Celes Renata
4ffde8cc06
- Database migration 018 with 13 tables for trading engine state - Trading engine service (services/trading/) with 12 pure computation modules: position sizer, stop-loss manager, reserve pool, circuit breaker, risk tier controller, correlation matrix, tax lots, trading window, gradual entry, notifications, micro-trading, backtester - Core TradingEngine with pre-trade evaluation pipeline and integration wiring - FastAPI HTTP service with 14 endpoints (health, config, decisions, metrics, backtest) - Performance tracker with Sharpe ratio, drawdown, profit factor computation - 194 Python tests (165 property-based + 29 integration) - Frontend: 13 TanStack Query hooks, 7 dashboard panels, tabbed Trading Engine page - Helm chart entry, network policy, nginx proxy, ingress for trading-engine - Shared infrastructure: enums, Redis keys, TradingConfig in AppConfig
133 lines
3.5 KiB
Python
133 lines
3.5 KiB
Python
"""Tax lot tracking for cost basis and wash sale detection.
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Feature: autonomous-trading-engine
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Pure computation module for FIFO tax lot closing and wash sale
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detection within the 30-day window. Used by the Trading Engine
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for tax-loss harvesting awareness.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from datetime import date, timedelta
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@dataclass
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class TaxLot:
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"""A single tax lot representing a purchase of shares."""
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ticker: str
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quantity: int
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cost_basis_per_share: float
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acquisition_date: date
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status: str = "open" # open | closed | washed
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@dataclass
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class ClosedLot:
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"""Result of closing a tax lot via FIFO."""
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ticker: str
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quantity: int
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cost_basis_per_share: float
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exit_price: float
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realized_pnl: float
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acquisition_date: date
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closed_date: date
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class TaxLotTracker:
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"""Pure computation for FIFO lot closing and wash sale detection."""
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def close_lots_fifo(
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self,
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lots: list[TaxLot],
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quantity: int,
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exit_price: float,
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exit_date: date,
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) -> list[ClosedLot]:
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"""Close lots in FIFO order (earliest acquired first).
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Processes open lots sorted by acquisition_date ascending,
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closing shares until the requested quantity is fulfilled.
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Returns a list of ClosedLot records with realized P&L.
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Parameters
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----------
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lots:
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All tax lots for the ticker (open ones will be selected).
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quantity:
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Number of shares to close.
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exit_price:
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Price per share at exit.
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exit_date:
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Date the lots are being closed.
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Returns
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-------
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list[ClosedLot]
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Closed lot records in FIFO order.
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"""
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open_lots = sorted(
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[lot for lot in lots if lot.status == "open"],
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key=lambda lot: lot.acquisition_date,
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)
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closed: list[ClosedLot] = []
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remaining = quantity
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for lot in open_lots:
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if remaining <= 0:
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break
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close_qty = min(lot.quantity, remaining)
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realized_pnl = (exit_price - lot.cost_basis_per_share) * close_qty
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closed.append(
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ClosedLot(
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ticker=lot.ticker,
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quantity=close_qty,
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cost_basis_per_share=lot.cost_basis_per_share,
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exit_price=exit_price,
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realized_pnl=realized_pnl,
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acquisition_date=lot.acquisition_date,
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closed_date=exit_date,
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)
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)
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remaining -= close_qty
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return closed
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def check_wash_sale(
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self,
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loss_date: date,
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purchases: list[TaxLot],
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) -> bool:
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"""Check whether any purchase falls within the 30-day wash sale window.
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A wash sale occurs when the same ticker is purchased within
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30 days before or after a loss-closing date.
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Parameters
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----------
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loss_date:
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The date the loss was realized.
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purchases:
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Tax lots representing purchases of the same ticker.
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Returns
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-------
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bool
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True if any purchase is within the 30-day window.
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"""
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window_start = loss_date - timedelta(days=30)
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window_end = loss_date + timedelta(days=30)
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for lot in purchases:
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if window_start <= lot.acquisition_date <= window_end:
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return True
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return False
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