{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select all symbols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Load Data for All Available Symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Found 2061 symbols in MT5\n",
      "📊 Data Loaded for 25 Symbols\n"
     ]
    }
   ],
   "source": [
    "import MetaTrader5 as mt5\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ta\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import euclidean\n",
    "from statsmodels.tsa.stattools import coint\n",
    "\n",
    "# ✅ Initialize MT5 Connection\n",
    "if not mt5.initialize():\n",
    "    print(\"MT5 Initialization Failed\")\n",
    "    mt5.shutdown()\n",
    "    quit()\n",
    "\n",
    "# ✅ Retrieve All Symbols in MT5\n",
    "symbols = [s.name for s in mt5.symbols_get()]\n",
    "print(f\"✅ Found {len(symbols)} symbols in MT5\")\n",
    "\n",
    "# ✅ Limit to Top 50 Symbols for Performance (adjust as needed)\n",
    "symbols = symbols[:50]\n",
    "\n",
    "# ✅ Function to Fetch Price Data from MT5\n",
    "def get_mt5_data(symbol, n_bars=1000, timeframe=mt5.TIMEFRAME_D1):\n",
    "    \"\"\"Fetches historical data from MT5\"\"\"\n",
    "    rates = mt5.copy_rates_from_pos(symbol, timeframe, 0, n_bars)\n",
    "    if rates is None:\n",
    "        print(f\"⚠️ Could not retrieve data for {symbol}\")\n",
    "        return None\n",
    "    df = pd.DataFrame(rates)\n",
    "    df[\"time\"] = pd.to_datetime(df[\"time\"], unit=\"s\")\n",
    "    df.set_index(\"time\", inplace=True)\n",
    "    return df[[\"close\"]]\n",
    "\n",
    "# ✅ Load Data for All Symbols\n",
    "data = {symbol: get_mt5_data(symbol) for symbol in symbols if get_mt5_data(symbol) is not None}\n",
    "\n",
    "# ✅ Merge Data into One DataFrame\n",
    "df = pd.concat(data.values(), axis=1, keys=data.keys()).dropna()\n",
    "\n",
    "print(f\"📊 Data Loaded for {len(df.columns)//2} Symbols\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2: Add Technical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Feature Engineering Complete. Shape: (947, 200)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:4: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
      "C:\\Users\\moham\\AppData\\Local\\Temp\\ipykernel_11396\\3836770481.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n"
     ]
    }
   ],
   "source": [
    "# ✅ Add Features\n",
    "def add_features(df):\n",
    "    for sym in symbols:\n",
    "        df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
    "                                  ta.volatility.bollinger_lband(df[(sym, \"close\")], window=20)\n",
    "        df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
    "        df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
    "\n",
    "    return df.dropna()\n",
    "\n",
    "df = add_features(df)\n",
    "print(f\"✅ Feature Engineering Complete. Shape: {df.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: PCA for Pair Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             PC1       PC2       PC3\n",
      "EURUSD -0.074120  0.270696  0.039489\n",
      "GBPUSD -0.061762  0.274868 -0.131038\n",
      "USDCHF -0.108607 -0.215683 -0.051013\n",
      "USDJPY  0.173301 -0.089506 -0.055852\n",
      "USDCAD  0.164355 -0.045407  0.066992\n",
      "AUDUSD -0.162769  0.089750 -0.103257\n",
      "AUDNZD  0.085779 -0.196785 -0.144085\n",
      "AUDCAD -0.086638  0.126954 -0.108870\n",
      "AUDCHF -0.174758 -0.053159 -0.103954\n",
      "AUDJPY  0.155454 -0.083599 -0.149799\n",
      "CHFJPY  0.182018 -0.008838 -0.035827\n",
      "EURGBP -0.009282 -0.105880  0.444556\n",
      "EURAUD  0.128375  0.172335  0.163352\n",
      "EURJPY  0.178240  0.011701 -0.047627\n",
      "EURCHF -0.166736  0.074977 -0.007875\n",
      "EURNZD  0.165860  0.075182  0.087998\n",
      "EURCAD  0.087572  0.246827  0.111828\n",
      "GBPCHF -0.151606  0.108690 -0.173693\n",
      "GBPJPY  0.175006  0.031103 -0.130606\n",
      "CADCHF -0.166122 -0.101996 -0.081290\n",
      "CADJPY  0.159921 -0.106765 -0.103625\n",
      "GBPAUD  0.122411  0.200551 -0.034171\n",
      "GBPCAD  0.081074  0.256699 -0.086531\n",
      "GBPNZD  0.154990  0.109517 -0.096284\n",
      "NZDCAD -0.109840  0.200551  0.019302\n",
      "NZDCHF -0.180453  0.004945 -0.058713\n",
      "NZDJPY  0.155090 -0.037133 -0.126900\n",
      "NZDUSD -0.158342  0.134926 -0.038115\n",
      "USDSGD -0.057412 -0.223057 -0.079595\n",
      "AUDSGD -0.174453  0.007578 -0.127653\n",
      "CHFSGD  0.125553  0.162212  0.016395\n",
      "EURDKK  0.168947  0.074703 -0.044841\n",
      "EURHKD -0.077918  0.264445  0.052825\n",
      "EURNOK  0.168174  0.075413  0.125895\n",
      "EURPLN -0.138813 -0.154186  0.145267\n",
      "EURSEK  0.166724 -0.012255  0.137284\n",
      "EURSGD -0.116597  0.205586  0.004615\n",
      "EURTRY  0.177953  0.036838 -0.081277\n",
      "EURZAR  0.151932  0.071716  0.207157\n",
      "GBPDKK  0.022237  0.108872 -0.439560\n",
      "GBPNOK  0.168420  0.100331  0.009281\n",
      "GBPSEK  0.170438  0.026468 -0.026887\n",
      "GBPSGD -0.095840  0.220007 -0.184375\n",
      "GBPTRY  0.176567  0.040673 -0.100254\n",
      "NOKJPY  0.100376 -0.101244 -0.281504\n",
      "NOKSEK -0.102030 -0.175865 -0.071520\n",
      "SEKJPY  0.149099  0.026119 -0.195165\n",
      "SGDJPY  0.179101 -0.048630 -0.042813\n",
      "USDCNH  0.172838 -0.067409  0.055439\n",
      "USDCZK  0.066981 -0.211104 -0.209816\n",
      "🎯 Best PCA-Based Pairs: [('EURUSD', 'EURHKD', 0.015209774849310981), ('NZDCAD', 'EURSGD', 0.016933687040313963), ('EURTRY', 'GBPTRY', 0.01940957888058514), ('CHFJPY', 'EURJPY', 0.02398707933964542), ('USDCAD', 'USDCNH', 0.026259049063238678), ('EURJPY', 'GBPSEK', 0.026627907561686315), ('GBPJPY', 'GBPTRY', 0.03186350419122791), ('EURNZD', 'EURNOK', 0.037968480416527), ('CHFJPY', 'GBPSEK', 0.03821655894445786), ('CHFJPY', 'SGDJPY', 0.04050607133426434)]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# ✅ Standardize the Data\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(df.xs(\"close\", axis=1, level=1))\n",
    "\n",
    "# ✅ PCA: Find Principal Components\n",
    "pca = PCA(n_components=3)  # Keep 3 Principal Components\n",
    "pca_components = pca.fit_transform(scaled_data)\n",
    "\n",
    "# ✅ Assign PCA Factor Weights to Each Symbol\n",
    "factor_df = pd.DataFrame(pca.components_.T, index=symbols, columns=[f\"PC{i+1}\" for i in range(pca.n_components_)])\n",
    "print(factor_df)\n",
    "\n",
    "# ✅ Find Closest Pairs Based on PCA Factor Similarity\n",
    "def find_pca_pairs(factor_df):\n",
    "    pairs = []\n",
    "    for i, sym1 in enumerate(factor_df.index):\n",
    "        for j, sym2 in enumerate(factor_df.index):\n",
    "            if i < j:\n",
    "                distance = euclidean(factor_df.loc[sym1], factor_df.loc[sym2])\n",
    "                pairs.append((sym1, sym2, distance))\n",
    "    \n",
    "    # ✅ Select Best Pairs with Smallest Distance\n",
    "    sorted_pairs = sorted(pairs, key=lambda x: x[2])\n",
    "    return sorted_pairs[:10]  # Return Top 10 Closest Pairs\n",
    "\n",
    "selected_pairs = find_pca_pairs(factor_df)\n",
    "print(\"🎯 Best PCA-Based Pairs:\", selected_pairs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4: K-Means Clustering for Pair Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📌 Symbol Clusters:\n",
      "        cluster\n",
      "EURUSD        1\n",
      "GBPUSD        1\n",
      "USDCHF        2\n",
      "USDJPY        0\n",
      "USDCAD        0\n",
      "AUDUSD        1\n",
      "AUDNZD        2\n",
      "AUDCAD        1\n",
      "AUDCHF        2\n",
      "AUDJPY        0\n",
      "CHFJPY        0\n",
      "EURGBP        2\n",
      "EURAUD        0\n",
      "EURJPY        0\n",
      "EURCHF        1\n",
      "EURNZD        0\n",
      "EURCAD        0\n",
      "GBPCHF        1\n",
      "GBPJPY        0\n",
      "CADCHF        2\n",
      "CADJPY        0\n",
      "GBPAUD        0\n",
      "GBPCAD        1\n",
      "GBPNZD        0\n",
      "NZDCAD        1\n",
      "NZDCHF        1\n",
      "NZDJPY        0\n",
      "NZDUSD        1\n",
      "USDSGD        2\n",
      "AUDSGD        1\n",
      "CHFSGD        0\n",
      "EURDKK        0\n",
      "EURHKD        1\n",
      "EURNOK        0\n",
      "EURPLN        2\n",
      "EURSEK        0\n",
      "EURSGD        1\n",
      "EURTRY        0\n",
      "EURZAR        0\n",
      "GBPDKK        1\n",
      "GBPNOK        0\n",
      "GBPSEK        0\n",
      "GBPSGD        1\n",
      "GBPTRY        0\n",
      "NOKJPY        0\n",
      "NOKSEK        2\n",
      "SEKJPY        0\n",
      "SGDJPY        0\n",
      "USDCNH        0\n",
      "USDCZK        2\n",
      "🎯 Cluster-Based Pairs: [('EURUSD', 'GBPUSD'), ('EURUSD', 'AUDUSD'), ('EURUSD', 'AUDCAD'), ('EURUSD', 'EURCHF'), ('EURUSD', 'GBPCHF'), ('EURUSD', 'GBPCAD'), ('EURUSD', 'NZDCAD'), ('EURUSD', 'NZDCHF'), ('EURUSD', 'NZDUSD'), ('EURUSD', 'AUDSGD'), ('EURUSD', 'EURHKD'), ('EURUSD', 'EURSGD'), ('EURUSD', 'GBPDKK'), ('EURUSD', 'GBPSGD'), ('GBPUSD', 'AUDUSD'), ('GBPUSD', 'AUDCAD'), ('GBPUSD', 'EURCHF'), ('GBPUSD', 'GBPCHF'), ('GBPUSD', 'GBPCAD'), ('GBPUSD', 'NZDCAD'), ('GBPUSD', 'NZDCHF'), ('GBPUSD', 'NZDUSD'), ('GBPUSD', 'AUDSGD'), ('GBPUSD', 'EURHKD'), ('GBPUSD', 'EURSGD'), ('GBPUSD', 'GBPDKK'), ('GBPUSD', 'GBPSGD'), ('AUDUSD', 'AUDCAD'), ('AUDUSD', 'EURCHF'), ('AUDUSD', 'GBPCHF'), ('AUDUSD', 'GBPCAD'), ('AUDUSD', 'NZDCAD'), ('AUDUSD', 'NZDCHF'), ('AUDUSD', 'NZDUSD'), ('AUDUSD', 'AUDSGD'), ('AUDUSD', 'EURHKD'), ('AUDUSD', 'EURSGD'), ('AUDUSD', 'GBPDKK'), ('AUDUSD', 'GBPSGD'), ('AUDCAD', 'EURCHF'), ('AUDCAD', 'GBPCHF'), ('AUDCAD', 'GBPCAD'), ('AUDCAD', 'NZDCAD'), ('AUDCAD', 'NZDCHF'), ('AUDCAD', 'NZDUSD'), ('AUDCAD', 'AUDSGD'), ('AUDCAD', 'EURHKD'), ('AUDCAD', 'EURSGD'), ('AUDCAD', 'GBPDKK'), ('AUDCAD', 'GBPSGD'), ('EURCHF', 'GBPCHF'), ('EURCHF', 'GBPCAD'), ('EURCHF', 'NZDCAD'), ('EURCHF', 'NZDCHF'), ('EURCHF', 'NZDUSD'), ('EURCHF', 'AUDSGD'), ('EURCHF', 'EURHKD'), ('EURCHF', 'EURSGD'), ('EURCHF', 'GBPDKK'), ('EURCHF', 'GBPSGD'), ('GBPCHF', 'GBPCAD'), ('GBPCHF', 'NZDCAD'), ('GBPCHF', 'NZDCHF'), ('GBPCHF', 'NZDUSD'), ('GBPCHF', 'AUDSGD'), ('GBPCHF', 'EURHKD'), ('GBPCHF', 'EURSGD'), ('GBPCHF', 'GBPDKK'), ('GBPCHF', 'GBPSGD'), ('GBPCAD', 'NZDCAD'), ('GBPCAD', 'NZDCHF'), ('GBPCAD', 'NZDUSD'), ('GBPCAD', 'AUDSGD'), ('GBPCAD', 'EURHKD'), ('GBPCAD', 'EURSGD'), ('GBPCAD', 'GBPDKK'), ('GBPCAD', 'GBPSGD'), ('NZDCAD', 'NZDCHF'), ('NZDCAD', 'NZDUSD'), ('NZDCAD', 'AUDSGD'), ('NZDCAD', 'EURHKD'), ('NZDCAD', 'EURSGD'), ('NZDCAD', 'GBPDKK'), ('NZDCAD', 'GBPSGD'), ('NZDCHF', 'NZDUSD'), ('NZDCHF', 'AUDSGD'), ('NZDCHF', 'EURHKD'), ('NZDCHF', 'EURSGD'), ('NZDCHF', 'GBPDKK'), ('NZDCHF', 'GBPSGD'), ('NZDUSD', 'AUDSGD'), ('NZDUSD', 'EURHKD'), ('NZDUSD', 'EURSGD'), ('NZDUSD', 'GBPDKK'), ('NZDUSD', 'GBPSGD'), ('AUDSGD', 'EURHKD'), ('AUDSGD', 'EURSGD'), ('AUDSGD', 'GBPDKK'), ('AUDSGD', 'GBPSGD'), ('EURHKD', 'EURSGD'), ('EURHKD', 'GBPDKK'), ('EURHKD', 'GBPSGD'), ('EURSGD', 'GBPDKK'), ('EURSGD', 'GBPSGD'), ('GBPDKK', 'GBPSGD'), ('USDCHF', 'AUDNZD'), ('USDCHF', 'AUDCHF'), ('USDCHF', 'EURGBP'), ('USDCHF', 'CADCHF'), ('USDCHF', 'USDSGD'), ('USDCHF', 'EURPLN'), ('USDCHF', 'NOKSEK'), ('USDCHF', 'USDCZK'), ('AUDNZD', 'AUDCHF'), ('AUDNZD', 'EURGBP'), ('AUDNZD', 'CADCHF'), ('AUDNZD', 'USDSGD'), ('AUDNZD', 'EURPLN'), ('AUDNZD', 'NOKSEK'), ('AUDNZD', 'USDCZK'), ('AUDCHF', 'EURGBP'), ('AUDCHF', 'CADCHF'), ('AUDCHF', 'USDSGD'), ('AUDCHF', 'EURPLN'), ('AUDCHF', 'NOKSEK'), ('AUDCHF', 'USDCZK'), ('EURGBP', 'CADCHF'), ('EURGBP', 'USDSGD'), ('EURGBP', 'EURPLN'), ('EURGBP', 'NOKSEK'), ('EURGBP', 'USDCZK'), ('CADCHF', 'USDSGD'), ('CADCHF', 'EURPLN'), ('CADCHF', 'NOKSEK'), ('CADCHF', 'USDCZK'), ('USDSGD', 'EURPLN'), ('USDSGD', 'NOKSEK'), ('USDSGD', 'USDCZK'), ('EURPLN', 'NOKSEK'), ('EURPLN', 'USDCZK'), ('NOKSEK', 'USDCZK'), ('USDJPY', 'USDCAD'), ('USDJPY', 'AUDJPY'), ('USDJPY', 'CHFJPY'), ('USDJPY', 'EURAUD'), ('USDJPY', 'EURJPY'), ('USDJPY', 'EURNZD'), ('USDJPY', 'EURCAD'), ('USDJPY', 'GBPJPY'), ('USDJPY', 'CADJPY'), ('USDJPY', 'GBPAUD'), ('USDJPY', 'GBPNZD'), ('USDJPY', 'NZDJPY'), ('USDJPY', 'CHFSGD'), ('USDJPY', 'EURDKK'), ('USDJPY', 'EURNOK'), ('USDJPY', 'EURSEK'), ('USDJPY', 'EURTRY'), ('USDJPY', 'EURZAR'), ('USDJPY', 'GBPNOK'), ('USDJPY', 'GBPSEK'), ('USDJPY', 'GBPTRY'), ('USDJPY', 'NOKJPY'), ('USDJPY', 'SEKJPY'), ('USDJPY', 'SGDJPY'), ('USDJPY', 'USDCNH'), ('USDCAD', 'AUDJPY'), ('USDCAD', 'CHFJPY'), ('USDCAD', 'EURAUD'), ('USDCAD', 'EURJPY'), ('USDCAD', 'EURNZD'), ('USDCAD', 'EURCAD'), ('USDCAD', 'GBPJPY'), ('USDCAD', 'CADJPY'), ('USDCAD', 'GBPAUD'), ('USDCAD', 'GBPNZD'), ('USDCAD', 'NZDJPY'), ('USDCAD', 'CHFSGD'), ('USDCAD', 'EURDKK'), ('USDCAD', 'EURNOK'), ('USDCAD', 'EURSEK'), ('USDCAD', 'EURTRY'), ('USDCAD', 'EURZAR'), ('USDCAD', 'GBPNOK'), ('USDCAD', 'GBPSEK'), ('USDCAD', 'GBPTRY'), ('USDCAD', 'NOKJPY'), ('USDCAD', 'SEKJPY'), ('USDCAD', 'SGDJPY'), ('USDCAD', 'USDCNH'), ('AUDJPY', 'CHFJPY'), ('AUDJPY', 'EURAUD'), ('AUDJPY', 'EURJPY'), ('AUDJPY', 'EURNZD'), ('AUDJPY', 'EURCAD'), ('AUDJPY', 'GBPJPY'), ('AUDJPY', 'CADJPY'), ('AUDJPY', 'GBPAUD'), ('AUDJPY', 'GBPNZD'), ('AUDJPY', 'NZDJPY'), ('AUDJPY', 'CHFSGD'), ('AUDJPY', 'EURDKK'), ('AUDJPY', 'EURNOK'), ('AUDJPY', 'EURSEK'), ('AUDJPY', 'EURTRY'), ('AUDJPY', 'EURZAR'), ('AUDJPY', 'GBPNOK'), ('AUDJPY', 'GBPSEK'), ('AUDJPY', 'GBPTRY'), ('AUDJPY', 'NOKJPY'), ('AUDJPY', 'SEKJPY'), ('AUDJPY', 'SGDJPY'), ('AUDJPY', 'USDCNH'), ('CHFJPY', 'EURAUD'), ('CHFJPY', 'EURJPY'), ('CHFJPY', 'EURNZD'), ('CHFJPY', 'EURCAD'), ('CHFJPY', 'GBPJPY'), ('CHFJPY', 'CADJPY'), ('CHFJPY', 'GBPAUD'), ('CHFJPY', 'GBPNZD'), ('CHFJPY', 'NZDJPY'), ('CHFJPY', 'CHFSGD'), ('CHFJPY', 'EURDKK'), ('CHFJPY', 'EURNOK'), ('CHFJPY', 'EURSEK'), ('CHFJPY', 'EURTRY'), ('CHFJPY', 'EURZAR'), ('CHFJPY', 'GBPNOK'), ('CHFJPY', 'GBPSEK'), ('CHFJPY', 'GBPTRY'), ('CHFJPY', 'NOKJPY'), ('CHFJPY', 'SEKJPY'), ('CHFJPY', 'SGDJPY'), ('CHFJPY', 'USDCNH'), ('EURAUD', 'EURJPY'), ('EURAUD', 'EURNZD'), ('EURAUD', 'EURCAD'), ('EURAUD', 'GBPJPY'), ('EURAUD', 'CADJPY'), ('EURAUD', 'GBPAUD'), ('EURAUD', 'GBPNZD'), ('EURAUD', 'NZDJPY'), ('EURAUD', 'CHFSGD'), ('EURAUD', 'EURDKK'), ('EURAUD', 'EURNOK'), ('EURAUD', 'EURSEK'), ('EURAUD', 'EURTRY'), ('EURAUD', 'EURZAR'), ('EURAUD', 'GBPNOK'), ('EURAUD', 'GBPSEK'), ('EURAUD', 'GBPTRY'), ('EURAUD', 'NOKJPY'), ('EURAUD', 'SEKJPY'), ('EURAUD', 'SGDJPY'), ('EURAUD', 'USDCNH'), ('EURJPY', 'EURNZD'), ('EURJPY', 'EURCAD'), ('EURJPY', 'GBPJPY'), ('EURJPY', 'CADJPY'), ('EURJPY', 'GBPAUD'), ('EURJPY', 'GBPNZD'), ('EURJPY', 'NZDJPY'), ('EURJPY', 'CHFSGD'), ('EURJPY', 'EURDKK'), ('EURJPY', 'EURNOK'), ('EURJPY', 'EURSEK'), ('EURJPY', 'EURTRY'), ('EURJPY', 'EURZAR'), ('EURJPY', 'GBPNOK'), ('EURJPY', 'GBPSEK'), ('EURJPY', 'GBPTRY'), ('EURJPY', 'NOKJPY'), ('EURJPY', 'SEKJPY'), ('EURJPY', 'SGDJPY'), ('EURJPY', 'USDCNH'), ('EURNZD', 'EURCAD'), ('EURNZD', 'GBPJPY'), ('EURNZD', 'CADJPY'), ('EURNZD', 'GBPAUD'), ('EURNZD', 'GBPNZD'), ('EURNZD', 'NZDJPY'), ('EURNZD', 'CHFSGD'), ('EURNZD', 'EURDKK'), ('EURNZD', 'EURNOK'), ('EURNZD', 'EURSEK'), ('EURNZD', 'EURTRY'), ('EURNZD', 'EURZAR'), ('EURNZD', 'GBPNOK'), ('EURNZD', 'GBPSEK'), ('EURNZD', 'GBPTRY'), ('EURNZD', 'NOKJPY'), ('EURNZD', 'SEKJPY'), ('EURNZD', 'SGDJPY'), ('EURNZD', 'USDCNH'), ('EURCAD', 'GBPJPY'), ('EURCAD', 'CADJPY'), ('EURCAD', 'GBPAUD'), ('EURCAD', 'GBPNZD'), ('EURCAD', 'NZDJPY'), ('EURCAD', 'CHFSGD'), ('EURCAD', 'EURDKK'), ('EURCAD', 'EURNOK'), ('EURCAD', 'EURSEK'), ('EURCAD', 'EURTRY'), ('EURCAD', 'EURZAR'), ('EURCAD', 'GBPNOK'), ('EURCAD', 'GBPSEK'), ('EURCAD', 'GBPTRY'), ('EURCAD', 'NOKJPY'), ('EURCAD', 'SEKJPY'), ('EURCAD', 'SGDJPY'), ('EURCAD', 'USDCNH'), ('GBPJPY', 'CADJPY'), ('GBPJPY', 'GBPAUD'), ('GBPJPY', 'GBPNZD'), ('GBPJPY', 'NZDJPY'), ('GBPJPY', 'CHFSGD'), ('GBPJPY', 'EURDKK'), ('GBPJPY', 'EURNOK'), ('GBPJPY', 'EURSEK'), ('GBPJPY', 'EURTRY'), ('GBPJPY', 'EURZAR'), ('GBPJPY', 'GBPNOK'), ('GBPJPY', 'GBPSEK'), ('GBPJPY', 'GBPTRY'), ('GBPJPY', 'NOKJPY'), ('GBPJPY', 'SEKJPY'), ('GBPJPY', 'SGDJPY'), ('GBPJPY', 'USDCNH'), ('CADJPY', 'GBPAUD'), ('CADJPY', 'GBPNZD'), ('CADJPY', 'NZDJPY'), ('CADJPY', 'CHFSGD'), ('CADJPY', 'EURDKK'), ('CADJPY', 'EURNOK'), ('CADJPY', 'EURSEK'), ('CADJPY', 'EURTRY'), ('CADJPY', 'EURZAR'), ('CADJPY', 'GBPNOK'), ('CADJPY', 'GBPSEK'), ('CADJPY', 'GBPTRY'), ('CADJPY', 'NOKJPY'), ('CADJPY', 'SEKJPY'), ('CADJPY', 'SGDJPY'), ('CADJPY', 'USDCNH'), ('GBPAUD', 'GBPNZD'), ('GBPAUD', 'NZDJPY'), ('GBPAUD', 'CHFSGD'), ('GBPAUD', 'EURDKK'), ('GBPAUD', 'EURNOK'), ('GBPAUD', 'EURSEK'), ('GBPAUD', 'EURTRY'), ('GBPAUD', 'EURZAR'), ('GBPAUD', 'GBPNOK'), ('GBPAUD', 'GBPSEK'), ('GBPAUD', 'GBPTRY'), ('GBPAUD', 'NOKJPY'), ('GBPAUD', 'SEKJPY'), ('GBPAUD', 'SGDJPY'), ('GBPAUD', 'USDCNH'), ('GBPNZD', 'NZDJPY'), ('GBPNZD', 'CHFSGD'), ('GBPNZD', 'EURDKK'), ('GBPNZD', 'EURNOK'), ('GBPNZD', 'EURSEK'), ('GBPNZD', 'EURTRY'), ('GBPNZD', 'EURZAR'), ('GBPNZD', 'GBPNOK'), ('GBPNZD', 'GBPSEK'), ('GBPNZD', 'GBPTRY'), ('GBPNZD', 'NOKJPY'), ('GBPNZD', 'SEKJPY'), ('GBPNZD', 'SGDJPY'), ('GBPNZD', 'USDCNH'), ('NZDJPY', 'CHFSGD'), ('NZDJPY', 'EURDKK'), ('NZDJPY', 'EURNOK'), ('NZDJPY', 'EURSEK'), ('NZDJPY', 'EURTRY'), ('NZDJPY', 'EURZAR'), ('NZDJPY', 'GBPNOK'), ('NZDJPY', 'GBPSEK'), ('NZDJPY', 'GBPTRY'), ('NZDJPY', 'NOKJPY'), ('NZDJPY', 'SEKJPY'), ('NZDJPY', 'SGDJPY'), ('NZDJPY', 'USDCNH'), ('CHFSGD', 'EURDKK'), ('CHFSGD', 'EURNOK'), ('CHFSGD', 'EURSEK'), ('CHFSGD', 'EURTRY'), ('CHFSGD', 'EURZAR'), ('CHFSGD', 'GBPNOK'), ('CHFSGD', 'GBPSEK'), ('CHFSGD', 'GBPTRY'), ('CHFSGD', 'NOKJPY'), ('CHFSGD', 'SEKJPY'), ('CHFSGD', 'SGDJPY'), ('CHFSGD', 'USDCNH'), ('EURDKK', 'EURNOK'), ('EURDKK', 'EURSEK'), ('EURDKK', 'EURTRY'), ('EURDKK', 'EURZAR'), ('EURDKK', 'GBPNOK'), ('EURDKK', 'GBPSEK'), ('EURDKK', 'GBPTRY'), ('EURDKK', 'NOKJPY'), ('EURDKK', 'SEKJPY'), ('EURDKK', 'SGDJPY'), ('EURDKK', 'USDCNH'), ('EURNOK', 'EURSEK'), ('EURNOK', 'EURTRY'), ('EURNOK', 'EURZAR'), ('EURNOK', 'GBPNOK'), ('EURNOK', 'GBPSEK'), ('EURNOK', 'GBPTRY'), ('EURNOK', 'NOKJPY'), ('EURNOK', 'SEKJPY'), ('EURNOK', 'SGDJPY'), ('EURNOK', 'USDCNH'), ('EURSEK', 'EURTRY'), ('EURSEK', 'EURZAR'), ('EURSEK', 'GBPNOK'), ('EURSEK', 'GBPSEK'), ('EURSEK', 'GBPTRY'), ('EURSEK', 'NOKJPY'), ('EURSEK', 'SEKJPY'), ('EURSEK', 'SGDJPY'), ('EURSEK', 'USDCNH'), ('EURTRY', 'EURZAR'), ('EURTRY', 'GBPNOK'), ('EURTRY', 'GBPSEK'), ('EURTRY', 'GBPTRY'), ('EURTRY', 'NOKJPY'), ('EURTRY', 'SEKJPY'), ('EURTRY', 'SGDJPY'), ('EURTRY', 'USDCNH'), ('EURZAR', 'GBPNOK'), ('EURZAR', 'GBPSEK'), ('EURZAR', 'GBPTRY'), ('EURZAR', 'NOKJPY'), ('EURZAR', 'SEKJPY'), ('EURZAR', 'SGDJPY'), ('EURZAR', 'USDCNH'), ('GBPNOK', 'GBPSEK'), ('GBPNOK', 'GBPTRY'), ('GBPNOK', 'NOKJPY'), ('GBPNOK', 'SEKJPY'), ('GBPNOK', 'SGDJPY'), ('GBPNOK', 'USDCNH'), ('GBPSEK', 'GBPTRY'), ('GBPSEK', 'NOKJPY'), ('GBPSEK', 'SEKJPY'), ('GBPSEK', 'SGDJPY'), ('GBPSEK', 'USDCNH'), ('GBPTRY', 'NOKJPY'), ('GBPTRY', 'SEKJPY'), ('GBPTRY', 'SGDJPY'), ('GBPTRY', 'USDCNH'), ('NOKJPY', 'SEKJPY'), ('NOKJPY', 'SGDJPY'), ('NOKJPY', 'USDCNH'), ('SEKJPY', 'SGDJPY'), ('SEKJPY', 'USDCNH'), ('SGDJPY', 'USDCNH')]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Use factor_df from PCA as input for clustering\n",
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "factor_df['cluster'] = kmeans.fit_predict(factor_df)\n",
    "\n",
    "print(\"📌 Symbol Clusters:\")\n",
    "print(factor_df[['cluster']])\n",
    "\n",
    "# Group symbols by cluster and form pairs within each cluster\n",
    "def find_cluster_pairs_from_factor_df(factor_df):\n",
    "    pairs = []\n",
    "    for cluster in factor_df['cluster'].unique():\n",
    "        symbols_in_cluster = factor_df[factor_df['cluster'] == cluster].index.tolist()\n",
    "        for i, sym1 in enumerate(symbols_in_cluster):\n",
    "            for j, sym2 in enumerate(symbols_in_cluster):\n",
    "                if i < j:\n",
    "                    pairs.append((sym1, sym2))\n",
    "    return pairs\n",
    "\n",
    "cluster_pairs = find_cluster_pairs_from_factor_df(factor_df)\n",
    "print(\"🎯 Cluster-Based Pairs:\", cluster_pairs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 5: Backtest the Pair Trading Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pair: EURUSD vs EURHKD\n",
      "Start                         2021-04-29 00:00:00\n",
      "End                           2025-03-05 00:00:00\n",
      "Period                           41 days 16:00:00\n",
      "Start Value                               10000.0\n",
      "End Value                             9726.830626\n",
      "Total Return [%]                        -2.731694\n",
      "Benchmark Return [%]                   -12.028977\n",
      "Max Gross Exposure [%]                      100.0\n",
      "Total Fees Paid                         95.663717\n",
      "Max Drawdown [%]                        11.550894\n",
      "Max Drawdown Duration            30 days 09:00:00\n",
      "Total Trades                                   25\n",
      "Total Closed Trades                            24\n",
      "Total Open Trades                               1\n",
      "Open Trade PnL                          31.724849\n",
      "Win Rate [%]                                 37.5\n",
      "Best Trade [%]                           4.555476\n",
      "Worst Trade [%]                         -1.846803\n",
      "Avg Winning Trade [%]                    1.590328\n",
      "Avg Losing Trade [%]                    -1.137923\n",
      "Avg Winning Trade Duration        1 days 16:13:20\n",
      "Avg Losing Trade Duration         0 days 13:08:00\n",
      "Profit Factor                            0.817878\n",
      "Expectancy                             -12.703926\n",
      "Sharpe Ratio                            -0.579445\n",
      "Calmar Ratio                            -1.865092\n",
      "Omega Ratio                              0.978605\n",
      "Sortino Ratio                           -0.829624\n",
      "Name: close, dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\moham\\miniconda3\\envs\\ml\\Lib\\site-packages\\vectorbt\\utils\\datetime_.py:24: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version. Please use 'h' instead of 'H'.\n",
      "\n"
     ]
    },
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   "source": [
    "import vectorbt as vbt\n",
    "\n",
    "# Option 1: Use the PCA-selected pair\n",
    "pair1, pair2 = selected_pairs[0][:2]\n",
    "\n",
    "# Option 2: Alternatively, use a cluster-based pair\n",
    "# pair1, pair2 = cluster_pairs[0]\n",
    "\n",
    "print(f\"Testing pair: {pair1} vs {pair2}\")\n",
    "\n",
    "df1, df2 = data[pair1], data[pair2]\n",
    "\n",
    "# Compute Spread & Z-Score\n",
    "spread = df1[\"close\"] - df2[\"close\"]\n",
    "z_score = (spread - spread.rolling(60).mean()) / spread.rolling(60).std()\n",
    "\n",
    "# Define Entry & Exit Signals\n",
    "entries = z_score < -1.5       # Buy pair1, Sell pair2\n",
    "short_entries = z_score > 1.5    # Sell pair1, Buy pair2\n",
    "exits = abs(z_score) < 0.5       # Exit when mean reversion occurs\n",
    "\n",
    "# Backtest using vectorbt\n",
    "portfolio = vbt.Portfolio.from_signals(\n",
    "    close=df1[\"close\"],\n",
    "    entries=entries,\n",
    "    exits=exits,\n",
    "    short_entries=short_entries,\n",
    "    short_exits=exits,\n",
    "    size=1,  \n",
    "    size_type=\"percent\",\n",
    "    init_cash=10000,\n",
    "    fees=0.0002,\n",
    "    freq=\"1H\"\n",
    ")\n",
    "\n",
    "print(portfolio.stats())\n",
    "portfolio.plot().show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 6: Live Trading with MT5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def live_pair_trading(pair1, pair2):\n",
    "    \"\"\"Executes pair trading strategy on MT5.\"\"\"\n",
    "    df_signals = generate_pair_signals(pair1, pair2)\n",
    "\n",
    "    latest = df_signals.iloc[-1]\n",
    "\n",
    "    if latest[\"long_signal\"]:\n",
    "        place_order(pair1, LOT_SIZE, is_buy=True, magic=MAGIC_NUMBER)\n",
    "        place_order(pair2, LOT_SIZE, is_buy=False, magic=MAGIC_NUMBER)\n",
    "    elif latest[\"short_signal\"]:\n",
    "        place_order(pair1, LOT_SIZE, is_buy=False, magic=MAGIC_NUMBER)\n",
    "        place_order(pair2, LOT_SIZE, is_buy=True, magic=MAGIC_NUMBER)\n",
    "    elif latest[\"exit_signal\"]:\n",
    "        close_positions(pair1, MAGIC_NUMBER)\n",
    "        close_positions(pair2, MAGIC_NUMBER)\n",
    "\n",
    "# ✅ Run Live Trading\n",
    "while True:\n",
    "    live_pair_trading(pair1, pair2)\n",
    "    time.sleep(3600)  # Run every hour\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select specific symbols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Load & Feature Engineer Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      AUDUSD   NZDUSD   EURUSD   GBPUSD   USDJPY   USDCAD  \\\n",
      "                       close    close    close    close    close    close   \n",
      "time                                                                        \n",
      "2024-11-07 19:00:00  0.66772  0.60247  1.08048  1.29899  153.016  1.38504   \n",
      "2024-11-07 20:00:00  0.66700  0.60199  1.07981  1.29726  152.963  1.38617   \n",
      "2024-11-07 21:00:00  0.66675  0.60170  1.07806  1.29645  153.085  1.38616   \n",
      "2024-11-07 22:00:00  0.66773  0.60238  1.08025  1.29827  152.853  1.38613   \n",
      "2024-11-07 23:00:00  0.66802  0.60252  1.08031  1.29871  152.899  1.38594   \n",
      "\n",
      "                        AUDUSD                              NZDUSD  ...  \\\n",
      "                    volatility  momentum mean_reversion volatility  ...   \n",
      "time                                                                ...   \n",
      "2024-11-07 19:00:00   0.014067  0.819883      -0.007538   0.011677  ...   \n",
      "2024-11-07 20:00:00   0.013384  0.521446      -0.006740   0.011059  ...   \n",
      "2024-11-07 21:00:00   0.012231  0.571679      -0.006423   0.010070  ...   \n",
      "2024-11-07 22:00:00   0.010929  0.689125      -0.007317   0.008573  ...   \n",
      "2024-11-07 23:00:00   0.009795  0.741970      -0.007517   0.007439  ...   \n",
      "\n",
      "                            EURUSD     GBPUSD                           \\\n",
      "                    mean_reversion volatility  momentum mean_reversion   \n",
      "time                                                                     \n",
      "2024-11-07 19:00:00      -0.002424   0.015236  0.462487      -0.006039   \n",
      "2024-11-07 20:00:00      -0.001998   0.014693  0.354303      -0.004392   \n",
      "2024-11-07 21:00:00      -0.000547   0.014055  0.441604      -0.003720   \n",
      "2024-11-07 22:00:00      -0.002993   0.013454  0.600533      -0.005625   \n",
      "2024-11-07 23:00:00      -0.003309   0.012935  0.633848      -0.006133   \n",
      "\n",
      "                        USDJPY                              USDCAD            \\\n",
      "                    volatility  momentum mean_reversion volatility  momentum   \n",
      "time                                                                           \n",
      "2024-11-07 19:00:00   1.974957 -0.640251        0.60886   0.010612 -0.267865   \n",
      "2024-11-07 20:00:00   2.063996 -0.698524        0.68188   0.009988 -0.118891   \n",
      "2024-11-07 21:00:00   2.118684 -0.561225        0.58790   0.008682 -0.215240   \n",
      "2024-11-07 22:00:00   2.138153 -0.756405        0.84584   0.007324 -0.169250   \n",
      "2024-11-07 23:00:00   2.109487 -0.759400        0.82878   0.005920 -0.241130   \n",
      "\n",
      "                                    \n",
      "                    mean_reversion  \n",
      "time                                \n",
      "2024-11-07 19:00:00       0.004597  \n",
      "2024-11-07 20:00:00       0.003484  \n",
      "2024-11-07 21:00:00       0.003525  \n",
      "2024-11-07 22:00:00       0.003583  \n",
      "2024-11-07 23:00:00       0.003809  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "import MetaTrader5 as mt5\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ta\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import euclidean\n",
    "from statsmodels.tsa.stattools import coint\n",
    "\n",
    "# ✅ Initialize MT5 Connection\n",
    "if not mt5.initialize():\n",
    "    print(\"MT5 Initialization Failed\")\n",
    "    mt5.shutdown()\n",
    "\n",
    "# ✅ Fetch Data for Multiple Symbols\n",
    "symbols = [\"AUDUSD\", \"NZDUSD\", \"EURUSD\", \"GBPUSD\", \"USDJPY\", \"USDCAD\"]  # Can be expanded\n",
    "n_bars = 2000  # Historical lookback\n",
    "\n",
    "def get_mt5_data(symbol, n_bars, timeframe=mt5.TIMEFRAME_H1):\n",
    "    \"\"\"Fetches historical data from MT5\"\"\"\n",
    "    rates = mt5.copy_rates_from_pos(symbol, timeframe, 0, n_bars)\n",
    "    if rates is None:\n",
    "        raise ValueError(f\"Could not retrieve data for {symbol}\")\n",
    "    df = pd.DataFrame(rates)\n",
    "    df[\"time\"] = pd.to_datetime(df[\"time\"], unit=\"s\")\n",
    "    df.set_index(\"time\", inplace=True)\n",
    "    return df[[\"close\"]]\n",
    "\n",
    "# ✅ Load Data for All Symbols\n",
    "data = {symbol: get_mt5_data(symbol, n_bars) for symbol in symbols}\n",
    "\n",
    "# ✅ Merge Data into One DataFrame\n",
    "df = pd.concat(data.values(), axis=1, keys=data.keys())\n",
    "\n",
    "# ✅ Add Technical Features (Volatility, Momentum, etc.)\n",
    "def add_features(df):\n",
    "    for sym in symbols:\n",
    "        df[(sym, \"volatility\")] = ta.volatility.bollinger_hband(df[(sym, \"close\")], window=20) - \\\n",
    "                                  ta.volatility.bollinger_lband(df[(sym, \"close\")], window=20)\n",
    "        df[(sym, \"momentum\")] = ta.momentum.ROCIndicator(df[(sym, \"close\")], window=10).roc()\n",
    "        df[(sym, \"mean_reversion\")] = df[(sym, \"close\")].rolling(50).mean() - df[(sym, \"close\")]\n",
    "\n",
    "    return df.dropna()\n",
    "\n",
    "df = add_features(df)\n",
    "\n",
    "print(df.head())  # Verify Features\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 2: PCA for Pair Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# ✅ Standardize the Data\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(df.xs(\"close\", axis=1, level=1))\n",
    "\n",
    "# ✅ PCA: Find Principal Components\n",
    "pca = PCA(n_components=3)  # Keep 3 Principal Components\n",
    "pca_components = pca.fit_transform(scaled_data)\n",
    "\n",
    "# ✅ Assign PCA Factor Weights to Each Symbol\n",
    "factor_df = pd.DataFrame(pca.components_.T, index=symbols, columns=[f\"PC{i+1}\" for i in range(pca.n_components_)])\n",
    "print(factor_df)\n",
    "\n",
    "# ✅ Find Closest Pairs Based on PCA Factor Similarity\n",
    "def find_pca_pairs(factor_df):\n",
    "    pairs = []\n",
    "    for i, sym1 in enumerate(factor_df.index):\n",
    "        for j, sym2 in enumerate(factor_df.index):\n",
    "            if i < j:\n",
    "                distance = euclidean(factor_df.loc[sym1], factor_df.loc[sym2])\n",
    "                pairs.append((sym1, sym2, distance))\n",
    "    \n",
    "    # ✅ Select Best Pairs with Smallest Distance\n",
    "    sorted_pairs = sorted(pairs, key=lambda x: x[2])\n",
    "    return sorted_pairs[:10]  # Return Top 10 Closest Pairs\n",
    "\n",
    "selected_pairs = find_pca_pairs(factor_df)\n",
    "print(\"🎯 Best PCA-Based Pairs:\", selected_pairs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Step 3: K-Means Clustering for Pair Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# ✅ K-Means Clustering\n",
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "df[\"cluster\"] = kmeans.fit_predict(scaled_data)\n",
    "\n",
    "# ✅ Group Stocks by Clusters\n",
    "clusters = df.groupby(\"cluster\").apply(lambda x: x.index.tolist())\n",
    "print(\"📌 Identified Clusters:\", clusters)\n",
    "\n",
    "# ✅ Select Best Pairs within Clusters\n",
    "def find_cluster_pairs(clusters):\n",
    "    best_pairs = []\n",
    "    for cluster in clusters:\n",
    "        for i, sym1 in enumerate(cluster):\n",
    "            for j, sym2 in enumerate(cluster):\n",
    "                if i < j:\n",
    "                    best_pairs.append((sym1, sym2))\n",
    "    return best_pairs\n",
    "\n",
    "cluster_pairs = find_cluster_pairs(clusters)\n",
    "print(\"🎯 Cluster-Based Pairs:\", cluster_pairs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4: Backtest the Pair Trading Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vectorbt as vbt\n",
    "\n",
    "# ✅ Select Pair for Backtest\n",
    "pair1, pair2 = selected_pairs[0][:2]\n",
    "df1, df2 = data[pair1], data[pair2]\n",
    "\n",
    "# ✅ Compute Spread & Z-Score\n",
    "spread = df1[\"close\"] - df2[\"close\"]\n",
    "z_score = (spread - spread.rolling(60).mean()) / spread.rolling(60).std()\n",
    "\n",
    "# ✅ Define Entry & Exit Signals\n",
    "entries = z_score < -1.5  # Buy Pair1, Sell Pair2\n",
    "exits = abs(z_score) < 0.5  # Exit when mean reversion happens\n",
    "short_entries = z_score > 1.5  # Sell Pair1, Buy Pair2\n",
    "\n",
    "# ✅ Backtest with vectorbt\n",
    "portfolio = vbt.Portfolio.from_signals(\n",
    "    close=df1[\"close\"],\n",
    "    entries=entries,\n",
    "    exits=exits,\n",
    "    short_entries=short_entries,\n",
    "    short_exits=exits,\n",
    "    size=1,  \n",
    "    size_type=\"percent\",\n",
    "    init_cash=10000,\n",
    "    fees=0.0002,\n",
    "    freq=\"1H\"\n",
    ")\n",
    "\n",
    "# ✅ Results\n",
    "print(portfolio.stats())\n",
    "portfolio.plot().show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 5: Live Trading with MT5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def live_pair_trading(pair1, pair2):\n",
    "    \"\"\"Executes pair trading strategy on MT5.\"\"\"\n",
    "    df_signals = generate_pair_signals(pair1, pair2)\n",
    "\n",
    "    latest = df_signals.iloc[-1]\n",
    "\n",
    "    if latest[\"long_signal\"]:\n",
    "        place_order(pair1, LOT_SIZE, is_buy=True, magic=MAGIC_NUMBER)\n",
    "        place_order(pair2, LOT_SIZE, is_buy=False, magic=MAGIC_NUMBER)\n",
    "    elif latest[\"short_signal\"]:\n",
    "        place_order(pair1, LOT_SIZE, is_buy=False, magic=MAGIC_NUMBER)\n",
    "        place_order(pair2, LOT_SIZE, is_buy=True, magic=MAGIC_NUMBER)\n",
    "    elif latest[\"exit_signal\"]:\n",
    "        close_positions(pair1, MAGIC_NUMBER)\n",
    "        close_positions(pair2, MAGIC_NUMBER)\n",
    "\n",
    "# ✅ Run Live Trading\n",
    "while True:\n",
    "    live_pair_trading(pair1, pair2)\n",
    "    time.sleep(3600)  # Run every hour\n"
   ]
  }
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