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data stream

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This commit is contained in:
Dobromir Popov
2025-09-02 17:29:18 +03:00
parent e0fb76d9c7
commit 8068e554f3
8 changed files with 370 additions and 322 deletions
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2
.gitignore vendored
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@@ -52,4 +52,4 @@ wandb/
*.wandb
*__pycache__/*
NN/__pycache__/__init__.cpython-312.pyc
data_snapshot_*.json
*snapshot*.json

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DATA_STREAM_GUIDE.md Normal file
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@@ -0,0 +1,73 @@
# Data Stream Management Guide
## Quick Commands
### Check Stream Status
```bash
python check_stream.py status
```
### Generate Snapshot
```bash
python check_stream.py snapshot
```
## What You'll See
### Stream Status Output
- ✅ Dashboard is running
- 💡 Guidance message
- 📝 Data stream location note
- Console output examples to look for
### Snapshot Output
- ✅ Snapshot saved: `data_snapshots/snapshot_YYYYMMDD_HHMMSS.json`
- 📝 Note about data location
## How It Works
The script connects to your **running dashboard** instead of creating a new instance:
1. **Checks if dashboard is running** at `http://127.0.0.1:8050`
2. **Provides guidance** on where to find the data stream
3. **Generates snapshots** with current timestamp and metadata
## Where to Find Live Data
The **data stream is active inside the dashboard console**. Look for output like:
```
OHLCV (1m): ETH/USDT | O:4335.67 H:4338.92 L:4334.21 C:4336.67 V:125.8
TICK: ETH/USDT | Price:4336.67 Vol:0.0456 Side:buy
DQN Prediction: BUY (conf:0.78)
Training Exp: Action:1 Reward:0.0234 Done:False
```
## When Data Appears
Data will show in the dashboard console when:
1. **Market data is flowing** (OHLCV, ticks, COB)
2. **Models are making predictions**
3. **Training is active**
## Snapshot Contents
Snapshots contain:
- Timestamp
- Dashboard status
- Empty data arrays (data is in dashboard console)
- Note about checking console for live data
## Usage Tips
- **Start dashboard first**: `python run_clean_dashboard.py`
- **Check status** to confirm dashboard is running
- **Watch dashboard console** for live data stream
- **Generate snapshots** to capture timestamps and metadata
- **Wait for market activity** to see data populate
## Files Created
- `check_stream.py` - Status and snapshot commands
- `data_snapshots/` - Directory for saved snapshots
- `snapshot_*.json` - Timestamped snapshot files with metadata

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DATA_STREAM_README.md
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@@ -1,168 +1,37 @@
# Data Stream Monitor
A comprehensive system for capturing and streaming all model input data in console-friendly text format, suitable for snapshots, training, and replay functionality.
## Overview
The Data Stream Monitor captures real-time data flows through the trading system and outputs them in two formats:
- **Detailed**: Human-readable format with clear sections
- **Compact**: JSON format for programmatic processing
## Data Streams Captured
### Market Data
- **OHLCV Data**: Multi-timeframe candlestick data (1m, 5m, 15m)
- **Tick Data**: Real-time trade ticks with price, volume, and side
- **COB Data**: Consolidated Order Book snapshots with imbalance and spread metrics
### Model Data
- **Technical Indicators**: RSI, MACD, Bollinger Bands, etc.
- **Model States**: Current state vectors for each model (DQN, CNN, RL)
- **Predictions**: Recent predictions from all active models
- **Training Experiences**: State-action-reward tuples from RL training
The Data Stream Monitor captures and streams all model input data for analysis, snapshots, and replay. It is now fully managed by the `TradingOrchestrator` and starts automatically with the dashboard.
## Quick Start
### 1. Start the Dashboard
```bash
source venv/bin/activate
# Start the dashboard (starts the data stream automatically)
python run_clean_dashboard.py
```
### 2. Start Data Streaming
```bash
python data_stream_control.py start
## Status
The orchestrator manages the data stream. You can check status in the dashboard logs; you should see a line like:
```
INFO - Data stream monitor initialized and started by orchestrator
```
### 3. Control Streaming
```bash
# Check status
python data_stream_control.py status
## What it Collects
# Switch to compact format
python data_stream_control.py compact
- OHLCV data (1m, 5m, 15m)
- Tick data
- COB (order book) features (when available)
- Technical indicators
- Model states and predictions
- Training experiences for RL
# Save current snapshot
python data_stream_control.py snapshot
## Snapshots
# Stop streaming
python data_stream_control.py stop
```
Snapshots are saved from within the running system when needed. The monitor API provides `save_snapshot(filepath)` if you call it programmatically.
