def performance_indicators():
total_loss = user_data['total_loss']
total_gain = user_data['total_gain']
avg_rrr = user_data['avg_rrr'][-1]
avg_timespan = user_data['avg_timespan'][-1]/60
trades = tl.read_trades(user_data['diary_file'], 'closed')
size = trades['cap. share (%)'].mean()
risk = trades['risk (%)'].mean()
no_trades = len(trades)
no_shorts = len(trades[trades['direction'] == 'SHORT'])
no_longs = len(trades[trades['direction'] == 'LONG'])
days_traded = trades['date'].apply(lambda x: datetime.date(x)).nunique()
trades_per_day = no_trades/days_traded
shortlongratio = no_shorts/no_longs
entry = trades['entry']
qty = trades['size']
total_volume = sum(entry.mul(qty))
max_loss = trades['P/L (%)'].min()
max_win = trades['P/L (%)'].max()
efficiency = (total_gain-total_loss)/total_volume*100
return [html.H5('Additional Performance Indicators',
style={'margin-bottom': '20px'}),
html.Div(
[
html.Div(
[
html.Pre('Average trades per day:\t {:.1f}'.format(trades_per_day)),
html.Pre('Average timespan: \t \t {:.1f} h'.format(avg_timespan)),
html.Pre('Average r2r: \t \t \t {:.2f}'.format(avg_rrr)),
html.Pre('Average size: \t \t \t {:.0f} %'.format(size)),
html.Pre('Average risk: \t \t \t {:.1f} %'.format(risk)),
html.Pre('Short/long ratio: \t \t {:.1f}'.format(shortlongratio)),
],
style={'width': '50%', 'display': 'flex', 'flex-flow': 'column',
'justify-content': 'space-between'}
),
html.Div(
[
html.Pre('Efficiency: \t \t \t \t {:.2f} %'.format(efficiency)),
html.Pre('Total volume traded: \t {:.2f} $'.format(total_volume)),
html.Pre('Total Gain: \t \t \t \t {:.2f} $'.format(total_gain)),
html.Pre('Total Loss: \t \t \t \t {:.2f} $'.format(total_loss)),
html.Pre('Max loss:\t \t \t \t{:.2f} %'.format(max_loss)),
html.Pre('Max Win: \t \t \t \t {:.2f} %'.format(max_win))
],
style={'width': '50%', 'display': 'flex', 'flex-flow': 'column',
'justify-content': 'space-between'}
)
],
style={'display': 'flex', 'height': '80%', 'overflow': 'auto'}
)
]
def close_trade(clicks, selected_row, close, note):
record_file = user_data['diary_file']
if clicks is None:
closed_trades = tl.read_trades(record_file, 'closed', dict_output=True)
open_trades = tl.read_trades(record_file, 'open', dict_output=True)
elif close is None:
closed_trades = tl.read_trades(record_file, 'closed', dict_output=True)
open_trades = tl.read_trades(record_file, 'open', dict_output=True)
style = {'color': '#fc3003', 'border-color': '#fc3003'}
else:
# Retrieve the trade that we want to close:
open_trades = tl.read_trades(record_file, 'open')
trade = open_trades.iloc[selected_row]
# NOTE that trade is still a df and not a series, because we put a list inside iloc
# Compute features of the closed trade:
closed_trade = tl.fill_trade(trade, close, note)
# Write the trade to the records:
tl.write_trade_to_records(record_file, 'closed', closed_trade)
# Remove the open trade from the open trade records:
tl.remove_trade_from_records(record_file, selected_row[0])
# Update the running average features:
tl.update_user_data(closed_trade)
closed_trades = tl.read_trades(record_file, 'closed', dict_output=True)
open_trades = tl.read_trades(record_file, 'open', dict_output=True)
style = {}
return closed_trades, open_trades, style
def open_profit_string():
profit = tl.open_profit(tl.read_trades(user_data['diary_file'], 'open'))
percent = profit / user_data['capital'][-1] * 100
if profit < 0:
color = 'red'
else:
color = 'green'
return [
html.Pre('Open profit/loss: \t '),
html.P('{:.2f}$ ({:.2f}%)'.format(profit, percent),
style={'color': color})
]
