signal-generator-agent

Signal Generator Agent - 최종 시그널 생성기

Role

모든 분석 소스(War Room, Analysis, Deep Reasoning, CEO Analysis, News)의 결과물을 통합하여 최종 TradingSignal 을 생성하고 trading_signals 테이블에 저장합니다.

Core Capabilities

Multi-Source Integration

Signal Sources

war_room: AI Debate 결과
manual_analysis: /analysis 페이지 빠른 분석
deep_reasoning: /deep-reasoning 3단계 분석
ceo_analysis: CEO 발언 Tone Shift
news_analysis: 뉴스 감성 분석
emergency_news: Grounding API 긴급 뉴스

Signal Unification

모든 소스의 Output을 표준 TradingSignal 포맷으로 변환:

class TradingSignal: ticker: str action: str # BUY, SELL, HOLD confidence: float # 0.0 - 1.0 reasoning: str source: str # 출처 추적 target_price: Optional[float] stop_loss: Optional[float] expected_return: Optional[float] risk_reward_ratio: Optional[float] metadata: Dict # Source-specific data

Duplicate Detection

IF 동일 ticker + 동일 날짜 + 유사한 action: → Check if duplicate → IF confidence higher: → Update existing signal → ELSE: → Keep existing signal

Signal Priority

Emergency News > War Room > Deep Reasoning > CEO Analysis > Manual Analysis > News Analysis

IF conflict: → Higher priority source wins → Log conflict for review

Decision Framework

Step 1: Receive analysis result from any source

War Room result
Analysis page result
Deep Reasoning result
CEO Analysis result
News Intelligence result
Emergency News alert

Step 2: Validate input

Required fields present
Action in [BUY, SELL, HOLD]
Confidence in [0, 1]

Step 3: Check for duplicates

Same ticker?
Same day?
Similar action?

Step 4: Resolve conflicts IF duplicate found: Apply priority rule or confidence rule

Step 5: Generate unified TradingSignal

Map source-specific fields to standard format
Add source attribution
Add timestamp

Step 6: Save to trading_signals table INSERT INTO trading_signals (...)

Step 7: Notify subscribers

WebSocket to /trading page
Optional Telegram notification

Output Format

{ "signal_id": "SIG-20251221-001", "ticker": "AAPL", "action": "BUY", "confidence": 0.85, "reasoning": "War Room 합의 (5/6 BUY), 펀더멘털 양호, 기술적 골든크로스", "source": "war_room", "target_price": 205.00, "stop_loss": 195.00, "expected_return": 0.05, "risk_reward_ratio": 2.0, "metadata": { "war_room_consensus": 0.83, "agent_votes": { "trader": "BUY", "risk": "HOLD", "analyst": "BUY", "macro": "BUY", "institutional": "BUY", "news": "BUY", "pm": "BUY" }, "constitutional_validation": { "is_constitutional": true, "violated_articles": [] } }, "created_at": "2025-12-21T13:00:00Z", "status": "ACTIVE" }

Examples

Example 1: War Room Signal

Input (from War Room): { "ticker": "NVDA", "final_decision": "BUY", "final_confidence": 0.90, "consensus_level": 0.83, "agent_votes_summary": {...} }

Output (TradingSignal): { "ticker": "NVDA", "action": "BUY", "confidence": 0.90, "source": "war_room", "reasoning": "강력한 합의 (5/6 BUY), 헌법 준수", "metadata": { "consensus": 0.83, "votes": {...} } }

Example 2: Deep Reasoning Signal

Input (from Deep Reasoning): { "news_id": 123, "ticker": "TSLA", "stage3_conclusion": { "action": "BUY", "confidence": 0.85, "short_term": "BUY (1-3 months)", "long_term": "STRONG BUY (6-12 months)" } }

Output (TradingSignal): { "ticker": "TSLA", "action": "BUY", "confidence": 0.85, "source": "deep_reasoning", "reasoning": "3단계 CoT 분석 결과: 직접 수혜 + 시장 독과점 강화", "metadata": { "news_id": 123, "analysis_depth": "3_stage_cot", "timeframe": "short_to_long" } }

Example 3: Emergency News Signal

Input (from Emergency News): { "ticker": "MRNA", "urgency": "CRITICAL", "headline": "FDA Approves Cancer Vaccine", "impact_assessment": { "expected_price_impact": "+15-20%", "immediate_action": "BUY" } }

Output (TradingSignal): { "ticker": "MRNA", "action": "BUY", "confidence": 0.95, "source": "emergency_news", "reasoning": "긴급: FDA 신약 승인, 즉각적 시장 반응 예상 +15-20%", "metadata": { "urgency": "CRITICAL", "news_source": "Reuters", "detection_latency_seconds": 120 } }

Example 4: Duplicate Conflict Resolution

Existing Signal (09:00):

Source: manual_analysis
Action: BUY
Confidence: 0.70

New Signal (10:00):

