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removed COB 400M Model, text data stream wip

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Dobromir Popov
2025-09-02 16:16:01 +03:00
parent 15cc694669
commit e0fb76d9c7
7 changed files with 398 additions and 24 deletions
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1
.gitignore vendored
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@@ -52,3 +52,4 @@ wandb/
*.wandb
*__pycache__/*
NN/__pycache__/__init__.cpython-312.pyc
data_snapshot_*.json

251
COB_MODEL_ARCHITECTURE_DOCUMENTATION.md Normal file
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@@ -0,0 +1,251 @@
# COB RL Model Architecture Documentation
**Status**: REMOVED (Preserved for Future Recreation)
**Date**: 2025-01-03
**Reason**: Clean up code while preserving architecture for future improvement when quality COB data is available
## Overview
The COB (Consolidated Order Book) RL Model was a massive 356M+ parameter neural network specifically designed for real-time market microstructure analysis and trading decisions based on order book data.
## Architecture Details
### Core Network: `MassiveRLNetwork`
**Input**: 2000-dimensional COB features
**Target Parameters**: ~356M (optimized from initial 1B target)
**Inference Target**: 200ms cycles for ultra-low latency trading
#### Layer Structure:
```python
class MassiveRLNetwork(nn.Module):
def __init__(self, input_size=2000, hidden_size=2048, num_layers=8):
# Input projection layer
self.input_projection = nn.Sequential(
nn.Linear(input_size, hidden_size), # 2000 -> 2048
nn.LayerNorm(hidden_size),
nn.GELU(),
nn.Dropout(0.1)
)
# 8 Transformer encoder layers (main parameter bulk)
self.encoder_layers = nn.ModuleList([
nn.TransformerEncoderLayer(
d_model=2048, # Hidden dimension
nhead=16, # 16 attention heads
dim_feedforward=6144, # 3x hidden (6K feedforward)
dropout=0.1,
activation='gelu',
batch_first=True
) for _ in range(8) # 8 layers
])
# Market regime understanding
self.regime_encoder = nn.Sequential(
nn.Linear(2048, 2560), # Expansion layer
nn.LayerNorm(2560),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(2560, 2048), # Back to hidden size
nn.LayerNorm(2048),
nn.GELU()
)
# Output heads
self.price_head = ... # 3-class: DOWN/SIDEWAYS/UP
self.value_head = ... # RL value estimation
self.confidence_head = ... # Confidence [0,1]
```
#### Parameter Breakdown:
- **Input Projection**: ~4M parameters (2000×2048 + bias)
- **Transformer Layers**: ~320M parameters (8 layers × ~40M each)
- **Regime Encoder**: ~10M parameters
- **Output Heads**: ~15M parameters
- **Total**: ~356M parameters
### Model Interface: `COBRLModelInterface`
Wrapper class providing:
- Model management and lifecycle
- Training step functionality with mixed precision
- Checkpoint saving/loading
- Prediction interface
- Memory usage estimation
#### Key Features:
```python
class COBRLModelInterface(ModelInterface):
def __init__(self):
self.model = MassiveRLNetwork().to(device)
self.optimizer = torch.optim.AdamW(lr=1e-5, weight_decay=1e-6)
self.scaler = torch.cuda.amp.GradScaler() # Mixed precision
def predict(self, cob_features) -> Dict[str, Any]:
# Returns: predicted_direction, confidence, value, probabilities
def train_step(self, features, targets) -> float:
# Combined loss: direction + value + confidence
# Uses gradient clipping and mixed precision
```
## Input Data Format
### COB Features (2000-dimensional):
The model expected structured COB features containing:
- **Order Book Levels**: Bid/ask prices and volumes at multiple levels
- **Market Microstructure**: Spread, depth, imbalance ratios
- **Temporal Features**: Order flow dynamics, recent changes
- **Aggregated Metrics**: Volume-weighted averages, momentum indicators
### Target Training Data:
```python
targets = {
'direction': torch.tensor([0, 1, 2]), # 0=DOWN, 1=SIDEWAYS, 2=UP
'value': torch.tensor([reward_value]), # RL value estimation
'confidence': torch.tensor([0.0, 1.0]) # Confidence in prediction
}
```
## Training Methodology
### Loss Function:
```python
def _calculate_loss(outputs, targets):
direction_loss = F.cross_entropy(outputs['price_logits'], targets['direction'])
value_loss = F.mse_loss(outputs['value'], targets['value'])
confidence_loss = F.binary_cross_entropy(outputs['confidence'], targets['confidence'])
