kumrzz · November 5, 2025 15:57
diff --git a/backtest_es_long_only_cooldown3y_05nov2025.ipynb b/backtest_es_long_only_cooldown3y_05nov2025.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ef45c721",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install pandas matplotlib numpy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f06ff118",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7g/23kt1h6917l_vg9gy8dq300m0000gp/T/ipykernel_48112/1673990973.py:7: FutureWarning: In a future version of pandas, parsing datetimes with mixed time zones will raise an error unless `utc=True`. Please specify `utc=True` to opt in to the new behaviour and silence this warning. To create a `Series` with mixed offsets and `object` dtype, please use `apply` and `datetime.datetime.strptime`\n",
      "  df['timestamp'] = pd.to_datetime(df['timestamp'], dayfirst=True)\n"
     ]
    },
    {
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       "      <th>open</th>\n",
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       "      <th>close</th>\n",
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       "                         timestamp      open      high       low     close  \\\n",
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       "...                            ...       ...       ...       ...       ...   \n",
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      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. LOAD 1-MINUTE DATA (CSV format)\n",
    "# Expected columns: ['Local time', 'Open', 'High', 'Low', 'Close']\n",
    "# Example: \"2025-06-01 00:00:00,5890.1,5890.5,5889.8,5890.0\"\n",
    "df = pd.read_csv(\"/Users/kumar.ghosh/kgtest/lemaske/USA500.IDXUSD_Candlestick_1_M_BID_05.11.2022-04.11.2025.csv\", parse_dates=['Local time'])\n",
    "df.rename(columns={'Local time':'timestamp','Open':'open','High':'high','Low':'low','Close':'close'}, inplace=True)\n",
    "# 1-minute timestamps for one month\n",
    "df['timestamp'] = pd.to_datetime(df['timestamp'], dayfirst=True)\n",
    "df = df.sort_values('timestamp').reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "85a6165f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. STRATEGY PARAMETERS\n",
    "initial_capital = 100_000\n",
    "contract_size = 20\n",
    "contract_value = 50  # $50 per point\n",
    "risk_per_trade = None  # Fixed size, not risk-based\n",
    "\n",
    "ema_fast = 50\n",
    "ema_slow = 100\n",
    "bb_period = 20\n",
    "bb_mult = 2.0\n",
    "rsi_period = 14\n",
    "rsi_oversold = 35\n",
    "atr_period = 14\n",
    "atr_mult = 1.5\n",
    "cooldown_bars = 10  # 10 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "11d0b57a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. INDICATOR CALCULATIONS\n",
    "df['ema_fast'] = df['close'].ewm(span=ema_fast, adjust=False).mean()\n",
    "df['ema_slow'] = df['close'].ewm(span=ema_slow, adjust=False).mean()\n",
    "df['basis'] = df['close'].rolling(bb_period).mean()\n",
    "df['dev'] = df['close'].rolling(bb_period).std()\n",
    "df['upper'] = df['basis'] + bb_mult * df['dev']\n",
    "df['lower'] = df['basis'] - bb_mult * df['dev']\n",
    "\n",
    "# RSI\n",
    "delta = df['close'].diff()\n",
    "gain = delta.where(delta > 0, 0)\n",
    "loss = -delta.where(delta < 0, 0)\n",
    "avg_gain = gain.rolling(rsi_period).mean()\n",
    "avg_loss = loss.rolling(rsi_period).mean()\n",
    "rs = avg_gain / avg_loss\n",
    "df['rsi'] = 100 - (100 / (1 + rs))\n",
    "\n",
    "# ATR\n",
    "high_low = df['high'] - df['low']\n",
    "high_close = np.abs(df['high'] - df['close'].shift())\n",
    "low_close = np.abs(df['low'] - df['close'].shift())\n",
    "tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)\n",
    "df['atr'] = tr.rolling(atr_period).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6a98f0cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop NaN\n",
    "df = df.dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3e5fdc18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. BACKTEST ENGINE\n",
    "equity = initial_capital\n",
    "peak_equity = initial_capital\n",
    "trades = []\n",
    "in_position = False\n",
    "entry_price = 0\n",
    "stop_price = 0\n",
    "target_price = 0\n",
    "cooldown_until = -1  # bar index\n",
    "current_bar = 0\n",
    "\n",
    "for i, row in df.iterrows():\n",
    "    close = row['close']\n",
    "    upper = row['upper']\n",
    "    lower = row['lower']\n",
    "    rsi = row['rsi']\n",
    "    atr = row['atr']\n",
    "    timestamp = row['timestamp']\n",
    "\n",
    "    # Update cooldown\n",
    "    if cooldown_until >= i:\n",
    "        can_trade = False\n",
    "    else:\n",
    "        can_trade = True\n",
    "\n",
    "    # Exit conditions\n",
    "    if in_position:\n",
    "        # Hit stop?\n",
    "        if row['low'] <= stop_price:\n",
    "            exit_price = stop_price\n",
    "            pnl_points = exit_price - entry_price\n",
    "            pnl_dollar = pnl_points * contract_value * contract_size\n",
    "            equity += pnl_dollar\n",
    "            trades.append({\n",
    "                'entry_time': entry_time,\n",
    "                'exit_time': timestamp,\n",
    "                'entry': entry_price,\n",
    "                'exit': exit_price,\n",
    "                'pnl_points': pnl_points,\n",
    "                'pnl_dollar': pnl_dollar,\n",
    "                'type': 'STOP'\n",
    "            })\n",
    "            in_position = False\n",
    "            cooldown_until = i + cooldown_bars\n",
    "            continue\n",
    "\n",
    "        # Hit target (upper band)?\n",
    "        if row['high'] >= target_price:\n",
    "            exit_price = target_price\n",
    "            pnl_points = exit_price - entry_price\n",
    "            pnl_dollar = pnl_points * contract_value * contract_size\n",
    "            equity += pnl_dollar\n",
    "            trades.append({\n",
    "                'entry_time': entry_time,\n",
    "                'exit_time': timestamp,\n",
    "                'entry': entry_price,\n",
    "                'exit': exit_price,\n",
    "                'pnl_points': pnl_points,\n",
    "                'pnl_dollar': pnl_dollar,\n",
    "                'type': 'TARGET'\n",
    "            })\n",
    "            in_position = False\n",
    "            cooldown_until = i + cooldown_bars\n",
    "            continue\n",
    "\n",
    "        # Close at upper band (if touched)\n",
    "        if close >= upper:\n",
    "            exit_price = close\n",
    "            pnl_points = exit_price - entry_price\n",
    "            pnl_dollar = pnl_points * contract_value * contract_size\n",
    "            equity += pnl_dollar\n",
    "            trades.append({\n",
    "                'entry_time': entry_time,\n",
    "                'exit_time': timestamp,\n",
    "                'entry': entry_price,\n",
    "                'exit': exit_price,\n",
    "                'pnl_points': pnl_points,\n",
    "                'pnl_dollar': pnl_dollar,\n",
    "                'type': 'BAND_EXIT'\n",
    "            })\n",
    "            in_position = False\n",
    "            cooldown_until = i + cooldown_bars\n",
    "            continue\n",
    "\n",
    "    # Entry condition (long only)\n",
    "    if not in_position and can_trade:\n",
    "        trend_up = row['ema_fast'] > row['ema_slow']\n",
    "        buy_signal = (close < lower) and (rsi < rsi_oversold) and trend_up\n",
    "\n",
    "        if buy_signal:\n",
    "            entry_price = close\n",
    "            stop_price = entry_price - atr_mult * atr\n",
    "            target_price = upper\n",
