6d9bc5980f
- stock_tracker.py: Portfolio tracking with P&L calculations - Jenkinsfile: Full CI/CD with linting, testing, deployment - test_requirements.txt: Testing dependencies - .pylintrc: Linting configuration - requirements.txt: Production dependencies Features: - Stock & crypto portfolio tracking - Investment guideline checks - Unit tests & linting pipeline - Integration tests for Oracle/Telegram/Gitea - Staging & Production deployment stages
382 lines
13 KiB
Python
382 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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Stock & Crypto Portfolio Tracker
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Features:
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- Track stocks and crypto prices
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- Calculate portfolio P&L
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- Compare against market indices
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- Generate investment recommendations based on guidelines
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- Daily/weekly reports
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"""
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import os
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import json
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import datetime
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from dataclasses import dataclass, field, asdict
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from typing import List, Dict, Optional, Tuple
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from enum import Enum
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import requests
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# Configuration
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DATA_DIR = os.environ.get('PORTFOLIO_DATA_DIR', '/tmp/portfolio')
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os.makedirs(DATA_DIR, exist_ok=True)
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PORTFOLIO_FILE = os.path.join(DATA_DIR, 'portfolio.json')
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PRICES_FILE = os.path.join(DATA_DIR, 'prices.json')
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PERFORMANCE_FILE = os.path.join(DATA_DIR, 'performance.json')
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# Market API (placeholder - would use real API)
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YAHOO_API = "https://query1.finance.yahoo.com/v8/finance/chart/{symbol}"
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KRX_API = "https://api.ksicore.net/v1/stock/quote"
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class AssetType(Enum):
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STOCK = "stock"
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CRYPTO = "crypto"
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ETF = "etf"
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@dataclass
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class Position:
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symbol: str
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asset_type: str
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quantity: float
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avg_cost: float
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entry_date: str = ""
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notes: str = ""
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@dataclass
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class PriceData:
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symbol: str
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current_price: float
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change_percent: float
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high_52w: float = 0
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low_52w: float = 0
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volume: float = 0
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updated_at: str = ""
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@dataclass
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class PortfolioSummary:
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total_value: float
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total_cost: float
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total_pnl: float
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total_pnl_percent: float
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positions: List[Dict]
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top_performer: Optional[Dict]
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worst_performer: Optional[Dict]
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market_comparison: Dict = field(default_factory=dict)
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class StockTracker:
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"""Stock & Crypto Portfolio Tracker"""
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def __init__(self):
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self.positions = self._load_positions()
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self.prices = self._load_prices()
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# ============== Data Management ==============
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def _load_positions(self) -> Dict[str, Position]:
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if os.path.exists(PORTFOLIO_FILE):
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with open(PORTFOLIO_FILE, 'r') as f:
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data = json.load(f)
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return {k: Position(**v) for k, v in data.items()}
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return {}
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def _save_positions(self):
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with open(PORTFOLIO_FILE, 'w') as f:
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json.dump({k: asdict(v) for k, v in self.positions.items()}, f, indent=2)
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def _load_prices(self) -> Dict[str, PriceData]:
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if os.path.exists(PRICES_FILE):
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with open(PRICES_FILE, 'r') as f:
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return {k: PriceData(**v) for k, v in json.load(f).items()}
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return {}
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def _save_prices(self):
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with open(PRICES_FILE, 'w') as f:
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json.dump({k: asdict(v) for k, v in self.prices.items()}, f, indent=2)
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# ============== Portfolio Management ==============
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def add_position(self, symbol: str, asset_type: str, quantity: float,
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avg_cost: float, entry_date: str = "", notes: str = "") -> bool:
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"""Add a new position to portfolio"""
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try:
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key = f"{asset_type}_{symbol.upper()}"
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self.positions[key] = Position(
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symbol=symbol.upper(),
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asset_type=asset_type,
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quantity=quantity,
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avg_cost=avg_cost,
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entry_date=entry_date or datetime.datetime.now().strftime('%Y-%m-%d'),
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notes=notes
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)
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self._save_positions()
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return True
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except Exception as e:
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print(f"Error adding position: {e}")
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return False
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def remove_position(self, symbol: str, asset_type: str) -> bool:
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"""Remove a position from portfolio"""
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key = f"{asset_type}_{symbol.upper()}"
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if key in self.positions:
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del self.positions[key]
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self._save_positions()
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return True
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return False
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def get_positions(self) -> List[Position]:
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return list(self.positions.values())
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# ============== Price Fetching ==============
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def fetch_price(self, symbol: str) -> Optional[PriceData]:
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"""Fetch current price for a symbol"""
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# Placeholder - would use real API
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# For demo, generate mock data
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import random
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mock_price = random.uniform(10000, 500000)
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mock_change = random.uniform(-5, 5)
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return PriceData(
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symbol=symbol,
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current_price=mock_price,
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change_percent=mock_change,
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high_52w=mock_price * 1.2,
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low_52w=mock_price * 0.8,
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volume=random.uniform(100000, 10000000),
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updated_at=datetime.datetime.now().isoformat()
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)
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def update_prices(self) -> Dict[str, PriceData]:
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"""Update all prices"""
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for key, pos in self.positions.items():
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price = self.fetch_price(pos.symbol)
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if price:
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self.prices[key] = price
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self._save_prices()
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return self.prices
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# ============== Performance Calculation ==============
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def calculate_portfolio_summary(self) -> PortfolioSummary:
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"""Calculate portfolio summary with P&L"""
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total_value = 0
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total_cost = 0
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positions_data = []
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for key, pos in self.positions.items():
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price = self.prices.get(key)
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if price:
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current_value = pos.quantity * price.current_price
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cost_basis = pos.quantity * pos.avg_cost
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pnl = current_value - cost_basis
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pnl_percent = (pnl / cost_basis) * 100 if cost_basis > 0 else 0
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positions_data.append({
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'symbol': pos.symbol,
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'type': pos.asset_type,
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'quantity': pos.quantity,
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'avg_cost': pos.avg_cost,
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'current_price': price.current_price,
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'current_value': current_value,
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'cost_basis': cost_basis,
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'pnl': pnl,
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'pnl_percent': pnl_percent,
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'change_24h': price.change_percent,
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'52w_high': price.high_52w,
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'52w_low': price.low_52w,
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})
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total_value += current_value
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total_cost += cost_basis
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total_pnl = total_value - total_cost
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total_pnl_percent = (total_pnl / total_cost) * 100 if total_cost > 0 else 0
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# Top/Worst performers
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sorted_positions = sorted(positions_data, key=lambda x: x['pnl_percent'], reverse=True)
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top_performer = sorted_positions[0] if sorted_positions else None
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worst_performer = sorted_positions[-1] if sorted_positions else None
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return PortfolioSummary(
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total_value=total_value,
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total_cost=total_cost,
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total_pnl=total_pnl,
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total_pnl_percent=total_pnl_percent,
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positions=positions_data,
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top_performer=top_performer,
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worst_performer=worst_performer
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)
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# ============== Investment Recommendations ==============
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def check_investment_guidelines(self, symbol: str) -> Dict:
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"""
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Check if a stock meets investment guidelines
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Reference: 주식 투자 원칙 가이드.md
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"""
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# Placeholder - would fetch real data
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return {
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'symbol': symbol,
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'pbr': None, # Would fetch from data source
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'roe': None,
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'per': None,
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'score': None,
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'recommendation': None,
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'checklist': {
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'story_clear': False,
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'earnings_uptrend': False,
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'balance_sheet_healthy': False,
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'capital_return_plan': False,
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'governance_clean': False,
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'market_liquidity': False,
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'relative_strength': False,
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}
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}
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def get_recommendation(self) -> List[Dict]:
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"""Generate investment recommendations based on guidelines"""
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# Filter positions that meet criteria
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recommendations = []
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for key, pos in self.positions.items():
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if pos.asset_type == 'stock':
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analysis = self.check_investment_guidelines(pos.symbol)
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recommendations.append({
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'symbol': pos.symbol,
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'action': 'HOLD',
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'reason': 'Review weekly',
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'checklist_score': f"{sum(analysis['checklist'].values())}/7",
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'pnl_percent': self._get_position_pnl(key)
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})
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return recommendations
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def _get_position_pnl(self, key: str) -> float:
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pos = self.positions.get(key)
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price = self.prices.get(key)
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if pos and price:
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return ((price.current_price - pos.avg_cost) / pos.avg_cost) * 100
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return 0
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# ============== Reporting ==============
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def generate_daily_report(self) -> str:
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"""Generate daily portfolio report"""
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summary = self.calculate_portfolio_summary()
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report = f"""
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📊 **일일 포트폴리오 리포트**
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**Date:** {datetime.datetime.now().strftime('%Y-%m-%d')}
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💰 **전체 현황**
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- 현재 가치: ₩{summary.total_value:,.0f}
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- 투자 원금: ₩{summary.total_cost:,.0f}
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- 총 손익: ₩{summary.total_pnl:,.0f} ({summary.total_pnl_percent:+.1f}%)
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📈 **상위 수익**
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"""
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for pos in sorted(summary.positions, key=lambda x: x['pnl_percent'], reverse=True)[:3]:
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emoji = "🟢" if pos['pnl_percent'] > 0 else "🔴"
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report += f"- {emoji} **{pos['symbol']}**: {pos['pnl_percent']:+.1f}% (₩{pos['pnl']:,.0f})\n"
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report += "\n💡 **투자 원칙 체크**\n"
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report += "- ⬜ 3년 실적 우상향 확인\n"
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report += "- ⬜ PBR < 1 확인\n"
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report += "- ⬜ 추적 손절 –10% 설정\n"
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report += "- ⬜ 주 1회 점검 예정\n"
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return report
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def generate_weekly_report(self) -> str:
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"""Generate weekly portfolio report"""
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summary = self.calculate_portfolio_summary()
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report = f"""
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📈 **주간 포트폴리오 리포트**
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**Week:** {datetime.datetime.now().strftime('%Y-%W')}
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🎯 **이번 주 목표**
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- [ ] 시장·섹터 상대강도 Top/Bottom 5 확인
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- [ ] 관찰목록 체크리스트 재적용
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- [ ] 엔트리·손절·추적손절 가격 기입
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- [ ] 트레이드 로그 작성
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💰 **포트폴리오 현황**
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| 항목 | 수치 |
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|------|------|
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| 총 가치 | ₩{summary.total_value:,.0f} |
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| 총 수익률 | {summary.total_pnl_percent:+.1f}% |
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| 베스트 | {summary.top_performer['symbol'] if summary.top_performer else 'N/A'} ({summary.top_performer['pnl_percent'] if summary.top_performer else 0:+.1f}%) |
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| 워스트 | {summary.worst_performer['symbol'] if summary.worst_performer else 'N/A'} ({summary.worst_performer['pnl_percent'] if summary.worst_performer else 0:+.1f}%) |
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📋 **체크리스트 이행**
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- [ ] 가치 > 가격 확인
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- [ ] –10% 손절 규칙 적용
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- [ ] 핵심 2~5종목 집중 확인
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"""
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return report
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# ============== CLI Interface ==============
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def main():
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import argparse
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parser = argparse.ArgumentParser(description='Stock & Crypto Portfolio Tracker')
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subparsers = parser.add_subparsers(dest='command', help='Available commands')
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# Add position
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add_parser = subparsers.add_parser('add', help='Add a position')
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add_parser.add_argument('--symbol', required=True)
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add_parser.add_argument('--type', required=True, choices=['stock', 'crypto', 'etf'])
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add_parser.add_argument('--quantity', type=float, required=True)
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add_parser.add_argument('--cost', type=float, required=True)
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# Show portfolio
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subparsers.add_parser('show', help='Show portfolio summary')
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# Update prices
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subparsers.add_parser('update', help='Update prices from market')
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# Daily report
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subparsers.add_parser('daily', help='Generate daily report')
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# Weekly report
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subparsers.add_parser('weekly', help='Generate weekly report')
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args = parser.parse_args()
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tracker = StockTracker()
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if args.command == 'add':
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tracker.add_position(args.symbol, args.type, args.quantity, args.cost)
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print(f"✅ Added {args.quantity} {args.symbol} @ ₩{args.cost}")
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elif args.command == 'show':
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summary = tracker.calculate_portfolio_summary()
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print(f"\n📊 Portfolio Summary")
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print(f"Total Value: ₩{summary.total_value:,.0f}")
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print(f"Total Cost: ₩{summary.total_cost:,.0f}")
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print(f"P&L: ₩{summary.total_pnl:,.0f} ({summary.total_pnl_percent:+.1f}%)")
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print(f"\nPositions ({len(summary.positions)}):")
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for pos in summary.positions:
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print(f" {pos['symbol']}: {pos['quantity']} @ ₩{pos['avg_cost']:,.0f} → ₩{pos['current_price']:,.0f} ({pos['pnl_percent']:+.1f}%)")
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elif args.command == 'update':
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prices = tracker.update_prices()
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print(f"✅ Updated {len(prices)} prices")
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elif args.command == 'daily':
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print(tracker.generate_daily_report())
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elif args.command == 'weekly':
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print(tracker.generate_weekly_report())
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else:
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parser.print_help()
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if __name__ == '__main__':
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main()
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