sqlite for checkpoints, cleanup
This commit is contained in:
Dobromir Popov
@ -1,11 +0,0 @@
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"""
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Neural Network Data
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=================
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This package is used to store datasets and model outputs.
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It does not contain any code, but serves as a storage location for:
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- Training datasets
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- Evaluation results
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- Inference outputs
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- Model checkpoints
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"""
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@ -1,104 +0,0 @@
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{
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@ -1 +0,0 @@
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@ -1,20 +0,0 @@
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@ -1,326 +0,0 @@
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@ -1,192 +0,0 @@
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||||
"train_pnl": 0.0,
|
||||
"val_pnl": 0.0,
|
||||
"train_win_rate": 0.0,
|
||||
"val_win_rate": 0.0,
|
||||
"best_position_size": 0.1,
|
||||
"signal_distribution": {
|
||||
"train": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.0,
|
||||
"HOLD": 1.0
|
||||
},
|
||||
"val": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.0,
|
||||
"HOLD": 1.0
|
||||
}
|
||||
},
|
||||
"timestamp": "2025-03-31T02:50:12.881423",
|
||||
"data_age": 2
|
||||
},
|
||||
{
|
||||
"epoch": 2,
|
||||
"train_loss": 1.1266120672225952,
|
||||
"val_loss": 1.072133183479309,
|
||||
"train_acc": 0.1171875,
|
||||
"val_acc": 0.25,
|
||||
"train_pnl": 0.0,
|
||||
"val_pnl": 0.0,
|
||||
"train_win_rate": 0.0,
|
||||
"val_win_rate": 0.0,
|
||||
"best_position_size": 0.1,
|
||||
"signal_distribution": {
|
||||
"train": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.0,
|
||||
"HOLD": 1.0
|
||||
},
|
||||
"val": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.0,
|
||||
"HOLD": 1.0
|
||||
}
|
||||
},
|
||||
"timestamp": "2025-03-31T02:50:13.186840",
|
||||
"data_age": 2
|
||||
},
|
||||
{
|
||||
"epoch": 3,
|
||||
"train_loss": 1.1415620843569438,
|
||||
"val_loss": 1.1701548099517822,
|
||||
"train_acc": 0.1015625,
|
||||
"val_acc": 0.5208333333333334,
|
||||
"train_pnl": 0.0,
|
||||
"val_pnl": 0.0,
|
||||
"train_win_rate": 0.0,
|
||||
"val_win_rate": 0.0,
|
||||
"best_position_size": 0.1,
|
||||
"signal_distribution": {
|
||||
"train": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.0,
|
||||
"HOLD": 1.0
|
||||
},
|
||||
"val": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.0,
|
||||
"HOLD": 1.0
|
||||
}
|
||||
},
|
||||
"timestamp": "2025-03-31T02:50:13.442018",
|
||||
"data_age": 3
|
||||
},
|
||||
{
|
||||
"epoch": 4,
|
||||
"train_loss": 1.1331567962964375,
|
||||
"val_loss": 1.070081114768982,
|
||||
"train_acc": 0.09375,
|
||||
"val_acc": 0.22916666666666666,
|
||||
"train_pnl": 0.010650217327384765,
|
||||
"val_pnl": -0.0007049481907895126,
|
||||
"train_win_rate": 0.49279538904899134,
|
||||
"val_win_rate": 0.40625,
|
||||
"best_position_size": 0.1,
|
||||
"signal_distribution": {
|
||||
"train": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.9036458333333334,
|
||||
"HOLD": 0.09635416666666667
|
||||
},
|
||||
"val": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.3333333333333333,
|
||||
"HOLD": 0.6666666666666666
|
||||
}
|
||||
},
|
||||
"timestamp": "2025-03-31T02:50:13.739899",
|
||||
"data_age": 3
|
||||
},
|
||||
{
|
||||
"epoch": 5,
|
||||
"train_loss": 1.10965762535731,
|
||||
"val_loss": 1.0485950708389282,
|
||||
"train_acc": 0.12239583333333333,
|
||||
"val_acc": 0.17708333333333334,
|
||||
"train_pnl": 0.011924086862580204,
|
||||
"val_pnl": 0.0,
|
||||
"train_win_rate": 0.5070422535211268,
|
||||
"val_win_rate": 0.0,
|
||||
"best_position_size": 0.1,
|
||||
"signal_distribution": {
|
||||
"train": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.7395833333333334,
|
||||
"HOLD": 0.2604166666666667
|
||||
},
|
||||
"val": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.0,
|
||||
"HOLD": 1.0
|
||||
}
|
||||
},
|
||||
"timestamp": "2025-03-31T02:50:14.073439",
|
||||
"data_age": 3
|
||||
},
|
||||
{
|
||||
"epoch": 6,
|
||||
"train_loss": 1.1272419293721516,
|
||||
"val_loss": 1.084235429763794,
|
||||
"train_acc": 0.1015625,
|
||||
"val_acc": 0.22916666666666666,
|
||||
"train_pnl": 0.014825159601390072,
|
||||
"val_pnl": 0.00405770620151887,
|
||||
"train_win_rate": 0.4908616187989556,
|
||||
"val_win_rate": 0.5157894736842106,
|
||||
"best_position_size": 2.0,
|
||||
"signal_distribution": {
|
||||
"train": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 1.0,
|
||||
"HOLD": 0.0
|
||||
},
|
||||
"val": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 1.0,
|
||||
"HOLD": 0.0
|
||||
}
|
||||
},
|
||||
"timestamp": "2025-03-31T02:50:14.658295",
|
||||
"data_age": 4
|
||||
},
|
||||
{
|
||||
"epoch": 7,
|
||||
"train_loss": 1.1171108484268188,
|
||||
"val_loss": 1.0741244554519653,
|
||||
"train_acc": 0.1171875,
|
||||
"val_acc": 0.22916666666666666,
|
||||
"train_pnl": 0.0059474696523706605,
|
||||
"val_pnl": 0.00405770620151887,
|
||||
"train_win_rate": 0.4838709677419355,
|
||||
"val_win_rate": 0.5157894736842106,
|
||||
"best_position_size": 2.0,
|
||||
"signal_distribution": {
|
||||
"train": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 0.7291666666666666,
|
||||
"HOLD": 0.2708333333333333
|
||||
},
|
||||
"val": {
|
||||
"BUY": 0.0,
|
||||
"SELL": 1.0,
|
||||
"HOLD": 0.0
|
||||
}
|
||||
},
|
||||
"timestamp": "2025-03-31T02:50:15.227593",
|
||||
"data_age": 4
|
||||
}
|
||||
]
|
||||
}
|
||||
@ -1,472 +0,0 @@
|
||||
# CNN Model Training, Decision Making, and Dashboard Visualization Analysis
|
||||
|
||||
## Comprehensive Analysis: Enhanced RL Training Systems
|
||||
|
||||
### User Questions Addressed:
|
||||
1. **CNN Model Training Implementation** ✅
|
||||
2. **Decision-Making Model Training System** ✅
|
||||
3. **Model Predictions and Training Progress Visualization on Clean Dashboard** ✅
|
||||
4. **🔧 FIXED: Signal Generation and Model Loading Issues** ✅
|
||||
5. **🎯 FIXED: Manual Trading Execution and Chart Visualization** ✅
|
||||
6. **🚫 CRITICAL FIX: Removed ALL Simulated COB Data - Using REAL COB Only** ✅
|
||||
|
||||
---
|
||||
|
||||
## 🚫 **MAJOR SYSTEM CLEANUP: NO MORE SIMULATED DATA**
|
||||
|
||||
### **🔥 REMOVED ALL SIMULATION COMPONENTS**
|
||||
|
||||
**Problem Identified**: The system was using simulated COB data instead of the real COB integration that's already implemented and working.
|
||||
|
||||
**Root Cause**: Dashboard was creating separate simulated COB components instead of connecting to the existing Enhanced Orchestrator's real COB integration.
|
||||
|
||||
### **💥 SIMULATION COMPONENTS REMOVED:**
|
||||
|
||||
#### **1. Removed Simulated COB Data Generation**
|
||||
- ❌ `_generate_simulated_cob_data()` - **DELETED**
|
||||
- ❌ `_start_cob_simulation_thread()` - **DELETED**
|
||||
- ❌ `_update_cob_cache_from_price_data()` - **DELETED**
|
||||
- ❌ All `random.uniform()` COB data generation - **ELIMINATED**
|
||||
- ❌ Fake bid/ask level creation - **REMOVED**
|
||||
- ❌ Simulated liquidity calculations - **PURGED**
|
||||
|
||||
#### **2. Removed Separate RL COB Trader**
|
||||
- ❌ `RealtimeRLCOBTrader` initialization - **DELETED**
|
||||
- ❌ `cob_rl_trader` instance variables - **REMOVED**
|
||||
- ❌ `cob_predictions` deque caches - **ELIMINATED**
|
||||
- ❌ `cob_data_cache_1d` buffers - **PURGED**
|
||||
- ❌ `cob_raw_ticks` collections - **DELETED**
|
||||
- ❌ `_start_cob_data_subscription()` - **REMOVED**
|
||||
- ❌ `_on_cob_prediction()` callback - **DELETED**
|
||||
|
||||
#### **3. Updated COB Status System**
|
||||
- ✅ **Real COB Integration Detection**: Connects to `orchestrator.cob_integration`
|
||||
- ✅ **Actual COB Statistics**: Uses `cob_integration.get_statistics()`
|
||||
- ✅ **Live COB Snapshots**: Uses `cob_integration.get_cob_snapshot(symbol)`
|
||||
- ✅ **No Simulation Status**: Removed all "Simulated" status messages
|
||||
|
||||
### **🔗 REAL COB INTEGRATION CONNECTION**
|
||||
|
||||
#### **How Real COB Data Works:**
|
||||
1. **Enhanced Orchestrator** initializes with real COB integration
|
||||
2. **COB Integration** connects to live market data streams (Binance, OKX, etc.)
|
||||
3. **Dashboard** connects to orchestrator's COB integration via callbacks
|
||||
4. **Real-time Updates** flow: `Market → COB Provider → COB Integration → Dashboard`
|
||||
|
||||
#### **Real COB Data Path:**
|
||||
```
|
||||
Live Market Data (Multiple Exchanges)
|
||||
↓
|
||||
Multi-Exchange COB Provider
|
||||
↓
|
||||
COB Integration (Real Consolidated Order Book)
|
||||
↓
|
||||
Enhanced Trading Orchestrator
|
||||
↓
|
||||
Clean Trading Dashboard (Real COB Display)
|
||||
```
|
||||
|
||||
### **✅ VERIFICATION IMPLEMENTED**
|
||||
|
||||
#### **Enhanced COB Status Checking:**
|
||||
```python
|
||||
# Check for REAL COB integration from enhanced orchestrator
|
||||
if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
|
||||
cob_integration = self.orchestrator.cob_integration
|
||||
|
||||
# Get real COB integration statistics
|
||||
cob_stats = cob_integration.get_statistics()
|
||||
if cob_stats:
|
||||
active_symbols = cob_stats.get('active_symbols', [])
|
||||
total_updates = cob_stats.get('total_updates', 0)
|
||||
provider_status = cob_stats.get('provider_status', 'Unknown')
|
||||
```
|
||||
|
||||
#### **Real COB Data Retrieval:**
|
||||
```python
|
||||
# Get from REAL COB integration via enhanced orchestrator
|
||||
snapshot = cob_integration.get_cob_snapshot(symbol)
|
||||
if snapshot:
|
||||
# Process REAL consolidated order book data
|
||||
return snapshot
|
||||
```
|
||||
|
||||
### **📊 STATUS MESSAGES UPDATED**
|
||||
|
||||
#### **Before (Simulation):**
|
||||
- ❌ `"COB-SIM BTC/USDT - Update #20, Mid: $107068.03, Spread: 7.1bps"`
|
||||
- ❌ `"Simulated (2 symbols)"`
|
||||
- ❌ `"COB simulation thread started"`
|
||||
|
||||
#### **After (Real Data Only):**
|
||||
- ✅ `"REAL COB Active (2 symbols)"`
|
||||
- ✅ `"No Enhanced Orchestrator COB Integration"` (when missing)
|
||||
- ✅ `"Retrieved REAL COB snapshot for ETH/USDT"`
|
||||
- ✅ `"REAL COB integration connected successfully"`
|
||||
|
||||
### **🚨 CRITICAL SYSTEM MESSAGES**
|
||||
|
||||
#### **If Enhanced Orchestrator Missing COB:**
|
||||
```
|
||||
CRITICAL: Enhanced orchestrator has NO COB integration!
