# Hyperparameter Tuning Configuration for Foxhunt ML Models
# Defines search spaces for Optuna optimization

# Global tuning settings
global:
  optimization_direction: maximize  # maximize sharpe_ratio
  pruning_enabled: true
  median_pruner:
    n_startup_trials: 5      # No pruning for first 5 trials (establish baseline)
    n_warmup_steps: 10       # Wait 10 epochs before starting to prune
    interval_steps: 5        # Check for pruning every 5 epochs
  sampler: TPE  # Tree-structured Parzen Estimator

# Model-specific search spaces
models:
  TLOB:
    epochs:
      type: int
      low: 10
      high: 100
      step: 10
    learning_rate:
      type: float
      low: 0.00001
      high: 0.01
      log: true
    batch_size:
      type: categorical
      choices: [32, 64, 128, 256]
    sequence_length:
      type: categorical
      choices: [50, 100, 200]
    hidden_dim:
      type: categorical
      choices: [64, 128, 256, 512]
    num_heads:
      type: categorical
      choices: [4, 8, 16]
    num_layers:
      type: int
      low: 2
      high: 8
      step: 1
    dropout_rate:
      type: float
      low: 0.0
      high: 0.5
      step: 0.05
    use_positional_encoding:
      type: categorical
      choices: [true, false]

  MAMBA_2:
    epochs:
      type: int
      low: 30
      high: 100
      step: 10
    learning_rate:
      type: categorical
      choices: [0.00001, 0.0001, 0.001]  # Conservative range for state-space models
    batch_size:
      type: categorical
      choices: [16, 32, 64]  # Memory-intensive due to state propagation (4GB VRAM constraint)
    hidden_dim:
      type: categorical
      choices: [128, 256, 512]  # d_model in MAMBA-2 architecture
    state_size:
      type: categorical
      choices: [8, 16, 32]  # d_state: Critical for state-space dynamics
    num_layers:
      type: categorical
      choices: [2, 4, 8]  # Layer depth affects memory and expressiveness
    expansion_factor:
      type: categorical
      choices: [2, 4]  # expand: Controls inner dimension (d_inner = expand * d_model)
    dropout:
      type: float
      low: 0.0
      high: 0.3
      step: 0.05
    # State-space specific parameters
    dt_min:
      type: float
      low: 0.0001
      high: 0.01
      log: true  # Time-step minimum for discrete-time dynamics
    dt_max:
      type: float
      low: 0.01
      high: 1.0
      log: true  # Time-step maximum
    # Architecture toggles
    use_ssd:
      type: categorical
      choices: [true, false]  # Structured State Duality
    use_selective_state:
      type: categorical
      choices: [true, false]  # Selective state mechanism
    hardware_aware:
      type: categorical
      choices: [true, false]  # Hardware-aware optimizations (RTX 3050 Ti)
    # Training parameters
    grad_clip:
      type: float
      low: 0.5
      high: 2.0
      step: 0.25  # Gradient clipping for state-space stability
    weight_decay:
      type: float
      low: 0.0001
      high: 0.01
      log: true
    warmup_steps:
      type: int
      low: 100
      high: 2000
      step: 100

  DQN:
    epochs:
      type: int
      low: 50
      high: 500
      step: 50
    learning_rate:
      type: float
      low: 0.00001
      high: 0.01
      log: true
    batch_size:
      type: categorical
      choices: [32, 64, 128, 256]
    replay_buffer_size:
      type: categorical
      choices: [10000, 50000, 100000, 500000]
    epsilon_start:
      type: float
      low: 0.9
      high: 1.0
      step: 0.05
    epsilon_end:
      type: float
      low: 0.01
      high: 0.1
      step: 0.01
    epsilon_decay_steps:
      type: int
      low: 1000
      high: 10000
      step: 1000
    gamma:
      type: float
      low: 0.9
      high: 0.999
      step: 0.01
    target_update_frequency:
      type: int
      low: 100
      high: 1000
      step: 100
    use_prioritized_replay:
      type: categorical
      choices: [true, false]

  PPO:
    epochs:
      type: int
      low: 50
      high: 500
      step: 50
    learning_rate:
      type: float
      low: 0.00001
      high: 0.01
      log: true
    batch_size:
      type: categorical
      choices: [64, 128, 256, 512]
    clip_ratio:
      type: float
      low: 0.1
      high: 0.3
      step: 0.05
    value_loss_coef:
      type: float
      low: 0.1
      high: 1.0
      step: 0.1
    entropy_coef:
      type: float
      low: 0.0
      high: 0.1
      step: 0.01
    rollout_steps:
      type: int
      low: 128
      high: 2048
      step: 128
    minibatch_size:
      type: categorical
      choices: [32, 64, 128, 256]
    gae_lambda:
      type: float
      low: 0.9
      high: 0.99
      step: 0.01

  LIQUID:
    epochs:
      type: int
      low: 20
      high: 100
      step: 10
    learning_rate:
      type: categorical
      choices: [0.0001, 0.001, 0.01]  # 3 learning rate options for fast convergence
    batch_size:
      type: categorical
      choices: [32, 64, 128]  # 3 batch size options
    hidden_dim:
      type: categorical
      choices: [64, 128, 256]  # 3 hidden dimension options for LTC/CfC cells
    # ODE Integration Parameters (Critical for continuous-time dynamics)
    ode_steps:
      type: categorical
      choices: [3, 5, 10, 20]  # Number of integration steps per forward pass
    solver_type:
      type: categorical
      choices: ["Euler", "RK4", "Adaptive"]  # ODE solver selection
    # Sparsity and Network Structure
    sparsity_level:
      type: categorical
      choices: [0.5, 0.7, 0.9]  # Connection sparsity (0.9 = 90% connections pruned)
    num_layers:
      type: categorical
      choices: [1, 2, 3]  # Depth of liquid network
    # Time Constant Parameters (tau)
    time_constant_tau:
      type: categorical
      choices: [0.01, 0.1, 1.0]  # Base time constant for ODE dynamics
    tau_min:
      type: float
      low: 0.001
      high: 0.05
      log: true
    tau_max:
      type: float
      low: 0.5
      high: 5.0
      log: true
    use_adaptive_tau:
      type: categorical
      choices: [true, false]  # Enable/disable volatility-aware tau adaptation
    # Activation Functions
    activation:
      type: categorical
      choices: ["Tanh", "Sigmoid", "ReLU"]  # Cell activation function
    output_activation:
      type: categorical
      choices: ["Linear", "Tanh", "Sigmoid"]  # Output layer activation
    # Regularization
    dropout_rate:
      type: float
      low: 0.0
      high: 0.3
      step: 0.05
    l2_regularization:
      type: float
      low: 0.00001
      high: 0.001
      log: true
    # Network Type
    network_type:
      type: categorical
      choices: ["LTC", "CfC", "Mixed"]  # Liquid Time-constant, Closed-form Continuous-time, or Mixed
    # Market Regime Adaptation
    market_regime_adaptation:
      type: categorical
      choices: [true, false]  # Enable regime-aware time step adaptation
    # Early Stopping
    early_stopping_patience:
      type: int
      low: 5
      high: 20
      step: 5
    # Default time step (dt) for ODE integration
    default_dt:
      type: float
      low: 0.001
      high: 0.1
      log: true

  TFT:
    epochs:
      type: int
      low: 10
      high: 100
      step: 10
    learning_rate:
      type: float
      low: 0.00001
      high: 0.01
      log: true
    batch_size:
      type: categorical
      choices: [32, 64, 128, 256]
    hidden_dim:
      type: categorical
      choices: [64, 128, 256, 512]
    num_heads:
      type: categorical
      choices: [4, 8, 16]
    num_layers:
      type: int
      low: 2
      high: 8
      step: 1
    lookback_window:
      type: int
      low: 10
      high: 100
      step: 10
    forecast_horizon:
      type: int
      low: 1
      high: 20
      step: 1
    dropout_rate:
      type: float
      low: 0.0
      high: 0.5
      step: 0.05