docs: update documentation for Smart Volatility Scanner
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Update project documentation to reflect new Smart Volatility Scanner feature: ## CLAUDE.md - Add Smart Volatility Scanner section with configuration guide - Update project structure to include analysis/ module - Update test count (273→343 tests) ## docs/architecture.md - Add Analysis component (VolatilityAnalyzer + SmartVolatilityScanner) - Add new KIS API methods (fetch_market_rankings, get_daily_prices) - Update data flow diagram to show Python-first filtering pipeline - Add selection_context to database schema documentation - Add Smart Scanner configuration section - Renumber components (Brain 2→3, Risk Manager 3→4, etc.) ## docs/requirements-log.md - Document 2026-02-06 requirement for Smart Volatility Scanner - Explain Python-First, AI-Last pipeline rationale - Record implementation details and benefits - Reference issue #76 and PR #77 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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@@ -64,7 +64,39 @@ High-frequency trading with individual stock analysis:
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- `get_open_markets()` returns currently active markets
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- `get_next_market_open()` finds next market to open and when
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### 2. Brain (`src/brain/gemini_client.py`)
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**New API Methods** (added in v0.9.0):
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- `fetch_market_rankings()` — Fetch volume surge rankings from KIS API
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- `get_daily_prices()` — Fetch OHLCV history for technical analysis
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### 2. Analysis (`src/analysis/`)
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**VolatilityAnalyzer** (`volatility.py`) — Technical indicator calculations
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- ATR (Average True Range) for volatility measurement
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- RSI (Relative Strength Index) using Wilder's smoothing method
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- Price change percentages across multiple timeframes
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- Volume surge ratios and price-volume divergence
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- Momentum scoring (0-100 scale)
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- Breakout/breakdown pattern detection
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**SmartVolatilityScanner** (`smart_scanner.py`) — Python-first filtering pipeline
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- **Step 1**: Fetch volume rankings from KIS API (top 30 stocks)
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- **Step 2**: Calculate RSI and volume ratio for each stock
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- **Step 3**: Apply filters:
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- Volume ratio >= `VOL_MULTIPLIER` (default 2.0x previous day)
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- RSI < `RSI_OVERSOLD_THRESHOLD` (30) OR RSI > `RSI_MOMENTUM_THRESHOLD` (70)
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- **Step 4**: Score candidates by RSI extremity (60%) + volume surge (40%)
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- **Step 5**: Return top N candidates (default 3) for AI analysis
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- **Fallback**: Uses static watchlist if ranking API unavailable
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- **Realtime mode only**: Daily mode uses batch processing for API efficiency
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**Benefits:**
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- Reduces Gemini API calls from 20-30 stocks to 1-3 qualified candidates
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- Fast Python-based filtering before expensive AI judgment
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- Logs selection context (RSI, volume_ratio, signal, score) for Evolution system
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### 3. Brain (`src/brain/gemini_client.py`)
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**GeminiClient** — AI decision engine powered by Google Gemini
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@@ -74,7 +106,7 @@ High-frequency trading with individual stock analysis:
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- Falls back to safe HOLD on any parse/API error
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- Handles markdown-wrapped JSON, malformed responses, invalid actions
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### 3. Risk Manager (`src/core/risk_manager.py`)
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### 4. Risk Manager (`src/core/risk_manager.py`)
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**RiskManager** — Safety circuit breaker and order validation
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@@ -86,7 +118,7 @@ High-frequency trading with individual stock analysis:
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- **Fat-Finger Protection**: Rejects orders exceeding 30% of available cash
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- Must always be enforced, cannot be disabled
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### 4. Notifications (`src/notifications/telegram_client.py`)
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### 5. Notifications (`src/notifications/telegram_client.py`)
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**TelegramClient** — Real-time event notifications via Telegram Bot API
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@@ -105,7 +137,7 @@ High-frequency trading with individual stock analysis:
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**Setup:** See [src/notifications/README.md](../src/notifications/README.md) for bot creation and configuration.
