Add DCA Calculator feature to dashboard

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

btc-portfolio/backend/app/routes/dca.py

@@ -0,0 +1,108 @@
+from datetime import date, timedelta
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.orm import Session
+
+from ..database import get_db
+from .. import models
+from ..dependencies import get_current_user
+from ..services.btc import get_btc_price_eur
+
+router = APIRouter()
+
+
+def _first_of_month(year: int, month: int) -> date:
+    return date(year, month, 1)
+
+
+def _next_month(year: int, month: int) -> tuple[int, int]:
+    if month == 12:
+        return year + 1, 1
+    return year, month + 1
+
+
+@router.get("/dca")
+def get_dca(
+    monthly_amount: float = Query(..., gt=0),
+    start_date: Optional[str] = Query(default=None),
+    db: Session = Depends(get_db),
+    current_user: models.User = Depends(get_current_user),
+):
+    # Determine start date
+    if start_date:
+        try:
+            sim_start = date.fromisoformat(start_date)
+        except ValueError:
+            sim_start = None
+    else:
+        sim_start = None
+
+    if sim_start is None:
+        earliest = (
+            db.query(models.Purchase)
+            .filter(models.Purchase.user_id == current_user.id)
+            .order_by(models.Purchase.created_at.asc())
+            .first()
+        )
+        if earliest:
+            sim_start = earliest.created_at.date()
+        else:
+            sim_start = date.today() - timedelta(days=365)
+
+    # Load all candles into a lookup dict {date_str: close_price}
+    candles_db = db.query(models.OHLCCandle).all()
+    price_by_date: dict[str, float] = {c.date: c.close for c in candles_db}
+
+    today = date.today()
+    current_price, _ = get_btc_price_eur()
+    # Patch today's price in case the candle isn't refreshed yet
+    if current_price:
+        price_by_date[today.isoformat()] = current_price
+
+    # Walk month by month and simulate buys
+    year, month = sim_start.year, sim_start.month
+    end_year, end_month = today.year, today.month
+
+    dca_invested = 0.0
+    dca_btc = 0.0
+    monthly_series = []
+
+    while (year, month) <= (end_year, end_month):
+        # Find the closest available candle on or after the 1st of this month
+        buy_date = None
+        buy_price = None
+        for day_offset in range(8):
+            candidate = _first_of_month(year, month) + timedelta(days=day_offset)
+            key = candidate.isoformat()
+            if key in price_by_date:
+                buy_date = key
+                buy_price = price_by_date[key]
+                break
+
+        if buy_price and buy_price > 0:
+            btc_bought = monthly_amount / buy_price
+            dca_btc += btc_bought
+            dca_invested += monthly_amount
+            monthly_series.append({
+                "month": f"{year:04d}-{month:02d}",
+                "price_used": round(buy_price, 2),
+                "btc_bought": round(btc_bought, 8),
+                "cumulative_btc": round(dca_btc, 8),
+                "cumulative_invested": round(dca_invested, 2),
+            })
+
+        year, month = _next_month(year, month)
+
+    dca_current_value = dca_btc * current_price if current_price else 0.0
+    dca_profit_loss = dca_current_value - dca_invested
+
+    return {
+        "start_date": sim_start.isoformat(),
+        "monthly_amount": monthly_amount,
+        "dca_total_invested": round(dca_invested, 2),
+        "dca_total_btc": round(dca_btc, 8),
+        "dca_current_value": round(dca_current_value, 2),
+        "dca_profit_loss": round(dca_profit_loss, 2),
+        "monthly_series": monthly_series,
+    }