67. Smart Signal Aggregation System¶

Overview¶

The Smart Signal Aggregation System dynamically aggregates trading signals from multiple sources (e.g., trend, mean reversion, momentum, macro) using performance-based weighting and advanced fusion logic. This enables unified, robust trading decisions that adapt to real-time strategy performance and signal quality.

Architecture & Module Breakdown¶

Microservice Directory¶

services/signal-aggregation-center/
├── src/
│   ├── main.py
│   ├── collector/signal_collector.py
│   ├── manager/weight_manager.py
│   ├── aggregator/signal_aggregator.py
│   ├── detector/conflict_detector.py
│   ├── api/aggregation_api.py
│   ├── config.py
│   └── requirements.txt
├── Dockerfile

Core Component Design¶

1. Signal Collector

class SignalCollector:
    def collect_signals(self):
        return fetch_all_strategy_signals()

2. Weight Manager

class WeightManager:
    def adjust_weights(self, strategy_performances):
        total_score = sum(perf["score"] for perf in strategy_performances)
        return {perf["strategy_id"]: perf["score"] / total_score for perf in strategy_performances}

3. Signal Aggregator

class SignalAggregator:
    def aggregate_signals(self, signals, weights):
        aggregated_signal = sum(signals[sid] * weights.get(sid, 0) for sid in signals.keys())
        return aggregated_signal

4. Conflict Detector

class ConflictDetector:
    def detect_conflict(self, signals):
        long_signals = sum(1 for sig in signals.values() if sig > 0)
        short_signals = sum(1 for sig in signals.values() if sig < 0)
        if abs(long_signals - short_signals) < len(signals) * 0.2:
            return True
        return False

5. API Example

from fastapi import APIRouter

router = APIRouter()

@router.get("/signal/aggregate")
async def get_aggregated_signal():
    return signal_aggregator.aggregate_current_signals()

Frontend Integration¶

SignalAggregationView.tsx - Signal source status (win rate, recent hit rate) - Aggregated signal real-time chart - Weight change curve over time - Conflict detection log

Implementation Roadmap¶

• Phase 1: Core collector, weight manager, aggregator, and API
• Phase 2: Conflict detector and frontend dashboard
• Phase 3: Source drift detection, meta-learning aggregation

System Integration¶

• Aggregates signals from all strategies and models
• Dynamically adapts to changing performance and signal quality
• Provides unified, robust trading instructions to execution layer

Business & Technical Value¶

• Robustness: Combines strengths of multiple strategies
• Adaptivity: Real-time weighting based on performance
• Transparency: Full signal and weight audit trail
• Scalability: Supports dozens to thousands of signal sources
• Competitive Edge: Meta-strategy decision making for superior stability