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>

backend/app/parsers/base_parser.py

@@ -0,0 +1,99 @@
+"""Base parser interface for brokerage CSV files."""
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any, NamedTuple
+from pathlib import Path
+import pandas as pd
+
+
+class ParseResult(NamedTuple):
+    """
+    Result of parsing a brokerage CSV file.
+
+    Attributes:
+        transactions: List of parsed transaction dictionaries
+        errors: List of error messages encountered during parsing
+        row_count: Total number of rows processed
+    """
+    transactions: List[Dict[str, Any]]
+    errors: List[str]
+    row_count: int
+
+
+class BaseParser(ABC):
+    """
+    Abstract base class for brokerage CSV parsers.
+
+    Provides a standard interface for parsing CSV files from different brokerages.
+    Subclasses must implement the parse() method for their specific format.
+    """
+
+    @abstractmethod
+    def parse(self, file_path: Path) -> ParseResult:
+        """
+        Parse a brokerage CSV file into standardized transaction dictionaries.
+
+        Args:
+            file_path: Path to the CSV file to parse
+
+        Returns:
+            ParseResult containing transactions, errors, and row count
+
+        Raises:
+            FileNotFoundError: If the file does not exist
+            ValueError: If the file format is invalid
+        """
+        pass
+
+    def _read_csv(self, file_path: Path, **kwargs) -> pd.DataFrame:
+        """
+        Read CSV file into a pandas DataFrame with error handling.
+
+        Args:
+            file_path: Path to CSV file
+            **kwargs: Additional arguments passed to pd.read_csv()
+
+        Returns:
+            DataFrame containing CSV data
+
+        Raises:
+            FileNotFoundError: If file does not exist
+            pd.errors.EmptyDataError: If file is empty
+        """
+        if not file_path.exists():
+            raise FileNotFoundError(f"CSV file not found: {file_path}")
+
+        return pd.read_csv(file_path, **kwargs)
+
+    @staticmethod
+    def _safe_decimal(value: Any) -> Any:
+        """
+        Safely convert value to decimal-compatible format, handling NaN and None.
+
+        Args:
+            value: Value to convert
+
+        Returns:
+            Converted value or None if invalid
+        """
+        if pd.isna(value):
+            return None
+        if value == "":
+            return None
+        return value
+
+    @staticmethod
+    def _safe_date(value: Any) -> Any:
+        """
+        Safely convert value to date, handling NaN and None.
+
+        Args:
+            value: Value to convert
+
+        Returns:
+            Converted date or None if invalid
+        """
+        if pd.isna(value):
+            return None
+        if value == "":
+            return None
+        return value