def update_picker_dropdown(index):
todaydate = datetime.now()
index = index.split('-')[1]
#options = am.DataHandler().IndexConstituentsDict()
dh = MSDataManager()
options = dh.IndexConstituentsDict(index)
#options = {'AAPL': 'AAPL', 'MSFT': 'MSFT'}
#dh = am.DataHandler()
data = None
# def Ret52wData(tick):
# return dh.ReturnData(
# tick,
# start_date=(todaydate - relativedelta(weeks=52)),
# end_date=todaydate,
# )
# data = [{
# 'id': tick,
# 'Symbol': tick,
# 'Name': tick,
# '52ws': round(MomentumIndicators(Ret52wData(tick)).calcSharpe(252), 2),
# '52wh': Ret52wData(tick)['Close'].max(),
# '52wl': Ret52wData(tick)['Close'].min()
# } for tick in dh.ReturnIndexConstituents(index)]
return index, options, data
def calcStrategy(start_date, end_date, index):
full_market_returns = []
full_returns_bh = []
#dh = am.DataHandler(index)
dh = MSDataManager()
for ticker in dh.ReturnIndexConstituents(index):
df1 = dh.ReturnData(ticker, start_date=start_date, end_date=end_date,)
df, returnsStrat, returnsBH = MOM1(df1, start_date, end_date, 5)
full_market_returns.append(returnsStrat*100)
full_returns_bh.append(returnsBH*100)
return [np.average(np.array(full_market_returns)),
np.average(np.array(full_returns_bh))]
def calcReturns(stockPicker, start_date, end_date, strategyToTest, index):
dh = MSDataManager()
df1 = dh.ReturnData(stockPicker, start_date=start_date, end_date=end_date)
df, returnsStrat, returnsBH = globals()[strategyToTest](df1, start_date,
end_date, 5)
tickerSymbol = []
buyPrices = []
buyDates = []
buySignals = []
sellPrices = []
sellDates = []
sellSignals = []
returns = []
for i in range(len(df)):
sig1 = df['bsSig'].iat[i]
val1 = df['Close'].iat[i]
date1 = df.index[i]
signal = str(int(df['SignalMACD'][i])) + str(int(
df['SignalROC'][i])) + str(int(df['SignalSTOC'][i])) + str(
int(df['SignalRSI'][i]))
if sig1 == 1:
tickerSymbol.append(stockPicker)
priceBuy = val1
buyPrices.append(val1)
buyDates.append(date1)
buySignals.append(signal)
elif sig1 == -1:
priceSell = val1
sellPrices.append(val1)
sellDates.append(date1)
profit = (priceSell - priceBuy) / priceBuy
sellSignals.append(signal)
returns.append(round(profit * 100, 2))
data = np.array([
tickerSymbol, buyDates, buyPrices, buySignals, sellDates, sellPrices,
sellSignals, returns
]).T.tolist()
dff = pd.DataFrame(data=data,
columns=[
'Symbol', 'datebuy', 'pricebuy', 'buysignals',
'datesell', 'pricesell', 'sellsignals', 'perprofit'
])
return dff, returnsStrat, returnsBH
def average_close(df, index, ticker, fig):
#dh = am.DataHandler(index)
dh = MSDataManager()
data = None
#data = dh.ReturnDataRT(ticker)
#if not data.empty
if data is not None:
ftwh = data['52_week_high'].iloc[-1]
ftwl = data['52_week_low'].iloc[-1]
trace = go.Scatter(x = df.index, y = (np.zeros(len(df.index)) + ftwh), mode = 'lines', name = '52wh', showlegend = False, line = {'color': 'rgb(255, 255, 255)', 'width': 1, 'dash': 'dot'})
fig.append_trace(trace, 1, 1)
trace = go.Scatter(x = df.index, y = (np.zeros(len(df.index)) + df['Close'].mean()), mode = 'lines', name = 'Average', showlegend = False, line = {'color': 'rgb(255, 255, 255)', 'width': 1, 'dash': 'dot'})
fig.append_trace(trace, 1, 1)
trace = go.Scatter(x = df.index, y = (np.zeros(len(df.index)) + ftwl), mode = 'lines', name = '52wl', showlegend = False, line = {'color': 'rgb(255, 255, 255)', 'width': 1, 'dash': 'dot'})
fig.append_trace(trace, 1, 1)
return fig
def info_div_callback(ticker, n_intervals):
ticker_sym = ticker.split('-')[0].strip()