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### 5. Evolution (`src/evolution/optimizer.py`)
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### 6. Evolution (`src/evolution/optimizer.py`)
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**StrategyOptimizer** — Self-improvement loop
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@@ -117,9 +149,11 @@ High-frequency trading with individual stock analysis:
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## Data Flow
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### Realtime Mode (with Smart Scanner)
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Main Loop (60s cycle per stock, per market) │
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│ Main Loop (60s cycle per market) │
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└─────────────────────────────────────────────────────────────┘
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│
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▼
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@@ -132,6 +166,21 @@ High-frequency trading with individual stock analysis:
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│
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▼
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┌──────────────────────────────────┐
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│ Smart Scanner (Python-first) │
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│ - Fetch volume rankings (KIS) │
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│ - Get 20d price history per stock│
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│ - Calculate RSI(14) + vol ratio │
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│ - Filter: vol>2x AND RSI extreme │
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│ - Return top 3 qualified stocks │
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└──────────────────┬────────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ For Each Qualified Candidate │
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└──────────────────┬────────────────┘
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│
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▼
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┌──────────────────────────────────┐
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│ Broker: Fetch Market Data │
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│ - Domestic: orderbook + balance │
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│ - Overseas: price + balance │
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@@ -145,7 +194,7 @@ High-frequency trading with individual stock analysis:
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│
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▼
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┌──────────────────────────────────┐
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│ Brain: Get Decision │
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│ Brain: Get Decision (AI) │
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│ - Build prompt with market data │
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│ - Call Gemini API │
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│ - Parse JSON response │
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@@ -181,6 +230,9 @@ High-frequency trading with individual stock analysis:
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│ - SQLite (data/trades.db) │
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│ - Track: action, confidence, │
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│ rationale, market, exchange │
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│ - NEW: selection_context (JSON) │
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│ - RSI, volume_ratio, signal │
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│ - For Evolution optimization │
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└───────────────────────────────────┘
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```
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@@ -200,11 +252,24 @@ CREATE TABLE trades (
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price REAL,
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pnl REAL DEFAULT 0.0,
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market TEXT DEFAULT 'KR', -- KR | US_NASDAQ | JP | etc.
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exchange_code TEXT DEFAULT 'KRX' -- KRX | NASD | NYSE | etc.
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exchange_code TEXT DEFAULT 'KRX', -- KRX | NASD | NYSE | etc.
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selection_context TEXT -- JSON: {rsi, volume_ratio, signal, score}
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);
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```
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Auto-migration: Adds `market` and `exchange_code` columns if missing for backward compatibility.
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**Selection Context** (new in v0.9.0): Stores scanner selection criteria as JSON:
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```json
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{
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"rsi": 28.5,
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"volume_ratio": 2.7,
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"signal": "oversold",
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"score": 85.2
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}
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```
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Enables Evolution system to analyze correlation between selection criteria and trade outcomes.
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Auto-migration: Adds `market`, `exchange_code`, and `selection_context` columns if missing for backward compatibility.
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## Configuration
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@@ -236,6 +301,12 @@ SESSION_INTERVAL_HOURS=6 # Hours between sessions (daily mode only)
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TELEGRAM_BOT_TOKEN=1234567890:ABCdefGHIjklMNOpqrsTUVwxyz
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TELEGRAM_CHAT_ID=123456789
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TELEGRAM_ENABLED=true
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# Smart Scanner (optional, realtime mode only)
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RSI_OVERSOLD_THRESHOLD=30 # 0-50, oversold threshold
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RSI_MOMENTUM_THRESHOLD=70 # 50-100, momentum threshold
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VOL_MULTIPLIER=2.0 # Minimum volume ratio (2.0 = 200%)
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SCANNER_TOP_N=3 # Max qualified candidates per scan
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```
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Tests use in-memory SQLite (`DB_PATH=":memory:"`) and dummy credentials via `tests/conftest.py`.
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@@ -26,3 +26,41 @@
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### 문서화
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- 시스템 구조, 기능별 설명 등 코드 문서화 항상 신경쓸 것
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- 새로운 기능 추가 시 관련 문서 업데이트 필수
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---
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## 2026-02-06
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### Smart Volatility Scanner (Python-First, AI-Last 파이프라인)
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**배경:**
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- 정적 종목 리스트를 순회하는 방식은 비효율적
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- KIS API 거래량 순위를 통해 시장 주도주를 자동 탐지해야 함
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- Gemini API 호출 전에 Python 기반 기술적 분석으로 필터링 필요
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**요구사항:**
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1. KIS API 거래량 순위 API 통합 (`fetch_market_rankings`)
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2. 일별 가격 히스토리 API 추가 (`get_daily_prices`)
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3. RSI(14) 계산 기능 구현 (Wilder's smoothing method)
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4. 필터 조건:
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- 거래량 > 전일 대비 200% (VOL_MULTIPLIER)
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- RSI < 30 (과매도) OR RSI > 70 (모멘텀)
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5. 상위 1-3개 적격 종목만 Gemini에 전달
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6. 종목 선정 배경(RSI, volume_ratio, signal, score) 데이터베이스 기록
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**구현 결과:**
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- `src/analysis/smart_scanner.py`: SmartVolatilityScanner 클래스
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- `src/analysis/volatility.py`: calculate_rsi() 메서드 추가
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- `src/broker/kis_api.py`: 2개 신규 API 메서드
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- `src/db.py`: selection_context 컬럼 추가
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- 설정 가능한 임계값: RSI_OVERSOLD_THRESHOLD, RSI_MOMENTUM_THRESHOLD, VOL_MULTIPLIER, SCANNER_TOP_N
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**효과:**
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- Gemini API 호출 20-30개 → 1-3개로 감소
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- Python 기반 빠른 필터링 → 비용 절감
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- 선정 기준 추적 → Evolution 시스템 최적화 가능
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- API 장애 시 정적 watchlist로 자동 전환
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**참고:** Realtime 모드 전용. Daily 모드는 배치 효율성을 위해 정적 watchlist 사용.
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**이슈/PR:** #76, #77
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