data stream working
This commit is contained in:
Dobromir Popov
@@ -232,6 +232,9 @@ class CleanTradingDashboard:
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</html>
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'''
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# Add API endpoints to the Flask server
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self._add_api_endpoints()
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# Suppress Dash development mode logging
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self.app.enable_dev_tools(debug=False, dev_tools_silence_routes_logging=True)
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@@ -265,6 +268,300 @@ class CleanTradingDashboard:
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logger.debug("Clean Trading Dashboard initialized with HIGH-FREQUENCY COB integration and signal generation")
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def _add_api_endpoints(self):
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"""Add API endpoints to the Flask server for data access"""
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from flask import jsonify, request
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@self.app.server.route('/api/stream-status', methods=['GET'])
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def get_stream_status():
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"""Get data stream status"""
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try:
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status = self.orchestrator.get_data_stream_status()
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summary = self.orchestrator.get_stream_summary()
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return jsonify({
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'status': status,
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'summary': summary,
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'timestamp': datetime.now().isoformat()
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})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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@self.app.server.route('/api/ohlcv-data', methods=['GET'])
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def get_ohlcv_data():
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"""Get OHLCV data with indicators"""
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try:
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symbol = request.args.get('symbol', 'ETH/USDT')
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timeframe = request.args.get('timeframe', '1m')
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limit = int(request.args.get('limit', 300))
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# Get OHLCV data from orchestrator
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ohlcv_data = self._get_ohlcv_data_with_indicators(symbol, timeframe, limit)
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return jsonify({
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'symbol': symbol,
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'timeframe': timeframe,
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'data': ohlcv_data,
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'timestamp': datetime.now().isoformat()
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})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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@self.app.server.route('/api/cob-data', methods=['GET'])
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def get_cob_data():
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"""Get COB data with price buckets"""
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try:
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symbol = request.args.get('symbol', 'ETH/USDT')
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limit = int(request.args.get('limit', 300))
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# Get COB data from orchestrator
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cob_data = self._get_cob_data_with_buckets(symbol, limit)
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return jsonify({
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'symbol': symbol,
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'data': cob_data,
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'timestamp': datetime.now().isoformat()
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})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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@self.app.server.route('/api/snapshot', methods=['POST'])
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def create_snapshot():
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"""Create a data snapshot"""
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try:
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filepath = self.orchestrator.save_data_snapshot()
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return jsonify({
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'filepath': filepath,
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'timestamp': datetime.now().isoformat()
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})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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@self.app.server.route('/api/health', methods=['GET'])
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def health_check():
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"""Health check endpoint"""
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return jsonify({
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'status': 'healthy',
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'dashboard_running': True,
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'orchestrator_active': hasattr(self, 'orchestrator'),
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'timestamp': datetime.now().isoformat()
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})
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def _get_ohlcv_data_with_indicators(self, symbol: str, timeframe: str, limit: int = 300):
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"""Get OHLCV data with technical indicators from data stream monitor"""
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try:
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# Get OHLCV data from data stream monitor
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if hasattr(self.orchestrator, 'data_stream_monitor') and self.orchestrator.data_stream_monitor:
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stream_key = f"ohlcv_{timeframe}"
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if stream_key in self.orchestrator.data_stream_monitor.data_streams:
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ohlcv_data = list(self.orchestrator.data_stream_monitor.data_streams[stream_key])
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# Take the last 'limit' items
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ohlcv_data = ohlcv_data[-limit:] if len(ohlcv_data) > limit else ohlcv_data
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if not ohlcv_data:
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return []
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# Convert to DataFrame for indicator calculation
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df_data = []
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for item in ohlcv_data:
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df_data.append({
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'timestamp': item.get('timestamp', ''),
