Add initial implementation of backtesting framework with CLI interface. Introduce core modules for data loading, trade management, performance metrics, and logging. Include Supertrend indicator calculations and slippage estimation. Update .gitignore to exclude logs and CSV files.

trade.py

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+from __future__ import annotations
+from dataclasses import dataclass
+import pandas as pd
+from market_costs import okx_fee, estimate_slippage_rate
+from intrabar import entry_slippage_row
+
+
+@dataclass
+class TradeState:
+    cash: float = 1000.0
+    qty: float = 0.0
+    entry_px: float | None = None
+    max_px: float | None = None
+    stop_loss_frac: float = 0.02
+    fee_bps: float = 10.0
+    slippage_bps: float = 2.0
+
+
+def enter_long(state: TradeState, price: float) -> dict:
+    if state.qty > 0:
+        return {}
+    px = entry_slippage_row(price, 0.0, state.slippage_bps)
+    qty = state.cash / px
+    fee = okx_fee(state.fee_bps, state.cash)
+    state.qty = max(qty - fee / px, 0.0)
+    state.cash = 0.0
+    state.entry_px = px
+    state.max_px = px
+    return {"side": "BUY", "price": px, "qty": state.qty, "fee": fee}
+
+
+def maybe_trailing_stop(state: TradeState, price: float) -> float:
+    if state.qty <= 0:
+        return float("inf")
+    state.max_px = max(state.max_px or price, price)
+    trail_px = state.max_px * (1.0 - state.stop_loss_frac)
+    return trail_px
+
+
+def exit_long(state: TradeState, price: float) -> dict:
+    if state.qty <= 0:
+        return {}
+    notional = state.qty * price
+    slip = estimate_slippage_rate(state.slippage_bps, notional)
+    fee = okx_fee(state.fee_bps, notional)
+    cash_back = notional - slip - fee
+    event = {"side": "SELL", "price": price, "qty": state.qty, "fee": fee, "slippage": slip}
+    state.cash = cash_back
+    state.qty = 0.0
+    state.entry_px = None
+    state.max_px = None
+    return event