raining normalization fix
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Dobromir Popov
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docs/main/_MODEL_INPUT_OUTPUT_STRUCTURE.md
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docs/main/_MODEL_INPUT_OUTPUT_STRUCTURE.md
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# Transformer Model Input/Output Structure
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## FIXED ISSUE: Batch Data Deletion Bug
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**Problem**: Training was failing after epoch 1 with "At least one timeframe must be provided"
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**Root Cause**: Batch tensors were being deleted after each use in the training loop, but the same batch dictionaries were being reused across all epochs.
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**Solution**: Removed batch deletion from inside the epoch loop and moved cleanup to after all epochs complete.
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## Current Model Architecture
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### INPUT Structure (Multi-Timeframe)
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The model accepts the following inputs in the `forward()` method:
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```python
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forward(
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# Price data for different timeframes - [batch, seq_len, 5] OHLCV
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price_data_1s=None, # 1-second timeframe
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price_data_1m=None, # 1-minute timeframe
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price_data_1h=None, # 1-hour timeframe
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price_data_1d=None, # 1-day timeframe
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# Reference data
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btc_data_1m=None, # BTC reference - [batch, seq_len, 5]
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# Additional features
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cob_data=None, # COB orderbook data - [batch, seq_len, 100]
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tech_data=None, # Technical indicators - [batch, 40]
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market_data=None, # Market context (pivots, volume) - [batch, 30]
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position_state=None, # Current position state - [batch, 5]
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# Legacy support
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price_data=None # Fallback to single timeframe
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)
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```
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**At least one timeframe** (price_data_1s, 1m, 1h, or 1d) must be provided, otherwise the model raises:
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```
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ValueError: At least one timeframe must be provided
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```
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### OUTPUT Structure
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The model returns a dictionary with the following predictions:
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```python
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outputs = {
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# PRIMARY OUTPUTS (trained with loss):
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'action_logits': tensor, # [batch, 3] - BUY/SELL/HOLD logits
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'action_probs': tensor, # [batch, 3] - softmax probabilities
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'price_prediction': tensor, # [batch, 1] - next price change ratio
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'confidence': tensor, # [batch, 1] - prediction confidence
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# TREND ANALYSIS (trained with loss):
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'trend_analysis': {
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'angle_radians': tensor, # [batch, 1] - trend angle in radians
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'steepness': tensor, # [batch, 1] - trend steepness (0-1)
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'direction': tensor # [batch, 1] - direction (-1/0/+1)
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},
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# NEXT CANDLE PREDICTIONS (evaluated but NOT trained):
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'next_candles': {
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'1s': tensor, # [batch, 5] - predicted OHLCV for 1s
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'1m': tensor, # [batch, 5] - predicted OHLCV for 1m
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'1h': tensor, # [batch, 5] - predicted OHLCV for 1h
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'1d': tensor, # [batch, 5] - predicted OHLCV for 1d
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},
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'btc_next_candle': tensor, # [batch, 5] - predicted BTC OHLCV
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# PIVOT PREDICTIONS:
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'next_pivots': {
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'L1': {
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'price': tensor, # [batch, 1] - pivot price
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'type_prob_high': tensor, # [batch, 1] - probability of high
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'type_prob_low': tensor, # [batch, 1] - probability of low
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'pivot_type': tensor, # [batch, 1] - 0=high, 1=low
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'confidence': tensor # [batch, 1] - confidence
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},
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# Same structure for L2, L3, L4, L5
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},
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# AUXILIARY OUTPUTS:
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'volatility_prediction': tensor, # [batch, 1]
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'trend_strength_prediction': tensor, # [batch, 1]
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'uncertainty_mean': tensor, # [batch, 1]
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'uncertainty_std': tensor # [batch, 1]
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}
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```
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### TRAINING TARGETS (in batch)
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```python
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batch = {
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# Input features (see INPUT Structure above)
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'price_data_1s': tensor,
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'price_data_1m': tensor,
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'price_data_1h': tensor,
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'price_data_1d': tensor,
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'btc_data_1m': tensor,
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'cob_data': tensor,
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'tech_data': tensor,
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'market_data': tensor,
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'position_state': tensor,
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# Training targets:
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'actions': tensor, # [batch] - target action (0/1/2)
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'future_prices': tensor, # [batch, 1] - actual price change ratio
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'trade_success': tensor, # [batch, 1] - 1.0 if profitable
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'trend_target': tensor, # [batch, 3] - [angle, steepness, direction]
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}
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```
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### LOSS CALCULATION
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Current loss function in `train_step()`:
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```python
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total_loss = action_loss + 0.1 * price_loss + 0.05 * trend_loss
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where:
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- action_loss: CrossEntropyLoss(action_logits, actions)
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- price_loss: MSELoss(price_prediction, future_prices)
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- trend_loss: MSELoss(trend_pred, trend_target)
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```
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**NOTE**: Next candle predictions are currently only used for accuracy evaluation, NOT trained directly.
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## CURRENT ISSUES AND RECOMMENDATIONS
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### Issue 1: Next Candle Predictions Not Trained
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**Status**: The model outputs next candle predictions for each timeframe, but these are NOT included in the loss function.
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**Impact**: The model is not explicitly learning to predict next candle OHLCV values.
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**Recommendation**: Add next candle loss to training:
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```python
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# Calculate next candle loss for each available timeframe
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candle_loss = 0.0
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if 'next_candles' in outputs:
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for tf in ['1s', '1m', '1h', '1d']:
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if tf in outputs['next_candles'] and f'future_candle_{tf}' in batch:
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pred_candle = outputs['next_candles'][tf] # [batch, 5]
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target_candle = batch[f'future_candle_{tf}'] # [batch, 5]
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candle_loss += MSELoss(pred_candle, target_candle)
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total_loss = action_loss + 0.1 * price_loss + 0.05 * trend_loss + 0.1 * candle_loss
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```
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### Issue 2: Annotation Timeframe vs Prediction Timeframe
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**Current Behavior**:
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- Annotations are created at a specific point in time
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- The model receives multiple timeframes (1s, 1m, 1h, 1d) as input
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- Predictions are made for ALL timeframes simultaneously
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- Only the 1m timeframe prediction is currently evaluated for accuracy
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**Question**: Should predictions be specific to the annotation's timeframe?
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**Options**:
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1. **Multi-timeframe predictions (current)**: Keep predicting all timeframes, add loss for each
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2. **Annotation-specific predictions**: Only predict/train on the timeframe that matches the annotation
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3. **Weighted predictions**: Weight the loss by the annotation's timeframe (e.g., if annotated on 1m, weight 1m prediction higher)
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### Issue 3: Missing Target Data for Next Candles
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**Current**: The batch only contains `future_prices` (next close price change)
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**Needed**: To train next candle predictions, we need full OHLCV targets:
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- `future_candle_1s`: [batch, 5] - next 1s candle OHLCV
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- `future_candle_1m`: [batch, 5] - next 1m candle OHLCV
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- `future_candle_1h`: [batch, 5] - next 1h candle OHLCV
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- `future_candle_1d`: [batch, 5] - next 1d candle OHLCV
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**Location to add**: `ANNOTATE/core/real_training_adapter.py` in `_convert_annotation_to_transformer_batch()`
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## SUMMARY
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✅ **Fixed**: Batch deletion bug causing epoch 2+ failures
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✅ **Working**: Model can predict next candles for all timeframes
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✅ **Working**: Model can predict trend vector (angle, steepness, direction)
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❌ **Missing**: Loss calculation for next candle predictions
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❌ **Missing**: Target data (future OHLCV) for next candle training
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⚠️ **Unclear**: Should predictions be timeframe-specific or multi-timeframe?
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## NEXT STEPS
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1. **Add future OHLCV target data** to training batches
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2. **Add next candle loss** to the training loop
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3. **Clarify prediction strategy**: Single timeframe vs multi-timeframe
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4. **Test training** with enhanced loss function
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