Initial release v1.1.0
- Complete MVP for tracking Fidelity brokerage account performance - Transaction import from CSV with deduplication - Automatic FIFO position tracking with options support - Real-time P&L calculations with market data caching - Dashboard with timeframe filtering (30/90/180 days, 1 year, YTD, all time) - Docker-based deployment with PostgreSQL backend - React/TypeScript frontend with TailwindCSS - FastAPI backend with SQLAlchemy ORM Features: - Multi-account support - Import via CSV upload or filesystem - Open and closed position tracking - Balance history charting - Performance analytics and metrics - Top trades analysis - Responsive UI design Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Chris
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backend/app/services/performance_calculator_v2.py
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backend/app/services/performance_calculator_v2.py
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"""
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Improved performance calculator with rate-limited market data fetching.
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This version uses the MarketDataService for efficient, cached price lookups.
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"""
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from sqlalchemy.orm import Session
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from sqlalchemy import and_
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from typing import Dict, Optional
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from decimal import Decimal
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from datetime import datetime, timedelta
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import logging
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from app.models import Position, Transaction
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from app.models.position import PositionStatus
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from app.services.market_data_service import MarketDataService
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logger = logging.getLogger(__name__)
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class PerformanceCalculatorV2:
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"""
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Enhanced performance calculator with efficient market data handling.
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Features:
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- Database-backed price caching
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- Rate-limited API calls
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- Batch price fetching
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- Stale-while-revalidate pattern
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"""
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def __init__(self, db: Session, cache_ttl: int = 300):
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"""
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Initialize performance calculator.
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Args:
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db: Database session
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cache_ttl: Cache time-to-live in seconds (default: 5 minutes)
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"""
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self.db = db
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self.market_data = MarketDataService(db, cache_ttl_seconds=cache_ttl)
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def calculate_unrealized_pnl(self, position: Position, current_price: Optional[Decimal] = None) -> Optional[Decimal]:
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"""
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Calculate unrealized P&L for an open position.
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Args:
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position: Open position to calculate P&L for
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current_price: Optional pre-fetched current price (avoids API call)
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Returns:
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Unrealized P&L or None if market data unavailable
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"""
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if position.status != PositionStatus.OPEN:
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return None
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# Use provided price or fetch it
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if current_price is None:
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current_price = self.market_data.get_price(position.symbol, allow_stale=True)
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if current_price is None or position.avg_entry_price is None:
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return None
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# Calculate P&L based on position direction
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quantity = abs(position.total_quantity)
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is_short = position.total_quantity < 0
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if is_short:
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# Short position: profit when price decreases
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pnl = (position.avg_entry_price - current_price) * quantity * 100
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else:
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# Long position: profit when price increases
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pnl = (current_price - position.avg_entry_price) * quantity * 100
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# Subtract fees and commissions from opening transactions
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total_fees = Decimal("0")
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for link in position.transaction_links:
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txn = link.transaction
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if txn.commission:
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total_fees += txn.commission
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if txn.fees:
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total_fees += txn.fees
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pnl -= total_fees
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return pnl
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def update_open_positions_pnl(
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self,
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account_id: int,
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max_api_calls: int = 10,
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allow_stale: bool = True
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) -> Dict[str, int]:
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"""
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Update unrealized P&L for all open positions in an account.
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Uses batch fetching with rate limiting to avoid overwhelming Yahoo Finance API.
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Args:
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account_id: Account ID to update
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max_api_calls: Maximum number of Yahoo Finance API calls to make
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allow_stale: Allow using stale cached prices
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Returns:
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Dictionary with update statistics
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"""
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open_positions = (
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self.db.query(Position)
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.filter(
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and_(
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Position.account_id == account_id,
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Position.status == PositionStatus.OPEN,
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)
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)
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.all()
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)
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if not open_positions:
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return {
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"total": 0,
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"updated": 0,
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"cached": 0,
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"failed": 0
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}
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# Get unique symbols
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symbols = list(set(p.symbol for p in open_positions))
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logger.info(f"Updating P&L for {len(open_positions)} positions across {len(symbols)} symbols")
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# Fetch prices in batch
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prices = self.market_data.get_prices_batch(
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symbols,
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allow_stale=allow_stale,
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max_fetches=max_api_calls
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)
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# Update P&L for each position
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updated = 0
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cached = 0
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failed = 0
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for position in open_positions:
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price = prices.get(position.symbol)
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if price is not None:
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unrealized_pnl = self.calculate_unrealized_pnl(position, current_price=price)
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if unrealized_pnl is not None:
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position.unrealized_pnl = unrealized_pnl
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updated += 1
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# Check if price was from cache (age > 0) or fresh fetch
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cached_info = self.market_data._get_cached_price(position.symbol)
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if cached_info:
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_, age = cached_info
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if age < self.market_data.cache_ttl:
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cached += 1
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else:
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failed += 1
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else:
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failed += 1
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logger.warning(f"Could not get price for {position.symbol}")
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self.db.commit()
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logger.info(
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f"Updated {updated}/{len(open_positions)} positions "
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f"(cached: {cached}, failed: {failed})"
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)
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return {
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"total": len(open_positions),
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"updated": updated,
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"cached": cached,
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"failed": failed
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}
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def calculate_account_stats(
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self,
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account_id: int,
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update_prices: bool = True,
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max_api_calls: int = 10,
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start_date = None,
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end_date = None
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) -> Dict:
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"""
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Calculate aggregate statistics for an account.
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Args:
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account_id: Account ID
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update_prices: Whether to fetch fresh prices (if False, uses cached only)
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max_api_calls: Maximum number of Yahoo Finance API calls
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start_date: Filter positions opened on or after this date
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end_date: Filter positions opened on or before this date
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Returns:
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Dictionary with performance metrics
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"""
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# Get all positions with optional date filtering
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query = self.db.query(Position).filter(Position.account_id == account_id)
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if start_date:
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query = query.filter(Position.open_date >= start_date)
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if end_date:
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query = query.filter(Position.open_date <= end_date)
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positions = query.all()
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total_positions = len(positions)
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open_positions_count = sum(
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1 for p in positions if p.status == PositionStatus.OPEN
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)
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closed_positions_count = sum(
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1 for p in positions if p.status == PositionStatus.CLOSED
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)
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# Calculate realized P&L (doesn't need market data)
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total_realized_pnl = sum(
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(p.realized_pnl or Decimal("0"))
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for p in positions
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if p.status == PositionStatus.CLOSED
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)
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# Update unrealized P&L for open positions
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update_stats = None
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if update_prices and open_positions_count > 0:
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update_stats = self.update_open_positions_pnl(
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account_id,
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max_api_calls=max_api_calls,
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allow_stale=True
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)
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# Calculate total unrealized P&L
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total_unrealized_pnl = sum(
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(p.unrealized_pnl or Decimal("0"))
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for p in positions
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if p.status == PositionStatus.OPEN
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)
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# Calculate win rate and average win/loss
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closed_with_pnl = [
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p for p in positions
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if p.status == PositionStatus.CLOSED and p.realized_pnl is not None
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]
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if closed_with_pnl:
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winning_trades = [p for p in closed_with_pnl if p.realized_pnl > 0]
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losing_trades = [p for p in closed_with_pnl if p.realized_pnl < 0]
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win_rate = (len(winning_trades) / len(closed_with_pnl)) * 100
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avg_win = (
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sum(p.realized_pnl for p in winning_trades) / len(winning_trades)
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if winning_trades
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else Decimal("0")
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)
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avg_loss = (
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sum(p.realized_pnl for p in losing_trades) / len(losing_trades)
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if losing_trades
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else Decimal("0")
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)
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else:
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win_rate = 0.0
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avg_win = Decimal("0")
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avg_loss = Decimal("0")
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# Get current account balance from latest transaction
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latest_txn = (
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self.db.query(Transaction)
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.filter(Transaction.account_id == account_id)
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.order_by(Transaction.run_date.desc(), Transaction.id.desc())
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.first()
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)
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current_balance = (
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latest_txn.cash_balance if latest_txn and latest_txn.cash_balance else Decimal("0")
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)
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result = {
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"total_positions": total_positions,
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"open_positions": open_positions_count,
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"closed_positions": closed_positions_count,
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"total_realized_pnl": float(total_realized_pnl),
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"total_unrealized_pnl": float(total_unrealized_pnl),
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"total_pnl": float(total_realized_pnl + total_unrealized_pnl),
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"win_rate": float(win_rate),
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"avg_win": float(avg_win),
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"avg_loss": float(avg_loss),
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"current_balance": float(current_balance),
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}
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# Add update stats if prices were fetched
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if update_stats:
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result["price_update_stats"] = update_stats
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return result
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def get_balance_history(
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self, account_id: int, days: int = 30
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) -> list[Dict]:
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"""
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Get account balance history for charting.
