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remove emojis from console

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Dobromir Popov
2025-10-25 16:35:08 +03:00
parent 5aa4925cff
commit b8f54e61fa
75 changed files with 828 additions and 828 deletions
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54
ANNOTATE/IMPLEMENTATION_SUMMARY.md
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@@ -4,22 +4,22 @@
The Manual Trade Annotation UI is now **functionally complete** with all core features implemented and ready for use.
## ✅ Completed Tasks (Tasks 1-5)
## Completed Tasks (Tasks 1-5)
### Task 1: Project Structure ✅
### Task 1: Project Structure
- Complete folder structure in `/ANNOTATE`
- Flask/Dash web application
- Template-based architecture (all HTML in separate files)
- Dark theme CSS
- Client-side JavaScript modules
### Task 2: Data Loading ✅
### Task 2: Data Loading
- `HistoricalDataLoader` - Integrates with existing DataProvider
- `TimeRangeManager` - Time navigation and prefetching
- Memory caching with TTL
- **Uses same data source as training/inference**
### Task 3: Chart Visualization ✅
### Task 3: Chart Visualization
- Multi-timeframe Plotly charts (1s, 1m, 1h, 1d)
- Candlestick + volume visualization
- Chart synchronization across timeframes
@@ -27,14 +27,14 @@ The Manual Trade Annotation UI is now **functionally complete** with all core fe
- Zoom and pan functionality
- Scroll zoom enabled
### Task 4: Time Navigation ✅
### Task 4: Time Navigation
- Date/time picker
- Quick range buttons (1h, 4h, 1d, 1w)
- Forward/backward navigation
- Keyboard shortcuts (arrow keys)
- Time range calculations
### Task 5: Trade Annotation ✅
### Task 5: Trade Annotation
- Click to mark entry/exit points
- Visual markers on charts (▲ entry, ▼ exit)
- P&L calculation and display
@@ -44,7 +44,7 @@ The Manual Trade Annotation UI is now **functionally complete** with all core fe
## 🎯 Key Features
### 1. Data Consistency ✅
### 1. Data Consistency
```python
# Same DataProvider used everywhere
DataProvider → HistoricalDataLoader → Annotation UI
@@ -52,7 +52,7 @@ DataProvider → HistoricalDataLoader → Annotation UI
Training/Inference
```
### 2. Test Case Generation ✅
### 2. Test Case Generation
```python
# Generates test cases in realtime format
{
@@ -75,14 +75,14 @@ DataProvider → HistoricalDataLoader → Annotation UI
}
```
### 3. Visual Annotation System ✅
### 3. Visual Annotation System
- **Entry markers**: Green/Red triangles (▲)
- **Exit markers**: Green/Red triangles (▼)
- **P&L labels**: Displayed with percentage
- **Connecting lines**: Dashed lines between entry/exit
- **Color coding**: Green for LONG, Red for SHORT
### 4. Chart Features ✅
### 4. Chart Features
- **Multi-timeframe**: 4 synchronized charts
- **Candlestick**: OHLC visualization
- **Volume bars**: Color-coded by direction
@@ -164,7 +164,7 @@ Save to test_cases/annotation_*.json
└─────────────────────────────────────┘
```
## 🚀 Usage Guide
## Usage Guide
### 1. Start the Application
```bash
@@ -278,12 +278,12 @@ Export annotations to JSON/CSV
## 🎯 Next Steps (Optional Enhancements)
### Task 6: Annotation Storage ✅ (Already Complete)
### Task 6: Annotation Storage (Already Complete)
- JSON-based storage implemented
- CRUD operations working
- Auto-save functionality
### Task 7: Test Case Generation ✅ (Already Complete)
### Task 7: Test Case Generation (Already Complete)
- Realtime format implemented
- Market context extraction working
- File storage implemented
@@ -304,14 +304,14 @@ Export annotations to JSON/CSV
## ✨ Key Achievements
1. **✅ Data Consistency**: Uses same DataProvider as training/inference
2. **✅ Template Architecture**: All HTML in separate files
3. **✅ Dark Theme**: Professional UI matching main dashboard
4. **✅ Multi-Timeframe**: 4 synchronized charts
5. **✅ Visual Annotations**: Clear entry/exit markers with P&L
6. **✅ Test Case Generation**: Realtime format with market context
7. **✅ Self-Contained**: Isolated in /ANNOTATE folder
8. **✅ Production Ready**: Functional core features complete
1. ** Data Consistency**: Uses same DataProvider as training/inference
2. ** Template Architecture**: All HTML in separate files
3. ** Dark Theme**: Professional UI matching main dashboard
4. ** Multi-Timeframe**: 4 synchronized charts
5. ** Visual Annotations**: Clear entry/exit markers with P&L
6. ** Test Case Generation**: Realtime format with market context
7. ** Self-Contained**: Isolated in /ANNOTATE folder
8. ** Production Ready**: Functional core features complete
## 🎊 Success Criteria Met
@@ -326,14 +326,14 @@ Export annotations to JSON/CSV
- [ ] Model training integration (optional)
- [ ] Inference simulation (optional)
## 🚀 Ready for Use!
## Ready for Use!
The ANNOTATE system is now **ready for production use**. You can:
1. ✅ Mark profitable trades on historical data
2. ✅ Generate training test cases
3. ✅ Visualize annotations on charts
4. ✅ Export annotations for analysis
5. ✅ Use same data as training/inference
1. Mark profitable trades on historical data
2. Generate training test cases
3. Visualize annotations on charts
4. Export annotations for analysis
5. Use same data as training/inference
The core functionality is complete and the system is ready to generate high-quality training data for your models! 🎉

52
ANNOTATE/PROGRESS.md
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@@ -1,8 +1,8 @@
# ANNOTATE Project Progress
## ✅ Completed Tasks
## Completed Tasks
### Task 1: Project Structure and Base Templates ✅
### Task 1: Project Structure and Base Templates
**Status**: Complete
**What was built**:
@@ -33,7 +33,7 @@ ANNOTATE/
└── data/ (storage directories)
```
### Task 2: Data Loading and Caching Layer ✅
### Task 2: Data Loading and Caching Layer
**Status**: Complete
**What was built**:
@@ -46,11 +46,11 @@ ANNOTATE/
- Prefetching for smooth scrolling
**Key Features**:
- ✅ Uses the **same DataProvider** as training/inference systems
- ✅ Ensures **data consistency** across annotation, training, and inference
- ✅ Caches data for performance
- ✅ Supports time-based navigation
- ✅ Prefetches adjacent ranges for smooth UX
- Uses the **same DataProvider** as training/inference systems
- Ensures **data consistency** across annotation, training, and inference
- Caches data for performance
- Supports time-based navigation
- Prefetches adjacent ranges for smooth UX
**Integration Points**:
```python
@@ -67,14 +67,14 @@ df = data_loader.get_data('ETH/USDT', '1m', limit=500)
## 🎯 Current Status
### Application Status
- ✅ Flask server running on http://127.0.0.1:8051
- ✅ Templates rendering correctly
- ✅ Data loading integrated with existing DataProvider
- ✅ Dark theme UI implemented
- ✅ Chart visualization (COMPLETE)
- ✅ Annotation functionality (COMPLETE)
- ✅ Test case generation (COMPLETE)
- ✅ **CORE FEATURES COMPLETE - READY FOR USE!**
- Flask server running on http://127.0.0.1:8051
- Templates rendering correctly
- Data loading integrated with existing DataProvider
- Dark theme UI implemented
- Chart visualization (COMPLETE)
- Annotation functionality (COMPLETE)
- Test case generation (COMPLETE)
- **CORE FEATURES COMPLETE - READY FOR USE!**
### Data Flow
```
@@ -146,11 +146,11 @@ The ANNOTATE system ensures data consistency by:
5. **Shared Configuration**: Uses main config.yaml
### Architecture Benefits
- ✅ **No Data Duplication**: Single source of truth
- ✅ **Consistent Quality**: Same data cleaning/validation
- ✅ **Performance**: Leverages existing caching
- ✅ **Maintainability**: Changes to DataProvider automatically propagate
- ✅ **Testing**: Annotations use same data as models see
- **No Data Duplication**: Single source of truth
- **Consistent Quality**: Same data cleaning/validation
- **Performance**: Leverages existing caching
- **Maintainability**: Changes to DataProvider automatically propagate
- **Testing**: Annotations use same data as models see
### Test Case Generation
When an annotation is created, the system will:
@@ -162,7 +162,7 @@ When an annotation is created, the system will:
This ensures models can be trained on manually validated scenarios using the exact same data structure.
## 🚀 Running the Application
## Running the Application
### Start the Server
```bash
@@ -198,10 +198,10 @@ test_cases = annotation_mgr.get_test_cases()
## 📝 Notes
- All HTML is in templates (requirement met ✅)
- Dark theme implemented (requirement met ✅)
- Data consistency ensured (requirement met ✅)
- Self-contained in /ANNOTATE folder (requirement met ✅)
- All HTML is in templates (requirement met )
- Dark theme implemented (requirement met )
- Data consistency ensured (requirement met )
- Self-contained in /ANNOTATE folder (requirement met )
- Ready for chart implementation (next step)
## 🎯 Success Criteria

6
ANNOTATE/README.md
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@@ -4,7 +4,7 @@
A professional web-based interface for manually marking profitable buy/sell signals on historical market data to generate high-quality training test cases for machine learning models.
**Status**: ✅ **Production Ready** - Core features complete and tested
**Status**: **Production Ready** - Core features complete and tested
## ✨ Key Features
@@ -27,7 +27,7 @@ A professional web-based interface for manually marking profitable buy/sell sign
- **Data consistency**: Uses same DataProvider as training/inference
- **Auto-save**: Test cases saved to JSON files
### 🔄 Data Integration
### Data Integration
- **Existing DataProvider**: No duplicate data fetching
- **Cached data**: Leverages existing cache
- **Same quality**: Identical data structure as models see
@@ -39,7 +39,7 @@ A professional web-based interface for manually marking profitable buy/sell sign
- **Responsive**: Works on different screen sizes
- **Keyboard shortcuts**: Arrow keys for navigation
## 🚀 Quick Start
## Quick Start
### Installation

56
ANNOTATE/REALTIME_INFERENCE_GUIDE.md
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@@ -6,7 +6,7 @@ Real-time inference mode runs your trained model on **live streaming data** from
---
## 🚀 Starting Real-Time Inference
## Starting Real-Time Inference
### Step 1: Select Model
Choose the model you want to run from the dropdown in the training panel.
@@ -97,16 +97,16 @@ Charts updating every 1s
- **False positives** - Signals that shouldn't happen
### Good Signs
- ✅ Signals at key levels (support/resistance)
- ✅ High confidence (>70%)
- ✅ Signals match your analysis
- ✅ Few false positives
- Signals at key levels (support/resistance)
- High confidence (>70%)
- Signals match your analysis
- Few false positives
### Warning Signs
- ⚠️ Too many signals (every second)
- ⚠️ Low confidence (<50%)
- ⚠️ Random signals
- ⚠️ Signals don't match patterns
- Too many signals (every second)
- Low confidence (<50%)
- Random signals
- Signals don't match patterns
---
@@ -234,7 +234,7 @@ All 4 charts update simultaneously. Watch for:
- Signals match training patterns
- Timing is precise
- No false positives
- Model learned correctly ✅
- Model learned correctly
**5. Stop Inference**
- Click "Stop Inference"
@@ -247,34 +247,34 @@ All 4 charts update simultaneously. Watch for:
## 🎯 Best Practices
### Before Starting
- ✅ Train model first
- ✅ Verify model loaded
- ✅ Check DataProvider has data
- ✅ Close unnecessary tabs
- Train model first
- Verify model loaded
- Check DataProvider has data
- Close unnecessary tabs
### During Inference
- ✅ Monitor all timeframes
- ✅ Note signal quality
- ✅ Check confidence levels
- ✅ Compare with your analysis
- Monitor all timeframes
- Note signal quality
- Check confidence levels
- Compare with your analysis
### After Stopping
- ✅ Review signal history
- ✅ Note performance
- ✅ Identify improvements
- ✅ Adjust training if needed
- Review signal history
- Note performance
- Identify improvements
- Adjust training if needed
---
## 🚀 Summary
## Summary
Real-time inference provides:
✅ **Live chart updates** (1/second)
✅ **Model predictions** in real-time
✅ **Signal markers** on charts
✅ **Confidence levels** displayed
✅ **Performance monitoring** built-in
**Live chart updates** (1/second)
**Model predictions** in real-time
**Signal markers** on charts
**Confidence levels** displayed
**Performance monitoring** built-in
Use it to:
- **Validate training** - Check model learned correctly

100
ANNOTATE/STATUS.md
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@@ -3,62 +3,62 @@
## 🎉 Project Complete!
**Date**: January 2025
**Status**: ✅ **Production Ready**
**Status**: **Production Ready**
**Completion**: **Tasks 1-8 Complete** (Core + Model Integration)
---
## ✅ Completed Tasks Summary
## Completed Tasks Summary
### ✅ Task 1: Project Structure and Base Templates
### Task 1: Project Structure and Base Templates
- Complete folder structure in `/ANNOTATE`
- Flask/Dash application framework
- Template-based architecture (all HTML separate)
- Dark theme CSS styling
- Client-side JavaScript modules
### ✅ Task 2: Data Loading and Caching Layer
### Task 2: Data Loading and Caching Layer
- `HistoricalDataLoader` class
- `TimeRangeManager` for navigation
- Integration with existing DataProvider
- Memory caching with TTL
- Multi-timeframe data loading
### ✅ Task 3: Multi-Timeframe Chart Visualization
### Task 3: Multi-Timeframe Chart Visualization
- Plotly candlestick charts (4 timeframes)
- Volume bars with color coding
- Chart synchronization
- Hover information display
- Zoom and pan functionality
### ✅ Task 4: Time Navigation System
### Task 4: Time Navigation System
- Date/time picker
- Quick range buttons
- Forward/backward navigation
- Keyboard shortcuts
- Time range calculations
### ✅ Task 5: Trade Annotation System
### Task 5: Trade Annotation System
- Click-to-mark entry/exit
- Visual markers (▲▼)
- P&L calculation
- Connecting lines
- Edit/delete functionality
### ✅ Task 6: Annotation Storage and Management
### Task 6: Annotation Storage and Management
- JSON-based storage
- CRUD operations
- Annotation validation
- Listing UI
- Export functionality
### ✅ Task 7: Test Case Generation System
### Task 7: Test Case Generation System
- Realtime format generation
- Market context extraction
- File storage
- DataProvider integration
### ✅ Task 8: Model Loading and Management
### Task 8: Model Loading and Management
- TrainingSimulator class
- Model loading from orchestrator
- Available models API
@@ -76,51 +76,51 @@
- **Total Lines**: ~2,500+ lines of code
### Features Implemented
- ✅ Multi-timeframe charts (4 timeframes)
- ✅ Visual annotations with P&L
- ✅ Test case generation
- ✅ Data consistency with training
- ✅ Model integration
- ✅ Dark theme UI
- ✅ Keyboard shortcuts
- ✅ Export functionality
- Multi-timeframe charts (4 timeframes)
- Visual annotations with P&L
- Test case generation
- Data consistency with training
- Model integration
- Dark theme UI
- Keyboard shortcuts
- Export functionality
### API Endpoints
- ✅ `/` - Main dashboard
- ✅ `/api/chart-data` - Get chart data
- ✅ `/api/save-annotation` - Save annotation
- ✅ `/api/delete-annotation` - Delete annotation
- ✅ `/api/generate-test-case` - Generate test case
- ✅ `/api/export-annotations` - Export annotations
- ✅ `/api/train-model` - Start training
- ✅ `/api/training-progress` - Get progress
- ✅ `/api/available-models` - List models
- `/` - Main dashboard
- `/api/chart-data` - Get chart data
- `/api/save-annotation` - Save annotation
- `/api/delete-annotation` - Delete annotation
- `/api/generate-test-case` - Generate test case
- `/api/export-annotations` - Export annotations
- `/api/train-model` - Start training
- `/api/training-progress` - Get progress
- `/api/available-models` - List models
---
## 🎯 Key Achievements
### 1. Data Consistency ✅
### 1. Data Consistency
**Problem**: Annotations need same data as training/inference
**Solution**: Integrated with existing DataProvider
**Result**: Perfect data consistency across all systems
### 2. Visual Annotation System ✅
### 2. Visual Annotation System
**Problem**: Need intuitive way to mark trades
**Solution**: Click-based marking with visual feedback
**Result**: Professional TradingView-like interface
### 3. Test Case Generation ✅
### 3. Test Case Generation
**Problem**: Need training data in correct format
**Solution**: Generate test cases with full market context
**Result**: Ready-to-use training data
### 4. Model Integration ✅
### 4. Model Integration
**Problem**: Need to load and use existing models
**Solution**: TrainingSimulator with orchestrator integration
**Result**: Can load CNN, DQN, Transformer, COB models
### 5. Template Architecture ✅
### 5. Template Architecture
**Problem**: Maintainable HTML structure
**Solution**: Jinja2 templates with component separation
**Result**: Clean, maintainable codebase
@@ -237,7 +237,7 @@ User Click → JavaScript → Flask API → AnnotationManager → JSON Storage
---
## 🚀 Deployment Checklist
## Deployment Checklist
- [x] Code complete and tested
- [x] Documentation written
@@ -255,22 +255,22 @@ User Click → JavaScript → Flask API → AnnotationManager → JSON Storage
## 📊 Success Metrics
### Functionality
- ✅ 100% of core features implemented
- ✅ 100% of API endpoints working
- ✅ 100% data consistency achieved
- ✅ 100% template-based architecture
- 100% of core features implemented
- 100% of API endpoints working
- 100% data consistency achieved
- 100% template-based architecture
### Quality
- ✅ Clean code structure
- ✅ Comprehensive documentation
- ✅ Error handling
- ✅ Performance optimized
- Clean code structure
- Comprehensive documentation
- Error handling
- Performance optimized
### Integration
- ✅ DataProvider integration
- ✅ Orchestrator integration
- ✅ Model loading
- ✅ Test case generation
- DataProvider integration
- Orchestrator integration
- Model loading
- Test case generation
---
@@ -308,11 +308,11 @@ The ANNOTATE project is **complete and production-ready**. All core features hav
5. **Production Ready**: Fully functional and documented
### Ready For
- ✅ Marking profitable trades
- ✅ Generating training test cases
- ✅ Model training integration
- ✅ Production deployment
- ✅ Team usage
- Marking profitable trades
- Generating training test cases
- Model training integration
- Production deployment
- Team usage
**Status**: 🎉 **COMPLETE AND READY FOR USE!**

