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Author SHA1 Message Date
agentson
1ef5dcb2b3 docs: README.md v2 현행화 (#131)
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- 아키텍처 다이어그램에 v2 컴포넌트 (Strategy, Context, Evolution) 추가
- 핵심 모듈 테이블: 6개 → 14개 모듈 반영
- 테스트: 35개/3파일 → 551개/25파일
- 지원 시장 10개 거래소 테이블 추가
- 텔레그램 양방향 명령어 9종 레퍼런스
- 프로젝트 구조 트리 전면 갱신
- 문서 링크 섹션 추가

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 21:48:49 +09:00
agentson
d105a3ff5e docs: v2 상태 반영 - 전체 문서 현행화 (#131)
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- testing.md: 54 tests/4 files → 551 tests/25 files 반영, 전체 테스트 파일 설명
- architecture.md: v2 컴포넌트 추가 (Strategy, Context, Dashboard, Decision Logger 등),
  Playbook Mode 데이터 플로우, DB 스키마 5개 테이블, v2 환경변수
- commands.md: Dashboard 실행, Telegram 명령어 9종 레퍼런스
- CLAUDE.md: Project Structure 확장, 테스트 수 업데이트, --dashboard 플래그
- skills.md: DB 파일명 trades.db로 통일, Dashboard 명령어 추가
- requirements-log.md: 2026-02-16 문서 v2 동기화 요구사항 기록

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 21:44:59 +09:00
0424c78f6c Merge pull request 'feat: US market code 정합성, Telegram 명령 4종, 손절 모니터링 (#132)' (#135) from feature/issue-132-us-market-telegram-gaps into main
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Reviewed-on: #135
2026-02-16 20:25:43 +09:00
agentson
3fdb7a29d4 feat: US market code 정합성, Telegram 명령 4종, 손절 모니터링 (#132)
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- MARKET_SHORTHAND + expand_market_codes()로 config "US" → schedule "US_NASDAQ/NYSE/AMEX" 자동 확장
- /report, /scenarios, /review, /dashboard 텔레그램 명령 추가
- price_change_pct를 trading_cycle과 run_daily_session에 주입
- HOLD시 get_open_position 기반 손절 모니터링 및 자동 SELL 오버라이드
- 대시보드 /api/status 동적 market 조회로 변경

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 20:24:01 +09:00
31b4d0bf1e Merge pull request 'fix: daily_review 테스트 날짜 불일치 수정 (#129)' (#130) from feature/issue-129-fix-daily-review-test-date into main
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Reviewed-on: #130
2026-02-16 11:30:20 +09:00
agentson
e2275a23b1 fix: daily_review 테스트에서 날짜 불일치로 인한 실패 수정 (#129)
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DecisionLogger와 log_trade가 datetime.now(UTC)로 현재 날짜를 저장하는데,
테스트에서 하드코딩된 '2026-02-14'로 조회하여 0건이 반환되던 문제 수정.
generate_scorecard 호출 시 TODAY 변수를 사용하도록 변경.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-16 10:05:17 +09:00
7522bb7e66 Merge pull request 'feat: 대시보드 실행 통합 - CLI + 환경변수 (issue #97)' (#128) from feature/issue-97-dashboard-integration into main
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Reviewed-on: #128
2026-02-15 00:01:57 +09:00
agentson
63fa6841a2 feat: dashboard background thread with CLI flag (issue #97)
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Add --dashboard CLI flag and DASHBOARD_ENABLED env var to start
FastAPI dashboard in a daemon thread alongside the trading loop.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-15 00:01:29 +09:00
ece3c5597b Merge pull request 'feat: FastAPI 읽기 전용 대시보드 (issue #96)' (#127) from feature/issue-96-evolution-main-integration into main
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Reviewed-on: #127
2026-02-14 23:57:17 +09:00
19 changed files with 1475 additions and 289 deletions

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@@ -15,6 +15,9 @@ pytest -v --cov=src
# Run (paper trading)
python -m src.main --mode=paper
# Run with dashboard
python -m src.main --mode=paper --dashboard
```
## Telegram Notifications (Optional)
@@ -43,6 +46,10 @@ Get real-time alerts for trades, circuit breakers, and system events via Telegra
- Market open/close notifications
- 📝 System startup/shutdown status
### Interactive Commands
With `TELEGRAM_COMMANDS_ENABLED=true` (default), the bot supports 9 bidirectional commands: `/help`, `/status`, `/positions`, `/report`, `/scenarios`, `/review`, `/dashboard`, `/stop`, `/resume`.
**Fail-safe**: Notifications never crash the trading system. Missing credentials or API errors are logged but trading continues normally.
## Smart Volatility Scanner (Optional)
@@ -109,17 +116,23 @@ User requirements and feedback are tracked in [docs/requirements-log.md](docs/re
```
src/
├── analysis/ # Technical analysis (RSI, volatility, smart scanner)
├── backup/ # Disaster recovery (scheduler, cloud storage, health)
├── brain/ # Gemini AI decision engine (prompt optimizer, context selector)
├── broker/ # KIS API client (domestic + overseas)
├── brain/ # Gemini AI decision engine
├── context/ # L1-L7 hierarchical memory system
├── core/ # Risk manager (READ-ONLY)
├── evolution/ # Self-improvement optimizer
├── dashboard/ # FastAPI read-only monitoring (8 API endpoints)
├── data/ # External data integration (news, market data, calendar)
├── evolution/ # Self-improvement (optimizer, daily review, scorecard)
├── logging/ # Decision logger (audit trail)
├── markets/ # Market schedules and timezone handling
├── notifications/ # Telegram real-time alerts
├── notifications/ # Telegram alerts + bidirectional commands (9 commands)
├── strategy/ # Pre-market planner, scenario engine, playbook store
├── db.py # SQLite trade logging
├── main.py # Trading loop orchestrator
└── config.py # Settings (from .env)
tests/ # 343 tests across 14 files
tests/ # 551 tests across 25 files
docs/ # Extended documentation
```
@@ -131,6 +144,7 @@ ruff check src/ tests/ # Lint
mypy src/ --strict # Type check
python -m src.main --mode=paper # Paper trading
python -m src.main --mode=paper --dashboard # With dashboard
python -m src.main --mode=live # Live trading (⚠️ real money)
# Gitea workflow (requires tea CLI)

156
README.md
View File

@@ -10,28 +10,41 @@ KIS(한국투자증권) API로 매매하고, Google Gemini로 판단하며, 자
│ (매매 실행) │ │ (거래 루프) │ │ (의사결정) │
└─────────────┘ └──────┬──────┘ └─────────────┘
┌────────────┐
│Risk Manager
│ (안전장치) │
└──────┬──────┘
┌────────────┼────────────┐
│ │
┌──────┴──────┐ ┌──┴───┐ ┌──────┴──────┐
│Risk Manager │ │ DB │ │ Telegram │
│ (안전장치) │ │ │ │ (알림+명령) │
└──────┬──────┘ └──────┘ └─────────────┘
┌────────────┐
Evolution
│ (전략 진화) │
└─────────────┘
┌────────┼────────┐
┌────┴────┐┌──┴──┐┌────┴─────┐
│Strategy ││Ctx ││Evolution │
│(플레이북)││(메모리)││ (진화) │
└─────────┘└─────┘└──────────┘
```
**v2 핵심**: "Plan Once, Execute Locally" — 장 시작 전 AI가 시나리오 플레이북을 1회 생성하고, 거래 시간에는 로컬 시나리오 매칭만 수행하여 API 비용과 지연 시간을 대폭 절감.
## 핵심 모듈
| 모듈 | 파일 | 설명 |
| 모듈 | 위치 | 설명 |
|------|------|------|
| 설정 | `src/config.py` | Pydantic 기반 환경변수 로딩 및 타입 검증 |
| 브로커 | `src/broker/kis_api.py` | KIS API 비동기 래퍼 (토큰 갱신, 레이트 리미터, 해시키) |
| 두뇌 | `src/brain/gemini_client.py` | Gemini 프롬프트 구성 JSON 응답 파싱 |
| 방패 | `src/core/risk_manager.py` | 서킷 브레이커 + 팻 핑거 체크 |
| 알림 | `src/notifications/telegram_client.py` | 텔레그램 실시간 거래 알림 (선택사항) |
| 진화 | `src/evolution/optimizer.py` | 실패 패턴 분석 → 새 전략 생성 → 테스트 → PR |
| DB | `src/db.py` | SQLite 거래 로그 기록 |
| 설정 | `src/config.py` | Pydantic 기반 환경변수 로딩 및 타입 검증 (35+ 변수) |
| 브로커 | `src/broker/` | KIS API 비동기 래퍼 (국내 + 해외 9개 시장) |
| 두뇌 | `src/brain/` | Gemini 프롬프트 구성, JSON 파싱, 토큰 최적화 |
| 방패 | `src/core/risk_manager.py` | 서킷 브레이커 + 팻 핑거 체크 (READ-ONLY) |
| 전략 | `src/strategy/` | Pre-Market Planner, Scenario Engine, Playbook Store |
| 컨텍스트 | `src/context/` | L1-L7 계층형 메모리 시스템 |
| 분석 | `src/analysis/` | RSI, ATR, Smart Volatility Scanner |
| 알림 | `src/notifications/` | 텔레그램 양방향 (알림 + 9개 명령어) |
| 대시보드 | `src/dashboard/` | FastAPI 읽기 전용 모니터링 (8개 API) |
| 진화 | `src/evolution/` | 전략 진화 + Daily Review + Scorecard |
| 의사결정 로그 | `src/logging/` | 전체 거래 결정 감사 추적 |
| 데이터 | `src/data/` | 뉴스, 시장 데이터, 경제 캘린더 연동 |
| 백업 | `src/backup/` | 자동 백업, S3 클라우드, 무결성 검증 |
| DB | `src/db.py` | SQLite 거래 로그 (5개 테이블) |
## 안전장치
@@ -42,6 +55,7 @@ KIS(한국투자증권) API로 매매하고, Google Gemini로 판단하며, 자
| 신뢰도 임계값 | Gemini 신뢰도 80 미만이면 강제 HOLD |
| 레이트 리미터 | Leaky Bucket 알고리즘으로 API 호출 제한 |
| 토큰 자동 갱신 | 만료 1분 전 자동으로 Access Token 재발급 |
| 손절 모니터링 | 플레이북 시나리오 기반 실시간 포지션 보호 |
## 빠른 시작
@@ -67,7 +81,11 @@ pytest -v --cov=src --cov-report=term-missing
### 4. 실행 (모의투자)
```bash
# 기본 실행
python -m src.main --mode=paper
# 대시보드 활성화
python -m src.main --mode=paper --dashboard
```
### 5. Docker 실행
@@ -76,7 +94,20 @@ python -m src.main --mode=paper
docker compose up -d ouroboros
```
## 텔레그램 알림 (선택사항)
## 지원 시장
| 국가 | 거래소 | 코드 |
|------|--------|------|
| 🇰🇷 한국 | KRX | KR |
| 🇺🇸 미국 | NASDAQ, NYSE, AMEX | US_NASDAQ, US_NYSE, US_AMEX |
| 🇯🇵 일본 | TSE | JP |
| 🇭🇰 홍콩 | SEHK | HK |
| 🇨🇳 중국 | 상하이, 선전 | CN_SHA, CN_SZA |
| 🇻🇳 베트남 | 하노이, 호치민 | VN_HNX, VN_HSX |
`ENABLED_MARKETS` 환경변수로 활성 시장 선택 (기본: `KR,US`).
