feat: strategy data access, sharpe ratio, and correctness fixes

- Trade.pnl now stores net pnl (after exit commission) so per-trade stats are accurate
- Position pyclass uses from_py_object to fix deprecation warning
- Removed dead AttributeError swallow in engine.rs
- Strategy gains self.data, self.index, self.cash, self.equity properties
- Broker.cash exposed to Python via pyo3(get)
- Sharpe ratio added to Stats (unannualized)
- Added examples/sma_cross.py and examples/compare_bt.py
- Logic verified against backtesting.py: 34 trades, 29.4% win rate match

examples/compare_bt.py

@@ -0,0 +1,90 @@
+"""
+Compares our SMA crossover logic against backtesting.py using the same data,
+same SMA periods, and zero commission/spread so only entry/exit logic is tested.
+
+Trade count and win rate should match between both libraries.
+Dollar PnL will differ because the sizing models are different:
+  - backtesting.py: buys fractional units based on available cash
+  - backtestingfx:  fixed lot size (0.1 lots = 10,000 units)
+"""
+
+import pandas as pd
+from backtesting import Backtest as BtBacktest, Strategy as BtStrategy
+from backtestingfx import Backtest, Strategy
+
+FAST = 10
+SLOW = 50
+
+
+# ── backtesting.py ────────────────────────────────────────────────────────────
+
+class SmaCrossBt(BtStrategy):
+    fast = FAST
+    slow = SLOW
+
+    def init(self):
+        pass
+
+    def next(self):
+        if len(self.data.Close) < self.slow:
+            return
+        fast_sma = self.data.Close[-self.fast:].mean()
+        slow_sma = self.data.Close[-self.slow:].mean()
+
+        if not self.position:
+            if fast_sma > slow_sma:
+                self.buy()
+        else:
+            if fast_sma < slow_sma:
+                self.position.close()
+
+
+# ── backtestingfx ─────────────────────────────────────────────────────────────
+
+class SmaCrossFx(Strategy):
+    fast = FAST
+    slow = SLOW
+
+    def next(self):
+        if self.index < self.slow:
+            return
+        closes = [b.close for b in self.data[-self.slow:]]
+        fast_sma = sum(closes[-self.fast:]) / self.fast
+        slow_sma = sum(closes) / self.slow
+
+        if not self.positions:
+            if fast_sma > slow_sma:
+                self.buy(0.1)
+        else:
+            if fast_sma < slow_sma:
+                self.close_all()
+
+
+# ── run both ──────────────────────────────────────────────────────────────────
+
+df_raw = pd.read_csv("data/EURUSD_1H.csv")
+
+# backtesting.py needs a DatetimeIndex and capitalised column names
+df_bt = df_raw.copy()
+df_bt["timestamp"] = pd.to_datetime(df_bt["timestamp"], utc=True)
+df_bt = df_bt.set_index("timestamp")
+df_bt.index = df_bt.index.tz_localize(None)
+df_bt = df_bt.rename(columns={"open": "Open", "high": "High", "low": "Low", "close": "Close", "volume": "Volume"})
+
+bt_result = BtBacktest(df_bt, SmaCrossBt, cash=10_000, commission=0).run()
+
+fx_result = Backtest(df_raw, SmaCrossFx, cash=10_000, commission=0, spread=0).run()
+
+# ── compare ───────────────────────────────────────────────────────────────────
+
+print("=" * 45)
+print(f"{'Metric':<20} {'backtesting.py':>12} {'backtestingfx':>12}")
+print("=" * 45)
+print(f"{'Trades':<20} {bt_result['# Trades']:>12} {fx_result.num_trades:>12}")
+print(f"{'Win Rate %':<20} {bt_result['Win Rate [%]']:>12.1f} {fx_result.win_rate_pct:>12.1f}")
+print(f"{'Return %':<20} {bt_result['Return [%]']:>12.2f} {fx_result.total_return_pct:>12.2f}")
+print(f"{'Max Drawdown %':<20} {bt_result['Max. Drawdown [%]']:>12.2f} {fx_result.max_drawdown_pct:>12.2f}")
+print("=" * 45)
+print()
+print("Note: Return % differs because backtesting.py sizes by available cash,")
+print("      backtestingfx uses fixed 0.1 lots. Trade count + win rate should match.")

examples/sma_cross.py

@@ -0,0 +1,36 @@
+import pandas as pd
+from backtestingfx import Backtest, Strategy
+
+
+class SmaCross(Strategy):
+    fast = 10
+    slow = 50
+
+    def next(self):
+        if self.index < self.slow:
+            return
+
+        closes = [b.close for b in self.data[-self.slow :]]
+        fast_sma = sum(closes[-self.fast :]) / self.fast
+        slow_sma = sum(closes) / self.slow
+
+        if not self.positions:
+            if fast_sma > slow_sma:
+                self.buy(0.1)
+        else:
+            if fast_sma < slow_sma:
+                self.close_all()
+
+
+df = pd.read_csv("data/EURUSD_1H.csv")
+
+stats = Backtest(
+    df,
+    SmaCross,
+    cash=10_000,
+    commission=3.5,
+    spread=0.00002,
+    contract_size=100_000,
+).run()
+
+print(stats)