Py.Cafe
Dash Color Selection Demo
- app.py
- main.py
- requirements.txt
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
import websocket
import json
import threading
import ssl
import certifi
import time
import pandas as pd
import numpy as np
import plotly.graph_objs as go
import dash
from dash import dcc, html
from dash.dependencies import Output, Input
from collections import defaultdict, deque
class MarketDataProcessor:
def __init__(self, symbol='ethusdt', max_length=50000):
self.symbol = symbol
self.max_length = max_length
self.trades = pd.DataFrame(columns=['timestamp', 'price', 'quantity', 'trade_type'])
self.order_flow_stats = {'buy_volume': 0, 'sell_volume': 0, 'total_volume': 0, 'order_imbalance': 0, 'vwap': 0}
self.cumulative_volume_delta = 0
self.cvd_history = deque(maxlen=1000)
self.order_book = {'bids': defaultdict(float), 'asks': defaultdict(float)}
self.price_buckets = defaultdict(lambda: {'buy_volume': 0, 'sell_volume': 0})
def process_trade(self, trade_data):
timestamp = pd.Timestamp.fromtimestamp(trade_data['T'] / 1000)
price = float(trade_data['p'])
quantity = float(trade_data['q'])
trade_type = 'sell' if trade_data['m'] else 'buy'
new_trade = pd.DataFrame([{'timestamp': timestamp, 'price': price, 'quantity': quantity, 'trade_type': trade_type}])
self.trades = pd.concat([self.trades, new_trade], ignore_index=True).tail(self.max_length)
self._update_order_flow_stats(price, quantity, trade_type)
self._update_cumulative_volume_delta(quantity, trade_type)
self._update_price_buckets(price, quantity, trade_type)
def _update_order_flow_stats(self, price, quantity, trade_type):
if trade_type == 'buy':
self.order_flow_stats['buy_volume'] += quantity
else:
self.order_flow_stats['sell_volume'] += quantity
self.order_flow_stats['total_volume'] += quantity
if self.order_flow_stats['total_volume'] > 0:
self.order_flow_stats['order_imbalance'] = (
self.order_flow_stats['buy_volume'] - self.order_flow_stats['sell_volume']
) / self.order_flow_stats['total_volume']
self.order_flow_stats['vwap'] = (
self.trades['price'] * self.trades['quantity']
).sum() / self.trades['quantity'].sum()
def _update_cumulative_volume_delta(self, quantity, trade_type):
self.cumulative_volume_delta += quantity if trade_type == 'buy' else -quantity
self.cvd_history.append((self.trades['timestamp'].iloc[-1], self.cumulative_volume_delta))
def _update_price_buckets(self, price, quantity, trade_type):
price_bucket = round(price, 2)
if trade_type == 'buy':
self.price_buckets[price_bucket]['buy_volume'] += quantity
else:
self.price_buckets[price_bucket]['sell_volume'] += quantity
def get_market_summary(self):
return {
'total_volume': self.order_flow_stats['total_volume'],
'buy_volume': self.order_flow_stats['buy_volume'],
'sell_volume': self.order_flow_stats['sell_volume'],
'order_imbalance': self.order_flow_stats['order_imbalance'],
'vwap': self.order_flow_stats['vwap'],
'cvd_history': list(self.cvd_history),
'price_buckets': dict(self.price_buckets)
}
class CryptoDashboard:
def __init__(self, symbol='ethusdt'):
self.symbol = symbol
self.base_url = "wss://stream.binance.com:9443/ws"
self.market_processor = MarketDataProcessor(symbol)
self._connect_websocket()
self.app = self._create_dashboard()
def _connect_websocket(self):
def on_message(ws, message):
trade_data = json.loads(message)