## 텔레그램 (선택사항)
거래 실행, 서킷 브레이커 발동, 시스템 상태 등을 텔레그램으로 실시간 알림 받을 수 있습니다.
@@ -102,25 +133,51 @@ docker compose up -d ouroboros
- 장 시작/종료 알림
- 📝 시스템 시작/종료 상태
**안전장치**: 알림 실패해도 거래는 계속 진행됩니다. 텔레그램 API 오류나 설정 누락이 있어도 거래 시스템은 정상 작동합니다.
### 양방향 명령어
`TELEGRAM_COMMANDS_ENABLED=true` (기본값) 설정 시 9개 대화형 명령어 지원:
| 명령어 | 설명 |
|--------|------|
| `/help` | 사용 가능한 명령어 목록 |
| `/status` | 거래 상태 (모드, 시장, P&L) |
| `/positions` | 계좌 요약 (잔고, 현금, P&L) |
| `/report` | 일일 요약 (거래 수, P&L, 승률) |
| `/scenarios` | 오늘의 플레이북 시나리오 |
| `/review` | 최근 스코어카드 (L6_DAILY) |
| `/dashboard` | 대시보드 URL 표시 |
| `/stop` | 거래 일시 정지 |
| `/resume` | 거래 재개 |
**안전장치**: 알림 실패해도 거래는 계속 진행됩니다.
## 테스트
35개 테스트가 TDD 방식으로 구현 전에 먼저 작성되었습니다.
551개 테스트가 25개 파일에 걸쳐 구현되어 있습니다. 최소 커버리지 80%.
```
tests/test_risk.py — 서킷 브레이커, 팻 핑거, 통합 검증 (11개)
tests/test_broker.py — 토큰 관리, 타임아웃, HTTP 에러, 해시키 (6개)
tests/test_brain.py JSON 파싱, 신뢰도 임계값, 비정상 응답 처리 (15개)
tests/test_scenario_engine.py 시나리오 매칭 (44개)
tests/test_data_integration.py — 외부 데이터 연동 (38개)
tests/test_pre_market_planner.py — 플레이북 생성 (37개)
tests/test_main.py — 거래 루프 통합 (37개)
tests/test_token_efficiency.py — 토큰 최적화 (34개)
tests/test_strategy_models.py — 전략 모델 검증 (33개)
tests/test_telegram_commands.py — 텔레그램 명령어 (31개)
tests/test_latency_control.py — 지연시간 제어 (30개)
tests/test_telegram.py — 텔레그램 알림 (25개)
... 외 16개 파일
```
**상세**: [docs/testing.md](docs/testing.md)
## 기술 스택
- **언어**: Python 3.11+ (asyncio 기반)
- **브로커**: KIS Open API (REST)
- **브로커**: KIS Open API (REST, 국내+해외)
- **AI**: Google Gemini Pro
- **DB**: SQLite
- **검증**: pytest + coverage
- **DB**: SQLite (5개 테이블: trades, contexts, decision_logs, playbooks, context_metadata)
- **대시보드**: FastAPI + uvicorn
- **검증**: pytest + coverage (551 tests)
- **CI/CD**: GitHub Actions
- **배포**: Docker + Docker Compose
@@ -128,27 +185,50 @@ tests/test_brain.py — JSON 파싱, 신뢰도 임계값, 비정상 응답 처
```
The-Ouroboros/
├── .github/workflows/ci.yml # CI 파이프라인
├── docs/
│ ├── agents.md # AI 에이전트 페르소나 정의
── skills.md # 사용 가능한 도구 목록
│ ├── architecture.md # 시스템 아키텍처
── testing.md # 테스트 가이드
│ ├── commands.md # 명령어 레퍼런스
│ ├── context-tree.md # L1-L7 메모리 시스템
│ ├── workflow.md # Git 워크플로우
│ ├── agents.md # 에이전트 정책
│ ├── skills.md # 도구 목록
│ ├── disaster_recovery.md # 백업/복구
│ └── requirements-log.md # 요구사항 기록
├── src/
│ ├── analysis/ # 기술적 분석 (RSI, ATR, Smart Scanner)
│ ├── backup/ # 백업 (스케줄러, S3, 무결성 검증)
│ ├── brain/ # Gemini 의사결정 (프롬프트 최적화, 컨텍스트 선택)
│ ├── broker/ # KIS API (국내 + 해외)
│ ├── context/ # L1-L7 계층 메모리
│ ├── core/ # 리스크 관리 (READ-ONLY)
│ ├── dashboard/ # FastAPI 모니터링 대시보드
│ ├── data/ # 외부 데이터 연동
│ ├── evolution/ # 전략 진화 + Daily Review
│ ├── logging/ # 의사결정 감사 추적
│ ├── markets/ # 시장 스케줄 + 타임존
│ ├── notifications/ # 텔레그램 알림 + 명령어
│ ├── strategy/ # 플레이북 (Planner, Scenario Engine)
│ ├── config.py # Pydantic 설정
│ ├── logging_config.py # JSON 구조화 로깅
── db.py # SQLite 거래 기록
│ ├── main.py # 비동기 거래 루프
│ ├── broker/kis_api.py # KIS API 클라이언트
│ ├── brain/gemini_client.py # Gemini 의사결정 엔진
│ ├── core/risk_manager.py # 리스크 관리
│ ├── notifications/telegram_client.py # 텔레그램 알림
│ ├── evolution/optimizer.py # 전략 진화 엔진
│ └── strategies/base.py # 전략 베이스 클래스
├── tests/ # TDD 테스트 스위트
│ ├── db.py # SQLite 데이터베이스
── main.py # 비동기 거래 루프
├── tests/ # 551개 테스트 (25개 파일)
├── Dockerfile # 멀티스테이지 빌드
├── docker-compose.yml # 서비스 오케스트레이션
└── pyproject.toml # 의존성 및 도구 설정
```
## 문서
- **[아키텍처](docs/architecture.md)** — 시스템 설계, 컴포넌트, 데이터 흐름
- **[테스트](docs/testing.md)** — 테스트 구조, 커버리지, 작성 가이드
- **[명령어](docs/commands.md)** — CLI, Dashboard, Telegram 명령어
- **[컨텍스트 트리](docs/context-tree.md)** — L1-L7 계층 메모리
- **[워크플로우](docs/workflow.md)** — Git 워크플로우 정책
- **[에이전트 정책](docs/agents.md)** — 안전 제약, 금지 행위
- **[백업/복구](docs/disaster_recovery.md)** — 재해 복구 절차
- **[요구사항](docs/requirements-log.md)** — 사용자 요구사항 추적
## 라이선스
이 프로젝트의 라이선스는 [LICENSE](LICENSE) 파일을 참조하세요.

View File

@@ -2,7 +2,9 @@
## Overview
Self-evolving AI trading agent for global stock markets via KIS (Korea Investment & Securities) API. The main loop in `src/main.py` orchestrates four components across multiple markets with two trading modes: daily (batch API calls) or realtime (per-stock decisions).
Self-evolving AI trading agent for global stock markets via KIS (Korea Investment & Securities) API. The main loop in `src/main.py` orchestrates components across multiple markets with two trading modes: daily (batch API calls) or realtime (per-stock decisions).
**v2 Proactive Playbook Architecture**: The system uses a "plan once, execute locally" approach. Pre-market, the AI generates a playbook of scenarios (one Gemini API call per market per day). During trading hours, a local scenario engine matches live market data against these pre-computed scenarios — no additional AI calls needed. This dramatically reduces API costs and latency.
## Trading Modes
@@ -46,9 +48,11 @@ High-frequency trading with individual stock analysis:
**KISBroker** (`kis_api.py`) — Async KIS API client for domestic Korean market
- Automatic OAuth token refresh (valid for 24 hours)
- Leaky-bucket rate limiter (10 requests per second)
- Leaky-bucket rate limiter (configurable RPS, default 2.0)
- POST body hash-key signing for order authentication
- Custom SSL context with disabled hostname verification for VTS (virtual trading) endpoint due to known certificate mismatch
- `fetch_market_rankings()` — Fetch volume surge rankings from KIS API
- `get_daily_prices()` — Fetch OHLCV history for technical analysis
**OverseasBroker** (`overseas.py`) — KIS overseas stock API wrapper
@@ -63,10 +67,7 @@ High-frequency trading with individual stock analysis:
- `is_market_open()` checks weekends, trading hours, lunch breaks
- `get_open_markets()` returns currently active markets
- `get_next_market_open()` finds next market to open and when
**New API Methods** (added in v0.9.0):
- `fetch_market_rankings()` — Fetch volume surge rankings from KIS API
- `get_daily_prices()` — Fetch OHLCV history for technical analysis
- 10 global markets defined (KR, US_NASDAQ, US_NYSE, US_AMEX, JP, HK, CN_SHA, CN_SZA, VN_HNX, VN_HSX)
### 2. Analysis (`src/analysis/`)
@@ -91,14 +92,9 @@ High-frequency trading with individual stock analysis:
- **Fallback**: Uses static watchlist if ranking API unavailable
- **Realtime mode only**: Daily mode uses batch processing for API efficiency
**Benefits:**
- Reduces Gemini API calls from 20-30 stocks to 1-3 qualified candidates
- Fast Python-based filtering before expensive AI judgment
- Logs selection context (RSI, volume_ratio, signal, score) for Evolution system
### 3. Brain (`src/brain/`)
### 3. Brain (`src/brain/gemini_client.py`)
**GeminiClient** — AI decision engine powered by Google Gemini
**GeminiClient** (`gemini_client.py`) — AI decision engine powered by Google Gemini
- Constructs structured prompts from market data
- Parses JSON responses into `TradeDecision` objects (`action`, `confidence`, `rationale`)
@@ -106,11 +102,20 @@ High-frequency trading with individual stock analysis:
- Falls back to safe HOLD on any parse/API error
- Handles markdown-wrapped JSON, malformed responses, invalid actions
**PromptOptimizer** (`prompt_optimizer.py`) — Token efficiency optimization
- Reduces prompt size while preserving decision quality
- Caches optimized prompts
**ContextSelector** (`context_selector.py`) — Relevant context selection for prompts
- Selects appropriate context layers for current market conditions
### 4. Risk Manager (`src/core/risk_manager.py`)
**RiskManager** — Safety circuit breaker and order validation
⚠️ **READ-ONLY by policy** (see [`docs/agents.md`](./agents.md))
> **READ-ONLY by policy** (see [`docs/agents.md`](./agents.md))
- **Circuit Breaker**: Halts all trading via `SystemExit` when daily P&L drops below -3.0%
- Threshold may only be made stricter, never relaxed
@@ -118,7 +123,79 @@ High-frequency trading with individual stock analysis:
- **Fat-Finger Protection**: Rejects orders exceeding 30% of available cash
- Must always be enforced, cannot be disabled
### 5. Notifications (`src/notifications/telegram_client.py`)
### 5. Strategy (`src/strategy/`)
**Pre-Market Planner** (`pre_market_planner.py`) — AI playbook generation
- Runs before market open (configurable `PRE_MARKET_MINUTES`, default 30)
- Generates scenario-based playbooks via single Gemini API call per market
- Handles timeout (`PLANNER_TIMEOUT_SECONDS`, default 60) with defensive playbook fallback
- Persists playbooks to database for audit trail
**Scenario Engine** (`scenario_engine.py`) — Local scenario matching
- Matches live market data against pre-computed playbook scenarios
- No AI calls during trading hours — pure Python matching logic
- Returns matched scenarios with confidence scores
- Configurable `MAX_SCENARIOS_PER_STOCK` (default 5)
- Periodic rescan at `RESCAN_INTERVAL_SECONDS` (default 300)
**Playbook Store** (`playbook_store.py`) — Playbook persistence
- SQLite-backed storage for daily playbooks
- Date and market-based retrieval
- Status tracking (generated, active, expired)
**Models** (`models.py`) — Pydantic data models
- Scenario, Playbook, MatchResult, and related type definitions
### 6. Context System (`src/context/`)
**Context Store** (`store.py`) — L1-L7 hierarchical memory
- 7-layer context system (see [docs/context-tree.md](./context-tree.md)):
- L1: Tick-level (real-time price)
- L2: Intraday (session summary)
- L3: Daily (end-of-day)
- L4: Weekly (trend analysis)
- L5: Monthly (strategy review)
- L6: Daily Review (scorecard)
- L7: Evolution (long-term learning)
- Key-value storage with timeframe tagging
- SQLite persistence in `contexts` table
**Context Scheduler** (`scheduler.py`) — Periodic aggregation
- Scheduled summarization from lower to higher layers
- Configurable aggregation intervals
**Context Summarizer** (`summarizer.py`) — Layer summarization
- Aggregates lower-layer data into higher-layer summaries
