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improved data structure

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This commit is contained in:
Dobromir Popov
2025-10-31 00:44:08 +02:00
parent b8f54e61fa
commit 7ddf98bf18
16 changed files with 5892 additions and 35 deletions
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12
ANNOTATE/core/real_training_adapter.py
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@@ -161,7 +161,7 @@ class RealTrainingAdapter:
session = self.training_sessions[training_id]
try:
logger.info(f"🎯 Executing REAL training for {model_name}")
logger.info(f"Executing REAL training for {model_name}")
logger.info(f" Training ID: {training_id}")
logger.info(f" Test cases: {len(test_cases)}")
@@ -299,8 +299,8 @@ class RealTrainingAdapter:
"""
training_data = []
logger.info(f"📦 Preparing training data from {len(test_cases)} test cases...")
logger.info(f" Negative sampling: ±{negative_samples_window} candles around signals")
logger.info(f"Preparing training data from {len(test_cases)} test cases...")
logger.info(f" Negative sampling: +/-{negative_samples_window} candles around signals")
logger.info(f" Training repetitions: {training_repetitions}x per sample")
for i, test_case in enumerate(test_cases):
@@ -316,7 +316,7 @@ class RealTrainingAdapter:
market_state = test_case.get('market_state', {})
if not market_state:
logger.info(f" 📡 Fetching market state dynamically for test case {i+1}...")
logger.info(f" Fetching market state dynamically for test case {i+1}...")
market_state = self._fetch_market_state_for_test_case(test_case)
if not market_state:
@@ -350,7 +350,7 @@ class RealTrainingAdapter:
)
training_data.extend(hold_samples)
logger.debug(f" 📊 Added {len(hold_samples)} HOLD samples (during position)")
logger.debug(f" Added {len(hold_samples)} HOLD samples (during position)")
# Create EXIT sample (where model SHOULD exit trade)
exit_timestamp = test_case.get('annotation_metadata', {}).get('exit_timestamp')
@@ -1023,7 +1023,7 @@ class RealTrainingAdapter:
if not trainer:
raise Exception("Transformer trainer not available in orchestrator")
logger.info(f"🎯 Using orchestrator's TradingTransformerTrainer")
logger.info(f"Using orchestrator's TradingTransformerTrainer")
logger.info(f" Trainer type: {type(trainer).__name__}")
# Use the trainer's train_step method for individual samples