Initial commit: MyMidas personal finance tracker

Full-stack self-hosted finance app with FastAPI backend and React frontend.

Features:
- Accounts, transactions, budgets, investments with GBP base currency
- CSV import with auto-detection for 10 UK bank formats
- ML predictions: spending forecast, net worth projection, Monte Carlo
- 7 selectable themes (Obsidian, Arctic, Midnight, Vault, Terminal, Synthwave, Ledger)
- Receipt/document attachments on transactions (JPEG, PNG, WebP, PDF)
- AES-256-GCM field encryption, RS256 JWT, TOTP 2FA, RLS, audit log
- Encrypted nightly backups + key rotation script
- Mobile-responsive layout with bottom nav

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/ml/feature_engineering.py

@@ -0,0 +1,119 @@
+from __future__ import annotations
+
+import pandas as pd
+from sqlalchemy import text
+from sqlalchemy.ext.asyncio import AsyncSession
+
+
+async def get_monthly_category_spending(db: AsyncSession, user_id: str) -> pd.DataFrame:
+    result = await db.execute(text("""
+        SELECT
+            COALESCE(t.category_id::text, 'uncategorised') AS category_id,
+            COALESCE(c.name, 'Uncategorised') AS category_name,
+            DATE_TRUNC('month', t.date)::date AS ds,
+            SUM(ABS(t.amount))::float AS y
+        FROM transactions t
+        LEFT JOIN categories c ON c.id = t.category_id
+        WHERE t.user_id = CAST(:uid AS uuid)
+          AND t.type = 'expense'
+          AND t.deleted_at IS NULL
+          AND t.status != 'void'
+        GROUP BY t.category_id, c.name, DATE_TRUNC('month', t.date)
+        ORDER BY ds ASC
+    """), {"uid": str(user_id)})
+    rows = result.fetchall()
+    if not rows:
+        return pd.DataFrame(columns=["category_id", "category_name", "ds", "y"])
+    df = pd.DataFrame(rows, columns=["category_id", "category_name", "ds", "y"])
+    df["ds"] = pd.to_datetime(df["ds"])
+    df["y"] = df["y"].astype(float)
+    return df
+
+
+async def get_monthly_net_worth(db: AsyncSession, user_id: str) -> pd.DataFrame:
+    result = await db.execute(text("""
+        SELECT date::text AS ds, net_worth::float AS y
+        FROM net_worth_snapshots
+        WHERE user_id = CAST(:uid AS uuid)
+        ORDER BY date ASC
+    """), {"uid": str(user_id)})
+    rows = result.fetchall()
+    if not rows:
+        return pd.DataFrame(columns=["ds", "y"])
+    df = pd.DataFrame(rows, columns=["ds", "y"])
+    df["ds"] = pd.to_datetime(df["ds"])
+    df["y"] = df["y"].astype(float)
+    # Resample to monthly end, keeping last value
+    df = df.set_index("ds").resample("ME").last().dropna().reset_index()
+    df.columns = ["ds", "y"]
+    return df
+
+
+async def get_current_month_spending(db: AsyncSession, user_id: str) -> pd.DataFrame:
+    result = await db.execute(text("""
+        SELECT
+            COALESCE(t.category_id::text, 'uncategorised') AS category_id,
+            COALESCE(c.name, 'Uncategorised') AS category_name,
+            SUM(ABS(t.amount))::float AS spent
+        FROM transactions t
+        LEFT JOIN categories c ON c.id = t.category_id
+        WHERE t.user_id = CAST(:uid AS uuid)
+          AND t.type = 'expense'
+          AND t.deleted_at IS NULL
+          AND t.status != 'void'
+          AND DATE_TRUNC('month', t.date) = DATE_TRUNC('month', CURRENT_DATE)
+        GROUP BY t.category_id, c.name
+    """), {"uid": str(user_id)})
+    rows = result.fetchall()
+    if not rows:
+        return pd.DataFrame(columns=["category_id", "category_name", "spent"])
+    df = pd.DataFrame(rows, columns=["category_id", "category_name", "spent"])
+    df["spent"] = df["spent"].astype(float)
+    return df
+
+
+async def get_portfolio_monthly_returns(db: AsyncSession, user_id: str) -> pd.DataFrame:
+    """Monthly close prices for each asset in user's portfolio."""
+    result = await db.execute(text("""
+        SELECT
+            a.symbol,
+            DATE_TRUNC('month', ap.date)::date AS month,
+            (ARRAY_AGG(ap.close ORDER BY ap.date DESC))[1]::float AS close
+        FROM investment_holdings h
+        JOIN assets a ON a.id = h.asset_id
+        JOIN asset_prices ap ON ap.asset_id = h.asset_id
+        WHERE h.user_id = CAST(:uid AS uuid)
+          AND h.deleted_at IS NULL
+        GROUP BY a.symbol, DATE_TRUNC('month', ap.date)
+        ORDER BY a.symbol, month ASC
+    """), {"uid": str(user_id)})
+    rows = result.fetchall()
+    if not rows:
+        return pd.DataFrame(columns=["symbol", "month", "close"])
+    df = pd.DataFrame(rows, columns=["symbol", "month", "close"])
+    df["month"] = pd.to_datetime(df["month"])
+    df["close"] = df["close"].astype(float)
+    return df
+
+
+async def get_daily_cash_flow(db: AsyncSession, user_id: str, days: int = 90) -> pd.DataFrame:
+    result = await db.execute(text("""
+        SELECT
+            t.date::date AS ds,
+            SUM(CASE WHEN t.amount > 0 THEN t.amount ELSE 0 END)::float AS inflow,
+            SUM(CASE WHEN t.amount < 0 THEN ABS(t.amount) ELSE 0 END)::float AS outflow
+        FROM transactions t
+        WHERE t.user_id = CAST(:uid AS uuid)
+          AND t.deleted_at IS NULL
+          AND t.status != 'void'
+          AND t.type IN ('income', 'expense')
+          AND t.date >= CURRENT_DATE - :days
+        GROUP BY t.date
+        ORDER BY t.date ASC
+    """), {"uid": str(user_id), "days": days})
+    rows = result.fetchall()
+    if not rows:
+        return pd.DataFrame(columns=["ds", "inflow", "outflow"])
+    df = pd.DataFrame(rows, columns=["ds", "inflow", "outflow"])
+    df["ds"] = pd.to_datetime(df["ds"])
+    return df