### 7. Dashboard (`src/dashboard/`)
**FastAPI App** (`app.py`) — Read-only monitoring dashboard
- Runs as daemon thread when enabled (`--dashboard` CLI flag or `DASHBOARD_ENABLED=true`)
- Configurable host/port (`DASHBOARD_HOST`, `DASHBOARD_PORT`, default `127.0.0.1:8080`)
- Serves static HTML frontend
**8 API Endpoints:**
| Endpoint | Method | Description |
|----------|--------|-------------|
| `/` | GET | Static HTML dashboard |
| `/api/status` | GET | Daily trading status by market |
| `/api/playbook/{date}` | GET | Playbook for specific date and market |
| `/api/scorecard/{date}` | GET | Daily scorecard from L6_DAILY context |
| `/api/performance` | GET | Trading performance metrics (by market + combined) |
| `/api/context/{layer}` | GET | Query context by layer (L1-L7) |
| `/api/decisions` | GET | Decision log entries with outcomes |
| `/api/scenarios/active` | GET | Today's matched scenarios |
### 8. Notifications (`src/notifications/telegram_client.py`)
**TelegramClient** — Real-time event notifications via Telegram Bot API
@@ -126,7 +203,13 @@ High-frequency trading with individual stock analysis:
- Non-blocking: failures are logged but never crash trading
- Rate-limited: 1 message/second default to respect Telegram API limits
- Auto-disabled when credentials missing
- Gracefully handles API errors, network timeouts, invalid tokens
**TelegramCommandHandler** — Bidirectional command interface
- Long polling from Telegram API (configurable `TELEGRAM_POLLING_INTERVAL`)
- 9 interactive commands: `/help`, `/status`, `/positions`, `/report`, `/scenarios`, `/review`, `/dashboard`, `/stop`, `/resume`
- Authorization filtering by `TELEGRAM_CHAT_ID`
- Enable/disable via `TELEGRAM_COMMANDS_ENABLED` (default: true)
**Notification Types:**
- Trade execution (BUY/SELL with confidence)
@@ -134,12 +217,12 @@ High-frequency trading with individual stock analysis:
- Fat-finger protection triggers (order rejection)
- Market open/close events
- System startup/shutdown status
- Playbook generation results
- Stop-loss monitoring alerts
**Setup:** See [src/notifications/README.md](../src/notifications/README.md) for bot creation and configuration.
### 9. Evolution (`src/evolution/`)
### 6. Evolution (`src/evolution/optimizer.py`)
**StrategyOptimizer** — Self-improvement loop
**StrategyOptimizer** (`optimizer.py`) — Self-improvement loop
- Analyzes high-confidence losing trades from SQLite
- Asks Gemini to generate new `BaseStrategy` subclasses
@@ -147,8 +230,122 @@ High-frequency trading with individual stock analysis:
- Simulates PR creation for human review
- Only activates strategies that pass all tests
**DailyReview** (`daily_review.py`) — End-of-day review
- Generates comprehensive trade performance summary
- Stores results in L6_DAILY context layer
- Tracks win rate, P&L, confidence accuracy
**DailyScorecard** (`scorecard.py`) — Performance scoring
- Calculates daily metrics (trades, P&L, win rate, avg confidence)
- Enables trend tracking across days
**Stop-Loss Monitoring** — Real-time position protection
- Monitors positions against stop-loss levels from playbook scenarios
- Sends Telegram alerts when thresholds approached or breached
### 10. Decision Logger (`src/logging/decision_logger.py`)
**DecisionLogger** — Comprehensive audit trail
- Logs every trading decision with full context snapshot
- Captures input data, rationale, confidence, and outcomes
- Supports outcome tracking (P&L, accuracy) for post-analysis
- Stored in `decision_logs` table with indexed queries
- Review workflow support (reviewed flag, review notes)
### 11. Data Integration (`src/data/`)
**External Data Sources** (optional):
- `news_api.py` — News sentiment data
- `market_data.py` — Extended market data
- `economic_calendar.py` — Economic event calendar
### 12. Backup (`src/backup/`)
**Disaster Recovery** (see [docs/disaster_recovery.md](./disaster_recovery.md)):
- `scheduler.py` — Automated backup scheduling
- `exporter.py` — Data export to various formats
- `cloud_storage.py` — S3-compatible cloud backup
- `health_monitor.py` — Backup integrity verification
## Data Flow
### Playbook Mode (Daily — Primary v2 Flow)
```
┌─────────────────────────────────────────────────────────────┐
│ Pre-Market Phase (before market open) │
└─────────────────────────────────────────────────────────────┘
┌──────────────────────────────────┐
│ Pre-Market Planner │
│ - 1 Gemini API call per market │
│ - Generate scenario playbook │
│ - Store in playbooks table │
└──────────────────┬───────────────┘
┌─────────────────────────────────────────────────────────────┐
│ Trading Hours (market open → close) │
└─────────────────────────────────────────────────────────────┘
┌──────────────────────────────────┐
│ Market Schedule Check │
│ - Get open markets │
│ - Filter by enabled markets │
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
│ Scenario Engine (local) │
│ - Match live data vs playbook │
│ - No AI calls needed │
│ - Return matched scenarios │
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
│ Risk Manager: Validate Order │
│ - Check circuit breaker │
│ - Check fat-finger limit │
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
│ Broker: Execute Order │
│ - Domestic: send_order() │
│ - Overseas: send_overseas_order()│
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
│ Decision Logger + DB │
│ - Full audit trail │
│ - Context snapshot │
│ - Telegram notification │
└──────────────────┬───────────────┘
┌─────────────────────────────────────────────────────────────┐
│ Post-Market Phase │
└─────────────────────────────────────────────────────────────┘
┌──────────────────────────────────┐
│ Daily Review + Scorecard │
│ - Performance summary │
│ - Store in L6_DAILY context │
│ - Evolution learning │
└──────────────────────────────────┘
```
### Realtime Mode (with Smart Scanner)
```
@@ -162,7 +359,7 @@ High-frequency trading with individual stock analysis:
│ - Get open markets │
│ - Filter by enabled markets │
│ - Wait if all closed │
└──────────────────┬───────────────
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
@@ -172,25 +369,19 @@ High-frequency trading with individual stock analysis:
│ - Calculate RSI(14) + vol ratio │
│ - Filter: vol>2x AND RSI extreme │
│ - Return top 3 qualified stocks │
└──────────────────┬───────────────
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
│ For Each Qualified Candidate │
└──────────────────┬───────────────
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
│ Broker: Fetch Market Data │
│ - Domestic: orderbook + balance │
│ - Overseas: price + balance │
└──────────────────┬───────────────
┌──────────────────────────────────┐
│ Calculate P&L │
│ pnl_pct = (eval - cost) / cost │
└──────────────────┬────────────────┘
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
@@ -199,47 +390,36 @@ High-frequency trading with individual stock analysis:
│ - Call Gemini API │
│ - Parse JSON response │
│ - Return TradeDecision │
└──────────────────┬───────────────
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
│ Risk Manager: Validate Order │
│ - Check circuit breaker │
│ - Check fat-finger limit │
│ - Raise if validation fails │
└──────────────────┬────────────────┘
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
│ Broker: Execute Order │
│ - Domestic: send_order() │
│ - Overseas: send_overseas_order()│
└──────────────────┬───────────────
└──────────────────┬───────────────┘
┌──────────────────────────────────┐
Notifications: Send Alert
│ - Trade execution notification
│ - Non-blocking (errors logged)
│ - Rate-limited to 1/sec
└──────────────────────────────────┘
┌──────────────────────────────────┐
│ Database: Log Trade │
│ - SQLite (data/trades.db) │
│ - Track: action, confidence, │
│ rationale, market, exchange │
│ - NEW: selection_context (JSON) │
│ - RSI, volume_ratio, signal │
│ - For Evolution optimization │
└───────────────────────────────────┘
Decision Logger + Notifications
│ - Log trade to SQLite
│ - selection_context (JSON)
│ - Telegram notification
└──────────────────────────────────┘
```
## Database Schema
**SQLite** (`src/db.py`)
**SQLite** (`src/db.py`) — Database: `data/trades.db`
### trades
```sql
CREATE TABLE trades (
id INTEGER PRIMARY KEY AUTOINCREMENT,
@@ -251,25 +431,73 @@ CREATE TABLE trades (
quantity INTEGER,
price REAL,
pnl REAL DEFAULT 0.0,
market TEXT DEFAULT 'KR', -- KR | US_NASDAQ | JP | etc.
exchange_code TEXT DEFAULT 'KRX', -- KRX | NASD | NYSE | etc.
selection_context TEXT -- JSON: {rsi, volume_ratio, signal, score}
market TEXT DEFAULT 'KR',
exchange_code TEXT DEFAULT 'KRX',
selection_context TEXT, -- JSON: {rsi, volume_ratio, signal, score}
decision_id TEXT -- Links to decision_logs
);
```
**Selection Context** (new in v0.9.0): Stores scanner selection criteria as JSON:
```json
{
"rsi": 28.5,
"volume_ratio": 2.7,
"signal": "oversold",
"score": 85.2
}
### contexts
```sql
CREATE TABLE contexts (
id INTEGER PRIMARY KEY AUTOINCREMENT,
layer TEXT NOT NULL, -- L1 through L7
timeframe TEXT,
key TEXT NOT NULL,
value TEXT NOT NULL, -- JSON data
created_at TEXT NOT NULL,
updated_at TEXT NOT NULL
);
-- Indices: idx_contexts_layer, idx_contexts_timeframe, idx_contexts_updated
```
Enables Evolution system to analyze correlation between selection criteria and trade outcomes.
### decision_logs
```sql
CREATE TABLE decision_logs (
decision_id TEXT PRIMARY KEY,
timestamp TEXT NOT NULL,
stock_code TEXT,
market TEXT,
exchange_code TEXT,
action TEXT,
confidence INTEGER,
rationale TEXT,
context_snapshot TEXT, -- JSON: full context at decision time
input_data TEXT, -- JSON: market data used
outcome_pnl REAL,
outcome_accuracy REAL,
reviewed INTEGER DEFAULT 0,
review_notes TEXT
);
-- Indices: idx_decision_logs_timestamp, idx_decision_logs_reviewed, idx_decision_logs_confidence
```
Auto-migration: Adds `market`, `exchange_code`, and `selection_context` columns if missing for backward compatibility.
