fixes
This commit is contained in:
Dobromir Popov
@@ -315,6 +315,16 @@ class TradingOrchestrator:
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logger.info(f"Using device: {self.device}")
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# Canonical model name aliases to eliminate ambiguity across UI/DB/FS
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# Canonical → accepted aliases (internal/legacy)
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self.model_name_aliases: Dict[str, list] = {
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"DQN": ["dqn_agent", "dqn"],
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"CNN": ["enhanced_cnn", "cnn", "cnn_model", "standardized_cnn"],
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"EXTREMA": ["extrema_trainer", "extrema"],
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"COB": ["cob_rl_model", "cob_rl"],
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"DECISION": ["decision_fusion", "decision"],
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}
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# Configuration - AGGRESSIVE for more training data
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self.confidence_threshold = self.config.orchestrator.get(
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"confidence_threshold", 0.15
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@@ -555,17 +565,16 @@ class TradingOrchestrator:
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def _normalize_model_name(self, name: str) -> str:
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"""Map various registry/UI names to canonical toggle keys."""
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try:
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mapping = {
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"dqn_agent": "dqn",
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"enhanced_cnn": "cnn",
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"cnn_model": "cnn",
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"decision": "decision_fusion",
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"decision_fusion": "decision_fusion",
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"cob_rl_model": "cob_rl",
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"cob_rl": "cob_rl",
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"transformer_model": "transformer",
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# Use alias map to unify names to canonical keys
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alias_to_canonical = {
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**{alias: "DQN" for alias in ["dqn_agent", "dqn"]},
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**{alias: "CNN" for alias in ["enhanced_cnn", "cnn", "cnn_model", "standardized_cnn"]},
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**{alias: "EXTREMA" for alias in ["extrema_trainer", "extrema"]},
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**{alias: "COB" for alias in ["cob_rl_model", "cob_rl"]},
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**{alias: "DECISION" for alias in ["decision_fusion", "decision"]},
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"transformer_model": "TRANSFORMER",
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}
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return mapping.get(name, name)
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return alias_to_canonical.get(name, name)
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except Exception:
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return name
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@@ -643,41 +652,44 @@ class TradingOrchestrator:
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)
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self.rl_agent.to(self.device) # Move DQN agent to the determined device
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# Load best checkpoint and capture initial state (using database metadata)
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# Load best checkpoint and capture initial state (using database metadata or filesystem fallback)
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checkpoint_loaded = False
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if hasattr(self.rl_agent, "load_best_checkpoint"):
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try:
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self.rl_agent.load_best_checkpoint() # This loads the state into the model
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# Check if we have checkpoints available using database metadata (fast!)
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db_manager = get_database_manager()
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checkpoint_metadata = db_manager.get_best_checkpoint_metadata(
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"dqn_agent"
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)
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self.rl_agent.load_best_checkpoint() # Load model state if available
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# 1) Try DB metadata first
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try:
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db_manager = get_database_manager()
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checkpoint_metadata = db_manager.get_best_checkpoint_metadata("dqn_agent")
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except Exception:
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checkpoint_metadata = None
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if checkpoint_metadata:
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self.model_states["dqn"]["initial_loss"] = 0.412
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self.model_states["dqn"]["current_loss"] = (
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checkpoint_metadata.performance_metrics.get("loss", 0.0)
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)
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self.model_states["dqn"]["best_loss"] = (
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checkpoint_metadata.performance_metrics.get("loss", 0.0)
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)
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self.model_states["dqn"]["current_loss"] = checkpoint_metadata.performance_metrics.get("loss", 0.0)
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self.model_states["dqn"]["best_loss"] = checkpoint_metadata.performance_metrics.get("loss", 0.0)
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self.model_states["dqn"]["checkpoint_loaded"] = True
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self.model_states["dqn"][
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"checkpoint_filename"
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] = checkpoint_metadata.checkpoint_id
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self.model_states["dqn"]["checkpoint_filename"] = checkpoint_metadata.checkpoint_id
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checkpoint_loaded = True
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loss_str = f"{checkpoint_metadata.performance_metrics.get('loss', 0.0):.4f}"
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logger.info(
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f"DQN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})"
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)
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logger.info(f"DQN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})")
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else:
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# 2) Filesystem fallback via CheckpointManager
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try:
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from utils.checkpoint_manager import get_checkpoint_manager
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cm = get_checkpoint_manager()
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result = cm.load_best_checkpoint("dqn_agent")
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if result:
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model_path, meta = result
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# We already loaded model weights via load_best_checkpoint; just record metadata
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self.model_states["dqn"]["checkpoint_loaded"] = True
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self.model_states["dqn"]["checkpoint_filename"] = getattr(meta, 'checkpoint_id', None)
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checkpoint_loaded = True
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logger.info(f"DQN checkpoint (fs) detected: {getattr(meta, 'checkpoint_id', 'unknown')}")
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except Exception:
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pass
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except Exception as e:
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logger.warning(
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f"Error loading DQN checkpoint (likely dimension mismatch): {e}"
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)
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logger.info(
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"DQN will start fresh due to checkpoint incompatibility"
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)
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# Reset the agent to handle dimension mismatch
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logger.warning(f"Error loading DQN checkpoint (likely dimension mismatch): {e}")
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logger.info("DQN will start fresh due to checkpoint incompatibility")
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checkpoint_loaded = False
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if not checkpoint_loaded:
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@@ -712,7 +724,7 @@ class TradingOrchestrator:
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self.cnn_model.parameters(), lr=0.001
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) # Initialize optimizer for CNN
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# Load best checkpoint and capture initial state (using database metadata)
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# Load best checkpoint and capture initial state (using database metadata or filesystem fallback)
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checkpoint_loaded = False
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try:
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db_manager = get_database_manager()
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@@ -738,6 +750,19 @@ class TradingOrchestrator:
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)
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except Exception as e:
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logger.warning(f"Error loading CNN checkpoint: {e}")
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# Filesystem fallback
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try:
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from utils.checkpoint_manager import get_checkpoint_manager
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cm = get_checkpoint_manager()
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result = cm.load_best_checkpoint("enhanced_cnn")
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if result:
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model_path, meta = result
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self.model_states["cnn"]["checkpoint_loaded"] = True
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self.model_states["cnn"]["checkpoint_filename"] = getattr(meta, 'checkpoint_id', None)
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checkpoint_loaded = True
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logger.info(f"CNN checkpoint (fs) detected: {getattr(meta, 'checkpoint_id', 'unknown')}")
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except Exception:
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pass
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if not checkpoint_loaded:
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# New model - no synthetic data
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@@ -7951,28 +7976,29 @@ class TradingOrchestrator:
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self.checkpoint_manager = get_checkpoint_manager()
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# Initialize model states dictionary to track performance
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self.model_states = {
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"dqn": {
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"initial_loss": None,
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"current_loss": None,
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"best_loss": float("inf"),
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"checkpoint_loaded": False,
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},
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"cnn": {
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"initial_loss": None,
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"current_loss": None,
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"best_loss": float("inf"),
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"checkpoint_loaded": False,
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},
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"cob_rl": {
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"initial_loss": None,
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"current_loss": None,
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"best_loss": float("inf"),
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"checkpoint_loaded": False,
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},
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"extrema": {
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"initial_loss": None,
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# Initialize model states dictionary to track performance (only if not already initialized)
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if not hasattr(self, 'model_states') or self.model_states is None:
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self.model_states = {
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"dqn": {
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"initial_loss": None,
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"current_loss": None,
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"best_loss": float("inf"),
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"checkpoint_loaded": False,
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},
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"cnn": {
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"initial_loss": None,
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"current_loss": None,
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"best_loss": float("inf"),
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"checkpoint_loaded": False,
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},
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"cob_rl": {
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"initial_loss": None,
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"current_loss": None,
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"best_loss": float("inf"),
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"checkpoint_loaded": False,
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},
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"extrema": {
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"initial_loss": None,
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"current_loss": None,
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"best_loss": float("inf"),
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"checkpoint_loaded": False,
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