ticker_sec = ticker.split('-')[1].strip()
dh = MSDataManager()
mh = MarketHours('NYSE')
mo_class = 'cgreen' if mh.MarketOpen else 'cred'
mo_str = 'open' if mh.MarketOpen else 'closed'
latest_trade_date = dh.ReturnLatestStoredDate(ticker_sym)
latest_trade_date_str = latest_trade_date.strftime(
'%d-%m-%Y - %H:%M:%S')
data = dh.ReturnData(ticker_sym,
start_date=latest_trade_date -
relativedelta(years=1),
end_date=latest_trade_date,
limit=None)
latest = data['Close'].iloc[-1]
diff = (data['Close'][-2] - data['Close'][-1])
pct_change = diff / data['Close'][-2]
pl_class = 'cgreen' if diff > 0 else 'cred'
pl_sign = '+' if diff > 0 else '-'
# pct_change = f''
ftwh = data['Close'].values.max()
ftwl = data['Close'].values.min()
#market_cap = data['market_cap'].iloc[-1]
# market_cap = 0
#curr = str(data['currency'].iloc[-1]).replace('EUR', '€')
curr = 'USD'
children = [
dbc.Row([html.H6(f'{ticker_sec} ({ticker_sym})', className='')]),
dbc.Row([
dbc.Col(
[
html.P([
html.Span(
html.Strong(
f'{curr} {latest:.2f} ',
className=pl_class + ' large bold')),
html.Span(f'{pl_sign}{diff:.2f} ',
className=pl_class + ' small'),
html.Span(f'({pl_sign}{pct_change:.2f}%)',
className=pl_class + ' small'),
], ),
html.P(
html.Span(
f'As of {latest_trade_date_str}. Market {mo_str}.',
className=' small'))
],
md=12,
),
]),
]
# dbc.Row(
# html.Div([
# dcc.RangeSlider(
# disabled = True,
# min=ftwl,
# max=ftwh,
# value=[ftwl, latest, ftwh],
# # value=[1, 20, 40]
# marks={
# float(ftwl): {'label': f'{curr} {ftwl:.2f}', 'style': {'color': '#77b0b1'}},
# float(latest): {'label': f'{curr} {latest:.2f}', 'style': {'color': '#77b0b1'}},
# float(ftwh): {'label': f'{curr} {ftwh:.2f}', 'style': {'color': '#77b0b1'}}
# }
# )
# ],
# style={'display': 'block', 'width': '60%', 'padding': ''},
# className='mt-1'
# )
# )
# {latest_trade_date}
# latest}+({pct_change:.2f})')
return children
def get_fig(ticker, type_trace, studies, start_date, end_date, index):
# for ticker in ticker_list:
#dh = am.DataHandler(index)
dh = MSDataManager()
df = dh.ReturnData(ticker, start_date=start_date, end_date=end_date)
selected_subplots_studies = []
selected_first_row_studies = []
row = 2 # number of subplots
if studies != []:
for study in studies:
if study in subplot_traces:
row += 1 # increment number of rows only if the study needs a subplot
selected_subplots_studies.append(study)
else:
selected_first_row_studies.append(study)
fig = tools.make_subplots(rows=row,
shared_xaxes=True,
shared_yaxes=False,
cols=1,
print_grid=True,
vertical_spacing=0.05,
row_width=[0.2] * (row - 1) +
[1 - (row - 1) * 0.2])
fig.append_trace(globals()[type_trace](df), 1, 1)
fig['layout'][f'xaxis{row}'].update(tickangle=-0,
tickformat='%Y-%m-%d',
autorange=True,
showgrid=True,
mirror='ticks',
color='rgba(255, 255, 255, 1)',
tickcolor='rgba(255,255,255,1)')
fig['layout']['yaxis1'].update(
range=[df['Close'].min() / 1.3, df['Close'].max() * 1.05],
showgrid=True,
anchor='x1',
mirror='ticks',
layer='above traces',
color='rgba(255, 255, 255, 1)',
gridcolor='rgba(255, 255, 255, 1)')
fig['layout']['yaxis2'].update(
# range= [0, df['Volume'].max()*4],
# overlaying = 'y2',
# layer = 'below traces',
autorange=True,
# anchor = 'x1',
side='left',
showgrid=True,
title='D. Trad. Vol.',
color='rgba(255, 255, 255, 1)',
)