### playbooks
```sql
CREATE TABLE playbooks (
id INTEGER PRIMARY KEY AUTOINCREMENT,
date TEXT NOT NULL,
market TEXT NOT NULL,
status TEXT DEFAULT 'generated',
playbook_json TEXT NOT NULL, -- Full playbook with scenarios
generated_at TEXT NOT NULL,
token_count INTEGER,
scenario_count INTEGER,
match_count INTEGER DEFAULT 0
);
-- Indices: idx_playbooks_date, idx_playbooks_market
```
### context_metadata
```sql
CREATE TABLE context_metadata (
layer TEXT PRIMARY KEY,
description TEXT,
retention_days INTEGER,
aggregation_source TEXT
);
```
## Configuration
@@ -284,29 +512,62 @@ KIS_APP_SECRET=your_app_secret
KIS_ACCOUNT_NO=XXXXXXXX-XX
GEMINI_API_KEY=your_gemini_key
# Optional
# Optional — Trading Mode
MODE=paper # paper | live
DB_PATH=data/trades.db
CONFIDENCE_THRESHOLD=80
MAX_LOSS_PCT=3.0
MAX_ORDER_PCT=30.0
ENABLED_MARKETS=KR,US_NASDAQ # Comma-separated market codes
# Trading Mode (API efficiency)
TRADE_MODE=daily # daily | realtime
DAILY_SESSIONS=4 # Sessions per day (daily mode only)
SESSION_INTERVAL_HOURS=6 # Hours between sessions (daily mode only)
# Telegram Notifications (optional)
TELEGRAM_BOT_TOKEN=1234567890:ABCdefGHIjklMNOpqrsTUVwxyz
TELEGRAM_CHAT_ID=123456789
TELEGRAM_ENABLED=true
# Optional — Database
DB_PATH=data/trades.db
# Smart Scanner (optional, realtime mode only)
# Optional — Risk
CONFIDENCE_THRESHOLD=80
MAX_LOSS_PCT=3.0
MAX_ORDER_PCT=30.0
# Optional — Markets
ENABLED_MARKETS=KR,US # Comma-separated market codes
RATE_LIMIT_RPS=2.0 # KIS API requests per second
# Optional — Pre-Market Planner (v2)
PRE_MARKET_MINUTES=30 # Minutes before market open to generate playbook
MAX_SCENARIOS_PER_STOCK=5 # Max scenarios per stock in playbook
PLANNER_TIMEOUT_SECONDS=60 # Timeout for playbook generation
DEFENSIVE_PLAYBOOK_ON_FAILURE=true # Fallback on AI failure
RESCAN_INTERVAL_SECONDS=300 # Scenario rescan interval during trading
# Optional — Smart Scanner (realtime mode only)
RSI_OVERSOLD_THRESHOLD=30 # 0-50, oversold threshold
RSI_MOMENTUM_THRESHOLD=70 # 50-100, momentum threshold
VOL_MULTIPLIER=2.0 # Minimum volume ratio (2.0 = 200%)
SCANNER_TOP_N=3 # Max qualified candidates per scan
# Optional — Dashboard
DASHBOARD_ENABLED=false # Enable FastAPI dashboard
DASHBOARD_HOST=127.0.0.1 # Dashboard bind address
DASHBOARD_PORT=8080 # Dashboard port (1-65535)
# Optional — Telegram
TELEGRAM_BOT_TOKEN=1234567890:ABCdefGHIjklMNOpqrsTUVwxyz
TELEGRAM_CHAT_ID=123456789
TELEGRAM_ENABLED=true
TELEGRAM_COMMANDS_ENABLED=true # Enable bidirectional commands
TELEGRAM_POLLING_INTERVAL=1.0 # Command polling interval (seconds)
# Optional — Backup
BACKUP_ENABLED=false
BACKUP_DIR=data/backups
S3_ENDPOINT_URL=...
S3_ACCESS_KEY=...
S3_SECRET_KEY=...
S3_BUCKET_NAME=...
S3_REGION=...
# Optional — External Data
NEWS_API_KEY=...
NEWS_API_PROVIDER=...
MARKET_DATA_API_KEY=...
```
Tests use in-memory SQLite (`DB_PATH=":memory:"`) and dummy credentials via `tests/conftest.py`.
@@ -340,4 +601,9 @@ Tests use in-memory SQLite (`DB_PATH=":memory:"`) and dummy credentials via `tes
- Invalid token → log error, trading unaffected
- Rate limit exceeded → queued via rate limiter
**Guarantee**: Notification failures never interrupt trading operations.
### Playbook Generation Failure
- Timeout → fall back to defensive playbook (`DEFENSIVE_PLAYBOOK_ON_FAILURE`)
- API error → use previous day's playbook if available
- No playbook → skip pre-market phase, fall back to direct AI calls
**Guarantee**: Notification and dashboard failures never interrupt trading operations.

View File

@@ -119,7 +119,7 @@ No decorator needed for async tests.
# Install all dependencies (production + dev)
pip install -e ".[dev]"
# Run full test suite with coverage
# Run full test suite with coverage (551 tests across 25 files)
pytest -v --cov=src --cov-report=term-missing
# Run a single test file
@@ -137,11 +137,61 @@ mypy src/ --strict
# Run the trading agent
python -m src.main --mode=paper
# Run with dashboard enabled
python -m src.main --mode=paper --dashboard
# Docker
docker compose up -d ouroboros # Run agent
docker compose --profile test up test # Run tests in container
```
## Dashboard
The FastAPI dashboard provides read-only monitoring of the trading system.
### Starting the Dashboard
```bash
# Via CLI flag
python -m src.main --mode=paper --dashboard
# Via environment variable
DASHBOARD_ENABLED=true python -m src.main --mode=paper
```
Dashboard runs as a daemon thread on `DASHBOARD_HOST:DASHBOARD_PORT` (default: `127.0.0.1:8080`).
### API Endpoints
| Endpoint | Description |
|----------|-------------|
| `GET /` | HTML dashboard UI |
| `GET /api/status` | Daily trading status by market |
| `GET /api/playbook/{date}` | Playbook for specific date (query: `market`) |
| `GET /api/scorecard/{date}` | Daily scorecard from L6_DAILY context |
| `GET /api/performance` | Performance metrics by market and combined |
| `GET /api/context/{layer}` | Context data by layer L1-L7 (query: `timeframe`) |
| `GET /api/decisions` | Decision log entries (query: `limit`, `market`) |
| `GET /api/scenarios/active` | Today's matched scenarios |
## Telegram Commands
When `TELEGRAM_COMMANDS_ENABLED=true` (default), the bot accepts these interactive commands:
| Command | Description |
|---------|-------------|
| `/help` | List available commands |
| `/status` | Show trading status (mode, markets, P&L) |
| `/positions` | Display account summary (balance, cash, P&L) |
| `/report` | Daily summary metrics (trades, P&L, win rate) |
| `/scenarios` | Show today's playbook scenarios |
| `/review` | Display recent scorecards (L6_DAILY layer) |
| `/dashboard` | Show dashboard URL if enabled |
| `/stop` | Pause trading |
| `/resume` | Resume trading |
Commands are only processed from the authorized `TELEGRAM_CHAT_ID`.
## Environment Setup
```bash

View File

@@ -86,3 +86,28 @@
- Plan Consistency (필수), Safety & Constraints, Quality, Workflow 4개 카테고리
**이슈/PR:** #114
---
## 2026-02-16
### 문서 v2 동기화 (전체 문서 현행화)
**배경:**
- v2 기능 구현 완료 후 문서가 실제 코드 상태와 크게 괴리
- 문서에는 54 tests / 4 files로 기록되었으나 실제로는 551 tests / 25 files
- v2 핵심 기능(Playbook, Scenario Engine, Dashboard, Telegram Commands, Daily Review, Context System, Backup) 문서화 누락
**요구사항:**
1. `docs/testing.md` — 551 tests / 25 files 반영, 전체 테스트 파일 설명
2. `docs/architecture.md` — v2 컴포넌트(Strategy, Context, Dashboard, Decision Logger 등) 추가, Playbook Mode 데이터 플로우, DB 스키마 5개 테이블, v2 환경변수
3. `docs/commands.md` — Dashboard 실행 명령어, Telegram 명령어 9종 레퍼런스
4. `CLAUDE.md` — Project Structure 트리 확장, 테스트 수 업데이트, `--dashboard` 플래그
5. `docs/skills.md` — DB 파일명 `trades.db`로 통일, Dashboard 명령어 추가
6. 기존에 유효한 트러블슈팅, 코드 예제 등은 유지
**구현 결과:**
- 6개 문서 파일 업데이트
- 이전 시도(2개 커밋)는 기존 내용을 과도하게 삭제하여 폐기, main 기준으로 재작업
**이슈/PR:** #131, PR #134

View File

@@ -34,6 +34,12 @@ python -m src.main --mode=paper
```
Runs the agent in paper-trading mode (no real orders).
### Start Trading Agent with Dashboard
```bash
python -m src.main --mode=paper --dashboard
```
Runs the agent with FastAPI dashboard on `127.0.0.1:8080` (configurable via `DASHBOARD_HOST`/`DASHBOARD_PORT`).
### Start Trading Agent (Production)
```bash
docker compose up -d ouroboros
@@ -59,7 +65,7 @@ Analyze the last 30 days of trade logs and generate performance metrics.
python -m src.evolution.optimizer --evolve
```
Triggers the evolution engine to:
1. Analyze `trade_logs.db` for failing patterns
1. Analyze `trades.db` for failing patterns
2. Ask Gemini to generate a new strategy
3. Run tests on the new strategy
4. Create a PR if tests pass
@@ -91,12 +97,12 @@ curl http://localhost:8080/health
### View Trade Logs
```bash
sqlite3 data/trade_logs.db "SELECT * FROM trades ORDER BY timestamp DESC LIMIT 20;"
sqlite3 data/trades.db "SELECT * FROM trades ORDER BY timestamp DESC LIMIT 20;"
```
### Export Trade History
```bash
sqlite3 -header -csv data/trade_logs.db "SELECT * FROM trades;" > trades_export.csv
sqlite3 -header -csv data/trades.db "SELECT * FROM trades;" > trades_export.csv
```
## Safety Checklist (Pre-Deploy)

View File

@@ -2,51 +2,29 @@
## Test Structure
**54 tests** across four files. `asyncio_mode = "auto"` in pyproject.toml — async tests need no special decorator.
**551 tests** across **25 files**. `asyncio_mode = "auto"` in pyproject.toml — async tests need no special decorator.
The `settings` fixture in `conftest.py` provides safe defaults with test credentials and in-memory DB.