30
ANNOTATE/TRAINING_DATA_FORMAT.md
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@@ -3,9 +3,9 @@
## 🎯 Overview
The ANNOTATE system generates training data that includes **±5 minutes of market data** around each trade signal. This allows models to learn:
- ✅ **WHERE to generate signals** (at entry/exit points)
- ✅ **WHERE NOT to generate signals** (before entry, after exit)
- ✅ **Context around the signal** (what led to the trade)
- **WHERE to generate signals** (at entry/exit points)
- **WHERE NOT to generate signals** (before entry, after exit)
- **Context around the signal** (what led to the trade)
---
@@ -259,11 +259,11 @@ for timestamp, label in zip(timestamps, labels):
```
**Model Learns:**
- ✅ Don't signal during consolidation
- ✅ Signal at breakout confirmation
- ✅ Hold during profitable move
- ✅ Exit at target
- ✅ Don't signal after exit
- Don't signal during consolidation
- Signal at breakout confirmation
- Hold during profitable move
- Exit at target
- Don't signal after exit
---
@@ -290,16 +290,16 @@ print(f"EXIT: {labels.count(3)}")
---
## 🚀 Summary
## Summary
The ANNOTATE system generates **production-ready training data** with:
✅ **±5 minutes of context** around each signal
✅ **Training labels** for each timestamp
✅ **Negative examples** (where NOT to signal)
✅ **Positive examples** (where TO signal)
✅ **All 4 timeframes** (1s, 1m, 1h, 1d)
✅ **Complete market state** (OHLCV data)
**±5 minutes of context** around each signal
**Training labels** for each timestamp
**Negative examples** (where NOT to signal)
**Positive examples** (where TO signal)
**All 4 timeframes** (1s, 1m, 1h, 1d)
**Complete market state** (OHLCV data)
This enables models to learn:
- **Precise timing** of entry/exit signals

26
ANNOTATE/TRAINING_GUIDE.md
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@@ -18,11 +18,11 @@ When you save an annotation, a test case is **automatically generated** and save
### What's Included
Each test case contains:
- ✅ **Market State** - OHLCV data for all 4 timeframes (100 candles each)
- ✅ **Entry/Exit Prices** - Exact prices from annotation
- ✅ **Expected Outcome** - Direction (LONG/SHORT) and P&L percentage
- ✅ **Timestamp** - When the trade occurred
- ✅ **Action** - BUY or SELL signal
- **Market State** - OHLCV data for all 4 timeframes (100 candles each)
- **Entry/Exit Prices** - Exact prices from annotation
- **Expected Outcome** - Direction (LONG/SHORT) and P&L percentage
- **Timestamp** - When the trade occurred
- **Action** - BUY or SELL signal
### Test Case Format
```json
@@ -105,7 +105,7 @@ The system integrates with your existing models:
---
## 🚀 Real-Time Inference
## Real-Time Inference
### Overview
Real-time inference mode runs your trained model on **live streaming data** from the DataProvider, generating predictions in real-time.
@@ -298,7 +298,7 @@ model = orchestrator.cob_rl_agent
---
## 🚀 Advanced Usage
## Advanced Usage
### Custom Training Parameters
Edit `ANNOTATE/core/training_simulator.py`:
@@ -345,11 +345,11 @@ models/checkpoints/ (main system)
The ANNOTATE system provides:
✅ **Automatic Test Case Generation** - From annotations
✅ **Production-Ready Training** - Integrates with orchestrator
✅ **Real-Time Inference** - Live predictions on streaming data
✅ **Data Consistency** - Same data as main system
✅ **Easy Monitoring** - Real-time progress and signals
**Automatic Test Case Generation** - From annotations
**Production-Ready Training** - Integrates with orchestrator
**Real-Time Inference** - Live predictions on streaming data
**Data Consistency** - Same data as main system
**Easy Monitoring** - Real-time progress and signals
**You can now:**
1. Mark profitable trades
@@ -360,4 +360,4 @@ The ANNOTATE system provides:
---
**Happy Training!** 🚀
**Happy Training!**

32
ANNOTATE/USAGE_GUIDE.md
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@@ -24,12 +24,12 @@ Access at: **http://127.0.0.1:8051**
### What Gets Captured
When you create an annotation, the system captures:
- ✅ **Entry timestamp and price**
- ✅ **Exit timestamp and price**
- ✅ **Full market state** (OHLCV for all 4 timeframes)
- ✅ **Direction** (LONG/SHORT)
- ✅ **P&L percentage**
- ✅ **Market context** at both entry and exit
- **Entry timestamp and price**
- **Exit timestamp and price**
- **Full market state** (OHLCV for all 4 timeframes)
- **Direction** (LONG/SHORT)
- **P&L percentage**
- **Market context** at both entry and exit
This ensures the annotation contains **exactly the same data** your models will see during training!
@@ -99,10 +99,10 @@ This ensures the annotation contains **exactly the same data** your models will
### Automatic Generation
When you save an annotation, the system:
1. ✅ Captures market state at entry time
2. ✅ Captures market state at exit time
3. ✅ Stores OHLCV data for all timeframes
4. ✅ Calculates expected outcome (P&L, direction)
1. Captures market state at entry time
2. Captures market state at exit time
3. Stores OHLCV data for all timeframes
4. Calculates expected outcome (P&L, direction)
### Manual Generation
1. Find annotation in sidebar
@@ -232,11 +232,11 @@ Export annotations regularly to backup your work.
### Why It Matters
The annotation system uses the **same DataProvider** as your training and inference systems. This means:
✅ **Same data source**
✅ **Same data quality**
✅ **Same data structure**
✅ **Same timeframes**
✅ **Same caching**
**Same data source**
**Same data quality**
**Same data structure**
**Same timeframes**
**Same caching**
### What This Guarantees
When you train a model on annotated data:
@@ -282,7 +282,7 @@ ANNOTATE/data/annotations/export_<timestamp>.json
---
## 🚀 Next Steps
## Next Steps
After creating annotations:

18
ANNOTATE/core/NO_SIMULATION_POLICY.md
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@@ -54,18 +54,18 @@ from utils.checkpoint_manager import get_checkpoint_manager
## NEVER DO THIS
❌ Create files with "simulator", "simulation", "mock", "fake" in the name
❌ Use placeholder/dummy training loops
❌ Return fake metrics or results
❌ Skip actual model training
Create files with "simulator", "simulation", "mock", "fake" in the name
Use placeholder/dummy training loops
Return fake metrics or results
Skip actual model training
## ALWAYS DO THIS
✅ Use real model training methods
✅ Integrate with existing training systems
✅ Save real checkpoints
✅ Track real metrics
✅ Handle real data
Use real model training methods
Integrate with existing training systems
Save real checkpoints
Track real metrics
Handle real data
---

6
ANNOTATE/core/data_loader.py
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@@ -82,7 +82,7 @@ class HistoricalDataLoader:
# Use cached data if we have enough candles
if len(cached_df) >= min(limit, 100): # Use cached if we have at least 100 candles
elapsed_ms = (time.time() - start_time_ms) * 1000
logger.debug(f"🚀 DataProvider cache hit for {symbol} {timeframe} ({len(cached_df)} candles, {elapsed_ms:.1f}ms)")
logger.debug(f" DataProvider cache hit for {symbol} {timeframe} ({len(cached_df)} candles, {elapsed_ms:.1f}ms)")
# Filter by time range with direction support
filtered_df = self._filter_by_time_range(
@@ -177,7 +177,7 @@ class HistoricalDataLoader:
if df is not None and not df.empty:
elapsed_ms = (time.time() - start_time_ms) * 1000
logger.info(f"✅ DuckDB hit for {symbol} {timeframe} ({len(df)} candles, {elapsed_ms:.1f}ms)")
logger.info(f" DuckDB hit for {symbol} {timeframe} ({len(df)} candles, {elapsed_ms:.1f}ms)")
# Cache in memory
self.memory_cache[cache_key] = (df.copy(), datetime.now())
return df
@@ -346,7 +346,7 @@ class HistoricalDataLoader:
df = df.set_index('timestamp')
df = df.sort_index()
logger.info(f"✅ Fetched {len(df)} candles from Binance for {symbol} {timeframe}")
logger.info(f" Fetched {len(df)} candles from Binance for {symbol} {timeframe}")
return df
except Exception as e:

40
ANNOTATE/core/real_training_adapter.py
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@@ -171,23 +171,23 @@ class RealTrainingAdapter:
if not training_data:
raise Exception("No valid training data prepared from test cases")
logger.info(f"✅ Prepared {len(training_data)} training samples")
logger.info(f" Prepared {len(training_data)} training samples")
# Route to appropriate REAL training method
if model_name in ["CNN", "StandardizedCNN"]:
logger.info("🔄 Starting CNN training...")
logger.info(" Starting CNN training...")
self._train_cnn_real(session, training_data)
elif model_name == "DQN":
logger.info("🔄 Starting DQN training...")
logger.info(" Starting DQN training...")
self._train_dqn_real(session, training_data)
elif model_name == "Transformer":
logger.info("🔄 Starting Transformer training...")
logger.info(" Starting Transformer training...")
self._train_transformer_real(session, training_data)
elif model_name == "COB":
logger.info("🔄 Starting COB training...")
logger.info(" Starting COB training...")
self._train_cob_real(session, training_data)
elif model_name == "Extrema":
logger.info("🔄 Starting Extrema training...")
logger.info(" Starting Extrema training...")
self._train_extrema_real(session, training_data)
else:
raise Exception(f"Unknown model type: {model_name}")
@@ -196,12 +196,12 @@ class RealTrainingAdapter:
session.status = 'completed'
session.duration_seconds = time.time() - session.start_time
logger.info(f"✅ REAL training completed: {training_id} in {session.duration_seconds:.2f}s")
logger.info(f" REAL training completed: {training_id} in {session.duration_seconds:.2f}s")
logger.info(f" Final loss: {session.final_loss}")
logger.info(f" Accuracy: {session.accuracy}")
except Exception as e:
logger.error(f"❌ REAL training failed: {e}", exc_info=True)
logger.error(f" REAL training failed: {e}", exc_info=True)
session.status = 'failed'
session.error = str(e)
session.duration_seconds = time.time() - session.start_time
@@ -266,15 +266,15 @@ class RealTrainingAdapter:
'close': df['close'].tolist(),
'volume': df['volume'].tolist()
}
logger.debug(f" ✅ {timeframe}: {len(df)} candles")
logger.debug(f" {timeframe}: {len(df)} candles")
else:
logger.warning(f" ❌ {timeframe}: No data")
logger.warning(f" {timeframe}: No data")
if market_state['timeframes']:
logger.info(f" ✅ Fetched market state with {len(market_state['timeframes'])} timeframes")
logger.info(f" Fetched market state with {len(market_state['timeframes'])} timeframes")
return market_state
else:
logger.warning(f" ❌ No market data fetched")
logger.warning(f" No market data fetched")
return {}
except Exception as e:
@@ -309,7 +309,7 @@ class RealTrainingAdapter:
expected_outcome = test_case.get('expected_outcome', {})
if not expected_outcome:
logger.warning(f"⚠️ Skipping test case {test_case.get('test_case_id')}: missing expected_outcome")
logger.warning(f" Skipping test case {test_case.get('test_case_id')}: missing expected_outcome")
continue
# Check if market_state is provided, if not, fetch it dynamically
@@ -320,7 +320,7 @@ class RealTrainingAdapter:
market_state = self._fetch_market_state_for_test_case(test_case)
if not market_state:
logger.warning(f"⚠️ Skipping test case {test_case.get('test_case_id')}: could not fetch market state")
logger.warning(f" Skipping test case {test_case.get('test_case_id')}: could not fetch market state")
continue
logger.debug(f" Test case {i+1}: has_market_state={bool(market_state)}, has_expected_outcome={bool(expected_outcome)}")
@@ -339,7 +339,7 @@ class RealTrainingAdapter:
}
training_data.append(entry_sample)
logger.debug(f" ✅ Entry sample: {entry_sample['direction']} @ {entry_sample['entry_price']}")
logger.debug(f" Entry sample: {entry_sample['direction']} @ {entry_sample['entry_price']}")
# Create HOLD samples (every candle while position is open)
# This teaches the model to maintain the position until exit
@@ -367,7 +367,7 @@ class RealTrainingAdapter:
'repetitions': training_repetitions
}
training_data.append(exit_sample)
logger.debug(f" ✅ Exit sample @ {exit_sample['exit_price']} ({exit_sample['profit_loss_pct']:.2f}%)")
logger.debug(f" Exit sample @ {exit_sample['exit_price']} ({exit_sample['profit_loss_pct']:.2f}%)")
# Create NEGATIVE samples (where model should NOT trade)
# These are candles before and after the signal
@@ -382,14 +382,14 @@ class RealTrainingAdapter:
logger.debug(f" ➕ Added {len(negative_samples)} negative samples (±{negative_samples_window} candles)")
except Exception as e:
logger.error(f"❌ Error preparing test case {i+1}: {e}")
logger.error(f" Error preparing test case {i+1}: {e}")
total_entry = sum(1 for s in training_data if s.get('label') == 'ENTRY')
total_hold = sum(1 for s in training_data if s.get('label') == 'HOLD')
total_exit = sum(1 for s in training_data if s.get('label') == 'EXIT')
total_no_trade = sum(1 for s in training_data if s.get('label') == 'NO_TRADE')
logger.info(f"✅ Prepared {len(training_data)} training samples from {len(test_cases)} test cases")
logger.info(f" Prepared {len(training_data)} training samples from {len(test_cases)} test cases")
logger.info(f" ENTRY samples: {total_entry}")
logger.info(f" HOLD samples: {total_hold}")
logger.info(f" EXIT samples: {total_exit}")
@@ -399,7 +399,7 @@ class RealTrainingAdapter:
logger.info(f" Ratio: 1:{total_no_trade/total_entry:.1f} (entry:no_trade)")
if len(training_data) < len(test_cases):
logger.warning(f"⚠️ Skipped {len(test_cases) - len(training_data)} test cases due to missing data")
logger.warning(f" Skipped {len(test_cases) - len(training_data)} test cases due to missing data")
return training_data
@@ -1048,7 +1048,7 @@ class RealTrainingAdapter:
if not converted_batches:
raise Exception("No valid training batches after conversion")
logger.info(f" ✅ Converted {len(training_data)} samples to {len(converted_batches)} training batches")
logger.info(f" Converted {len(training_data)} samples to {len(converted_batches)} training batches")
# Train using train_step for each batch
for epoch in range(session.total_epochs):

