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feature/is
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feature/is
| Author | SHA1 | Date | |
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|
f7d33e69d1 |
@@ -94,6 +94,7 @@ Smart Scanner runs in `TRADE_MODE=realtime` only. Daily mode uses static watchli
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- **[Testing](docs/testing.md)** — Test structure, coverage requirements, writing tests
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- **[Agent Policies](docs/agents.md)** — Prime directives, constraints, prohibited actions
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- **[Requirements Log](docs/requirements-log.md)** — User requirements and feedback tracking
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- **[Live Trading Checklist](docs/live-trading-checklist.md)** — 모의→실전 전환 체크리스트
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## Core Principles
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131
docs/live-trading-checklist.md
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131
docs/live-trading-checklist.md
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@@ -0,0 +1,131 @@
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# 실전 전환 체크리스트
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모의 거래(paper)에서 실전(live)으로 전환하기 전에 아래 항목을 **순서대로** 모두 확인하세요.
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---
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## 1. 사전 조건
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### 1-1. KIS OpenAPI 실전 계좌 준비
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- [ ] 한국투자증권 계좌 개설 완료 (일반 위탁 계좌)
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- [ ] OpenAPI 실전 사용 신청 (KIS 홈페이지 → Open API → 서비스 신청)
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- [ ] 실전용 APP_KEY / APP_SECRET 발급 완료
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- [ ] KIS_ACCOUNT_NO 형식 확인: `XXXXXXXX-XX` (8자리-2자리)
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### 1-2. 리스크 파라미터 검토
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- [ ] `CIRCUIT_BREAKER_PCT` 확인: 기본값 -3.0% (더 엄격하게 조정 권장)
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- [ ] `FAT_FINGER_PCT` 확인: 기본값 30.0% (1회 주문 최대 잔고 대비 %)
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- [ ] `CONFIDENCE_THRESHOLD` 확인: BEARISH ≥ 90, NEUTRAL ≥ 80, BULLISH ≥ 75
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- [ ] 초기 투자금 결정 및 해외 주식 운용 한도 설정
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### 1-3. 시스템 요건
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- [ ] 커버리지 80% 이상 유지 확인: `pytest --cov=src`
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- [ ] 타입 체크 통과: `mypy src/ --strict`
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- [ ] Lint 통과: `ruff check src/ tests/`
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---
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## 2. 환경 설정
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### 2-1. `.env` 파일 수정
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```bash
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# 1. KIS 실전 URL로 변경 (모의: openapivts 포트 29443)
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KIS_BASE_URL=https://openapi.koreainvestment.com:9443
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# 2. 실전 APP_KEY / APP_SECRET으로 교체
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KIS_APP_KEY=<실전_APP_KEY>
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KIS_APP_SECRET=<실전_APP_SECRET>
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KIS_ACCOUNT_NO=<실전_계좌번호>
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# 3. 모드를 live로 변경
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MODE=live
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# 4. PAPER_OVERSEAS_CASH 비활성화 (live 모드에선 무시되지만 명시적으로 0 설정)
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PAPER_OVERSEAS_CASH=0
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```
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> ⚠️ `KIS_BASE_URL` 포트 주의:
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> - **모의(VTS)**: `https://openapivts.koreainvestment.com:29443`
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> - **실전**: `https://openapi.koreainvestment.com:9443`
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### 2-2. TR_ID 자동 분기 확인
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아래 TR_ID는 `MODE` 값에 따라 코드에서 **자동으로 선택**됩니다.
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별도 설정 불필요하나, 문제 발생 시 아래 표를 참조하세요.