def submit_trade(clicks, pair, entry, qty, stop, idea, direction, confidence):
forgotten_input = [
x == '' or x is None for x in [entry, qty, stop, idea, direction]
]
if clicks is None:
trades = tl.read_trades(user_data['diary_file'],
'open',
dict_output=True)
elif any(forgotten_input):
trades = tl.read_trades(user_data['diary_file'],
'open',
dict_output=True)
style = {'color': '#fc3003', 'border-color': '#fc3003'}
return trades, 'Missing!', entry, 'Missing!', qty, 'Missing!', stop, idea, direction, confidence, \
open_risk_string(), style
else:
# Add new trade at the top of the diary excel file:
index = pd.DatetimeIndex([datetime.datetime.now()])
trade = pd.DataFrame(
{
'pair': pair,
'size': qty,
'entry': entry,
'stop': stop,
'type': idea,
'direction': direction,
'confidence': confidence
},
index=index)
tl.write_trade_to_records(user_data['diary_file'], 'open', trade)
trades = tl.read_trades(user_data['diary_file'],
'open',
dict_output=True)
return trades, 0, '', 0, '', 0, '', '', '', 2, open_risk_string(), {}
def open_risk_string():
current_capital = user_data['capital'][-1]
open_risk = sum(
tl.trade_risk(current_capital,
tl.read_trades(user_data['diary_file'], 'open')))
if open_risk > tl.max_open_risk * 100:
color = 'red'
else:
color = 'green'
return [
html.Pre('Open risk: \t'),
html.P(' {:.2f}%'.format(open_risk), style={'color': color})
]
def timespan_figure():
# Extract the timespans of winners and losers from the records:
trades = tl.read_trades(user_data['diary_file'], 'closed')
data = trades[['P/L (%)', 'timespan (min)']]
winners = data[data['P/L (%)'] >= 0]
losers = data[data['P/L (%)'] < 0]
win_spans = winners.drop(columns=['P/L (%)'])/60 # convert to hours
lose_spans = losers.drop(columns=['P/L (%)'])/60 # convert to hours
# Plot the histogram of this data:
graph_data = [
{
'x': list(win_spans.values.squeeze()),
'name': 'won',
'type': 'histogram',
'nbins': 10,
'histnorm': 'percent',
'opacity': '0.75',
'bingroup': 1,
'marker': {'color': '#3D9970'}
},
{
'x': list(lose_spans.values.squeeze()),
'name': 'lost',
'type': 'histogram',
'nbins': 10,
'histnorm': 'percent',
'opacity': '0.75',
'bingroup': 1,
'marker': {'color': '#A83232'}
}
]
figure = {
'data': graph_data,
'layout': dict(
barmode='overlay',
margin={'l': 40, 'r': 15, 't': 10, 'b': 35},
paper_bgcolor='#e8e8e8',
font={'family': 'Dosis', 'size': 13},
showlegend=False,
yaxis={'title': 'Win/Lose (%)'},
xaxis={'title': 'Time [h]'},
)
}
return figure
def shortlong_figure():
trades = tl.read_trades(user_data['diary_file'], 'closed')
shorts = trades[trades['direction'] == 'SHORT']
longs = trades[trades['direction'] == 'LONG']
win_s = sum(shorts['P/L (%)'] >= 0)
win_l = sum(longs['P/L (%)'] >= 0)
lose_s = sum(shorts['P/L (%)'] < 0)
lose_l = sum(longs['P/L (%)'] < 0)
graph_data = [
{'x': ['SHORT', 'LONG'],
'y': [win_s, win_l],
'name': 'won',
'type': 'bar',
'marker': {'color': '#3D9970'}
},
{'x': ['SHORT', 'LONG'],
'y': [lose_s, lose_l],
'name': 'lost',
'type': 'bar',
'marker': {'color': '#A83232'}
}
]
figure = {
'data': graph_data,
'layout': dict(
barmode='stack',
margin={'l': 20, 'r': 15, 't': 10, 'b': 15},
height=250,
paper_bgcolor='#e8e8e8',
font={'family': 'Dosis', 'size': 13},
showlegend=False,
)
}
return figure
def serve_layout():
return html.Div(
[
html.Div(
[
html.Div(id='selected_trade',
style={'visibility': 'hidden'}),
html.Div([
html.H5('Select Position To Close:',
style={'margin-bottom': '10px'}),
dash_table.DataTable(
id='open_table2',
columns=tl.open_trade_dict,
data=tl.read_trades(user_data['diary_file'],
'open',
