diff --git a/apps/paper_trader/backtest_sim.py b/apps/paper_trader/backtest_sim.py
new file mode 100644
index 0000000..9c74536
--- /dev/null
+++ b/apps/paper_trader/backtest_sim.py
@@ -0,0 +1,236 @@
+"""Paper-trading backtest simulator.
+
+Refactored to use the SAME BacktestRunner + SnapshotStore as
+apps/backtester/run.py. This guarantees identical scoring, engine
+matching, and position sizing between `fithia2 paper backtest` and
+the research backtester.
+
+Previous implementation used PaperTradingEngine + EventDetector +
+MockBroker which had different scoring functions, feature computation,
+and data sources — causing divergent results.
+"""
+from __future__ import annotations
+
+import datetime as dt
+import math
+import statistics
+import tempfile
+from pathlib import Path
+from typing import Any
+
+from libs.common.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+def run_backtest_session_sync(
+    session_name: str,
+    config_path: str,
+    initial_equity: float,
+    start_date: dt.date,
+    end_date: dt.date,
+) -> dict[str, Any]:
+    """Run a single strategy using BacktestRunner (same as research backtester).
+
+    Uses the existing Parquet snapshot + BacktestRunner pipeline so results
+    match `python -m apps.backtester.run --manifest <config>` exactly.
+    """
+    from apps.backtester.run import (
+        BacktestRunner,
+        _build_store,
+        _build_merged_snapshot_store,
+        load_manifest,
+        resolve_config,
+    )
+
+    manifest = load_manifest(config_path)
+    config = resolve_config(manifest)
+
+    # Use merged store (train+valid+test) to cover the full date range
+    store = _build_merged_snapshot_store(manifest, config, snapshot_dir_override=None)
+
+    # Slice to requested date range
+    store = store.slice_by_date_range(start_date, end_date)
+
+    runner = BacktestRunner(
+        manifest=manifest,
+        config=config,
+        store=store,
+        initial_equity=initial_equity,
+        split_name="paper_backtest",
+    )
+
+    # Run with temporary output directory
+    tmp_dir = tempfile.mkdtemp(prefix="paper_bt_")
+    try:
+        result = runner.run(output_root=tmp_dir)
+    except Exception:
+        result = runner.run(output_root=None)
+
+    # Convert to paper backtest format
+    return _convert_from_runner(session_name, config_path, initial_equity, result, tmp_dir)
+
+
+def _convert_from_runner(
+    session_name: str,
+    config_path: str,
+    initial_equity: float,
+    result: Any,
+    tmp_dir: str,
+) -> dict[str, Any]:
+    """Convert BacktestRunner result to paper backtest output format."""
+    equity_curve: list[dict] = []
+    trades: list[dict] = []
+
+    # Load from Parquet artifacts
+    try:
+        import pyarrow.parquet as pq
+        import glob
+        import os
+
+        run_dirs = sorted(glob.glob(os.path.join(tmp_dir, "bt_*")))
+        if run_dirs:
+            run_dir = Path(run_dirs[0])
+
+            # Equity curve
+            eq_path = run_dir / "artifacts" / "daily_equity_curve.parquet"
+            if eq_path.exists():
+                eq_df = pq.read_table(str(eq_path)).to_pandas()
+                for _, row in eq_df.iterrows():
+                    d = row.get("date")
+                    if isinstance(d, str):
+                        d = dt.date.fromisoformat(d[:10])
+                    equity_curve.append({
+                        "date": d,
+                        "equity": float(row.get("equity", initial_equity)),
+                    })
+
+            # Trade blotter
+            bl_path = run_dir / "artifacts" / "trade_blotter.parquet"
+            if bl_path.exists():
+                bl_df = pq.read_table(str(bl_path)).to_pandas()
+                for _, row in bl_df.iterrows():
+                    entry_px = row.get("entry_price")
+                    exit_px = row.get("exit_price")
+                    shares = int(row.get("shares", 0))
+                    pnl_pct = float(row.get("pnl_pct", 0.0))
+                    pnl_dollar = pnl_pct * float(entry_px or 0) * shares if entry_px else 0.0
+
+                    trades.append({
+                        "symbol": str(row.get("symbol", "")),