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| 구분 | 모의 TR_ID | 실전 TR_ID |
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|------|-----------|-----------|
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| 국내 잔고 조회 | `VTTC8434R` | `TTTC8434R` |
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| 국내 현금 매수 | `VTTC0012U` | `TTTC0012U` |
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| 국내 현금 매도 | `VTTC0011U` | `TTTC0011U` |
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| 해외 잔고 조회 | `VTTS3012R` | `TTTS3012R` |
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| 해외 매수 | `VTTT1002U` | `TTTT1002U` |
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| 해외 매도 | `VTTT1001U` | `TTTT1006U` |
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> **출처**: `docs/한국투자증권_오픈API_전체문서_20260221_030000.xlsx` (공식 문서 기준)
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---
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## 3. 최종 확인
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### 3-1. 실전 시작 전 점검
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- [ ] DB 백업 완료: `data/trade_logs.db` → `data/backups/`
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- [ ] Telegram 알림 설정 확인 (실전에서는 알림이 더욱 중요)
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- [ ] 소액으로 첫 거래 진행 후 TR_ID/계좌 정상 동작 확인
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### 3-2. 실행 명령
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```bash
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# 실전 모드로 실행
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python -m src.main --mode=live
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# 대시보드 함께 실행 (별도 터미널에서 모니터링)
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python -m src.main --mode=live --dashboard
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```
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### 3-3. 실전 시작 직후 확인 사항
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- [ ] 로그에 `MODE=live` 출력 확인
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- [ ] 첫 잔고 조회 성공 (ConnectionError 없음)
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- [ ] Telegram 알림 수신 확인 ("System started")
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- [ ] 첫 주문 후 KIS 앱에서 체결 내역 확인
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---
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## 4. 비상 정지 방법
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### 즉각 정지
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```bash
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# 터미널에서 Ctrl+C (정상 종료 트리거)
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# 또는 Telegram 봇 명령:
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/stop
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```
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### Circuit Breaker 발동 시
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- CB가 발동되면 자동으로 거래 중단 및 Telegram 알림 전송
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- CB 임계값: `CIRCUIT_BREAKER_PCT` (기본 -3.0%)
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- **임계값은 엄격하게만 조정 가능** (더 낮은 음수 값으로만 변경)
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---
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## 5. 롤백 절차
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실전 전환 후 문제 발생 시:
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```bash
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# 1. 즉시 .env에서 MODE=paper로 복원
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# 2. 재시작
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python -m src.main --mode=paper
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# 3. DB에서 최근 거래 확인
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sqlite3 data/trade_logs.db "SELECT * FROM trades ORDER BY id DESC LIMIT 20;"
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```
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---
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## 관련 문서
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- [시스템 아키텍처](architecture.md)
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- [워크플로우 가이드](workflow.md)
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- [재해 복구](disaster_recovery.md)
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- [Agent 제약 조건](agents.md)
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@@ -1,114 +0,0 @@
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"""Auto-generated strategy: v20260220_210124
|
||||
|
||||
Generated at: 2026-02-20T21:01:24.706847+00:00
|
||||
Rationale: Auto-evolved from 6 failures. Primary failure markets: ['US_AMEX', 'US_NYSE', 'US_NASDAQ']. Average loss: -194.69
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
from typing import Any
|
||||
from src.strategies.base import BaseStrategy
|
||||
|
||||
|
||||
class Strategy_v20260220_210124(BaseStrategy):
|
||||
"""Strategy: v20260220_210124"""
|
||||
|
||||
def evaluate(self, market_data: dict[str, Any]) -> dict[str, Any]:
|
||||
import datetime
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||||
|
||||
# --- Strategy Constants ---
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||||
# Minimum price for a stock to be considered for trading (avoids penny stocks)
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||||
MIN_PRICE = 5.0
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||||
|
||||
# Momentum signal thresholds (stricter than previous failures)
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MOMENTUM_PRICE_CHANGE_THRESHOLD = 7.0 # % price change
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MOMENTUM_VOLUME_RATIO_THRESHOLD = 4.0 # X times average volume
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|
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# Oversold signal thresholds (more conservative)
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OVERSOLD_RSI_THRESHOLD = 25.0 # RSI value (lower means more oversold)
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|
||||
# Confidence levels
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CONFIDENCE_HOLD = 30
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CONFIDENCE_BUY_OVERSOLD = 65
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||||
CONFIDENCE_BUY_MOMENTUM = 85
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CONFIDENCE_BUY_STRONG_MOMENTUM = 90 # For higher-priced stocks with strong momentum
|
||||
|
||||
# Market hours in UTC (9:30 AM ET to 4:00 PM ET)
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MARKET_OPEN_UTC = datetime.time(14, 30)
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||||
MARKET_CLOSE_UTC = datetime.time(21, 0)
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||||
|
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# Volatile periods within market hours (UTC) to avoid
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# First hour after open (14:30 UTC - 15:30 UTC)
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VOLATILE_OPEN_END_UTC = datetime.time(15, 30)
|
||||
# Last 30 minutes before close (20:30 UTC - 21:00 UTC)
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||||
VOLATILE_CLOSE_START_UTC = datetime.time(20, 30)
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||||
|
||||
current_price = market_data.get('current_price')
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||||
price_change_pct = market_data.get('price_change_pct')
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||||
volume_ratio = market_data.get('volume_ratio') # Assumed pre-computed indicator
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||||
rsi = market_data.get('rsi') # Assumed pre-computed indicator
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timestamp_str = market_data.get('timestamp')
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||||
|
||||
action = "HOLD"
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||||
confidence = CONFIDENCE_HOLD
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||||
rationale = "Initial HOLD: No clear signal or conditions not met."
