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Replaces the plain quantity field with a CryptoTransaction log (same
"record events, derive the total" shape as Goal/GoalContribution): quantity
and average buy price are now computed via weighted-average cost — a buy
blends into the running average, a sell reduces quantity but leaves the
average cost of what's still held unchanged.
Switches price fetching from /simple/price to CoinGecko's /coins/markets
(still one batched call for every coin), which also returns 1h/24h/7d %
change in the same request. Old PATCH /crypto/holdings/{id} is replaced by
POST .../transactions (buy/sell, rejects selling more than held), GET
.../transactions (history), and DELETE /crypto/transactions/{id}.
Frontend: CryptoHoldingsTable replaces the simple list with a real table
(Name/Price/1h%/24h%/7d%/Holdings/Avg Buy Price/Profit-Loss/Actions),
horizontally scrollable on mobile rather than dropping columns. New
CryptoTransactionModal (buy/sell) and CryptoTransactionHistoryModal.
Backend: 79/79 tests passing (13 in test_crypto.py, up from 9). Also
verified live end-to-end against the real CoinGecko API with the user's own
key — create, buy, sell, oversell rejection, history, manual refresh, all
against real BTC/ETH prices in EUR — test data cleaned up after.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01855PiW5Wd8n4aRW4n9kByY
98 lines
5.0 KiB
Python
98 lines
5.0 KiB
Python
"""Crypto holdings: a thin layer on top of the existing Asset/AssetValuation
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engine (see models/asset.py) rather than a parallel system. Net worth's
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number for a holding still lives as ordinary AssetValuation rows, kept
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current by services/crypto_service.py — Net Worth requires zero changes to
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treat a crypto holding like any other manually tracked asset.
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Quantity and average buy price are NOT stored — they're computed from the
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CryptoTransaction log (same "you record it, we derive it" shape as
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Goal/GoalContribution), because a sell doesn't just subtract quantity, it
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also has to leave the average cost basis of what's still held unchanged
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(see services/crypto_service.py's _compute_position).
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"""
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from datetime import date as date_
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from datetime import datetime
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from sqlalchemy import Date, DateTime, Enum, ForeignKey, Numeric, String, Text
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from app.db.base import Base
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from app.models.asset import Asset
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from app.models.enums import CryptoTransactionType
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from app.models.mixins import TimestampMixin
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class CryptoHolding(Base, TimestampMixin):
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"""1:1 extension of an Asset (asset_class=CRYPTO) — asset_id doubles as
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this table's own primary key rather than a separate id+unique
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constraint, so the relationship can never drift into a 1:many by
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accident. Deleting the Asset (the existing DELETE /assets/{id}) cascades
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here too, and cascades again to every CryptoTransaction — there's no
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separate "delete the whole holding" endpoint."""
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__tablename__ = "crypto_holdings"
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asset_id: Mapped[int] = mapped_column(ForeignKey("assets.id", ondelete="CASCADE"), primary_key=True)
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# CoinGecko's stable coin id (e.g. "bitcoin") — NOT the ticker symbol,
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# which collides across unrelated coins (CoinGecko lists dozens of
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# differently-named coins that all use e.g. "SOL"-like tickers).
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coingecko_id: Mapped[str] = mapped_column(String(100), nullable=False)
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# Denormalized from CoinGecko at add-time so the holdings list can
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# render a ticker/name/logo without an extra lookup per row.
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symbol: Mapped[str] = mapped_column(String(20), nullable=False)
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name: Mapped[str] = mapped_column(String(150), nullable=False)
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thumb_url: Mapped[str | None] = mapped_column(String(500), nullable=True)
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# Cached from the last successful sync (services/crypto_service.py) —
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# in the app's display currency (see AppSettings.currency). Null until
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# the first sync completes.
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last_price: Mapped[Numeric | None] = mapped_column(Numeric(38, 18), nullable=True)
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price_change_1h: Mapped[Numeric | None] = mapped_column(Numeric(10, 4), nullable=True)
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price_change_24h: Mapped[Numeric | None] = mapped_column(Numeric(10, 4), nullable=True)
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price_change_7d: Mapped[Numeric | None] = mapped_column(Numeric(10, 4), nullable=True)
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asset: Mapped["Asset"] = relationship()
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transactions: Mapped[list["CryptoTransaction"]] = relationship(
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back_populates="holding", cascade="all, delete-orphan", order_by="CryptoTransaction.date"
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)
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class CryptoTransaction(Base, TimestampMixin):
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"""One buy or sell against a holding. Quantity held and average buy
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price are both derived by replaying this log in date order — see
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services/crypto_service.py's _compute_position for why a sell can't
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just be "negative quantity" the way it can for a Goal contribution: it
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must leave the remaining coins' average cost basis alone."""
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__tablename__ = "crypto_transactions"
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id: Mapped[int] = mapped_column(primary_key=True)
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asset_id: Mapped[int] = mapped_column(ForeignKey("crypto_holdings.asset_id", ondelete="CASCADE"), nullable=False)
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type: Mapped[CryptoTransactionType] = mapped_column(
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Enum(CryptoTransactionType, name="crypto_transaction_type", native_enum=False, length=10), nullable=False
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)
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quantity: Mapped[Numeric] = mapped_column(Numeric(38, 18), nullable=False)
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# Price paid (buy) or received (sell) per unit, in the app's display
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# currency at the time of the transaction — not re-derived later, since
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# what you actually paid doesn't change with today's market price.
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price_per_unit: Mapped[Numeric] = mapped_column(Numeric(38, 18), nullable=False)
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date: Mapped[date_] = mapped_column(Date, nullable=False)
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note: Mapped[str | None] = mapped_column(Text, nullable=True)
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holding: Mapped["CryptoHolding"] = relationship(back_populates="transactions")
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class CryptoSyncState(Base):
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"""Singleton row (id=1, same get-or-create pattern as AppSettings) — the
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single global timestamp of the last successful CoinGecko price refresh.
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One timestamp for every holding, not one per holding: a refresh always
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re-prices every tracked coin in a single batched request (see
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services/crypto_service.py), so there's only ever one "last synced"
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moment to track."""
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__tablename__ = "crypto_sync_state"
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id: Mapped[int] = mapped_column(primary_key=True)
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last_synced_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
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