# Final Data Structure Implementation Summary

## What Was Implemented

### ✅ 5 Batches of 600 Candles Each

**Primary Symbol** (e.g., ETH/USDT):
- 1s timeframe: 600 candles (10 minutes of data)
- 1m timeframe: 600 candles (10 hours of data)
- 1h timeframe: 600 candles (25 days of data)
- 1d timeframe: 600 candles (~1.6 years of data)

**Secondary Symbol** (BTC/USDT or ETH/USDT):
- 1m timeframe: 600 candles (10 hours of data)

**Total**: 3,000 candles per annotation

---

## Symbol Pairing Logic

```python
def _get_secondary_symbol(primary_symbol):
    """
    ETH/USDT → BTC/USDT
    SOL/USDT → BTC/USDT
    BTC/USDT → ETH/USDT
    """
    if 'BTC' in primary_symbol:
        return 'ETH/USDT'
    else:
        return 'BTC/USDT'
```

---

## Data Structure

```python
market_state = {
    'symbol': 'ETH/USDT',
    'timestamp': '2025-10-27 14:00:00',
    
    # Primary symbol: 4 timeframes × 600 candles
    'timeframes': {
        '1s': {'timestamps': [...], 'open': [...], 'high': [...], 'low': [...], 'close': [...], 'volume': [...]},
        '1m': {'timestamps': [...], 'open': [...], 'high': [...], 'low': [...], 'close': [...], 'volume': [...]},
        '1h': {'timestamps': [...], 'open': [...], 'high': [...], 'low': [...], 'close': [...], 'volume': [...]},
        '1d': {'timestamps': [...], 'open': [...], 'high': [...], 'low': [...], 'close': [...], 'volume': [...]}
    },
    
    'secondary_symbol': 'BTC/USDT',
    
    # Secondary symbol: 1 timeframe × 600 candles
    'secondary_timeframes': {
        '1m': {'timestamps': [...], 'open': [...], 'high': [...], 'low': [...], 'close': [...], 'volume': [...]}
    }
}
```

---

## Key Features

### 1. Fixed Candle Count ✅
- Always fetches 600 candles per batch
- Configurable via `candles_per_timeframe` parameter
- Consistent data structure for all models

### 2. Historical Data Fetching ✅
- Fetches data at annotation timestamp (not current)
- Uses DuckDB for historical queries
- Fallback to replay and latest data

### 3. Multi-Symbol Support ✅
- Primary symbol: All timeframes
- Secondary symbol: 1m only (for correlation)
- Automatic symbol pairing

### 4. Time Window Calculation ✅
```python
time_windows = {
    '1s': 600 seconds = 10 minutes,
    '1m': 600 minutes = 10 hours,
    '1h': 600 hours = 25 days,
    '1d': 600 days = 1.6 years
}
```

---

## Example Training Log

```
Fetching HISTORICAL market state for ETH/USDT at 2025-10-27 14:00:00
   Primary symbol: ETH/USDT - Timeframes: ['1s', '1m', '1h', '1d']
   Secondary symbol: BTC/USDT - Timeframe: 1m
   Candles per batch: 600

   Fetching primary symbol data: ETH/USDT
       ETH/USDT 1s: 600 candles
       ETH/USDT 1m: 600 candles
       ETH/USDT 1h: 600 candles
       ETH/USDT 1d: 600 candles

   Fetching secondary symbol data: BTC/USDT (1m)
       BTC/USDT 1m: 600 candles

    ✓ Fetched 4 primary timeframes (2400 total candles)
    ✓ Fetched 1 secondary timeframes (600 total candles)

   Test case 1: ENTRY sample - LONG @ 2500.0
   Test case 1: Added 30 HOLD samples (during position)
   Test case 1: Added 30 NO_TRADE samples (±15 candles)
      → 15 before signal, 15 after signal
```

---

## Memory & Storage

### Per Annotation
- **Values**: 18,000 (3,000 candles × 6 OHLCV fields)
- **Memory**: ~144 KB (float64)
- **Disk**: Minimal (metadata only, data fetched from DuckDB)

### 100 Annotations
- **Memory**: ~14.4 MB
- **Training batches**: ~12,250 (with repetitions)

---

## Integration Points

### 1. Annotation Manager
```python
# Saves lightweight metadata only
test_case = {
    'symbol': 'ETH/USDT',
    'timestamp': '2025-10-27 14:00',
    'training_config': {
        'timeframes': ['1s', '1m', '1h', '1d'],
        'candles_per_timeframe': 600
    }
}
```

### 2. Real Training Adapter
```python
# Fetches full OHLCV data dynamically
market_state = _fetch_market_state_for_test_case(test_case)
# Returns 3,000 candles (5 batches × 600)
```

### 3. Model Training
```python
# Converts to model input format
batch = _convert_annotation_to_transformer_batch(training_sample)
# Uses all 3,000 candles for context
```

---

## Configuration

### Default Settings
```python
candles_per_timeframe = 600
timeframes = ['1s', '1m', '1h', '1d']
```

### Adjustable
```python
# Reduce for faster training
candles_per_timeframe = 300

# Increase for more context
candles_per_timeframe = 1000

# Limit timeframes
timeframes = ['1m', '1h']
```

---

## Validation

### Data Quality Checks
- ✅ Minimum 500 candles per batch (83% threshold)
- ✅ Continuous timestamps (no large gaps)
- ✅ Valid OHLCV values (no NaN/Inf)
- ✅ Secondary symbol data available

### Warning Conditions
```python
if len(candles) < 500:
    logger.warning("Insufficient data")

if len(candles) < 300:
    logger.error("Critical: skipping batch")
```

---

## Files Modified

1. **ANNOTATE/core/real_training_adapter.py**
   - Added `_get_secondary_symbol()` method
   - Updated `_fetch_market_state_for_test_case()` to fetch 5 batches
   - Fixed candle count to 600 per batch
   - Added secondary symbol fetching

---

## Documentation Created

1. **ANNOTATE/DATA_STRUCTURE_SPECIFICATION.md**
   - Complete data structure specification
   - Symbol pairing rules
   - Time window calculations
   - Integration guide

2. **ANNOTATE/CONTINUOUS_DATA_TRAINING_STRATEGY.md**
   - Training strategy explanation
   - Negative sampling details
   - Sample distribution

3. **ANNOTATE/DATA_LOADING_ARCHITECTURE.md**
   - Storage architecture
   - Dynamic loading strategy
   - Troubleshooting guide

---

## Summary

✅ **5 batches** of 600 candles each  
✅ **Primary symbol**: 4 timeframes (1s, 1m, 1h, 1d)  
✅ **Secondary symbol**: 1 timeframe (1m) - BTC or ETH  
✅ **3,000 total candles** per annotation  
✅ **Historical data** from DuckDB at annotation timestamp  
✅ **Automatic symbol pairing** (ETH→BTC, BTC→ETH)  
✅ **Fallback strategy** for missing data  
✅ **144 KB memory** per annotation  
✅ **Continuous training** with negative sampling  

The system now properly fetches and structures data according to the BaseDataInput specification!