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'open': float(item.get('open', 0)),
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'high': float(item.get('high', 0)),
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'low': float(item.get('low', 0)),
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'close': float(item.get('close', 0)),
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'volume': float(item.get('volume', 0))
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})
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if not df_data:
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return []
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df = pd.DataFrame(df_data)
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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df.set_index('timestamp', inplace=True)
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# Add technical indicators
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df['sma_20'] = df['close'].rolling(window=20).mean()
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df['sma_50'] = df['close'].rolling(window=50).mean()
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df['ema_12'] = df['close'].ewm(span=12).mean()
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df['ema_26'] = df['close'].ewm(span=26).mean()
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# RSI
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delta = df['close'].diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
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rs = gain / loss
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df['rsi'] = 100 - (100 / (1 + rs))
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# MACD
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df['macd'] = df['ema_12'] - df['ema_26']
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df['macd_signal'] = df['macd'].ewm(span=9).mean()
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df['macd_histogram'] = df['macd'] - df['macd_signal']
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# Bollinger Bands
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df['bb_middle'] = df['close'].rolling(window=20).mean()
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bb_std = df['close'].rolling(window=20).std()
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df['bb_upper'] = df['bb_middle'] + (bb_std * 2)
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df['bb_lower'] = df['bb_middle'] - (bb_std * 2)
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# Volume indicators
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df['volume_sma'] = df['volume'].rolling(window=20).mean()
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df['volume_ratio'] = df['volume'] / df['volume_sma']
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# Convert to list of dictionaries
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result = []
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for _, row in df.iterrows():
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data_point = {
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'timestamp': row.name.isoformat() if hasattr(row.name, 'isoformat') else str(row.name),
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'open': float(row['open']),
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'high': float(row['high']),
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'low': float(row['low']),
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'close': float(row['close']),
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'volume': float(row['volume']),
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'indicators': {
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'sma_20': float(row['sma_20']) if pd.notna(row['sma_20']) else None,
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'sma_50': float(row['sma_50']) if pd.notna(row['sma_50']) else None,
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'ema_12': float(row['ema_12']) if pd.notna(row['ema_12']) else None,
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'ema_26': float(row['ema_26']) if pd.notna(row['ema_26']) else None,
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'rsi': float(row['rsi']) if pd.notna(row['rsi']) else None,
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'macd': float(row['macd']) if pd.notna(row['macd']) else None,
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'macd_signal': float(row['macd_signal']) if pd.notna(row['macd_signal']) else None,
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'macd_histogram': float(row['macd_histogram']) if pd.notna(row['macd_histogram']) else None,
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'bb_upper': float(row['bb_upper']) if pd.notna(row['bb_upper']) else None,
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'bb_middle': float(row['bb_middle']) if pd.notna(row['bb_middle']) else None,
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'bb_lower': float(row['bb_lower']) if pd.notna(row['bb_lower']) else None,
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'volume_ratio': float(row['volume_ratio']) if pd.notna(row['volume_ratio']) else None
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}
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}
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result.append(data_point)
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return result
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# Fallback to data provider if stream monitor not available
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ohlcv_data = self.data_provider.get_ohlcv(symbol, timeframe, limit=limit)
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if ohlcv_data is None or ohlcv_data.empty:
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return []
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# Add technical indicators
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df = ohlcv_data.copy()
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# Basic indicators
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df['sma_20'] = df['close'].rolling(window=20).mean()
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df['sma_50'] = df['close'].rolling(window=50).mean()
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df['ema_12'] = df['close'].ewm(span=12).mean()
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df['ema_26'] = df['close'].ewm(span=26).mean()
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# RSI
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delta = df['close'].diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
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rs = gain / loss
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df['rsi'] = 100 - (100 / (1 + rs))
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# MACD
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df['macd'] = df['ema_12'] - df['ema_26']
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df['macd_signal'] = df['macd'].ewm(span=9).mean()
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df['macd_histogram'] = df['macd'] - df['macd_signal']
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# Bollinger Bands
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df['bb_middle'] = df['close'].rolling(window=20).mean()
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bb_std = df['close'].rolling(window=20).std()
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df['bb_upper'] = df['bb_middle'] + (bb_std * 2)
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df['bb_lower'] = df['bb_middle'] - (bb_std * 2)
