improve stream
This commit is contained in:
Dobromir Popov
132
check_stream.py
132
check_stream.py
@@ -108,7 +108,7 @@ def check_stream():
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print("❌ Could not get stream status from API")
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def show_ohlcv_data():
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"""Show OHLCV data with indicators."""
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"""Show OHLCV data with indicators for all required timeframes and symbols."""
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print("=" * 60)
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print("OHLCV DATA WITH INDICATORS")
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print("=" * 60)
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@@ -120,30 +120,118 @@ def show_ohlcv_data():
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print("💡 Start dashboard first: python run_clean_dashboard.py")
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return
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# Get OHLCV data for different timeframes
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timeframes = ['1s', '1m', '1h', '1d']
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symbol = 'ETH/USDT'
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# Check all required datasets for models
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datasets = [
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("ETH/USDT", "1m"),
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("ETH/USDT", "1h"),
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("ETH/USDT", "1d"),
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("BTC/USDT", "1m")
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]
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for timeframe in timeframes:
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print(f"\n📊 {symbol} {timeframe} Data:")
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print("📊 Checking all required datasets for model training:")
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for symbol, timeframe in datasets:
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print(f"\n📈 {symbol} {timeframe} Data:")
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data = get_ohlcv_data_from_api(symbol, timeframe, 300)
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if data and data.get('data'):
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if data and isinstance(data, dict) and 'data' in data:
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ohlcv_data = data['data']
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print(f" Records: {len(ohlcv_data)}")
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if ohlcv_data:
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if ohlcv_data and len(ohlcv_data) > 0:
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print(f" ✅ Records: {len(ohlcv_data)}")
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latest = ohlcv_data[-1]
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print(f" Latest: {latest['timestamp']}")
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print(f" Price: ${latest['close']:.2f}")
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oldest = ohlcv_data[0]
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print(f" 📅 Range: {oldest['timestamp'][:10]} to {latest['timestamp'][:10]}")
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print(f" 💰 Latest Price: ${latest['close']:.2f}")
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print(f" 📊 Volume: {latest['volume']:.2f}")
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indicators = latest.get('indicators', {})
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if indicators:
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print(f" RSI: {indicators.get('rsi', 'N/A')}")
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print(f" MACD: {indicators.get('macd', 'N/A')}")
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print(f" SMA20: {indicators.get('sma_20', 'N/A')}")
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rsi = indicators.get('rsi')
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macd = indicators.get('macd')
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sma_20 = indicators.get('sma_20')
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print(f" 📉 RSI: {rsi:.2f}" if rsi else " 📉 RSI: N/A")
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print(f" 🔄 MACD: {macd:.4f}" if macd else " 🔄 MACD: N/A")
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print(f" 📈 SMA20: ${sma_20:.2f}" if sma_20 else " 📈 SMA20: N/A")
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# Check if we have enough data for training
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if len(ohlcv_data) >= 300:
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print(f" 🎯 Model Ready: {len(ohlcv_data)}/300 candles")
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else:
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print(f" ⚠️ Need More: {len(ohlcv_data)}/300 candles ({300-len(ohlcv_data)} missing)")
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else:
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print(f" ❌ Empty data array")
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elif data and isinstance(data, list) and len(data) > 0:
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# Direct array format
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print(f" ✅ Records: {len(data)}")
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latest = data[-1]
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oldest = data[0]
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print(f" 📅 Range: {oldest['timestamp'][:10]} to {latest['timestamp'][:10]}")
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print(f" 💰 Latest Price: ${latest['close']:.2f}")
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elif data:
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print(f" ⚠️ Unexpected format: {type(data)}")
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else:
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print(f" No data available")
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print(f" ❌ No data available")
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print(f"\n🎯 Expected: 300 candles per dataset (1200 total)")
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def show_detailed_ohlcv(symbol="ETH/USDT", timeframe="1m"):
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"""Show detailed OHLCV data for a specific symbol/timeframe."""