dict_output=True),
style_table={
'height': '102px',
'overflow-y': 'scroll',
},
style_cell_conditional=[{
'if': {
'column_id': c
},
'text-align': 'center'
} for c in ['pair', 'direction']],
row_selectable='single',
selected_rows=[0],
style_as_list_view=True,
style_cell={'padding': '5px'},
style_header={
'background-color': 'white',
'font-weight': 'bold'
})
],
className='pretty_container seven columns',
style={
'margin-left': '0',
'padding-top': '5px'
}),
html.Div(
[
html.Div(
[
html.H5('Close Position:',
style={'margin-bottom': '20px'}),
html.Button('Close',
id='close_trade_button',
style={'margin-top': '10px'})
],
style={
'justify-content': 'space-between',
'display': 'flex'
}),
html.Div(
[
html.Pre('Exit Price: \t',
style={'line-height': '2.5'}),
dcc.Input(id='exit',
placeholder=0,
type='number',
min=0,
style={'width': '30%'}),
],
style={
'display':
'flex', # 'justify-content': 'space-between',
'vertical-align': 'center'
}),
html.P('Note:',
style={
'margin-top': '0',
'margin-bottom': '0'
}),
dcc.Input(
id='note', style={'width': '100%'}, value='')
],
className='pretty_container five columns',
style={
'margin-right': '0',
'margin-left': '0',
'padding-top': '5px'
})
],
className='row flex-display',
),
html.Div(
[
html.H5('Closed Trades:'),
dash_table.DataTable(
id='closed_table',
columns=tl.closed_trade_dict,
data=tl.read_trades(user_data['diary_file'],
'closed',
dict_output=True),
style_table={
'height': '270px',
'overflow-y': 'scroll'
},
# You can use style conditional to color profitable and losing trades!
style_cell_conditional=[{
'if': {
'column_id': c
},
'text-align': 'center'
} for c in ['pair', 'direction', 'type']],
style_data_conditional=[{
'if': {
'column_id': 'P/L (%)',
'filter_query': '{P/L (%)} > 0',
},
'backgroundColor': '#3D9970',
'color': 'white',
}, {
'if': {
'column_id': 'P/L (%)',
'filter_query': '{P/L (%)} < 0',
},
'backgroundColor': '#A83232',
'color': 'white',
}],
style_as_list_view=True,
style_cell={'padding': '5px'},
style_header={
'background-color': 'white',
'font-weight': 'bold'
})
],
className='pretty_container twelve columns',
style={
'margin-top': '0',
'padding-top': '5px'
})
], )
def serve_layout():
return html.Div([
# CONTAINER FOR ENTERING A NEW TRADE
dcc.Interval(id='price_call'),
html.Div(
[
html.Div(
[
html.H5('Add New Trade:'),
html.Div([
html.P('Pair:'),
dcc.Dropdown(id='pair',
options=tl.pairs,
value='ETHUSDT',
style={
'width': '97%',
'display': 'block',
'height': '40px'
}),
],
style={
'width': '25%',
'display': 'inline-block'
}),
html.Div(
[
html.P('Amount:'),
dcc.Input(id='qty',
placeholder=0,
type='number',
min=0,
style={
'width': '90%',
'display': 'inline',
'height': '40px'
})
],
style={
'width': '15%',
'display': 'inline-block',
'vertical-align': 'top'
}),
html.Div(
[
html.P('Entry:'),
dcc.Input(id='entry',
placeholder=0,
type='number',
min=0,
style={
'width': '90%',
'height': '40px'
})
],
style={
'width': '15%',
'display': 'inline-block',
'vertical-align': 'top'
}),
html.Div(
[
html.P('Stop loss:'),
dcc.Input(id='stop',
placeholder=0,
type='number',
min=0,
style={
'width': '90%',
'height': '40px'
})
],
style={
'width': '15%',
'display': 'inline-block',
'vertical-align': 'top'
}),
html.Div(
[
html.P('Trade type:'),
dcc.Dropdown(id='type',
options=tl.types,
value='',
style={
'width': '100%',
'display': 'block',
'height': '40px'
})
],
style={
'width': '30%',
'display': 'inline-block'
},
),
html.Div(
[
html.Div(
[
html.P('Confidence:'),
dcc.Slider(id='confidence',
min=1,
max=3,
step=None,
value=2,
marks={
1: 'Unsure',
2: 'OK',
3: 'Gonna Win!'
