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/net_worth_projection.py

@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+import warnings
+from datetime import date
+from dateutil.relativedelta import relativedelta
+
+import numpy as np
+import pandas as pd
+
+warnings.filterwarnings("ignore")
+
+
+def _project_months(from_date: date, n: int) -> list[str]:
+    months = []
+    d = from_date.replace(day=1)
+    for i in range(1, n + 1):
+        months.append((d + relativedelta(months=i)).strftime("%Y-%m"))
+    return months
+
+
+def project_net_worth(df: pd.DataFrame, years: int = 5) -> dict:
+    """
+    df columns: ds (monthly datetime), y (net_worth float)
+    Returns history + 3-scenario projections.
+    """
+    n_months = years * 12
+    today = date.today()
+    future_dates = _project_months(today, n_months)
+
+    history = [
+        {"date": row["ds"].strftime("%Y-%m"), "value": round(float(row["y"]), 2)}
+        for _, row in df.iterrows()
+    ]
+
+    if df.empty or len(df) < 2:
+        # No data — return flat projection from 0
+        last_val = float(df["y"].iloc[-1]) if not df.empty else 0.0
+        flat = [{"date": d, "value": round(last_val, 2)} for d in future_dates]
+        return {
+            "history": history,
+            "projections": {"conservative": flat, "base": flat, "optimistic": flat},
+            "insufficient_data": True,
+        }
+
+    try:
+        from statsmodels.tsa.holtwinters import ExponentialSmoothing
+
+        values = df["y"].tolist()
+
+        if len(values) >= 12:
+            model = ExponentialSmoothing(values, trend="add", seasonal="add", seasonal_periods=12)
+        elif len(values) >= 4:
+            model = ExponentialSmoothing(values, trend="add", seasonal=None)
+        else:
+            model = ExponentialSmoothing(values, trend="add", seasonal=None)
+
+        fit = model.fit(optimized=True, disp=False)
+        base_fcast = fit.forecast(n_months)
+
+        # Estimate monthly trend from the fit
+        monthly_trend = float(np.mean(np.diff(base_fcast[:12]))) if len(base_fcast) >= 12 else 0.0
+        last_val = float(values[-1])
+
+        # Scale trends for scenarios
+        def build_scenario(scale: float) -> list[dict]:
+            pts = []
+            v = last_val
+            for i, d in enumerate(future_dates):
+                v = float(base_fcast[i]) + (scale - 1.0) * monthly_trend * (i + 1)
+                pts.append({"date": d, "value": round(v, 2)})
+            return pts
+
+        return {
+            "history": history,
+            "projections": {
+                "conservative": build_scenario(0.5),
+                "base": [{"date": d, "value": round(float(v), 2)} for d, v in zip(future_dates, base_fcast)],
+                "optimistic": build_scenario(1.5),
+            },
+            "insufficient_data": False,
+        }
+
+    except Exception:
+        # Fallback: linear trend from last 2 values
+        trend = float(df["y"].iloc[-1]) - float(df["y"].iloc[-2])
+        last_val = float(df["y"].iloc[-1])
+
+        def linear_scenario(t_scale: float) -> list[dict]:
+            return [
+                {"date": d, "value": round(last_val + t_scale * trend * (i + 1), 2)}
+                for i, d in enumerate(future_dates)
+            ]
+
+        return {
+            "history": history,
+            "projections": {
+                "conservative": linear_scenario(0.5),
+                "base": linear_scenario(1.0),
+                "optimistic": linear_scenario(1.5),
+            },
+            "insufficient_data": False,
+        }