# ML Model Parameters Configuration
# Centralized configuration for all AI/ML model parameters

[dqn]
# Deep Q-Network parameters
state_size = 50              # Market features input size
action_size = 3              # Buy, Sell, Hold
hidden_sizes = [256, 256]    # Hidden layer dimensions
learning_rate = 0.001        # Learning rate for training
gamma = 0.99                 # Discount factor for future rewards
target_update_freq = 1000    # Target network update frequency
dropout_rate = 0.1           # Dropout rate for regularization
memory_size = 100000         # Replay buffer capacity
batch_size = 32              # Training batch size

[dqn.hft_optimized]
# HFT-specific optimized parameters
state_size = 40              # Reduced feature set for speed
action_size = 3              # Buy, Sell, Hold
hidden_sizes = [128, 128]    # Smaller networks for latency
learning_rate = 0.0005       # Lower learning rate for stability
gamma = 0.95                 # Lower discount for HFT (shorter horizon)
target_update_freq = 500     # More frequent target updates
dropout_rate = 0.05          # Lower dropout for HFT
memory_size = 50000          # Smaller buffer for recent data focus
batch_size = 64              # Larger batches for stable gradients

[agent]
# DQN Agent parameters
epsilon = 1.0                # Initial exploration rate
epsilon_min = 0.01           # Minimum exploration rate
epsilon_decay = 0.995        # Exploration decay rate
min_replay_size = 1000       # Minimum samples before training

[agent.hft_optimized]
# HFT-specific agent parameters
epsilon = 0.3                # Lower initial exploration for HFT
epsilon_min = 0.001          # Very low minimum exploration
epsilon_decay = 0.9995       # Slower decay for stable learning
min_replay_size = 2000       # More samples before training

[mamba]
# MAMBA model parameters
model_dim = 768              # Model dimension
state_size = 64              # State space size
conv_kernel = 4              # Convolution kernel size
expand_factor = 2            # Expansion factor
dt_rank = "auto"             # Delta time rank

[liquid]
# Liquid AI model parameters
model_type = "LFM1B"         # Model type (LFM1B, LFM3B, LFM40B, Custom)
hidden_size = 768            # Hidden layer size
num_layers = 12              # Number of transformer layers
num_heads = 12               # Number of attention heads
intermediate_size = 3072     # Feed-forward intermediate size

[tft]
# Temporal Fusion Transformer parameters
hidden_size = 128            # Hidden layer size
num_encoder_layers = 2       # Number of encoder layers
num_decoder_layers = 2       # Number of decoder layers
num_heads = 8                # Number of attention heads
dropout_rate = 0.1           # Dropout rate
attention_dropout = 0.1      # Attention dropout rate

[tggn]
# Temporal Graph Generation Network parameters
hidden_size = 256            # Hidden layer size
num_layers = 4               # Number of graph layers
num_heads = 8                # Number of attention heads
edge_dim = 64                # Edge feature dimension
max_nodes = 1000             # Maximum nodes in graph

[nlp]
# NLP model parameters
model_type = "FinBERT"       # Model type (BERT, RoBERTa, DistilBERT, FinBERT, Custom)
hidden_size = 768            # Hidden size
num_layers = 12              # Number of layers
num_heads = 12               # Number of attention heads
max_sequence_length = 512    # Maximum sequence length
vocab_size = 30522           # Vocabulary size

[inference]
# Inference parameters
max_latency_us = 100         # Maximum inference latency target
inference_threads = 8        # Number of inference threads  
batch_size = 32              # Batch size for batch inference
warmup_iterations = 100      # Model warmup iterations on startup
enable_gpu = true            # Enable GPU acceleration
device_id = 0                # CUDA device ID
memory_pool_mb = 1024        # GPU memory pool size
enable_mixed_precision = true # Enable mixed precision inference

[model_paths]
# Model file paths
models_dir = "./models"
venue_selection = "./models/venue_selection.onnx"
risk_assessment = "./models/risk_assessment.onnx"
sentiment_analysis = "./models/sentiment.onnx"

[model_configs]
# Model type configurations
venue_selection_type = "onnx"
venue_selection_input_size = 50
venue_selection_output_size = 10
risk_assessment_type = "onnx"
risk_assessment_input_size = 30
risk_assessment_output_size = 1
sentiment_analysis_type = "onnx"
sentiment_analysis_input_size = 512
sentiment_analysis_output_size = 3

[normalization]
# Feature normalization parameters
enable_normalization = true
means = []                   # Will be populated during training
stds = []                    # Will be populated during training
min_vals = []                # Will be populated during training
max_vals = []                # Will be populated during training

[validation]
# Model validation parameters
clamp_min = -1000.0          # Minimum value clamp
clamp_max = 1000.0           # Maximum value clamp
finite_check = true          # Check for NaN/Inf values