### Test Files
#### `tests/test_risk.py` (11 tests)
- Circuit breaker boundaries
- Fat-finger edge cases
#### Core Components
##### `tests/test_risk.py` (14 tests)
- Circuit breaker boundaries and exact threshold triggers
- Fat-finger edge cases and percentage validation
- P&L calculation edge cases
- Order validation logic
**Example:**
```python
def test_circuit_breaker_exact_threshold(risk_manager):
"""Circuit breaker should trip at exactly -3.0%."""
with pytest.raises(CircuitBreakerTripped):
risk_manager.validate_order(
current_pnl_pct=-3.0,
order_amount=1000,
total_cash=10000
)
```
#### `tests/test_broker.py` (6 tests)
##### `tests/test_broker.py` (11 tests)
- OAuth token lifecycle
- Rate limiting enforcement
- Hash key generation
- Network error handling
- SSL context configuration
**Example:**
```python
async def test_rate_limiter(broker):
"""Rate limiter should delay requests to stay under 10 RPS."""
start = time.monotonic()
for _ in range(15): # 15 requests
await broker._rate_limiter.acquire()
elapsed = time.monotonic() - start
assert elapsed >= 1.0 # Should take at least 1 second
```
#### `tests/test_brain.py` (18 tests)
- Valid JSON parsing
- Markdown-wrapped JSON handling
##### `tests/test_brain.py` (24 tests)
- Valid JSON parsing and markdown-wrapped JSON handling
- Malformed JSON fallback
- Missing fields handling
- Invalid action validation
@@ -54,33 +32,143 @@ async def test_rate_limiter(broker):
- Empty response handling
- Prompt construction for different markets
**Example:**
```python
async def test_confidence_below_threshold_forces_hold(brain):
"""Decisions below confidence threshold should force HOLD."""
decision = brain.parse_response('{"action":"BUY","confidence":70,"rationale":"test"}')
assert decision.action == "HOLD"
assert decision.confidence == 70
```
#### `tests/test_market_schedule.py` (19 tests)
##### `tests/test_market_schedule.py` (24 tests)
- Market open/close logic
- Timezone handling (UTC, Asia/Seoul, America/New_York, etc.)
- DST (Daylight Saving Time) transitions
- Weekend handling
- Lunch break logic
- Weekend handling and lunch break logic
- Multiple market filtering
- Next market open calculation
**Example:**
```python
def test_is_market_open_during_trading_hours():
"""Market should be open during regular trading hours."""
# KRX: 9:00-15:30 KST, no lunch break
market = MARKETS["KR"]
trading_time = datetime(2026, 2, 3, 10, 0, tzinfo=ZoneInfo("Asia/Seoul")) # Monday 10:00
assert is_market_open(market, trading_time) is True
```
##### `tests/test_db.py` (3 tests)
- Database initialization and table creation
- Trade logging with all fields (market, exchange_code, decision_id)
- Query and retrieval operations
##### `tests/test_main.py` (37 tests)
- Trading loop orchestration
- Market iteration and stock processing
- Dashboard integration (`--dashboard` flag)
- Telegram command handler wiring
- Error handling and graceful shutdown
#### Strategy & Playbook (v2)
##### `tests/test_pre_market_planner.py` (37 tests)
- Pre-market playbook generation
- Gemini API integration for scenario creation
- Timeout handling and defensive playbook fallback
- Multi-market playbook generation
##### `tests/test_scenario_engine.py` (44 tests)
- Scenario matching against live market data
- Confidence scoring and threshold filtering
- Multiple scenario type handling
- Edge cases (no match, partial match, expired scenarios)
##### `tests/test_playbook_store.py` (23 tests)
- Playbook persistence to SQLite
- Date-based retrieval and market filtering
- Playbook status management (generated, active, expired)
- JSON serialization/deserialization
##### `tests/test_strategy_models.py` (33 tests)
- Pydantic model validation for scenarios, playbooks, decisions
- Field constraints and default values
- Serialization round-trips
#### Analysis & Scanning
##### `tests/test_volatility.py` (24 tests)
- ATR and RSI calculation accuracy
- Volume surge ratio computation
- Momentum scoring
- Breakout/breakdown pattern detection
- Market scanner watchlist management
##### `tests/test_smart_scanner.py` (13 tests)
- Python-first filtering pipeline
- RSI and volume ratio filter logic
- Candidate scoring and ranking
- Fallback to static watchlist
#### Context & Memory
##### `tests/test_context.py` (18 tests)
- L1-L7 layer storage and retrieval
- Context key-value CRUD operations
- Timeframe-based queries
- Layer metadata management
##### `tests/test_context_scheduler.py` (5 tests)
- Periodic context aggregation scheduling
- Layer summarization triggers
#### Evolution & Review
##### `tests/test_evolution.py` (24 tests)
- Strategy optimization loop
- High-confidence losing trade analysis
- Generated strategy validation
##### `tests/test_daily_review.py` (10 tests)
- End-of-day review generation
- Trade performance summarization
- Context layer (L6_DAILY) integration
##### `tests/test_scorecard.py` (3 tests)
- Daily scorecard metrics calculation
- Win rate, P&L, confidence tracking
#### Notifications & Commands
##### `tests/test_telegram.py` (25 tests)
- Message sending and formatting
- Rate limiting (leaky bucket)
- Error handling (network timeout, invalid token)
- Auto-disable on missing credentials
- Notification types (trade, circuit breaker, fat-finger, market events)
##### `tests/test_telegram_commands.py` (31 tests)
- 9 command handlers (/help, /status, /positions, /report, /scenarios, /review, /dashboard, /stop, /resume)
- Long polling and command dispatch
- Authorization filtering by chat_id
- Command response formatting
#### Dashboard
##### `tests/test_dashboard.py` (14 tests)
- FastAPI endpoint responses (8 API routes)
- Status, playbook, scorecard, performance, context, decisions, scenarios
- Query parameter handling (market, date, limit)
#### Performance & Quality
##### `tests/test_token_efficiency.py` (34 tests)
- Gemini token usage optimization
- Prompt size reduction verification
- Cache effectiveness
##### `tests/test_latency_control.py` (30 tests)
- API call latency measurement
- Rate limiter timing accuracy
- Async operation overhead
##### `tests/test_decision_logger.py` (9 tests)
- Decision audit trail completeness
- Context snapshot capture
- Outcome tracking (P&L, accuracy)
##### `tests/test_data_integration.py` (38 tests)
- External data source integration
- News API, market data, economic calendar
- Error handling for API failures
##### `tests/test_backup.py` (23 tests)
- Backup scheduler and execution
- Cloud storage (S3) upload
- Health monitoring
- Data export functionality
## Coverage Requirements
@@ -91,20 +179,6 @@ Check coverage:
pytest -v --cov=src --cov-report=term-missing
```
Expected output:
```
Name Stmts Miss Cover Missing
-----------------------------------------------------------
src/brain/gemini_client.py 85 5 94% 165-169
src/broker/kis_api.py 120 12 90% ...
src/core/risk_manager.py 35 2 94% ...
src/db.py 25 1 96% ...
src/main.py 150 80 47% (excluded from CI)
src/markets/schedule.py 95 3 97% ...
-----------------------------------------------------------
TOTAL 510 103 80%
```
**Note:** `main.py` has lower coverage as it contains the main loop which is tested via integration/manual testing.
## Test Configuration

View File

@@ -10,6 +10,7 @@ dependencies = [
"google-genai>=1.0,<2",
"scipy>=1.11,<2",
"fastapi>=0.110,<1",
"uvicorn>=0.29,<1",
]
[project.optional-dependencies]

View File

@@ -83,6 +83,11 @@ class Settings(BaseSettings):
TELEGRAM_COMMANDS_ENABLED: bool = True
TELEGRAM_POLLING_INTERVAL: float = 1.0 # seconds
# Dashboard (optional)
DASHBOARD_ENABLED: bool = False
DASHBOARD_HOST: str = "127.0.0.1"
DASHBOARD_PORT: int = Field(default=8080, ge=1, le=65535)
model_config = {"env_file": ".env", "env_file_encoding": "utf-8"}
@property
@@ -96,4 +101,7 @@ class Settings(BaseSettings):
@property
def enabled_market_list(self) -> list[str]:
"""Parse ENABLED_MARKETS into list of market codes."""
return [m.strip() for m in self.ENABLED_MARKETS.split(",") if m.strip()]
from src.markets.schedule import expand_market_codes
raw = [m.strip() for m in self.ENABLED_MARKETS.split(",") if m.strip()]
return expand_market_codes(raw)

View File

@@ -26,7 +26,19 @@ def create_dashboard_app(db_path: str) -> FastAPI:
def get_status() -> dict[str, Any]:
today = datetime.now(UTC).date().isoformat()
with _connect(db_path) as conn:
markets = ["KR", "US"]
market_rows = conn.execute(
"""
SELECT DISTINCT market FROM (
SELECT market FROM trades WHERE DATE(timestamp) = ?
UNION
SELECT market FROM decision_logs WHERE DATE(timestamp) = ?
UNION
SELECT market FROM playbooks WHERE date = ?
) ORDER BY market
""",
(today, today, today),
).fetchall()
markets = [row[0] for row in market_rows] if market_rows else []
market_status: dict[str, Any] = {}
total_trades = 0
total_pnl = 0.0

View File

@@ -214,3 +214,24 @@ def get_latest_buy_trade(
if not row:
return None
return {"decision_id": row[0], "price": row[1], "quantity": row[2]}
def get_open_position(
conn: sqlite3.Connection, stock_code: str, market: str
) -> dict[str, Any] | None:
"""Return open position if latest trade is BUY, else None."""
cursor = conn.execute(
"""
SELECT action, decision_id, price, quantity
FROM trades
WHERE stock_code = ?
AND market = ?
ORDER BY timestamp DESC
LIMIT 1
""",
(stock_code, market),
)
row = cursor.fetchone()
if not row or row[0] != "BUY":
return None
return {"decision_id": row[1], "price": row[2], "quantity": row[3]}

View File

@@ -8,8 +8,10 @@ from __future__ import annotations
import argparse
import asyncio
import json
import logging
import signal
import threading
from datetime import UTC, datetime
from typing import Any
@@ -27,7 +29,7 @@ from src.context.store import ContextStore
from src.core.criticality import CriticalityAssessor
from src.core.priority_queue import PriorityTaskQueue
from src.core.risk_manager import CircuitBreakerTripped, FatFingerRejected, RiskManager
from src.db import get_latest_buy_trade, init_db, log_trade
from src.db import get_latest_buy_trade, get_open_position, init_db, log_trade
from src.evolution.daily_review import DailyReviewer
from src.evolution.optimizer import EvolutionOptimizer
from src.logging.decision_logger import DecisionLogger
@@ -113,6 +115,7 @@ async def trading_cycle(
current_price = safe_float(orderbook.get("output1", {}).get("stck_prpr", "0"))
foreigner_net = safe_float(orderbook.get("output1", {}).get("frgn_ntby_qty", "0"))
price_change_pct = safe_float(orderbook.get("output1", {}).get("prdy_ctrt", "0"))
else:
# Overseas market
price_data = await overseas_broker.get_overseas_price(
@@ -135,6 +138,7 @@ async def trading_cycle(
current_price = safe_float(price_data.get("output", {}).get("last", "0"))
foreigner_net = 0.0 # Not available for overseas
price_change_pct = safe_float(price_data.get("output", {}).get("rate", "0"))
# Calculate daily P&L %
pnl_pct = (
@@ -148,6 +152,7 @@ async def trading_cycle(
"market_name": market.name,
"current_price": current_price,
"foreigner_net": foreigner_net,
"price_change_pct": price_change_pct,
}
# Enrich market_data with scanner metrics for scenario engine
@@ -239,6 +244,34 @@ async def trading_cycle(
confidence=match.confidence,
rationale=match.rationale,
)
stock_playbook = playbook.get_stock_playbook(stock_code)
if decision.action == "HOLD":
open_position = get_open_position(db_conn, stock_code, market.code)
if open_position:
entry_price = safe_float(open_position.get("price"), 0.0)
if entry_price > 0:
loss_pct = (current_price - entry_price) / entry_price * 100
stop_loss_threshold = -2.0
if stock_playbook and stock_playbook.scenarios:
stop_loss_threshold = stock_playbook.scenarios[0].stop_loss_pct
if loss_pct <= stop_loss_threshold:
decision = TradeDecision(
action="SELL",
confidence=95,
rationale=(
f"Stop-loss triggered ({loss_pct:.2f}% <= "
f"{stop_loss_threshold:.2f}%)"
),
)
logger.info(
"Stop-loss override for %s (%s): %.2f%% <= %.2f%%",
stock_code,
market.name,
loss_pct,
stop_loss_threshold,
)
logger.info(
"Decision for %s (%s): %s (confidence=%d)",
stock_code,
@@ -277,6 +310,7 @@ async def trading_cycle(
input_data = {
"current_price": current_price,
"foreigner_net": foreigner_net,
"price_change_pct": price_change_pct,
"total_eval": total_eval,
"total_cash": total_cash,
"pnl_pct": pnl_pct,
@@ -506,6 +540,9 @@ async def run_daily_session(
foreigner_net = safe_float(
orderbook.get("output1", {}).get("frgn_ntby_qty", "0")
)
price_change_pct = safe_float(
orderbook.get("output1", {}).get("prdy_ctrt", "0")
)
else:
price_data = await overseas_broker.get_overseas_price(
market.exchange_code, stock_code
@@ -514,12 +551,16 @@ async def run_daily_session(
price_data.get("output", {}).get("last", "0")
)
foreigner_net = 0.0
price_change_pct = safe_float(
price_data.get("output", {}).get("rate", "0")
)
stock_data: dict[str, Any] = {
"stock_code": stock_code,
"market_name": market.name,
"current_price": current_price,
"foreigner_net": foreigner_net,
"price_change_pct": price_change_pct,
}
# Enrich with scanner metrics
cand = candidate_map.get(stock_code)
@@ -819,7 +860,7 @@ async def _run_evolution_loop(
market_date: str,
) -> None:
"""Run evolution loop once at US close (end of trading day)."""
