072b6059d4
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Deploy to Production / deploy (push) Waiting to run
- Upgrade pykrx 1.2.3 → 1.2.6 (KRX login session support) - Add KRX_ID/KRX_PW env vars for KRX authentication - Enhance error handling in all pykrx-dependent collectors - ETFCollector: raise KRXDataError with login hint - ValuationCollector: raise RuntimeError with login hint - StockCollector/PriceCollector/ETFPriceCollector: JSONDecodeError handling - Fix TIGER 200 ticker: 069500 → 102110 in seed data - Rebuild seed_data.py from actual 33 trade records - Add trade_history_raw.csv as source data - Fix pension_allocation recommendation: KODEX 200 → TIGER 200 - Add ticker dropdown to transaction add modal (frontend) - Update .env.example with KRX credentials - All 276 tests passing
199 lines
6.8 KiB
Python
199 lines
6.8 KiB
Python
"""
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One-time script to import historical portfolio data.
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Builds portfolio from actual trade history with accurate average prices
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and cumulative holdings.
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Usage:
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cd backend && python -m scripts.seed_data
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Requires: DATABASE_URL environment variable or default dev connection.
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"""
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import sys
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import os
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from datetime import date, datetime
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from decimal import Decimal, ROUND_HALF_UP
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# Add backend to path
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from sqlalchemy.orm import Session
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from app.core.database import SessionLocal
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from app.models.portfolio import (
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Portfolio, PortfolioType, Target, Holding,
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Transaction, TransactionType,
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)
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from app.models.user import User
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# ETF name -> ticker mapping
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ETF_MAP = {
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"TIGER 200": "102110",
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"KIWOOM 국고채10년": "148070",
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"KODEX 200미국채혼합": "284430",
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"TIGER 미국S&P500": "360750",
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"ACE KRX금현물": "411060",
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}
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# Target ratios (percentage of total portfolio)
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TARGETS = {
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"102110": Decimal("0.83"),
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"148070": Decimal("25"),
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"284430": Decimal("41.67"),
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"360750": Decimal("17.5"),
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"411060": Decimal("15"),
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}
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# Actual trade history (date, name, quantity, price_per_unit)
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TRADES = [
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# 2025-04-29: Initial purchases
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(date(2025, 4, 29), "ACE KRX금현물", 1, Decimal("21620")),
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(date(2025, 4, 29), "TIGER 미국S&P500", 329, Decimal("19770")),
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(date(2025, 4, 29), "KIWOOM 국고채10년", 1, Decimal("118000")),
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(date(2025, 4, 29), "TIGER 200", 16, Decimal("33815")),
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(date(2025, 4, 30), "KODEX 200미국채혼합", 355, Decimal("13235")),
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# 2025-05-13 ~ 05-16
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(date(2025, 5, 13), "ACE KRX금현물", 260, Decimal("20820")),
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(date(2025, 5, 13), "KODEX 200미국채혼합", 14, Decimal("13165")),
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(date(2025, 5, 14), "ACE KRX금현물", 45, Decimal("20760")),
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(date(2025, 5, 14), "TIGER 미국S&P500", 45, Decimal("20690")),
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(date(2025, 5, 14), "KODEX 200미국채혼합", 733, Decimal("13220")),
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(date(2025, 5, 14), "KIWOOM 국고채10년", 90, Decimal("116939")),
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(date(2025, 5, 16), "KODEX 200미국채혼합", 169, Decimal("13125")),
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# 2025-06-12
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(date(2025, 6, 12), "ACE KRX금현물", 14, Decimal("20855")),
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(date(2025, 6, 12), "TIGER 미국S&P500", 3, Decimal("20355")),
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(date(2025, 6, 12), "KODEX 200미국채혼합", 88, Decimal("13570")),
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(date(2025, 6, 12), "KIWOOM 국고채10년", 5, Decimal("115945")),
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# 2025-07-30
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(date(2025, 7, 30), "KIWOOM 국고채10년", 6, Decimal("116760")),
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# 2025-08-14 ~ 08-19
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(date(2025, 8, 14), "ACE KRX금현물", 6, Decimal("21095")),
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(date(2025, 8, 14), "TIGER 미국S&P500", 3, Decimal("22200")),
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(date(2025, 8, 14), "KODEX 200미국채혼합", 27, Decimal("14465")),
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(date(2025, 8, 14), "KIWOOM 국고채10년", 1, Decimal("117075")),
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(date(2025, 8, 19), "ACE KRX금현물", 2, Decimal("21030")),
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# 2025-10-13
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(date(2025, 10, 13), "TIGER 미국S&P500", 3, Decimal("23480")),
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(date(2025, 10, 13), "KIWOOM 국고채10년", 12, Decimal("116465")),
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# 2025-12-05
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(date(2025, 12, 5), "KIWOOM 국고채10년", 7, Decimal("112830")),
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# 2026-01-07 ~ 01-08
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(date(2026, 1, 7), "TIGER 미국S&P500", 2, Decimal("25015")),
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(date(2026, 1, 8), "KIWOOM 국고채10년", 11, Decimal("109527")),
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# 2026-02-20
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(date(2026, 2, 20), "TIGER 미국S&P500", 20, Decimal("24685")),