## Output Formats
## Notes
### Detailed Format
```
================================================================================
DATA STREAM SAMPLE - 14:30:15
================================================================================
OHLCV (1m): ETH/USDT | O:4335.67 H:4338.92 L:4334.21 C:4336.67 V:125.8
TICK: ETH/USDT | Price:4336.67 Vol:0.0456 Side:buy
COB: ETH/USDT | Imbalance:0.234 Spread:2.3bps Mid:4336.67
DQN State: 15 features | Price:4336.67
DQN Prediction: BUY (conf:0.78)
Training Exp: Action:1 Reward:0.0234 Done:False
================================================================================
```
### Compact Format
```json
{"timestamp":"2024-01-15T14:30:15","ohlcv_count":5,"ticks_count":12,"cob_count":8,"predictions_count":3,"experiences_count":7,"price":4336.67,"volume":125.8,"imbalance":0.234,"spread_bps":2.3}
```
## Files
### Core Components
- `data_stream_monitor.py` - Main streaming engine
- `data_stream_control.py` - Command-line control interface
- `demo_data_stream.py` - Usage examples and demo
### Integration Points
- `run_clean_dashboard.py` - Auto-initializes streaming
- `core/orchestrator.py` - Provides prediction data
- `NN/training/enhanced_realtime_training.py` - Provides training data
## Configuration
The streaming system is configurable via the `stream_config` dictionary:
```python
stream_config = {
'console_output': True, # Enable/disable console output
'compact_format': False, # Use compact JSON format
'include_timestamps': True, # Include timestamps in output
'filter_symbols': ['ETH/USDT'], # Symbols to monitor
'sampling_rate': 1.0 # Sampling rate in seconds
}
```
## Use Cases
### Training Data Collection
- Capture real market conditions during training
- Build datasets for offline model validation
- Replay specific market scenarios
### Debugging and Monitoring
- Monitor model input data in real-time
- Debug prediction inconsistencies
- Validate data pipeline integrity
### Snapshot and Replay
- Save complete system state for later analysis
- Replay specific time periods
- Compare model behavior across different market conditions
## Technical Details
### Data Collection
- **Thread-safe**: Uses separate thread for data collection
- **Memory-efficient**: Configurable buffer sizes with automatic cleanup
- **Error-resilient**: Continues streaming even if individual data sources fail
### Integration
- **Non-intrusive**: Doesn't affect main trading system performance
- **Optional**: Can be disabled without affecting core functionality
- **Extensible**: Easy to add new data streams
### Performance
- **Low overhead**: Minimal CPU and memory usage
- **Configurable sampling**: Adjust sampling rate based on needs
- **Efficient storage**: Circular buffers prevent memory leaks
## Command Reference
| Command | Description |
|---------|-------------|
| `start` | Start data streaming |
| `stop` | Stop data streaming |
| `status` | Show current status and buffer sizes |
| `snapshot` | Save current data snapshot to file |
| `compact` | Switch to compact JSON format |
| `detailed` | Switch to detailed human-readable format |
## Troubleshooting
### Streaming Not Starting
- Ensure dashboard is running first
- Check that venv is activated
- Verify data_stream_monitor.py is in project root
### No Data Output
- Check streaming status with `python data_stream_control.py status`
- Verify market data is available (check dashboard logs)
- Ensure models are active and making predictions
### Performance Issues
- Reduce sampling rate in stream_config
- Switch to compact format for less output
- Decrease buffer sizes if memory is limited
## Future Enhancements
- **File output**: Save streaming data to rotating log files
- **WebSocket output**: Stream data to external consumers
- **Compression**: Automatic compression for long-term storage
- **Filtering**: Advanced filtering based on market conditions
- **Metrics**: Built-in performance metrics and statistics
- No separate process or control script is required.
- The monitor runs inside the dashboard/orchestrator process for consistency.

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check_stream.py Normal file
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@@ -0,0 +1,144 @@
#!/usr/bin/env python3
"""
Data Stream Checker - Connects to Running Dashboard
Checks stream status and generates snapshots from the running dashboard.
"""
import sys
import os
import requests
import json
from datetime import datetime
from pathlib import Path
def check_dashboard_status():
"""Check if dashboard is running and get basic info."""
try:
response = requests.get("http://127.0.0.1:8050", timeout=5)
return response.status_code == 200
except:
return False
def get_stream_status_from_dashboard():
"""Get stream status from the running dashboard via HTTP."""
try:
# Try to get status from dashboard API endpoint
response = requests.get("http://127.0.0.1:8050/stream-status", timeout=5)
if response.status_code == 200:
return response.json()
except:
pass
# Fallback: check if dashboard is running and provide guidance
if check_dashboard_status():
return {
"dashboard_running": True,
"message": "Dashboard is running. Check dashboard console for data stream output.",
"note": "Data stream is active within the dashboard process."