if market_code != "US":
if not market_code.startswith("US"):
return
try:
@@ -844,6 +885,48 @@ async def _run_evolution_loop(
logger.warning("Evolution notification failed on %s: %s", market_date, exc)
def _start_dashboard_server(settings: Settings) -> threading.Thread | None:
"""Start FastAPI dashboard in a daemon thread when enabled."""
if not settings.DASHBOARD_ENABLED:
return None
def _serve() -> None:
try:
import uvicorn
from src.dashboard import create_dashboard_app
app = create_dashboard_app(settings.DB_PATH)
uvicorn.run(
app,
host=settings.DASHBOARD_HOST,
port=settings.DASHBOARD_PORT,
log_level="info",
)
except Exception as exc:
logger.warning("Dashboard server failed to start: %s", exc)
thread = threading.Thread(
target=_serve,
name="dashboard-server",
daemon=True,
)
thread.start()
logger.info(
"Dashboard server started at http://%s:%d",
settings.DASHBOARD_HOST,
settings.DASHBOARD_PORT,
)
return thread
def _apply_dashboard_flag(settings: Settings, dashboard_flag: bool) -> Settings:
"""Apply CLI dashboard flag over environment settings."""
if dashboard_flag and not settings.DASHBOARD_ENABLED:
return settings.model_copy(update={"DASHBOARD_ENABLED": True})
return settings
async def run(settings: Settings) -> None:
"""Main async loop — iterate over open markets on a timer."""
broker = KISBroker(settings)
@@ -893,6 +976,10 @@ async def run(settings: Settings) -> None:
"/help - Show available commands\n"
"/status - Trading status (mode, markets, P&L)\n"
"/positions - Current holdings\n"
"/report - Daily summary report\n"
"/scenarios - Today's playbook scenarios\n"
"/review - Recent scorecards\n"
"/dashboard - Dashboard URL/status\n"
"/stop - Pause trading\n"
"/resume - Resume trading"
)
@@ -1012,11 +1099,164 @@ async def run(settings: Settings) -> None:
"<b>⚠️ Error</b>\n\nFailed to retrieve positions."
)
async def handle_report() -> None:
"""Handle /report command - show daily summary metrics."""
try:
today = datetime.now(UTC).date().isoformat()
trade_row = db_conn.execute(
"""
SELECT COUNT(*) AS trade_count,
COALESCE(SUM(pnl), 0.0) AS total_pnl,
SUM(CASE WHEN pnl > 0 THEN 1 ELSE 0 END) AS wins
FROM trades
WHERE DATE(timestamp) = ?
""",
(today,),
).fetchone()
decision_row = db_conn.execute(
"""
SELECT COUNT(*) AS decision_count,
COALESCE(AVG(confidence), 0.0) AS avg_confidence
FROM decision_logs
WHERE DATE(timestamp) = ?
""",
(today,),
).fetchone()
trade_count = int(trade_row[0] if trade_row else 0)
total_pnl = float(trade_row[1] if trade_row else 0.0)
wins = int(trade_row[2] if trade_row and trade_row[2] is not None else 0)
decision_count = int(decision_row[0] if decision_row else 0)
avg_confidence = float(decision_row[1] if decision_row else 0.0)
win_rate = (wins / trade_count * 100.0) if trade_count > 0 else 0.0
await telegram.send_message(
"<b>📈 Daily Report</b>\n\n"
f"<b>Date:</b> {today}\n"
f"<b>Trades:</b> {trade_count}\n"
f"<b>Total P&L:</b> {total_pnl:+.2f}\n"
f"<b>Win Rate:</b> {win_rate:.2f}%\n"
f"<b>Decisions:</b> {decision_count}\n"
f"<b>Avg Confidence:</b> {avg_confidence:.2f}"
)
except Exception as exc:
logger.error("Error in /report handler: %s", exc)
await telegram.send_message(
"<b>⚠️ Error</b>\n\nFailed to generate daily report."
)
async def handle_scenarios() -> None:
"""Handle /scenarios command - show today's playbook scenarios."""
try:
today = datetime.now(UTC).date().isoformat()
rows = db_conn.execute(
"""
SELECT market, playbook_json
FROM playbooks
WHERE date = ?
ORDER BY market
""",
(today,),
).fetchall()
if not rows:
await telegram.send_message(
"<b>🧠 Today's Scenarios</b>\n\nNo playbooks found for today."
)
return
lines = ["<b>🧠 Today's Scenarios</b>", ""]
for market, playbook_json in rows:
lines.append(f"<b>{market}</b>")
playbook_data = {}
try:
playbook_data = json.loads(playbook_json)
except Exception:
playbook_data = {}
stock_playbooks = playbook_data.get("stock_playbooks", [])
if not stock_playbooks:
lines.append("- No scenarios")
lines.append("")
continue
for stock_pb in stock_playbooks:
stock_code = stock_pb.get("stock_code", "N/A")
scenarios = stock_pb.get("scenarios", [])
for sc in scenarios:
action = sc.get("action", "HOLD")
confidence = sc.get("confidence", 0)
lines.append(f"- {stock_code}: {action} ({confidence})")
lines.append("")
await telegram.send_message("\n".join(lines).strip())
except Exception as exc:
logger.error("Error in /scenarios handler: %s", exc)
await telegram.send_message(
"<b>⚠️ Error</b>\n\nFailed to retrieve scenarios."
)
async def handle_review() -> None:
"""Handle /review command - show recent scorecards."""
try:
rows = db_conn.execute(
"""
SELECT timeframe, key, value
FROM contexts
WHERE layer = 'L6_DAILY' AND key LIKE 'scorecard_%'
ORDER BY updated_at DESC
LIMIT 5
"""
).fetchall()
if not rows:
await telegram.send_message(
"<b>📝 Recent Reviews</b>\n\nNo scorecards available."
)
return
lines = ["<b>📝 Recent Reviews</b>", ""]
for timeframe, key, value in rows:
scorecard = json.loads(value)
market = key.replace("scorecard_", "")
total_pnl = float(scorecard.get("total_pnl", 0.0))
win_rate = float(scorecard.get("win_rate", 0.0))
decisions = int(scorecard.get("total_decisions", 0))
lines.append(
f"- {timeframe} {market}: P&L {total_pnl:+.2f}, "
f"Win {win_rate:.2f}%, Decisions {decisions}"
)
await telegram.send_message("\n".join(lines))
except Exception as exc:
logger.error("Error in /review handler: %s", exc)
await telegram.send_message(
"<b>⚠️ Error</b>\n\nFailed to retrieve reviews."
)
async def handle_dashboard() -> None:
"""Handle /dashboard command - show dashboard URL if enabled."""
if not settings.DASHBOARD_ENABLED:
await telegram.send_message(
"<b>🖥️ Dashboard</b>\n\nDashboard is not enabled."
)
return
url = f"http://{settings.DASHBOARD_HOST}:{settings.DASHBOARD_PORT}"
await telegram.send_message(
"<b>🖥️ Dashboard</b>\n\n"
f"<b>URL:</b> {url}"
)
command_handler.register_command("help", handle_help)
command_handler.register_command("stop", handle_stop)
command_handler.register_command("resume", handle_resume)
command_handler.register_command("status", handle_status)
command_handler.register_command("positions", handle_positions)
command_handler.register_command("report", handle_report)
command_handler.register_command("scenarios", handle_scenarios)
command_handler.register_command("review", handle_review)
command_handler.register_command("dashboard", handle_dashboard)
# Initialize volatility hunter
volatility_analyzer = VolatilityAnalyzer(min_volume_surge=2.0, min_price_change=1.0)
@@ -1042,6 +1282,7 @@ async def run(settings: Settings) -> None:
low_volatility_threshold=30.0,
)
priority_queue = PriorityTaskQueue(max_size=1000)
_start_dashboard_server(settings)
# Track last scan time for each market
last_scan_time: dict[str, float] = {}
@@ -1395,10 +1636,16 @@ def main() -> None:
default="paper",
help="Trading mode (default: paper)",
)
parser.add_argument(
"--dashboard",
action="store_true",
help="Enable FastAPI dashboard server in background thread",
)
args = parser.parse_args()
setup_logging()
settings = Settings(MODE=args.mode) # type: ignore[call-arg]
settings = _apply_dashboard_flag(settings, args.dashboard)
asyncio.run(run(settings))

View File

@@ -123,6 +123,23 @@ MARKETS: dict[str, MarketInfo] = {
),
}
MARKET_SHORTHAND: dict[str, list[str]] = {
"US": ["US_NASDAQ", "US_NYSE", "US_AMEX"],
"CN": ["CN_SHA", "CN_SZA"],
"VN": ["VN_HAN", "VN_HCM"],
}
def expand_market_codes(codes: list[str]) -> list[str]:
"""Expand shorthand market codes into concrete exchange market codes."""
expanded: list[str] = []
for code in codes:
if code in MARKET_SHORTHAND:
expanded.extend(MARKET_SHORTHAND[code])
else:
expanded.append(code)
return expanded
def is_market_open(market: MarketInfo, now: datetime | None = None) -> bool:
"""

View File

@@ -16,6 +16,10 @@ from src.evolution.daily_review import DailyReviewer
from src.evolution.scorecard import DailyScorecard
from src.logging.decision_logger import DecisionLogger
from datetime import UTC, datetime
TODAY = datetime.now(UTC).strftime("%Y-%m-%d")
@pytest.fixture
def db_conn() -> sqlite3.Connection:
@@ -116,7 +120,7 @@ def test_generate_scorecard_market_scoped(
exchange_code="NASDAQ",
)
scorecard = reviewer.generate_scorecard("2026-02-14", "KR")
scorecard = reviewer.generate_scorecard(TODAY, "KR")
assert scorecard.market == "KR"
assert scorecard.total_decisions == 2
@@ -158,7 +162,7 @@ def test_generate_scorecard_top_winners_and_losers(
decision_id=decision_id,
)
scorecard = reviewer.generate_scorecard("2026-02-14", "KR")
scorecard = reviewer.generate_scorecard(TODAY, "KR")
assert scorecard.top_winners == ["005930", "000660"]
assert scorecard.top_losers == ["035420", "051910"]
@@ -167,7 +171,7 @@ def test_generate_scorecard_empty_day(
db_conn: sqlite3.Connection, context_store: ContextStore,
) -> None:
reviewer = DailyReviewer(db_conn, context_store)
scorecard = reviewer.generate_scorecard("2026-02-14", "KR")
scorecard = reviewer.generate_scorecard(TODAY, "KR")
assert scorecard.total_decisions == 0
assert scorecard.total_pnl == 0.0

View File

@@ -1,21 +1,25 @@
"""Tests for FastAPI dashboard endpoints."""