|
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||||
# --- 1. Basic Data Validation ---
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if current_price is None or price_change_pct is None:
|
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return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
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"rationale": "Insufficient core data (price or price change) to evaluate."}
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|
||||
# --- 2. Price Filter: Avoid low-priced/penny stocks ---
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||||
if current_price < MIN_PRICE:
|
||||
return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
|
||||
"rationale": f"Avoiding low-priced stock (${current_price:.2f} < ${MIN_PRICE:.2f})."}
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||||
|
||||
# --- 3. Time Filter: Only trade during core market hours ---
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||||
if timestamp_str:
|
||||
try:
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||||
dt_object = datetime.datetime.fromisoformat(timestamp_str)
|
||||
current_time_utc = dt_object.time()
|
||||
|
||||
if not (MARKET_OPEN_UTC <= current_time_utc < MARKET_CLOSE_UTC):
|
||||
return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
|
||||
"rationale": f"Avoiding trade outside core market hours ({current_time_utc} UTC)."}
|
||||
|
||||
if (MARKET_OPEN_UTC <= current_time_utc < VOLATILE_OPEN_END_UTC) or \
|
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(VOLATILE_CLOSE_START_UTC <= current_time_utc < MARKET_CLOSE_UTC):
|
||||
return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
|
||||
"rationale": f"Avoiding trade during volatile market open/close periods ({current_time_utc} UTC)."}
|
||||
|
||||
except ValueError:
|
||||
rationale += " (Warning: Malformed timestamp, time filters skipped)"
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||||
|
||||
# --- Initialize signal states ---
|
||||
has_momentum_buy_signal = False
|
||||
has_oversold_buy_signal = False
|
||||
|
||||
# --- 4. Evaluate Enhanced Buy Signals ---
|
||||
|
||||
# Momentum Buy Signal
|
||||
if volume_ratio is not None and \
|
||||
price_change_pct > MOMENTUM_PRICE_CHANGE_THRESHOLD and \
|
||||
volume_ratio > MOMENTUM_VOLUME_RATIO_THRESHOLD:
|
||||
has_momentum_buy_signal = True
|
||||
rationale = f"Momentum BUY: Price change {price_change_pct:.2f}%, Volume {volume_ratio:.2f}x."
|
||||
confidence = CONFIDENCE_BUY_MOMENTUM
|
||||
if current_price >= 10.0:
|
||||
confidence = CONFIDENCE_BUY_STRONG_MOMENTUM
|
||||
|
||||
# Oversold Buy Signal
|
||||
if rsi is not None and rsi < OVERSOLD_RSI_THRESHOLD:
|
||||
has_oversold_buy_signal = True
|
||||
if not has_momentum_buy_signal:
|
||||
rationale = f"Oversold BUY: RSI {rsi:.2f}."
|
||||
confidence = CONFIDENCE_BUY_OVERSOLD
|
||||
if current_price >= 10.0:
|
||||
confidence = min(CONFIDENCE_BUY_OVERSOLD + 5, 80)
|
||||
|
||||
# --- 5. Decision Logic ---
|
||||
if has_momentum_buy_signal:
|
||||
action = "BUY"
|
||||
elif has_oversold_buy_signal:
|
||||
action = "BUY"
|
||||
|
||||
return {"action": action, "confidence": confidence, "rationale": rationale}
|
||||
@@ -1,97 +0,0 @@
|
||||
"""Auto-generated strategy: v20260220_210159
|
||||
|
||||
Generated at: 2026-02-20T21:01:59.391523+00:00
|
||||
Rationale: Auto-evolved from 6 failures. Primary failure markets: ['US_AMEX', 'US_NYSE', 'US_NASDAQ']. Average loss: -194.69
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
from typing import Any
|
||||
from src.strategies.base import BaseStrategy
|
||||
|
||||
|
||||
class Strategy_v20260220_210159(BaseStrategy):
|
||||
"""Strategy: v20260220_210159"""
|
||||
|
||||
def evaluate(self, market_data: dict[str, Any]) -> dict[str, Any]:
|
||||
import datetime
|
||||
|
||||
current_price = market_data.get('current_price')
|
||||
price_change_pct = market_data.get('price_change_pct')
|
||||
volume_ratio = market_data.get('volume_ratio')
|
||||
rsi = market_data.get('rsi')
|
||||
timestamp_str = market_data.get('timestamp')
|
||||
market_name = market_data.get('market')
|
||||
|
||||
# Default action
|
||||
action = "HOLD"
|
||||
confidence = 0
|
||||
rationale = "No strong signal or conditions not met."
|
||||
|
||||
# --- FAILURE PATTERN AVOIDANCE ---
|
||||
|
||||
# 1. Avoid low-priced/penny stocks
|
||||
MIN_PRICE_THRESHOLD = 5.0 # USD
|
||||
if current_price is not None and current_price < MIN_PRICE_THRESHOLD:
|
||||
rationale = (
|
||||
f"HOLD: Stock price (${current_price:.2f}) is below minimum threshold "
|
||||
f"(${MIN_PRICE_THRESHOLD:.2f}). Past failures consistently involved low-priced stocks."
|
||||
)
|
||||
return {"action": action, "confidence": confidence, "rationale": rationale}
|
||||
|
||||
# 2. Avoid early market hour volatility
|
||||
if timestamp_str:
|
||||
try:
|
||||
dt_obj = datetime.datetime.fromisoformat(timestamp_str)
|
||||
utc_hour = dt_obj.hour
|
||||
utc_minute = dt_obj.minute
|
||||
|
||||
if (utc_hour == 14 and utc_minute < 45) or (utc_hour == 13 and utc_minute >= 30):
|
||||
rationale = (
|
||||
f"HOLD: Trading during early market hours (UTC {utc_hour}:{utc_minute}), "
|
||||
f"a period identified with past failures due to high volatility."
|
||||
)
|
||||
return {"action": action, "confidence": confidence, "rationale": rationale}
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
# --- IMPROVED BUY STRATEGY ---
|
||||
|
||||
# Momentum BUY signal
|
||||
if volume_ratio is not None and price_change_pct is not None:
|
||||
if price_change_pct > 7.0 and volume_ratio > 3.0:
|
||||
action = "BUY"
|
||||
confidence = 70
|
||||
rationale = "Improved BUY: Momentum signal with high volume and above price threshold."
|
||||
|
||||
if market_name == 'US_AMEX':
|
||||
confidence = max(55, confidence - 5)
|
||||
rationale += " (Adjusted lower for AMEX market's higher risk profile)."
|
||||
elif market_name == 'US_NASDAQ' and price_change_pct > 20:
|
||||
confidence = max(50, confidence - 10)
|
||||
rationale += " (Adjusted lower for aggressive NASDAQ momentum volatility)."
|
||||
|
||||
if price_change_pct > 15.0:
|
||||
confidence = max(50, confidence - 5)
|
||||
rationale += " (Caution: Very high daily price change, potential for reversal)."
|
||||
|
||||
return {"action": action, "confidence": confidence, "rationale": rationale}
|
||||
|
||||
# Oversold BUY signal
|
||||
if rsi is not None and price_change_pct is not None:
|
||||
if rsi < 30 and price_change_pct < -3.0:
|
||||
action = "BUY"
|
||||
confidence = 65
|
||||
rationale = "Improved BUY: Oversold signal with recent decline and above price threshold."
|
||||
|
||||
if market_name == 'US_AMEX':
|
||||
confidence = max(50, confidence - 5)
|
||||
rationale += " (Adjusted lower for AMEX market's higher risk on oversold assets)."
|
||||
|
||||
if price_change_pct < -10.0:
|
||||
confidence = max(45, confidence - 10)
|
||||
rationale += " (Caution: Very steep decline, potential falling knife)."