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# Volume indicators
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df['volume_sma'] = df['volume'].rolling(window=20).mean()
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df['volume_ratio'] = df['volume'] / df['volume_sma']
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# Convert to list of dictionaries
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result = []
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for _, row in df.iterrows():
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data_point = {
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'timestamp': row.name.isoformat() if hasattr(row.name, 'isoformat') else str(row.name),
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'open': float(row['open']),
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'high': float(row['high']),
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'low': float(row['low']),
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'close': float(row['close']),
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'volume': float(row['volume']),
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'indicators': {
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'sma_20': float(row['sma_20']) if pd.notna(row['sma_20']) else None,
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'sma_50': float(row['sma_50']) if pd.notna(row['sma_50']) else None,
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'ema_12': float(row['ema_12']) if pd.notna(row['ema_12']) else None,
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'ema_26': float(row['ema_26']) if pd.notna(row['ema_26']) else None,
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'rsi': float(row['rsi']) if pd.notna(row['rsi']) else None,
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'macd': float(row['macd']) if pd.notna(row['macd']) else None,
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'macd_signal': float(row['macd_signal']) if pd.notna(row['macd_signal']) else None,
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'macd_histogram': float(row['macd_histogram']) if pd.notna(row['macd_histogram']) else None,
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'bb_upper': float(row['bb_upper']) if pd.notna(row['bb_upper']) else None,
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'bb_middle': float(row['bb_middle']) if pd.notna(row['bb_middle']) else None,
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'bb_lower': float(row['bb_lower']) if pd.notna(row['bb_lower']) else None,
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'volume_ratio': float(row['volume_ratio']) if pd.notna(row['volume_ratio']) else None
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}
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}
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result.append(data_point)
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return result
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except Exception as e:
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logger.error(f"Error getting OHLCV data: {e}")
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return []
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def _get_cob_data_with_buckets(self, symbol: str, limit: int = 300):
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"""Get COB data with price buckets ($1 increments)"""
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try:
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# Get COB data from orchestrator
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cob_data = self.orchestrator.get_cob_data(symbol, limit)
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if not cob_data:
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return []
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# Process COB data into price buckets
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result = []
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for cob_snapshot in cob_data:
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# Create price buckets ($1 increments)
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price_buckets = {}
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mid_price = cob_snapshot.mid_price
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# Create buckets around mid price
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for i in range(-50, 51): # -$50 to +$50 from mid price
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bucket_price = mid_price + i
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bucket_key = f"{bucket_price:.2f}"
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price_buckets[bucket_key] = {
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'bid_volume': 0,
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'ask_volume': 0,
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'bid_count': 0,
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'ask_count': 0
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}
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# Fill buckets with order book data
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for level in cob_snapshot.bids:
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bucket_price = f"{level.price:.2f}"
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if bucket_price in price_buckets:
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price_buckets[bucket_price]['bid_volume'] += level.volume
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price_buckets[bucket_price]['bid_count'] += 1
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for level in cob_snapshot.asks:
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bucket_price = f"{level.price:.2f}"
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if bucket_price in price_buckets:
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price_buckets[bucket_price]['ask_volume'] += level.volume
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price_buckets[bucket_price]['ask_count'] += 1
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data_point = {
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'timestamp': cob_snapshot.timestamp.isoformat() if hasattr(cob_snapshot.timestamp, 'isoformat') else str(cob_snapshot.timestamp),
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'mid_price': float(cob_snapshot.mid_price),
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'spread': float(cob_snapshot.spread),
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'imbalance': float(cob_snapshot.imbalance),
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'price_buckets': price_buckets,
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'total_bid_volume': float(cob_snapshot.total_bid_volume),
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'total_ask_volume': float(cob_snapshot.total_ask_volume)
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}
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result.append(data_point)
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return result
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except Exception as e:
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logger.error(f"Error getting COB data: {e}")
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return []
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def _get_universal_data_from_orchestrator(self) -> Optional[UniversalDataStream]:
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"""Get universal data through orchestrator as per architecture."""
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try:
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