52
ANNOTATE/web/app.py
View File

@@ -196,14 +196,14 @@ class AnnotationDashboard:
for attempt in range(max_retries):
try:
if attempt > 0:
logger.info(f"🔄 Retry attempt {attempt + 1}/{max_retries} for model loading...")
logger.info(f" Retry attempt {attempt + 1}/{max_retries} for model loading...")
time.sleep(retry_delay)
else:
logger.info("🔄 Starting async model loading...")
logger.info(" Starting async model loading...")
# Check if TradingOrchestrator is available
if not TradingOrchestrator:
logger.error("❌ TradingOrchestrator class not available")
logger.error(" TradingOrchestrator class not available")
self.models_loading = False
self.available_models = []
return
@@ -214,48 +214,48 @@ class AnnotationDashboard:
data_provider=self.data_provider,
enhanced_rl_training=True
)
logger.info(" ✅ Orchestrator created")
logger.info(" Orchestrator created")
# Initialize ML models
logger.info(" Initializing ML models...")
self.orchestrator._initialize_ml_models()
logger.info(" ✅ ML models initialized")
logger.info(" ML models initialized")
# Update training adapter with orchestrator
self.training_adapter.orchestrator = self.orchestrator
logger.info(" ✅ Training adapter updated")
logger.info(" Training adapter updated")
# Get available models from orchestrator
available = []
if hasattr(self.orchestrator, 'rl_agent') and self.orchestrator.rl_agent:
available.append('DQN')
logger.info(" ✅ DQN model available")
logger.info(" DQN model available")
if hasattr(self.orchestrator, 'cnn_model') and self.orchestrator.cnn_model:
available.append('CNN')
logger.info(" ✅ CNN model available")
logger.info(" CNN model available")
if hasattr(self.orchestrator, 'transformer_model') and self.orchestrator.transformer_model:
available.append('Transformer')
logger.info(" ✅ Transformer model available")
logger.info(" Transformer model available")
self.available_models = available
if available:
logger.info(f"✅ Models loaded successfully: {', '.join(available)}")
logger.info(f" Models loaded successfully: {', '.join(available)}")
else:
logger.warning("⚠️ No models were initialized (this might be normal if models aren't configured)")
logger.warning(" No models were initialized (this might be normal if models aren't configured)")
self.models_loading = False
logger.info("✅ Async model loading complete")
logger.info(" Async model loading complete")
return # Success - exit retry loop
except Exception as e:
logger.error(f"❌ Error loading models (attempt {attempt + 1}/{max_retries}): {e}")
logger.error(f" Error loading models (attempt {attempt + 1}/{max_retries}): {e}")
import traceback
logger.error(f"Traceback:\n{traceback.format_exc()}")
if attempt == max_retries - 1:
# Final attempt failed
logger.error(f"❌ Model loading failed after {max_retries} attempts")
logger.error(f" Model loading failed after {max_retries} attempts")
self.models_loading = False
self.available_models = []
else:
@@ -264,7 +264,7 @@ class AnnotationDashboard:
# Start loading in background thread
thread = threading.Thread(target=load_models, daemon=True, name="ModelLoader")
thread.start()
logger.info(f"🚀 Model loading started in background thread (ID: {thread.ident}, Name: {thread.name})")
logger.info(f" Model loading started in background thread (ID: {thread.ident}, Name: {thread.name})")
logger.info(" UI remains responsive while models load...")
logger.info(" Will retry up to 3 times if loading fails")
@@ -284,7 +284,7 @@ class AnnotationDashboard:
)
if success:
logger.info("✅ ANNOTATE: Unified storage enabled for real-time data")
logger.info(" ANNOTATE: Unified storage enabled for real-time data")
# Get statistics
stats = self.data_provider.get_unified_storage_stats()
@@ -293,7 +293,7 @@ class AnnotationDashboard:
logger.info(" Historical data access: <100ms")
logger.info(" Annotation data: Available at any timestamp")
else:
logger.warning("⚠️ ANNOTATE: Unified storage not available, using cached data only")
logger.warning(" ANNOTATE: Unified storage not available, using cached data only")
except Exception as e:
logger.warning(f"ANNOTATE: Could not enable unified storage: {e}")
@@ -312,7 +312,7 @@ class AnnotationDashboard:
# Wait for app to fully start
time.sleep(5)
logger.info("🔄 Starting one-time background data refresh (fetching only recent missing data)")
logger.info(" Starting one-time background data refresh (fetching only recent missing data)")
# Disable startup mode to fetch fresh data
self.data_loader.disable_startup_mode()
@@ -321,7 +321,7 @@ class AnnotationDashboard:
logger.info("Using on-demand refresh for recent data")
self.data_provider.refresh_data_on_demand()
logger.info("✅ One-time background data refresh completed")
logger.info(" One-time background data refresh completed")
except Exception as e:
logger.error(f"Error in background data refresh: {e}")
@@ -488,9 +488,9 @@ class AnnotationDashboard:
<h1 class="text-center">📝 ANNOTATE - Manual Trade Annotation UI</h1>
<div class="alert alert-info">
<h4>System Status</h4>
<p>✅ Annotation Manager: Active</p>
<p>⚠️ Data Provider: {'Available' if self.data_provider else 'Not Available (Standalone Mode)'}</p>
<p>⚠️ Trading Orchestrator: {'Available' if self.orchestrator else 'Not Available (Standalone Mode)'}</p>
<p> Annotation Manager: Active</p>
<p> Data Provider: {'Available' if self.data_provider else 'Not Available (Standalone Mode)'}</p>
<p> Trading Orchestrator: {'Available' if self.orchestrator else 'Not Available (Standalone Mode)'}</p>
</div>
<div class="row">
<div class="col-md-6">
@@ -537,7 +537,7 @@ class AnnotationDashboard:
'error': {'code': 'INVALID_REQUEST', 'message': 'Missing timeframe or timestamps'}
})
logger.info(f"🔄 Recalculating pivots for {symbol} {timeframe} with {len(timestamps)} candles")
logger.info(f" Recalculating pivots for {symbol} {timeframe} with {len(timestamps)} candles")
# Convert to DataFrame
df = pd.DataFrame({
@@ -552,7 +552,7 @@ class AnnotationDashboard:
# Recalculate pivot markers
pivot_markers = self._get_pivot_markers_for_timeframe(symbol, timeframe, df)
logger.info(f" ✅ Recalculated {len(pivot_markers)} pivot candles")
logger.info(f" Recalculated {len(pivot_markers)} pivot candles")
return jsonify({
'success': True,
@@ -614,7 +614,7 @@ class AnnotationDashboard:
)
if df is not None and not df.empty:
logger.info(f" ✅ {timeframe}: {len(df)} candles ({df.index[0]} to {df.index[-1]})")
logger.info(f" {timeframe}: {len(df)} candles ({df.index[0]} to {df.index[-1]})")
# Get pivot points for this timeframe
pivot_markers = self._get_pivot_markers_for_timeframe(symbol, timeframe, df)
@@ -630,7 +630,7 @@ class AnnotationDashboard:
'pivot_markers': pivot_markers # Optional: only present if pivots exist
}
else:
logger.warning(f" ❌ {timeframe}: No data returned")
logger.warning(f" {timeframe}: No data returned")
# Get pivot bounds for the symbol
pivot_bounds = None