# if model != '' and model is not None:
# # bsSig, retStrat, retBH = globals()[strategyTest](df, start_date, end_date, 5)
# ml = load(model)
# fig = bsMarkers(df, fig)
# fig['layout'].update(annotations=[
# dict(
# x = df.index[int(round(len(df.index)/2, 0))],
# y = df['Close'].max()*1.02,
# text = 'Your Strategy: ' + str(round(retStrat*100, 0)) + ' % \nBuy&Hold: ' + str(round(retBH*100, 0)) + '%',
# align = 'center',
# font = dict(
# size = 16,
# color = 'rgb(255, 255, 255)'),
# showarrow = False,
# )
# ]
# )
for study in selected_first_row_studies:
fig = globals()[study](df, fig) # add trace(s) on fig's first row
row = 2
fig = vol_traded(df, fig, row)
for study in selected_subplots_studies:
row += 1
fig = globals()[study](df, fig, row) # plot trace on new row
fig['layout'].update(chart_layout)
fig['layout']['xaxis'].update(rangeselector=dict(buttons=list([
dict(count=1, label='1M', step='month', stepmode='backward'),
dict(count=6, label='6M', step='month', stepmode='backward'),
dict(count=1, label='YTD', step='year', stepmode='todate'),
dict(count=1, label='1Y', step='year', stepmode='backward'),
dict(count=3, label='3Y', step='year', stepmode='backward'),
dict(step='all', label='MAX')
])),
rangeslider=dict(visible=False),
type="date")
return fig
import colorlover as cl
colorscale = cl.scales['12']['qual']['Paired']
cgreen = 'rgb(12, 205, 24)'
cred = 'rgb(205, 12, 24)'
# Set number of allowed charts
num_charts = 4
server = app.server
#conf = am.Config()
#dh = am.DataHandler('DAX')
dh = MSDataManager()
options = dh.IndexConstituentsDict('SPY')
spy_info = pd.read_csv('./data/spy.csv')
# options = [{'label': 'AAPL', 'value': 'AAPL'}, {'label': 'MSFT', 'value': 'TT'}]
indicators = [
{
'label': '10 day exponetial moving average',
'value': 'EMA10',
'subplot': False
},
{
'label': '30 day exponetial moving average',
'value': 'EMA30',
'subplot': False
},
{
def info_div_callback(ticker, n_intervals, index, timeRange):
#dh = am.DataHandler(index)
dh = MSDataManager()
#print(index)
mh = MarketHours('NYSE')
data = None
#data = dh.ReturnDataRT(ticker)
#children = ''
#if not data.empty
data = dh.ReturnData(ticker, limit=2)
#if data is not None:
latest = data['Close'].iloc[-1]
latest_trade_date = dh.ReturnLatestStoredDate(ticker).strftime(
'%Y:%M:%d - %H:%M:%S')
diff = data['Close'].diff()[-1]
pct_change = data['Close'].pct_change()[-1]
ftwh = data['Close'].values.max()
ftwl = data['Close'].values.min()
#market_cap = data['market_cap'].iloc[-1]
market_cap = 0
#curr = str(data['currency'].iloc[-1]).replace('EUR', '€')
curr = 'USD'
children = [
html.Div([
html.Table(
[
# html.Tr([
# html.Td(html.H2(ticker)),
# html.Td(),
# html.Td(),
# html.Td('Market cap:'),
# html.Td(f'{curr} {market_cap:.2f}')
# ]),
# html.Tr([
# html.Td([
# html.Span(f'{curr}{latest:.2f}'),
# html.Span(f'{diff:.2f} + ({pct_change:.2f})')
# ], className = 'small'),
# html.Td(),
# html.Td(),
# html.Td(html.Span('Market open', style={'color': 'green'}) if mh.MarketOpen() else html.Span('Market closed', style={'color': 'red'})),
# html.Td()
# ]),
# html.Tr([
# html.Td([html.Span(str(latest_trade_date))]),
# html.Td(),
# html.Td(),
# html.Td(),
# html.Td()
# ]),
],
className='small'),
]),
# html.Div([
# dcc.RangeSlider(
# disabled = True,
# min=ftwl,
# max=ftwh,
# value=[ftwl, latest, ftwh],
# marks={
# ftwl: {'label': curr + str(ftwl), 'style': {'color': '#77b0b1'}},
# latest: {'label': curr + str(latest), 'style': {'color': '#77b0b1'}},
# ftwh: {'label': curr + str(ftwh), 'style': {'color': '#77b0b1'}}
# }
# )
# ], style={'display': 'block', 'width': '60%', 'padding': '8px 40px'})
]
# children = html.H1(ticker)
return children
def get_fig(ticker, type_trace, studies, strategyTest, start_date, end_date,
index):
# for ticker in ticker_list:
#dh = am.DataHandler(index)
dh = MSDataManager()
df = dh.ReturnData(ticker, start_date=start_date, end_date=end_date)
subplot_traces = [ # first row traces
'RSI', 'ROC', 'MACD', 'STOC', 'D_RETURNS', 'ATR', 'ADX', 'WR'
]
selected_subplots_studies = []
selected_first_row_studies = []
row = 2 # number of subplots
if studies != []:
for study in studies:
if study in subplot_traces:
row += 1 # increment number of rows only if the study needs a subplot
selected_subplots_studies.append(study)
else:
selected_first_row_studies.append(study)
fig = tools.make_subplots(rows=row,
shared_xaxes=True,
shared_yaxes=False,
cols=1,
print_grid=True,
vertical_spacing=0.05,
row_width=[0.2] * (row - 1) +
[1 - (row - 1) * 0.2])
fig.append_trace(globals()[type_trace](df), 1, 1)
fig = average_close(df, index, ticker, fig)
fig['layout'][f'xaxis{row}'].update(tickangle=-0,
tickformat='%Y-%m-%d',
autorange=True,
showgrid=True,
mirror='ticks',
color='rgba(255, 255, 255, 1)',
tickcolor='rgba(255,255,255,1)')
fig['layout']['yaxis1'].update(
range=[df['Close'].min() / 1.3, df['Close'].max() * 1.05],
showgrid=True,
anchor='x1',
mirror='ticks',
layer='above traces',
color='rgba(255, 255, 255, 1)',
gridcolor='rgba(255, 255, 255, 1)')
fig['layout']['yaxis2'].update(
# range= [0, df['Volume'].max()*4],
# overlaying = 'y2',
# layer = 'below traces',
autorange=True,
# anchor = 'x1',
side='left',
showgrid=True,
title='D. Trad. Vol.',
color='rgba(255, 255, 255, 1)',
)
if strategyTest != '' and strategyTest is not None:
bsSig, retStrat, retBH = globals()[strategyTest](df, start_date,
end_date, 5)
fig = bsMarkers(df, fig)
fig['layout'].update(annotations=[
dict(
x=df.index[int(round(len(df.index) / 2, 0))],
y=df['Close'].max() * 1.02,
text='Your Strategy: ' + str(round(retStrat * 100, 0)) +
' % \nBuy&Hold: ' + str(round(retBH * 100, 0)) + '%',
align='center',
font=dict(size=16, color='rgb(255, 255, 255)'),
showarrow=False,
)
])
for study in selected_first_row_studies:
fig = globals()[study](df, fig) # add trace(s) on fig's first row
row = 2
fig = vol_traded(df, fig, row)
for study in selected_subplots_studies:
row += 1
fig = globals()[study](df, fig, row) # plot trace on new row
fig['layout']['margin'] = {'b': 30, 'r': 60, 'l': 40, 't': 10}
fig['layout'].update(
autosize=True,
uirevision='The User is always right',
height=600,
# paper_bgcolor = '#000000',
paper_bgcolor='#222',
# paper_bgcolor = 'rgb(64, 68, 72)',
# paper_bgcolor = 'rgb(219, 240, 238)',
# plot_bgcolor = '#000000',
plot_bgcolor='#222',
# plot_bgcolor = 'rgb(64, 68, 72)',
# plot_bgcolor = 'rgb(219, 240, 238)',
hovermode='x',
spikedistance=-1,
xaxis={
'gridcolor': 'rgba(255, 255, 255, 0.5)',
'gridwidth': .5,
'showspikes': True,
'spikemode': 'across',
'spikesnap': 'data',
'spikecolor': 'rgb(220, 200, 220)',
'spikethickness': 1,
'spikedash': 'dot'
},
yaxis={
'gridcolor': 'rgba(255, 255, 255, 0.5)',
'gridwidth': .5
},
legend=dict(x=1.05, y=1))
fig['layout']['xaxis'].update(rangeselector=dict(buttons=list([
dict(count=1, label='1M', step='month', stepmode='backward'),
dict(count=6, label='6M', step='month', stepmode='backward'),
dict(count=1, label='YTD', step='year', stepmode='todate'),
dict(count=1, label='1Y', step='year', stepmode='backward'),
dict(count=3, label='3Y', step='year', stepmode='backward'),
dict(step='all', label='MAX')
])),
rangeslider=dict(visible=False),
type="date")
return fig