if trade_data.get('e') == 'trade':
self.market_processor.process_trade(trade_data)
def on_error(ws, error):
print(f"WebSocket Error: {error}")
def on_close(ws, close_status_code, close_msg):
print(f"WebSocket closed: {close_msg}")
def run_websocket():
while True:
try:
ws = websocket.WebSocketApp(
f"{self.base_url}/{self.symbol.lower()}@trade",
on_message=on_message,
on_error=on_error,
on_close=on_close
)
ssl_context = ssl.create_default_context(cafile=certifi.where())
ws.run_forever(sslopt={"context": ssl_context})
except Exception as e:
print(f"WebSocket connection error: {e}")
time.sleep(5)
threading.Thread(target=run_websocket, daemon=True).start()
def _create_dashboard(self):
app = dash.Dash(__name__)
app.layout = html.Div([
html.H1(f'{self.symbol.upper()} Market Dashboard'),
html.Div(id='market-summary'),
dcc.Graph(id='volume-pie-chart'),
dcc.Graph(id='cvd-chart'),
dcc.Graph(id='dom-heatmap'),
dcc.Graph(id='volume-profile'),
dcc.Interval(id='interval-component', interval=2*1000, n_intervals=0)
])
@app.callback(
[
Output('market-summary', 'children'),
Output('volume-pie-chart', 'figure'),
Output('cvd-chart', 'figure'),
Output('dom-heatmap', 'figure'),
Output('volume-profile', 'figure')
],
[Input('interval-component', 'n_intervals')]
)
def update_dashboard(n):
market_summary = self.market_processor.get_market_summary()
# Market Summary Text
market_summary_text = html.Div([
html.P(f"Total Volume: {market_summary['total_volume']}"),
html.P(f"Buy Volume: {market_summary['buy_volume']}"),
html.P(f"Sell Volume: {market_summary['sell_volume']}"),
html.P(f"Order Imbalance: {market_summary['order_imbalance']:.2f}"),
html.P(f"VWAP: {market_summary['vwap']:.2f}")
])
# Volume Pie Chart
volume_pie_chart = go.Figure(
data=[go.Pie(
labels=['Buy Volume', 'Sell Volume'],
values=[market_summary['buy_volume'], market_summary['sell_volume']],
hole=0.4
)]
)
volume_pie_chart.update_layout(title="Volume Distribution")
# Cumulative Volume Delta Chart
timestamps, cvd_values = zip(*market_summary['cvd_history']) if market_summary['cvd_history'] else ([], [])
cvd_chart = go.Figure(
data=[go.Scatter(x=timestamps, y=cvd_values, mode='lines', name='CVD')]
)
cvd_chart.update_layout(title="Cumulative Volume Delta (CVD)", xaxis_title="Time", yaxis_title="CVD")
# DOM Heatmap
dom_data = market_summary['price_buckets']
dom_heatmap = go.Figure(
data=go.Heatmap(
z=[
[dom_data[price]['buy_volume'], dom_data[price]['sell_volume']]
for price in sorted(dom_data.keys())
],
x=['Buy Volume', 'Sell Volume'],
y=[f"{price}" for price in sorted(dom_data.keys())],
colorscale='Viridis'
)
)
dom_heatmap.update_layout(title="Depth of Market (DOM) Heatmap", xaxis_title="Order Type", yaxis_title="Price Level")
# Volume Profile
volume_profile = go.Figure()
for price in sorted(dom_data.keys()):
volume_profile.add_trace(
go.Bar(
x=[price],
y=[dom_data[price]['buy_volume']],
name='Buy Volume',
orientation='v'
)
)
volume_profile.add_trace(
go.Bar(
x=[price],
y=[dom_data[price]['sell_volume']],
name='Sell Volume',
orientation='v'
)
)
volume_profile.update_layout(
barmode='stack',
title="Volume Profile",
xaxis_title="Price Level",
yaxis_title="Volume"
)
return market_summary_text, volume_pie_chart, cvd_chart, dom_heatmap, volume_profile
return app
if __name__ == "__main__":
dashboard = CryptoDashboard(symbol="ethusdt")
dashboard.app.run_server(debug=True)