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This doesn't need market data, just transaction history.
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Args:
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account_id: Account ID
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days: Number of days to retrieve
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Returns:
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List of {date, balance} dictionaries
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"""
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cutoff_date = datetime.now().date() - timedelta(days=days)
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transactions = (
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self.db.query(Transaction.run_date, Transaction.cash_balance)
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.filter(
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and_(
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Transaction.account_id == account_id,
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Transaction.run_date >= cutoff_date,
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Transaction.cash_balance.isnot(None),
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)
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)
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.order_by(Transaction.run_date)
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.all()
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)
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# Get one balance per day (use last transaction of the day)
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daily_balances = {}
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for txn in transactions:
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daily_balances[txn.run_date] = float(txn.cash_balance)
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return [
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{"date": date.isoformat(), "balance": balance}
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for date, balance in sorted(daily_balances.items())
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]
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def get_top_trades(
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self, account_id: int, limit: int = 10, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None
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) -> list[Dict]:
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"""
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Get top performing trades (by realized P&L).
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This doesn't need market data, just closed positions.
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Args:
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account_id: Account ID
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limit: Maximum number of trades to return
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start_date: Filter positions closed on or after this date
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end_date: Filter positions closed on or before this date
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Returns:
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List of trade dictionaries
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"""
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query = self.db.query(Position).filter(
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and_(
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Position.account_id == account_id,
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Position.status == PositionStatus.CLOSED,
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Position.realized_pnl.isnot(None),
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)
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)
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# Apply date filters if provided
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if start_date:
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query = query.filter(Position.close_date >= start_date)
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if end_date:
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query = query.filter(Position.close_date <= end_date)
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positions = query.order_by(Position.realized_pnl.desc()).limit(limit).all()
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return [
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{
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"symbol": p.symbol,
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"option_symbol": p.option_symbol,
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"position_type": p.position_type.value,
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"open_date": p.open_date.isoformat(),
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"close_date": p.close_date.isoformat() if p.close_date else None,
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"quantity": float(p.total_quantity),
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"entry_price": float(p.avg_entry_price) if p.avg_entry_price else None,
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"exit_price": float(p.avg_exit_price) if p.avg_exit_price else None,
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"realized_pnl": float(p.realized_pnl),
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}
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for p in positions
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]
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def get_worst_trades(
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self, account_id: int, limit: int = 10, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None
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) -> list[Dict]:
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"""
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Get worst performing trades (by realized P&L).
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This doesn't need market data, just closed positions.
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Args:
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account_id: Account ID
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limit: Maximum number of trades to return
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start_date: Filter positions closed on or after this date
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end_date: Filter positions closed on or before this date
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Returns:
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List of trade dictionaries
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"""
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query = self.db.query(Position).filter(
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and_(
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Position.account_id == account_id,
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Position.status == PositionStatus.CLOSED,
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Position.realized_pnl.isnot(None),
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)
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)
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# Apply date filters if provided
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if start_date:
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query = query.filter(Position.close_date >= start_date)
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if end_date:
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query = query.filter(Position.close_date <= end_date)
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positions = query.order_by(Position.realized_pnl.asc()).limit(limit).all()
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return [
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{
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"symbol": p.symbol,
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"option_symbol": p.option_symbol,
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"position_type": p.position_type.value,
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"open_date": p.open_date.isoformat(),
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"close_date": p.close_date.isoformat() if p.close_date else None,
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"quantity": float(p.total_quantity),
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"entry_price": float(p.avg_entry_price) if p.avg_entry_price else None,
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"exit_price": float(p.avg_exit_price) if p.avg_exit_price else None,
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"realized_pnl": float(p.realized_pnl),
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}
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for p in positions
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]
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