})
],
style={
'width': '40%',
'display': 'block-inline',
'vertical-align': 'top'
}),
html.Div(
[
# It would be possible to determine SHORT/LONG from entry and stop!
html.P('Direction:'),
dcc.RadioItems(id='direction',
options=tl.directions,
value='',
style={
'display': 'flex',
'margin-top':
'10px',
'margin-bottom': '0'
},
labelStyle={
'margin-right':
'10px'
})
],
style={'display': 'block-inline'}),
html.Div(
[
html.Button('Enter Trade',
id='enter_trade_button')
],
style={
'display': 'block',
'align-items': 'flex-end',
'margin-top': '35px'
})
],
style={
'justify-content': 'space-between',
'display': 'flex'
})
],
className="pretty_container twelve columns",
style={
'margin-right': '0',
'padding-top': '5px'
},
id="enter-trade"),
],
className='row flex-display'),
# CONTAINER FOR OPEN POSITIONS
html.Div(
[
html.Div(
[
html.H5('Open Positions:',
style={'margin-bottom': '10px'}),
dash_table.DataTable(
id='open_table',
columns=tl.open_trade_dict,
data=tl.read_trades(user_data['diary_file'],
'open',
dict_output=True),
style_table={
'height': '126px',
'overflow-y': 'scroll'
},
# You can use style conditional to color profitable and losing trades!
style_cell_conditional=[{
'if': {
'column_id': c
},
'text-align': 'center'
} for c in ['pair', 'direction']],
style_as_list_view=True,
style_cell={'padding': '5px'},
style_header={
'background-color': 'white',
'font-weight': 'bold'
}),
html.Div(
[
html.Div(id='open_risk',
children=open_risk_string(),
style={'display': 'flex'}),
html.Div(id='open_profit',
children=open_profit_string(),
style={
'display': 'flex',
'width': '60%'
})
],
style={
'justify-content': 'space-between',
'display': 'flex',
'margin-top': '10px'
})
],
className="pretty_container seven columns",
style={
'margin-left': '0',
'margin-top': '0',
'padding-top': '5px'
},
id="open-positions"),
# CONTAINER FOR CALCULATING TRADE SIZE
html.Div(
[
html.H5('Calculate Position Size:'),
html.Div([
html.Div([
html.P('Entry:'),
dcc.Input(id='entry2',
placeholder=0.0,
type='number',
value=np.nan,
min=0,
style={
'display': 'inline',
'width': '90%'
})
], ),
html.Div([
html.P('Stop loss:'),
dcc.Input(id='stop2',
placeholder=0.0,
type='number',
value=np.nan,
min=0,
style={
'display': 'inline',
'width': '90%',
})
],
style={'display': 'inline-block'}),
html.Div([
html.P('Allowed size:',
style={'display': 'block'}),
dcc.Input(id='qty2',
placeholder=0.0,
type='number',
value=np.nan,
min=0,
readOnly=True,
style={
'display': 'inline',
'width': '100%'
})
],
style={'display': 'inline-block'}),
],
style={
'justify-content': 'space-between',
'display': 'flex'
}),
html.Div([
html.Div(
[
html.Pre('Risk:'),
dcc.RadioItems(id='risk',
options=[{
'label': '1% ',
'value': 0.01
}, {
'label': '2%',
'value': 0.02
}],
value=0.01,
style={
'display': 'flex',
'margin-right': '2px'
},
labelStyle={
'margin-right': '24px'
})
],
style={
'display': 'flex',
'justify-content': 'space-between',
'margin-top': '10px'
}),
html.Div(
[
html.P('Use Leverage:'),
dcc.RadioItems(id='leverage',
options=[{
'label': 'Yes',
'value': 5
}, {
'label': 'No',
'value': 1
}],
value=1,
style={
'display': 'flex',
'margin-right': '10px'
},
labelStyle={
'margin-right': '20px'
})
],
style={
'display': 'flex',
'justify-content': 'space-between'
}),
html.Div(
[html.Button('GO!', id='calc_size_button')],
style={
'display': 'flex',
'justify-content': 'flex-end'
})
],
style={'display': 'inline'})
],
className="pretty_container five columns",
style={
'margin-right': '0',
'margin-left': '0',
'margin-top': '0',
'padding-top': '5px'
},
id="calculate_size")
],
className='row flex-display')
])