diff --git a/services/trading/backtest_replay.py b/services/trading/backtest_replay.py
index 5fbb240..c8315de 100644
--- a/services/trading/backtest_replay.py
+++ b/services/trading/backtest_replay.py
@@ -212,7 +212,7 @@ class BacktestReplay:
                     )
 
                 # Simulate simple exit logic: close positions held > 5 days
-                # (simplified — real engine uses stop-loss/take-profit)
+                # or use actual market price if available
                 tickers_to_close = []
                 for ticker, pos_info in simulated_positions.items():
                     hold_days = (current_date - pos_info["entry_date"]).days
@@ -221,8 +221,8 @@ class BacktestReplay:
 
                 for ticker in tickers_to_close:
                     pos_info = simulated_positions.pop(ticker)
-                    # Simulate a small random-ish exit based on entry price
-                    exit_price = pos_info["entry_price"] * 1.01  # simplified
+                    # Use actual market price if available, otherwise estimate
+                    exit_price = company_prices.get(ticker, pos_info["entry_price"] * 1.01)
                     qty = pos_info["quantity"]
                     pnl = (exit_price - pos_info["entry_price"]) * qty
                     pnl_pct = (
@@ -253,10 +253,10 @@ class BacktestReplay:
                         0, portfolio_state.open_position_count - 1
                     )
 
-                # Compute daily portfolio value
+                # Compute daily portfolio value using latest market prices
                 positions_value = sum(
-                    p["entry_price"] * p["quantity"]
-                    for p in simulated_positions.values()
+                    company_prices.get(t, p["entry_price"]) * p["quantity"]
+                    for t, p in simulated_positions.items()
                 )
                 current_value = portfolio_state.active_pool + positions_value
                 portfolio_state.total_value = current_value
@@ -277,6 +277,42 @@ class BacktestReplay:
 
                 current_date += timedelta(days=1)
 
+            # Force-close any remaining open positions at end of backtest
+            # using the latest available market prices
+            for ticker in list(simulated_positions.keys()):
+                pos_info = simulated_positions.pop(ticker)
+                exit_price = company_prices.get(ticker, pos_info["entry_price"])
+                qty = pos_info["quantity"]
+                pnl = (exit_price - pos_info["entry_price"]) * qty
+                pnl_pct = (
+                    (exit_price - pos_info["entry_price"]) / pos_info["entry_price"]
+                    if pos_info["entry_price"] > 0
+                    else 0.0
+                )
+                hold_duration = timedelta(
+                    days=(config.end_date - pos_info["entry_date"]).days
+                )
+
+                trade = ClosedTrade(
+                    ticker=ticker,
+                    entry_price=pos_info["entry_price"],
+                    exit_price=exit_price,
+                    quantity=qty,
+                    pnl=pnl,
+                    pnl_pct=pnl_pct,
+                    hold_duration=hold_duration,
+                    recommendation_id=pos_info.get("recommendation_id"),
+                )
+                closed_trades.append(trade)
+                trade_log.append(self._perf.compute_trade_metrics(trade))
+                portfolio_state.active_pool += exit_price * qty
+
+            if closed_trades:
+                logger.info(
+                    "Backtest %s completed: %d trades, final value=$%.2f",
+                    backtest_id, len(closed_trades), portfolio_state.active_pool,
+                )
+
             # Compute final metrics
             metrics = self._perf.compute_metrics(
                 closed_trades=closed_trades,