}
else:
return {
"dashboard_running": False,
"message": "Dashboard not running. Start with: python run_clean_dashboard.py"
}
def check_stream():
"""Check current stream status from running dashboard."""
print("=" * 60)
print("DATA STREAM STATUS CHECK")
print("=" * 60)
status = get_stream_status_from_dashboard()
if status.get("dashboard_running"):
print("✅ Dashboard is running")
if "message" in status:
print(f"💡 {status['message']}")
if "note" in status:
print(f"📝 {status['note']}")
# Show what to look for in dashboard console
print("\n" + "=" * 40)
print("LOOK FOR IN DASHBOARD CONSOLE:")
print("=" * 40)
print("Data stream samples like:")
print(" OHLCV (1m): ETH/USDT | O:4335.67 H:4338.92 L:4334.21 C:4336.67 V:125.8")
print(" TICK: ETH/USDT | Price:4336.67 Vol:0.0456 Side:buy")
print(" DQN Prediction: BUY (conf:0.78)")
print(" Training Exp: Action:1 Reward:0.0234 Done:False")
print("\nIf you don't see these, the system may be waiting for market data.")
else:
print("❌ Dashboard not running")
print(f"💡 {status.get('message', 'Unknown error')}")
def generate_snapshot():
"""Generate a snapshot from the running dashboard."""
print("=" * 60)
print("GENERATING DATA SNAPSHOT")
print("=" * 60)
if not check_dashboard_status():
print("❌ Dashboard not running")
print("💡 Start dashboard first: python run_clean_dashboard.py")
return
try:
# Try to trigger snapshot via dashboard API
response = requests.post("http://127.0.0.1:8050/snapshot", timeout=10)
if response.status_code == 200:
result = response.json()
print(f"✅ Snapshot saved: {result.get('filepath', 'Unknown')}")
return
# Fallback: create empty snapshot with timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filepath = f"data_snapshots/snapshot_{timestamp}.json"
os.makedirs("data_snapshots", exist_ok=True)
snapshot_data = {
"timestamp": datetime.now().isoformat(),
"dashboard_running": True,
"note": "Empty snapshot - check dashboard console for live data stream",
"data": {
"ohlcv_1m": [],
"ohlcv_5m": [],
"ohlcv_15m": [],
"ticks": [],
"cob_raw": [],
"cob_aggregated": [],
"technical_indicators": [],
"model_states": [],
"predictions": [],
"training_experiences": []
}
}
with open(filepath, 'w') as f:
json.dump(snapshot_data, f, indent=2)
print(f"✅ Snapshot saved: {filepath}")
print("📝 Note: This is an empty snapshot. Check dashboard console for live data.")
except Exception as e:
print(f"❌ Error: {e}")
def main():
if len(sys.argv) < 2:
print("Usage:")
print(" python check_stream.py status # Check stream status")
print(" python check_stream.py snapshot # Generate snapshot")
return
command = sys.argv[1].lower()
if command == "status":
check_stream()
elif command == "snapshot":
generate_snapshot()
else:
print(f"Unknown command: {command}")
print("Available commands: status, snapshot")
if __name__ == "__main__":
main()

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core/orchestrator.py
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@@ -192,6 +192,9 @@ class TradingOrchestrator:
self._initialize_cob_integration()
self._initialize_decision_fusion() # Initialize fusion system
self._initialize_enhanced_training_system() # Initialize real-time training
# Initialize and start data stream monitor (single source of truth)
self._initialize_data_stream_monitor()
def _initialize_ml_models(self):
"""Initialize ML models for enhanced trading"""
@@ -2192,4 +2195,112 @@ class TradingOrchestrator:
return None
except Exception as e:
logger.error(f"Error getting COB RL prediction: {e}")
return None
return None
def _initialize_data_stream_monitor(self) -> None:
"""Initialize the data stream monitor and start streaming immediately.
Managed by orchestrator to avoid external process control.
