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train feedback - norm fix

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Dobromir Popov
2025-11-12 15:20:42 +02:00
parent 352dc9cbeb
commit 7cb4201bc0
1 changed files with 22 additions and 23 deletions
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45
ANNOTATE/core/real_training_adapter.py
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@@ -1211,16 +1211,16 @@ class RealTrainingAdapter:
logger.error(f"Error extracting timeframe data: {e}") logger.error(f"Error extracting timeframe data: {e}")
return None return None
def _extract_next_candle(self, tf_data: Dict, reference_data = None) -> Optional[torch.Tensor]: def _extract_next_candle(self, tf_data: Dict, norm_params: Dict = None) -> Optional[torch.Tensor]:
""" """
Extract the NEXT candle OHLCV (after the current sequence) as training target Extract the NEXT candle OHLCV (after the current sequence) as training target
This extracts the candle that comes immediately after the sequence used for input. This extracts the candle that comes immediately after the sequence used for input.
Normalized using the same price range as the reference data for consistency. Normalized using the ORIGINAL price range (not the already-normalized sequence data).
Args: Args:
tf_data: Timeframe data dictionary with 'open', 'high', 'low', 'close', 'volume' tf_data: Timeframe data dictionary with 'open', 'high', 'low', 'close', 'volume'
reference_data: Optional reference OHLCV data [seq_len, 5] to get normalization params norm_params: Normalization parameters dict with 'price_min', 'price_max', 'volume_min', 'volume_max'
Returns: Returns:
Tensor of shape [1, 5] representing next candle OHLCV, or None if not available Tensor of shape [1, 5] representing next candle OHLCV, or None if not available
@@ -1251,17 +1251,20 @@ class RealTrainingAdapter:
# Create OHLCV array [5] # Create OHLCV array [5]
next_candle = np.array([next_open, next_high, next_low, next_close, next_volume], dtype=np.float32) next_candle = np.array([next_open, next_high, next_low, next_close, next_volume], dtype=np.float32)
# Normalize using reference data if provided # CRITICAL FIX: Normalize using ORIGINAL price bounds, not already-normalized data
if reference_data is not None: # The bug was: reference_data was already normalized to [0,1], so its min/max
# Use same normalization as input sequence # would be ~0 and ~1, which when used to normalize raw prices ($3000+) created
price_min = np.min(reference_data[:, :4]) # astronomically large values (e.g., $3000 / 1.0 = still $3000 in "normalized" space!)
price_max = np.max(reference_data[:, :4]) if norm_params is not None:
# Use ORIGINAL normalization bounds
price_min = norm_params.get('price_min', 0.0)
price_max = norm_params.get('price_max', 1.0)
if price_max > price_min: if price_max > price_min:
next_candle[:4] = (next_candle[:4] - price_min) / (price_max - price_min) next_candle[:4] = (next_candle[:4] - price_min) / (price_max - price_min)
volume_min = np.min(reference_data[:, 4]) volume_min = norm_params.get('volume_min', 0.0)
volume_max = np.max(reference_data[:, 4]) volume_max = norm_params.get('volume_max', 1.0)
if volume_max > volume_min: if volume_max > volume_min:
next_candle[4] = (next_candle[4] - volume_min) / (volume_max - volume_min) next_candle[4] = (next_candle[4] - volume_min) / (volume_max - volume_min)
@@ -1598,22 +1601,18 @@ class RealTrainingAdapter:
future_candle_1d = None future_candle_1d = None
# For each timeframe, extract the next candle if data is available # For each timeframe, extract the next candle if data is available
# Use the input sequence data as reference for normalization # CRITICAL: Pass the ORIGINAL normalization parameters, not the normalized data!
if price_data_1s is not None and '1s' in timeframes: if price_data_1s is not None and '1s' in timeframes and '1s' in norm_params_dict:
ref_data_1s = price_data_1s.squeeze(0).numpy() # [seq_len, 5] future_candle_1s = self._extract_next_candle(timeframes['1s'], norm_params_dict['1s'])
future_candle_1s = self._extract_next_candle(timeframes['1s'], ref_data_1s)
if price_data_1m is not None and '1m' in timeframes: if price_data_1m is not None and '1m' in timeframes and '1m' in norm_params_dict:
ref_data_1m = price_data_1m.squeeze(0).numpy() # [seq_len, 5] future_candle_1m = self._extract_next_candle(timeframes['1m'], norm_params_dict['1m'])
future_candle_1m = self._extract_next_candle(timeframes['1m'], ref_data_1m)
if price_data_1h is not None and '1h' in timeframes: if price_data_1h is not None and '1h' in timeframes and '1h' in norm_params_dict:
ref_data_1h = price_data_1h.squeeze(0).numpy() # [seq_len, 5] future_candle_1h = self._extract_next_candle(timeframes['1h'], norm_params_dict['1h'])
future_candle_1h = self._extract_next_candle(timeframes['1h'], ref_data_1h)
if price_data_1d is not None and '1d' in timeframes: if price_data_1d is not None and '1d' in timeframes and '1d' in norm_params_dict:
ref_data_1d = price_data_1d.squeeze(0).numpy() # [seq_len, 5] future_candle_1d = self._extract_next_candle(timeframes['1d'], norm_params_dict['1d'])
future_candle_1d = self._extract_next_candle(timeframes['1d'], ref_data_1d)
# Return batch dictionary with ALL timeframes # Return batch dictionary with ALL timeframes
batch = { batch = {