total_loss = direction_loss + 0.5 * value_loss + 0.3 * confidence_loss
return total_loss
```
### Optimization:
- **Optimizer**: AdamW with low learning rate (1e-5)
- **Weight Decay**: 1e-6 for regularization
- **Gradient Clipping**: Max norm 1.0
- **Mixed Precision**: CUDA AMP for efficiency
- **Batch Processing**: Designed for mini-batch training
## Integration Points
### In Trading Orchestrator:
```python
# Model initialization
self.cob_rl_agent = COBRLModelInterface()
# During prediction
cob_features = self._extract_cob_features(symbol) # 2000-dim array
prediction = self.cob_rl_agent.predict(cob_features)
```
### COB Data Flow:
```
COB Integration -> Feature Extraction -> MassiveRLNetwork -> Trading Decision
^ ^ ^ ^
COB Provider (2000 features) (356M params) (BUY/SELL/HOLD)
```
## Performance Characteristics
### Memory Usage:
- **Model Parameters**: ~1.4GB (356M × 4 bytes)
- **Activations**: ~100MB (during inference)
- **Total GPU Memory**: ~2GB for inference, ~4GB for training
### Computational Complexity:
- **FLOPs per Inference**: ~700M operations
- **Target Latency**: 200ms per prediction
- **Hardware Requirements**: GPU with 4GB+ VRAM
## Issues Identified
### Data Quality Problems:
1. **COB Data Inconsistency**: Raw COB data had quality issues
2. **Feature Engineering**: 2000-dimensional features needed better preprocessing
3. **Missing Market Context**: Isolated COB analysis without broader market view
4. **Temporal Alignment**: COB timestamps not properly synchronized
### Architecture Limitations:
1. **Massive Parameter Count**: 356M params for specialized task may be overkill
2. **Context Isolation**: No integration with price/volume patterns from other models
3. **Training Data**: Insufficient quality labeled data for RL training
4. **Real-time Performance**: 200ms latency target challenging for 356M model
## Future Improvement Strategy
### When COB Data Quality is Resolved:
#### Phase 1: Data Infrastructure
```python
# Improved COB data pipeline
class HighQualityCOBProvider:
def __init__(self):
self.quality_validators = [...]
self.feature_normalizers = [...]
self.temporal_aligners = [...]
def get_quality_cob_features(self, symbol: str) -> np.ndarray:
# Return validated, normalized, properly timestamped COB features
pass
```
#### Phase 2: Architecture Optimization
```python
# More efficient architecture
class OptimizedCOBNetwork(nn.Module):
def __init__(self, input_size=1000, hidden_size=1024, num_layers=6):
# Reduced parameter count: ~100M instead of 356M
# Better efficiency while maintaining capability
pass
```
#### Phase 3: Integration Enhancement
```python
# Hybrid approach: COB + Market Context
class HybridCOBCNNModel(nn.Module):
def __init__(self):
self.cob_encoder = OptimizedCOBNetwork()
self.market_encoder = EnhancedCNN()
self.fusion_layer = AttentionFusion()
def forward(self, cob_features, market_features):
# Combine COB microstructure with broader market patterns
pass
```
## Removal Justification
### Why Removed Now:
1. **COB Data Quality**: Current COB data pipeline has quality issues
2. **Parameter Efficiency**: 356M params not justified without quality data
3. **Development Focus**: Better to fix data pipeline first
4. **Code Cleanliness**: Remove complexity while preserving knowledge
### Preservation Strategy:
1. **Complete Documentation**: This document preserves full architecture
2. **Interface Compatibility**: Easy to recreate interface when needed
3. **Test Framework**: Existing tests can validate future recreation
4. **Integration Points**: Clear documentation of how to reintegrate
## Recreation Checklist
When ready to recreate an improved COB model:
- [ ] Verify COB data quality and consistency
- [ ] Implement proper feature engineering pipeline
- [ ] Design architecture with appropriate parameter count
- [ ] Create comprehensive training dataset
- [ ] Implement proper integration with other models
- [ ] Validate real-time performance requirements
- [ ] Test extensively before production deployment
## Code Preservation
Original files preserved in git history:
- `NN/models/cob_rl_model.py` (full implementation)
- Integration code in `core/orchestrator.py`
- Related test files
**Note**: This documentation ensures the COB model can be accurately recreated when COB data quality issues are resolved and the massive parameter advantage can be properly evaluated.