    "            entry_time = timestamp\n",
    "            in_position = True\n",
    "\n",
    "    # Track equity\n",
    "    df.at[i, 'equity'] = equity\n",
    "    if equity > peak_equity:\n",
    "        peak_equity = equity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5d7b9f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. PERFORMANCE METRICS\n",
    "trades_df = pd.DataFrame(trades)\n",
    "total_trades = len(trades_df)\n",
    "win_trades = len(trades_df[trades_df['pnl_dollar'] > 0])\n",
    "win_rate = win_trades / total_trades * 100 if total_trades > 0 else 0\n",
    "avg_win = trades_df[trades_df['pnl_dollar'] > 0]['pnl_dollar'].mean() if win_trades > 0 else 0\n",
    "avg_loss = trades_df[trades_df['pnl_dollar'] < 0]['pnl_dollar'].mean() if (total_trades - win_trades) > 0 else 0\n",
    "profit_factor = abs(avg_win * win_trades / (avg_loss * (total_trades - win_trades))) if avg_loss != 0 else float('inf')\n",
    "\n",
    "final_equity = equity\n",
    "net_profit = final_equity - initial_capital\n",
    "return_pct = net_profit / initial_capital * 100\n",
    "\n",
    "# Drawdown\n",
    "df['peak'] = df['equity'].cummax()\n",
    "df['drawdown'] = df['equity'] - df['peak']\n",
    "max_dd = df['drawdown'].min()\n",
    "\n",
    "# Sharpe (daily returns)\n",
    "df['daily_return'] = df['equity'].pct_change(fill_method=None)\n",
    "daily_vol = df['daily_return'].std()\n",
    "sharpe = (df['daily_return'].mean() / daily_vol) * np.sqrt(252 * 390) if daily_vol > 0 else 0  # 390 min/day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "29ec5ff3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "        LONG-ONLY + 10-MIN COOLDOWN BACKTEST\n",
      "============================================================\n",
      "Initial Capital:     $100,000\n",
      "Final Equity:        $1,124,551\n",
      "Net Profit:          $1,024,551 (1,024.6%)\n",
      "Total Trades:        7309\n",
      "Win Rate:            30.9%\n",
      "Avg Win:             $4,364\n",
      "Avg Loss:            $-1,744\n",
      "Profit Factor:       1.12\n",
      "Max Drawdown:        $200,096\n",
      "Sharpe Ratio:        0.00\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "# 6. PRINT SUMMARY\n",
    "print(\"=\"*60)\n",
    "print(\"        LONG-ONLY + 10-MIN COOLDOWN BACKTEST\")\n",
    "print(\"=\"*60)\n",
    "print(f\"Initial Capital:     ${initial_capital:,.0f}\")\n",
    "print(f\"Final Equity:        ${final_equity:,.0f}\")\n",
    "print(f\"Net Profit:          ${net_profit:,.0f} ({return_pct:,.1f}%)\")\n",
    "print(f\"Total Trades:        {total_trades}\")\n",
    "print(f\"Win Rate:            {win_rate:.1f}%\")\n",
    "print(f\"Avg Win:             ${avg_win:,.0f}\")\n",
    "print(f\"Avg Loss:            ${avg_loss:,.0f}\")\n",
    "print(f\"Profit Factor:       {profit_factor:.2f}\")\n",
    "print(f\"Max Drawdown:        ${-max_dd:,.0f}\")\n",
    "print(f\"Sharpe Ratio:        {sharpe:.2f}\")\n",
    "print(\"=\"*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "39278772",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8. PLOT EQUITY CURVE\n",
    "plt.figure(figsize=(14, 8))\n",
    "plt.plot(df['timestamp'], df['equity'], label='Equity Curve', color='blue')\n",
    "plt.fill_between(df['timestamp'], df['equity'], df['peak'], where=df['drawdown'] < 0, color='red', alpha=0.3, label='Drawdown')\n",
    "plt.title('Equity Curve - Long-Only + 10-Min Cooldown (20 Contracts)')\n",
    "plt.ylabel('Equity ($)')\n",
    "plt.xlabel('Date')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "# plt.savefig('/Users/kumar.ghosh/kgtest/lemaske/backtest_es_long_only_cooldown_equity_curve05nov2025.png', dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db4d7527",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
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