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print("=" * 60)
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print(f"DETAILED {symbol} {timeframe} DATA")
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print("=" * 60)
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# Check dashboard health
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dashboard_running, _ = check_dashboard_status()
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if not dashboard_running:
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print("❌ Dashboard not running")
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return
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data = get_ohlcv_data_from_api(symbol, timeframe, 300)
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if data and isinstance(data, dict) and 'data' in data:
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ohlcv_data = data['data']
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if ohlcv_data and len(ohlcv_data) > 0:
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print(f"📈 Total candles loaded: {len(ohlcv_data)}")
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if len(ohlcv_data) >= 2:
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oldest = ohlcv_data[0]
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latest = ohlcv_data[-1]
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print(f"📅 Date range: {oldest['timestamp']} to {latest['timestamp']}")
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# Calculate price statistics
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closes = [item['close'] for item in ohlcv_data]
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volumes = [item['volume'] for item in ohlcv_data]
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print(f"💰 Price range: ${min(closes):.2f} - ${max(closes):.2f}")
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print(f"📊 Average volume: {sum(volumes)/len(volumes):.2f}")
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# Show sample data
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print(f"\n🔍 First 3 candles:")
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for i in range(min(3, len(ohlcv_data))):
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candle = ohlcv_data[i]
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ts = candle['timestamp'][:19] if len(candle['timestamp']) > 19 else candle['timestamp']
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print(f" {ts} | ${candle['close']:.2f} | Vol:{candle['volume']:.2f}")
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print(f"\n🔍 Last 3 candles:")
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for i in range(max(0, len(ohlcv_data)-3), len(ohlcv_data)):
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candle = ohlcv_data[i]
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ts = candle['timestamp'][:19] if len(candle['timestamp']) > 19 else candle['timestamp']
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print(f" {ts} | ${candle['close']:.2f} | Vol:{candle['volume']:.2f}")
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# Model training readiness check
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if len(ohlcv_data) >= 300:
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print(f"\n✅ Model Training Ready: {len(ohlcv_data)}/300 candles loaded")
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else:
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print(f"\n⚠️ Insufficient Data: {len(ohlcv_data)}/300 candles (need {300-len(ohlcv_data)} more)")
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else:
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print("❌ Empty data array")
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elif data and isinstance(data, list) and len(data) > 0:
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# Direct array format
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print(f"📈 Total candles loaded: {len(data)}")
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# ... (same processing as above for array format)
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else:
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print(f"❌ No data returned: {type(data)}")
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def show_cob_data():
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"""Show COB data with price buckets."""
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@@ -213,9 +301,13 @@ def main():
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if len(sys.argv) < 2:
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print("Usage:")
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print(" python check_stream.py status # Check stream status")
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print(" python check_stream.py ohlcv # Show OHLCV data")
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print(" python check_stream.py ohlcv # Show all OHLCV datasets")
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print(" python check_stream.py detail [symbol] [timeframe] # Show detailed data")
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print(" python check_stream.py cob # Show COB data")
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print(" python check_stream.py snapshot # Generate snapshot")
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print("\nExamples:")
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print(" python check_stream.py detail ETH/USDT 1h")
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print(" python check_stream.py detail BTC/USDT 1m")
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return
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command = sys.argv[1].lower()
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@@ -224,13 +316,17 @@ def main():
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check_stream()
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elif command == "ohlcv":
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show_ohlcv_data()
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elif command == "detail":
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symbol = sys.argv[2] if len(sys.argv) > 2 else "ETH/USDT"
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timeframe = sys.argv[3] if len(sys.argv) > 3 else "1m"
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show_detailed_ohlcv(symbol, timeframe)
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elif command == "cob":
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show_cob_data()
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elif command == "snapshot":
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generate_snapshot()
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else:
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print(f"Unknown command: {command}")
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print("Available commands: status, ohlcv, cob, snapshot")
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print("Available commands: status, ohlcv, detail, cob, snapshot")
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if __name__ == "__main__":
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main()
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@@ -195,6 +195,9 @@ class TradingOrchestrator:
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# Initialize and start data stream monitor (single source of truth)
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self._initialize_data_stream_monitor()
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# Load historical data for models and RL training
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self._load_historical_data_for_models()
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def _initialize_ml_models(self):
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"""Initialize ML models for enhanced trading"""
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@@ -2315,6 +2318,58 @@ class TradingOrchestrator:
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logger.error(f"Error getting COB data: {e}")
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return []
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def _load_historical_data_for_models(self):
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"""Load 300 historical candles for all required timeframes and symbols for model training"""
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logger.info("Loading 300 historical candles for model training and RL context...")