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(date(2026, 2, 20), "KIWOOM 국고채10년", 9, Decimal("108500")),
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# 2026-03-23
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(date(2026, 3, 23), "ACE KRX금현물", 41, Decimal("30095")),
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(date(2026, 3, 23), "TIGER 미국S&P500", 128, Decimal("24290")),
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(date(2026, 3, 23), "KODEX 200미국채혼합", 188, Decimal("19579")),
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(date(2026, 3, 23), "KIWOOM 국고채10년", 10, Decimal("106780")),
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]
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def _compute_holdings(trades: list) -> dict:
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"""Compute current holdings with weighted average prices from trade history."""
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holdings = {}
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for _, name, qty, price in trades:
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ticker = ETF_MAP[name]
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if ticker not in holdings:
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holdings[ticker] = {"qty": 0, "total_cost": Decimal("0")}
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holdings[ticker]["qty"] += qty
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holdings[ticker]["total_cost"] += qty * price
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return holdings
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def seed(db: Session):
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"""Import historical data into database."""
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# Find admin user (first user in DB)
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user = db.query(User).first()
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if not user:
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print("ERROR: No user found in database. Create a user first.")
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return
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# Delete existing portfolio if present (cascade deletes related records)
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existing = db.query(Portfolio).filter(
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Portfolio.user_id == user.id,
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Portfolio.name == "연금 포트폴리오",
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).first()
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if existing:
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db.delete(existing)
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db.flush()
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print(f"Deleted existing portfolio (id={existing.id})")
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# Create portfolio
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portfolio = Portfolio(
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user_id=user.id,
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name="연금 포트폴리오",
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portfolio_type=PortfolioType.PENSION,
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)
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db.add(portfolio)
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db.flush()
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print(f"Created portfolio id={portfolio.id}")
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# Set targets
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for ticker, ratio in TARGETS.items():
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db.add(Target(portfolio_id=portfolio.id, ticker=ticker, target_ratio=ratio))
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print(f"Set {len(TARGETS)} targets")
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# Create transactions from trade history
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tx_count = 0
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for trade_date, name, qty, price in TRADES:
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ticker = ETF_MAP[name]
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db.add(Transaction(
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portfolio_id=portfolio.id,
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ticker=ticker,
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tx_type=TransactionType.BUY,
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quantity=qty,
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price=price,
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executed_at=datetime.combine(trade_date, datetime.min.time()),
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memo=f"{name} 매수",
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))
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tx_count += 1
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print(f"Created {tx_count} transactions")
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# Set current holdings from computed totals
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computed = _compute_holdings(TRADES)
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for ticker, data in computed.items():
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qty = data["qty"]
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avg_price = (data["total_cost"] / qty).quantize(Decimal("0.01"), rounding=ROUND_HALF_UP)
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db.add(Holding(
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portfolio_id=portfolio.id,
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ticker=ticker,
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quantity=qty,
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avg_price=avg_price,
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))
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print(f"Set {len(computed)} current holdings")
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# Print summary
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print("\n=== Holdings Summary ===")
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total_invested = Decimal("0")
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for ticker in sorted(computed.keys()):
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d = computed[ticker]
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avg = (d["total_cost"] / d["qty"]).quantize(Decimal("0.01"), rounding=ROUND_HALF_UP)
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name = [n for n, t in ETF_MAP.items() if t == ticker][0]
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print(f" {name:20s} ({ticker}) qty={d['qty']:>5d} avg={avg:>12} invested={d['total_cost']:>15}")
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total_invested += d["total_cost"]
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print(f" {'TOTAL':20s} invested={total_invested:>15}")
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db.commit()
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print("\nDone!")
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if __name__ == "__main__":
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db = SessionLocal()
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try:
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seed(db)
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finally:
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db.close()
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