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    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# ============================================================\n",
    "# Backtesting Pair Trading Strategy (Version 2)\n",
    "# ============================================================\n",
    "# This version builds trading signals (based on rolling mean/std of spread)\n",
    "# and runs a backtest comparing the strategy against benchmarks:\n",
    "# - Buy & Hold BTC\n",
    "# - Buy & Hold ETH\n",
    "# - 50/50 Portfolio\n",
    "#\n",
    "# Signals are cached in CSV files under \"rolling_mean_cache/\" so that\n",
    "# they do not need to be recomputed every time.\n",
    "# ============================================================\n",
    "\n",
    "\n",
    "def build_signals(btc, eth, k=1, window_days=10, cache_dir=\"rolling_mean_cache\"):\n",
    "    \"\"\"\n",
    "    Build trading signals based on the spread between BTC and ETH.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    btc : DataFrame\n",
    "        BTC OHLCV data (must include 'open_time' and 'close').\n",
    "    eth : DataFrame\n",
    "        ETH OHLCV data (must include 'open_time' and 'close').\n",
    "    k : int, optional\n",
    "        Standard deviation multiplier for signal thresholds.\n",
    "    window_days : int, optional\n",
    "        Rolling window size in days.\n",
    "    cache_dir : str, optional\n",
    "        Directory to save signal CSV files.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    str\n",
    "        Path to the saved signal file.\n",
    "    \"\"\"\n",
    "    os.makedirs(cache_dir, exist_ok=True)\n",
    "    output_path = os.path.join(cache_dir, f\"signals_k{k}_w{window_days}.csv\")\n",
    "\n",
    "    df = pd.DataFrame({\n",
    "        \"time\": pd.to_datetime(btc[\"open_time\"]),\n",
    "        \"btc_close\": btc[\"close\"],\n",
    "        \"eth_close\": eth[\"close\"]\n",
    "    })\n",
    "\n",
    "    # Spread\n",
    "    df[\"spread\"] = df[\"btc_close\"] - df[\"eth_close\"]\n",
    "\n",
    "    # Rolling mean & std\n",
    "    window_size = 60 * 24 * window_days\n",
    "    df[\"mean\"] = df[\"spread\"].rolling(window_size).mean()\n",
    "    df[\"std\"] = df[\"spread\"].rolling(window_size).std()\n",
    "\n",
    "    # Upper / lower bounds\n",
    "    df[\"upper\"] = df[\"mean\"] + k * df[\"std\"]\n",
    "    df[\"lower\"] = df[\"mean\"] - k * df[\"std\"]\n",
    "\n",
    "    # Generate trading signals\n",
    "    conditions = [\n",
    "        df[\"spread\"] > df[\"upper\"],\n",
    "        df[\"spread\"] < df[\"lower\"]\n",
    "    ]\n",
    "    choices = [\"sell_btc_buy_eth\", \"buy_btc_sell_eth\"]\n",
    "    df[\"signal\"] = np.select(conditions, choices, default=\"hold\")\n",
    "\n",
    "    df.to_csv(output_path, index=False)\n",
    "    print(f\"✅ Signals saved to {output_path}\")\n",
    "    return output_path\n",
    "\n",
    "\n",
    "def run_backtest(btc, eth, k=1, window_days=10,\n",
    "                 start_date=None, end_date=None,\n",
    "                 initial_capital=1000, trade_size=200,\n",
    "                 fee_rate=0.001, cache_dir=\"rolling_mean_cache\"):\n",
    "    \"\"\"\n",
    "    Run backtest for pair trading strategy and compare with benchmarks.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    btc : DataFrame\n",
    "        BTC OHLCV data.\n",
    "    eth : DataFrame\n",
    "        ETH OHLCV data.\n",
    "    k : int\n",
    "        Standard deviation multiplier for signal thresholds.\n",
    "    window_days : int\n",
    "        Rolling window size in days.\n",
    "    start_date : str or None\n",
    "        Backtest start date (inclusive).\n",
    "    end_date : str or None\n",
    "        Backtest end date (inclusive).\n",
    "    initial_capital : float\n",
    "        Starting portfolio value in USD.\n",
    "    trade_size : float\n",
    "        Trade size per signal in USD.\n",
    "    fee_rate : float\n",
    "        Transaction fee rate.\n",
    "    cache_dir : str\n",
    "        Directory where signals are stored.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    dict\n",
    "        Final portfolio values for benchmarks and strategy.\n",
    "    Series\n",
    "        Strategy equity curve.\n",
    "    list\n",
    "        Log of executed trades.\n",
    "    DataFrame\n",
    "        DataFrame with signals and spread data.\n",
    "    \"\"\"\n",
    "    signal_file = os.path.join(cache_dir, f\"signals_k{k}_w{window_days}.csv\")\n",
    "\n",
    "    # Build signals if not cached\n",
    "    if not os.path.exists(signal_file):\n",
    "        print(f\"⚠️ Signal file {signal_file} not found → building...\")\n",
    "        build_signals(btc, eth, k, window_days, cache_dir)\n",
    "\n",
    "    # Load signals\n",
    "    df = pd.read_csv(signal_file, parse_dates=[\"time\"])\n",
    "\n",
    "    # Filter by date range\n",
    "    if start_date:\n",
    "        df = df[df[\"time\"] >= pd.to_datetime(start_date)]\n",
    "    if end_date:\n",
    "        df = df[df[\"time\"] <= pd.to_datetime(end_date)]\n",
    "    df = df.reset_index(drop=True)\n",
    "\n",
    "    # ------------------------------\n",
    "    # Benchmarks\n",
    "    # ------------------------------\n",
    "    # 100% BTC\n",
    "    bh_btc_units = initial_capital / df[\"btc_close\"].iloc[0]\n",
    "    bh_btc_value = bh_btc_units * df[\"btc_close\"]\n",
    "\n",
    "    # 100% ETH\n",
    "    bh_eth_units = initial_capital / df[\"eth_close\"].iloc[0]\n",
    "    bh_eth_value = bh_eth_units * df[\"eth_close\"]\n",
    "\n",
    "    # 50/50 portfolio\n",
    "    bh_half_btc = (initial_capital / 2) / df[\"btc_close\"].iloc[0]\n",
    "    bh_half_eth = (initial_capital / 2) / df[\"eth_close\"].iloc[0]\n",
    "    bh_equal_value = bh_half_btc * df[\"btc_close\"] + bh_half_eth * df[\"eth_close\"]\n",
    "\n",
    "    # ------------------------------\n",
    "    # Strategy\n",
    "    # ------------------------------\n",
    "    btc_units = (initial_capital / 2) / df[\"btc_close\"].iloc[0]\n",
    "    eth_units = (initial_capital / 2) / df[\"eth_close\"].iloc[0]\n",
    "    strat_values = []\n",
    "    trade_log = []\n",
    "\n",
    "    for _, row in df.iterrows():\n",
    "        btc_price, eth_price, signal = row[\"btc_close\"], row[\"eth_close\"], row[\"signal\"]\n",
    "        total_value = btc_units * btc_price + eth_units * eth_price\n",
    "\n",
    "        if signal == \"sell_btc_buy_eth\":\n",
    "            btc_value = btc_units * btc_price\n",
    "            if btc_value > 0:\n",
    "                sell_value = min(trade_size, btc_value)\n",
    "                fee = sell_value * fee_rate\n",
    "                btc_units -= sell_value / btc_price\n",
    "                eth_units += (sell_value - fee) / eth_price\n",
    "                trade_log.append((row[\"time\"], signal, total_value))\n",
    "\n",
    "        elif signal == \"buy_btc_sell_eth\":\n",
    "            eth_value = eth_units * eth_price\n",
    "            if eth_value > 0:\n",
    "                sell_value = min(trade_size, eth_value)\n",
    "                fee = sell_value * fee_rate\n",
    "                eth_units -= sell_value / eth_price\n",
    "                btc_units += (sell_value - fee) / btc_price\n",
    "                trade_log.append((row[\"time\"], signal, total_value))\n",
    "\n",
    "        strat_values.append(btc_units * btc_price + eth_units * eth_price)\n",
    "\n",
    "    strat_values = pd.Series(strat_values, index=df[\"time\"])\n",
    "\n",
    "    # ------------------------------\n",
    "    # Results summary\n",
    "    # ------------------------------\n",
    "    results = {\n",
    "        \"BTC Buy&Hold\": bh_btc_value.iloc[-1],\n",
    "        \"ETH Buy&Hold\": bh_eth_value.iloc[-1],\n",
    "        \"50/50 Portfolio\": bh_equal_value.iloc[-1],\n",
    "        \"Strategy\": strat_values.iloc[-1]\n",
    "    }\n",
    "\n",
    "    print(\"========== 📊 Backtest ==========\")\n",
    "    print(f\"File: {signal_file}\")\n",
    "    print(f\"Period: {start_date} → {end_date}\")\n",
    "    print(f\"Initial Capital: ${initial_capital}\")\n",
    "    for name, val in results.items():\n",
    "        ret = (val / initial_capital - 1) * 100\n",
    "        print(f\"{name:15s}: ${val:.2f} | Return: {ret:.2f}%\")\n",
    "    print(f\"Total Trades: {len(trade_log)}\")\n",
    "\n",
    "    return results, strat_values, trade_log, df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d92e6b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========== 📊 Backtest ==========\n",
      "File: rolling_mean_cache\\signals_k2.5_w7.csv\n",
      "Period: 2025-01-01 → 2025-09-01\n",
      "Initial Capital: $1000\n",
      "BTC Buy&Hold   : $1156.21 | Return: 15.62%\n",
      "ETH Buy&Hold   : $1311.57 | Return: 31.16%\n",
      "50/50 Portfolio: $1233.89 | Return: 23.39%\n",
      "Strategy       : $2210.08 | Return: 121.01%\n",
      "Total Trades: 105\n"
     ]
    }
   ],
   "source": [
    "# load data\n",
    "# btc = pd.read_csv(r\"C:\\Users\\amirs\\OneDrive\\Desktop\\myAlgoCode\\detector\\BTCUSDT_1m_1000d.csv\")\n",
    "# eth = pd.read_csv(r\"C:\\Users\\amirs\\OneDrive\\Desktop\\myAlgoCode\\detector\\ETHUSDT_1m_1000d.csv\")\n",
    "results, strat_values, trade_log, df = run_backtest(\n",
    "    btc, eth,\n",
    "    k=2.5, window_days=7,\n",
    "    start_date=\"2025-01-01\", end_date=\"2025-09-01\",\n",
    "    initial_capital=1000, fee_rate=0.001\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aef6e2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "========== 📊 Backtest ==========\n",
    "File: rolling_mean_cache\\signals_k1.5_w7.csv\n",
    "Period: 2025-01-01 → 2025-09-01\n",
    "Initial Capital: $1000\n",
    "BTC Buy&Hold   : $1156.21 | Return: 15.62%\n",
    "ETH Buy&Hold   : $1311.57 | Return: 31.16%\n",
    "50/50 Portfolio: $1233.89 | Return: 23.39%\n",
    "Strategy       : $1927.83 | Return: 92.78%\n",
    "Total Trades: 229"
   ]
  }
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