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try:
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# Required data for models:
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# ETH/USDT: 1m, 1h, 1d (300 candles each)
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# BTC/USDT: 1m (300 candles)
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symbols_timeframes = [
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('ETH/USDT', '1m'),
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('ETH/USDT', '1h'),
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('ETH/USDT', '1d'),
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('BTC/USDT', '1m')
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]
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loaded_data = {}
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total_candles = 0
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for symbol, timeframe in symbols_timeframes:
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try:
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logger.info(f"Loading {symbol} {timeframe} historical data...")
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df = self.data_provider.get_historical_data(symbol, timeframe, limit=300)
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if df is not None and not df.empty:
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loaded_data[f"{symbol}_{timeframe}"] = df
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total_candles += len(df)
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logger.info(f"✅ Loaded {len(df)} {timeframe} candles for {symbol}")
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# Store in data provider's historical cache for quick access
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cache_key = f"{symbol}_{timeframe}_300"
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if not hasattr(self.data_provider, 'model_data_cache'):
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self.data_provider.model_data_cache = {}
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self.data_provider.model_data_cache[cache_key] = df
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else:
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logger.warning(f"❌ No {timeframe} data available for {symbol}")
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except Exception as e:
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logger.error(f"Error loading {symbol} {timeframe} data: {e}")
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# Initialize model context data
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if hasattr(self, 'extrema_trainer') and self.extrema_trainer:
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logger.info("Initializing ExtremaTrainer with historical context...")
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self.extrema_trainer.initialize_context_data()
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logger.info(f"🎯 Historical data loading complete: {total_candles} total candles loaded")
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logger.info(f"📊 Available datasets: {list(loaded_data.keys())}")
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except Exception as e:
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logger.error(f"Error in historical data loading: {e}")
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def get_ohlcv_data(self, symbol: str, timeframe: str, limit: int = 300) -> List:
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"""Get OHLCV data for a symbol with specified timeframe and limit."""
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try:
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@@ -25,11 +25,15 @@ class DataStreamMonitor:
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self.data_provider = data_provider
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self.training_system = training_system
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# Data buffers for streaming
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# Data buffers for streaming (expanded for accessing historical data)
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self.data_streams = {
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'ohlcv_1m': deque(maxlen=100),
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'ohlcv_5m': deque(maxlen=50),
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'ohlcv_15m': deque(maxlen=20),
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'ohlcv_1s': deque(maxlen=300), # 300 seconds for 1s data
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'ohlcv_1m': deque(maxlen=300), # 300 minutes for 1m data (ETH)
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'ohlcv_1h': deque(maxlen=300), # 300 hours for 1h data (ETH)
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'ohlcv_1d': deque(maxlen=300), # 300 days for 1d data (ETH)
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'btc_1m': deque(maxlen=300), # 300 minutes for BTC 1m data
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'ohlcv_5m': deque(maxlen=100), # Keep for compatibility
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'ohlcv_15m': deque(maxlen=100), # Keep for compatibility
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'ticks': deque(maxlen=200),
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'cob_raw': deque(maxlen=100),
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'cob_aggregated': deque(maxlen=50),
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@@ -39,12 +43,15 @@ class DataStreamMonitor:
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'training_experiences': deque(maxlen=200)
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}
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# Streaming configuration
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# Streaming configuration - expanded for model requirements