+                        "entry_date": str(row.get("entry_date", "-")),
+                        "exit_date": str(row.get("exit_date", "-")),
+                        "entry_price": float(entry_px) if entry_px is not None else None,
+                        "exit_price": float(exit_px) if exit_px is not None else None,
+                        "shares": shares,
+                        "pnl": pnl_dollar,
+                        "reason": str(row.get("exit_reason", "-")),
+                        "event_type": str(row.get("event_type", "-")),
+                        "score": float(row.get("score", 0.0)),
+                        "engine_id": str(row.get("engine_id", "")),
+                    })
+    except Exception as exc:
+        logger.warning("backtest_sim_artifact_load_failed", error=str(exc))
+
+    # Compute summary stats
+    final_equity = equity_curve[-1]["equity"] if equity_curve else initial_equity
+    total_return_pct = (final_equity - initial_equity) / initial_equity * 100
+
+    pnls = [t["pnl"] for t in trades]
+    wins = [p for p in pnls if p > 0]
+    win_rate = len(wins) / len(pnls) * 100 if pnls else 0.0
+
+    equities = [r["equity"] for r in equity_curve]
+    daily_returns = [
+        (equities[i] - equities[i - 1]) / equities[i - 1]
+        for i in range(1, len(equities))
+        if equities[i - 1] > 0
+    ]
+    if len(daily_returns) >= 2:
+        mean_r = statistics.mean(daily_returns)
+        std_r = statistics.stdev(daily_returns)
+        sharpe = (mean_r / std_r) * math.sqrt(252) if std_r > 0 else 0.0
+    else:
+        sharpe = 0.0
+
+    peak = initial_equity
+    max_dd_pct = 0.0
+    for eq in equities:
+        if eq > peak:
+            peak = eq
+        dd = (peak - eq) / peak * 100 if peak > 0 else 0.0
+        if dd > max_dd_pct:
+            max_dd_pct = dd
+
+    return {
+        "session_name": session_name,
+        "config_path": config_path,
+        "initial_equity": initial_equity,
+        "equity_curve": equity_curve,
+        "trades": trades,
+        "all_entries": [],
+        "all_exits": [],
+        "summary": {
+            "return_pct": total_return_pct,
+            "final_equity": final_equity,
+            "max_dd_pct": max_dd_pct,
+            "trade_count": len(trades),
+            "win_rate": win_rate,
+            "sharpe": sharpe,
+        },
+    }
+
+
+async def run_backtest(
+    configs: list[str],
+    capital: float,
+    start_date: dt.date,
+    end_date: dt.date,
+    db_dsn: str,
+    oracle_url: str,
+    console=None,
+) -> list[dict[str, Any]]:
+    """Run multiple strategies sequentially using BacktestRunner."""
+    from libs.common.time_utils import is_trading_day
+    from libs.common.logging import configure_logging
+
+    all_days = [
+        start_date + dt.timedelta(days=i)
+        for i in range((end_date - start_date).days + 1)
+    ]
+    trading_days = [d for d in all_days if is_trading_day(d)]
+
+    if not trading_days:
+        raise ValueError(f"No trading days found between {start_date} and {end_date}")
+
+    if console:
+        console.print(f"[bold]Trading days:[/] {trading_days[0]} → {trading_days[-1]} ({len(trading_days)} days)")
+        console.print("[bold]Engine:[/] BacktestRunner (identical to research backtester)")
+
+    configure_logging("WARNING")
+
+    results = []
+    for config_path in configs:
+        session_name = Path(config_path).stem
+        if console:
+            console.print(f"\n[bold cyan]Running:[/] {session_name}")
+
+        result = run_backtest_session_sync(
+            session_name=session_name,
+            config_path=config_path,
+            initial_equity=capital,
+            start_date=start_date,
+            end_date=end_date,
+        )
+        results.append(result)
+
+        if console and result["summary"]["trade_count"] > 0:
+            s = result["summary"]
+            console.print(
+                f"  Trades: {s['trade_count']}, "
+                f"Return: {s['return_pct']:+.2f}%, "
+                f"MaxDD: {s['max_dd_pct']:.2f}%, "
+                f"WR: {s['win_rate']:.0f}%"
+            )
+
+    return results