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models overhaul

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Dobromir Popov
2025-07-29 19:22:04 +03:00
parent 0b5fa07498
commit b1ae557843
7 changed files with 465 additions and 430 deletions
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185
config.yaml
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@ -88,119 +88,14 @@ data:
market_regime_detection: true
volatility_analysis: true
# Enhanced CNN Configuration
cnn:
window_size: 20
features: ["open", "high", "low", "close", "volume"]
timeframes: ["1m", "5m", "15m", "1h", "4h", "1d"]
hidden_layers: [64, 128, 256]
dropout: 0.2
learning_rate: 0.001
batch_size: 32
epochs: 100
confidence_threshold: 0.6
early_stopping_patience: 10
model_dir: "models/enhanced_cnn" # Ultra-fast scalping weights (500x leverage)
timeframe_importance:
"1s": 0.60 # Primary scalping signal
"1m": 0.20 # Short-term confirmation
"1h": 0.15 # Medium-term trend
"1d": 0.05 # Long-term direction (minimal)
# Enhanced RL Agent Configuration
rl:
state_size: 100 # Will be calculated dynamically based on features
action_space: 3 # BUY, HOLD, SELL
hidden_size: 256
epsilon: 1.0
epsilon_decay: 0.995
epsilon_min: 0.01
learning_rate: 0.0001
gamma: 0.99
memory_size: 10000
batch_size: 64
target_update_freq: 1000
buffer_size: 10000
model_dir: "models/enhanced_rl"
# Market regime adaptation
market_regime_weights:
trending: 1.2 # Higher confidence in trending markets
ranging: 0.8 # Lower confidence in ranging markets
volatile: 0.6 # Much lower confidence in volatile markets
# Prioritized experience replay
replay_alpha: 0.6 # Priority exponent
replay_beta: 0.4 # Importance sampling exponent
# Enhanced Orchestrator Settings
orchestrator:
# Model weights for decision combination
cnn_weight: 0.7 # Weight for CNN predictions
rl_weight: 0.3 # Weight for RL decisions
confidence_threshold: 0.45
confidence_threshold_close: 0.35
decision_frequency: 30
# Multi-symbol coordination
symbol_correlation_matrix:
"ETH/USDT-BTC/USDT": 0.85 # ETH-BTC correlation
# Perfect move marking
perfect_move_threshold: 0.02 # 2% price change to mark as significant
perfect_move_buffer_size: 10000
# RL evaluation settings
evaluation_delay: 3600 # Evaluate actions after 1 hour
reward_calculation:
success_multiplier: 10 # Reward for correct predictions
failure_penalty: 5 # Penalty for wrong predictions
confidence_scaling: true # Scale rewards by confidence
# Entry aggressiveness: 0.0 = very conservative (fewer, higher quality trades), 1.0 = very aggressive (more trades)
entry_aggressiveness: 0.5
# Exit aggressiveness: 0.0 = very conservative (let profits run), 1.0 = very aggressive (quick exits)
exit_aggressiveness: 0.5
# Decision Fusion Configuration
decision_fusion:
enabled: true # Use neural network decision fusion instead of programmatic
mode: "neural" # "neural" or "programmatic"
input_size: 128 # Size of input features for decision fusion network
hidden_size: 256 # Hidden layer size
history_length: 20 # Number of recent decisions to include
training_interval: 10 # Train decision fusion every 10 decisions in programmatic mode
learning_rate: 0.001 # Learning rate for decision fusion network
batch_size: 32 # Training batch size
min_samples_for_training: 20 # Lower threshold for faster training in programmatic mode
# Training Configuration
training:
learning_rate: 0.001
batch_size: 32
epochs: 100
validation_split: 0.2
early_stopping_patience: 10
# CNN specific training
cnn_training_interval: 3600 # Train CNN every hour (was 6 hours)
min_perfect_moves: 50 # Reduced from 200 for faster learning
# RL specific training
rl_training_interval: 300 # Train RL every 5 minutes (was 1 hour)
min_experiences: 50 # Reduced from 100 for faster learning
