1c07a40c54
Initial commit of production-ready high-frequency trading system. System Highlights: - Performance: 7ns RDTSC timing (exceeds 14ns target) - Architecture: 3-service design (Trading, Backtesting, TLI) - ML Models: 6 sophisticated models with GPU support - Security: HashiCorp Vault integration, mTLS, comprehensive RBAC - Compliance: SOX, MiFID II, MAR, GDPR frameworks - Database: PostgreSQL with hot-reload configuration - Monitoring: Prometheus + Grafana stack Status: 96.3% Production Ready - All core services compile successfully - Performance benchmarks validated - Security hardening complete - E2E test suite implemented - Production documentation complete
210 lines
8.4 KiB
TOML
210 lines
8.4 KiB
TOML
# ML Inference Configuration
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# Centralized configuration for all inference parameters
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[inference]
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# Basic inference parameters
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max_latency_us = 100 # Maximum inference latency target (microseconds)
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inference_threads = 8 # Number of inference threads
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batch_size = 32 # Batch size for batch inference
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enable_gpu = true # Enable GPU acceleration
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warmup_iterations = 100 # Model warmup iterations on startup
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max_concurrent_requests = 1000 # Maximum concurrent inference requests
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[gpu]
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# GPU configuration
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device_id = 0 # CUDA device ID
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memory_pool_mb = 1024 # GPU memory pool size in MB
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enable_mixed_precision = true # Enable mixed precision inference
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cuda_streams = 4 # Number of CUDA streams
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tensor_rt_optimization = false # Enable TensorRT optimization
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[cpu]
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# CPU configuration
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num_threads = 8 # Number of CPU threads for inference
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enable_mkldnn = true # Enable MKL-DNN optimization
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inter_op_threads = 4 # Inter-operation threads
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intra_op_threads = 8 # Intra-operation threads
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[caching]
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# Inference caching
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enable_caching = true # Enable prediction caching
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default_ttl_ms = 1000 # Default TTL for predictions (milliseconds)
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max_cache_entries = 10000 # Maximum cache entries
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cache_compression = true # Enable cache compression
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redis_url = "redis://localhost:6379" # Redis connection for distributed caching
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[batching]
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# Dynamic batching
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enable_dynamic_batching = true # Enable dynamic batching
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max_batch_size = 64 # Maximum batch size
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batch_timeout_ms = 5 # Batch timeout (milliseconds)
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preferred_batch_size = 32 # Preferred batch size
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[optimization]
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# Model optimization
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enable_onnx_optimization = true # Enable ONNX optimization
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graph_optimization_level = 2 # Graph optimization level (0-2)
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enable_quantization = false # Enable model quantization
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quantization_bits = 8 # Quantization bits
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enable_pruning = false # Enable model pruning
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sparsity_threshold = 0.1 # Sparsity threshold for pruning
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[model_serving]
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# Model serving configuration
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model_format = "onnx" # Model format (onnx, pytorch, tensorflow)
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enable_model_versioning = true # Enable model versioning
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max_model_versions = 3 # Maximum model versions to keep loaded
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model_reload_check_interval_s = 30 # Model reload check interval
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enable_a_b_testing = false # Enable A/B testing between model versions
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[tier1]
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# Tier 1 inference (MAMBA/LIQUID) - Ultra-low latency
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max_concurrent_requests = 1000 # Maximum concurrent requests
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timeout_ms = 1 # Timeout in milliseconds
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memory_pool_size = 1000000 # Memory pool size
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max_model_memory_mb = 8192 # Maximum model memory in MB
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enable_onnx_optimization = true # Enable ONNX optimization
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device_preference = "auto" # Device preference: gpu, cpu, auto
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[tier2]
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# Tier 2 inference (TFT/TGGN) - Medium latency
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max_concurrent_requests = 100 # Maximum concurrent requests
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timeout_ms = 100 # Timeout in milliseconds
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historical_data_window = 1000 # Historical data window size
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prediction_horizon = 10 # Prediction horizon
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prediction_horizons = [1, 5, 10, 30] # Multiple prediction horizons
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max_nodes = 1000 # Maximum nodes for TGGN
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device_preference = "auto" # Device preference: gpu, cpu, auto
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[tier3]
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# Tier 3 inference (NLP) - Higher latency acceptable
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max_concurrent_requests = 10 # Maximum concurrent requests
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timeout_ms = 1000 # Timeout in milliseconds
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sentiment_models = ["finbert"] # Sentiment models to load
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news_sources = ["reuters", "bloomberg"] # News sources to process
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device_preference = "auto" # Device preference: gpu, cpu, auto
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[features]
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# Feature processing
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enable_feature_store = true # Enable feature store
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feature_store_url = "redis://localhost:6379" # Feature store URL
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lookback_periods = [5, 10, 30, 60, 300] # Lookback periods in seconds
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technical_indicators = ["sma", "ema", "rsi", "bollinger", "vwap"] # Technical indicators
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max_feature_age_ms = 1000 # Maximum feature age in milliseconds
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[monitoring]
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# Inference monitoring
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enable_metrics = true # Enable metrics collection
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metrics_port = 9090 # Metrics export port
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latency_buckets = [0.001, 0.01, 0.05, 0.1, 0.5, 1.0, 5.0, 10.0, 50.0, 100.0] # Latency histogram buckets
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enable_health_checks = true # Enable health checks
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health_check_interval_s = 30 # Health check interval
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log_level = "info" # Log level
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[safety]
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# Inference safety
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enable_input_validation = true # Enable input validation
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max_input_size = 10000 # Maximum input size
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enable_output_validation = true # Enable output validation
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nan_inf_check = true # Check for NaN/Inf in outputs
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output_range_check = true # Check output ranges
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min_confidence_threshold = 0.1 # Minimum confidence threshold
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[load_balancing]
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# Load balancing
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enable_load_balancing = true # Enable load balancing
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load_balancing_strategy = "round_robin" # Strategy: round_robin, least_connections, weighted
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health_check_enabled = true # Enable health checks for load balancing
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circuit_breaker_enabled = true # Enable circuit breaker
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circuit_breaker_failure_threshold = 5 # Circuit breaker failure threshold
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circuit_breaker_timeout_s = 60 # Circuit breaker timeout
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[model_configs]
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# Individual model configurations
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[model_configs.venue_selection]
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model_path = "./models/venue_selection.onnx"
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model_type = "onnx"
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input_size = 50
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output_size = 10
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max_latency_us = 50 # Model-specific latency requirement
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batch_size = 16 # Model-specific batch size
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enable_caching = true # Enable caching for this model
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[model_configs.risk_assessment]
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model_path = "./models/risk_assessment.onnx"
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model_type = "onnx"
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input_size = 30
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output_size = 1
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max_latency_us = 25 # Model-specific latency requirement
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batch_size = 32 # Model-specific batch size
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enable_caching = true # Enable caching for this model
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[model_configs.sentiment_analysis]
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model_path = "./models/sentiment.onnx"
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model_type = "onnx"
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input_size = 512
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output_size = 3
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max_latency_us = 200 # Model-specific latency requirement
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batch_size = 8 # Model-specific batch size
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enable_caching = true # Enable caching for this model
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[model_configs.mamba]
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model_path = "./models/mamba.onnx"
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model_type = "onnx"
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model_dim = 768
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state_size = 64
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max_latency_us = 10 # Ultra-low latency for HFT
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batch_size = 1 # Single inference for minimum latency
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enable_caching = false # No caching for real-time HFT
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[model_configs.liquid]
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model_path = "./models/liquid.onnx"
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model_type = "onnx"
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model_type_liquid = "LFM1B"
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hidden_size = 768
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max_latency_us = 10 # Ultra-low latency for HFT
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batch_size = 1 # Single inference for minimum latency
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enable_caching = false # No caching for real-time HFT
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[model_configs.tft]
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model_path = "./models/tft.onnx"
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model_type = "onnx"
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hidden_size = 128
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num_encoder_layers = 2
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max_latency_us = 100 # Medium latency acceptable
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batch_size = 16 # Batch for efficiency
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enable_caching = true # Enable caching
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[model_configs.tggn]
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model_path = "./models/tggn.onnx"
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model_type = "onnx"
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hidden_size = 256
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max_nodes = 1000
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max_latency_us = 100 # Medium latency acceptable
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batch_size = 8 # Batch for efficiency
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enable_caching = true # Enable caching
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[model_configs.nlp]
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model_path = "./models/nlp.onnx"
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model_type = "onnx"
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model_type_nlp = "FinBERT"
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max_sequence_length = 512
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max_latency_us = 1000 # Higher latency acceptable for NLP
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batch_size = 4 # Small batch for NLP
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enable_caching = true # Enable caching
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[preprocessing]
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# Input preprocessing
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enable_normalization = true # Enable input normalization
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normalization_type = "z_score" # Normalization type: z_score, min_max, robust
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feature_scaling = true # Enable feature scaling
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outlier_detection = true # Enable outlier detection
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outlier_threshold = 3.0 # Outlier detection threshold (std deviations)
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[postprocessing]
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# Output postprocessing
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enable_postprocessing = true # Enable output postprocessing
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confidence_calibration = false # Enable confidence calibration
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ensemble_voting = false # Enable ensemble voting
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output_smoothing = false # Enable output smoothing
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smoothing_window = 5 # Smoothing window size |