Phase 1: initial codes

stockmind/core/portfolio.py

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+from __future__ import annotations
+import pandas as pd
+import yfinance as yf
+from core.market_data import get_quote, get_history
+
+
+def calculate_pnl(positions: list[dict], quotes: dict[str, dict]) -> list[dict]:
+    result = []
+    for pos in positions:
+        sym = pos["symbol"]
+        q = quotes.get(sym, {})
+        price = q.get("price", 0.0)
+        shares = pos.get("shares", 0.0)
+        avg_cost = pos.get("avg_cost", 0.0)
+        market_value = price * shares
+        cost_basis = avg_cost * shares
+        pnl = market_value - cost_basis
+        pnl_pct = (pnl / cost_basis * 100) if cost_basis else 0.0
+        result.append({
+            **pos,
+            "price": price,
+            "market_value": market_value,
+            "cost_basis": cost_basis,
+            "pnl": pnl,
+            "pnl_pct": pnl_pct,
+            "change_pct": q.get("change_pct", 0.0),
+        })
+    return result
+
+
+def get_benchmark_performance(period: str = "1y") -> pd.DataFrame:
+    return get_history("SPY", period=period)
+
+
+def calculate_sector_allocation(positions: list[dict]) -> dict[str, float]:
+    sector_values: dict[str, float] = {}
+    for pos in positions:
+        sector = pos.get("sector", "Unknown") or "Unknown"
+        value = pos.get("market_value", 0.0)
+        sector_values[sector] = sector_values.get(sector, 0.0) + value
+    total = sum(sector_values.values())
+    if total == 0:
+        return {}
+    return {s: (v / total * 100) for s, v in sorted(sector_values.items(), key=lambda x: -x[1])}
+
+
+def get_portfolio_performance(positions: list[dict], period: str = "1y") -> pd.DataFrame:
+    if not positions:
+        return pd.DataFrame()
+
+    symbols = [p["symbol"] for p in positions]
+    weights = {}
+    total_value = sum(p.get("market_value", 0) for p in positions)
+
+    if total_value == 0:
+        return pd.DataFrame()
+
+    for p in positions:
+        weights[p["symbol"]] = p.get("market_value", 0) / total_value
+
+    frames = []
+    for sym in symbols:
+        hist = get_history(sym, period=period)
+        if not hist.empty:
+            pct = hist["Close"].pct_change().fillna(0)
+            pct.name = sym
+            frames.append(pct * weights.get(sym, 0))
+
+    if not frames:
+        return pd.DataFrame()
+
+    combined = pd.concat(frames, axis=1).fillna(0)
+    portfolio_returns = combined.sum(axis=1)
+    portfolio_cumulative = (1 + portfolio_returns).cumprod() - 1
+
+    spy = get_history("SPY", period=period)
+    if not spy.empty:
+        spy_returns = spy["Close"].pct_change().fillna(0)
+        spy_cumulative = (1 + spy_returns).cumprod() - 1
+        return pd.DataFrame({
+            "portfolio": portfolio_cumulative,
+            "spy": spy_cumulative,
+        }).dropna()
+
+    return pd.DataFrame({"portfolio": portfolio_cumulative}).dropna()