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self.stream_config = {
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'console_output': True,
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'compact_format': False,
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'include_timestamps': True,
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'filter_symbols': ['ETH/USDT'], # Focus on primary symbols
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'filter_symbols': ['ETH/USDT', 'BTC/USDT'], # Primary and secondary symbols
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'primary_symbol': 'ETH/USDT',
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'secondary_symbol': 'BTC/USDT',
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'timeframes': ['1s', '1m', '1h', '1d'], # Required timeframes for models
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'sampling_rate': 1.0 # seconds between samples
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}
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@@ -118,32 +125,114 @@ class DataStreamMonitor:
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logger.error(f"Error collecting data sample: {e}")
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def _collect_ohlcv_data(self, timestamp: datetime):
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"""Collect OHLCV data for all timeframes"""
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"""Collect OHLCV data for all timeframes and symbols"""
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try:
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for symbol in self.stream_config['filter_symbols']:
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for timeframe in ['1m', '5m', '15m']:
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if self.data_provider:
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df = self.data_provider.get_historical_data(symbol, timeframe, limit=5)
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if df is not None and not df.empty:
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latest_bar = {
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'timestamp': timestamp.isoformat(),
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'symbol': symbol,
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'timeframe': timeframe,
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'open': float(df['open'].iloc[-1]),
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'high': float(df['high'].iloc[-1]),
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'low': float(df['low'].iloc[-1]),
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'close': float(df['close'].iloc[-1]),
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'volume': float(df['volume'].iloc[-1])
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}
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# ETH/USDT data for all required timeframes
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primary_symbol = self.stream_config['primary_symbol']
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for timeframe in ['1m', '1h', '1d']:
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if self.data_provider:
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# Get recent data (limit=1 for latest, but access historical data when needed)
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df = self.data_provider.get_historical_data(primary_symbol, timeframe, limit=300)
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if df is not None and not df.empty:
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# Get the latest bar
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latest_bar = {
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'timestamp': timestamp.isoformat(),
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'symbol': primary_symbol,
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'timeframe': timeframe,
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'open': float(df['open'].iloc[-1]),
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'high': float(df['high'].iloc[-1]),
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'low': float(df['low'].iloc[-1]),
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'close': float(df['close'].iloc[-1]),
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'volume': float(df['volume'].iloc[-1])
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}
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stream_key = f'ohlcv_{timeframe}'
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if len(self.data_streams[stream_key]) == 0 or \
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self.data_streams[stream_key][-1]['timestamp'] != latest_bar['timestamp']:
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self.data_streams[stream_key].append(latest_bar)
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stream_key = f'ohlcv_{timeframe}'
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# Only add if different from last entry or if stream is empty
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if len(self.data_streams[stream_key]) == 0 or \
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self.data_streams[stream_key][-1]['close'] != latest_bar['close']:
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self.data_streams[stream_key].append(latest_bar)
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# If stream was empty, populate with historical data
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if len(self.data_streams[stream_key]) == 1:
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logger.info(f"Populating {stream_key} with historical data...")
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self._populate_historical_data(df, stream_key, primary_symbol, timeframe)
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# BTC/USDT 1m data (secondary symbol)
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secondary_symbol = self.stream_config['secondary_symbol']
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if self.data_provider:
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df = self.data_provider.get_historical_data(secondary_symbol, '1m', limit=300)
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if df is not None and not df.empty:
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latest_bar = {
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'timestamp': timestamp.isoformat(),
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'symbol': secondary_symbol,
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'timeframe': '1m',
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'open': float(df['open'].iloc[-1]),
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'high': float(df['high'].iloc[-1]),
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'low': float(df['low'].iloc[-1]),
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'close': float(df['close'].iloc[-1]),
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'volume': float(df['volume'].iloc[-1])
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}
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# Only add if different from last entry or if stream is empty
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if len(self.data_streams['btc_1m']) == 0 or \
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self.data_streams['btc_1m'][-1]['close'] != latest_bar['close']:
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self.data_streams['btc_1m'].append(latest_bar)
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# If stream was empty, populate with historical data
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if len(self.data_streams['btc_1m']) == 1:
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logger.info("Populating btc_1m with historical data...")
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self._populate_historical_data(df, 'btc_1m', secondary_symbol, '1m')
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# Legacy timeframes for compatibility
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for timeframe in ['5m', '15m']:
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if self.data_provider:
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df = self.data_provider.get_historical_data(primary_symbol, timeframe, limit=5)
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if df is not None and not df.empty:
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latest_bar = {
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'timestamp': timestamp.isoformat(),
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'symbol': primary_symbol,
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'timeframe': timeframe,
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'open': float(df['open'].iloc[-1]),
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'high': float(df['high'].iloc[-1]),
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'low': float(df['low'].iloc[-1]),
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'close': float(df['close'].iloc[-1]),
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'volume': float(df['volume'].iloc[-1])
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}
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stream_key = f'ohlcv_{timeframe}'
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if len(self.data_streams[stream_key]) == 0 or \
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self.data_streams[stream_key][-1]['timestamp'] != latest_bar['timestamp']:
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self.data_streams[stream_key].append(latest_bar)
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except Exception as e:
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logger.debug(f"Error collecting OHLCV data: {e}")
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def _populate_historical_data(self, df, stream_key, symbol, timeframe):
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"""Populate stream with historical data from DataFrame"""
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try:
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# Clear the stream first (it should only have 1 latest entry)
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self.data_streams[stream_key].clear()
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# Add all historical data
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for _, row in df.iterrows():
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bar_data = {
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'timestamp': row.name.isoformat() if hasattr(row.name, 'isoformat') else str(row.name),
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'symbol': symbol,
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'timeframe': timeframe,
|
||||
'open': float(row['open']),
|
||||
'high': float(row['high']),
|
||||
'low': float(row['low']),
|
||||
'close': float(row['close']),
|
||||
'volume': float(row['volume'])
|
||||
}
|
||||
self.data_streams[stream_key].append(bar_data)
|
||||
|
||||
logger.info(f"✅ Loaded {len(df)} historical candles for {stream_key} ({symbol} {timeframe})")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error populating historical data for {stream_key}: {e}")
|
||||
|
||||
def _collect_tick_data(self, timestamp: datetime):
|
||||
"""Collect real-time tick data"""
|
||||
try:
|
||||
|
||||
@@ -347,17 +347,27 @@ class CleanTradingDashboard:
|
||||
def _get_ohlcv_data_with_indicators(self, symbol: str, timeframe: str, limit: int = 300):
|
||||
"""Get OHLCV data with technical indicators from data stream monitor"""
|
||||
try:
|
||||
# Get OHLCV data from data stream monitor
|
||||
# Get OHLCV data from data stream monitor based on symbol and timeframe
|
||||
if hasattr(self.orchestrator, 'data_stream_monitor') and self.orchestrator.data_stream_monitor:
|
||||
stream_key = f"ohlcv_{timeframe}"
|
||||
# Determine stream key based on symbol and timeframe
|
||||
if symbol == 'BTC/USDT' and timeframe == '1m':
|
||||
stream_key = 'btc_1m'
|
||||
else:
|
||||
stream_key = f"ohlcv_{timeframe}"
|
||||
|
||||
if stream_key in self.orchestrator.data_stream_monitor.data_streams:
|
||||
ohlcv_data = list(self.orchestrator.data_stream_monitor.data_streams[stream_key])
|
||||
|
||||
# Filter by symbol if needed (for ETH data in mixed streams)
|
||||
if symbol != 'BTC/USDT':
|
||||
ohlcv_data = [item for item in ohlcv_data if item.get('symbol') == symbol]
|
||||
|
||||
# Take the last 'limit' items
|
||||
ohlcv_data = ohlcv_data[-limit:] if len(ohlcv_data) > limit else ohlcv_data
|
||||
|
||||
if not ohlcv_data:
|
||||
return []
|
||||
# Fallback to data provider if stream is empty
|
||||
return self._get_ohlcv_from_provider(symbol, timeframe, limit)
|
||||
|
||||
# Convert to DataFrame for indicator calculation
|
||||
df_data = []
|
||||
@@ -372,78 +382,46 @@ class CleanTradingDashboard:
|
||||
})
|
||||
|
||||
if not df_data:
|
||||
return []
|
||||
return self._get_ohlcv_from_provider(symbol, timeframe, limit)
|
||||
|
||||
df = pd.DataFrame(df_data)
|
||||
df['timestamp'] = pd.to_datetime(df['timestamp'])
|
||||
df.set_index('timestamp', inplace=True)
|
||||
|
||||
# Add technical indicators
|
||||
df['sma_20'] = df['close'].rolling(window=20).mean()
|
||||
df['sma_50'] = df['close'].rolling(window=50).mean()
|
||||
df['ema_12'] = df['close'].ewm(span=12).mean()
|
||||
df['ema_26'] = df['close'].ewm(span=26).mean()
|
||||
|
||||
# RSI
|
||||
delta = df['close'].diff()
|
||||
gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
|
||||
loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
|
||||
rs = gain / loss
|
||||
df['rsi'] = 100 - (100 / (1 + rs))
|
||||
|
||||
# MACD
|
||||
df['macd'] = df['ema_12'] - df['ema_26']
|
||||
df['macd_signal'] = df['macd'].ewm(span=9).mean()
|
||||
df['macd_histogram'] = df['macd'] - df['macd_signal']
|
||||
|
||||
# Bollinger Bands
|
||||
df['bb_middle'] = df['close'].rolling(window=20).mean()
|
||||
bb_std = df['close'].rolling(window=20).std()
|
||||
df['bb_upper'] = df['bb_middle'] + (bb_std * 2)
|
||||
df['bb_lower'] = df['bb_middle'] - (bb_std * 2)
|
||||
|
||||
# Volume indicators
|
||||
df['volume_sma'] = df['volume'].rolling(window=20).mean()
|
||||
df['volume_ratio'] = df['volume'] / df['volume_sma']
|
||||
df = self._add_technical_indicators(df)
|
||||
|
||||
# Convert to list of dictionaries
|
||||
result = []
|
||||
for _, row in df.iterrows():
|
||||
data_point = {
|
||||
'timestamp': row.name.isoformat() if hasattr(row.name, 'isoformat') else str(row.name),
|
||||
'open': float(row['open']),
|
||||
'high': float(row['high']),
|
||||
'low': float(row['low']),
|
||||
'close': float(row['close']),
|
||||
'volume': float(row['volume']),
|
||||
'indicators': {
|
||||
'sma_20': float(row['sma_20']) if pd.notna(row['sma_20']) else None,
|
||||
'sma_50': float(row['sma_50']) if pd.notna(row['sma_50']) else None,
|
||||
'ema_12': float(row['ema_12']) if pd.notna(row['ema_12']) else None,
|
||||
'ema_26': float(row['ema_26']) if pd.notna(row['ema_26']) else None,
|
||||
'rsi': float(row['rsi']) if pd.notna(row['rsi']) else None,
|
||||
'macd': float(row['macd']) if pd.notna(row['macd']) else None,
|
||||
'macd_signal': float(row['macd_signal']) if pd.notna(row['macd_signal']) else None,
|
||||
'macd_histogram': float(row['macd_histogram']) if pd.notna(row['macd_histogram']) else None,
|
||||
'bb_upper': float(row['bb_upper']) if pd.notna(row['bb_upper']) else None,
|
||||
'bb_middle': float(row['bb_middle']) if pd.notna(row['bb_middle']) else None,
|
||||
'bb_lower': float(row['bb_lower']) if pd.notna(row['bb_lower']) else None,
|
||||
'volume_ratio': float(row['volume_ratio']) if pd.notna(row['volume_ratio']) else None
|
||||
}
|
||||
}
|
||||
result.append(data_point)
|
||||
|
||||
return result
|
||||
return self._dataframe_to_api_format(df)
|
||||
|
||||
# Fallback to data provider if stream monitor not available
|
||||
return self._get_ohlcv_from_provider(symbol, timeframe, limit)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting OHLCV data: {e}")
|
||||
return []
|
||||
|
||||
def _get_ohlcv_from_provider(self, symbol: str, timeframe: str, limit: int = 300):
|
||||
"""Fallback to get OHLCV data directly from data provider"""
|
||||
try:
|
||||
ohlcv_data = self.data_provider.get_ohlcv(symbol, timeframe, limit=limit)
|
||||
|
||||
if ohlcv_data is None or ohlcv_data.empty:
|
||||
return []
|
||||
|
||||
# Add technical indicators
|
||||
df = ohlcv_data.copy()
|
||||
df = self._add_technical_indicators(ohlcv_data.copy())
|
||||
|
||||
# Convert to list of dictionaries
|
||||
return self._dataframe_to_api_format(df)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting OHLCV from provider: {e}")
|
||||
return []
|
||||
|
||||
def _add_technical_indicators(self, df):
|
||||
"""Add technical indicators to DataFrame"""
|
||||
try:
|
||||
# Basic indicators
|
||||
df['sma_20'] = df['close'].rolling(window=20).mean()
|
||||
df['sma_50'] = df['close'].rolling(window=50).mean()
|
||||
@@ -472,7 +450,15 @@ class CleanTradingDashboard:
|
||||
df['volume_sma'] = df['volume'].rolling(window=20).mean()
|
||||
df['volume_ratio'] = df['volume'] / df['volume_sma']
|
||||
|
||||
# Convert to list of dictionaries
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error adding technical indicators: {e}")
|
||||
return df
|
||||
|
||||
def _dataframe_to_api_format(self, df):
|
||||
"""Convert DataFrame to API format with indicators"""
|
||||
try:
|
||||
result = []
|
||||
for _, row in df.iterrows():
|
||||
data_point = {
|
||||
@@ -502,7 +488,7 @@ class CleanTradingDashboard:
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting OHLCV data: {e}")
|
||||
logger.error(f"Error converting to API format: {e}")
|
||||
return []
|
||||
|
||||
def _get_cob_data_with_buckets(self, symbol: str, limit: int = 300):
|
||||
|
||||
Reference in New Issue
Block a user