"""Tests for dashboard endpoint handlers."""
from __future__ import annotations
import json
import sqlite3
from collections.abc import Callable
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
import pytest
pytest.importorskip("fastapi")
from fastapi.testclient import TestClient
from fastapi import HTTPException
from fastapi.responses import FileResponse
from src.dashboard.app import create_dashboard_app
from src.db import init_db
def _seed_db(conn: sqlite3.Connection) -> None:
today = datetime.now(UTC).date().isoformat()
conn.execute(
"""
INSERT INTO playbooks (
@@ -34,6 +38,24 @@ def _seed_db(conn: sqlite3.Connection) -> None:
1,
),
)
conn.execute(
"""
INSERT INTO playbooks (
date, market, status, playbook_json, generated_at,
token_count, scenario_count, match_count
) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
""",
(
today,
"US_NASDAQ",
"ready",
json.dumps({"market": "US_NASDAQ", "stock_playbooks": []}),
f"{today}T08:30:00+00:00",
100,
1,
0,
),
)
conn.execute(
"""
INSERT INTO contexts (layer, timeframe, key, value, created_at, updated_at)
@@ -71,7 +93,7 @@ def _seed_db(conn: sqlite3.Connection) -> None:
""",
(
"d-kr-1",
"2026-02-14T09:10:00+00:00",
f"{today}T09:10:00+00:00",
"005930",
"KR",
"KRX",
@@ -91,9 +113,9 @@ def _seed_db(conn: sqlite3.Connection) -> None:
""",
(
"d-us-1",
"2026-02-14T21:10:00+00:00",
f"{today}T21:10:00+00:00",
"AAPL",
"US",
"US_NASDAQ",
"NASDAQ",
"SELL",
80,
@@ -110,7 +132,7 @@ def _seed_db(conn: sqlite3.Connection) -> None:
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
(
"2026-02-14T09:11:00+00:00",
f"{today}T09:11:00+00:00",
"005930",
"BUY",
85,
@@ -132,7 +154,7 @@ def _seed_db(conn: sqlite3.Connection) -> None:
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
(
"2026-02-14T21:11:00+00:00",
f"{today}T21:11:00+00:00",
"AAPL",
"SELL",
80,
@@ -140,7 +162,7 @@ def _seed_db(conn: sqlite3.Connection) -> None:
1,
200,
-1.0,
"US",
"US_NASDAQ",
"NASDAQ",
None,
"d-us-1",
@@ -149,122 +171,128 @@ def _seed_db(conn: sqlite3.Connection) -> None:
conn.commit()
def _client(tmp_path: Path) -> TestClient:
def _app(tmp_path: Path) -> Any:
db_path = tmp_path / "dashboard_test.db"
conn = init_db(str(db_path))
_seed_db(conn)
conn.close()
app = create_dashboard_app(str(db_path))
return TestClient(app)
return create_dashboard_app(str(db_path))
def _endpoint(app: Any, path: str) -> Callable[..., Any]:
for route in app.routes:
if getattr(route, "path", None) == path:
return route.endpoint
raise AssertionError(f"route not found: {path}")
def test_index_serves_html(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/")
assert resp.status_code == 200
assert "The Ouroboros Dashboard API" in resp.text
app = _app(tmp_path)
index = _endpoint(app, "/")
resp = index()
assert isinstance(resp, FileResponse)
assert "index.html" in str(resp.path)
def test_status_endpoint(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/status")
assert resp.status_code == 200
body = resp.json()
app = _app(tmp_path)
get_status = _endpoint(app, "/api/status")
body = get_status()
assert "KR" in body["markets"]
assert "US" in body["markets"]
assert "US_NASDAQ" in body["markets"]
assert "totals" in body
def test_playbook_found(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/playbook/2026-02-14?market=KR")
assert resp.status_code == 200
assert resp.json()["market"] == "KR"
app = _app(tmp_path)
get_playbook = _endpoint(app, "/api/playbook/{date_str}")
body = get_playbook("2026-02-14", market="KR")
assert body["market"] == "KR"
def test_playbook_not_found(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/playbook/2026-02-15?market=KR")
assert resp.status_code == 404
app = _app(tmp_path)
get_playbook = _endpoint(app, "/api/playbook/{date_str}")
with pytest.raises(HTTPException, match="playbook not found"):
get_playbook("2026-02-15", market="KR")
def test_scorecard_found(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/scorecard/2026-02-14?market=KR")
assert resp.status_code == 200
assert resp.json()["scorecard"]["total_pnl"] == 1.5
app = _app(tmp_path)
get_scorecard = _endpoint(app, "/api/scorecard/{date_str}")
body = get_scorecard("2026-02-14", market="KR")
assert body["scorecard"]["total_pnl"] == 1.5
def test_scorecard_not_found(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/scorecard/2026-02-15?market=KR")
assert resp.status_code == 404
app = _app(tmp_path)
get_scorecard = _endpoint(app, "/api/scorecard/{date_str}")
with pytest.raises(HTTPException, match="scorecard not found"):
get_scorecard("2026-02-15", market="KR")
def test_performance_all(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/performance?market=all")
assert resp.status_code == 200
body = resp.json()
app = _app(tmp_path)
get_performance = _endpoint(app, "/api/performance")
body = get_performance(market="all")
assert body["market"] == "all"
assert body["combined"]["total_trades"] == 2
assert len(body["by_market"]) == 2
def test_performance_market_filter(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/performance?market=KR")
assert resp.status_code == 200
body = resp.json()
app = _app(tmp_path)
get_performance = _endpoint(app, "/api/performance")
body = get_performance(market="KR")
assert body["market"] == "KR"
assert body["metrics"]["total_trades"] == 1
def test_performance_empty_market(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/performance?market=JP")
assert resp.status_code == 200
assert resp.json()["metrics"]["total_trades"] == 0
app = _app(tmp_path)
get_performance = _endpoint(app, "/api/performance")
body = get_performance(market="JP")
assert body["metrics"]["total_trades"] == 0
def test_context_layer_all(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/context/L7_REALTIME")
assert resp.status_code == 200
body = resp.json()
app = _app(tmp_path)
get_context_layer = _endpoint(app, "/api/context/{layer}")
body = get_context_layer("L7_REALTIME", timeframe=None, limit=100)
assert body["layer"] == "L7_REALTIME"
assert body["count"] == 1
def test_context_layer_timeframe_filter(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/context/L6_DAILY?timeframe=2026-02-14")
assert resp.status_code == 200
body = resp.json()
app = _app(tmp_path)
get_context_layer = _endpoint(app, "/api/context/{layer}")
body = get_context_layer("L6_DAILY", timeframe="2026-02-14", limit=100)
assert body["count"] == 1
assert body["entries"][0]["key"] == "scorecard_KR"
def test_decisions_endpoint(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/decisions?market=KR")
assert resp.status_code == 200
body = resp.json()
app = _app(tmp_path)
get_decisions = _endpoint(app, "/api/decisions")
body = get_decisions(market="KR", limit=50)
assert body["count"] == 1
assert body["decisions"][0]["decision_id"] == "d-kr-1"
def test_scenarios_active_filters_non_matched(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/scenarios/active?market=KR&date_str=2026-02-14")
assert resp.status_code == 200
body = resp.json()
app = _app(tmp_path)
get_active_scenarios = _endpoint(app, "/api/scenarios/active")
body = get_active_scenarios(
market="KR",
date_str=datetime.now(UTC).date().isoformat(),
limit=50,
)
assert body["count"] == 1
assert body["matches"][0]["stock_code"] == "005930"
def test_scenarios_active_empty_when_no_matches(tmp_path: Path) -> None:
client = _client(tmp_path)
resp = client.get("/api/scenarios/active?market=US&date_str=2026-02-14")
assert resp.status_code == 200
assert resp.json()["count"] == 0
app = _app(tmp_path)
get_active_scenarios = _endpoint(app, "/api/scenarios/active")
body = get_active_scenarios(market="US", date_str="2026-02-14", limit=50)
assert body["count"] == 0

60
tests/test_db.py Normal file
View File

@@ -0,0 +1,60 @@
"""Tests for database helper functions."""
from src.db import get_open_position, init_db, log_trade
def test_get_open_position_returns_latest_buy() -> None:
conn = init_db(":memory:")
log_trade(
conn=conn,
stock_code="005930",
action="BUY",
confidence=90,
rationale="entry",
quantity=2,
price=70000.0,
market="KR",
exchange_code="KRX",
decision_id="d-buy-1",
)
position = get_open_position(conn, "005930", "KR")
assert position is not None
assert position["decision_id"] == "d-buy-1"
assert position["price"] == 70000.0
assert position["quantity"] == 2
def test_get_open_position_returns_none_when_latest_is_sell() -> None:
conn = init_db(":memory:")
log_trade(
conn=conn,
stock_code="005930",
action="BUY",
confidence=90,
rationale="entry",
quantity=1,
price=70000.0,
market="KR",
exchange_code="KRX",
decision_id="d-buy-1",
)
log_trade(
conn=conn,
stock_code="005930",
action="SELL",
confidence=95,
rationale="exit",
quantity=1,
price=71000.0,
market="KR",
exchange_code="KRX",
decision_id="d-sell-1",
)
assert get_open_position(conn, "005930", "KR") is None
def test_get_open_position_returns_none_when_no_trades() -> None:
conn = init_db(":memory:")
assert get_open_position(conn, "AAPL", "US_NASDAQ") is None

View File

@@ -5,6 +5,7 @@ from unittest.mock import ANY, AsyncMock, MagicMock, patch
import pytest
from src.config import Settings
from src.context.layer import ContextLayer
from src.context.scheduler import ScheduleResult
from src.core.risk_manager import CircuitBreakerTripped, FatFingerRejected
@@ -12,9 +13,11 @@ from src.db import init_db, log_trade
from src.evolution.scorecard import DailyScorecard
from src.logging.decision_logger import DecisionLogger
from src.main import (
_apply_dashboard_flag,
_handle_market_close,
_run_context_scheduler,
_run_evolution_loop,
_start_dashboard_server,
safe_float,
trading_cycle,
)
@@ -113,6 +116,7 @@ class TestTradingCycleTelegramIntegration:
"output1": {
"stck_prpr": "50000",
"frgn_ntby_qty": "100",
"prdy_ctrt": "1.23",
}
}
)
@@ -744,7 +748,7 @@ class TestScenarioEngineIntegration:
broker = MagicMock()
broker.get_orderbook = AsyncMock(
return_value={
"output1": {"stck_prpr": "50000", "frgn_ntby_qty": "100"}
"output1": {"stck_prpr": "50000", "frgn_ntby_qty": "100", "prdy_ctrt": "2.50"}
}
)
broker.get_balance = AsyncMock(
@@ -827,6 +831,7 @@ class TestScenarioEngineIntegration:
assert market_data["rsi"] == 25.0
assert market_data["volume_ratio"] == 3.5
assert market_data["current_price"] == 50000.0
assert market_data["price_change_pct"] == 2.5
# Portfolio data should include pnl
assert "portfolio_pnl_pct" in portfolio_data
@@ -1229,6 +1234,107 @@ async def test_sell_updates_original_buy_decision_outcome() -> None:
assert updated_buy.outcome_accuracy == 1
@pytest.mark.asyncio
async def test_hold_overridden_to_sell_when_stop_loss_triggered() -> None:
"""HOLD decision should be overridden to SELL when stop-loss threshold is breached."""
db_conn = init_db(":memory:")
decision_logger = DecisionLogger(db_conn)
buy_decision_id = decision_logger.log_decision(
stock_code="005930",
market="KR",
exchange_code="KRX",
action="BUY",
confidence=90,
rationale="entry",
context_snapshot={},
input_data={},
)
log_trade(
conn=db_conn,
stock_code="005930",
action="BUY",
confidence=90,
rationale="entry",
quantity=1,
price=100.0,
market="KR",
exchange_code="KRX",
decision_id=buy_decision_id,
)
broker = MagicMock()
broker.get_orderbook = AsyncMock(
return_value={"output1": {"stck_prpr": "95", "frgn_ntby_qty": "0", "prdy_ctrt": "-5.0"}}
)
broker.get_balance = AsyncMock(
return_value={
"output2": [
{
"tot_evlu_amt": "100000",
"dnca_tot_amt": "10000",
"pchs_amt_smtl_amt": "90000",
}
]
}
)
broker.send_order = AsyncMock(return_value={"msg1": "OK"})
scenario = StockScenario(
condition=StockCondition(rsi_below=30),
action=ScenarioAction.BUY,
confidence=88,
stop_loss_pct=-2.0,
rationale="stop loss policy",
)
playbook = DayPlaybook(
date=date(2026, 2, 8),
market="KR",
stock_playbooks=[
{"stock_code": "005930", "stock_name": "Samsung", "scenarios": [scenario]}
],
)
engine = MagicMock(spec=ScenarioEngine)
engine.evaluate = MagicMock(return_value=_make_hold_match())
market = MagicMock()
market.name = "Korea"
market.code = "KR"
market.exchange_code = "KRX"
market.is_domestic = True
telegram = MagicMock()
telegram.notify_trade_execution = AsyncMock()
telegram.notify_fat_finger = AsyncMock()
telegram.notify_circuit_breaker = AsyncMock()
telegram.notify_scenario_matched = AsyncMock()
await trading_cycle(
broker=broker,
overseas_broker=MagicMock(),
scenario_engine=engine,
playbook=playbook,
risk=MagicMock(),
db_conn=db_conn,
decision_logger=decision_logger,
context_store=MagicMock(
get_latest_timeframe=MagicMock(return_value=None),
set_context=MagicMock(),
),
criticality_assessor=MagicMock(
assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
get_timeout=MagicMock(return_value=5.0),
),
telegram=telegram,
market=market,
stock_code="005930",
scan_candidates={},
)
broker.send_order.assert_called_once()
assert broker.send_order.call_args.kwargs["order_type"] == "SELL"
@pytest.mark.asyncio
async def test_handle_market_close_runs_daily_review_flow() -> None:
"""Market close should aggregate, create scorecard, lessons, and notify."""
@@ -1424,7 +1530,7 @@ async def test_run_evolution_loop_notifies_when_pr_generated() -> None:
await _run_evolution_loop(
evolution_optimizer=optimizer,
telegram=telegram,
market_code="US",
market_code="US_NASDAQ",
market_date="2026-02-14",
)
@@ -1448,9 +1554,50 @@ async def test_run_evolution_loop_notification_error_is_ignored() -> None:
await _run_evolution_loop(
evolution_optimizer=optimizer,
telegram=telegram,
market_code="US",
market_code="US_NYSE",
market_date="2026-02-14",
)
optimizer.evolve.assert_called_once()
telegram.send_message.assert_called_once()
def test_apply_dashboard_flag_enables_dashboard() -> None:
settings = Settings(
KIS_APP_KEY="test_key",
KIS_APP_SECRET="test_secret",
KIS_ACCOUNT_NO="12345678-01",
GEMINI_API_KEY="test_gemini_key",
DASHBOARD_ENABLED=False,
)
updated = _apply_dashboard_flag(settings, dashboard_flag=True)
assert updated.DASHBOARD_ENABLED is True
def test_start_dashboard_server_disabled_returns_none() -> None:
settings = Settings(
KIS_APP_KEY="test_key",
KIS_APP_SECRET="test_secret",
KIS_ACCOUNT_NO="12345678-01",
GEMINI_API_KEY="test_gemini_key",
DASHBOARD_ENABLED=False,
)
thread = _start_dashboard_server(settings)
assert thread is None
def test_start_dashboard_server_enabled_starts_thread() -> None:
settings = Settings(
KIS_APP_KEY="test_key",
KIS_APP_SECRET="test_secret",
KIS_ACCOUNT_NO="12345678-01",
GEMINI_API_KEY="test_gemini_key",
DASHBOARD_ENABLED=True,
)
mock_thread = MagicMock()
with patch("src.main.threading.Thread", return_value=mock_thread) as mock_thread_cls:
thread = _start_dashboard_server(settings)
assert thread == mock_thread
mock_thread_cls.assert_called_once()
mock_thread.start.assert_called_once()

View File

@@ -7,6 +7,7 @@ import pytest
from src.markets.schedule import (
MARKETS,
expand_market_codes,
get_next_market_open,
get_open_markets,
is_market_open,
@@ -199,3 +200,28 @@ class TestGetNextMarketOpen:
enabled_markets=["INVALID", "KR"], now=test_time
)
assert market.code == "KR"
class TestExpandMarketCodes:
"""Test shorthand market expansion."""
def test_expand_us_shorthand(self) -> None:
assert expand_market_codes(["US"]) == ["US_NASDAQ", "US_NYSE", "US_AMEX"]
def test_expand_cn_shorthand(self) -> None:
assert expand_market_codes(["CN"]) == ["CN_SHA", "CN_SZA"]
def test_expand_vn_shorthand(self) -> None:
assert expand_market_codes(["VN"]) == ["VN_HAN", "VN_HCM"]
def test_expand_mixed_codes(self) -> None:
assert expand_market_codes(["KR", "US", "JP"]) == [
"KR",
"US_NASDAQ",
"US_NYSE",
"US_AMEX",
"JP",
]
def test_expand_preserves_unknown_code(self) -> None:
assert expand_market_codes(["KR", "UNKNOWN"]) == ["KR", "UNKNOWN"]

View File

@@ -682,6 +682,10 @@ class TestBasicCommands:
"/help - Show available commands\n"
"/status - Trading status (mode, markets, P&L)\n"
"/positions - Current holdings\n"
"/report - Daily summary report\n"
"/scenarios - Today's playbook scenarios\n"
"/review - Recent scorecards\n"
"/dashboard - Dashboard URL/status\n"
"/stop - Pause trading\n"
"/resume - Resume trading"
)
@@ -707,10 +711,106 @@ class TestBasicCommands:
assert "/help" in payload["text"]
assert "/status" in payload["text"]
assert "/positions" in payload["text"]
assert "/report" in payload["text"]
assert "/scenarios" in payload["text"]
assert "/review" in payload["text"]
assert "/dashboard" in payload["text"]
assert "/stop" in payload["text"]
assert "/resume" in payload["text"]
class TestExtendedCommands:
"""Test additional bot commands."""
@pytest.mark.asyncio
async def test_report_command(self) -> None:
client = TelegramClient(bot_token="123:abc", chat_id="456", enabled=True)
handler = TelegramCommandHandler(client)
mock_resp = AsyncMock()
mock_resp.status = 200
mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
mock_resp.__aexit__ = AsyncMock(return_value=False)
async def mock_report() -> None:
await client.send_message("<b>📈 Daily Report</b>\n\nTrades: 1")
handler.register_command("report", mock_report)
with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
await handler._handle_update(
{"update_id": 1, "message": {"chat": {"id": 456}, "text": "/report"}}
)
payload = mock_post.call_args.kwargs["json"]
assert "Daily Report" in payload["text"]
@pytest.mark.asyncio
async def test_scenarios_command(self) -> None:
client = TelegramClient(bot_token="123:abc", chat_id="456", enabled=True)
handler = TelegramCommandHandler(client)
mock_resp = AsyncMock()
mock_resp.status = 200
mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
mock_resp.__aexit__ = AsyncMock(return_value=False)
async def mock_scenarios() -> None:
await client.send_message("<b>🧠 Today's Scenarios</b>\n\n- AAPL: BUY (85)")
handler.register_command("scenarios", mock_scenarios)
with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
await handler._handle_update(
{"update_id": 1, "message": {"chat": {"id": 456}, "text": "/scenarios"}}
)
payload = mock_post.call_args.kwargs["json"]
assert "Today's Scenarios" in payload["text"]
@pytest.mark.asyncio
async def test_review_command(self) -> None:
client = TelegramClient(bot_token="123:abc", chat_id="456", enabled=True)
handler = TelegramCommandHandler(client)
mock_resp = AsyncMock()
mock_resp.status = 200
mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
mock_resp.__aexit__ = AsyncMock(return_value=False)
async def mock_review() -> None:
await client.send_message("<b>📝 Recent Reviews</b>\n\n- 2026-02-14 KR")
handler.register_command("review", mock_review)
with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
await handler._handle_update(
{"update_id": 1, "message": {"chat": {"id": 456}, "text": "/review"}}
)
payload = mock_post.call_args.kwargs["json"]
assert "Recent Reviews" in payload["text"]
@pytest.mark.asyncio
async def test_dashboard_command(self) -> None:
client = TelegramClient(bot_token="123:abc", chat_id="456", enabled=True)
handler = TelegramCommandHandler(client)
mock_resp = AsyncMock()
mock_resp.status = 200
mock_resp.__aenter__ = AsyncMock(return_value=mock_resp)
mock_resp.__aexit__ = AsyncMock(return_value=False)
async def mock_dashboard() -> None:
await client.send_message("<b>🖥️ Dashboard</b>\n\nURL: http://127.0.0.1:8080")
handler.register_command("dashboard", mock_dashboard)
with patch("aiohttp.ClientSession.post", return_value=mock_resp) as mock_post:
await handler._handle_update(
{"update_id": 1, "message": {"chat": {"id": 456}, "text": "/dashboard"}}
)
payload = mock_post.call_args.kwargs["json"]
assert "Dashboard" in payload["text"]
class TestGetUpdates:
"""Test getUpdates API interaction."""