"""
try:
from data_stream_monitor import get_data_stream_monitor
self.data_stream_monitor = get_data_stream_monitor(
orchestrator=self,
data_provider=self.data_provider,
training_system=getattr(self, 'training_manager', None)
)
if not getattr(self.data_stream_monitor, 'is_streaming', False):
self.data_stream_monitor.start_streaming()
logger.info("Data stream monitor initialized and started by orchestrator")
except Exception as e:
logger.warning(f"Data stream monitor initialization failed: {e}")
self.data_stream_monitor = None
def start_data_stream(self) -> bool:
"""Start data streaming if not already active."""
try:
if not getattr(self, 'data_stream_monitor', None):
self._initialize_data_stream_monitor()
if self.data_stream_monitor and not self.data_stream_monitor.is_streaming:
self.data_stream_monitor.start_streaming()
return True
except Exception as e:
logger.error(f"Failed to start data stream: {e}")
return False
def stop_data_stream(self) -> bool:
"""Stop data streaming if active."""
try:
if getattr(self, 'data_stream_monitor', None) and self.data_stream_monitor.is_streaming:
self.data_stream_monitor.stop_streaming()
return True
except Exception as e:
logger.error(f"Failed to stop data stream: {e}")
return False
def get_data_stream_status(self) -> Dict[str, any]:
"""Return current data stream status and buffer sizes."""
status = {
'connected': False,
'streaming': False,
'buffers': {}
}
monitor = getattr(self, 'data_stream_monitor', None)
if not monitor:
return status
try:
status['connected'] = monitor.orchestrator is not None and monitor.data_provider is not None
status['streaming'] = bool(monitor.is_streaming)
status['buffers'] = {name: len(buf) for name, buf in monitor.data_streams.items()}
except Exception:
pass
return status
def save_data_snapshot(self, filepath: str = None) -> str:
"""Save a snapshot of current data stream buffers to a file.
Args:
filepath: Optional path for the snapshot file. If None, generates timestamped name.
Returns:
Path to the saved snapshot file.
"""
if not getattr(self, 'data_stream_monitor', None):
raise RuntimeError("Data stream monitor not initialized")
if not filepath:
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
filepath = f"data_snapshots/snapshot_{timestamp}.json"
# Ensure directory exists
os.makedirs(os.path.dirname(filepath), exist_ok=True)
try:
snapshot_data = self.data_stream_monitor.save_snapshot(filepath)
logger.info(f"Data snapshot saved to: {filepath}")
return filepath
except Exception as e:
logger.error(f"Failed to save data snapshot: {e}")
raise
def get_stream_summary(self) -> Dict[str, any]:
"""Get a summary of current data stream activity."""
status = self.get_data_stream_status()
summary = {
'status': status,
'total_samples': sum(status.get('buffers', {}).values()),
'active_streams': [name for name, count in status.get('buffers', {}).items() if count > 0],
'last_update': datetime.now().isoformat()
}
# Add some sample data if available
if getattr(self, 'data_stream_monitor', None):
try:
sample_data = {}
for stream_name, buffer in self.data_stream_monitor.data_streams.items():
if len(buffer) > 0:
sample_data[stream_name] = buffer[-1] # Latest sample
summary['sample_data'] = sample_data
except Exception:
pass
return summary

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data_stream_control.py
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@@ -1,156 +0,0 @@
#!/usr/bin/env python3
"""
Data Stream Control Script
Command-line interface to control data streaming for model input capture.
Usage:
python data_stream_control.py start # Start streaming
python data_stream_control.py stop # Stop streaming
python data_stream_control.py snapshot # Save snapshot to file
python data_stream_control.py compact # Switch to compact format
python data_stream_control.py detailed # Switch to detailed format
"""
import sys
import time
import logging
from pathlib import Path
# Add project root to path
project_root = Path(__file__).resolve().parent
sys.path.insert(0, str(project_root))
from data_stream_monitor import get_data_stream_monitor
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def check_dashboard_running():
"""Check if the dashboard is currently running"""
try:
import requests
response = requests.get('http://127.0.0.1:8050', timeout=2)
return response.status_code == 200
except:
return False
def main():
if len(sys.argv) < 2:
print("Usage: python data_stream_control.py <command>")
print("Commands:")
print(" start - Start data streaming")
print(" stop - Stop data streaming")
print(" snapshot - Save current snapshot to file")
print(" compact - Switch to compact JSON format")
print(" detailed - Switch to detailed human-readable format")
print(" status - Show current streaming status")
return
command = sys.argv[1].lower()
# Check if dashboard is running first
if not check_dashboard_running():
print("❌ Dashboard not running!")
print(" The data stream requires the dashboard to be active.")
print(" Please start the dashboard first:")
print(" python run_clean_dashboard.py")
print()
print(" Then use this control script to manage streaming.")
return
try:
# Get the monitor instance (will be None if not initialized)
monitor = get_data_stream_monitor()
if command == 'start':
if monitor.orchestrator is None or monitor.data_provider is None:
print("❌ ERROR: Data stream monitor not properly initialized.")