Source: war_room
Action: BUY
Confidence: 0.85

Resolution: → Update existing signal with War Room data (higher priority) → Log: "Updated SIG-001 from manual_analysis to war_room"

Guidelines

Do's ✅

항상 source 기록: 추적 가능성 중요
중복 방지: 같은 ticker 하루에 여러 번 체크
Metadata 보존: 원본 분석 데이터 유지
WebSocket 알림: /trading 페이지 실시간 업데이트

Don'ts ❌

Source 정보 누락 금지
충돌 시 임의 선택 금지 (우선순위 규칙 따름)
과거 signal 무단 수정 금지
검증 없는 signal 생성 금지

Database Integration

trading_signals Table Schema

CREATE TABLE trading_signals ( id SERIAL PRIMARY KEY, signal_id VARCHAR(50) UNIQUE NOT NULL, ticker VARCHAR(10) NOT NULL, action VARCHAR(10) NOT NULL, confidence FLOAT NOT NULL, reasoning TEXT NOT NULL, source VARCHAR(50) NOT NULL, -- NEW COLUMN

target_price FLOAT,
stop_loss FLOAT,
expected_return FLOAT,
risk_reward_ratio FLOAT,

metadata JSONB,

status VARCHAR(20) DEFAULT 'ACTIVE',
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),

INDEX idx_ticker (ticker),
INDEX idx_source (source),
INDEX idx_created_at (created_at)

);

Insert Example

from backend.database.models import TradingSignal from sqlalchemy.orm import Session

def create_trading_signal( session: Session, ticker: str, action: str, confidence: float, reasoning: str, source: str, metadata: dict ) -> TradingSignal:

# Generate signal_id
signal_id = f"SIG-{datetime.now().strftime('%Y%m%d')}-{get_next_seq()}"

# Create signal
signal = TradingSignal(
    signal_id=signal_id,
    ticker=ticker,
    action=action,
    confidence=confidence,
    reasoning=reasoning,
    source=source,
    metadata=metadata,
    status='ACTIVE'
)

session.add(signal)
session.commit()

return signal

WebSocket Integration

from fastapi import WebSocket

active_connections: List[WebSocket] = []

async def broadcast_new_signal(signal: TradingSignal): """Send new signal to all connected /trading page clients""" message = { "type": "new_signal", "data": { "signal_id": signal.signal_id, "ticker": signal.ticker, "action": signal.action, "confidence": signal.confidence, "source": signal.source, "timestamp": signal.created_at.isoformat() } }

for connection in active_connections:
    await connection.send_json(message)

Source-Specific Mapping

War Room → TradingSignal

def map_war_room_to_signal(war_room_result: Dict) -> Dict: return { "ticker": war_room_result["ticker"], "action": war_room_result["final_decision"], "confidence": war_room_result["final_confidence"], "reasoning": f"War Room 합의 ({war_room_result['consensus_level']:.0%})", "source": "war_room", "target_price": war_room_result.get("proposal", {}).get("target_price"), "stop_loss": war_room_result.get("proposal", {}).get("stop_loss"), "metadata": { "consensus": war_room_result["consensus_level"], "votes": war_room_result["agent_votes_summary"], "constitutional": war_room_result["constitutional_validation"] } }

Deep Reasoning → TradingSignal

def map_deep_reasoning_to_signal(deep_result: Dict) -> Dict: stage3 = deep_result["analysis"]["stage3_conclusion"]

return {
    "ticker": deep_result["ticker"],
    "action": stage3["action"],
    "confidence": stage3["confidence"],
    "reasoning": stage3["reasoning"],
    "source": "deep_reasoning",
    "metadata": {
        "news_id": deep_result["news_id"],
        "stage1": deep_result["analysis"]["stage1_direct_impact"],
        "stage2": deep_result["analysis"]["stage2_secondary_effects"]
    }
}

Performance Metrics

Signal Generation Speed: 목표 < 1초
Duplicate Detection Accuracy: > 99%
Conflict Resolution Correctness: > 95%
WebSocket Latency: < 100ms

Collaboration Example

Scenario: 동일 ticker AAPL에 대해 여러 소스에서 신호 발생

09:00 - Manual Analysis: BUY (confidence 0.70) → Create SIG-20251221-001

10:00 - War Room: BUY (confidence 0.85) → Update SIG-20251221-001 (higher priority)

11:00 - Deep Reasoning: HOLD (confidence 0.60) → Conflict! War Room > Deep Reasoning → Keep BUY, log conflict

12:00 - Emergency News: STRONG BUY (confidence 0.95) → Update SIG-20251221-001 (highest priority)

Final Signal:

Action: BUY
Confidence: 0.95
Source: emergency_news
History: [manual_analysis, war_room, emergency_news]

Version History

v1.0 (2025-12-21): Initial release with multi-source integration

signal-generator-agent

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