|
||||
This means we're using basic orchestrator instead of enhanced one
|
||||
Dashboard will NOT have real COB data until this is fixed
|
||||
```
|
||||
|
||||
#### **Success Messages:**
|
||||
```
|
||||
REAL COB integration found: <class 'core.cob_integration.COBIntegration'>
|
||||
Registered dashboard callback with REAL COB integration
|
||||
NO SIMULATION - Using live market data only
|
||||
```
|
||||
|
||||
### **🔧 NEXT STEPS REQUIRED**
|
||||
|
||||
#### **1. Verify Enhanced Orchestrator Usage**
|
||||
- ✅ **main.py** correctly uses `EnhancedTradingOrchestrator`
|
||||
- ✅ **COB Integration** properly initialized in orchestrator
|
||||
- 🔍 **Need to verify**: Dashboard receives real COB callbacks
|
||||
|
||||
#### **2. Debug Connection Issues**
|
||||
- Dashboard shows connection attempts but no listening port
|
||||
- Enhanced orchestrator may need COB integration startup verification
|
||||
- Real COB data flow needs testing
|
||||
|
||||
#### **3. Test Real COB Data Display**
|
||||
- Verify COB snapshots contain real market data
|
||||
- Confirm bid/ask levels from actual exchanges
|
||||
- Validate liquidity and spread calculations
|
||||
|
||||
### **💡 VERIFICATION COMMANDS**
|
||||
|
||||
#### **Check COB Integration Status:**
|
||||
```python
|
||||
# In dashboard initialization:
|
||||
logger.info(f"Orchestrator type: {type(self.orchestrator)}")
|
||||
logger.info(f"Has COB integration: {hasattr(self.orchestrator, 'cob_integration')}")
|
||||
logger.info(f"COB integration active: {self.orchestrator.cob_integration is not None}")
|
||||
```
|
||||
|
||||
#### **Test Real COB Data:**
|
||||
```python
|
||||
# Test real COB snapshot retrieval:
|
||||
snapshot = self.orchestrator.cob_integration.get_cob_snapshot('ETH/USDT')
|
||||
logger.info(f"Real COB snapshot: {snapshot}")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🚀 LATEST FIXES IMPLEMENTED (Manual Trading & Chart Visualization)
|
||||
|
||||
### 🔧 Manual Trading Buttons - FULLY FIXED ✅
|
||||
|
||||
**Problem**: Manual buy/sell buttons weren't executing trades properly
|
||||
|
||||
**Root Cause Analysis**:
|
||||
- Missing `execute_trade` method in `TradingExecutor`
|
||||
- Missing `get_closed_trades` and `get_current_position` methods
|
||||
- No proper trade record creation and tracking
|
||||
|
||||
**Solution Applied**:
|
||||
1. **Added missing methods to TradingExecutor**:
|
||||
- `execute_trade()` - Direct trade execution with proper error handling
|
||||
- `get_closed_trades()` - Returns trade history in dashboard format
|
||||
- `get_current_position()` - Returns current position information
|
||||
|
||||
2. **Enhanced manual trading execution**:
|
||||
- Proper error handling and trade recording
|
||||
- Real P&L tracking (+$0.05 demo profit for SELL orders)
|
||||
- Session metrics updates (trade count, total P&L, fees)
|
||||
- Visual confirmation of executed vs blocked trades
|
||||
|
||||
3. **Trade record structure**:
|
||||
```python
|
||||
trade_record = {
|
||||
'symbol': symbol,
|
||||
'side': action, # 'BUY' or 'SELL'
|
||||
'quantity': 0.01,
|
||||
'entry_price': current_price,
|
||||
'exit_price': current_price,
|
||||
'entry_time': datetime.now(),
|
||||
'exit_time': datetime.now(),
|
||||
'pnl': demo_pnl, # Real P&L calculation
|
||||
'fees': 0.0,
|
||||
'confidence': 1.0 # Manual trades = 100% confidence
|
||||
}
|
||||
```
|
||||
|
||||
### 📊 Chart Visualization - COMPLETELY SEPARATED ✅
|
||||
|
||||
**Problem**: All signals and trades were mixed together on charts
|
||||
|
||||
**Requirements**:
|
||||
- **1s mini chart**: Show ALL signals (executed + non-executed)
|
||||
- **1m main chart**: Show ONLY executed trades
|
||||
|
||||
**Solution Implemented**:
|
||||
|
||||
#### **1s Mini Chart (Row 2) - ALL SIGNALS:**
|
||||
- ✅ **Executed BUY signals**: Solid green triangles-up
|
||||
- ✅ **Executed SELL signals**: Solid red triangles-down
|
||||
- ✅ **Pending BUY signals**: Hollow green triangles-up
|
||||
- ✅ **Pending SELL signals**: Hollow red triangles-down
|
||||
- ✅ **Independent axis**: Can zoom/pan separately from main chart
|
||||
- ✅ **Real-time updates**: Shows all trading activity
|
||||
|
||||
#### **1m Main Chart (Row 1) - EXECUTED TRADES ONLY:**
|
||||
- ✅ **Executed BUY trades**: Large green circles with confidence hover
|
||||
- ✅ **Executed SELL trades**: Large red circles with confidence hover
|
||||
- ✅ **Professional display**: Clean execution-only view
|
||||
- ✅ **P&L information**: Hover shows actual profit/loss
|
||||
|
||||
#### **Chart Architecture:**
|
||||
```python
|
||||
# Main 1m chart - EXECUTED TRADES ONLY
|
||||
executed_signals = [signal for signal in self.recent_decisions if signal.get('executed', False)]
|
||||
|
||||
# 1s mini chart - ALL SIGNALS
|
||||
all_signals = self.recent_decisions[-50:] # Last 50 signals
|
||||
executed_buys = [s for s in buy_signals if s['executed']]
|
||||
pending_buys = [s for s in buy_signals if not s['executed']]
|
||||
```
|
||||
|
||||
### 🎯 Variable Scope Error - FIXED ✅
|
||||
|
||||
**Problem**: `cannot access local variable 'last_action' where it is not associated with a value`
|
||||
|
||||
**Root Cause**: Variables declared inside conditional blocks weren't accessible when conditions were False
|
||||
|
||||
**Solution Applied**:
|
||||
```python
|
||||
# BEFORE (caused error):
|
||||
if condition:
|
||||
last_action = 'BUY'
|
||||
last_confidence = 0.8
|
||||
# last_action accessed here would fail if condition was False
|
||||
|
||||
# AFTER (fixed):
|
||||
last_action = 'NONE'
|
||||
last_confidence = 0.0
|
||||
if condition:
|
||||
last_action = 'BUY'
|
||||
last_confidence = 0.8
|
||||
# Variables always defined
|
||||
```
|
||||
|
||||
### 🔇 Unicode Logging Errors - FIXED ✅
|
||||
|
||||
**Problem**: `UnicodeEncodeError: 'charmap' codec can't encode character '\U0001f4c8'`
|
||||
|
||||
**Root Cause**: Windows console (cp1252) can't handle Unicode emoji characters
|
||||
|
||||
**Solution Applied**: Removed ALL emoji icons from log messages:
|
||||
- `🚀 Starting...` → `Starting...`
|
||||
- `✅ Success` → `Success`
|
||||
- `📊 Data` → `Data`
|
||||
- `🔧 Fixed` → `Fixed`
|
||||
- `❌ Error` → `Error`
|
||||
|
||||
**Result**: Clean ASCII-only logging compatible with Windows console
|
||||
|
||||
---
|
||||
|
||||
## 🧠 CNN Model Training Implementation
|
||||
|
||||
### A. Williams Market Structure CNN Architecture
|
||||
|
||||
**Model Specifications:**
|
||||
- **Architecture**: Enhanced CNN with ResNet blocks, self-attention, and multi-task learning
|
||||
- **Parameters**: ~50M parameters (Williams) + 400M parameters (COB-RL optimized)
|
||||
- **Input Shape**: (900, 50) - 900 timesteps (1s bars), 50 features per timestep
|
||||
- **Output**: 10-class direction prediction + confidence scores
|
||||
|
||||
**Training Triggers:**
|
||||
1. **Real-time Pivot Detection**: Confirmed local extrema (tops/bottoms)
|
||||
2. **Perfect Move Identification**: >2% price moves within prediction window
|
||||
3. **Negative Case Training**: Failed predictions for intensive learning
|
||||
4. **Multi-timeframe Validation**: 1s, 1m, 1h, 1d consistency checks
|
||||
|
||||
### B. Feature Engineering Pipeline
|
||||
|
||||
**5 Timeseries Universal Format:**
|
||||
1. **ETH/USDT Ticks** (1s) - Primary trading pair real-time data
|
||||
2. **ETH/USDT 1m** - Short-term price action and patterns
|
||||
3. **ETH/USDT 1h** - Medium-term trends and momentum
|
||||
4. **ETH/USDT 1d** - Long-term market structure
|
||||
5. **BTC/USDT Ticks** (1s) - Reference asset for correlation analysis
|
||||
|
||||
**Feature Matrix Construction:**
|
||||
```python
|
||||
# Williams Market Structure Features (900x50 matrix)
|
||||
- OHLCV data (5 cols)
|
||||
- Technical indicators (15 cols)
|
||||
- Market microstructure (10 cols)
|
||||
- COB integration features (10 cols)
|
||||
- Cross-asset correlation (5 cols)
|
||||
- Temporal dynamics (5 cols)
|
||||
```
|
||||
|
||||
### C. Retrospective Training System
|
||||
|
||||
**Perfect Move Detection:**
|
||||
- **Threshold**: 2% price change within 15-minute window
|
||||
- **Context**: 200-candle history for enhanced pattern recognition
|
||||
- **Validation**: Multi-timeframe confirmation (1s→1m→1h consistency)
|
||||
- **Auto-labeling**: Optimal action determination for supervised learning
|
||||
|
||||
**Training Data Pipeline:**
|
||||
```
|
||||
Market Event → Extrema Detection → Perfect Move Validation → Feature Matrix → CNN Training
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Decision-Making Model Training System
|
||||
|
||||
### A. Neural Decision Fusion Architecture
|
||||
|
||||
**Model Integration Weights:**
|
||||
- **CNN Predictions**: 70% weight (Williams Market Structure)
|
||||
- **RL Agent Decisions**: 30% weight (DQN with sensitivity levels)
|
||||
- **COB RL Integration**: Dynamic weight based on market conditions
|
||||
|
||||
**Decision Fusion Process:**
|
||||
```python
|
||||
# Neural Decision Fusion combines all model predictions
|
||||
williams_pred = cnn_model.predict(market_state) # 70% weight
|
||||
dqn_action = rl_agent.act(state_vector) # 30% weight
|
||||
cob_signal = cob_rl.get_direction(order_book_state) # Variable weight
|
||||
|
||||
final_decision = neural_fusion.combine(williams_pred, dqn_action, cob_signal)
|
||||
```
|
||||
|
||||
### B. Enhanced Training Weight System
|
||||
|
||||
**Training Weight Multipliers:**
|
||||
- **Regular Predictions**: 1× base weight
|
||||
- **Signal Accumulation**: 1× weight (3+ confident predictions)
|
||||
- **🔥 Actual Trade Execution**: 10× weight multiplier**
|
||||
- **P&L-based Reward**: Enhanced feedback loop
|
||||
|
||||
**Trade Execution Enhanced Learning:**
|
||||
```python
|
||||
# 10× weight for actual trade outcomes
|
||||
if trade_executed:
|
||||
enhanced_reward = pnl_ratio * 10.0
|
||||
model.train_on_batch(state, action, enhanced_reward)
|
||||
|
||||
# Immediate training on last 3 signals that led to trade
|
||||
for signal in last_3_signals:
|
||||
model.retrain_signal(signal, actual_outcome)
|
||||
```
|
||||
|
||||
### C. Sensitivity Learning DQN
|
||||
|
||||
**5 Sensitivity Levels:**
|
||||
- **very_low** (0.1): Conservative, high-confidence only
|
||||
- **low** (0.3): Selective entry/exit
|
||||
- **medium** (0.5): Balanced approach
|
||||
- **high** (0.7): Aggressive trading
|
||||
- **very_high** (0.9): Maximum activity
|
||||
|
||||
**Adaptive Threshold System:**
|
||||
```python
|
||||
# Sensitivity affects confidence thresholds
|
||||
entry_threshold = base_threshold * sensitivity_multiplier
|
||||
exit_threshold = base_threshold * (1 - sensitivity_level)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📊 Dashboard Visualization and Model Monitoring
|
||||
|
||||
### A. Real-time Model Predictions Display
|
||||
|
||||
**Model Status Section:**
|
||||
- ✅ **Loaded Models**: DQN (5M params), CNN (50M params), COB-RL (400M params)
|
||||
- ✅ **Real-time Loss Tracking**: 5-MA loss for each model
|
||||
- ✅ **Prediction Counts**: Total predictions generated per model
|
||||
- ✅ **Last Prediction**: Timestamp, action, confidence for each model
|
||||
|
||||
**Training Metrics Visualization:**
|
||||
```python
|
||||
# Real-time model performance tracking
|
||||
{
|
||||
'dqn': {
|
||||
'active': True,
|
||||
'parameters': 5000000,
|
||||
'loss_5ma': 0.0234,
|
||||
'last_prediction': {'action': 'BUY', 'confidence': 0.67},
|
||||
'epsilon': 0.15 # Exploration rate
|
||||
},
|
||||
'cnn': {
|
||||
'active': True,
|
||||
'parameters': 50000000,
|
||||
'loss_5ma': 0.0198,
|
||||
'last_prediction': {'action': 'HOLD', 'confidence': 0.45}
|
||||
},
|
||||
'cob_rl': {
|
||||
'active': True,
|
||||
'parameters': 400000000,
|
||||
'loss_5ma': 0.012,
|
||||
'predictions_count': 1247
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### B. Training Progress Monitoring
|
||||
|
||||
**Loss Visualization:**
|
||||
- **Real-time Loss Charts**: 5-minute moving average for each model
|
||||
- **Training Status**: Active sessions, parameter counts, update frequencies
|
||||
- **Signal Generation**: ACTIVE/INACTIVE status with last update timestamps
|
||||
|
||||
**Performance Metrics Dashboard:**
|
||||
- **Session P&L**: Real-time profit/loss tracking
|
||||
- **Trade Accuracy**: Success rate of executed trades
|
||||
- **Model Confidence Trends**: Average confidence over time
|
||||
- **Training Iterations**: Progress tracking for continuous learning
|
||||
|
||||
### C. COB Integration Visualization
|
||||
|
||||
**Real-time COB Data Display:**
|
||||
- **Order Book Levels**: Bid/ask spreads and liquidity depth
|
||||
- **Exchange Breakdown**: Multi-exchange liquidity sources
|
||||
- **Market Microstructure**: Imbalance ratios and flow analysis
|
||||
- **COB Feature Status**: CNN features and RL state availability
|
||||
|
||||
**Training Pipeline Integration:**
|
||||
- **COB → CNN Features**: Real-time market microstructure patterns
|
||||
- **COB → RL States**: Enhanced state vectors for decision making
|
||||
- **Performance Tracking**: COB integration health monitoring
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Key System Capabilities
|
||||
|
||||
### Real-time Learning Pipeline
|
||||
1. **Market Data Ingestion**: 5 timeseries universal format
|
||||
2. **Feature Engineering**: Multi-timeframe analysis with COB integration
|
||||
3. **Model Predictions**: CNN, DQN, and COB-RL ensemble
|
||||
4. **Decision Fusion**: Neural network combines all predictions
|
||||
5. **Trade Execution**: 10× enhanced learning from actual trades
|
||||
6. **Retrospective Training**: Perfect move detection and model updates
|
||||
|
||||
### Enhanced Training Systems
|
||||
- **Continuous Learning**: Models update in real-time from market outcomes
|
||||
- **Multi-modal Integration**: CNN + RL + COB predictions combined intelligently
|
||||
- **Sensitivity Adaptation**: DQN adjusts risk appetite based on performance
|
||||
- **Perfect Move Detection**: Automatic identification of optimal trading opportunities
|
||||
- **Negative Case Training**: Intensive learning from failed predictions
|
||||
|
||||
### Dashboard Monitoring
|
||||
- **Real-time Model Status**: Active models, parameters, loss tracking
|
||||
- **Live Predictions**: Current model outputs with confidence scores
|
||||
- **Training Metrics**: Loss trends, accuracy rates, iteration counts
|
||||
- **COB Integration**: Real-time order book analysis and microstructure data
|
||||
- **Performance Tracking**: P&L, trade accuracy, model effectiveness
|
||||
|
||||
The system provides a comprehensive ML-driven trading environment with real-time learning, multi-modal decision making, and advanced market microstructure analysis through COB integration.
|
||||
|
||||
**Dashboard URL**: http://127.0.0.1:8051
|
||||
**Status**: ✅ FULLY OPERATIONAL
|
||||
@ -1,194 +0,0 @@
|
||||
# Enhanced Training Integration Report
|
||||
*Generated: 2024-12-19*
|
||||
|
||||
## 🎯 Integration Objective
|
||||
|
||||
Integrate the restored `EnhancedRealtimeTrainingSystem` into the orchestrator and audit the `EnhancedRLTrainingIntegrator` to determine if it can be used for comprehensive RL training.
|
||||
|
||||
## 📊 EnhancedRealtimeTrainingSystem Analysis
|
||||
|
||||
### **✅ Successfully Integrated**
|
||||
|
||||
The `EnhancedRealtimeTrainingSystem` has been successfully integrated into the orchestrator with the following capabilities:
|
||||
|
||||
#### **Core Features**
|
||||
- **Real-time Data Collection**: Multi-timeframe OHLCV, tick data, COB snapshots
|
||||
- **Enhanced DQN Training**: Prioritized experience replay with market-aware rewards
|
||||
- **CNN Training**: Real-time pattern recognition training
|
||||
- **Forward-looking Predictions**: Generates predictions for future validation
|
||||
- **Adaptive Learning**: Adjusts training frequency based on performance
|
||||
- **Comprehensive State Building**: 13,400+ feature states for RL training
|
||||
|
||||
#### **Integration Points in Orchestrator**
|
||||
```python
|
||||
# New orchestrator capabilities:
|
||||
self.enhanced_training_system: Optional[EnhancedRealtimeTrainingSystem] = None
|
||||
self.training_enabled: bool = enhanced_rl_training and ENHANCED_TRAINING_AVAILABLE
|
||||
|
||||
# Methods added:
|
||||
def _initialize_enhanced_training_system()
|
||||
def start_enhanced_training()
|
||||
def stop_enhanced_training()
|
||||
def get_enhanced_training_stats()
|
||||
def set_training_dashboard(dashboard)
|
||||
```
|
||||
|
||||
#### **Training Capabilities**
|
||||
1. **Real-time Data Streams**:
|
||||
- OHLCV data (1m, 5m intervals)
|
||||
- Tick-level market data
|
||||
- COB (Change of Bid) snapshots
|
||||
- Market event detection
|
||||
|
||||
2. **Enhanced Model Training**:
|
||||
- DQN with prioritized experience replay
|
||||
- CNN with multi-timeframe features
|
||||
- Comprehensive reward engineering
|
||||
- Performance-based adaptation
|
||||
|
||||
3. **Prediction Tracking**:
|
||||
- Forward-looking predictions with validation
|
||||
- Accuracy measurement and tracking
|
||||
- Model confidence scoring
|
||||
|
||||
## 🔍 EnhancedRLTrainingIntegrator Audit
|
||||
|
||||
### **Purpose & Scope**
|
||||
The `EnhancedRLTrainingIntegrator` is a comprehensive testing and validation system designed to:
|
||||
- Verify 13,400-feature comprehensive state building
|
||||
- Test enhanced pivot-based reward calculation
|
||||
- Validate Williams market structure integration
|
||||
- Demonstrate live comprehensive training
|
||||
|
||||
### **Audit Results**
|
||||
|
||||
#### **✅ Valuable Components**
|
||||
1. **Comprehensive State Verification**: Tests for exactly 13,400 features
|
||||
2. **Feature Distribution Analysis**: Analyzes non-zero vs zero features
|
||||
3. **Enhanced Reward Testing**: Validates pivot-based reward calculations
|
||||
4. **Williams Integration**: Tests market structure feature extraction
|
||||
5. **Live Training Demo**: Demonstrates coordinated decision making
|
||||
|
||||
#### **🔧 Integration Challenges**
|
||||
1. **Dependency Issues**: References `core.enhanced_orchestrator.EnhancedTradingOrchestrator` (not available)
|
||||
2. **Missing Methods**: Expects methods not present in current orchestrator:
|
||||
- `build_comprehensive_rl_state()`
|
||||
- `calculate_enhanced_pivot_reward()`
|
||||
- `make_coordinated_decisions()`
|
||||
3. **Williams Module**: Depends on `training.williams_market_structure` (needs verification)
|
||||
|
||||
#### **💡 Recommended Usage**
|
||||
The `EnhancedRLTrainingIntegrator` should be used as a **testing and validation tool** rather than direct integration:
|
||||
|
||||
```python
|
||||
# Use as standalone testing script
|
||||
python enhanced_rl_training_integration.py
|
||||
|
||||
# Or import specific testing functions
|
||||
from enhanced_rl_training_integration import EnhancedRLTrainingIntegrator
|
||||
integrator = EnhancedRLTrainingIntegrator()
|
||||
await integrator._verify_comprehensive_state_building()
|
||||
```
|
||||
|
||||
## 🚀 Implementation Strategy
|
||||
|
||||
### **Phase 1: EnhancedRealtimeTrainingSystem (✅ COMPLETE)**
|
||||
- [x] Integrated into orchestrator
|
||||
- [x] Added initialization methods
|
||||
- [x] Connected to data provider
|
||||
- [x] Dashboard integration support
|
||||
|
||||
### **Phase 2: Enhanced Methods (🔄 IN PROGRESS)**
|
||||
Add missing methods expected by the integrator:
|
||||
|
||||
```python
|
||||
# Add to orchestrator:
|
||||
def build_comprehensive_rl_state(self, symbol: str) -> Optional[np.ndarray]:
|
||||
"""Build comprehensive 13,400+ feature state for RL training"""
|
||||
|
||||
def calculate_enhanced_pivot_reward(self, trade_decision: Dict,
|
||||
market_data: Dict,
|
||||
trade_outcome: Dict) -> float:
|
||||
"""Calculate enhanced pivot-based rewards"""
|
||||
|
||||
async def make_coordinated_decisions(self) -> Dict[str, TradingDecision]:
|
||||
"""Make coordinated decisions across all symbols"""
|
||||
```
|
||||
|
||||
### **Phase 3: Validation Integration (📋 PLANNED)**
|
||||
Use `EnhancedRLTrainingIntegrator` as a validation tool:
|
||||
|
||||
```python
|
||||
# Integration validation workflow:
|
||||
1. Start enhanced training system
|
||||
2. Run comprehensive state building tests
|
||||
3. Validate reward calculation accuracy
|
||||
4. Test Williams market structure integration
|
||||
5. Monitor live training performance
|
||||
```
|
||||
|
||||
## 📈 Benefits of Integration
|
||||
|
||||
### **Real-time Learning**
|
||||
- Continuous model improvement during live trading
|
||||
- Adaptive learning based on market conditions
|
||||
- Forward-looking prediction validation
|
||||
|
||||
### **Comprehensive Features**
|
||||
- 13,400+ feature comprehensive states
|
||||
- Multi-timeframe market analysis
|
||||
- COB microstructure integration
|
||||
- Enhanced reward engineering
|
||||
|
||||
### **Performance Monitoring**
|
||||
- Real-time training statistics
|
||||
- Model accuracy tracking
|
||||
- Adaptive parameter adjustment
|
||||
- Comprehensive logging
|
||||
|
||||
## 🎯 Next Steps
|
||||
|
||||
### **Immediate Actions**
|
||||
1. **Complete Method Implementation**: Add missing orchestrator methods
|
||||
2. **Williams Module Verification**: Ensure market structure module is available
|
||||
3. **Testing Integration**: Use integrator for validation testing
|
||||
4. **Dashboard Connection**: Connect training system to dashboard
|
||||
|
||||
### **Future Enhancements**
|
||||
1. **Multi-Symbol Coordination**: Enhance coordinated decision making
|
||||
2. **Advanced Reward Engineering**: Implement sophisticated reward functions
|
||||
3. **Model Ensemble**: Combine multiple model predictions
|
||||
4. **Performance Optimization**: GPU acceleration for training
|
||||
|
||||
## 📊 Integration Status
|
||||
|
||||
| Component | Status | Notes |
|
||||
|-----------|--------|-------|
|
||||
| EnhancedRealtimeTrainingSystem | ✅ Integrated | Fully functional in orchestrator |
|
||||
| Real-time Data Collection | ✅ Available | Multi-timeframe data streams |
|
||||
| Enhanced DQN Training | ✅ Available | Prioritized experience replay |
|
||||
| CNN Training | ✅ Available | Pattern recognition training |
|
||||
| Forward Predictions | ✅ Available | Prediction validation system |
|
||||
| EnhancedRLTrainingIntegrator | 🔧 Partial | Use as validation tool |
|
||||
| Comprehensive State Building | 📋 Planned | Need to implement method |
|
||||
| Enhanced Reward Calculation | 📋 Planned | Need to implement method |
|
||||
| Williams Integration | ❓ Unknown | Need to verify module |
|
||||
|
||||
## 🏆 Conclusion
|
||||
|
||||
The `EnhancedRealtimeTrainingSystem` has been successfully integrated into the orchestrator, providing comprehensive real-time training capabilities. The `EnhancedRLTrainingIntegrator` serves as an excellent validation and testing tool, but requires additional method implementations in the orchestrator for full functionality.
|
||||
|
||||
**Key Achievements:**
|
||||
- ✅ Real-time training system fully integrated
|
||||
- ✅ Comprehensive feature extraction capabilities
|
||||
- ✅ Enhanced reward engineering framework
|
||||
- ✅ Forward-looking prediction validation
|
||||
- ✅ Performance monitoring and adaptation
|
||||
|
||||
**Recommended Actions:**
|
||||
1. Use the integrated training system for live model improvement
|
||||
2. Implement missing orchestrator methods for full integrator compatibility
|
||||
3. Use the integrator as a comprehensive testing and validation tool
|
||||
4. Monitor training performance and adapt parameters as needed
|
||||
|
||||
The integration provides a solid foundation for advanced ML-driven trading with continuous learning capabilities.
|
||||
@ -201,6 +201,9 @@ class DataProvider:
|
||||
self.last_pivot_calculation: Dict[str, datetime] = {}
|
||||
self.pivot_calculation_interval = timedelta(minutes=5) # Recalculate every 5 minutes
|
||||
|
||||
# Auto-fix corrupted cache files on startup
|
||||
self._auto_fix_corrupted_cache()
|
||||
|
||||
# Load existing pivot bounds from cache
|
||||
self._load_all_pivot_bounds()
|
||||
|
||||
@ -1231,6 +1234,36 @@ class DataProvider:
|
||||
return symbol # Return first symbol for now - can be improved
|
||||
return None
|
||||
|
||||
# === CACHE MANAGEMENT ===
|
||||
|
||||
def _auto_fix_corrupted_cache(self):
|
||||
"""Automatically fix corrupted cache files on startup"""
|
||||
try:
|
||||
from utils.cache_manager import get_cache_manager
|
||||
cache_manager = get_cache_manager()
|
||||
|
||||
# Quick health check
|
||||
health_summary = cache_manager.get_cache_summary()
|
||||
|
||||
if health_summary['corrupted_files'] > 0:
|
||||
logger.warning(f"Found {health_summary['corrupted_files']} corrupted cache files, cleaning up...")
|
||||
|
||||
# Auto-cleanup corrupted files (no confirmation needed)
|
||||
deleted_files = cache_manager.cleanup_corrupted_files(dry_run=False)
|
||||
|
||||
deleted_count = 0
|
||||
for cache_dir, files in deleted_files.items():
|
||||
for file_info in files:
|
||||
if "DELETED:" in file_info:
|
||||
deleted_count += 1
|
||||
|
||||
logger.info(f"Auto-cleaned {deleted_count} corrupted cache files")
|
||||
else:
|
||||
logger.debug("Cache health check passed - no corrupted files found")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Cache auto-fix failed: {e}")
|
||||
|
||||
# === PIVOT BOUNDS CACHING ===
|
||||
|
||||
def _load_all_pivot_bounds(self):
|
||||
@ -1285,13 +1318,25 @@ class DataProvider:
|
||||
logger.info(f"Loaded {len(df)} 1m candles from cache for {symbol}")
|
||||
return df
|
||||
except Exception as parquet_e:
|
||||
# Handle corrupted Parquet file
|
||||
if "Parquet magic bytes not found" in str(parquet_e) or "corrupted" in str(parquet_e).lower():
|
||||
# Handle corrupted Parquet file - expanded error detection
|
||||
error_str = str(parquet_e).lower()
|
||||
corrupted_indicators = [
|
||||
"parquet magic bytes not found",
|
||||
"corrupted",
|
||||
"couldn't deserialize thrift",
|
||||
"don't know what type",
|
||||
"invalid parquet file",
|
||||
"unexpected end of file",
|
||||
"invalid metadata"
|
||||
]
|
||||
|
||||
if any(indicator in error_str for indicator in corrupted_indicators):
|
||||
logger.warning(f"Corrupted Parquet cache file for {symbol}, removing and returning None: {parquet_e}")
|
||||
try:
|
||||
cache_file.unlink() # Delete corrupted file
|
||||
except Exception:
|
||||
pass
|
||||
logger.info(f"Deleted corrupted monthly cache file: {cache_file}")
|
||||
except Exception as delete_e:
|
||||
logger.error(f"Failed to delete corrupted monthly cache file: {delete_e}")
|
||||
return None
|
||||
else:
|
||||
raise parquet_e
|
||||
@ -1393,13 +1438,25 @@ class DataProvider:
|
||||
logger.debug(f"Loaded {len(df)} rows from cache for {symbol} {timeframe} (age: {cache_age/60:.1f}min)")
|
||||
return df
|
||||
except Exception as parquet_e:
|
||||
# Handle corrupted Parquet file
|
||||
if "Parquet magic bytes not found" in str(parquet_e) or "corrupted" in str(parquet_e).lower():
|
||||
# Handle corrupted Parquet file - expanded error detection
|
||||
error_str = str(parquet_e).lower()
|
||||
corrupted_indicators = [
|
||||
"parquet magic bytes not found",
|
||||
"corrupted",
|
||||
"couldn't deserialize thrift",
|
||||
"don't know what type",
|
||||
"invalid parquet file",
|
||||
"unexpected end of file",
|
||||
"invalid metadata"
|
||||
]
|
||||
|
||||
if any(indicator in error_str for indicator in corrupted_indicators):
|
||||
logger.warning(f"Corrupted Parquet cache file for {symbol} {timeframe}, removing and returning None: {parquet_e}")
|
||||
try:
|
||||
cache_file.unlink() # Delete corrupted file
|
||||
except Exception:
|
||||
pass
|
||||
logger.info(f"Deleted corrupted cache file: {cache_file}")
|
||||
except Exception as delete_e:
|
||||
logger.error(f"Failed to delete corrupted cache file: {delete_e}")
|
||||
return None
|
||||
else:
|
||||
raise parquet_e
|
||||
|
||||
@ -38,6 +38,11 @@ from NN.models.cob_rl_model import COBRLModelInterface # Specific import for COB
|
||||
from NN.models.model_interfaces import ModelInterface as NNModelInterface, CNNModelInterface as NNCNNModelInterface, RLAgentInterface as NNRLAgentInterface, ExtremaTrainerInterface as NNExtremaTrainerInterface # Import from new file
|
||||
from core.extrema_trainer import ExtremaTrainer # Import ExtremaTrainer for its interface
|
||||
|
||||
# Import new logging and database systems
|
||||
from utils.inference_logger import get_inference_logger, log_model_inference
|
||||
from utils.database_manager import get_database_manager
|
||||
from utils.checkpoint_manager import load_best_checkpoint
|
||||
|
||||
# Import COB integration for real-time market microstructure data
|
||||
try:
|
||||
from .cob_integration import COBIntegration
|
||||
@ -213,6 +218,10 @@ class TradingOrchestrator:
|
||||
# Initialize inference history for each model (will be populated as models make predictions)
|
||||
# We'll create entries dynamically as models are used
|
||||
|
||||
# Initialize inference logger
|
||||
self.inference_logger = get_inference_logger()
|
||||
self.db_manager = get_database_manager()
|
||||
|
||||
# ENHANCED: Real-time Training System Integration
|
||||
self.enhanced_training_system = None # Will be set to EnhancedRealtimeTrainingSystem if available
|
||||
# Enable training by default - don't depend on external training system
|
||||
@ -232,6 +241,9 @@ class TradingOrchestrator:
|
||||
self.data_provider.start_centralized_data_collection()
|
||||
logger.info("Centralized data collection started - all models and dashboard will receive data")
|
||||
|
||||
# Initialize database cleanup task
|
||||
self._schedule_database_cleanup()
|
||||
|
||||
# CRITICAL: Initialize checkpoint manager for saving training progress
|
||||
self.checkpoint_manager = None
|
||||
self.training_iterations = 0 # Track training iterations for periodic saves
|
||||
@ -265,24 +277,23 @@ class TradingOrchestrator:
|
||||
self.rl_agent = DQNAgent(state_shape=state_size, n_actions=action_size)
|
||||
self.rl_agent.to(self.device) # Move DQN agent to the determined device
|
||||
|
||||
# Load best checkpoint and capture initial state
|
||||
# Load best checkpoint and capture initial state (using database metadata)
|
||||
checkpoint_loaded = False
|
||||
if hasattr(self.rl_agent, 'load_best_checkpoint'):
|
||||
try:
|
||||
self.rl_agent.load_best_checkpoint() # This loads the state into the model
|
||||
# Check if we have checkpoints available
|
||||
from utils.checkpoint_manager import load_best_checkpoint
|
||||
result = load_best_checkpoint("dqn_agent")
|
||||
if result:
|
||||
file_path, metadata = result
|
||||
# Check if we have checkpoints available using database metadata (fast!)
|
||||
db_manager = get_database_manager()
|
||||
checkpoint_metadata = db_manager.get_best_checkpoint_metadata("dqn_agent")
|
||||
if checkpoint_metadata:
|
||||
self.model_states['dqn']['initial_loss'] = 0.412
|
||||
self.model_states['dqn']['current_loss'] = metadata.loss
|
||||
self.model_states['dqn']['best_loss'] = metadata.loss
|
||||
self.model_states['dqn']['current_loss'] = checkpoint_metadata.performance_metrics.get('loss', 0.0)
|
||||
self.model_states['dqn']['best_loss'] = checkpoint_metadata.performance_metrics.get('loss', 0.0)
|
||||
self.model_states['dqn']['checkpoint_loaded'] = True
|
||||
self.model_states['dqn']['checkpoint_filename'] = metadata.checkpoint_id
|
||||
self.model_states['dqn']['checkpoint_filename'] = checkpoint_metadata.checkpoint_id
|
||||
checkpoint_loaded = True
|
||||
loss_str = f"{metadata.loss:.4f}" if metadata.loss is not None else "unknown"
|
||||
logger.info(f"DQN checkpoint loaded: {metadata.checkpoint_id} (loss={loss_str})")
|
||||
loss_str = f"{checkpoint_metadata.performance_metrics.get('loss', 0.0):.4f}"
|
||||
logger.info(f"DQN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})")
|
||||
except Exception as e:
|
||||
logger.warning(f"Error loading DQN checkpoint: {e}")
|
||||
|
||||
@ -307,21 +318,20 @@ class TradingOrchestrator:
|
||||
self.cnn_model = self.cnn_adapter.model # Keep reference for compatibility
|
||||
self.cnn_optimizer = optim.Adam(self.cnn_model.parameters(), lr=0.001) # Initialize optimizer for CNN
|
||||
|
||||
# Load best checkpoint and capture initial state
|
||||
# Load best checkpoint and capture initial state (using database metadata)
|
||||
checkpoint_loaded = False
|
||||
try:
|
||||
from utils.checkpoint_manager import load_best_checkpoint
|
||||
result = load_best_checkpoint("enhanced_cnn")
|
||||
if result:
|
||||
file_path, metadata = result
|
||||
db_manager = get_database_manager()
|
||||
checkpoint_metadata = db_manager.get_best_checkpoint_metadata("enhanced_cnn")
|
||||
if checkpoint_metadata:
|
||||
self.model_states['cnn']['initial_loss'] = 0.412
|
||||
self.model_states['cnn']['current_loss'] = metadata.loss or 0.0187
|
||||
self.model_states['cnn']['best_loss'] = metadata.loss or 0.0134
|
||||
self.model_states['cnn']['current_loss'] = checkpoint_metadata.performance_metrics.get('loss', 0.0187)
|
||||
self.model_states['cnn']['best_loss'] = checkpoint_metadata.performance_metrics.get('loss', 0.0134)
|
||||
self.model_states['cnn']['checkpoint_loaded'] = True
|
||||
self.model_states['cnn']['checkpoint_filename'] = metadata.checkpoint_id
|
||||
self.model_states['cnn']['checkpoint_filename'] = checkpoint_metadata.checkpoint_id
|
||||
checkpoint_loaded = True
|
||||
loss_str = f"{metadata.loss:.4f}" if metadata.loss is not None else "unknown"
|
||||
logger.info(f"CNN checkpoint loaded: {metadata.checkpoint_id} (loss={loss_str})")
|
||||
loss_str = f"{checkpoint_metadata.performance_metrics.get('loss', 0.0):.4f}"
|
||||
logger.info(f"CNN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})")
|
||||
except Exception as e:
|
||||
logger.warning(f"Error loading CNN checkpoint: {e}")
|
||||
|
||||
@ -399,23 +409,22 @@ class TradingOrchestrator:
|
||||
if hasattr(self.cob_rl_agent, 'to'):
|
||||
self.cob_rl_agent.to(self.device)
|
||||
|
||||
# Load best checkpoint and capture initial state
|
||||
# Load best checkpoint and capture initial state (using database metadata)
|
||||
checkpoint_loaded = False
|
||||
if hasattr(self.cob_rl_agent, 'load_model'):
|
||||
try:
|
||||
self.cob_rl_agent.load_model() # This loads the state into the model
|
||||
from utils.checkpoint_manager import load_best_checkpoint
|
||||
result = load_best_checkpoint("cob_rl_model")
|
||||
if result:
|
||||
file_path, metadata = result
|
||||
self.model_states['cob_rl']['initial_loss'] = getattr(metadata, 'initial_loss', None)
|
||||
self.model_states['cob_rl']['current_loss'] = metadata.loss
|
||||
self.model_states['cob_rl']['best_loss'] = metadata.loss
|
||||
db_manager = get_database_manager()
|
||||
checkpoint_metadata = db_manager.get_best_checkpoint_metadata("cob_rl")
|
||||
if checkpoint_metadata:
|
||||
self.model_states['cob_rl']['initial_loss'] = checkpoint_metadata.training_metadata.get('initial_loss', None)
|
||||
self.model_states['cob_rl']['current_loss'] = checkpoint_metadata.performance_metrics.get('loss', 0.0)
|
||||
self.model_states['cob_rl']['best_loss'] = checkpoint_metadata.performance_metrics.get('loss', 0.0)
|
||||
self.model_states['cob_rl']['checkpoint_loaded'] = True
|
||||
self.model_states['cob_rl']['checkpoint_filename'] = metadata.checkpoint_id
|
||||
self.model_states['cob_rl']['checkpoint_filename'] = checkpoint_metadata.checkpoint_id
|
||||
checkpoint_loaded = True
|
||||
loss_str = f"{metadata.loss:.4f}" if metadata.loss is not None else "unknown"
|
||||
logger.info(f"COB RL checkpoint loaded: {metadata.checkpoint_id} (loss={loss_str})")
|
||||
loss_str = f"{checkpoint_metadata.performance_metrics.get('loss', 0.0):.4f}"
|
||||
logger.info(f"COB RL checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})")
|
||||
except Exception as e:
|
||||
logger.warning(f"Error loading COB RL checkpoint: {e}")
|
||||
|
||||
@ -1247,51 +1256,210 @@ class TradingOrchestrator:
|
||||
logger.error(f"Error storing inference data for {model_name}: {e}")
|
||||
|
||||
async def _save_inference_to_disk_async(self, model_name: str, inference_record: Dict):
|
||||
"""Async save inference record to disk with file capping"""
|
||||
"""Async save inference record to SQLite database and model-specific log"""
|
||||
try:
|
||||
# Create model-specific directory
|
||||
model_dir = Path(f"training_data/inference_history/{model_name}")
|
||||
model_dir.mkdir(parents=True, exist_ok=True)
|
||||
# Use SQLite for comprehensive storage
|
||||
await self._save_to_sqlite_db(model_name, inference_record)
|
||||
|
||||
# Create filename with timestamp
|
||||
timestamp_str = datetime.fromisoformat(inference_record['timestamp']).strftime('%Y%m%d_%H%M%S_%f')[:-3]
|
||||
filename = f"inference_{timestamp_str}.json"
|
||||
filepath = model_dir / filename
|
||||
|
||||
# Convert to JSON-serializable format
|
||||
serializable_record = self._make_json_serializable(inference_record)
|
||||
|
||||
# Save to file
|
||||
with open(filepath, 'w') as f:
|
||||
json.dump(serializable_record, f, indent=2)
|
||||
|
||||
# Cap files per model (keep only latest 200)
|
||||
await self._cap_model_files(model_dir)
|
||||
|
||||
logger.debug(f"Saved inference record to disk: {filepath}")
|
||||
# Also save key metrics to model-specific log for debugging
|
||||
await self._save_to_model_log(model_name, inference_record)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving inference to disk for {model_name}: {e}")
|
||||
|
||||
async def _cap_model_files(self, model_dir: Path):
|
||||
"""Cap the number of files per model to max_disk_files_per_model"""
|
||||
async def _save_to_sqlite_db(self, model_name: str, inference_record: Dict):
|
||||
"""Save inference record to SQLite database"""
|
||||
import sqlite3
|
||||
import asyncio
|
||||
|
||||
def save_to_db():
|
||||
try:
|
||||
# Create database directory
|
||||
db_dir = Path("training_data/inference_db")
|
||||
db_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Connect to SQLite database
|
||||
db_path = db_dir / "inference_history.db"
|
||||
conn = sqlite3.connect(str(db_path))
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Create table if it doesn't exist
|
||||
cursor.execute('''
|
||||
CREATE TABLE IF NOT EXISTS inference_records (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
model_name TEXT NOT NULL,
|
||||
symbol TEXT NOT NULL,
|
||||
timestamp TEXT NOT NULL,
|
||||
action TEXT NOT NULL,
|
||||
confidence REAL NOT NULL,
|
||||
probabilities TEXT,
|
||||
timeframe TEXT,
|
||||
metadata TEXT,
|
||||
created_at DATETIME DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
''')
|
||||
|
||||
# Create index for faster queries
|
||||
cursor.execute('''
|
||||
CREATE INDEX IF NOT EXISTS idx_model_timestamp
|
||||
ON inference_records(model_name, timestamp)
|
||||
''')
|
||||
|
||||
# Extract data from inference record
|
||||
prediction = inference_record.get('prediction', {})
|
||||
probabilities_str = str(prediction.get('probabilities', {}))
|
||||
metadata_str = str(inference_record.get('metadata', {}))
|
||||
|
||||
# Insert record
|
||||
cursor.execute('''
|
||||
INSERT INTO inference_records
|
||||
(model_name, symbol, timestamp, action, confidence, probabilities, timeframe, metadata)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
''', (
|
||||
model_name,
|
||||
inference_record.get('symbol', 'ETH/USDT'),
|
||||
inference_record.get('timestamp', ''),
|
||||
prediction.get('action', 'HOLD'),
|
||||
prediction.get('confidence', 0.0),
|
||||
probabilities_str,
|
||||
prediction.get('timeframe', '1m'),
|
||||
metadata_str
|
||||
))
|
||||
|
||||
# Clean up old records (keep only last 1000 per model)
|
||||
cursor.execute('''
|
||||
DELETE FROM inference_records
|
||||
WHERE model_name = ? AND id NOT IN (
|
||||
SELECT id FROM inference_records
|
||||
WHERE model_name = ?
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT 1000
|
||||
)
|
||||
''', (model_name, model_name))
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving to SQLite database: {e}")
|
||||
|
||||
# Run database operation in thread pool to avoid blocking
|
||||
await asyncio.get_event_loop().run_in_executor(None, save_to_db)
|
||||
|
||||
async def _save_to_model_log(self, model_name: str, inference_record: Dict):
|
||||
"""Save key inference metrics to model-specific log file for debugging"""
|
||||
import asyncio
|
||||
|
||||
def save_to_log():
|
||||
try:
|
||||
# Create logs directory
|
||||
logs_dir = Path("logs/model_inference")
|
||||
logs_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Create model-specific log file
|
||||
log_file = logs_dir / f"{model_name}_inference.log"
|
||||
|
||||
# Extract key metrics
|
||||
prediction = inference_record.get('prediction', {})
|
||||
timestamp = inference_record.get('timestamp', '')
|
||||
symbol = inference_record.get('symbol', 'N/A')
|
||||
|
||||
# Format log entry with key metrics
|
||||
log_entry = (
|
||||
f"{timestamp} | "
|
||||
f"Symbol: {symbol} | "
|
||||
f"Action: {prediction.get('action', 'N/A'):4} | "
|
||||
f"Confidence: {prediction.get('confidence', 0.0):6.3f} | "
|
||||
f"Timeframe: {prediction.get('timeframe', 'N/A'):3} | "
|
||||
f"Probs: BUY={prediction.get('probabilities', {}).get('BUY', 0.0):5.3f} "
|
||||
f"SELL={prediction.get('probabilities', {}).get('SELL', 0.0):5.3f} "
|
||||
f"HOLD={prediction.get('probabilities', {}).get('HOLD', 0.0):5.3f}\n"
|
||||
)
|
||||
|
||||
# Append to log file
|
||||
with open(log_file, 'a', encoding='utf-8') as f:
|
||||
f.write(log_entry)
|
||||
|
||||
# Keep log files manageable (rotate when > 10MB)
|
||||
if log_file.stat().st_size > 10 * 1024 * 1024: # 10MB
|
||||
self._rotate_log_file(log_file)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving to model log: {e}")
|
||||
|
||||
# Run log operation in thread pool to avoid blocking
|
||||
await asyncio.get_event_loop().run_in_executor(None, save_to_log)
|
||||
|
||||
def _rotate_log_file(self, log_file: Path):
|
||||
"""Rotate log file when it gets too large"""
|
||||
try:
|
||||
# Get all inference files
|
||||
files = list(model_dir.glob("inference_*.json"))
|
||||
# Keep last 1000 lines
|
||||
with open(log_file, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
|
||||
if len(files) > self.max_disk_files_per_model:
|
||||
# Sort by modification time (oldest first)
|
||||
files.sort(key=lambda x: x.stat().st_mtime)
|
||||
|
||||
# Remove oldest files
|
||||
files_to_remove = files[:-self.max_disk_files_per_model]
|
||||
for file_path in files_to_remove:
|
||||
file_path.unlink()
|
||||
|
||||
logger.debug(f"Removed {len(files_to_remove)} old inference files from {model_dir.name}")
|
||||
# Write back only the last 1000 lines
|
||||
with open(log_file, 'w', encoding='utf-8') as f:
|
||||
f.writelines(lines[-1000:])
|
||||
|
||||
logger.debug(f"Rotated log file {log_file.name} (kept last 1000 lines)")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error capping model files in {model_dir}: {e}")
|
||||
logger.error(f"Error rotating log file {log_file}: {e}")
|
||||
|
||||
def get_inference_records_from_db(self, model_name: str = None, limit: int = 100) -> List[Dict]:
|
||||
"""Get inference records from SQLite database"""
|
||||
import sqlite3
|
||||
|
||||
try:
|
||||
# Connect to database
|
||||
db_path = Path("training_data/inference_db/inference_history.db")
|
||||
if not db_path.exists():
|
||||
return []
|
||||
|
||||
conn = sqlite3.connect(str(db_path))
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Query records
|
||||
if model_name:
|
||||
cursor.execute('''
|
||||
SELECT model_name, symbol, timestamp, action, confidence, probabilities, timeframe, metadata
|
||||
FROM inference_records
|
||||
WHERE model_name = ?
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT ?
|
||||
''', (model_name, limit))
|
||||
else:
|
||||
cursor.execute('''
|
||||
SELECT model_name, symbol, timestamp, action, confidence, probabilities, timeframe, metadata
|
||||
FROM inference_records
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT ?
|
||||
''', (limit,))
|
||||
|
||||
records = []
|
||||
for row in cursor.fetchall():
|
||||
record = {
|
||||
'model_name': row[0],
|
||||
'symbol': row[1],
|
||||
'timestamp': row[2],
|
||||
'prediction': {
|
||||
'action': row[3],
|
||||
'confidence': row[4],
|
||||
'probabilities': eval(row[5]) if row[5] else {},
|
||||
'timeframe': row[6]
|
||||
},
|
||||
'metadata': eval(row[7]) if row[7] else {}
|
||||
}
|
||||
records.append(record)
|
||||
|
||||
conn.close()
|
||||
return records
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error querying SQLite database: {e}")
|
||||
return []
|
||||
|
||||
|
||||
|
||||
def _prepare_cnn_input_data(self, ohlcv_data: Dict, cob_data: Any, technical_indicators: Dict) -> torch.Tensor:
|
||||
"""Prepare standardized input data for CNN models with proper GPU device placement"""
|
||||
@ -1472,67 +1640,60 @@ class TradingOrchestrator:
|
||||
return obj
|
||||
|
||||
def load_inference_history_from_disk(self, symbol: str, days_back: int = 7) -> List[Dict]:
|
||||
"""Load inference history from disk for training replay"""
|
||||
"""Load inference history from SQLite database for training replay"""
|
||||
try:
|
||||
inference_dir = Path("training_data/inference_history")
|
||||
if not inference_dir.exists():
|
||||
import sqlite3
|
||||
|
||||
# Connect to database
|
||||
db_path = Path("training_data/inference_db/inference_history.db")
|
||||
if not db_path.exists():
|
||||
return []
|
||||
|
||||
# Get files for the symbol from the last N days
|
||||
cutoff_date = datetime.now() - timedelta(days=days_back)
|
||||
conn = sqlite3.connect(str(db_path))
|
||||
cursor = conn.cursor()
|
||||
|
||||
# Get records for the symbol from the last N days
|
||||
cutoff_date = (datetime.now() - timedelta(days=days_back)).isoformat()
|
||||
|
||||
cursor.execute('''
|
||||
SELECT model_name, symbol, timestamp, action, confidence, probabilities, timeframe, metadata
|
||||
FROM inference_records
|
||||
WHERE symbol = ? AND timestamp >= ?
|
||||
ORDER BY timestamp ASC
|
||||
''', (symbol, cutoff_date))
|
||||
|
||||
inference_records = []
|
||||
for row in cursor.fetchall():
|
||||
record = {
|
||||
'model_name': row[0],
|
||||
'symbol': row[1],
|
||||
'timestamp': row[2],
|
||||
'prediction': {
|
||||
'action': row[3],
|
||||
'confidence': row[4],
|
||||
'probabilities': eval(row[5]) if row[5] else {},
|
||||
'timeframe': row[6]
|
||||
},
|
||||
'metadata': eval(row[7]) if row[7] else {}
|
||||
}
|
||||
inference_records.append(record)
|
||||
|
||||
for filepath in inference_dir.glob(f"{symbol}_*.json"):
|
||||
try:
|
||||
# Extract timestamp from filename
|
||||
filename_parts = filepath.stem.split('_')
|
||||
if len(filename_parts) >= 3:
|
||||
timestamp_str = f"{filename_parts[-2]}_{filename_parts[-1]}"
|
||||
file_timestamp = datetime.strptime(timestamp_str, '%Y%m%d_%H%M%S')
|
||||
|
||||
if file_timestamp >= cutoff_date:
|
||||
with open(filepath, 'r') as f:
|
||||
record = json.load(f)
|
||||
inference_records.append(record)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error loading inference file {filepath}: {e}")
|
||||
continue
|
||||
|
||||
# Sort by timestamp
|
||||
inference_records.sort(key=lambda x: x['timestamp'])
|
||||
logger.info(f"Loaded {len(inference_records)} inference records for {symbol} from disk")
|
||||
conn.close()
|
||||
logger.info(f"Loaded {len(inference_records)} inference records for {symbol} from SQLite database")
|
||||
|
||||
return inference_records
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading inference history from disk: {e}")
|
||||
logger.error(f"Error loading inference history from database: {e}")
|
||||
return []
|
||||
|
||||
async def load_model_inference_history(self, model_name: str, limit: int = 50) -> List[Dict]:
|
||||
"""Load inference history for a specific model from disk"""
|
||||
"""Load inference history for a specific model from SQLite database"""
|
||||
try:
|
||||
model_dir = Path(f"training_data/inference_history/{model_name}")
|
||||
if not model_dir.exists():
|
||||
return []
|
||||
|
||||
# Get all inference files
|
||||
files = list(model_dir.glob("inference_*.json"))
|
||||
files.sort(key=lambda x: x.stat().st_mtime, reverse=True) # Newest first
|
||||
|
||||
# Load up to 'limit' files
|
||||
inference_records = []
|
||||
for filepath in files[:limit]:
|
||||
try:
|
||||
with open(filepath, 'r') as f:
|
||||
record = json.load(f)
|
||||
inference_records.append(record)
|
||||
except Exception as e:
|
||||
logger.warning(f"Error loading inference file {filepath}: {e}")
|
||||
continue
|
||||
|
||||
logger.info(f"Loaded {len(inference_records)} inference records for {model_name}")
|
||||
return inference_records
|
||||
# Use the SQLite database method
|
||||
records = self.get_inference_records_from_db(model_name, limit)
|
||||
logger.info(f"Loaded {len(records)} inference records for {model_name} from database")
|
||||
return records
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading model inference history for {model_name}: {e}")
|
||||
@ -3284,6 +3445,15 @@ class TradingOrchestrator:
|
||||
logger.error(f"Error initializing checkpoint manager: {e}")
|
||||
self.checkpoint_manager = None
|
||||
|
||||
def _schedule_database_cleanup(self):
|
||||
"""Schedule periodic database cleanup"""
|
||||
try:
|
||||
# Clean up old inference records (keep 30 days)
|
||||
self.inference_logger.cleanup_old_logs(days_to_keep=30)
|
||||
logger.info("Database cleanup completed")
|
||||
except Exception as e:
|
||||
logger.error(f"Database cleanup failed: {e}")
|
||||
|
||||
def _save_training_checkpoints(self, models_trained: List[str], performance_score: float):
|
||||
"""Save checkpoints for trained models if performance improved
|
||||
|
||||
@ -3419,4 +3589,45 @@ class TradingOrchestrator:
|
||||
logger.error(f"Error saving checkpoint for {model_name}: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in _save_training_checkpoints: {e}")
|
||||
logger.error(f"Error in _save_training_checkpoints: {e}")
|
||||
def _schedule_database_cleanup(self):
|
||||
"""Schedule periodic database cleanup"""
|
||||
try:
|
||||
# Clean up old inference records (keep 30 days)
|
||||
self.inference_logger.cleanup_old_logs(days_to_keep=30)
|
||||
logger.info("Database cleanup completed")
|
||||
except Exception as e:
|
||||
logger.error(f"Database cleanup failed: {e}")
|
||||
|
||||
def log_model_inference(self, model_name: str, symbol: str, action: str,
|
||||
confidence: float, probabilities: Dict[str, float],
|
||||
input_features: Any, processing_time_ms: float,
|
||||
checkpoint_id: str = None, metadata: Dict[str, Any] = None) -> bool:
|
||||
"""
|
||||
Centralized method for models to log their inferences
|
||||
|
||||
This replaces scattered logger.info() calls throughout the codebase
|
||||
"""
|
||||
return log_model_inference(
|
||||
model_name=model_name,
|
||||
symbol=symbol,
|
||||
action=action,
|
||||
confidence=confidence,
|
||||
probabilities=probabilities,
|
||||
input_features=input_features,
|
||||
processing_time_ms=processing_time_ms,
|
||||
checkpoint_id=checkpoint_id,
|
||||
metadata=metadata
|
||||
)
|
||||
|
||||
def get_model_inference_stats(self, model_name: str, hours: int = 24) -> Dict[str, Any]:
|
||||
"""Get inference statistics for a model"""
|
||||
return self.inference_logger.get_model_stats(model_name, hours)
|
||||
|
||||
def get_checkpoint_metadata_fast(self, model_name: str) -> Optional[Any]:
|
||||
"""
|
||||
Get checkpoint metadata without loading the full model
|
||||
|
||||
This is much faster than loading the entire checkpoint just to get metadata
|
||||
"""
|
||||
return self.db_manager.get_best_checkpoint_metadata(model_name)
|
||||
BIN
data/trading_system.db
Normal file
BIN
data/trading_system.db
Normal file
Binary file not shown.
280
docs/logging_system_upgrade.md
Normal file
280
docs/logging_system_upgrade.md
Normal file
@ -0,0 +1,280 @@
|
||||
# Trading System Logging Upgrade
|
||||
|
||||
## Overview
|
||||
|
||||
This upgrade implements a comprehensive logging and metadata management system that addresses the key issues:
|
||||
|
||||
1. **Eliminates scattered "No checkpoints found" logs** during runtime
|
||||
2. **Fast checkpoint metadata access** without loading full models
|
||||
3. **Centralized inference logging** with database and text file storage
|
||||
4. **Structured tracking** of model performance and checkpoints
|
||||
|
||||
## Key Components
|
||||
|
||||
### 1. Database Manager (`utils/database_manager.py`)
|
||||
|
||||
**Purpose**: SQLite-based storage for structured data
|
||||
|
||||
**Features**:
|
||||
- Inference records logging with deduplication
|
||||
- Checkpoint metadata storage (separate from model weights)
|
||||
- Model performance tracking
|
||||
- Fast queries without loading model files
|
||||
|
||||
**Tables**:
|
||||
- `inference_records`: All model predictions with metadata
|
||||
- `checkpoint_metadata`: Checkpoint info without model weights
|
||||
- `model_performance`: Daily aggregated statistics
|
||||
|
||||
### 2. Inference Logger (`utils/inference_logger.py`)
|
||||
|
||||
**Purpose**: Centralized logging for all model inferences
|
||||
|
||||
**Features**:
|
||||
- Single function call replaces scattered `logger.info()` calls
|
||||
- Automatic feature hashing for deduplication
|
||||
- Memory usage tracking
|
||||
- Processing time measurement
|
||||
- Dual storage (database + text files)
|
||||
|
||||
**Usage**:
|
||||
```python
|
||||
from utils.inference_logger import log_model_inference
|
||||
|
||||
log_model_inference(
|
||||
model_name="dqn_agent",
|
||||
symbol="ETH/USDT",
|
||||
action="BUY",
|
||||
confidence=0.85,
|
||||
probabilities={"BUY": 0.85, "SELL": 0.10, "HOLD": 0.05},
|
||||
input_features=features_array,
|
||||
processing_time_ms=12.5,
|
||||
checkpoint_id="dqn_agent_20250725_143500"
|
||||
)
|
||||
```
|
||||
|
||||
### 3. Text Logger (`utils/text_logger.py`)
|
||||
|
||||
**Purpose**: Human-readable log files for tracking
|
||||
|
||||
**Features**:
|
||||
- Separate files for different event types
|
||||
- Clean, tabular format
|
||||
- Automatic cleanup of old entries
|
||||
- Easy to read and grep
|
||||
|
||||
**Files**:
|
||||
- `logs/inference_records.txt`: All model predictions
|
||||
- `logs/checkpoint_events.txt`: Save/load events
|
||||
- `logs/system_events.txt`: General system events
|
||||
|
||||
### 4. Enhanced Checkpoint Manager (`utils/checkpoint_manager.py`)
|
||||
|
||||
**Purpose**: Improved checkpoint handling with metadata separation
|
||||
|
||||
**Features**:
|
||||
- Database-backed metadata storage
|
||||
- Fast metadata queries without loading models
|
||||
- Eliminates "No checkpoints found" spam
|
||||
- Backward compatibility with existing code
|
||||
|
||||
## Benefits
|
||||
|
||||
### 1. Performance Improvements
|
||||
|
||||
**Before**: Loading full checkpoint just to get metadata
|
||||
```python
|
||||
# Old way - loads entire model!
|
||||
checkpoint_path, metadata = load_best_checkpoint("dqn_agent")
|
||||
loss = metadata.loss # Expensive operation
|
||||
```
|
||||
|
||||
**After**: Fast metadata access from database
|
||||
```python
|
||||
# New way - database query only
|
||||
metadata = db_manager.get_best_checkpoint_metadata("dqn_agent")
|
||||
loss = metadata.performance_metrics['loss'] # Fast!
|
||||
```
|
||||
|
||||
### 2. Cleaner Runtime Logs
|
||||
|
||||
**Before**: Scattered logs everywhere
|
||||
```
|
||||
2025-07-25 14:34:39,749 - utils.checkpoint_manager - INFO - No checkpoints found for dqn_agent
|
||||
2025-07-25 14:34:39,754 - utils.checkpoint_manager - INFO - No checkpoints found for enhanced_cnn
|
||||
2025-07-25 14:34:39,756 - utils.checkpoint_manager - INFO - No checkpoints found for extrema_trainer
|
||||
```
|
||||
|
||||
**After**: Clean, structured logging
|
||||
```
|
||||
2025-07-25 14:34:39 | dqn_agent | ETH/USDT | BUY | conf=0.850 | time= 12.5ms [checkpoint: dqn_agent_20250725_143500]
|
||||
2025-07-25 14:34:40 | enhanced_cnn | ETH/USDT | HOLD | conf=0.720 | time= 8.2ms [checkpoint: enhanced_cnn_20250725_143501]
|
||||
```
|
||||
|
||||
### 3. Structured Data Storage
|
||||
|
||||
**Database Schema**:
|
||||
```sql
|
||||
-- Fast metadata queries
|
||||
SELECT * FROM checkpoint_metadata WHERE model_name = 'dqn_agent' AND is_active = TRUE;
|
||||
|
||||
-- Performance analysis
|
||||
SELECT model_name, AVG(confidence), COUNT(*)
|
||||
FROM inference_records
|
||||
WHERE timestamp > datetime('now', '-24 hours')
|
||||
GROUP BY model_name;
|
||||
```
|
||||
|
||||
### 4. Easy Integration
|
||||
|
||||
**In Model Code**:
|
||||
```python
|
||||
# Replace scattered logging
|
||||
# OLD: logger.info(f"DQN prediction: {action} confidence={conf}")
|
||||
|
||||
# NEW: Centralized logging
|
||||
self.orchestrator.log_model_inference(
|
||||
model_name="dqn_agent",
|
||||
symbol=symbol,
|
||||
action=action,
|
||||
confidence=confidence,
|
||||
probabilities=probs,
|
||||
input_features=features,
|
||||
processing_time_ms=processing_time
|
||||
)
|
||||
```
|
||||
|
||||
## Implementation Guide
|
||||
|
||||
### 1. Update Model Classes
|
||||
|
||||
Add inference logging to prediction methods:
|
||||
|
||||
```python
|
||||
class DQNAgent:
|
||||
def predict(self, state):
|
||||
start_time = time.time()
|
||||
|
||||
# Your prediction logic here
|
||||
action = self._predict_action(state)
|
||||
confidence = self._calculate_confidence()
|
||||
|
||||
processing_time = (time.time() - start_time) * 1000
|
||||
|
||||
# Log the inference
|
||||
self.orchestrator.log_model_inference(
|
||||
model_name="dqn_agent",
|
||||
symbol=self.symbol,
|
||||
action=action,
|
||||
confidence=confidence,
|
||||
probabilities=self.action_probabilities,
|
||||
input_features=state,
|
||||
processing_time_ms=processing_time,
|
||||
checkpoint_id=self.current_checkpoint_id
|
||||
)
|
||||
|
||||
return action
|
||||
```
|
||||
|
||||
### 2. Update Checkpoint Saving
|
||||
|
||||
Use the enhanced checkpoint manager:
|
||||
|
||||
```python
|
||||
from utils.checkpoint_manager import save_checkpoint
|
||||
|
||||
# Save with metadata
|
||||
checkpoint_metadata = save_checkpoint(
|
||||
model=self.model,
|
||||
model_name="dqn_agent",
|
||||
model_type="rl",
|
||||
performance_metrics={"loss": 0.0234, "accuracy": 0.87},
|
||||
training_metadata={"epochs": 100, "lr": 0.001}
|
||||
)
|
||||
```
|
||||
|
||||
### 3. Fast Metadata Access
|
||||
|
||||
Get checkpoint info without loading models:
|
||||
|
||||
```python
|
||||
# Fast metadata access
|
||||
metadata = orchestrator.get_checkpoint_metadata_fast("dqn_agent")
|
||||
if metadata:
|
||||
current_loss = metadata.performance_metrics['loss']
|
||||
checkpoint_id = metadata.checkpoint_id
|
||||
```
|
||||
|
||||
## Migration Steps
|
||||
|
||||
1. **Install new dependencies** (if any)
|
||||
2. **Update model classes** to use centralized logging
|
||||
3. **Replace checkpoint loading** with database queries where possible
|
||||
4. **Remove scattered logger.info()** calls for inferences
|
||||
5. **Test with demo script**: `python demo_logging_system.py`
|
||||
|
||||
## File Structure
|
||||
|
||||
```
|
||||
utils/
|
||||
├── database_manager.py # SQLite database management
|
||||
├── inference_logger.py # Centralized inference logging
|
||||
├── text_logger.py # Human-readable text logs
|
||||
└── checkpoint_manager.py # Enhanced checkpoint handling
|
||||
|
||||
logs/ # Text log files
|
||||
├── inference_records.txt
|
||||
├── checkpoint_events.txt
|
||||
└── system_events.txt
|
||||
|
||||
data/
|
||||
└── trading_system.db # SQLite database
|
||||
|
||||
demo_logging_system.py # Demonstration script
|
||||
```
|
||||
|
||||
## Monitoring and Maintenance
|
||||
|
||||
### Daily Tasks
|
||||
- Check `logs/inference_records.txt` for recent activity
|
||||
- Monitor database size: `ls -lh data/trading_system.db`
|
||||
|
||||
### Weekly Tasks
|
||||
- Run cleanup: `inference_logger.cleanup_old_logs(days_to_keep=30)`
|
||||
- Check model performance trends in database
|
||||
|
||||
### Monthly Tasks
|
||||
- Archive old log files
|
||||
- Analyze model performance statistics
|
||||
- Review checkpoint storage usage
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
1. **Database locked**: Multiple processes accessing SQLite
|
||||
- Solution: Use connection timeout and proper context managers
|
||||
|
||||
2. **Log files growing too large**:
|
||||
- Solution: Run `text_logger.cleanup_old_logs(max_lines=10000)`
|
||||
|
||||
3. **Missing checkpoint metadata**:
|
||||
- Solution: System falls back to file-based approach automatically
|
||||
|
||||
### Debug Commands
|
||||
|
||||
```python
|
||||
# Check database status
|
||||
db_manager = get_database_manager()
|
||||
checkpoints = db_manager.list_checkpoints("dqn_agent")
|
||||
|
||||
# Check recent inferences
|
||||
inference_logger = get_inference_logger()
|
||||
stats = inference_logger.get_model_stats("dqn_agent", hours=24)
|
||||
|
||||
# View text logs
|
||||
text_logger = get_text_logger()
|
||||
recent = text_logger.get_recent_inferences(lines=50)
|
||||
```
|
||||
|
||||
This upgrade provides a solid foundation for tracking model performance, eliminating log spam, and enabling fast metadata access without the overhead of loading full model checkpoints.
|
||||
133
fix_cache.py
Normal file
133
fix_cache.py
Normal file
@ -0,0 +1,133 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Cache Fix Script
|
||||
|
||||
Quick script to diagnose and fix cache issues, including the Parquet deserialization error
|
||||
"""
|
||||
|
||||
import sys
|
||||
import logging
|
||||
from utils.cache_manager import get_cache_manager, cleanup_corrupted_cache, get_cache_health
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
def main():
|
||||
"""Main cache fix routine"""
|
||||
print("=== Trading System Cache Fix ===")
|
||||
print()
|
||||
|
||||
# Get cache manager
|
||||
cache_manager = get_cache_manager()
|
||||
|
||||
# 1. Scan cache health
|
||||
print("1. Scanning cache health...")
|
||||
health_summary = get_cache_health()
|
||||
|
||||
print(f"Total files: {health_summary['total_files']}")
|
||||
print(f"Valid files: {health_summary['valid_files']}")
|
||||
print(f"Corrupted files: {health_summary['corrupted_files']}")
|
||||
print(f"Health percentage: {health_summary['health_percentage']:.1f}%")
|
||||
print(f"Total cache size: {health_summary['total_size_mb']:.1f} MB")
|
||||
print()
|
||||
|
||||
# Show detailed report
|
||||
for cache_dir, report in health_summary['directories'].items():
|
||||
if report['total_files'] > 0:
|
||||
print(f"Directory: {cache_dir}")
|
||||
print(f" Files: {report['valid_files']}/{report['total_files']} valid")
|
||||
print(f" Size: {report['total_size_mb']:.1f} MB")
|
||||
|
||||
if report['corrupted_files'] > 0:
|
||||
print(f" CORRUPTED FILES ({report['corrupted_files']}):")
|
||||
for corrupted in report['corrupted_files_list']:
|
||||
print(f" - {corrupted['file']}: {corrupted['error']}")
|
||||
|
||||
if report['old_files']:
|
||||
print(f" OLD FILES ({len(report['old_files'])}):")
|
||||
for old_file in report['old_files'][:3]: # Show first 3
|
||||
print(f" - {old_file['file']}: {old_file['age_days']} days old")
|
||||
if len(report['old_files']) > 3:
|
||||
print(f" ... and {len(report['old_files']) - 3} more")
|
||||
print()
|
||||
|
||||
# 2. Fix corrupted files
|
||||
if health_summary['corrupted_files'] > 0:
|
||||
print("2. Fixing corrupted files...")
|
||||
|
||||
# First show what would be deleted
|
||||
print("Files that will be deleted:")
|
||||
dry_run_result = cleanup_corrupted_cache(dry_run=True)
|
||||
for cache_dir, files in dry_run_result.items():
|
||||
if files:
|
||||
print(f" {cache_dir}:")
|
||||
for file_info in files:
|
||||
print(f" {file_info}")
|
||||
|
||||
# Ask for confirmation
|
||||
response = input("\nProceed with deletion? (y/N): ").strip().lower()
|
||||
if response == 'y':
|
||||
print("Deleting corrupted files...")
|
||||
actual_result = cleanup_corrupted_cache(dry_run=False)
|
||||
|
||||
deleted_count = 0
|
||||
for cache_dir, files in actual_result.items():
|
||||
for file_info in files:
|
||||
if "DELETED:" in file_info:
|
||||
deleted_count += 1
|
||||
|
||||
print(f"Deleted {deleted_count} corrupted files")
|
||||
else:
|
||||
print("Skipped deletion")
|
||||
else:
|
||||
print("2. No corrupted files found - cache is healthy!")
|
||||
|
||||
print()
|
||||
|
||||
# 3. Optional: Clean old files
|
||||
print("3. Checking for old files...")
|
||||
old_files_result = cache_manager.cleanup_old_files(days_to_keep=7, dry_run=True)
|
||||
|
||||
old_file_count = sum(len(files) for files in old_files_result.values())
|
||||
if old_file_count > 0:
|
||||
print(f"Found {old_file_count} old files (>7 days)")
|
||||
response = input("Clean up old files? (y/N): ").strip().lower()
|
||||
if response == 'y':
|
||||
actual_old_result = cache_manager.cleanup_old_files(days_to_keep=7, dry_run=False)
|
||||
deleted_old_count = sum(len([f for f in files if "DELETED:" in f]) for files in actual_old_result.values())
|
||||
print(f"Deleted {deleted_old_count} old files")
|
||||
else:
|
||||
print("Skipped old file cleanup")
|
||||
else:
|
||||
print("No old files found")
|
||||
|
||||
print()
|
||||
print("=== Cache Fix Complete ===")
|
||||
print("The system should now work without Parquet deserialization errors.")
|
||||
print("If you continue to see issues, consider running with --emergency-reset")
|
||||
|
||||
def emergency_reset():
|
||||
"""Emergency cache reset"""
|
||||
print("=== EMERGENCY CACHE RESET ===")
|
||||
print("WARNING: This will delete ALL cache files!")
|
||||
print("You will need to re-download all historical data.")
|
||||
print()
|
||||
|
||||
response = input("Are you sure you want to proceed? Type 'DELETE ALL CACHE' to confirm: ")
|
||||
if response == "DELETE ALL CACHE":
|
||||
cache_manager = get_cache_manager()
|
||||
success = cache_manager.emergency_cache_reset(confirm=True)
|
||||
if success:
|
||||
print("Emergency cache reset completed.")
|
||||
print("All cache files have been deleted.")
|
||||
else:
|
||||
print("Emergency reset failed.")
|
||||
else:
|
||||
print("Emergency reset cancelled.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
if len(sys.argv) > 1 and sys.argv[1] == "--emergency-reset":
|
||||
emergency_reset()
|
||||
else:
|
||||
main()
|
||||
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295
utils/cache_manager.py
Normal file
295
utils/cache_manager.py
Normal file
@ -0,0 +1,295 @@
|
||||
"""
|
||||
Cache Manager for Trading System
|
||||
|
||||
Utilities for managing and cleaning up cache files, including:
|
||||
- Parquet file validation and repair
|
||||
- Cache cleanup and maintenance
|
||||
- Cache health monitoring
|
||||
"""
|
||||
|
||||
import os
|
||||
import logging
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Optional, Tuple
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class CacheManager:
|
||||
"""Manages cache files for the trading system"""
|
||||
|
||||
def __init__(self, cache_dirs: List[str] = None):
|
||||
"""
|
||||
Initialize cache manager
|
||||
|
||||
Args:
|
||||
cache_dirs: List of cache directories to manage
|
||||
"""
|
||||
self.cache_dirs = cache_dirs or [
|
||||
"data/cache",
|
||||
"data/monthly_cache",
|
||||
"data/pivot_cache"
|
||||
]
|
||||
|
||||
# Ensure cache directories exist
|
||||
for cache_dir in self.cache_dirs:
|
||||
Path(cache_dir).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def validate_parquet_file(self, file_path: Path) -> Tuple[bool, Optional[str]]:
|
||||
"""
|
||||
Validate a Parquet file
|
||||
|
||||
Args:
|
||||
file_path: Path to the Parquet file
|
||||
|
||||
Returns:
|
||||
Tuple of (is_valid, error_message)
|
||||
"""
|
||||
try:
|
||||
if not file_path.exists():
|
||||
return False, "File does not exist"
|
||||
|
||||
if file_path.stat().st_size == 0:
|
||||
return False, "File is empty"
|
||||
|
||||
# Try to read the file
|
||||
df = pd.read_parquet(file_path)
|
||||
|
||||
if df.empty:
|
||||
return False, "File contains no data"
|
||||
|
||||
# Check for required columns (basic validation)
|
||||
required_columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume']
|
||||
missing_columns = [col for col in required_columns if col not in df.columns]
|
||||
|
||||
if missing_columns:
|
||||
return False, f"Missing required columns: {missing_columns}"
|
||||
|
||||
return True, None
|
||||
|
||||
except Exception as e:
|
||||
error_str = str(e).lower()
|
||||
corrupted_indicators = [
|
||||
"parquet magic bytes not found",
|
||||
"corrupted",
|
||||
"couldn't deserialize thrift",
|
||||
"don't know what type",
|
||||
"invalid parquet file",
|
||||
"unexpected end of file",
|
||||
"invalid metadata"
|
||||
]
|
||||
|
||||
if any(indicator in error_str for indicator in corrupted_indicators):
|
||||
return False, f"Corrupted Parquet file: {e}"
|
||||
else:
|
||||
return False, f"Validation error: {e}"
|
||||
|
||||
def scan_cache_health(self) -> Dict[str, Dict]:
|
||||
"""
|
||||
Scan all cache directories for file health
|
||||
|
||||
Returns:
|
||||
Dictionary with cache health information
|
||||
"""
|
||||
health_report = {}
|
||||
|
||||
for cache_dir in self.cache_dirs:
|
||||
cache_path = Path(cache_dir)
|
||||
if not cache_path.exists():
|
||||
continue
|
||||
|
||||
dir_report = {
|
||||
'total_files': 0,
|
||||
'valid_files': 0,
|
||||
'corrupted_files': 0,
|
||||
'empty_files': 0,
|
||||
'total_size_mb': 0.0,
|
||||
'corrupted_files_list': [],
|
||||
'old_files': []
|
||||
}
|
||||
|
||||
# Scan all Parquet files
|
||||
for file_path in cache_path.glob("*.parquet"):
|
||||
dir_report['total_files'] += 1
|
||||
file_size_mb = file_path.stat().st_size / (1024 * 1024)
|
||||
dir_report['total_size_mb'] += file_size_mb
|
||||
|
||||
# Check file age
|
||||
file_age = datetime.now() - datetime.fromtimestamp(file_path.stat().st_mtime)
|
||||
if file_age > timedelta(days=7): # Files older than 7 days
|
||||
dir_report['old_files'].append({
|
||||
'file': str(file_path),
|
||||
'age_days': file_age.days,
|
||||
'size_mb': file_size_mb
|
||||
})
|
||||
|
||||
# Validate file
|
||||
is_valid, error_msg = self.validate_parquet_file(file_path)
|
||||
|
||||
if is_valid:
|
||||
dir_report['valid_files'] += 1
|
||||
else:
|
||||
if "empty" in error_msg.lower():
|
||||
dir_report['empty_files'] += 1
|
||||
else:
|
||||
dir_report['corrupted_files'] += 1
|
||||
dir_report['corrupted_files_list'].append({
|
||||
'file': str(file_path),
|
||||
'error': error_msg,
|
||||
'size_mb': file_size_mb
|
||||
})
|
||||
|
||||
health_report[cache_dir] = dir_report
|
||||
|
||||
return health_report
|
||||
|
||||
def cleanup_corrupted_files(self, dry_run: bool = True) -> Dict[str, List[str]]:
|
||||
"""
|
||||
Clean up corrupted cache files
|
||||
|
||||
Args:
|
||||
dry_run: If True, only report what would be deleted
|
||||
|
||||
Returns:
|
||||
Dictionary of deleted files by directory
|
||||
"""
|
||||
deleted_files = {}
|
||||
|
||||
for cache_dir in self.cache_dirs:
|
||||
cache_path = Path(cache_dir)
|
||||
if not cache_path.exists():
|
||||
continue
|
||||
|
||||
deleted_files[cache_dir] = []
|
||||
|
||||
for file_path in cache_path.glob("*.parquet"):
|
||||
is_valid, error_msg = self.validate_parquet_file(file_path)
|
||||
|
||||
if not is_valid:
|
||||
if dry_run:
|
||||
deleted_files[cache_dir].append(f"WOULD DELETE: {file_path} ({error_msg})")
|
||||
logger.info(f"Would delete corrupted file: {file_path} ({error_msg})")
|
||||
else:
|
||||
try:
|
||||
file_path.unlink()
|
||||
deleted_files[cache_dir].append(f"DELETED: {file_path} ({error_msg})")
|
||||
logger.info(f"Deleted corrupted file: {file_path}")
|
||||
except Exception as e:
|
||||
deleted_files[cache_dir].append(f"FAILED TO DELETE: {file_path} ({e})")
|
||||
logger.error(f"Failed to delete corrupted file {file_path}: {e}")
|
||||
|
||||
return deleted_files
|
||||
|
||||
def cleanup_old_files(self, days_to_keep: int = 7, dry_run: bool = True) -> Dict[str, List[str]]:
|
||||
"""
|
||||
Clean up old cache files
|
||||
|
||||
Args:
|
||||
days_to_keep: Number of days to keep files
|
||||
dry_run: If True, only report what would be deleted
|
||||
|
||||
Returns:
|
||||
Dictionary of deleted files by directory
|
||||
"""
|
||||
deleted_files = {}
|
||||
cutoff_date = datetime.now() - timedelta(days=days_to_keep)
|
||||
|
||||
for cache_dir in self.cache_dirs:
|
||||
cache_path = Path(cache_dir)
|
||||
if not cache_path.exists():
|
||||
continue
|
||||
|
||||
deleted_files[cache_dir] = []
|
||||
|
||||
for file_path in cache_path.glob("*.parquet"):
|
||||
file_mtime = datetime.fromtimestamp(file_path.stat().st_mtime)
|
||||
|
||||
if file_mtime < cutoff_date:
|
||||
age_days = (datetime.now() - file_mtime).days
|
||||
|
||||
if dry_run:
|
||||
deleted_files[cache_dir].append(f"WOULD DELETE: {file_path} (age: {age_days} days)")
|
||||
logger.info(f"Would delete old file: {file_path} (age: {age_days} days)")
|
||||
else:
|
||||
try:
|
||||
file_path.unlink()
|
||||
deleted_files[cache_dir].append(f"DELETED: {file_path} (age: {age_days} days)")
|
||||
logger.info(f"Deleted old file: {file_path}")
|
||||
except Exception as e:
|
||||
deleted_files[cache_dir].append(f"FAILED TO DELETE: {file_path} ({e})")
|
||||
logger.error(f"Failed to delete old file {file_path}: {e}")
|
||||
|
||||
return deleted_files
|
||||
|
||||
def get_cache_summary(self) -> Dict[str, any]:
|
||||
"""Get a summary of cache usage"""
|
||||
health_report = self.scan_cache_health()
|
||||
|
||||
total_files = sum(report['total_files'] for report in health_report.values())
|
||||
total_valid = sum(report['valid_files'] for report in health_report.values())
|
||||
total_corrupted = sum(report['corrupted_files'] for report in health_report.values())
|
||||
total_size_mb = sum(report['total_size_mb'] for report in health_report.values())
|
||||
|
||||
return {
|
||||
'total_files': total_files,
|
||||
'valid_files': total_valid,
|
||||
'corrupted_files': total_corrupted,
|
||||
'health_percentage': (total_valid / total_files * 100) if total_files > 0 else 0,
|
||||
'total_size_mb': total_size_mb,
|
||||
'directories': health_report
|
||||
}
|
||||
|
||||
def emergency_cache_reset(self, confirm: bool = False) -> bool:
|
||||
"""
|
||||
Emergency cache reset - deletes all cache files
|
||||
|
||||
Args:
|
||||
confirm: Must be True to actually delete files
|
||||
|
||||
Returns:
|
||||
True if reset was performed
|
||||
"""
|
||||
if not confirm:
|
||||
logger.warning("Emergency cache reset called but not confirmed")
|
||||
return False
|
||||
|
||||
deleted_count = 0
|
||||
|
||||
for cache_dir in self.cache_dirs:
|
||||
cache_path = Path(cache_dir)
|
||||
if not cache_path.exists():
|
||||
continue
|
||||
|
||||
for file_path in cache_path.glob("*"):
|
||||
try:
|
||||
if file_path.is_file():
|
||||
file_path.unlink()
|
||||
deleted_count += 1
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete {file_path}: {e}")
|
||||
|
||||
logger.warning(f"Emergency cache reset completed: deleted {deleted_count} files")
|
||||
return True
|
||||
|
||||
# Global cache manager instance
|
||||
_cache_manager_instance = None
|
||||
|
||||
def get_cache_manager() -> CacheManager:
|
||||
"""Get the global cache manager instance"""
|
||||
global _cache_manager_instance
|
||||
|
||||
if _cache_manager_instance is None:
|
||||
_cache_manager_instance = CacheManager()
|
||||
|
||||
return _cache_manager_instance
|
||||
|
||||
def cleanup_corrupted_cache(dry_run: bool = True) -> Dict[str, List[str]]:
|
||||
"""Convenience function to clean up corrupted cache files"""
|
||||
cache_manager = get_cache_manager()
|
||||
return cache_manager.cleanup_corrupted_files(dry_run=dry_run)
|
||||
|
||||
def get_cache_health() -> Dict[str, any]:
|
||||
"""Convenience function to get cache health summary"""
|
||||
cache_manager = get_cache_manager()
|
||||
return cache_manager.get_cache_summary()
|
||||
@ -5,6 +5,7 @@ This module provides functionality for managing model checkpoints, including:
|
||||
- Saving checkpoints with metadata
|
||||
- Loading the best checkpoint based on performance metrics
|
||||
- Cleaning up old or underperforming checkpoints
|
||||
- Database-backed metadata storage for efficient access
|
||||
"""
|
||||
|
||||
import os
|
||||
@ -13,9 +14,13 @@ import glob
|
||||
import logging
|
||||
import shutil
|
||||
import torch
|
||||
import hashlib
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Optional, Any, Tuple
|
||||
|
||||
from .database_manager import get_database_manager, CheckpointMetadata
|
||||
from .text_logger import get_text_logger
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Global checkpoint manager instance
|
||||
@ -46,7 +51,7 @@ def get_checkpoint_manager(checkpoint_dir: str = "models/checkpoints", max_check
|
||||
|
||||
def save_checkpoint(model, model_name: str, model_type: str, performance_metrics: Dict[str, float], training_metadata: Dict[str, Any] = None, checkpoint_dir: str = "models/checkpoints") -> Any:
|
||||
"""
|
||||
Save a checkpoint with metadata
|
||||
Save a checkpoint with metadata to both filesystem and database
|
||||
|
||||
Args:
|
||||
model: The model to save
|
||||
@ -64,57 +69,90 @@ def save_checkpoint(model, model_name: str, model_type: str, performance_metrics
|
||||
os.makedirs(checkpoint_dir, exist_ok=True)
|
||||
|
||||
# Create timestamp
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
timestamp = datetime.now()
|
||||
timestamp_str = timestamp.strftime("%Y%m%d_%H%M%S")
|
||||
|
||||
# Create checkpoint path
|
||||
model_dir = os.path.join(checkpoint_dir, model_name)
|
||||
os.makedirs(model_dir, exist_ok=True)
|
||||
checkpoint_path = os.path.join(model_dir, f"{model_name}_{timestamp}")
|
||||
checkpoint_path = os.path.join(model_dir, f"{model_name}_{timestamp_str}")
|
||||
checkpoint_id = f"{model_name}_{timestamp_str}"
|
||||
|
||||
# Save model
|
||||
torch_path = f"{checkpoint_path}.pt"
|
||||
if hasattr(model, 'save'):
|
||||
# Use model's save method if available
|
||||
model.save(checkpoint_path)
|
||||
else:
|
||||
# Otherwise, save state_dict
|
||||
torch_path = f"{checkpoint_path}.pt"
|
||||
torch.save({
|
||||
'model_state_dict': model.state_dict() if hasattr(model, 'state_dict') else None,
|
||||
'model_name': model_name,
|
||||
'model_type': model_type,
|
||||
'timestamp': timestamp
|
||||
'timestamp': timestamp_str,
|
||||
'checkpoint_id': checkpoint_id
|
||||
}, torch_path)
|
||||
|
||||
# Create metadata
|
||||
checkpoint_metadata = {
|
||||
# Calculate file size
|
||||
file_size_mb = os.path.getsize(torch_path) / (1024 * 1024) if os.path.exists(torch_path) else 0.0
|
||||
|
||||
# Save metadata to database
|
||||
db_manager = get_database_manager()
|
||||
checkpoint_metadata = CheckpointMetadata(
|
||||
checkpoint_id=checkpoint_id,
|
||||
model_name=model_name,
|
||||
model_type=model_type,
|
||||
timestamp=timestamp,
|
||||
performance_metrics=performance_metrics,
|
||||
training_metadata=training_metadata or {},
|
||||
file_path=torch_path,
|
||||
file_size_mb=file_size_mb,
|
||||
is_active=True # New checkpoint is active by default
|
||||
)
|
||||
|
||||
# Save to database
|
||||
if db_manager.save_checkpoint_metadata(checkpoint_metadata):
|
||||
# Log checkpoint save event to text file
|
||||
text_logger = get_text_logger()
|
||||
text_logger.log_checkpoint_event(
|
||||
model_name=model_name,
|
||||
event_type="SAVED",
|
||||
checkpoint_id=checkpoint_id,
|
||||
details=f"loss={performance_metrics.get('loss', 'N/A')}, size={file_size_mb:.1f}MB"
|
||||
)
|
||||
logger.info(f"Checkpoint saved: {checkpoint_id}")
|
||||
else:
|
||||
logger.warning(f"Failed to save checkpoint metadata to database: {checkpoint_id}")
|
||||
|
||||
# Also save legacy JSON metadata for backward compatibility
|
||||
legacy_metadata = {
|
||||
'model_name': model_name,
|
||||
'model_type': model_type,
|
||||
'timestamp': timestamp,
|
||||
'timestamp': timestamp_str,
|
||||
'performance_metrics': performance_metrics,
|
||||
'training_metadata': training_metadata or {},
|
||||
'checkpoint_id': f"{model_name}_{timestamp}"
|
||||
'checkpoint_id': checkpoint_id,
|
||||
'performance_score': performance_metrics.get('accuracy', performance_metrics.get('reward', 0.0)),
|
||||
'created_at': timestamp_str
|
||||
}
|
||||
|
||||
# Add performance score for sorting
|
||||
primary_metric = 'accuracy' if 'accuracy' in performance_metrics else 'reward'
|
||||
checkpoint_metadata['performance_score'] = performance_metrics.get(primary_metric, 0.0)
|
||||
checkpoint_metadata['created_at'] = timestamp
|
||||
|
||||
# Save metadata
|
||||
with open(f"{checkpoint_path}_metadata.json", 'w') as f:
|
||||
json.dump(checkpoint_metadata, f, indent=2)
|
||||
json.dump(legacy_metadata, f, indent=2)
|
||||
|
||||
# Get checkpoint manager and clean up old checkpoints
|
||||
checkpoint_manager = get_checkpoint_manager(checkpoint_dir=checkpoint_dir)
|
||||
checkpoint_manager._cleanup_checkpoints(model_name)
|
||||
|
||||
# Return metadata as an object
|
||||
class CheckpointMetadata:
|
||||
# Return metadata as an object for backward compatibility
|
||||
class CheckpointMetadataObj:
|
||||
def __init__(self, metadata):
|
||||
for key, value in metadata.items():
|
||||
setattr(self, key, value)
|
||||
# Add database fields
|
||||
self.checkpoint_id = checkpoint_id
|
||||
self.loss = performance_metrics.get('loss', performance_metrics.get('accuracy', 0.0))
|
||||
|
||||
return CheckpointMetadata(checkpoint_metadata)
|
||||
return CheckpointMetadataObj(legacy_metadata)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving checkpoint: {e}")
|
||||
@ -122,7 +160,7 @@ def save_checkpoint(model, model_name: str, model_type: str, performance_metrics
|
||||
|
||||
def load_best_checkpoint(model_name: str, checkpoint_dir: str = "models/checkpoints") -> Optional[Tuple[str, Any]]:
|
||||
"""
|
||||
Load the best checkpoint based on performance metrics
|
||||
Load the best checkpoint based on performance metrics using database metadata
|
||||
|
||||
Args:
|
||||
model_name: Name of the model
|
||||
@ -132,29 +170,77 @@ def load_best_checkpoint(model_name: str, checkpoint_dir: str = "models/checkpoi
|
||||
Optional[Tuple[str, Any]]: Path to the best checkpoint and its metadata, or None if not found
|
||||
"""
|
||||
try:
|
||||
checkpoint_manager = get_checkpoint_manager(checkpoint_dir=checkpoint_dir)
|
||||
checkpoint_path, checkpoint_metadata = checkpoint_manager.load_best_checkpoint(model_name)
|
||||
# First try to get from database (fast metadata access)
|
||||
db_manager = get_database_manager()
|
||||
checkpoint_metadata = db_manager.get_best_checkpoint_metadata(model_name, "accuracy")
|
||||
|
||||
if not checkpoint_path:
|
||||
if not checkpoint_metadata:
|
||||
# Fallback to legacy file-based approach (no more scattered "No checkpoints found" logs)
|
||||
pass # Silent fallback
|
||||
checkpoint_manager = get_checkpoint_manager(checkpoint_dir=checkpoint_dir)
|
||||
checkpoint_path, legacy_metadata = checkpoint_manager.load_best_checkpoint(model_name)
|
||||
|
||||
if not checkpoint_path:
|
||||
return None
|
||||
|
||||
# Convert legacy metadata to object
|
||||
class CheckpointMetadataObj:
|
||||
def __init__(self, metadata):
|
||||
for key, value in metadata.items():
|
||||
setattr(self, key, value)
|
||||
|
||||
# Add performance score if not present
|
||||
if not hasattr(self, 'performance_score'):
|
||||
metrics = getattr(self, 'metrics', {})
|
||||
primary_metric = 'accuracy' if 'accuracy' in metrics else 'reward'
|
||||
self.performance_score = metrics.get(primary_metric, 0.0)
|
||||
|
||||
# Add created_at if not present
|
||||
if not hasattr(self, 'created_at'):
|
||||
self.created_at = getattr(self, 'timestamp', 'unknown')
|
||||
|
||||
# Add loss for compatibility
|
||||
self.loss = metrics.get('loss', self.performance_score)
|
||||
self.checkpoint_id = getattr(self, 'checkpoint_id', f"{model_name}_unknown")
|
||||
|
||||
return f"{checkpoint_path}.pt", CheckpointMetadataObj(legacy_metadata)
|
||||
|
||||
# Check if checkpoint file exists
|
||||
if not os.path.exists(checkpoint_metadata.file_path):
|
||||
logger.warning(f"Checkpoint file not found: {checkpoint_metadata.file_path}")
|
||||
return None
|
||||
|
||||
# Convert metadata to object
|
||||
class CheckpointMetadata:
|
||||
def __init__(self, metadata):
|
||||
for key, value in metadata.items():
|
||||
setattr(self, key, value)
|
||||
|
||||
# Add performance score if not present
|
||||
if not hasattr(self, 'performance_score'):
|
||||
metrics = getattr(self, 'metrics', {})
|
||||
primary_metric = 'accuracy' if 'accuracy' in metrics else 'reward'
|
||||
self.performance_score = metrics.get(primary_metric, 0.0)
|
||||
|
||||
# Add created_at if not present
|
||||
if not hasattr(self, 'created_at'):
|
||||
self.created_at = getattr(self, 'timestamp', 'unknown')
|
||||
# Log checkpoint load event to text file
|
||||
text_logger = get_text_logger()
|
||||
text_logger.log_checkpoint_event(
|
||||
model_name=model_name,
|
||||
event_type="LOADED",
|
||||
checkpoint_id=checkpoint_metadata.checkpoint_id,
|
||||
details=f"loss={checkpoint_metadata.performance_metrics.get('loss', 'N/A')}"
|
||||
)
|
||||
|
||||
return f"{checkpoint_path}.pt", CheckpointMetadata(checkpoint_metadata)
|
||||
# Convert database metadata to object for backward compatibility
|
||||
class CheckpointMetadataObj:
|
||||
def __init__(self, db_metadata: CheckpointMetadata):
|
||||
self.checkpoint_id = db_metadata.checkpoint_id
|
||||
self.model_name = db_metadata.model_name
|
||||
self.model_type = db_metadata.model_type
|
||||
self.timestamp = db_metadata.timestamp.strftime("%Y%m%d_%H%M%S")
|
||||
self.performance_metrics = db_metadata.performance_metrics
|
||||
self.training_metadata = db_metadata.training_metadata
|
||||
self.file_path = db_metadata.file_path
|
||||
self.file_size_mb = db_metadata.file_size_mb
|
||||
self.is_active = db_metadata.is_active
|
||||
|
||||
# Backward compatibility fields
|
||||
self.metrics = db_metadata.performance_metrics
|
||||
self.metadata = db_metadata.training_metadata
|
||||
self.created_at = self.timestamp
|
||||
self.performance_score = db_metadata.performance_metrics.get('accuracy',
|
||||
db_metadata.performance_metrics.get('reward', 0.0))
|
||||
self.loss = db_metadata.performance_metrics.get('loss', self.performance_score)
|
||||
|
||||
return checkpoint_metadata.file_path, CheckpointMetadataObj(checkpoint_metadata)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error loading best checkpoint: {e}")
|
||||
@ -254,7 +340,7 @@ class CheckpointManager:
|
||||
metadata_files = glob.glob(os.path.join(checkpoint_dir, f"{model_name}_*_metadata.json"))
|
||||
|
||||
if not metadata_files:
|
||||
logger.info(f"No checkpoints found for {model_name}")
|
||||
# No more scattered "No checkpoints found" logs - handled by database system
|
||||
return "", {}
|
||||
|
||||
# Load metadata for each checkpoint
|
||||
@ -278,7 +364,7 @@ class CheckpointManager:
|
||||
logger.error(f"Error loading checkpoint metadata {metadata_file}: {e}")
|
||||
|
||||
if not checkpoints:
|
||||
logger.info(f"No valid checkpoints found for {model_name}")
|
||||
# No more scattered logs - handled by database system
|
||||
return "", {}
|
||||
|
||||
# Sort by metric (highest first)
|
||||
|
||||
408
utils/database_manager.py
Normal file
408
utils/database_manager.py
Normal file
@ -0,0 +1,408 @@
|
||||
"""
|
||||
Database Manager for Trading System
|
||||
|
||||
Manages SQLite database for:
|
||||
1. Inference records logging
|
||||
2. Checkpoint metadata storage
|
||||
3. Model performance tracking
|
||||
"""
|
||||
|
||||
import sqlite3
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from datetime import datetime
|
||||
from typing import Dict, List, Optional, Any, Tuple
|
||||
from contextlib import contextmanager
|
||||
from dataclasses import dataclass, asdict
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@dataclass
|
||||
class InferenceRecord:
|
||||
"""Structure for inference logging"""
|
||||
model_name: str
|
||||
timestamp: datetime
|
||||
symbol: str
|
||||
action: str
|
||||
confidence: float
|
||||
probabilities: Dict[str, float]
|
||||
input_features_hash: str # Hash of input features for deduplication
|
||||
processing_time_ms: float
|
||||
memory_usage_mb: float
|
||||
checkpoint_id: Optional[str] = None
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
|
||||
@dataclass
|
||||
class CheckpointMetadata:
|
||||
"""Structure for checkpoint metadata"""
|
||||
checkpoint_id: str
|
||||
model_name: str
|
||||
model_type: str
|
||||
timestamp: datetime
|
||||
performance_metrics: Dict[str, float]
|
||||
training_metadata: Dict[str, Any]
|
||||
file_path: str
|
||||
file_size_mb: float
|
||||
is_active: bool = False # Currently loaded checkpoint
|
||||
|
||||
class DatabaseManager:
|
||||
"""Manages SQLite database for trading system logging and metadata"""
|
||||
|
||||
def __init__(self, db_path: str = "data/trading_system.db"):
|
||||
self.db_path = db_path
|
||||
self._ensure_db_directory()
|
||||
self._initialize_database()
|
||||
|
||||
def _ensure_db_directory(self):
|
||||
"""Ensure database directory exists"""
|
||||
os.makedirs(os.path.dirname(self.db_path), exist_ok=True)
|
||||
|
||||
def _initialize_database(self):
|
||||
"""Initialize database tables"""
|
||||
with self._get_connection() as conn:
|
||||
# Inference records table
|
||||
conn.execute("""
|
||||
CREATE TABLE IF NOT EXISTS inference_records (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
model_name TEXT NOT NULL,
|
||||
timestamp TEXT NOT NULL,
|
||||
symbol TEXT NOT NULL,
|
||||
action TEXT NOT NULL,
|
||||
confidence REAL NOT NULL,
|
||||
probabilities TEXT NOT NULL, -- JSON
|
||||
input_features_hash TEXT NOT NULL,
|
||||
processing_time_ms REAL NOT NULL,
|
||||
memory_usage_mb REAL NOT NULL,
|
||||
checkpoint_id TEXT,
|
||||
metadata TEXT, -- JSON
|
||||
created_at TEXT DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
|
||||
# Checkpoint metadata table
|
||||
conn.execute("""
|
||||
CREATE TABLE IF NOT EXISTS checkpoint_metadata (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
checkpoint_id TEXT UNIQUE NOT NULL,
|
||||
model_name TEXT NOT NULL,
|
||||
model_type TEXT NOT NULL,
|
||||
timestamp TEXT NOT NULL,
|
||||
performance_metrics TEXT NOT NULL, -- JSON
|
||||
training_metadata TEXT NOT NULL, -- JSON
|
||||
file_path TEXT NOT NULL,
|
||||
file_size_mb REAL NOT NULL,
|
||||
is_active BOOLEAN DEFAULT FALSE,
|
||||
created_at TEXT DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
|
||||
# Model performance tracking table
|
||||
conn.execute("""
|
||||
CREATE TABLE IF NOT EXISTS model_performance (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
model_name TEXT NOT NULL,
|
||||
date TEXT NOT NULL,
|
||||
total_predictions INTEGER DEFAULT 0,
|
||||
correct_predictions INTEGER DEFAULT 0,
|
||||
accuracy REAL DEFAULT 0.0,
|
||||
avg_confidence REAL DEFAULT 0.0,
|
||||
avg_processing_time_ms REAL DEFAULT 0.0,
|
||||
created_at TEXT DEFAULT CURRENT_TIMESTAMP,
|
||||
UNIQUE(model_name, date)
|
||||
)
|
||||
""")
|
||||
|
||||
# Create indexes for better performance
|
||||
conn.execute("CREATE INDEX IF NOT EXISTS idx_inference_model_timestamp ON inference_records(model_name, timestamp)")
|
||||
conn.execute("CREATE INDEX IF NOT EXISTS idx_inference_symbol ON inference_records(symbol)")
|
||||
conn.execute("CREATE INDEX IF NOT EXISTS idx_checkpoint_model ON checkpoint_metadata(model_name)")
|
||||
conn.execute("CREATE INDEX IF NOT EXISTS idx_checkpoint_active ON checkpoint_metadata(is_active)")
|
||||
|
||||
logger.info(f"Database initialized at {self.db_path}")
|
||||
|
||||
@contextmanager
|
||||
def _get_connection(self):
|
||||
"""Get database connection with proper error handling"""
|
||||
conn = None
|
||||
try:
|
||||
conn = sqlite3.connect(self.db_path, timeout=30.0)
|
||||
conn.row_factory = sqlite3.Row # Enable dict-like access
|
||||
yield conn
|
||||
except Exception as e:
|
||||
if conn:
|
||||
conn.rollback()
|
||||
logger.error(f"Database error: {e}")
|
||||
raise
|
||||
finally:
|
||||
if conn:
|
||||
conn.close()
|
||||
|
||||
def log_inference(self, record: InferenceRecord) -> bool:
|
||||
"""Log an inference record"""
|
||||
try:
|
||||
with self._get_connection() as conn:
|
||||
conn.execute("""
|
||||
INSERT INTO inference_records (
|
||||
model_name, timestamp, symbol, action, confidence,
|
||||
probabilities, input_features_hash, processing_time_ms,
|
||||
memory_usage_mb, checkpoint_id, metadata
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
record.model_name,
|
||||
record.timestamp.isoformat(),
|
||||
record.symbol,
|
||||
record.action,
|
||||
record.confidence,
|
||||
json.dumps(record.probabilities),
|
||||
record.input_features_hash,
|
||||
record.processing_time_ms,
|
||||
record.memory_usage_mb,
|
||||
record.checkpoint_id,
|
||||
json.dumps(record.metadata) if record.metadata else None
|
||||
))
|
||||
conn.commit()
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to log inference record: {e}")
|
||||
return False
|
||||
|
||||
def save_checkpoint_metadata(self, metadata: CheckpointMetadata) -> bool:
|
||||
"""Save checkpoint metadata"""
|
||||
try:
|
||||
with self._get_connection() as conn:
|
||||
# First, set all other checkpoints for this model as inactive
|
||||
conn.execute("""
|
||||
UPDATE checkpoint_metadata
|
||||
SET is_active = FALSE
|
||||
WHERE model_name = ?
|
||||
""", (metadata.model_name,))
|
||||
|
||||
# Insert or replace the new checkpoint metadata
|
||||
conn.execute("""
|
||||
INSERT OR REPLACE INTO checkpoint_metadata (
|
||||
checkpoint_id, model_name, model_type, timestamp,
|
||||
performance_metrics, training_metadata, file_path,
|
||||
file_size_mb, is_active
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
metadata.checkpoint_id,
|
||||
metadata.model_name,
|
||||
metadata.model_type,
|
||||
metadata.timestamp.isoformat(),
|
||||
json.dumps(metadata.performance_metrics),
|
||||
json.dumps(metadata.training_metadata),
|
||||
metadata.file_path,
|
||||
metadata.file_size_mb,
|
||||
metadata.is_active
|
||||
))
|
||||
conn.commit()
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to save checkpoint metadata: {e}")
|
||||
return False
|
||||
|
||||
def get_checkpoint_metadata(self, model_name: str, checkpoint_id: str = None) -> Optional[CheckpointMetadata]:
|
||||
"""Get checkpoint metadata without loading the actual model"""
|
||||
try:
|
||||
with self._get_connection() as conn:
|
||||
if checkpoint_id:
|
||||
# Get specific checkpoint
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM checkpoint_metadata
|
||||
WHERE model_name = ? AND checkpoint_id = ?
|
||||
""", (model_name, checkpoint_id))
|
||||
else:
|
||||
# Get active checkpoint
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM checkpoint_metadata
|
||||
WHERE model_name = ? AND is_active = TRUE
|
||||
ORDER BY timestamp DESC LIMIT 1
|
||||
""", (model_name,))
|
||||
|
||||
row = cursor.fetchone()
|
||||
if row:
|
||||
return CheckpointMetadata(
|
||||
checkpoint_id=row['checkpoint_id'],
|
||||
model_name=row['model_name'],
|
||||
model_type=row['model_type'],
|
||||
timestamp=datetime.fromisoformat(row['timestamp']),
|
||||
performance_metrics=json.loads(row['performance_metrics']),
|
||||
training_metadata=json.loads(row['training_metadata']),
|
||||
file_path=row['file_path'],
|
||||
file_size_mb=row['file_size_mb'],
|
||||
is_active=bool(row['is_active'])
|
||||
)
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get checkpoint metadata: {e}")
|
||||
return None
|
||||
|
||||
def get_best_checkpoint_metadata(self, model_name: str, metric_name: str = "accuracy") -> Optional[CheckpointMetadata]:
|
||||
"""Get best checkpoint metadata based on performance metric"""
|
||||
try:
|
||||
with self._get_connection() as conn:
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM checkpoint_metadata
|
||||
WHERE model_name = ?
|
||||
ORDER BY json_extract(performance_metrics, '$.' || ?) DESC
|
||||
LIMIT 1
|
||||
""", (model_name, metric_name))
|
||||
|
||||
row = cursor.fetchone()
|
||||
if row:
|
||||
return CheckpointMetadata(
|
||||
checkpoint_id=row['checkpoint_id'],
|
||||
model_name=row['model_name'],
|
||||
model_type=row['model_type'],
|
||||
timestamp=datetime.fromisoformat(row['timestamp']),
|
||||
performance_metrics=json.loads(row['performance_metrics']),
|
||||
training_metadata=json.loads(row['training_metadata']),
|
||||
file_path=row['file_path'],
|
||||
file_size_mb=row['file_size_mb'],
|
||||
is_active=bool(row['is_active'])
|
||||
)
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get best checkpoint metadata: {e}")
|
||||
return None
|
||||
|
||||
def list_checkpoints(self, model_name: str) -> List[CheckpointMetadata]:
|
||||
"""List all checkpoints for a model"""
|
||||
try:
|
||||
with self._get_connection() as conn:
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM checkpoint_metadata
|
||||
WHERE model_name = ?
|
||||
ORDER BY timestamp DESC
|
||||
""", (model_name,))
|
||||
|
||||
checkpoints = []
|
||||
for row in cursor.fetchall():
|
||||
checkpoints.append(CheckpointMetadata(
|
||||
checkpoint_id=row['checkpoint_id'],
|
||||
model_name=row['model_name'],
|
||||
model_type=row['model_type'],
|
||||
timestamp=datetime.fromisoformat(row['timestamp']),
|
||||
performance_metrics=json.loads(row['performance_metrics']),
|
||||
training_metadata=json.loads(row['training_metadata']),
|
||||
file_path=row['file_path'],
|
||||
file_size_mb=row['file_size_mb'],
|
||||
is_active=bool(row['is_active'])
|
||||
))
|
||||
return checkpoints
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to list checkpoints: {e}")
|
||||
return []
|
||||
|
||||
def set_active_checkpoint(self, model_name: str, checkpoint_id: str) -> bool:
|
||||
"""Set a checkpoint as active for a model"""
|
||||
try:
|
||||
with self._get_connection() as conn:
|
||||
# First, set all checkpoints for this model as inactive
|
||||
conn.execute("""
|
||||
UPDATE checkpoint_metadata
|
||||
SET is_active = FALSE
|
||||
WHERE model_name = ?
|
||||
""", (model_name,))
|
||||
|
||||
# Set the specified checkpoint as active
|
||||
cursor = conn.execute("""
|
||||
UPDATE checkpoint_metadata
|
||||
SET is_active = TRUE
|
||||
WHERE model_name = ? AND checkpoint_id = ?
|
||||
""", (model_name, checkpoint_id))
|
||||
|
||||
conn.commit()
|
||||
return cursor.rowcount > 0
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to set active checkpoint: {e}")
|
||||
return False
|
||||
|
||||
def get_recent_inferences(self, model_name: str, limit: int = 100) -> List[InferenceRecord]:
|
||||
"""Get recent inference records for a model"""
|
||||
try:
|
||||
with self._get_connection() as conn:
|
||||
cursor = conn.execute("""
|
||||
SELECT * FROM inference_records
|
||||
WHERE model_name = ?
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT ?
|
||||
""", (model_name, limit))
|
||||
|
||||
records = []
|
||||
for row in cursor.fetchall():
|
||||
records.append(InferenceRecord(
|
||||
model_name=row['model_name'],
|
||||
timestamp=datetime.fromisoformat(row['timestamp']),
|
||||
symbol=row['symbol'],
|
||||
action=row['action'],
|
||||
confidence=row['confidence'],
|
||||
probabilities=json.loads(row['probabilities']),
|
||||
input_features_hash=row['input_features_hash'],
|
||||
processing_time_ms=row['processing_time_ms'],
|
||||
memory_usage_mb=row['memory_usage_mb'],
|
||||
checkpoint_id=row['checkpoint_id'],
|
||||
metadata=json.loads(row['metadata']) if row['metadata'] else None
|
||||
))
|
||||
return records
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get recent inferences: {e}")
|
||||
return []
|
||||
|
||||
def update_model_performance(self, model_name: str, date: str,
|
||||
total_predictions: int, correct_predictions: int,
|
||||
avg_confidence: float, avg_processing_time: float) -> bool:
|
||||
"""Update daily model performance statistics"""
|
||||
try:
|
||||
accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0.0
|
||||
|
||||
with self._get_connection() as conn:
|
||||
conn.execute("""
|
||||
INSERT OR REPLACE INTO model_performance (
|
||||
model_name, date, total_predictions, correct_predictions,
|
||||
accuracy, avg_confidence, avg_processing_time_ms
|
||||
) VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
model_name, date, total_predictions, correct_predictions,
|
||||
accuracy, avg_confidence, avg_processing_time
|
||||
))
|
||||
conn.commit()
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update model performance: {e}")
|
||||
return False
|
||||
|
||||
def cleanup_old_records(self, days_to_keep: int = 30) -> bool:
|
||||
"""Clean up old inference records"""
|
||||
try:
|
||||
cutoff_date = datetime.now() - timedelta(days=days_to_keep)
|
||||
|
||||
with self._get_connection() as conn:
|
||||
cursor = conn.execute("""
|
||||
DELETE FROM inference_records
|
||||
WHERE timestamp < ?
|
||||
""", (cutoff_date.isoformat(),))
|
||||
|
||||
deleted_count = cursor.rowcount
|
||||
conn.commit()
|
||||
|
||||
if deleted_count > 0:
|
||||
logger.info(f"Cleaned up {deleted_count} old inference records")
|
||||
|
||||
return True
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to cleanup old records: {e}")
|
||||
return False
|
||||
|
||||
# Global database manager instance
|
||||
_db_manager_instance = None
|
||||
|
||||
def get_database_manager(db_path: str = "data/trading_system.db") -> DatabaseManager:
|
||||
"""Get the global database manager instance"""
|
||||
global _db_manager_instance
|
||||
|
||||
if _db_manager_instance is None:
|
||||
_db_manager_instance = DatabaseManager(db_path)
|
||||
|
||||
return _db_manager_instance
|
||||
226
utils/inference_logger.py
Normal file
226
utils/inference_logger.py
Normal file
@ -0,0 +1,226 @@
|
||||
"""
|
||||
Inference Logger
|
||||
|
||||
Centralized logging system for model inferences with database storage
|
||||
Eliminates scattered logging throughout the codebase
|
||||
"""
|
||||
|
||||
import time
|
||||
import hashlib
|
||||
import logging
|
||||
import psutil
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, List, Optional, Any, Union
|
||||
from dataclasses import dataclass
|
||||
import numpy as np
|
||||
|
||||
from .database_manager import get_database_manager, InferenceRecord
|
||||
from .text_logger import get_text_logger
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class InferenceLogger:
|
||||
"""Centralized inference logging system"""
|
||||
|
||||
def __init__(self):
|
||||
self.db_manager = get_database_manager()
|
||||
self.text_logger = get_text_logger()
|
||||
self._process = psutil.Process()
|
||||
|
||||
def log_inference(self,
|
||||
model_name: str,
|
||||
symbol: str,
|
||||
action: str,
|
||||
confidence: float,
|
||||
probabilities: Dict[str, float],
|
||||
input_features: Union[np.ndarray, Dict, List],
|
||||
processing_time_ms: float,
|
||||
checkpoint_id: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None) -> bool:
|
||||
"""
|
||||
Log a model inference with all relevant details
|
||||
|
||||
Args:
|
||||
model_name: Name of the model making the prediction
|
||||
symbol: Trading symbol
|
||||
action: Predicted action (BUY/SELL/HOLD)
|
||||
confidence: Confidence score (0.0 to 1.0)
|
||||
probabilities: Action probabilities dict
|
||||
input_features: Input features used for prediction
|
||||
processing_time_ms: Time taken for inference in milliseconds
|
||||
checkpoint_id: ID of the checkpoint used
|
||||
metadata: Additional metadata
|
||||
|
||||
Returns:
|
||||
bool: True if logged successfully
|
||||
"""
|
||||
try:
|
||||
# Create feature hash for deduplication
|
||||
feature_hash = self._hash_features(input_features)
|
||||
|
||||
# Get current memory usage
|
||||
memory_usage_mb = self._get_memory_usage()
|
||||
|
||||
# Create inference record
|
||||
record = InferenceRecord(
|
||||
model_name=model_name,
|
||||
timestamp=datetime.now(),
|
||||
symbol=symbol,
|
||||
action=action,
|
||||
confidence=confidence,
|
||||
probabilities=probabilities,
|
||||
input_features_hash=feature_hash,
|
||||
processing_time_ms=processing_time_ms,
|
||||
memory_usage_mb=memory_usage_mb,
|
||||
checkpoint_id=checkpoint_id,
|
||||
metadata=metadata
|
||||
)
|
||||
|
||||
# Log to database
|
||||
db_success = self.db_manager.log_inference(record)
|
||||
|
||||
# Log to text file
|
||||
text_success = self.text_logger.log_inference(
|
||||
model_name=model_name,
|
||||
symbol=symbol,
|
||||
action=action,
|
||||
confidence=confidence,
|
||||
processing_time_ms=processing_time_ms,
|
||||
checkpoint_id=checkpoint_id
|
||||
)
|
||||
|
||||
if db_success:
|
||||
# Reduced logging - no more scattered logs at runtime
|
||||
pass # Database logging successful, text file provides human-readable record
|
||||
else:
|
||||
logger.error(f"Failed to log inference for {model_name}")
|
||||
|
||||
return db_success and text_success
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error logging inference: {e}")
|
||||
return False
|
||||
|
||||
def _hash_features(self, features: Union[np.ndarray, Dict, List]) -> str:
|
||||
"""Create a hash of input features for deduplication"""
|
||||
try:
|
||||
if isinstance(features, np.ndarray):
|
||||
# Hash numpy array
|
||||
return hashlib.md5(features.tobytes()).hexdigest()[:16]
|
||||
elif isinstance(features, (dict, list)):
|
||||
# Hash dict or list by converting to string
|
||||
feature_str = str(sorted(features.items()) if isinstance(features, dict) else features)
|
||||
return hashlib.md5(feature_str.encode()).hexdigest()[:16]
|
||||
else:
|
||||
# Hash string representation
|
||||
return hashlib.md5(str(features).encode()).hexdigest()[:16]
|
||||
except Exception:
|
||||
# Fallback to timestamp-based hash
|
||||
return hashlib.md5(str(time.time()).encode()).hexdigest()[:16]
|
||||
|
||||
def _get_memory_usage(self) -> float:
|
||||
"""Get current memory usage in MB"""
|
||||
try:
|
||||
return self._process.memory_info().rss / (1024 * 1024)
|
||||
except Exception:
|
||||
return 0.0
|
||||
|
||||
def get_model_stats(self, model_name: str, hours: int = 24) -> Dict[str, Any]:
|
||||
"""Get inference statistics for a model"""
|
||||
try:
|
||||
# Get recent inferences
|
||||
recent_inferences = self.db_manager.get_recent_inferences(model_name, limit=1000)
|
||||
|
||||
if not recent_inferences:
|
||||
return {
|
||||
'total_inferences': 0,
|
||||
'avg_confidence': 0.0,
|
||||
'avg_processing_time_ms': 0.0,
|
||||
'action_distribution': {},
|
||||
'symbol_distribution': {}
|
||||
}
|
||||
|
||||
# Filter by time window
|
||||
cutoff_time = datetime.now() - timedelta(hours=hours)
|
||||
recent_inferences = [r for r in recent_inferences if r.timestamp >= cutoff_time]
|
||||
|
||||
if not recent_inferences:
|
||||
return {
|
||||
'total_inferences': 0,
|
||||
'avg_confidence': 0.0,
|
||||
'avg_processing_time_ms': 0.0,
|
||||
'action_distribution': {},
|
||||
'symbol_distribution': {}
|
||||
}
|
||||
|
||||
# Calculate statistics
|
||||
total_inferences = len(recent_inferences)
|
||||
avg_confidence = sum(r.confidence for r in recent_inferences) / total_inferences
|
||||
avg_processing_time = sum(r.processing_time_ms for r in recent_inferences) / total_inferences
|
||||
|
||||
# Action distribution
|
||||
action_counts = {}
|
||||
for record in recent_inferences:
|
||||
action_counts[record.action] = action_counts.get(record.action, 0) + 1
|
||||
|
||||
# Symbol distribution
|
||||
symbol_counts = {}
|
||||
for record in recent_inferences:
|
||||
symbol_counts[record.symbol] = symbol_counts.get(record.symbol, 0) + 1
|
||||
|
||||
return {
|
||||
'total_inferences': total_inferences,
|
||||
'avg_confidence': avg_confidence,
|
||||
'avg_processing_time_ms': avg_processing_time,
|
||||
'action_distribution': action_counts,
|
||||
'symbol_distribution': symbol_counts,
|
||||
'latest_inference': recent_inferences[0].timestamp.isoformat() if recent_inferences else None
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting model stats: {e}")
|
||||
return {}
|
||||
|
||||
def cleanup_old_logs(self, days_to_keep: int = 30) -> bool:
|
||||
"""Clean up old inference logs"""
|
||||
return self.db_manager.cleanup_old_records(days_to_keep)
|
||||
|
||||
# Global inference logger instance
|
||||
_inference_logger_instance = None
|
||||
|
||||
def get_inference_logger() -> InferenceLogger:
|
||||
"""Get the global inference logger instance"""
|
||||
global _inference_logger_instance
|
||||
|
||||
if _inference_logger_instance is None:
|
||||
_inference_logger_instance = InferenceLogger()
|
||||
|
||||
return _inference_logger_instance
|
||||
|
||||
def log_model_inference(model_name: str,
|
||||
symbol: str,
|
||||
action: str,
|
||||
confidence: float,
|
||||
probabilities: Dict[str, float],
|
||||
input_features: Union[np.ndarray, Dict, List],
|
||||
processing_time_ms: float,
|
||||
checkpoint_id: Optional[str] = None,
|
||||
metadata: Optional[Dict[str, Any]] = None) -> bool:
|
||||
"""
|
||||
Convenience function to log model inference
|
||||
|
||||
This is the main function that should be called throughout the codebase
|
||||
instead of scattered logger.info() calls
|
||||
"""
|
||||
inference_logger = get_inference_logger()
|
||||
return inference_logger.log_inference(
|
||||
model_name=model_name,
|
||||
symbol=symbol,
|
||||
action=action,
|
||||
confidence=confidence,
|
||||
probabilities=probabilities,
|
||||
input_features=input_features,
|
||||
processing_time_ms=processing_time_ms,
|
||||
checkpoint_id=checkpoint_id,
|
||||
metadata=metadata
|
||||
)
|
||||
156
utils/text_logger.py
Normal file
156
utils/text_logger.py
Normal file
@ -0,0 +1,156 @@
|
||||
"""
|
||||
Text File Logger for Trading System
|
||||
|
||||
Simple text file logging for tracking inference records and system events
|
||||
Provides human-readable logs alongside database storage
|
||||
"""
|
||||
|
||||
import os
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from typing import Dict, Any, Optional
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class TextLogger:
|
||||
"""Simple text file logger for trading system events"""
|
||||
|
||||
def __init__(self, log_dir: str = "logs"):
|
||||
self.log_dir = Path(log_dir)
|
||||
self.log_dir.mkdir(exist_ok=True)
|
||||
|
||||
# Create separate log files for different types of events
|
||||
self.inference_log = self.log_dir / "inference_records.txt"
|
||||
self.checkpoint_log = self.log_dir / "checkpoint_events.txt"
|
||||
self.system_log = self.log_dir / "system_events.txt"
|
||||
|
||||
def log_inference(self, model_name: str, symbol: str, action: str,
|
||||
confidence: float, processing_time_ms: float,
|
||||
checkpoint_id: str = None) -> bool:
|
||||
"""Log inference record to text file"""
|
||||
try:
|
||||
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
checkpoint_info = f" [checkpoint: {checkpoint_id}]" if checkpoint_id else ""
|
||||
|
||||
log_entry = (
|
||||
f"{timestamp} | {model_name:15} | {symbol:10} | "
|
||||
f"{action:4} | conf={confidence:.3f} | "
|
||||
f"time={processing_time_ms:6.1f}ms{checkpoint_info}\n"
|
||||
)
|
||||
|
||||
with open(self.inference_log, 'a', encoding='utf-8') as f:
|
||||
f.write(log_entry)
|
||||
f.flush()
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to log inference to text file: {e}")
|
||||
return False
|
||||
|
||||
def log_checkpoint_event(self, model_name: str, event_type: str,
|
||||
checkpoint_id: str, details: str = "") -> bool:
|
||||
"""Log checkpoint events (save, load, etc.)"""
|
||||
try:
|
||||
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
details_str = f" - {details}" if details else ""
|
||||
|
||||
log_entry = (
|
||||
f"{timestamp} | {model_name:15} | {event_type:10} | "
|
||||
f"{checkpoint_id}{details_str}\n"
|
||||
)
|
||||
|
||||
with open(self.checkpoint_log, 'a', encoding='utf-8') as f:
|
||||
f.write(log_entry)
|
||||
f.flush()
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to log checkpoint event to text file: {e}")
|
||||
return False
|
||||
|
||||
def log_system_event(self, event_type: str, message: str,
|
||||
component: str = "system") -> bool:
|
||||
"""Log general system events"""
|
||||
try:
|
||||
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
|
||||
log_entry = (
|
||||
f"{timestamp} | {component:15} | {event_type:10} | {message}\n"
|
||||
)
|
||||
|
||||
with open(self.system_log, 'a', encoding='utf-8') as f:
|
||||
f.write(log_entry)
|
||||
f.flush()
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to log system event to text file: {e}")
|
||||
return False
|
||||
|
||||
def get_recent_inferences(self, lines: int = 50) -> str:
|
||||
"""Get recent inference records from text file"""
|
||||
try:
|
||||
if not self.inference_log.exists():
|
||||
return "No inference records found"
|
||||
|
||||
with open(self.inference_log, 'r', encoding='utf-8') as f:
|
||||
all_lines = f.readlines()
|
||||
recent_lines = all_lines[-lines:] if len(all_lines) > lines else all_lines
|
||||
return ''.join(recent_lines)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to read inference log: {e}")
|
||||
return f"Error reading log: {e}"
|
||||
|
||||
def get_recent_checkpoint_events(self, lines: int = 20) -> str:
|
||||
"""Get recent checkpoint events from text file"""
|
||||
try:
|
||||
if not self.checkpoint_log.exists():
|
||||
return "No checkpoint events found"
|
||||
|
||||
with open(self.checkpoint_log, 'r', encoding='utf-8') as f:
|
||||
all_lines = f.readlines()
|
||||
recent_lines = all_lines[-lines:] if len(all_lines) > lines else all_lines
|
||||
return ''.join(recent_lines)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to read checkpoint log: {e}")
|
||||
return f"Error reading log: {e}"
|
||||
|
||||
def cleanup_old_logs(self, max_lines: int = 10000) -> bool:
|
||||
"""Keep only the most recent log entries"""
|
||||
try:
|
||||
for log_file in [self.inference_log, self.checkpoint_log, self.system_log]:
|
||||
if log_file.exists():
|
||||
with open(log_file, 'r', encoding='utf-8') as f:
|
||||
lines = f.readlines()
|
||||
|
||||
if len(lines) > max_lines:
|
||||
# Keep only the most recent lines
|
||||
recent_lines = lines[-max_lines:]
|
||||
with open(log_file, 'w', encoding='utf-8') as f:
|
||||
f.writelines(recent_lines)
|
||||
|
||||
logger.info(f"Cleaned up {log_file.name}: kept {len(recent_lines)} lines")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to cleanup logs: {e}")
|
||||
return False
|
||||
|
||||
# Global text logger instance
|
||||
_text_logger_instance = None
|
||||
|
||||
def get_text_logger(log_dir: str = "logs") -> TextLogger:
|
||||
"""Get the global text logger instance"""
|
||||
global _text_logger_instance
|
||||
|
||||
if _text_logger_instance is None:
|
||||
_text_logger_instance = TextLogger(log_dir)
|
||||
|
||||
return _text_logger_instance
|
||||
Reference in New Issue
Block a user