zephyrdark
653fa08fa4
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The seed script was incorrectly using the latest snapshot's market price as avg_price, resulting in inflated average costs. Now computes avg_price from actual total invested amounts per ticker. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
279 lines
11 KiB
Python
279 lines
11 KiB
Python
"""
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One-time script to import historical portfolio data from data.txt.
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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
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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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PortfolioSnapshot, SnapshotHolding,
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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": "069500",
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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
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TARGETS = {
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"069500": 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 total invested amounts per ticker (from brokerage records)
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TOTAL_INVESTED = {
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"069500": Decimal("541040"),
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"148070": Decimal("15432133"),
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"284430": Decimal("18375975"),
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"360750": Decimal("7683515"),
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"411060": Decimal("6829620"),
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}
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# Historical snapshots from data.txt
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SNAPSHOTS = [
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{
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"date": date(2025, 4, 28),
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"total_assets": Decimal("42485834"),
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"holdings": [
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{"ticker": "069500", "qty": 16, "price": Decimal("33815"), "value": Decimal("541040")},
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{"ticker": "148070", "qty": 1, "price": Decimal("118000"), "value": Decimal("118000")},
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{"ticker": "284430", "qty": 355, "price": Decimal("13235"), "value": Decimal("4698435")},
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{"ticker": "360750", "qty": 329, "price": Decimal("19770"), "value": Decimal("6504330")},
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{"ticker": "411060", "qty": 1, "price": Decimal("21620"), "value": Decimal("21620")},
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],
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},
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{
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"date": date(2025, 5, 13),
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"total_assets": Decimal("42485834"),
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"holdings": [
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{"ticker": "069500", "qty": 16, "price": Decimal("34805"), "value": Decimal("556880")},
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{"ticker": "148070", "qty": 1, "price": Decimal("117010"), "value": Decimal("117010")},
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{"ticker": "284430", "qty": 369, "price": Decimal("13175"), "value": Decimal("4861575")},
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{"ticker": "360750", "qty": 329, "price": Decimal("20490"), "value": Decimal("6741210")},
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{"ticker": "411060", "qty": 261, "price": Decimal("20840"), "value": Decimal("5439240")},
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],
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},
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{
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"date": date(2025, 6, 11),
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"total_assets": Decimal("44263097"),
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"holdings": [
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{"ticker": "069500", "qty": 16, "price": Decimal("39110"), "value": Decimal("625760")},
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{"ticker": "148070", "qty": 91, "price": Decimal("115790"), "value": Decimal("10536890")},
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{"ticker": "284430", "qty": 1271, "price": Decimal("13570"), "value": Decimal("17247470")},
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{"ticker": "360750", "qty": 374, "price": Decimal("20570"), "value": Decimal("7693180")},
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{"ticker": "411060", "qty": 306, "price": Decimal("20670"), "value": Decimal("6325020")},
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],
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},
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{
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"date": date(2025, 7, 30),
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"total_assets": Decimal("47395573"),
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"holdings": [
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{"ticker": "069500", "qty": 16, "price": Decimal("43680"), "value": Decimal("698880")},
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{"ticker": "148070", "qty": 96, "price": Decimal("116470"), "value": Decimal("11181120")},
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{"ticker": "284430", "qty": 1359, "price": Decimal("14550"), "value": Decimal("19773450")},
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{"ticker": "360750", "qty": 377, "price": Decimal("22085"), "value": Decimal("8326045")},
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{"ticker": "411060", "qty": 320, "price": Decimal("20870"), "value": Decimal("6678400")},
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],
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},
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{
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"date": date(2025, 8, 13),
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"total_assets": Decimal("47997732"),
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"holdings": [
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{"ticker": "069500", "qty": 16, "price": Decimal("43795"), "value": Decimal("700720")},
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{"ticker": "148070", "qty": 102, "price": Decimal("116800"), "value": Decimal("11913600")},
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{"ticker": "284430", "qty": 1359, "price": Decimal("14435"), "value": Decimal("19617165")},
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{"ticker": "360750", "qty": 377, "price": Decimal("22090"), "value": Decimal("8327930")},
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{"ticker": "411060", "qty": 320, "price": Decimal("20995"), "value": Decimal("6718400")},
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],
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},
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{
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"date": date(2025, 10, 12),
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"total_assets": Decimal("54188966"),
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"holdings": [
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{"ticker": "069500", "qty": 16, "price": Decimal("50850"), "value": Decimal("813600")},
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{"ticker": "148070", "qty": 103, "price": Decimal("116070"), "value": Decimal("11955210")},
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{"ticker": "284430", "qty": 1386, "price": Decimal("15665"), "value": Decimal("21711690")},
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{"ticker": "360750", "qty": 380, "price": Decimal("23830"), "value": Decimal("9055400")},
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{"ticker": "411060", "qty": 328, "price": Decimal("27945"), "value": Decimal("9165960")},
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],
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},
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{
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"date": date(2025, 12, 4),