8
ANNOTATE/web/static/js/chart_manager.js
View File

@@ -1215,10 +1215,10 @@ class ChartManager {
// Merge with existing data
this.mergeChartData(timeframe, newData, direction);
console.log(`✅ Loaded ${newData.timestamps.length} new candles for ${timeframe}`);
console.log(` Loaded ${newData.timestamps.length} new candles for ${timeframe}`);
window.showSuccess(`Loaded ${newData.timestamps.length} more candles`);
} else {
console.warn(`❌ No more data available for ${timeframe} ${direction}`);
console.warn(` No more data available for ${timeframe} ${direction}`);
console.warn('Full result:', result);
window.showWarning('No more historical data available');
}
@@ -1312,7 +1312,7 @@ class ChartManager {
*/
async recalculatePivots(timeframe, data) {
try {
console.log(`🔄 Recalculating pivots for ${timeframe} with ${data.timestamps.length} candles...`);
console.log(` Recalculating pivots for ${timeframe} with ${data.timestamps.length} candles...`);
const response = await fetch('/api/recalculate-pivots', {
method: 'POST',
@@ -1338,7 +1338,7 @@ class ChartManager {
const chart = this.charts[timeframe];
if (chart && chart.data) {
chart.data.pivot_markers = result.pivot_markers;
console.log(`✅ Pivots recalculated: ${Object.keys(result.pivot_markers).length} pivot candles`);
console.log(` Pivots recalculated: ${Object.keys(result.pivot_markers).length} pivot candles`);
// Redraw the chart with updated pivots
this.redrawChartWithPivots(timeframe, chart.data);

10
ANNOTATE/web/templates/components/training_panel.html
View File

@@ -113,7 +113,7 @@
if (data.loading) {
// Models still loading - show loading message and poll
modelSelect.innerHTML = '<option value="">🔄 Loading models...</option>';
modelSelect.innerHTML = '<option value=""> Loading models...</option>';
// Start polling if not already polling
if (!modelLoadingPollInterval) {
@@ -132,7 +132,7 @@
if (data.success && data.models.length > 0) {
// Show success notification
if (window.showSuccess) {
window.showSuccess(`✅ ${data.models.length} models loaded and ready for training`);
window.showSuccess(` ${data.models.length} models loaded and ready for training`);
}
data.models.forEach(model => {
@@ -142,7 +142,7 @@
modelSelect.appendChild(option);
});
console.log(`✅ Models loaded: ${data.models.join(', ')}`);
console.log(` Models loaded: ${data.models.join(', ')}`);
} else {
const option = document.createElement('option');
option.value = '';
@@ -157,12 +157,12 @@
// Don't stop polling on network errors - keep trying
if (!modelLoadingPollInterval) {
modelSelect.innerHTML = '<option value="">⚠️ Connection error, retrying...</option>';
modelSelect.innerHTML = '<option value=""> Connection error, retrying...</option>';
// Start polling to retry
modelLoadingPollInterval = setInterval(loadAvailableModels, 3000); // Poll every 3 seconds
} else {
// Already polling, just update the message
modelSelect.innerHTML = '<option value="">🔄 Retrying...</option>';
modelSelect.innerHTML = '<option value=""> Retrying...</option>';
}
});
}