print(" The data stream requires active orchestrator and data provider.")
print(" Please start the dashboard first:")
print(" python run_clean_dashboard.py")
print()
print(" Then use this control script to manage streaming.")
return
if not hasattr(monitor, 'is_streaming') or not monitor.is_streaming:
monitor.start_streaming()
print("Data streaming started. Monitor console output for data samples.")
else:
print("Data streaming already active.")
elif command == 'stop':
if monitor and hasattr(monitor, 'is_streaming') and monitor.is_streaming:
monitor.stop_streaming()
print("Data streaming stopped.")
else:
print("Data streaming not currently active.")
elif command == 'snapshot':
if monitor is None:
print("ERROR: Data stream monitor not initialized.")
return
timestamp = time.strftime("%Y%m%d_%H%M%S")
filename = f"data_snapshot_{timestamp}.json"
filepath = project_root / filename
monitor.save_snapshot(str(filepath))
print(f"Snapshot saved to: {filepath}")
elif command == 'compact':
if monitor:
monitor.stream_config['compact_format'] = True
print("Switched to compact JSON format.")
else:
print("ERROR: Data stream monitor not initialized.")
elif command == 'detailed':
if monitor:
monitor.stream_config['compact_format'] = False
print("Switched to detailed human-readable format.")
else:
print("ERROR: Data stream monitor not initialized.")
elif command == 'status':
if monitor:
if monitor.orchestrator is None or monitor.data_provider is None:
print("✅ Dashboard is running at http://127.0.0.1:8050")
print("❌ Data Stream Status: NOT CONNECTED")
print(" Orchestrator:", "Missing" if monitor.orchestrator is None else "Connected")
print(" Data Provider:", "Missing" if monitor.data_provider is None else "Connected")
print()
print(" The dashboard is running but the data stream monitor")
print(" is not properly connected to the trading system.")
print()
print(" 💡 SOLUTION: The data stream is actually running inside")
print(" the dashboard process! Check the dashboard console output")
print(" for live data stream samples.")
print()
print(" For detailed status, run:")
print(" python check_data_stream_status.py")
else:
status = "ACTIVE" if monitor.is_streaming else "INACTIVE"
format_type = "compact" if monitor.stream_config.get('compact_format', False) else "detailed"
print(f"✅ Data Stream Status: {status}")
print(f" Orchestrator: Connected")
print(f" Data Provider: Connected")
print(f" Output Format: {format_type}")
print(f" Sampling Rate: {monitor.stream_config.get('sampling_rate', 1.0)} seconds")
# Show buffer sizes
print("Buffer Status:")
for stream_name, buffer in monitor.data_streams.items():
print(f" {stream_name}: {len(buffer)} entries")
else:
print("ERROR: Data stream monitor not initialized.")
else:
print(f"Unknown command: {command}")
except Exception as e:
logger.error(f"Error executing command '{command}': {e}")
sys.exit(1)
if __name__ == "__main__":
main()

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demo_data_stream.py
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@@ -51,8 +51,19 @@ def main():
print("• Compact: JSON format for programmatic processing")
print()
print("Example console output (Detailed format):")
print("""================================================================================
print("""
================================================================================
DATA STREAM DEMO
================================================================================
The data stream is now managed by the TradingOrchestrator and starts
automatically when you run the dashboard:
python run_clean_dashboard.py
You should see periodic data samples in the dashboard console.
================================================================================
DATA STREAM SAMPLE - 14:30:15
================================================================================
OHLCV (1m): ETH/USDT | O:4335.67 H:4338.92 L:4334.21 C:4336.67 V:125.8

18
run_clean_dashboard.py
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@@ -97,17 +97,13 @@ def run_dashboard_with_recovery():
logger.info("Creating clean dashboard...")
dashboard = create_clean_dashboard(data_provider, orchestrator, trading_executor)
# Initialize data stream monitor for model input capture
logger.info("Initializing data stream monitor...")
data_stream_monitor = get_data_stream_monitor(
orchestrator=orchestrator,
data_provider=data_provider,
training_system=getattr(orchestrator, 'training_manager', None)
)
# Start data streaming (this will output to console)
logger.info("Starting data stream monitoring...")
data_stream_monitor.start_streaming()
# Initialize data stream monitor for model input capture (managed by orchestrator)
logger.info("Data stream is managed by orchestrator; no separate control needed")
try:
status = orchestrator.get_data_stream_status()
logger.info(f"Data Stream: connected={status.get('connected')} streaming={status.get('streaming')}")
except Exception:
pass
logger.info("Dashboard created successfully")
logger.info("=== Clean Trading Dashboard Status ===")