
fig.layout.xaxis.color = 'white'
fig.layout.yaxis.color = 'white'
fig.layout.autosize = True
fig["layout"]["height"] = 700
fig["layout"]["width"] = 2100
# fig.layout.hovermode = 'x' # UNcomment this if you want to see insights for both squares and bubbles
fig.update_xaxes(title_text='Time',
title_font=dict(size=18, color='white'),
tickangle=45,
tickfont=dict(size=8, color='white'),
showgrid=False,
dtick=len(dfmp_list))
fig.update_yaxes(title_text=symbol,
title_font=dict(size=18, color='white'),
tickfont=dict(size=12, color='white'),
showgrid=False)
fig.layout.update(template="plotly_dark",
title="@" + abc()[1],
autosize=True,
xaxis=dict(showline=True, color='white'),
yaxis=dict(showline=True,
color='white',
autorange=True,
fixedrange=False))
fig["layout"]["xaxis"]["rangeslider"]["visible"] = False
fig["layout"]["xaxis"]["tickformat"] = "%H:%M:%S"
return fig
def update_figure(value):
dfresample = dfhist[(dfhist.datetime > dates[value[0]])
& (dfhist.datetime < dates[value[1]])]
DFList = [group[1] for group in dfresample.groupby(dfresample.index.date)]
# !!! for context based bubbles at the top with text hovers
dfcontext = get_context(dfresample,
freq=freq,
ticksize=ticksz,
style=mode,
session_hr=trading_hr)
# get market profile DataFrame and ranking as a series for each day.
# @todo: IN next version, display the ranking DataFrame with drop-down menu
dfmp_list = dfcontext[0]
df_distribution = dfcontext[1]
df_ranking = get_dayrank(df_distribution.copy(), mean_val)
ranking = df_ranking[0]
power1 = ranking.power1 # Non-normalised IB strength
power = ranking.power # Normalised IB strength for dynamic shape size for markers at bottom
breakdown = df_ranking[1]
dh_list = ranking.highd
dl_list = ranking.lowd
# IB is 1st 1 hour of the session. Not useful for scrips with global 24 x 7 session
context_ibdf = get_ibrank(mean_val, ranking)
ibpower1 = context_ibdf[0].ibpower1 # Non-normalised IB strength
ibpower = context_ibdf[
0].IB_power # Normalised IB strength for dynamic shape size for markers at bottom
ibbreakdown = context_ibdf[1]
ib_high_list = context_ibdf[0].ibh
ib_low_list = context_ibdf[0].ibl
fig = go.Figure(data=[
go.Candlestick(x=dfresample['datetime'],
open=dfresample['Open'],
high=dfresample['High'],
low=dfresample['Low'],
close=dfresample['Close'],
showlegend=True,
name=symbol,
opacity=0.3)
]) # To make candlesticks more prominent increase the opacity
for i in range(len(dfmp_list)): # test the loop with i=1
df1 = DFList[i].copy()
df_mp = dfmp_list[i]
irank = ranking.iloc[i] # select single row from ranking df
df_mp['i_date'] = irank.date
# # @todo: background color for text
df_mp['color'] = np.where(
np.logical_and(df_mp['close'] > irank.vallist,
df_mp['close'] < irank.vahlist), 'green', 'white')
df_mp = df_mp.set_index('i_date', inplace=False)
fig.add_trace(
go.Scattergl(x=df_mp.index,
y=df_mp.close,
mode="text",
name=str(df_mp.index[0]),
text=df_mp.alphabets,
showlegend=False,
textposition="top right",
textfont=dict(family="verdana",
size=6,
color=df_mp.color)))
if power1[i] < 0:
my_rgb = 'rgba({power}, 3, 252, 0.5)'.format(power=abs(165))
else:
my_rgb = 'rgba(23, {power}, 3, 0.5)'.format(power=abs(252))
brk_f_list_maj = []
f = 0
for f in range(len(breakdown.columns)):
brk_f_list_min = []
for index, rows in breakdown.iterrows():
brk_f_list_min.append(index + str(': ') + str(rows[f]) +
'<br />')
brk_f_list_maj.append(brk_f_list_min)
breakdown_values = '' # for bubbles
for st in brk_f_list_maj[i]:
breakdown_values += st
# .........................
ibrk_f_list_maj = []
g = 0
for g in range(len(ibbreakdown.columns)):
ibrk_f_list_min = []
for index, rows in ibbreakdown.iterrows():
ibrk_f_list_min.append(index + str(': ') + str(rows[g]) +
'<br />')
ibrk_f_list_maj.append(ibrk_f_list_min)
ibreakdown_values = '' # for squares
for ist in ibrk_f_list_maj[i]:
ibreakdown_values += ist
# ..................................
fig.add_trace(
go.Scattergl(
# x=[df1.iloc[4]['datetime']],
x=[irank.date],
y=[dfresample['High'].max()],
mode="markers",
marker=dict(color=my_rgb,
size=0.90 * power[i],
line=dict(color='rgb(17, 17, 17)', width=2)),
# marker_symbol='square',
hovertext=[
'<br />Insights:<br />VAH: {}<br /> POC: {}<br /> VAL: {}<br /> Balance Target: {}<br /> Day Type: {}<br />strength: {}<br />BreakDown: {}<br />{}<br />{}'
.format(irank.vahlist, irank.poclist, irank.vallist,
irank.btlist, irank.daytype, irank.power, '',
'-------------------', breakdown_values)
],
showlegend=False))
if ibpower1[i] < 0:
ib_rgb = 'rgba(165, 3, 252, 0.5)'
else:
ib_rgb = 'rgba(23, 252, 3, 0.5)'
fig.add_trace(
go.Scattergl(
# x=[df1.iloc[4]['datetime']],
x=[irank.date],
y=[dfresample['Low'].min()],
mode="markers",
marker=dict(color=ib_rgb,
size=0.40 * ibpower[i],
line=dict(color='rgb(17, 17, 17)', width=2)),
marker_symbol='square',
hovertext=[
'<br />Insights:<br />Vol_mean: {}<br /> Vol_Daily: {}<br /> RF_mean: {}<br /> RF_daily: {}<br /> IBvol_mean: {}<br /> IBvol_day: {}<br /> IB_RFmean: {}<br /> IB_RFday: {}<br />strength: {}<br />BreakDown: {}<br />{}<br />{}'
.format(mean_val['volume_mean'], irank.volumed,
mean_val['rf_mean'], irank.rfd,
mean_val['volib_mean'], irank.ibvol,
mean_val['ibrf_mean'], irank.ibrf, ibpower[i], '',
'......................', ibreakdown_values)
],
showlegend=False))
lvns = irank.lvnlist
for lvn in lvns:
if lvn > irank.vallist and lvn < irank.vahlist:
fig.add_shape(
# Line Horizontal
type="line",
x0=df1.iloc[0]['datetime'],
y0=lvn,
x1=df1.iloc[5]['datetime'],
y1=lvn,
line=dict(
color="darksalmon",
width=2,
dash="dashdot",
),
)
fig.add_shape(
# Line Horizontal
type="line",
x0=df1.iloc[0]['datetime'],
y0=ib_low_list[i],
x1=df1.iloc[0]['datetime'],
y1=ib_high_list[i],
line=dict(
color="cyan",
width=3,
),
)
# day high and low
fig.add_shape(
# Line Horizontal
type="line",
x0=df1.iloc[0]['datetime'],
y0=dl_list[i],
x1=df1.iloc[0]['datetime'],
y1=dh_list[i],
line=dict(
color="gray",
width=1,
dash="dashdot",
),
)
ltp = dfresample.iloc[-1]['Close']
if ltp >= irank.poclist:
ltp_color = 'green'
else:
ltp_color = 'red'
fig.add_trace(
go.Scatter(x=[df1.iloc[-1]['datetime']],
y=[df1.iloc[-1]['Close']],
mode="text",
name="last traded price",
text=['last ' + str(df1.iloc[-1]['Close'])],
textposition="bottom right",
textfont=dict(size=11, color=ltp_color),
showlegend=False))
fig.layout.xaxis.color = 'white'
fig.layout.yaxis.color = 'white'
fig.layout.autosize = True
fig["layout"]["height"] = 550
fig.update_xaxes(title_text='Time',
title_font=dict(size=18, color='white'),
tickangle=45,
tickfont=dict(size=8, color='white'),
showgrid=False,
dtick=len(dfmp_list))
fig.update_yaxes(title_text=symbol,
title_font=dict(size=18, color='white'),
tickfont=dict(size=12, color='white'),
showgrid=False)
fig.layout.update(template="plotly_dark",
title="@" + abc()[1],
autosize=True,
xaxis=dict(showline=True, color='white'),
yaxis=dict(showline=True,
color='white',
autorange=True,
fixedrange=False))
fig["layout"]["xaxis"]["rangeslider"]["visible"] = False
fig["layout"]["xaxis"]["tickformat"] = "%H:%M:%S"
return fig
'High': 'max',
'Low': 'min',
'Close': 'last',
'Volume': 'sum',
'rf': 'sum'
})
dfresample = dfresample.dropna()
# slice df based on days_to_display parameter
dt1 = dfresample.index[-1]
sday1 = dt1 - timedelta(days_to_display)
dfresample = dfresample[(dfresample.index.date > sday1.date())]
# to save memory do all the calculations for context outside the loop
dfcontext = get_context(dfresample.copy(),
freq=freq,
ticksize=ticksz,
style=mode,
session_hr=trading_hr)
# warning: do not chnage the settings below. There are HTML tags get triggered for live updates. Took me a while to figure out
# ------------------------------------------------------------------------------
# App layout
app.layout = html.Div(
html.Div([
dcc.Location(id='url', refresh=False),
dcc.Link('For questions, ping me on Twitter',
href='https://twitter.com/beinghorizontal'),
html.Br(),
dcc.Link('FAQ and python source code',
href='http://www.github.com/beinghorizontal/tpo_project'),
html.H4('@beinghorizontal'),