gogo2/ANNOTATE/TRAINING_IMPROVEMENTS_SUMMARY.md

# Training Improvements Summary

## What Changed

### 1. Extended Data Fetching Window ✅

**Before:**
```python
context_window = 5  # Only ±5 minutes
start_time = timestamp - 5 minutes
end_time = timestamp + 5 minutes
```

**After:**
```python
context_window = 5
negative_samples_window = 15  # ±15 candles
extended_window = max(5, 15 + 10)  # = 25 minutes

start_time = timestamp - 25 minutes
end_time = timestamp + 25 minutes
```

**Impact**: Fetches enough data to create ±15 candle negative samples

---

### 2. Dynamic Candle Limits ✅

**Before:**
```python
limit = 200  # Fixed for all timeframes
```

**After:**
```python
if timeframe == '1s':
    limit = extended_window_minutes * 60 * 2 + 100  # ~3100
elif timeframe == '1m':
    limit = extended_window_minutes * 2 + 50        # ~100
elif timeframe == '1h':
    limit = max(200, extended_window_minutes // 30) # 200+
elif timeframe == '1d':
    limit = 200
```

**Impact**: Requests appropriate amount of data per timeframe

---

### 3. Improved Logging ✅

**Before:**
```
DEBUG - Added 30 negative samples
```

**After:**
```
INFO - Test case 1: ENTRY sample - LONG @ 2500.0
INFO - Test case 1: Added 30 HOLD samples (during position)
INFO - Test case 1: EXIT sample @ 2562.5 (2.50%)
INFO - Test case 1: Added 30 NO_TRADE samples (±15 candles)
INFO -    → 15 before signal, 15 after signal
```

**Impact**: Clear visibility into training data composition

---

### 4. Historical Data Priority ✅

**Before:**
```python
df = data_provider.get_historical_data(limit=100)  # Latest data
```

**After:**
```python
# Try DuckDB first (historical at specific timestamp)
df = duckdb_storage.get_ohlcv_data(
    start_time=start_time,
    end_time=end_time
)

# Fallback to replay
if df is None:
    df = data_provider.get_historical_data_replay(
        start_time=start_time,
        end_time=end_time
    )

# Last resort: latest data (with warning)
if df is None:
    logger.warning("Using latest data as fallback")
    df = data_provider.get_historical_data(limit=limit)
```

**Impact**: Trains on correct historical data, not current data

---

## Training Data Composition

### Per Annotation

| Sample Type | Count | Repetitions | Total Batches |
|------------|-------|-------------|---------------|
| ENTRY | 1 | 100 | 100 |
| HOLD | ~30 | 25 | 750 |
| EXIT | 1 | 100 | 100 |
| NO_TRADE | ~30 | 50 | 1,500 |
| **Total** | **~62** | **-** | **~2,450** |

### 5 Annotations

| Sample Type | Count | Total Batches |
|------------|-------|---------------|
| ENTRY | 5 | 500 |
| HOLD | ~150 | 3,750 |
| EXIT | 5 | 500 |
| NO_TRADE | ~150 | 7,500 |
| **Total** | **~310** | **~12,250** |

**Key Ratio**: 1:30 (entry:no_trade) - Model learns to be selective!

---

## What This Achieves

### 1. Continuous Data Training ✅
- Trains on every candle ±15 around signals
- Not just isolated entry/exit points
- Learns from continuous price action

### 2. Negative Sampling ✅
- 30 NO_TRADE samples per annotation
- 15 before signal (don't enter too early)
- 15 after signal (don't chase)

### 3. Context Learning ✅
- Model sees what happened before signal
- Model sees what happened after signal
- Learns timing and context

### 4. Selective Trading ✅
- High ratio of NO_TRADE samples
- Teaches model to wait for quality setups
- Reduces false signals

---

## Example Training Output

```
Starting REAL training with 5 test cases for model Transformer

Preparing training data from 5 test cases...
   Negative sampling: +/-15 candles around signals
   Training repetitions: 100x per sample

   Fetching market state dynamically for test case 1...
   Fetching HISTORICAL market state for ETH/USDT at 2025-10-27 14:00
   Timeframes: ['1s', '1m', '1h', '1d'], Extended window: ±25 minutes
   (Includes ±15 candles for negative sampling)
       1m: 100 candles from DuckDB (historical)
       1h: 200 candles from DuckDB (historical)
       1d: 200 candles from DuckDB (historical)
    Fetched market state with 3 timeframes

   Test case 1: ENTRY sample - LONG @ 2500.0
   Test case 1: Added 30 HOLD samples (during position)
   Test case 1: EXIT sample @ 2562.5 (2.50%)
   Test case 1: Added 30 NO_TRADE samples (±15 candles)
      → 15 before signal, 15 after signal

 Prepared 310 training samples from 5 test cases
   ENTRY samples: 5
   HOLD samples: 150
   EXIT samples: 5
   NO_TRADE samples: 150
   Ratio: 1:30.0 (entry:no_trade)

 Starting Transformer training...
    Converting annotation data to transformer format...
     Converted 310 samples to 12,250 training batches
```

---

## Files Modified

1. `ANNOTATE/core/real_training_adapter.py`
   - Extended data fetching window
   - Dynamic candle limits
   - Improved logging
   - Historical data priority

---

## New Documentation

1. `ANNOTATE/CONTINUOUS_DATA_TRAINING_STRATEGY.md`
   - Detailed explanation of training strategy
   - Sample composition breakdown
   - Configuration guidelines
   - Monitoring tips

2. `ANNOTATE/DATA_LOADING_ARCHITECTURE.md`
   - Data storage architecture
   - Dynamic loading strategy
   - Troubleshooting guide

3. `MODEL_INPUTS_OUTPUTS_REFERENCE.md`
   - All model inputs/outputs
   - Shape specifications
   - Integration examples

---

## Next Steps

1. **Test Training**
   - Run training with 5+ annotations
   - Verify NO_TRADE samples are created
   - Check logs for data fetching

2. **Monitor Ratios**
   - Ideal: 1:20 to 1:40 (entry:no_trade)
   - Adjust `negative_samples_window` if needed

3. **Verify Data**
   - Ensure DuckDB has historical data
   - Check for "fallback" warnings
   - Confirm timestamps match annotations

4. **Tune Parameters**
   - Adjust `extended_window_minutes` if needed
   - Modify repetitions based on dataset size
   - Balance training time vs accuracy