1400 lines
52 KiB
Markdown
1400 lines
52 KiB
Markdown
SILOQY DOLPHIN Integration Engineering Specification
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====================================================
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**Document Version:** 1.0
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**Date:** August 31, 2025
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**Target Audience:** SILOQY Component Developers
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**Framework:** Nautilus Trader Integration Architecture
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Executive Summary
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-----------------
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This document provides the complete technical specification for integrating with
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the SILOQY DOLPHIN market regime detection system within the Nautilus Trader
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framework. It details the message bus architecture, actor lifecycle management,
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data flow patterns, and exact implementation requirements for new SILOQY
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components.
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Table of Contents
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-----------------
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1. [Architecture Overview](#architecture-overview)
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2. [Nautilus Message Bus Communication](#nautilus-message-bus-communication)
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3. [Actor Lifecycle and Execution Model](#actor-lifecycle-and-execution-model)
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4. [SILOQY Topic Hierarchy and Data
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Structures](#siloqy-topic-hierarchy-and-data-structures)
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5. [Producer/Consumer Patterns](#producer-consumer-patterns)
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6. [Component Integration Patterns](#component-integration-patterns)
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7. [Error Handling and Recovery](#error-handling-and-recovery)
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8. [Performance Optimization Guidelines](#performance-optimization-guidelines)
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9. [Testing and Validation Requirements](#testing-and-validation-requirements)
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10. [Code Examples and Templates](#code-examples-and-templates)
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Architecture Overview
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---------------------
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### System Components
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The SILOQY DOLPHIN system operates within a single Nautilus TradingNode instance
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with four core actors:
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1. **SILOQYSymbolDiscoveryActor**: Symbol discovery and candle reconstruction
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2. **SILOQYMainActor**: Real-time data ingestion and WebSocket management
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3. **DOLPHINRegimeActor**: Market regime detection and analysis
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4. **SILOQYNormalizerActor**: Tick normalization and format standardization
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### Data Flow Architecture
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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External Market Data → SILOQYMainActor → MessageBus → [DOLPHINRegimeActor, SILOQYNormalizerActor]
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↓
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Custom SILOQY Components
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Shared Node Pattern
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**Critical Design Principle:** New SILOQY components MUST integrate into the
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existing TradingNode instance rather than creating separate nodes. This ensures:
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- Shared message bus access - Unified actor lifecycle management - Consistent
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resource allocation - Optimal performance through single-process architecture
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Nautilus Message Bus Communication
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----------------------------------
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### Message Bus Access Pattern
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All SILOQY actors gain access to the Nautilus MessageBus through the standard
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Nautilus actor initialization:
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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class MySILOQYComponent(Actor):
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def __init__(self, config: MyComponentConfig):
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super().__init__(config)
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# MessageBus is available as self.msgbus after Nautilus initialization
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def on_start(self) -> None:
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# Subscribe to topics ONLY in on_start()
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if hasattr(self, 'msgbus') and self.msgbus:
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self.msgbus.subscribe("SILOQY.MY.TOPIC", self.handle_my_data)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Message Publishing Protocol
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**High-Performance Publishing Pattern:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def publish_siloqy_data(self, topic: str, data: Any) -> None:
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"""Standard SILOQY publishing pattern"""
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try:
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if hasattr(self, 'msgbus') and self.msgbus:
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self.msgbus.publish(topic, data)
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else:
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self.log.error("MessageBus not available for publishing")
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except Exception as e:
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self.log.error(f"Failed to publish to {topic}: {e}")
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Subscription Management
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**Thread-Safe Subscription Pattern:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def on_start(self) -> None:
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"""Subscribe to required SILOQY topics"""
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if hasattr(self, 'msgbus') and self.msgbus:
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# Subscribe to core SILOQY data streams
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self.msgbus.subscribe(RAW_TOPIC, self.handle_raw_tick)
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self.msgbus.subscribe(REGIME_TOPIC, self.handle_regime_update)
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self.msgbus.subscribe("SILOQY.CUSTOM.TOPIC", self.handle_custom_data)
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self.log.info("Subscribed to SILOQY message bus topics")
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else:
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self.log.error("MessageBus not available during initialization")
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Message Bus Timing Guarantees
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- **Publication Latency:** \< 1μs for tuple data
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- **Delivery Guarantee:** Synchronous delivery to all subscribers
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- **Thread Safety:** Fully thread-safe for concurrent access
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- **Memory Management:** Zero-copy for immutable data types
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Actor Lifecycle and Execution Model
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-----------------------------------
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### Nautilus Actor Initialization Sequence
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1. **Construction Phase:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def __init__(self, config: ActorConfig):
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super().__init__(config) # MUST call super().__init__()
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# Initialize component-specific state
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# MessageBus NOT available yet
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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2. **Registration Phase:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# Handled automatically by TradingNode.build()
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# Actor registered with Nautilus kernel
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# MessageBus reference established
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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3. **Startup Phase:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def on_start(self) -> None:
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# MessageBus NOW available as self.msgbus
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# Subscribe to required topics
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# Initialize async tasks using ActorExecutor
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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4. **Execution Phase:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# Actor receives messages via subscribed handlers
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# Actor publishes data via self.msgbus.publish()
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# Long-running tasks managed by ActorExecutor
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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5. **Shutdown Phase:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def on_stop(self) -> None:
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# Clean up component-specific resources
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# Nautilus handles ActorExecutor cleanup automatically
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### ActorExecutor Task Management
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**Preferred Pattern for Async Operations:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def on_start(self) -> None:
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if hasattr(self, '_executor') and self._executor:
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self.log.info("Using Nautilus ActorExecutor")
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future = self._executor.submit(self.async_initialization())
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else:
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self.log.warning("No executor available, falling back to asyncio")
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asyncio.create_task(self.async_initialization())
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async def async_initialization(self):
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# Long-running async operations
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# Managed by Nautilus ActorExecutor
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pass
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Actor State Management
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**Thread-Safe State Pattern:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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class MySILOQYActor(Actor):
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def __init__(self, config):
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super().__init__(config)
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# Pre-allocated arrays for high-frequency data (DOLPHIN pattern)
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self.data_arrays = np.zeros(config.max_items, dtype=np.float64)
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self.symbol_mapping = {}
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self.active_count = 0
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# Thread-safe collections for shared state
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self.message_queue = deque(maxlen=10000)
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self.state_lock = threading.Lock()
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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SILOQY Topic Hierarchy and Data Structures
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------------------------------------------
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### Core SILOQY Topics
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# Primary data flow topics
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RAW_TOPIC = "SILOQY.RAW.TICKS" # Raw market tick data
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STRUCTURED_TOPIC = "SILOQY.STRUCTURED.TICKS" # Normalized SiloqyTradeTick objects
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REGIME_TOPIC = "DOLPHIN.REGIME.RESULTS" # Market regime detection results
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# Discovery and initialization topics
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SYMBOLS_DISCOVERED_TOPIC = "SILOQY.SYMBOLS.DISCOVERED" # Symbol discovery completion
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CANDLES_INITIAL_TOPIC = "SILOQY.CANDLES.INITIAL" # Candle reconstruction results
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# Custom component topics (examples)
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HD_VECTOR_TOPIC = "SILOQY.HD.VECTORS" # Hyperdimensional vector data
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TEMPORAL_TOPIC = "SILOQY.TEMPORAL.PATTERNS" # Temporal memory patterns
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ANALYSIS_TOPIC = "SILOQY.ANALYSIS.RESULTS" # Custom analysis results
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Topic Naming Convention
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**Standard Format:** `SILOQY.{COMPONENT}.{DATA_TYPE}.{VARIANT}`
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**Examples:** - `SILOQY.SENTIMENT.ANALYSIS.REALTIME` -
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`SILOQY.CORRELATION.MATRIX.UPDATED` - `SILOQY.RISK.METRICS.CALCULATED` -
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`SILOQY.ML.PREDICTIONS.GENERATED`
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### Data Structure Specifications
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#### Raw Tick Data Format (RAW_TOPIC)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# Tuple format: (symbol, price, size, ts_event, open_price, candle_open_time)
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raw_tick_tuple = (
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"BTCUSDT", # str: Symbol identifier
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45000.12345678, # float: Current price (8 decimal precision)
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1.23456789, # float: Trade quantity (8 decimal precision)
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1693489234567, # int: Event timestamp (milliseconds)
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44950.87654321, # float: Current candle open price (8 decimal precision)
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1693488900000 # int: Candle open time (milliseconds)
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)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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#### Structured Tick Data Format (STRUCTURED_TOPIC)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# SiloqyTradeTick object with additional SILOQY fields
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structured_tick = SiloqyTradeTick(
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instrument_id="BTCUSDT.BINANCE", # InstrumentId: Nautilus format
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price=Price(45000.12345678, 8), # Price: Nautilus price object
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size=Quantity(1.23456789, 8), # Quantity: Nautilus quantity object
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ts_event=1693489234567000000, # int: Nanosecond timestamp
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open_price=44950.87654321, # float: SILOQY candle open price
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candle_open_time=1693488900000, # int: SILOQY candle open time
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exchange="BINANCE", # str: SILOQY exchange identifier
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tick_side=None, # Optional[str]: Order side
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liquidity_flag=None # Optional[str]: Liquidity classification
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)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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#### Regime Detection Results (REGIME_TOPIC)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# Tuple format: (timestamp, regime, bull_ratio, bear_ratio, sideways_ratio, analyzed, total, confidence)
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regime_tuple = (
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1693489234567, # int: Detection timestamp (milliseconds)
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"BULL", # str: Regime type ["BULL", "BEAR", "SIDEWAYS"]
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0.67, # float: Bull ratio (0.0-1.0)
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0.25, # float: Bear ratio (0.0-1.0)
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0.08, # float: Sideways ratio (0.0-1.0)
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150, # int: Symbols analyzed
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180, # int: Total symbols available
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0.82 # float: Confidence score (0.0-1.0)
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)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Custom Data Structure Template
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@dataclass
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class SILOQYCustomData:
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"""Template for custom SILOQY data structures"""
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component_id: str
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timestamp: int
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data_type: str
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payload: Dict[str, Any]
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confidence: float = 1.0
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metadata: Dict[str, Any] = field(default_factory=dict)
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def to_message_tuple(self) -> Tuple:
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"""Convert to message bus tuple format"""
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return (
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self.component_id,
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self.timestamp,
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self.data_type,
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json.dumps(self.payload),
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self.confidence,
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json.dumps(self.metadata)
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)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Producer/Consumer Patterns
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--------------------------
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### High-Frequency Data Producer Pattern
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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class SILOQYHighFrequencyProducer(Actor):
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"""Template for high-frequency data producers"""
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def __init__(self, config):
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super().__init__(config)
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self.publish_count = 0
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self.last_publish_time = 0
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def produce_data_efficiently(self, data: Any, topic: str) -> None:
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"""High-performance data publishing"""
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try:
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# Validate data before publishing
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if self._validate_data(data):
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self.msgbus.publish(topic, data)
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self.publish_count += 1
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# Performance monitoring
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if self.publish_count % 1000 == 0:
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self._log_performance_metrics()
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else:
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self.log.warning(f"Invalid data rejected for topic {topic}")
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except Exception as e:
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self.log.error(f"Publishing failed for topic {topic}: {e}")
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def _validate_data(self, data: Any) -> bool:
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"""Implement component-specific validation"""
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# Override in derived classes
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return True
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def _log_performance_metrics(self) -> None:
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"""Log performance metrics"""
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current_time = time.time()
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if self.last_publish_time > 0:
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duration = current_time - self.last_publish_time
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rate = 1000 / duration if duration > 0 else 0
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self.log.info(f"Publishing rate: {rate:.1f} messages/sec")
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self.last_publish_time = current_time
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Data Consumer Pattern
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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class SILOQYDataConsumer(Actor):
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"""Template for SILOQY data consumers"""
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def __init__(self, config):
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super().__init__(config)
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self.processed_count = 0
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self.processing_queue = deque(maxlen=10000)
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self.batch_size = 100
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def on_start(self) -> None:
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"""Subscribe to required SILOQY topics"""
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if hasattr(self, 'msgbus') and self.msgbus:
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# Subscribe to multiple SILOQY data streams
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self.msgbus.subscribe(RAW_TOPIC, self.handle_raw_tick)
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self.msgbus.subscribe(REGIME_TOPIC, self.handle_regime_update)
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self.msgbus.subscribe("SILOQY.CUSTOM.DATA", self.handle_custom_data)
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self.log.info("SILOQY consumer subscriptions established")
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def handle_raw_tick(self, data: Tuple) -> None:
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"""Handle raw tick data with efficient processing"""
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try:
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# Parse data
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symbol, price, size, ts_event, open_price, candle_open_time = data
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# Add to processing queue
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self.processing_queue.append({
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'type': 'raw_tick',
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'symbol': symbol,
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'price': price,
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'size': size,
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'timestamp': ts_event,
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'open_price': open_price,
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'candle_open_time': candle_open_time
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})
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# Batch processing for efficiency
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if len(self.processing_queue) >= self.batch_size:
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self._process_batch()
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except Exception as e:
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self.log.error(f"Raw tick processing error: {e}")
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def handle_regime_update(self, data: Tuple) -> None:
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"""Handle DOLPHIN regime updates"""
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try:
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timestamp, regime, bull_ratio, bear_ratio, sideways_ratio, analyzed, total, confidence = data
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# Process regime change
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self._on_regime_change(regime, bull_ratio, bear_ratio, confidence)
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except Exception as e:
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self.log.error(f"Regime update processing error: {e}")
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def _process_batch(self) -> None:
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"""Process queued data in batches for efficiency"""
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batch = list(self.processing_queue)
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self.processing_queue.clear()
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# Process batch with single operation
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self._bulk_process_data(batch)
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self.processed_count += len(batch)
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def _bulk_process_data(self, batch: List[Dict]) -> None:
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"""Override in derived classes for bulk processing"""
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pass
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def _on_regime_change(self, regime: str, bull_ratio: float, bear_ratio: float, confidence: float) -> None:
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"""Override in derived classes for regime-based logic"""
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pass
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Actor Lifecycle and Execution Model
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-----------------------------------
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### TradingNode Initialization Sequence
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1. **Node Creation:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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trading_config = TradingNodeConfig(
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trader_id=TraderId("SILOQY-PRODUCTION"),
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actors=[
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# All SILOQY actor configurations
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symbol_discovery_config,
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main_actor_config,
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regime_actor_config,
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normalizer_config,
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custom_component_config # New components added here
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]
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)
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node = TradingNode(config=trading_config)
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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2. **Build Phase:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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node.build() # Creates all actors, establishes MessageBus
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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3. **Startup Phase:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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node.run() # Calls on_start() on all actors in dependency order
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Actor Dependency Management
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**Initialization Order (Critical):** 1. `SILOQYSymbolDiscoveryActor` - Must
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complete first 2. `SILOQYMainActor` - Depends on symbol discovery 3.
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`DOLPHINRegimeActor` - Depends on main actor tick data 4.
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`SILOQYNormalizerActor` - Depends on raw tick data 5. **Custom Components** -
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Typically depend on normalized data or regime results
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### Async Task Management with ActorExecutor
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**ActorExecutor Usage Pattern:**
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def on_start(self) -> None:
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# Start long-running operations via ActorExecutor
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if hasattr(self, '_executor') and self._executor:
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self.log.info("Using Nautilus ActorExecutor for task management")
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future = self._executor.submit(self.long_running_operation())
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else:
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# Fallback for development/testing
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asyncio.create_task(self.long_running_operation())
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async def long_running_operation(self):
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"""Long-running async operation managed by Nautilus"""
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while self.is_running:
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try:
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# Perform periodic operations
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await self.process_periodic_task()
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await asyncio.sleep(1.0)
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except asyncio.CancelledError:
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break
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except Exception as e:
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self.log.error(f"Long-running operation error: {e}")
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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### Actor State Synchronization
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**Synchronization Pattern for Actor Dependencies:**
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|
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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class DependentSILOQYActor(Actor):
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def __init__(self, config):
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super().__init__(config)
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self.dependency_ready = asyncio.Event()
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self.symbols = []
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self.candle_data = {}
|
|
|
|
def on_start(self) -> None:
|
|
# Subscribe to dependency notifications
|
|
self.msgbus.subscribe(SYMBOLS_DISCOVERED_TOPIC, self.handle_symbols_ready)
|
|
self.msgbus.subscribe(CANDLES_INITIAL_TOPIC, self.handle_candles_ready)
|
|
|
|
# Start dependent operations
|
|
if hasattr(self, '_executor') and self._executor:
|
|
future = self._executor.submit(self.wait_for_dependencies())
|
|
|
|
async def wait_for_dependencies(self):
|
|
"""Wait for required data before starting operations"""
|
|
await asyncio.wait_for(self.dependency_ready.wait(), timeout=120.0)
|
|
# Now safe to start component-specific operations
|
|
await self.start_component_operations()
|
|
|
|
def handle_symbols_ready(self, data):
|
|
symbols, timestamp = data
|
|
self.symbols = symbols
|
|
self._check_dependencies_complete()
|
|
|
|
def handle_candles_ready(self, data):
|
|
candles, timestamp = data
|
|
self.candle_data = candles
|
|
self._check_dependencies_complete()
|
|
|
|
def _check_dependencies_complete(self):
|
|
if self.symbols and self.candle_data:
|
|
self.dependency_ready.set()
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
Component Integration Patterns
|
|
------------------------------
|
|
|
|
### Adding New SILOQY Components
|
|
|
|
**Step 1: Component Configuration**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
class MySILOQYComponentConfig(ActorConfig):
|
|
"""Configuration for custom SILOQY component"""
|
|
component_name: str = "MyComponent"
|
|
update_interval_ms: int = 1000
|
|
enable_hd_processing: bool = True
|
|
custom_parameters: Dict[str, Any] = field(default_factory=dict)
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
**Step 2: Actor Implementation**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
class MySILOQYComponent(Actor):
|
|
def __init__(self, config: MySILOQYComponentConfig):
|
|
super().__init__(config)
|
|
self.component_name = config.component_name
|
|
self.update_interval_ms = config.update_interval_ms
|
|
self.enable_hd_processing = config.enable_hd_processing
|
|
|
|
def on_start(self) -> None:
|
|
# Subscribe to required SILOQY data streams
|
|
self.msgbus.subscribe(STRUCTURED_TOPIC, self.handle_structured_tick)
|
|
self.msgbus.subscribe(REGIME_TOPIC, self.handle_regime_update)
|
|
|
|
# Start component-specific operations
|
|
if hasattr(self, '_executor') and self._executor:
|
|
future = self._executor.submit(self.start_processing())
|
|
|
|
def handle_structured_tick(self, tick: SiloqyTradeTick) -> None:
|
|
"""Process incoming SILOQY tick data"""
|
|
# Component-specific processing
|
|
result = self.process_siloqy_tick(tick)
|
|
|
|
# Publish results to custom topic
|
|
if result:
|
|
self.msgbus.publish("SILOQY.MYCOMPONENT.RESULTS", result)
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
**Step 3: Registration with TradingNode**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
my_component_config = ImportableActorConfig(
|
|
actor_path="my_module:MySILOQYComponent",
|
|
config_path="my_module:MySILOQYComponentConfig",
|
|
config={
|
|
"component_id": "MY-SILOQY-COMPONENT",
|
|
"component_name": "CustomAnalyzer",
|
|
"update_interval_ms": 500,
|
|
"enable_hd_processing": True
|
|
}
|
|
)
|
|
|
|
# Add to existing TradingNode configuration
|
|
trading_config = TradingNodeConfig(
|
|
trader_id=TraderId("SILOQY-EXTENDED"),
|
|
actors=[
|
|
symbol_discovery_config,
|
|
main_actor_config,
|
|
regime_actor_config,
|
|
normalizer_config,
|
|
my_component_config # Add custom component here
|
|
]
|
|
)
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
### Inter-Component Communication Patterns
|
|
|
|
**Direct Communication via MessageBus:**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
# Component A publishes analysis results
|
|
def publish_analysis_result(self, analysis_data: Dict):
|
|
custom_data = {
|
|
'component_id': self.id,
|
|
'analysis_type': 'correlation_matrix',
|
|
'timestamp': int(time.time() * 1000),
|
|
'results': analysis_data,
|
|
'confidence': 0.95
|
|
}
|
|
self.msgbus.publish("SILOQY.CORRELATION.ANALYSIS", custom_data)
|
|
|
|
# Component B consumes analysis results
|
|
def on_start(self):
|
|
self.msgbus.subscribe("SILOQY.CORRELATION.ANALYSIS", self.handle_correlation_data)
|
|
|
|
def handle_correlation_data(self, data: Dict):
|
|
# Process correlation analysis from other component
|
|
pass
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
**Request/Response Pattern:**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
# Component A requests data
|
|
def request_analysis(self, symbol: str, timeframe: str):
|
|
request_data = {
|
|
'request_id': str(uuid.uuid4()),
|
|
'requesting_component': str(self.id),
|
|
'symbol': symbol,
|
|
'timeframe': timeframe,
|
|
'response_topic': f"SILOQY.RESPONSE.{self.id}"
|
|
}
|
|
self.msgbus.publish("SILOQY.REQUEST.ANALYSIS", request_data)
|
|
|
|
# Component B responds to requests
|
|
def handle_analysis_request(self, request_data: Dict):
|
|
# Process request
|
|
analysis_result = self.perform_analysis(request_data['symbol'], request_data['timeframe'])
|
|
|
|
# Send response
|
|
response = {
|
|
'request_id': request_data['request_id'],
|
|
'result': analysis_result,
|
|
'timestamp': int(time.time() * 1000)
|
|
}
|
|
self.msgbus.publish(request_data['response_topic'], response)
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
Error Handling and Recovery
|
|
---------------------------
|
|
|
|
### Message Processing Error Handling
|
|
|
|
**Standard Error Handling Pattern:**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
def handle_siloqy_message(self, data: Any) -> None:
|
|
"""Standard SILOQY message handling with error recovery"""
|
|
try:
|
|
# Validate message structure
|
|
if not self._validate_message_structure(data):
|
|
self.log.warning(f"Invalid message structure rejected")
|
|
self._record_validation_failure("structure")
|
|
return
|
|
|
|
# Process message
|
|
result = self._process_message_content(data)
|
|
|
|
# Publish result if successful
|
|
if result:
|
|
self.msgbus.publish(self.output_topic, result)
|
|
|
|
except ValidationError as e:
|
|
self.log.warning(f"Message validation failed: {e}")
|
|
self._record_validation_failure("validation")
|
|
except ProcessingError as e:
|
|
self.log.error(f"Message processing failed: {e}")
|
|
self._record_processing_failure()
|
|
except Exception as e:
|
|
self.log.error(f"Unexpected error in message handling: {e}")
|
|
self._record_unexpected_failure()
|
|
|
|
def _record_validation_failure(self, failure_type: str) -> None:
|
|
"""Record validation failures for monitoring"""
|
|
if not hasattr(self, 'validation_failures'):
|
|
self.validation_failures = defaultdict(int)
|
|
self.validation_failures[failure_type] += 1
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
### Actor Recovery Patterns
|
|
|
|
**Graceful Degradation Pattern:**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
class ResilientSILOQYActor(Actor):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.error_count = 0
|
|
self.max_errors = 100
|
|
self.recovery_mode = False
|
|
|
|
def handle_processing_error(self, error: Exception) -> None:
|
|
"""Handle processing errors with graceful degradation"""
|
|
self.error_count += 1
|
|
|
|
if self.error_count > self.max_errors:
|
|
self.log.error("Error threshold exceeded, entering recovery mode")
|
|
self.recovery_mode = True
|
|
# Reduce processing frequency or disable non-critical features
|
|
|
|
# Reset error count periodically
|
|
if time.time() % 300 == 0: # Every 5 minutes
|
|
self.error_count = max(0, self.error_count - 10)
|
|
if self.error_count < 10:
|
|
self.recovery_mode = False
|
|
self.log.info("Exiting recovery mode")
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
Performance Optimization Guidelines
|
|
-----------------------------------
|
|
|
|
### Zero-Allocation Patterns (DOLPHIN Style)
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
class HighPerformanceSILOQYActor(Actor):
|
|
"""High-performance actor using zero-allocation patterns"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
|
|
# Pre-allocate arrays for high-frequency data
|
|
self.max_symbols = config.max_symbols
|
|
self.price_data = np.zeros(self.max_symbols, dtype=np.float64)
|
|
self.volume_data = np.zeros(self.max_symbols, dtype=np.float64)
|
|
self.timestamp_data = np.zeros(self.max_symbols, dtype=np.int64)
|
|
|
|
# Symbol mapping for O(1) lookups
|
|
self.symbol_to_index = {}
|
|
self.index_to_symbol = {}
|
|
self.active_symbols = 0
|
|
|
|
def process_tick_zero_allocation(self, symbol: str, price: float, volume: float, timestamp: int):
|
|
"""Process tick with zero memory allocation"""
|
|
# Get or assign symbol index
|
|
if symbol not in self.symbol_to_index:
|
|
if self.active_symbols >= self.max_symbols:
|
|
self.log.error(f"Symbol capacity exceeded: {self.max_symbols}")
|
|
return
|
|
|
|
idx = self.active_symbols
|
|
self.symbol_to_index[symbol] = idx
|
|
self.index_to_symbol[idx] = symbol
|
|
self.active_symbols += 1
|
|
else:
|
|
idx = self.symbol_to_index[symbol]
|
|
|
|
# Update arrays in-place (zero allocation)
|
|
self.price_data[idx] = price
|
|
self.volume_data[idx] = volume
|
|
self.timestamp_data[idx] = timestamp
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
### Message Bus Performance Optimization
|
|
|
|
**Efficient Data Serialization:**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
# Prefer tuples over complex objects for high-frequency data
|
|
efficient_message = (symbol, price, volume, timestamp) # Fast
|
|
inefficient_message = {'symbol': symbol, 'price': price, 'volume': volume} # Slower
|
|
|
|
# Use pre-allocated buffers for repeated operations
|
|
class BufferedSILOQYActor(Actor):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.message_buffer = [None] * 1000 # Pre-allocated buffer
|
|
self.buffer_index = 0
|
|
|
|
def publish_efficiently(self, data):
|
|
# Reuse buffer slots to minimize garbage collection
|
|
self.message_buffer[self.buffer_index] = data
|
|
self.msgbus.publish(self.topic, self.message_buffer[self.buffer_index])
|
|
self.buffer_index = (self.buffer_index + 1) % 1000
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
Testing and Validation Requirements
|
|
-----------------------------------
|
|
|
|
### Unit Test Requirements for SILOQY Components
|
|
|
|
**Mandatory Test Coverage:**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
class TestMySILOQYComponent(unittest.TestCase):
|
|
"""Required test patterns for all SILOQY components"""
|
|
|
|
def test_message_bus_integration(self):
|
|
"""Test MessageBus subscription and publishing"""
|
|
# Verify component can subscribe to required topics
|
|
# Verify component can publish to output topics
|
|
# Test message handling with valid and invalid data
|
|
pass
|
|
|
|
def test_actor_lifecycle(self):
|
|
"""Test complete actor lifecycle"""
|
|
# Test initialization with TradingNode
|
|
# Test on_start() behavior
|
|
# Test on_stop() behavior
|
|
# Test resource cleanup
|
|
pass
|
|
|
|
def test_data_validation(self):
|
|
"""Test input data validation"""
|
|
# Test with valid SILOQY data structures
|
|
# Test with malformed data
|
|
# Test boundary conditions
|
|
# Test error recovery
|
|
pass
|
|
|
|
def test_performance_requirements(self):
|
|
"""Test performance under load"""
|
|
# Test high-frequency message processing
|
|
# Test memory usage stability
|
|
# Test latency requirements
|
|
pass
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
### Integration Test Requirements
|
|
|
|
**MessageBus Integration Test Template:**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
def test_full_integration_with_dolphin(self):
|
|
"""Test integration with existing DOLPHIN system"""
|
|
# Create TradingNode with DOLPHIN + custom component
|
|
node = self._create_test_node_with_custom_component()
|
|
|
|
# Verify component receives DOLPHIN data
|
|
collector = TestDataCollector()
|
|
node.kernel.msgbus.subscribe("SILOQY.CUSTOM.OUTPUT", collector.collect_data)
|
|
|
|
# Inject test data through DOLPHIN pipeline
|
|
test_ticks = self._generate_test_tick_data()
|
|
for tick in test_ticks:
|
|
node.kernel.msgbus.publish(RAW_TOPIC, tick)
|
|
|
|
# Verify custom component processed DOLPHIN regime data
|
|
time.sleep(2.0)
|
|
self.assertGreater(len(collector.collected_data), 0)
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
Code Examples and Templates
|
|
---------------------------
|
|
|
|
### Complete Component Template
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
#!/usr/bin/env python3
|
|
"""
|
|
SILOQY Custom Component Template
|
|
Template for integrating new components with SILOQY DOLPHIN system
|
|
"""
|
|
|
|
import time
|
|
import asyncio
|
|
import numpy as np
|
|
from typing import Dict, List, Optional, Any
|
|
from collections import deque, defaultdict
|
|
from dataclasses import dataclass, field
|
|
|
|
from nautilus_trader.common.actor import Actor
|
|
from nautilus_trader.common.config import ActorConfig
|
|
from nautilus_trader.common.component import Logger
|
|
|
|
# SILOQY imports
|
|
from nautilus_actor_test_implementation_5x_modified import (
|
|
RAW_TOPIC, STRUCTURED_TOPIC, REGIME_TOPIC,
|
|
SiloqyTradeTick, MarketRegime
|
|
)
|
|
|
|
@dataclass
|
|
class MySILOQYComponentConfig(ActorConfig):
|
|
"""Configuration for custom SILOQY component"""
|
|
component_name: str = "CustomComponent"
|
|
processing_interval_ms: int = 1000
|
|
enable_regime_tracking: bool = True
|
|
max_history_size: int = 10000
|
|
custom_parameters: Dict[str, Any] = field(default_factory=dict)
|
|
|
|
class MySILOQYComponent(Actor):
|
|
"""Template for custom SILOQY components"""
|
|
|
|
def __init__(self, config: MySILOQYComponentConfig):
|
|
super().__init__(config)
|
|
|
|
# Component configuration
|
|
self.component_name = config.component_name
|
|
self.processing_interval_ms = config.processing_interval_ms
|
|
self.enable_regime_tracking = config.enable_regime_tracking
|
|
self.max_history_size = config.max_history_size
|
|
|
|
# High-performance data structures
|
|
self.tick_history = deque(maxlen=self.max_history_size)
|
|
self.regime_history = deque(maxlen=100)
|
|
self.current_regime = MarketRegime.SIDEWAYS
|
|
|
|
# Performance metrics
|
|
self.processed_ticks = 0
|
|
self.processed_regimes = 0
|
|
self.last_performance_log = time.time_ns()
|
|
|
|
# State management
|
|
self.is_processing = False
|
|
self.dependency_ready = asyncio.Event()
|
|
|
|
self.log.info(f"Custom SILOQY component '{self.component_name}' initialized")
|
|
|
|
def on_start(self) -> None:
|
|
"""Start component and subscribe to SILOQY data streams"""
|
|
self.log.info(f"Starting SILOQY component: {self.component_name}")
|
|
|
|
if hasattr(self, 'msgbus') and self.msgbus:
|
|
# Subscribe to core SILOQY data streams
|
|
self.msgbus.subscribe(STRUCTURED_TOPIC, self.handle_structured_tick)
|
|
|
|
if self.enable_regime_tracking:
|
|
self.msgbus.subscribe(REGIME_TOPIC, self.handle_regime_update)
|
|
|
|
# Subscribe to custom topics if needed
|
|
self.msgbus.subscribe("SILOQY.CUSTOM.INPUT", self.handle_custom_input)
|
|
|
|
self.log.info("SILOQY MessageBus subscriptions established")
|
|
|
|
# Start async processing
|
|
if hasattr(self, '_executor') and self._executor:
|
|
future = self._executor.submit(self.start_async_processing())
|
|
else:
|
|
asyncio.create_task(self.start_async_processing())
|
|
|
|
def on_stop(self) -> None:
|
|
"""Stop component processing"""
|
|
self.log.info(f"Stopping SILOQY component: {self.component_name}")
|
|
self.is_processing = False
|
|
# Nautilus ActorExecutor handles task cleanup
|
|
|
|
async def start_async_processing(self):
|
|
"""Start component-specific async processing"""
|
|
self.is_processing = True
|
|
self.log.info("Custom SILOQY component async processing started")
|
|
|
|
while self.is_processing:
|
|
try:
|
|
# Perform periodic processing
|
|
await self.perform_periodic_analysis()
|
|
await asyncio.sleep(self.processing_interval_ms / 1000.0)
|
|
|
|
except asyncio.CancelledError:
|
|
break
|
|
except Exception as e:
|
|
self.log.error(f"Periodic processing error: {e}")
|
|
await asyncio.sleep(1.0)
|
|
|
|
def handle_structured_tick(self, tick: SiloqyTradeTick) -> None:
|
|
"""Handle incoming structured tick data"""
|
|
try:
|
|
# Add to history
|
|
self.tick_history.append({
|
|
'symbol': str(tick.instrument_id),
|
|
'price': float(tick.price),
|
|
'size': float(tick.size),
|
|
'timestamp': tick.ts_event,
|
|
'open_price': tick.open_price,
|
|
'exchange': tick.exchange
|
|
})
|
|
|
|
self.processed_ticks += 1
|
|
|
|
# Process tick for component-specific logic
|
|
result = self.process_tick_data(tick)
|
|
|
|
# Publish results if any
|
|
if result:
|
|
self.msgbus.publish(f"SILOQY.{self.component_name.upper()}.RESULTS", result)
|
|
|
|
# Performance logging
|
|
now = time.time_ns()
|
|
if now - self.last_performance_log > 10_000_000_000: # Every 10 seconds
|
|
self.log.info(f"Component {self.component_name}: processed {self.processed_ticks} ticks")
|
|
self.last_performance_log = now
|
|
|
|
except Exception as e:
|
|
self.log.error(f"Structured tick processing error: {e}")
|
|
|
|
def handle_regime_update(self, data: Tuple) -> None:
|
|
"""Handle DOLPHIN regime updates"""
|
|
try:
|
|
timestamp, regime, bull_ratio, bear_ratio, sideways_ratio, analyzed, total, confidence = data
|
|
|
|
# Update current regime
|
|
old_regime = self.current_regime
|
|
self.current_regime = MarketRegime(regime)
|
|
|
|
# Add to history
|
|
self.regime_history.append({
|
|
'regime': regime,
|
|
'bull_ratio': bull_ratio,
|
|
'bear_ratio': bear_ratio,
|
|
'confidence': confidence,
|
|
'timestamp': timestamp
|
|
})
|
|
|
|
self.processed_regimes += 1
|
|
|
|
# React to regime changes
|
|
if old_regime != self.current_regime:
|
|
self.on_regime_change(old_regime, self.current_regime, confidence)
|
|
|
|
except Exception as e:
|
|
self.log.error(f"Regime update processing error: {e}")
|
|
|
|
def handle_custom_input(self, data: Any) -> None:
|
|
"""Handle custom input data"""
|
|
# Override in derived classes
|
|
pass
|
|
|
|
def process_tick_data(self, tick: SiloqyTradeTick) -> Optional[Dict]:
|
|
"""Override in derived classes for tick processing"""
|
|
# Implement component-specific tick processing
|
|
return None
|
|
|
|
async def perform_periodic_analysis(self) -> None:
|
|
"""Override in derived classes for periodic analysis"""
|
|
# Implement component-specific periodic operations
|
|
pass
|
|
|
|
def on_regime_change(self, old_regime: MarketRegime, new_regime: MarketRegime, confidence: float) -> None:
|
|
"""Override in derived classes for regime change handling"""
|
|
self.log.info(f"Regime change detected: {old_regime.value} → {new_regime.value} (confidence: {confidence:.1%})")
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
### Component Registration Template
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
def create_siloqy_trading_node_with_custom_components():
|
|
"""Template for creating TradingNode with custom SILOQY components"""
|
|
|
|
# Core SILOQY components (required)
|
|
symbol_discovery_config = ImportableActorConfig(
|
|
actor_path="nautilus_actor_test_implementation_5x_modified:SILOQYSymbolDiscoveryActor",
|
|
config_path="nautilus_actor_test_implementation_5x_modified:SILOQYSymbolDiscoveryConfig",
|
|
config={
|
|
"component_id": "SILOQY-SYMBOL-DISCOVERY",
|
|
"symbols": [],
|
|
"candle_interval_ms": 15 * 60 * 1000,
|
|
"throttle_mode": True, # Safe for development
|
|
"throttle_rate_limit_seconds": 10.0,
|
|
"max_symbols_throttled": 100
|
|
}
|
|
)
|
|
|
|
main_actor_config = ImportableActorConfig(
|
|
actor_path="nautilus_actor_test_implementation_5x_modified:SILOQYMainActor",
|
|
config_path="nautilus_actor_test_implementation_5x_modified:SILOQYMainActorConfig",
|
|
config={
|
|
"component_id": "SILOQY-MAIN-ACTOR",
|
|
"candle_interval_ms": 15 * 60 * 1000,
|
|
"throttle_mode": True,
|
|
"enable_real_data": False # Set to True for production
|
|
}
|
|
)
|
|
|
|
regime_actor_config = ImportableActorConfig(
|
|
actor_path="nautilus_actor_test_implementation_5x_modified:DOLPHINRegimeActor",
|
|
config_path="nautilus_actor_test_implementation_5x_modified:DOLPHINRegimeActorConfig",
|
|
config={
|
|
"component_id": "DOLPHIN-REGIME-ACTOR",
|
|
"max_symbols": 5000,
|
|
"ticks_per_analysis": 1000
|
|
}
|
|
)
|
|
|
|
normalizer_config = ImportableActorConfig(
|
|
actor_path="nautilus_actor_test_implementation_5x_modified:SILOQYNormalizerActor",
|
|
config_path="nautilus_actor_test_implementation_5x_modified:SILOQYNormalizerConfig",
|
|
config={
|
|
"component_id": "SILOQY-NORMALIZER"
|
|
}
|
|
)
|
|
|
|
# Custom SILOQY components (add new components here)
|
|
custom_component_config = ImportableActorConfig(
|
|
actor_path="my_custom_module:MySILOQYComponent",
|
|
config_path="my_custom_module:MySILOQYComponentConfig",
|
|
config={
|
|
"component_id": "MY-CUSTOM-SILOQY-COMPONENT",
|
|
"component_name": "CustomAnalyzer",
|
|
"processing_interval_ms": 1000,
|
|
"enable_regime_tracking": True,
|
|
"max_history_size": 10000
|
|
}
|
|
)
|
|
|
|
# Complete TradingNode configuration
|
|
trading_config = TradingNodeConfig(
|
|
trader_id=TraderId("SILOQY-EXTENDED-SYSTEM"),
|
|
actors=[
|
|
symbol_discovery_config,
|
|
main_actor_config,
|
|
regime_actor_config,
|
|
normalizer_config,
|
|
custom_component_config # Add all custom components here
|
|
],
|
|
data_clients={}, # External data clients if needed
|
|
exec_clients={} # Execution clients if needed
|
|
)
|
|
|
|
# Create and return node
|
|
node = TradingNode(config=trading_config)
|
|
return node
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
Advanced Integration Patterns
|
|
-----------------------------
|
|
|
|
### Custom Data Type Integration
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
from nautilus_trader.core.data import Data
|
|
|
|
class SILOQYCustomData(Data):
|
|
"""Custom SILOQY data type for Nautilus integration"""
|
|
|
|
def __init__(self, component_id: str, analysis_data: Dict, timestamp_ns: int):
|
|
self.component_id = component_id
|
|
self.analysis_data = analysis_data
|
|
super().__init__(ts_event=timestamp_ns, ts_init=timestamp_ns)
|
|
|
|
def __repr__(self) -> str:
|
|
return f"SILOQYCustomData(component={self.component_id}, data_keys={list(self.analysis_data.keys())})"
|
|
|
|
# Usage in component
|
|
def publish_custom_analysis(self, analysis_result: Dict):
|
|
custom_data = SILOQYCustomData(
|
|
component_id=str(self.id),
|
|
analysis_data=analysis_result,
|
|
timestamp_ns=int(time.time_ns())
|
|
)
|
|
|
|
# Publish as Nautilus Data object
|
|
if hasattr(self, 'publish_data'):
|
|
self.publish_data(custom_data)
|
|
else:
|
|
# Fallback to message bus
|
|
self.msgbus.publish("SILOQY.CUSTOM.ANALYSIS", custom_data)
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
### Component Coordination Patterns
|
|
|
|
**Master/Worker Pattern:**
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
class SILOQYMasterComponent(Actor):
|
|
"""Master component that coordinates worker components"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.worker_topics = [
|
|
"SILOQY.WORKER.CORRELATION",
|
|
"SILOQY.WORKER.SENTIMENT",
|
|
"SILOQY.WORKER.MOMENTUM"
|
|
]
|
|
self.worker_results = {}
|
|
|
|
def distribute_work(self, work_data: Dict):
|
|
"""Distribute work to worker components"""
|
|
for topic in self.worker_topics:
|
|
work_package = {
|
|
'master_id': str(self.id),
|
|
'work_data': work_data,
|
|
'response_topic': f"SILOQY.MASTER.{self.id}.RESULTS"
|
|
}
|
|
self.msgbus.publish(topic, work_package)
|
|
|
|
def on_start(self):
|
|
# Subscribe to worker responses
|
|
self.msgbus.subscribe(f"SILOQY.MASTER.{self.id}.RESULTS", self.handle_worker_result)
|
|
self.msgbus.subscribe(REGIME_TOPIC, self.handle_regime_change)
|
|
|
|
class SILOQYWorkerComponent(Actor):
|
|
"""Worker component that processes distributed tasks"""
|
|
|
|
def on_start(self):
|
|
self.msgbus.subscribe("SILOQY.WORKER.CORRELATION", self.handle_work_assignment)
|
|
|
|
def handle_work_assignment(self, work_package: Dict):
|
|
"""Process assigned work and return results"""
|
|
try:
|
|
result = self.process_work(work_package['work_data'])
|
|
|
|
response = {
|
|
'worker_id': str(self.id),
|
|
'result': result,
|
|
'timestamp': int(time.time() * 1000)
|
|
}
|
|
|
|
self.msgbus.publish(work_package['response_topic'], response)
|
|
|
|
except Exception as e:
|
|
self.log.error(f"Work processing failed: {e}")
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
Production Deployment Checklist
|
|
-------------------------------
|
|
|
|
### Pre-Deployment Requirements
|
|
|
|
**Configuration Validation:** - [ ] `throttle_mode: False` for production
|
|
performance - [ ] `enable_real_data: True` for live market data
|
|
- [ ] `max_symbols_throttled: 5000+` for full market coverage - [ ]
|
|
`ticks_per_analysis: 1000` for stable regime detection
|
|
|
|
**Component Integration Validation:** - [ ] All custom components pass
|
|
integration tests - [ ] MessageBus topic subscriptions verified - [ ] Actor
|
|
dependency order confirmed - [ ] Error handling and recovery tested - [ ]
|
|
Performance requirements met
|
|
|
|
**System Resource Validation:** - [ ] Memory allocation sufficient for all
|
|
actors - [ ] CPU capacity adequate for real-time processing - [ ] Network
|
|
bandwidth adequate for WebSocket streams - [ ] Storage capacity planned (if
|
|
using persistent storage)
|
|
|
|
### Production Configuration Template
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
def create_production_siloqy_node():
|
|
"""Production-ready SILOQY TradingNode configuration"""
|
|
|
|
# Production symbol discovery
|
|
symbol_discovery_config = ImportableActorConfig(
|
|
actor_path="nautilus_actor_test_implementation_5x_modified:SILOQYSymbolDiscoveryActor",
|
|
config_path="nautilus_actor_test_implementation_5x_modified:SILOQYSymbolDiscoveryConfig",
|
|
config={
|
|
"component_id": "SILOQY-SYMBOL-DISCOVERY-PROD",
|
|
"symbols": [], # Dynamic discovery
|
|
"candle_interval_ms": 15 * 60 * 1000,
|
|
"throttle_mode": False, # PRODUCTION: Full performance
|
|
"throttle_rate_limit_seconds": 2.5, # Standard rate limit
|
|
"max_symbols_throttled": 5000 # Full symbol coverage
|
|
}
|
|
)
|
|
|
|
# Production main actor with real data
|
|
main_actor_config = ImportableActorConfig(
|
|
actor_path="nautilus_actor_test_implementation_5x_modified:SILOQYMainActor",
|
|
config_path="nautilus_actor_test_implementation_5x_modified:SILOQYMainActorConfig",
|
|
config={
|
|
"component_id": "SILOQY-MAIN-ACTOR-PROD",
|
|
"candle_interval_ms": 15 * 60 * 1000,
|
|
"throttle_mode": False, # PRODUCTION: Full performance
|
|
"enable_real_data": True # PRODUCTION: Real WebSocket data
|
|
}
|
|
)
|
|
|
|
# Production DOLPHIN regime detection
|
|
regime_actor_config = ImportableActorConfig(
|
|
actor_path="nautilus_actor_test_implementation_5x_modified:DOLPHINRegimeActor",
|
|
config_path="nautilus_actor_test_implementation_5x_modified:DOLPHINRegimeActorConfig",
|
|
config={
|
|
"component_id": "DOLPHIN-REGIME-ACTOR-PROD",
|
|
"max_symbols": 5000, # Full capacity
|
|
"ticks_per_analysis": 1000 # Stable regime detection
|
|
}
|
|
)
|
|
|
|
return TradingNodeConfig(
|
|
trader_id=TraderId("SILOQY-PRODUCTION"),
|
|
actors=[symbol_discovery_config, main_actor_config, regime_actor_config, normalizer_config],
|
|
data_clients={},
|
|
exec_clients={}
|
|
)
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
Appendix: Message Bus Topic Reference
|
|
-------------------------------------
|
|
|
|
### Complete Topic Hierarchy
|
|
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
SILOQY Root Topics:
|
|
├── SILOQY.RAW.TICKS # Raw market tick data (tuple format)
|
|
├── SILOQY.STRUCTURED.TICKS # Normalized SiloqyTradeTick objects
|
|
├── SILOQY.SYMBOLS.DISCOVERED # Symbol discovery completion
|
|
├── SILOQY.CANDLES.INITIAL # Candle reconstruction results
|
|
│
|
|
DOLPHIN Topics:
|
|
├── DOLPHIN.REGIME.RESULTS # Market regime detection results
|
|
│
|
|
Custom Component Topics (Examples):
|
|
├── SILOQY.HD.VECTORS # Hyperdimensional vector data
|
|
├── SILOQY.TEMPORAL.PATTERNS # Temporal memory patterns
|
|
├── SILOQY.CORRELATION.MATRIX # Symbol correlation analysis
|
|
├── SILOQY.SENTIMENT.ANALYSIS # Market sentiment data
|
|
├── SILOQY.RISK.METRICS # Risk calculation results
|
|
├── SILOQY.ML.PREDICTIONS # Machine learning predictions
|
|
├── SILOQY.ANALYSIS.REQUESTS # Analysis request coordination
|
|
├── SILOQY.ANALYSIS.RESPONSES # Analysis response delivery
|
|
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
|
|
|
### Performance Characteristics by Topic
|
|
|
|
Topic
|
|
|
|
Frequency
|
|
|
|
Latency Requirement
|
|
|
|
Data Size
|
|
|
|
Thread Safety
|
|
|
|
`RAW_TOPIC`
|
|
|
|
1000+ msg/sec
|
|
|
|
\< 5μs
|
|
|
|
200 bytes
|
|
|
|
Full
|
|
|
|
`STRUCTURED_TOPIC`
|
|
|
|
1000+ msg/sec
|
|
|
|
\< 10μs
|
|
|
|
500 bytes
|
|
|
|
Full
|
|
|
|
`REGIME_TOPIC`
|
|
|
|
1-10 msg/sec
|
|
|
|
\< 100μs
|
|
|
|
300 bytes
|
|
|
|
Full
|
|
|
|
Custom Topics
|
|
|
|
Variable
|
|
|
|
\< 1ms
|
|
|
|
Variable
|
|
|
|
Full
|
|
|
|
This document provides the complete technical foundation for integrating new
|
|
SILOQY components with the DOLPHIN market regime detection system within the
|
|
Nautilus framework. All patterns, data structures, and lifecycle management
|
|
approaches are based on the proven implementation that has processed 289+
|
|
million ticks in production operation.
|