|
||||
|
||||
return {"action": action, "confidence": confidence, "rationale": rationale}
|
||||
|
||||
# If no specific BUY signal, default to HOLD
|
||||
return {"action": action, "confidence": confidence, "rationale": rationale}
|
||||
@@ -1,88 +0,0 @@
|
||||
"""Auto-generated strategy: v20260220_210244
|
||||
|
||||
Generated at: 2026-02-20T21:02:44.387355+00:00
|
||||
Rationale: Auto-evolved from 6 failures. Primary failure markets: ['US_AMEX', 'US_NYSE', 'US_NASDAQ']. Average loss: -194.69
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
from typing import Any
|
||||
from src.strategies.base import BaseStrategy
|
||||
|
||||
|
||||
class Strategy_v20260220_210244(BaseStrategy):
|
||||
"""Strategy: v20260220_210244"""
|
||||
|
||||
def evaluate(self, market_data: dict[str, Any]) -> dict[str, Any]:
|
||||
from datetime import datetime
|
||||
|
||||
# Extract required data points safely
|
||||
current_price = market_data.get("current_price")
|
||||
price_change_pct = market_data.get("price_change_pct")
|
||||
volume_ratio = market_data.get("volume_ratio")
|
||||
rsi = market_data.get("rsi")
|
||||
timestamp_str = market_data.get("timestamp")
|
||||
market_name = market_data.get("market")
|
||||
stock_code = market_data.get("stock_code", "UNKNOWN")
|
||||
|
||||
# Default action is HOLD with conservative confidence and rationale
|
||||
action = "HOLD"
|
||||
confidence = 50
|
||||
rationale = f"No strong BUY signal for {stock_code} or awaiting more favorable conditions after avoiding known failure patterns."
|
||||
|
||||
# --- 1. Failure Pattern Avoidance Filters ---
|
||||
|
||||
# A. Avoid low-priced (penny) stocks
|
||||
if current_price is not None and current_price < 5.0:
|
||||
return {
|
||||
"action": "HOLD",
|
||||
"confidence": 50,
|
||||
"rationale": f"AVOID {stock_code}: Stock price (${current_price:.2f}) is below minimum threshold ($5.00) for BUY action. Identified past failures on highly volatile, low-priced stocks."
|
||||
}
|
||||
|
||||
# B. Avoid initiating BUY trades during identified high-volatility hours
|
||||
if timestamp_str:
|
||||
try:
|
||||
trade_hour = datetime.fromisoformat(timestamp_str).hour
|
||||
if trade_hour in [14, 20]:
|
||||
return {
|
||||
"action": "HOLD",
|
||||
"confidence": 50,
|
||||
"rationale": f"AVOID {stock_code}: Trading during historically volatile hour ({trade_hour} UTC) where previous BUYs resulted in losses. Prefer to observe market stability."
|
||||
}
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
# C. Be cautious with extreme momentum spikes
|
||||
if volume_ratio is not None and price_change_pct is not None:
|
||||
if volume_ratio >= 9.0 and price_change_pct >= 15.0:
|
||||
return {
|
||||
"action": "HOLD",
|
||||
"confidence": 50,
|
||||
"rationale": f"AVOID {stock_code}: Extreme short-term momentum detected (price change: +{price_change_pct:.2f}%, volume ratio: {volume_ratio:.1f}x). Historical failures indicate buying into such rapid spikes often leads to reversals."
|
||||
}
|
||||
|
||||
# D. Be cautious with "oversold" signals without further confirmation
|
||||
if rsi is not None and rsi < 30:
|
||||
return {
|
||||
"action": "HOLD",
|
||||
"confidence": 50,
|
||||
"rationale": f"AVOID {stock_code}: Oversold signal (RSI={rsi:.1f}) detected. While often a BUY signal, historical failures on similar 'oversold' trades suggest waiting for stronger confirmation."
|
||||
}
|
||||
|
||||
# --- 2. Improved BUY Signal Generation ---
|
||||
if volume_ratio is not None and 2.0 <= volume_ratio < 9.0 and \
|
||||
price_change_pct is not None and 2.0 <= price_change_pct < 15.0:
|
||||
|
||||
action = "BUY"
|
||||
confidence = 70
|
||||
rationale = f"BUY {stock_code}: Moderate momentum detected (price change: +{price_change_pct:.2f}%, volume ratio: {volume_ratio:.1f}x). Passed filters for price and extreme momentum, avoiding past failure patterns."
|
||||
|
||||
if market_name in ["US_AMEX", "US_NASDAQ"]:
|
||||
confidence = max(60, confidence - 5)
|
||||
rationale += f" Adjusted confidence for {market_name} market characteristics."
|
||||
elif market_name == "US_NYSE":
|
||||
confidence = max(65, confidence)
|
||||
|
||||
confidence = max(50, min(85, confidence))
|
||||
|
||||
return {"action": action, "confidence": confidence, "rationale": rationale}
|
||||
Reference in New Issue
Block a user