Add backtesting engine with SMA cross strategy

Reads features from Supabase, simulates SMA(3)/SMA(20) crossover signals,
and outputs P&L, drawdown, and trade logs per instrument/granularity.

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

logs/backtest_trades_EUR_USD_M1.csv

@@ -0,0 +1,19 @@
+time,instrument,granularity,side,price,position_size,equity,drawdown
+2026-02-16 02:52:00+00:00,EUR_USD,M1,BUY,1.18646,100.0,10000.0,0.0
+2026-02-16 03:03:00+00:00,EUR_USD,M1,FLAT,1.18654,0.0,10000.01,2e-06
+2026-02-16 03:13:00+00:00,EUR_USD,M1,BUY,1.18672,100.0,10000.01,2e-06
+2026-02-16 03:30:00+00:00,EUR_USD,M1,FLAT,1.18659,0.0,10000.0,3e-06
+2026-02-16 03:44:00+00:00,EUR_USD,M1,BUY,1.18676,100.0,10000.0,3e-06
+2026-02-16 03:47:00+00:00,EUR_USD,M1,FLAT,1.18666,0.0,9999.99,3e-06
+2026-02-16 03:55:00+00:00,EUR_USD,M1,BUY,1.18672,100.0,9999.99,3e-06
+2026-02-16 04:07:00+00:00,EUR_USD,M1,FLAT,1.18664,0.0,9999.98,4e-06
+2026-02-16 04:10:00+00:00,EUR_USD,M1,BUY,1.18674,100.0,9999.98,4e-06
+2026-02-16 04:15:00+00:00,EUR_USD,M1,FLAT,1.18666,0.0,9999.97,5e-06
+2026-02-16 04:27:00+00:00,EUR_USD,M1,BUY,1.18672,100.0,9999.97,5e-06
+2026-02-16 04:31:00+00:00,EUR_USD,M1,FLAT,1.18661,0.0,9999.96,6e-06
+2026-02-16 05:01:00+00:00,EUR_USD,M1,BUY,1.18627,100.0,9999.96,6e-06
+2026-02-16 05:02:00+00:00,EUR_USD,M1,FLAT,1.18619,0.0,9999.96,6e-06
+2026-02-16 05:09:00+00:00,EUR_USD,M1,BUY,1.18628,100.0,9999.96,6e-06
+2026-02-16 05:19:00+00:00,EUR_USD,M1,FLAT,1.1862,0.0,9999.95,7e-06
+2026-02-16 05:25:00+00:00,EUR_USD,M1,BUY,1.18635,100.0,9999.95,7e-06
+2026-02-16 05:32:00+00:00,EUR_USD,M1,FLAT,1.18623,0.0,9999.94,8e-06

logs/backtest_trades_EUR_USD_M5.csv

@@ -0,0 +1,15 @@
+time,instrument,granularity,side,price,position_size,equity,drawdown
+2026-02-13 16:20:00+00:00,EUR_USD,M5,BUY,1.18684,100.0,10000.0,0.0
+2026-02-13 18:15:00+00:00,EUR_USD,M5,FLAT,1.18742,0.0,10000.05,7e-06
+2026-02-13 18:30:00+00:00,EUR_USD,M5,BUY,1.1878,100.0,10000.05,7e-06
+2026-02-13 19:00:00+00:00,EUR_USD,M5,FLAT,1.18712,0.0,9999.99,1.2e-05
+2026-02-13 20:50:00+00:00,EUR_USD,M5,BUY,1.18708,100.0,9999.99,1.2e-05
+2026-02-13 21:40:00+00:00,EUR_USD,M5,FLAT,1.18682,0.0,9999.97,1.5e-05
+2026-02-15 22:10:00+00:00,EUR_USD,M5,BUY,1.18719,100.0,9999.97,1.5e-05
+2026-02-15 22:55:00+00:00,EUR_USD,M5,FLAT,1.18708,0.0,9999.96,1.6e-05
+2026-02-16 01:20:00+00:00,EUR_USD,M5,BUY,1.18645,100.0,9999.96,1.6e-05
+2026-02-16 01:40:00+00:00,EUR_USD,M5,FLAT,1.1864,0.0,9999.96,1.6e-05
+2026-02-16 01:55:00+00:00,EUR_USD,M5,BUY,1.18654,100.0,9999.96,1.6e-05
+2026-02-16 02:15:00+00:00,EUR_USD,M5,FLAT,1.18625,0.0,9999.93,1.8e-05
+2026-02-16 02:30:00+00:00,EUR_USD,M5,BUY,1.18654,100.0,9999.93,1.8e-05
+2026-02-16 04:20:00+00:00,EUR_USD,M5,FLAT,1.18663,0.0,9999.94,1.8e-05

logs/backtest_trades_USD_JPY_M1.csv

@@ -0,0 +1,11 @@
+time,instrument,granularity,side,price,position_size,equity,drawdown
+2026-02-16 03:05:00+00:00,USD_JPY,M1,BUY,153.046,100.0,10000.0,0.0
+2026-02-16 03:19:00+00:00,USD_JPY,M1,FLAT,153.054,0.0,10000.01,3e-06
+2026-02-16 03:34:00+00:00,USD_JPY,M1,BUY,153.057,100.0,10000.01,3e-06
+2026-02-16 04:04:00+00:00,USD_JPY,M1,FLAT,153.06,0.0,10000.01,3e-06
+2026-02-16 04:16:00+00:00,USD_JPY,M1,BUY,153.056,100.0,10000.01,3e-06
+2026-02-16 05:09:00+00:00,USD_JPY,M1,FLAT,153.215,0.0,10000.11,2e-06
+2026-02-16 05:23:00+00:00,USD_JPY,M1,BUY,153.228,100.0,10000.11,2e-06
+2026-02-16 05:29:00+00:00,USD_JPY,M1,FLAT,153.184,0.0,10000.08,5e-06
+2026-02-16 05:30:00+00:00,USD_JPY,M1,BUY,153.214,100.0,10000.08,5e-06
+2026-02-16 05:44:00+00:00,USD_JPY,M1,FLAT,153.205,0.0,10000.08,5e-06

logs/backtest_trades_USD_JPY_M5.csv

@@ -0,0 +1,24 @@
+time,instrument,granularity,side,price,position_size,equity,drawdown
+2026-02-13 15:15:00+00:00,USD_JPY,M5,BUY,153.089,100.0,10000.0,0.0
+2026-02-13 15:45:00+00:00,USD_JPY,M5,FLAT,152.948,0.0,9999.91,9e-06
+2026-02-13 15:55:00+00:00,USD_JPY,M5,BUY,153.132,100.0,9999.91,9e-06
+2026-02-13 16:20:00+00:00,USD_JPY,M5,FLAT,152.894,0.0,9999.75,2.5e-05
+2026-02-13 17:45:00+00:00,USD_JPY,M5,BUY,152.828,100.0,9999.75,2.5e-05
+2026-02-13 17:50:00+00:00,USD_JPY,M5,FLAT,152.757,0.0,9999.71,2.9e-05
+2026-02-13 17:55:00+00:00,USD_JPY,M5,BUY,152.808,100.0,9999.71,2.9e-05
+2026-02-13 18:00:00+00:00,USD_JPY,M5,FLAT,152.792,0.0,9999.7,3e-05
+2026-02-13 18:05:00+00:00,USD_JPY,M5,BUY,152.818,100.0,9999.7,3e-05
+2026-02-13 19:10:00+00:00,USD_JPY,M5,FLAT,152.81,0.0,9999.69,3.1e-05
+2026-02-13 20:10:00+00:00,USD_JPY,M5,BUY,152.788,100.0,9999.69,3.1e-05
+2026-02-13 20:25:00+00:00,USD_JPY,M5,FLAT,152.734,0.0,9999.65,3.5e-05
+2026-02-13 21:50:00+00:00,USD_JPY,M5,BUY,152.717,100.0,9999.65,3.5e-05
+2026-02-15 22:05:00+00:00,USD_JPY,M5,FLAT,152.632,0.0,9999.6,4e-05
+2026-02-15 23:00:00+00:00,USD_JPY,M5,BUY,152.695,100.0,9999.6,4e-05
+2026-02-16 01:30:00+00:00,USD_JPY,M5,FLAT,153.008,0.0,9999.8,2e-05
+2026-02-16 02:10:00+00:00,USD_JPY,M5,BUY,153.089,100.0,9999.8,2e-05
+2026-02-16 02:55:00+00:00,USD_JPY,M5,FLAT,153.012,0.0,9999.75,2.5e-05
+2026-02-16 03:05:00+00:00,USD_JPY,M5,BUY,153.087,100.0,9999.75,2.5e-05
+2026-02-16 03:20:00+00:00,USD_JPY,M5,FLAT,153.022,0.0,9999.71,2.9e-05
+2026-02-16 03:50:00+00:00,USD_JPY,M5,BUY,153.062,100.0,9999.71,2.9e-05
+2026-02-16 04:05:00+00:00,USD_JPY,M5,FLAT,153.02,0.0,9999.68,3.2e-05
+2026-02-16 04:15:00+00:00,USD_JPY,M5,BUY,153.092,100.0,9999.68,3.2e-05

src/backtester.py

@@ -0,0 +1,343 @@
+# src/backtester.py
+"""
+Backtesting engine for fx-quant.
+Reads features from Supabase, simulates the SMA cross strategy,
+and produces P&L, drawdown, and trade log outputs.
+"""
+
+import os
+import math
+from pathlib import Path
+
+import pandas as pd
+import numpy as np
+from supabase import create_client
+
+from config_loader import load_config, get_project_root
+
+
+# ---------------------------------------------------------------------------
+# Data fetching
+# ---------------------------------------------------------------------------
+
+def fetch_candles_from_supabase(instrument, granularity, supabase_client, table):
+    """
+    Query Supabase fx_candles table filtered by instrument + granularity.
+    Returns a pandas DataFrame indexed by time, sorted ascending.
+    Drops rows where sma columns are NaN (warmup period).
+    """
+    print(f"Fetching {instrument} / {granularity} from Supabase...")
+
+    # Supabase JS-style pagination: fetch in pages of 1000
+    all_rows = []
+    page_size = 1000
+    offset = 0
+    while True:
+        resp = (
+            supabase_client.table(table)
+            .select("*")
+            .eq("instrument", instrument)
+            .eq("granularity", granularity)
+            .order("time", desc=False)
+            .range(offset, offset + page_size - 1)
+            .execute()
+        )
+        rows = resp.data or []
+        all_rows.extend(rows)
+        if len(rows) < page_size:
+            break
+        offset += page_size
+
+    if not all_rows:
+        print(f"  No data found for {instrument} / {granularity}.")
+        return pd.DataFrame()
+
+    df = pd.DataFrame(all_rows)
+    df["time"] = pd.to_datetime(df["time"])
+    df = df.set_index("time").sort_index()
+
+    # Convert numeric columns from possible string types
+    numeric_cols = [c for c in df.columns if c not in ("instrument", "granularity")]
+    for col in numeric_cols:
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+
+    print(f"  Fetched {len(df)} rows.")
+    return df
+
+
+# ---------------------------------------------------------------------------
+# Signal generation
+# ---------------------------------------------------------------------------
+
+def generate_signals(df, strategy_cfg):
+    """
+    Generate trading signals based on strategy config.
+    Currently supports 'sma_cross' rule only.
+
+    Signal logic (long-only / flat):
+      sma_short > sma_long  →  signal = 1  (long)
+      sma_short < sma_long  →  signal = 0  (flat)
+    Position changes only when signal changes.
+    """
+    rule = strategy_cfg["rule"]
+    if rule != "sma_cross":
+        raise ValueError(f"Unsupported strategy rule: {rule}")
+
+    short_w = strategy_cfg["params"]["short"]
+    long_w = strategy_cfg["params"]["long"]
+    sma_short_col = f"sma_{short_w}"
+    sma_long_col = f"sma_{long_w}"
+
+    # Drop rows where indicator columns are NaN (warmup)
+    required_cols = [sma_short_col, sma_long_col, "close", "ret"]
+    for col in required_cols:
+        if col not in df.columns:
+            raise KeyError(f"Missing required column: {col}")
+
+    df = df.dropna(subset=[sma_short_col, sma_long_col, "ret"]).copy()
+
+    # Signal: 1 when short SMA above long SMA, else 0
+    df["signal"] = np.where(df[sma_short_col] > df[sma_long_col], 1, 0)
+
+    # Position changes only on crossover (detect changes)
+    df["position"] = df["signal"]
+
+    return df
+
+
+# ---------------------------------------------------------------------------
+# Backtest engine
+# ---------------------------------------------------------------------------
+
+def run_backtest(df, strategy_cfg):
+    """
+    Walk through bars, track position, equity, trades.
+    Returns dict with equity_curve (Series), trades (list of dicts), metrics (dict).
+    """
+    trade_size_pct = strategy_cfg.get("trade_size_pct_of_equity", 0.01)
+    max_dd_pct = strategy_cfg.get("max_drawdown_pct", 0.05)
+
+    starting_equity = 10_000.0
+    equity = starting_equity
+    peak_equity = equity
+    position = 0  # 0 = flat, 1 = long
+    position_size = 0.0
+    stopped = False
+
+    equity_curve = []
+    trades = []
+
+    times = df.index.tolist()
+    signals = df["signal"].values
+    closes = df["close"].values
+    rets = df["ret"].values
+
+    for i in range(len(df)):
+        bar_time = times[i]
+        sig = signals[i]
+        price = closes[i]
+        bar_ret = rets[i]
+
+        # Check max drawdown stop
+        if stopped:
+            equity_curve.append(equity)
+            continue
+
+        # P&L from existing position
+        if position == 1 and i > 0:
+            pnl = position_size * bar_ret
+            equity += pnl
+
+        # Update peak and drawdown
+        if equity > peak_equity:
+            peak_equity = equity
+        drawdown = (peak_equity - equity) / peak_equity if peak_equity > 0 else 0.0
+
+        # Max drawdown breached — stop trading
+        if drawdown >= max_dd_pct:
+            if position == 1:
+                trades.append({
+                    "time": bar_time,
+                    "side": "FLAT",
+                    "price": price,
+                    "position_size": 0.0,
+                    "equity": round(equity, 2),
+                    "drawdown": round(drawdown, 6),
+                })
+            stopped = True
+            position = 0
+            position_size = 0.0
+            equity_curve.append(equity)
+            print(f"  Max drawdown {max_dd_pct:.1%} breached at {bar_time}. Stopping.")
+            continue
+
+        # Position change
+        if sig != position:
+            if sig == 1 and position == 0:
+                # Enter long
+                position_size = equity * trade_size_pct
+                trades.append({
+                    "time": bar_time,
+                    "side": "BUY",
+                    "price": price,
+                    "position_size": round(position_size, 2),
+                    "equity": round(equity, 2),
+                    "drawdown": round(drawdown, 6),
+                })
+            elif sig == 0 and position == 1:
+                # Exit long
+                trades.append({
+                    "time": bar_time,
+                    "side": "FLAT",
+                    "price": price,
+                    "position_size": 0.0,
+                    "equity": round(equity, 2),
+                    "drawdown": round(drawdown, 6),
+                })
+                position_size = 0.0
+            position = sig
+
+        equity_curve.append(equity)
+
+    equity_series = pd.Series(equity_curve, index=df.index, name="equity")
+    metrics = compute_metrics(equity_series, trades, starting_equity)
+
+    return {
+        "equity_curve": equity_series,
+        "trades": trades,
+        "metrics": metrics,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Metrics
+# ---------------------------------------------------------------------------
+
+def compute_metrics(equity_curve, trades, starting_equity=10_000.0):
+    """
+    Compute summary metrics from equity curve and trade list.
+    """
+    final_equity = equity_curve.iloc[-1] if len(equity_curve) > 0 else starting_equity
+    total_return_pct = ((final_equity - starting_equity) / starting_equity) * 100
+
+    # Max drawdown from equity curve
+    peak = equity_curve.cummax()
+    dd = (peak - equity_curve) / peak
+    max_drawdown_pct = dd.max() * 100 if len(dd) > 0 else 0.0
+
+    # Trade stats
+    num_trades = len(trades)
+    # Count winning round-trips (BUY followed by FLAT with higher equity)
+    wins = 0
+    buy_equity = None
+    for t in trades:
+        if t["side"] == "BUY":
+            buy_equity = t["equity"]
+        elif t["side"] == "FLAT" and buy_equity is not None:
+            if t["equity"] > buy_equity:
+                wins += 1
+            buy_equity = None
+
+    round_trips = sum(1 for t in trades if t["side"] == "FLAT")
+    win_rate = (wins / round_trips * 100) if round_trips > 0 else 0.0
+
+    # Sharpe ratio (annualized, from per-bar returns of the equity curve)
+    eq_returns = equity_curve.pct_change().dropna()
+    sharpe = 0.0
+    if len(eq_returns) > 1 and eq_returns.std() > 0:
+        sharpe = (eq_returns.mean() / eq_returns.std()) * math.sqrt(252 * 24 * 60)  # per-minute approx
+
+    return {
+        "total_return_pct": round(total_return_pct, 4),
+        "max_drawdown_pct": round(max_drawdown_pct, 4),
+        "num_trades": num_trades,
+        "round_trips": round_trips,
+        "win_rate_pct": round(win_rate, 2),
+        "sharpe_ratio": round(sharpe, 4),
+        "final_equity": round(final_equity, 2),
+    }
+
+
+# ---------------------------------------------------------------------------
+# Results output
+# ---------------------------------------------------------------------------
+
+def save_results(instrument, granularity, trades, metrics):
+    """
+    Save trade log CSV and print summary metrics.
+    """
+    root = get_project_root()
+    logs_dir = root / "logs"
+    logs_dir.mkdir(exist_ok=True)
+
+    # Trade log CSV
+    if trades:
+        trade_df = pd.DataFrame(trades)
+        trade_df["instrument"] = instrument
+        trade_df["granularity"] = granularity
+        # Reorder columns
+        cols = ["time", "instrument", "granularity", "side", "price",
+                "position_size", "equity", "drawdown"]
+        trade_df = trade_df[cols]
+        csv_path = logs_dir / f"backtest_trades_{instrument}_{granularity}.csv"
+        trade_df.to_csv(csv_path, index=False)
+        print(f"  Trade log saved: {csv_path}")
+    else:
+        print("  No trades to log.")
+
+    # Console summary
+    print(f"\n  --- {instrument} / {granularity} Summary ---")
+    print(f"  Total return:   {metrics['total_return_pct']:.4f}%")
+    print(f"  Max drawdown:   {metrics['max_drawdown_pct']:.4f}%")
+    print(f"  Trades:         {metrics['num_trades']}  (round-trips: {metrics['round_trips']})")
+    print(f"  Win rate:       {metrics['win_rate_pct']:.2f}%")
+    print(f"  Sharpe ratio:   {metrics['sharpe_ratio']:.4f}")
+    print(f"  Final equity:   ${metrics['final_equity']:,.2f}")
+    print()
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+
+def main():
+    cfg = load_config()
+
+    # Supabase client
+    supabase_url = os.getenv("SUPABASE_URL")
+    supabase_key = os.getenv("SUPABASE_KEY")
+    if not supabase_url or not supabase_key:
+        raise SystemExit("Missing SUPABASE_URL or SUPABASE_KEY in config/.env")
+    sb = create_client(supabase_url, supabase_key)
+    table = cfg.get("supabase", {}).get("table", "fx_candles")
+
+    strategy_cfg = cfg["strategy"]
+    instruments = cfg["brokers"][0]["instruments"]
+    granularities = cfg["data"]["candle_granularities"]
+
+    print(f"Backtesting strategy: {strategy_cfg['rule']}")
+    print(f"Instruments: {instruments}")
+    print(f"Granularities: {granularities}\n")
+
+    for instrument in instruments:
+        for granularity in granularities:
+            print(f"=== {instrument} / {granularity} ===")
+
+            df = fetch_candles_from_supabase(instrument, granularity, sb, table)
+            if df.empty:
+                print("  Skipping — no data.\n")
+                continue
+
+            df = generate_signals(df, strategy_cfg)
+            if df.empty:
+                print("  Skipping — no valid rows after warmup.\n")
+                continue
+
+            results = run_backtest(df, strategy_cfg)
+            save_results(instrument, granularity, results["trades"], results["metrics"])
+
+    print("Backtesting complete.")
+
+
+if __name__ == "__main__":
+    main()