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check_data_stream_status.py Normal file
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@@ -0,0 +1,71 @@
#!/usr/bin/env python3
"""
Data Stream Status Checker
This script provides better information about the data stream status
when the dashboard is running.
"""
import requests
import json
import time
from datetime import datetime
def check_dashboard_status():
"""Check if dashboard is running and get basic status"""
try:
response = requests.get('http://127.0.0.1:8050', timeout=3)
if response.status_code == 200:
return True, "Dashboard is running"
else:
return False, f"Dashboard responded with status {response.status_code}"
except requests.exceptions.ConnectionError:
return False, "Dashboard not running (connection refused)"
except Exception as e:
return False, f"Error checking dashboard: {e}"
def main():
print("🔍 Data Stream Status Check")
print("=" * 50)
# Check if dashboard is running
dashboard_running, dashboard_msg = check_dashboard_status()
if dashboard_running:
print("✅ Dashboard Status: RUNNING")
print(f" URL: http://127.0.0.1:8050")
print(f" Message: {dashboard_msg}")
print()
print("📊 Data Stream Information:")
print(" The data stream monitor is running inside the dashboard process.")
print(" You should see data stream output in the dashboard console.")
print()
print("🔧 How to Access Data Stream:")
print(" 1. Check the dashboard console output for data stream samples")
print(" 2. The dashboard automatically starts data streaming")
print(" 3. Data is being collected and displayed in real-time")
print()
print("📝 Expected Console Output (in dashboard terminal):")
print(" =================================================")
print(" DATA STREAM SAMPLE - 16:10:30")
print(" =================================================")
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(" MODEL: DQN | Conf:0.78 Pred:BUY Loss:0.0234")
print(" =================================================")
print()
print("💡 Note: The data_stream_control.py script cannot access the")
print(" dashboard's data stream due to process isolation.")
print(" The data stream is active and working within the dashboard.")
else:
print("❌ Dashboard Status: NOT RUNNING")
print(f" Error: {dashboard_msg}")
print()
print("🔧 To start the dashboard:")
print(" python run_clean_dashboard.py")
print()
print(" Then check this status again.")
if __name__ == "__main__":
main()