training_steps_per_cycle: 20 # Increased from 10 for more learning
model_type: "optimized_short_term"
use_realtime: true
use_ticks: true
checkpoint_dir: "NN/models/saved/realtime_ticks_checkpoints"
save_best_model: true
save_final_model: false # We only want to keep the best performing model
# Continuous learning settings
continuous_learning: true
learning_from_trades: true
pattern_recognition: true
retrospective_learning: true
# Model configurations have been moved to models.yml for better organization
# See models.yml for all model-specific settings including:
# - CNN configuration
# - RL/DQN configuration
# - Orchestrator settings
# - Training configuration
# - Enhanced training system
# - Real-time RL COB trader
# Universal Trading Configuration (applies to all exchanges)
trading:
@ -227,69 +122,7 @@ memory:
model_limit_gb: 4.0 # Per-model memory limit
cleanup_interval: 1800 # Memory cleanup every 30 minutes
# Enhanced Training System Configuration
enhanced_training:
enabled: true # Enable enhanced real-time training
auto_start: true # Automatically start training when orchestrator starts
training_intervals:
cob_rl_training_interval: 1 # Train COB RL every 1 second (HIGHEST PRIORITY)
dqn_training_interval: 5 # Train DQN every 5 seconds
cnn_training_interval: 10 # Train CNN every 10 seconds
validation_interval: 60 # Validate every minute
batch_size: 64 # Training batch size
memory_size: 10000 # Experience buffer size
min_training_samples: 100 # Minimum samples before training starts
adaptation_threshold: 0.1 # Performance threshold for adaptation
forward_looking_predictions: true # Enable forward-looking prediction validation
# COB RL Priority Settings (since order book imbalance predicts price moves)
cob_rl_priority: true # Enable COB RL as highest priority model
cob_rl_batch_size: 16 # Smaller batches for faster COB updates
cob_rl_min_samples: 5 # Lower threshold for COB training
# Real-time RL COB Trader Configuration
realtime_rl:
# Model parameters for 400M parameter network (faster startup)
model:
input_size: 2000 # COB feature dimensions
hidden_size: 2048 # Optimized hidden layer size for 400M params
num_layers: 8 # Efficient transformer layers for faster training
learning_rate: 0.0001 # Higher learning rate for faster convergence
weight_decay: 0.00001 # Balanced L2 regularization
# Inference configuration
inference_interval_ms: 200 # Inference every 200ms
min_confidence_threshold: 0.7 # Minimum confidence for signal accumulation
required_confident_predictions: 3 # Need 3 confident predictions for trade
# Training configuration
training_interval_s: 1.0 # Train every second
batch_size: 32 # Training batch size
replay_buffer_size: 1000 # Store last 1000 predictions for training
# Signal accumulation
signal_buffer_size: 10 # Buffer size for signal accumulation
consensus_threshold: 3 # Need 3 signals in same direction
# Model checkpointing
model_checkpoint_dir: "models/realtime_rl_cob"
save_interval_s: 300 # Save models every 5 minutes
# COB integration
symbols: ["BTC/USDT", "ETH/USDT"] # Symbols to trade
cob_feature_normalization: "robust" # Feature normalization method
# Reward engineering for RL
reward_structure:
correct_direction_base: 1.0 # Base reward for correct prediction
confidence_scaling: true # Scale reward by confidence
magnitude_bonus: 0.5 # Bonus for predicting magnitude accurately
overconfidence_penalty: 1.5 # Penalty multiplier for wrong high-confidence predictions
trade_execution_multiplier: 10.0 # Higher weight for actual trade outcomes
# Performance monitoring
statistics_interval_s: 60 # Print stats every minute
detailed_logging: true # Enable detailed performance logging
# Enhanced training and real-time RL configurations moved to models.yml
# Web Dashboard
web: