Best checkpoint file not found
This commit is contained in:
Dobromir Popov
1
.env
1
.env
@ -1,6 +1,7 @@
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# MEXC API Configuration (Spot Trading)
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MEXC_API_KEY=mx0vglhVPZeIJ32Qw1
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MEXC_SECRET_KEY=3bfe4bd99d5541e4a1bca87ab257cc7e
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#3bfe4bd99d5541e4a1bca87ab257cc7e 45d0b3c26f2644f19bfb98b07741b2f5
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# BASE ENDPOINTS: https://api.mexc.com wss://wbs-api.mexc.com/ws !!! DO NOT CHANGE THIS
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292
config.yaml.backup_20250702_202543
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292
config.yaml.backup_20250702_202543
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# Enhanced Multi-Modal Trading System Configuration
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# System Settings
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system:
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timezone: "Europe/Sofia" # Configurable timezone for all timestamps
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log_level: "INFO" # DEBUG, INFO, WARNING, ERROR
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session_timeout: 3600 # Session timeout in seconds
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# Trading Symbols Configuration
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# Primary trading pair: ETH/USDT (main signals generation)
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# Reference pair: BTC/USDT (correlation analysis only, no trading signals)
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symbols:
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- "ETH/USDT" # MAIN TRADING PAIR - Generate signals and execute trades
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- "BTC/USDT" # REFERENCE ONLY - For correlation analysis, no direct trading
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# Timeframes for ultra-fast scalping (500x leverage)
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timeframes:
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- "1s" # Primary scalping timeframe
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- "1m" # Short-term confirmation
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- "1h" # Medium-term trend
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- "1d" # Long-term direction
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# Data Provider Settings
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data:
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provider: "binance"
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cache_enabled: true
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cache_dir: "cache"
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historical_limit: 1000
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real_time_enabled: true
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websocket_reconnect: true
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feature_engineering:
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technical_indicators: true
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market_regime_detection: true
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volatility_analysis: true
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# Enhanced CNN Configuration
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cnn:
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window_size: 20
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features: ["open", "high", "low", "close", "volume"]
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timeframes: ["1m", "5m", "15m", "1h", "4h", "1d"]
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hidden_layers: [64, 128, 256]
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dropout: 0.2
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learning_rate: 0.001
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batch_size: 32
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epochs: 100
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confidence_threshold: 0.6
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early_stopping_patience: 10
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model_dir: "models/enhanced_cnn" # Ultra-fast scalping weights (500x leverage)
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timeframe_importance:
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"1s": 0.60 # Primary scalping signal
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"1m": 0.20 # Short-term confirmation
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"1h": 0.15 # Medium-term trend
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"1d": 0.05 # Long-term direction (minimal)
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# Enhanced RL Agent Configuration
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rl:
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state_size: 100 # Will be calculated dynamically based on features
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action_space: 3 # BUY, HOLD, SELL
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hidden_size: 256
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epsilon: 1.0
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epsilon_decay: 0.995
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epsilon_min: 0.01
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learning_rate: 0.0001
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gamma: 0.99
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memory_size: 10000
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batch_size: 64
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target_update_freq: 1000
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buffer_size: 10000
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model_dir: "models/enhanced_rl"
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# Market regime adaptation
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market_regime_weights:
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trending: 1.2 # Higher confidence in trending markets
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ranging: 0.8 # Lower confidence in ranging markets
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volatile: 0.6 # Much lower confidence in volatile markets
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# Prioritized experience replay
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replay_alpha: 0.6 # Priority exponent
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replay_beta: 0.4 # Importance sampling exponent
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# Enhanced Orchestrator Settings
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orchestrator:
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# Model weights for decision combination
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cnn_weight: 0.7 # Weight for CNN predictions
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rl_weight: 0.3 # Weight for RL decisions
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confidence_threshold: 0.20 # Lowered from 0.35 for low-volatility markets
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confidence_threshold_close: 0.10 # Lowered from 0.15 for easier exits
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decision_frequency: 30 # Seconds between decisions (faster)
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# Multi-symbol coordination
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symbol_correlation_matrix:
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"ETH/USDT-BTC/USDT": 0.85 # ETH-BTC correlation
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# Perfect move marking
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perfect_move_threshold: 0.02 # 2% price change to mark as significant
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perfect_move_buffer_size: 10000
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# RL evaluation settings
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evaluation_delay: 3600 # Evaluate actions after 1 hour
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reward_calculation:
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success_multiplier: 10 # Reward for correct predictions
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failure_penalty: 5 # Penalty for wrong predictions
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confidence_scaling: true # Scale rewards by confidence
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# Training Configuration
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training:
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learning_rate: 0.001
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batch_size: 32
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epochs: 100
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validation_split: 0.2
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early_stopping_patience: 10
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# CNN specific training
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cnn_training_interval: 3600 # Train CNN every hour (was 6 hours)
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min_perfect_moves: 50 # Reduced from 200 for faster learning
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# RL specific training
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rl_training_interval: 300 # Train RL every 5 minutes (was 1 hour)
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min_experiences: 50 # Reduced from 100 for faster learning
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training_steps_per_cycle: 20 # Increased from 10 for more learning
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model_type: "optimized_short_term"
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use_realtime: true
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use_ticks: true
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checkpoint_dir: "NN/models/saved/realtime_ticks_checkpoints"
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save_best_model: true
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save_final_model: false # We only want to keep the best performing model
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# Continuous learning settings
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continuous_learning: true
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learning_from_trades: true
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pattern_recognition: true
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retrospective_learning: true
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# Trading Execution
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trading:
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max_position_size: 0.05 # Maximum position size (5% of balance)
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stop_loss: 0.02 # 2% stop loss
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take_profit: 0.05 # 5% take profit
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trading_fee: 0.0005 # 0.05% trading fee (MEXC taker fee - fallback)
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# MEXC Fee Structure (asymmetrical) - Updated 2025-05-28
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trading_fees:
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maker: 0.0000 # 0.00% maker fee (adds liquidity)
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taker: 0.0005 # 0.05% taker fee (takes liquidity)
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default: 0.0005 # Default fallback fee (taker rate)
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# Risk management
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max_daily_trades: 20 # Maximum trades per day
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max_concurrent_positions: 2 # Max positions across symbols
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position_sizing:
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confidence_scaling: true # Scale position by confidence
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base_size: 0.02 # 2% base position
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max_size: 0.05 # 5% maximum position
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# MEXC Trading API Configuration
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mexc_trading:
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enabled: true
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trading_mode: simulation # simulation, testnet, live
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# FIXED: Meaningful position sizes for learning
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base_position_usd: 25.0 # $25 base position (was $1)
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max_position_value_usd: 50.0 # $50 max position (was $1)
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min_position_value_usd: 10.0 # $10 min position (was $0.10)
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# Risk management
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max_daily_trades: 100
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max_daily_loss_usd: 200.0
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max_concurrent_positions: 3
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min_trade_interval_seconds: 30
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# Order configuration
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order_type: market # market or limit
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# Enhanced fee structure for better calculation
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trading_fees:
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maker_fee: 0.0002 # 0.02% maker fee
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taker_fee: 0.0006 # 0.06% taker fee
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default_fee: 0.0006 # Default to taker fee
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# Memory Management
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memory:
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total_limit_gb: 28.0 # Total system memory limit
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model_limit_gb: 4.0 # Per-model memory limit
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cleanup_interval: 1800 # Memory cleanup every 30 minutes
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# Real-time RL COB Trader Configuration
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realtime_rl:
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# Model parameters for 400M parameter network (faster startup)
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model:
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input_size: 2000 # COB feature dimensions
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hidden_size: 2048 # Optimized hidden layer size for 400M params
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num_layers: 8 # Efficient transformer layers for faster training
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learning_rate: 0.0001 # Higher learning rate for faster convergence
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weight_decay: 0.00001 # Balanced L2 regularization
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# Inference configuration
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inference_interval_ms: 200 # Inference every 200ms
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min_confidence_threshold: 0.7 # Minimum confidence for signal accumulation
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required_confident_predictions: 3 # Need 3 confident predictions for trade
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# Training configuration
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training_interval_s: 1.0 # Train every second
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batch_size: 32 # Training batch size
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replay_buffer_size: 1000 # Store last 1000 predictions for training
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# Signal accumulation
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signal_buffer_size: 10 # Buffer size for signal accumulation
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consensus_threshold: 3 # Need 3 signals in same direction
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# Model checkpointing
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model_checkpoint_dir: "models/realtime_rl_cob"
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save_interval_s: 300 # Save models every 5 minutes
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# COB integration
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symbols: ["BTC/USDT", "ETH/USDT"] # Symbols to trade
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cob_feature_normalization: "robust" # Feature normalization method
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# Reward engineering for RL
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reward_structure:
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correct_direction_base: 1.0 # Base reward for correct prediction
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confidence_scaling: true # Scale reward by confidence
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magnitude_bonus: 0.5 # Bonus for predicting magnitude accurately
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overconfidence_penalty: 1.5 # Penalty multiplier for wrong high-confidence predictions
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trade_execution_multiplier: 10.0 # Higher weight for actual trade outcomes
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# Performance monitoring
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statistics_interval_s: 60 # Print stats every minute
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detailed_logging: true # Enable detailed performance logging
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# Web Dashboard
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web:
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host: "127.0.0.1"
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port: 8050
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debug: false
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update_interval: 500 # Milliseconds
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chart_history: 200 # Number of candles to show
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# Enhanced dashboard features
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show_timeframe_analysis: true
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show_confidence_scores: true
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show_perfect_moves: true
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show_rl_metrics: true
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# Logging
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logging:
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level: "INFO"
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format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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file: "logs/enhanced_trading.log"
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max_size: 10485760 # 10MB
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backup_count: 5
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# Component-specific logging
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orchestrator_level: "INFO"
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cnn_level: "INFO"
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rl_level: "INFO"
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training_level: "INFO"
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# Model Directories
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model_dir: "models"
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data_dir: "data"
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cache_dir: "cache"
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logs_dir: "logs"
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# GPU/Performance
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gpu:
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enabled: true
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memory_fraction: 0.8 # Use 80% of GPU memory
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allow_growth: true # Allow dynamic memory allocation
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# Monitoring and Alerting
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monitoring:
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tensorboard_enabled: true
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tensorboard_log_dir: "logs/tensorboard"
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metrics_interval: 300 # Log metrics every 5 minutes
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performance_alerts: true
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# Performance thresholds
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min_confidence_threshold: 0.3
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max_memory_usage: 0.9 # 90% of available memory
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max_decision_latency: 10 # 10 seconds max per decision
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# Backtesting (for future implementation)
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backtesting:
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start_date: "2024-01-01"
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end_date: "2024-12-31"
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initial_balance: 10000
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commission: 0.0002
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slippage: 0.0001
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model_paths:
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realtime_model: "NN/models/saved/optimized_short_term_model_realtime_best.pt"
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ticks_model: "NN/models/saved/optimized_short_term_model_ticks_best.pt"
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backup_model: "NN/models/saved/realtime_ticks_checkpoints/checkpoint_epoch_50449_backup/model.pt"
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41
tests/test_mexc_account_privte.py
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41
tests/test_mexc_account_privte.py
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from NN.exchanges.mexc_interface import MEXCInterface
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import logging
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# Set up logging to see debug info
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logging.basicConfig(level=logging.INFO)
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# Create interface with API credentials
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mexc = MEXCInterface(
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api_key='mx0aBYs33eIilxBWC5',
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api_secret='45d0b3c26f2644f19bfb98b07741b2f5',
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trading_mode='simulation'
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)
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print('MEXC Interface created successfully')
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# Test signature generation
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import time
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timestamp = int(time.time() * 1000)
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test_params = 'quantity=1&price=11&symbol=BTCUSDT&side=BUY&type=LIMIT×tamp=' + str(timestamp)
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signature = mexc._generate_signature(timestamp, test_params)
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print(f'Generated signature: {signature}')
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# Test account info
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print('Testing account info...')
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account_info = mexc.get_account_info()
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print(f'Account info result: {account_info}')
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# Test ticker data
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print('Testing ticker data...')
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ticker = mexc.get_ticker('ETH/USDT')
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print(f'ETH/USDT ticker: {ticker}')
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# Test balance retrieval
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print('Testing balance retrieval...')
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usdt_balance = mexc.get_balance('USDT')
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print(f'USDT balance: {usdt_balance}')
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# Test a small order placement (simulation mode)
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print('Testing order placement in simulation mode...')
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order_result = mexc.place_order('ETH/USDT', 'buy', 'market', 0.001)
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print(f'Order result: {order_result}')
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@ -87,7 +87,7 @@ class CheckpointManager:
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performance_score = self._calculate_performance_score(performance_metrics)
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if not force_save and not self._should_save_checkpoint(model_name, performance_score):
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logger.info(f"Skipping checkpoint save for {model_name} - performance not improved")
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logger.debug(f"Skipping checkpoint save for {model_name} - performance not improved")
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return None
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success = self._save_model_file(model, checkpoint_path, model_type)
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@ -140,10 +140,11 @@ class CheckpointManager:
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best_checkpoint = max(self.checkpoints[model_name], key=lambda x: x.performance_score)
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if not Path(best_checkpoint.file_path).exists():
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logger.error(f"Best checkpoint file not found: {best_checkpoint.file_path}")
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# temporary disable logging to avoid spam
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# logger.error(f"Best checkpoint file not found: {best_checkpoint.file_path}")
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return None
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logger.info(f"Loading best checkpoint for {model_name}: {best_checkpoint.checkpoint_id}")
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logger.debug(f"Loading best checkpoint for {model_name}: {best_checkpoint.checkpoint_id}")
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return best_checkpoint.file_path, best_checkpoint
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except Exception as e:
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@ -5474,7 +5474,7 @@ class CleanTradingDashboard:
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if training_samples > 0:
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avg_loss_info = f", avg_loss={total_loss/loss_count:.6f}" if loss_count > 0 else ""
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performance_score = 100 / (1 + (total_loss/loss_count)) if loss_count > 0 else 0.1
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logger.info(f"DECISION TRAINING: Processed {training_samples} decision fusion samples{avg_loss_info}, perf_score={performance_score:.4f}")
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logger.debug(f"DECISION TRAINING: Processed {training_samples} decision fusion samples{avg_loss_info}, perf_score={performance_score:.4f}")
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except Exception as e:
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logger.error(f"Error in real decision fusion training: {e}")
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Reference in New Issue
Block a user