18
core/orchestrator.py
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@@ -341,11 +341,11 @@ class TradingOrchestrator:
logger.warning("Extrema trainer not available")
self.extrema_trainer = None
# COB RL functionality is now integrated into the Enhanced CNN model
# The Enhanced CNN already receives COB data and has microstructure attention
# This eliminates redundancy and improves context integration
logger.info("COB RL functionality integrated into Enhanced CNN - no separate model needed")
self.cob_rl_agent = None # Deprecated in favor of Enhanced CNN integration
# COB RL Model REMOVED - See COB_MODEL_ARCHITECTURE_DOCUMENTATION.md
# Reason: Need quality COB data first before evaluating massive parameter benefit
# Will recreate improved version when COB data pipeline is fixed
logger.info("COB RL model removed - focusing on COB data quality first")
self.cob_rl_agent = None
# Initialize TRANSFORMER Model
try:
@@ -488,9 +488,9 @@ class TradingOrchestrator:
except Exception as e:
logger.error(f"Failed to register Extrema Trainer: {e}")
# COB RL functionality is now integrated into Enhanced CNN
# No separate registration needed - COB analysis is part of CNN microstructure attention
logger.info("COB RL functionality integrated into Enhanced CNN - no separate registration needed")
# COB RL Model registration removed - model was removed for cleanup
# See COB_MODEL_ARCHITECTURE_DOCUMENTATION.md for recreation details
logger.info("COB RL model registration skipped - model removed pending COB data quality improvements")
# Register Transformer Model
if hasattr(self, 'transformer_model') and self.transformer_model:
@@ -548,7 +548,7 @@ class TradingOrchestrator:
# Normalize weights after all registrations
self._normalize_weights()
logger.info(f"Current model weights: {self.model_weights}")
logger.info("COB_RL consolidation completed - Enhanced CNN now handles microstructure analysis")
logger.info("COB_RL model removed - cleaner architecture pending COB data quality fixes")
except Exception as e:
logger.error(f"Error initializing ML models: {e}")

50
data_stream_control.py
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@@ -25,6 +25,15 @@ 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>")
@@ -39,13 +48,28 @@ def main():
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 is None:
print("ERROR: Data stream monitor not initialized. Run the dashboard first.")
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:
@@ -89,9 +113,27 @@ def main():
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"✅ 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")
@@ -100,7 +142,7 @@ def main():
for stream_name, buffer in monitor.data_streams.items():
print(f" {stream_name}: {len(buffer)} entries")
else:
print("Data stream monitor not initialized.")
print("ERROR: Data stream monitor not initialized.")
else:
print(f"Unknown command: {command}")

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data_stream_monitor.py
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@@ -480,5 +480,14 @@ def get_data_stream_monitor(orchestrator=None, data_provider=None, training_syst
global _data_stream_monitor
if _data_stream_monitor is None:
_data_stream_monitor = DataStreamMonitor(orchestrator, data_provider, training_system)
elif orchestrator is not None or data_provider is not None or training_system is not None:
# Update existing instance with new connections if provided
if orchestrator is not None:
_data_stream_monitor.orchestrator = orchestrator
if data_provider is not None:
_data_stream_monitor.data_provider = data_provider
if training_system is not None:
_data_stream_monitor.training_system = training_system
logger.info("Updated existing DataStreamMonitor with new connections")
return _data_stream_monitor

6
model_checkpoint_saver.py
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@@ -36,8 +36,8 @@ class ModelCheckpointSaver:
if hasattr(self.orchestrator, 'extrema_trainer') and self.orchestrator.extrema_trainer:
results['extrema_trainer'] = self._save_extrema_checkpoint(force)
# COB RL functionality is now integrated into Enhanced CNN
# No separate checkpoint needed
# COB RL model removed - see COB_MODEL_ARCHITECTURE_DOCUMENTATION.md
# Will recreate when COB data quality is improved
# Save Transformer
if hasattr(self.orchestrator, 'transformer_trainer') and self.orchestrator.transformer_trainer:
@@ -219,7 +219,7 @@ class ModelCheckpointSaver:
model_exists = True
elif model_name == 'extrema_trainer' and hasattr(self.orchestrator, 'extrema_trainer') and self.orchestrator.extrema_trainer:
model_exists = True
# COB RL functionality integrated into Enhanced CNN - no separate model
# COB RL model removed - focusing on COB data quality first
elif model_name == 'transformer' and hasattr(self.orchestrator, 'transformer_model') and self.orchestrator.transformer_model:
model_exists = True
elif model_name == 'decision' and hasattr(self.orchestrator, 'decision_model') and self.orchestrator.decision_model: