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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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34
docs/CURRENT_RL_INPUT_ANALYSIS.md
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@@ -26,14 +26,14 @@ def _market_state_to_rl_state(self, market_state: MarketState) -> np.ndarray:
**Total Current Input: ~100 basic features (CRITICALLY INSUFFICIENT)**
### What's Missing from Current Implementation:
- ❌ **300s of raw tick data** (0 features vs required 3000+ features)
- ❌ **Multi-timeframe OHLCV data** (4 basic prices vs required 9600+ features)
- ❌ **BTC reference data** (0 features vs required 2400+ features)
- ❌ **CNN hidden layer features** (0 features vs required 512 features)
- ❌ **CNN predictions** (0 features vs required 16 features)
- ❌ **Pivot point data** (0 features vs required 250+ features)
- ❌ **Momentum detection from ticks** (completely missing)
- ❌ **Market regime analysis** (basic vs sophisticated analysis)
- **300s of raw tick data** (0 features vs required 3000+ features)
- **Multi-timeframe OHLCV data** (4 basic prices vs required 9600+ features)
- **BTC reference data** (0 features vs required 2400+ features)
- **CNN hidden layer features** (0 features vs required 512 features)
- **CNN predictions** (0 features vs required 16 features)
- **Pivot point data** (0 features vs required 250+ features)
- **Momentum detection from ticks** (completely missing)
- **Market regime analysis** (basic vs sophisticated analysis)
## What Dashboard Currently Shows
@@ -52,37 +52,37 @@ This shows the data is being **collected** but **NOT being fed to the RL model**
### ETH Data Requirements:
1. **300s max of raw ticks data** → ~3000 features
- Important for detecting single big moves and momentum
- Currently: 0 features ❌
- Currently: 0 features
2. **300s of 1s OHLCV data (5 min)** → 2400 features
- 300 bars × 8 features (OHLC + volume + indicators)
- Currently: 0 features ❌
- Currently: 0 features
3. **300 OHLCV + indicators bars for each timeframe** → 7200 features
- 1m: 300 bars × 8 features = 2400
- 1h: 300 bars × 8 features = 2400
- 1d: 300 bars × 8 features = 2400
- Currently: ~4 basic price features ❌
- Currently: ~4 basic price features
### BTC Reference Data:
4. **BTC data for all timeframes** → 2400 features
- Same structure as ETH for correlation analysis
- Currently: 0 features ❌
- Currently: 0 features
### CNN Integration:
5. **CNN hidden layer features** → 512 features
- Last hidden layers where patterns are learned
- Currently: 0 features ❌
- Currently: 0 features
6. **CNN predictions for each timeframe** → 16 features
- 1s, 1m, 1h, 1d predictions (4 timeframes × 4 outputs)
- Currently: 0 features ❌
- Currently: 0 features
### Pivot Points:
7. **Williams Market Structure pivot points** → 250+ features
- 5-level recursive pivot point calculation
- Standard pivot points for all timeframes
- Currently: 0 features ❌
- Currently: 0 features
## Total Required vs Current
@@ -113,12 +113,12 @@ This explains why RL performance may be poor - the model is essentially "blind"
## Solution Implementation Status
✅ **Already Created**:
**Already Created**:
- `training/enhanced_rl_state_builder.py` - Implements comprehensive state building
- `training/williams_market_structure.py` - Williams pivot point system
- `docs/RL_TRAINING_AUDIT_AND_IMPROVEMENTS.md` - Complete improvement plan
⚠️ **Next Steps**:
**Next Steps**:
1. Integrate the enhanced state builder into the current RL training pipeline
2. Update MarketState class to include all required data
3. Connect tick cache and OHLCV data to state builder

38
docs/ENHANCED_RL_REAL_DATA_INTEGRATION.md
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@@ -34,7 +34,7 @@ comprehensive_state = self.state_builder.build_rl_state(
## Real Data Sources Integration
### 1. Tick Data (300s Window) ✅
### 1. Tick Data (300s Window)
**Source**: Your dashboard's "Tick Cache: 129 ticks"
```python
def _get_recent_tick_data_for_rl(self, symbol: str, seconds: int = 300):
@@ -43,7 +43,7 @@ def _get_recent_tick_data_for_rl(self, symbol: str, seconds: int = 300):
# Converts to RL format with momentum detection
```
### 2. Multi-timeframe OHLCV ✅
### 2. Multi-timeframe OHLCV
**Source**: Your dashboard's "1s Bars: 128 bars" + historical data
```python
def _get_multiframe_ohlcv_for_rl(self, symbol: str):
@@ -51,21 +51,21 @@ def _get_multiframe_ohlcv_for_rl(self, symbol: str):
# Gets real OHLCV data with technical indicators (RSI, MACD, BB, etc.)
```
### 3. BTC Reference Data ✅
### 3. BTC Reference Data
**Source**: Same data provider, BTC/USDT symbol
```python
btc_reference_data = self._get_multiframe_ohlcv_for_rl('BTC/USDT')
# Provides correlation analysis for ETH decisions
```
### 4. Williams Market Structure ✅
### 4. Williams Market Structure
**Source**: Calculated from real 1m OHLCV data
```python
pivot_data = self.williams_structure.calculate_recursive_pivot_points(ohlc_array)
# Implements your specified 5-level recursive pivot system
```
### 5. CNN Integration Framework ✅
### 5. CNN Integration Framework
**Ready for**: CNN hidden features and predictions
```python
def _get_cnn_features_for_rl(self, symbol: str):
@@ -75,21 +75,21 @@ def _get_cnn_features_for_rl(self, symbol: str):
## Files Modified/Created
### 1. Enhanced RL Trainer (`training/enhanced_rl_trainer.py`) ✅
### 1. Enhanced RL Trainer (`training/enhanced_rl_trainer.py`)
- **Replaced** mock `_market_state_to_rl_state()` with comprehensive state building
- **Integrated** with EnhancedRLStateBuilder (~13,400 features)
- **Connected** to real data sources (ticks, OHLCV, BTC reference)
- **Added** Williams pivot point calculation
- **Enhanced** agent initialization with larger state space (1024 hidden units)
### 2. Enhanced Orchestrator (`core/enhanced_orchestrator.py`) ✅
### 2. Enhanced Orchestrator (`core/enhanced_orchestrator.py`)
- **Expanded** MarketState class with comprehensive data fields
- **Added** real tick data extraction methods
- **Implemented** multi-timeframe OHLCV processing with technical indicators
- **Integrated** market microstructure analysis
- **Added** CNN feature extraction framework
### 3. Comprehensive Launcher (`run_enhanced_rl_training.py`) ✅
### 3. Comprehensive Launcher (`run_enhanced_rl_training.py`)
- **Created** complete training system launcher
- **Implements** real-time data collection and verification
- **Provides** comprehensive training loop with real market states
@@ -122,7 +122,7 @@ Stream: LIVE + Technical Indic. + CNN features + Pivots
## New Capabilities Unlocked
### 1. Momentum Detection 🚀
### 1. Momentum Detection
- **Real tick-level analysis** for detecting single big moves
- **Volume-weighted price momentum** from 300s of tick data
- **Market microstructure patterns** (order flow, tick frequency)
@@ -188,16 +188,16 @@ The system includes comprehensive data quality monitoring:
## Integration Status
✅ **COMPLETE**: Real tick data integration (300s window)
✅ **COMPLETE**: Multi-timeframe OHLCV processing
✅ **COMPLETE**: BTC reference data integration
✅ **COMPLETE**: Williams Market Structure implementation
✅ **COMPLETE**: Technical indicators (RSI, MACD, BB, ATR)
✅ **COMPLETE**: Market microstructure analysis
✅ **COMPLETE**: Comprehensive state building (~13,400 features)
✅ **COMPLETE**: Real-time training loop
✅ **COMPLETE**: Data quality monitoring
⚠️ **FRAMEWORK READY**: CNN hidden feature extraction (when CNN models available)
**COMPLETE**: Real tick data integration (300s window)
**COMPLETE**: Multi-timeframe OHLCV processing
**COMPLETE**: BTC reference data integration
**COMPLETE**: Williams Market Structure implementation
**COMPLETE**: Technical indicators (RSI, MACD, BB, ATR)
**COMPLETE**: Market microstructure analysis
**COMPLETE**: Comprehensive state building (~13,400 features)
**COMPLETE**: Real-time training loop
**COMPLETE**: Data quality monitoring
**FRAMEWORK READY**: CNN hidden feature extraction (when CNN models available)
## Performance Impact Expected

66
docs/UNIFIED_STORAGE_COMPLETE.md
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@@ -4,9 +4,9 @@
The unified data storage system has been successfully implemented and integrated into the existing DataProvider.
## ✅ Completed Tasks (8 out of 10)
## Completed Tasks (8 out of 10)
### Task 1: TimescaleDB Schema and Infrastructure ✅
### Task 1: TimescaleDB Schema and Infrastructure
**Files:**
- `core/unified_storage_schema.py` - Schema manager with migrations
- `scripts/setup_unified_storage.py` - Automated setup script
@@ -19,7 +19,7 @@ The unified data storage system has been successfully implemented and integrated
- Compression policies (>80% compression)
- Retention policies (30 days to 2 years)
### Task 2: Data Models and Validation ✅
### Task 2: Data Models and Validation
**Files:**
- `core/unified_data_models.py` - Data structures
- `core/unified_data_validator.py` - Validation logic
@@ -30,7 +30,7 @@ The unified data storage system has been successfully implemented and integrated
- `OHLCVCandle`, `TradeEvent` - Individual data types
- Comprehensive validation and sanitization
### Task 3: Cache Layer ✅
### Task 3: Cache Layer
**Files:**
- `core/unified_cache_manager.py` - In-memory caching
@@ -41,7 +41,7 @@ The unified data storage system has been successfully implemented and integrated
- Automatic eviction
- Statistics tracking
### Task 4: Database Connection and Query Layer ✅
### Task 4: Database Connection and Query Layer
**Files:**
- `core/unified_database_manager.py` - Connection pool and queries
@@ -52,7 +52,7 @@ The unified data storage system has been successfully implemented and integrated
- <100ms query latency
- Multi-timeframe support
### Task 5: Data Ingestion Pipeline ✅
### Task 5: Data Ingestion Pipeline
**Files:**
- `core/unified_ingestion_pipeline.py` - Real-time ingestion
@@ -63,7 +63,7 @@ The unified data storage system has been successfully implemented and integrated
- >1000 ops/sec throughput
- Error handling and retry logic
### Task 6: Unified Data Provider API ✅
### Task 6: Unified Data Provider API
**Files:**
- `core/unified_data_provider_extension.py` - Main API
@@ -74,10 +74,10 @@ The unified data storage system has been successfully implemented and integrated
- Order book data access
- Statistics tracking
### Task 7: Data Migration System ✅
### Task 7: Data Migration System
**Status:** Skipped (decided to drop existing Parquet data)
### Task 8: Integration with Existing DataProvider ✅
### Task 8: Integration with Existing DataProvider
**Files:**
- `core/data_provider.py` - Updated with unified storage methods
- `docs/UNIFIED_STORAGE_INTEGRATION.md` - Integration guide
@@ -115,27 +115,27 @@ The unified data storage system has been successfully implemented and integrated
└──────────────┘ └──────────────┘
```
## 🚀 Key Features
## Key Features
### Performance
- ✅ Cache reads: <10ms
- ✅ Database queries: <100ms
- ✅ Ingestion: >1000 ops/sec
- ✅ Compression: >80%
- Cache reads: <10ms
- Database queries: <100ms
- Ingestion: >1000 ops/sec
- Compression: >80%
### Reliability
- ✅ Data validation
- ✅ Error handling
- ✅ Health monitoring
- ✅ Statistics tracking
- ✅ Automatic reconnection
- Data validation
- Error handling
- Health monitoring
- Statistics tracking
- Automatic reconnection
### Usability
- ✅ Single endpoint for all data
- ✅ Automatic routing (cache vs database)
- ✅ Type-safe interfaces
- ✅ Backward compatible
- ✅ Easy to integrate
- Single endpoint for all data
- Automatic routing (cache vs database)
- Type-safe interfaces
- Backward compatible
- Easy to integrate
## 📝 Quick Start
@@ -302,9 +302,9 @@ print(f"Ingestion rate: {stats['ingestion']['total_ingested']}")
### Check Health
```python
if data_provider.is_unified_storage_enabled():
print("✅ Unified storage is running")
print(" Unified storage is running")
else:
print("❌ Unified storage is not enabled")
print(" Unified storage is not enabled")
```
## 🚧 Remaining Tasks (Optional)
@@ -323,12 +323,12 @@ else:
## 🎉 Success Metrics
✅ **Completed**: 8 out of 10 major tasks (80%)
✅ **Core Functionality**: 100% complete
✅ **Integration**: Seamless with existing code
✅ **Performance**: Meets all targets
✅ **Documentation**: Comprehensive guides
✅ **Examples**: Working code samples
**Completed**: 8 out of 10 major tasks (80%)
**Core Functionality**: 100% complete
**Integration**: Seamless with existing code
**Performance**: Meets all targets
**Documentation**: Comprehensive guides
**Examples**: Working code samples
## 🙏 Next Steps
@@ -349,7 +349,7 @@ For issues or questions:
---
**Status**: ✅ Production Ready
**Status**: Production Ready
**Version**: 1.0.0
**Last Updated**: 2024
**Completion**: 80% (8/10 tasks)

14
docs/UNIFIED_STORAGE_INTEGRATION.md
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@@ -6,11 +6,11 @@ The unified storage system has been integrated into the existing `DataProvider`
## Key Features
✅ **Single Endpoint**: One method for all data access
✅ **Automatic Routing**: Cache for real-time, database for historical
✅ **Backward Compatible**: All existing methods still work
✅ **Opt-In**: Only enabled when explicitly initialized
✅ **Fast**: <10ms cache reads, <100ms database queries
**Single Endpoint**: One method for all data access
**Automatic Routing**: Cache for real-time, database for historical
**Backward Compatible**: All existing methods still work
**Opt-In**: Only enabled when explicitly initialized
**Fast**: <10ms cache reads, <100ms database queries
## Quick Start
@@ -27,9 +27,9 @@ data_provider = DataProvider()
async def setup():
success = await data_provider.enable_unified_storage()
if success:
print("✅ Unified storage enabled!")
print(" Unified storage enabled!")
else:
print("❌ Failed to enable unified storage")
print(" Failed to enable unified storage")
asyncio.run(setup())
```

12
docs/fifo_queue_system.md
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@@ -250,12 +250,12 @@ python test_fifo_queues.py
```
**Test Coverage**:
- ✅ FIFO queue operations (add, retrieve, status)
- ✅ Data queue filling with multiple timeframes
- ✅ BaseDataInput building from queues
- ✅ Consistent feature vector size (always 7850)
- ✅ Thread safety under concurrent access
- ✅ Minimum data requirement validation
- FIFO queue operations (add, retrieve, status)
- Data queue filling with multiple timeframes
- BaseDataInput building from queues
- Consistent feature vector size (always 7850)
- Thread safety under concurrent access
- Minimum data requirement validation
## Monitoring