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"total_assets": Decimal("56860460"),
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"holdings": [
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{"ticker": "069500", "qty": 16, "price": Decimal("57190"), "value": Decimal("915040")},
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{"ticker": "148070", "qty": 115, "price": Decimal("112900"), "value": Decimal("12983500")},
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{"ticker": "284430", "qty": 1386, "price": Decimal("16825"), "value": Decimal("23319450")},
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{"ticker": "360750", "qty": 383, "price": Decimal("25080"), "value": Decimal("9605640")},
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{"ticker": "411060", "qty": 328, "price": Decimal("27990"), "value": Decimal("9180720")},
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],
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},
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{
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"date": date(2026, 1, 6),
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"total_assets": Decimal("58949962"),
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"holdings": [
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{"ticker": "069500", "qty": 16, "price": Decimal("66255"), "value": Decimal("1060080")},
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{"ticker": "148070", "qty": 122, "price": Decimal("108985"), "value": Decimal("13296170")},
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{"ticker": "284430", "qty": 1386, "price": Decimal("17595"), "value": Decimal("24386670")},
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{"ticker": "360750", "qty": 383, "price": Decimal("24840"), "value": Decimal("9513720")},
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{"ticker": "411060", "qty": 328, "price": Decimal("29605"), "value": Decimal("9710440")},
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],
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},
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{
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"date": date(2026, 2, 16),
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"total_assets": Decimal("62433665"),
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"holdings": [
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{"ticker": "069500", "qty": 16, "price": Decimal("81835"), "value": Decimal("1309360")},
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{"ticker": "148070", "qty": 133, "price": Decimal("108290"), "value": Decimal("14402570")},
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{"ticker": "284430", "qty": 1386, "price": Decimal("19250"), "value": Decimal("26680500")},
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{"ticker": "360750", "qty": 385, "price": Decimal("24435"), "value": Decimal("9407475")},
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{"ticker": "411060", "qty": 328, "price": Decimal("32420"), "value": Decimal("10633760")},
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],
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},
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]
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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 snapshots
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for snap in SNAPSHOTS:
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snapshot = PortfolioSnapshot(
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portfolio_id=portfolio.id,
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total_value=snap["total_assets"],
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snapshot_date=snap["date"],
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)
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db.add(snapshot)
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db.flush()
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total = snap["total_assets"]
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for h in snap["holdings"]:
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ratio = (h["value"] / total * 100).quantize(Decimal("0.01")) if total > 0 else Decimal("0")
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db.add(SnapshotHolding(
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snapshot_id=snapshot.id,
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ticker=h["ticker"],
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quantity=h["qty"],
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price=h["price"],
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value=h["value"],
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current_ratio=ratio,
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))
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print(f" Snapshot {snap['date']}: {len(snap['holdings'])} holdings")
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# Create transactions by comparing consecutive snapshots
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tx_count = 0
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for i, snap in enumerate(SNAPSHOTS):
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current_holdings = {h["ticker"]: h for h in snap["holdings"]}
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if i == 0:
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# First snapshot: all holdings are initial buys
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prev_holdings = {}
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else:
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prev_holdings = {h["ticker"]: h for h in SNAPSHOTS[i - 1]["holdings"]}
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all_tickers = set(current_holdings.keys()) | set(prev_holdings.keys())
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for ticker in all_tickers:
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cur_qty = current_holdings[ticker]["qty"] if ticker in current_holdings else 0
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prev_qty = prev_holdings[ticker]["qty"] if ticker in prev_holdings else 0
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diff = cur_qty - prev_qty
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if diff == 0:
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continue
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if diff > 0:
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tx_type = TransactionType.BUY
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price = current_holdings[ticker]["price"]
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else:
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tx_type = TransactionType.SELL
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price = prev_holdings[ticker]["price"]
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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=tx_type,
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quantity=abs(diff),
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price=price,
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executed_at=datetime.combine(snap["date"], datetime.min.time()),
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))
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tx_count += 1
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print(f"Created {tx_count} transactions from snapshot diffs")
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# Set current holdings from latest snapshot
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# avg_price = total invested amount / quantity (from actual brokerage records)
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latest = SNAPSHOTS[-1]
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for h in latest["holdings"]:
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ticker = h["ticker"]
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qty = h["qty"]
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invested = TOTAL_INVESTED[ticker]
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avg_price = (invested / qty).quantize(Decimal("0.01"))
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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(latest['holdings'])} current holdings from {latest['date']}")
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db.commit()
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print("Done!")
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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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