kumrzz · November 5, 2025 16:19
diff --git a/backtest_es_long_only_cooldown3y_atrtweaks_05nov2025.ipynb b/backtest_es_long_only_cooldown3y_atrtweaks_05nov2025.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "id": "f06ff118",
   "metadata": {},
   "outputs": [
    {
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       "      <th>timestamp</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
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       "                        timestamp      open      high       low     close  \\\n",
       "0       2022-11-05 00:00:00+00:00  3766.331  3766.331  3766.331  3766.331   \n",
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       "4       2022-11-05 00:04:00+00:00  3766.331  3766.331  3766.331  3766.331   \n",
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     },
     "execution_count": 2,
     "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, utc=True)\n",
    "df = df.sort_values('timestamp').reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 = 3\n",
    "cooldown_bars = 10  # 10 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "id": "6a98f0cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop NaN\n",
    "df = df.dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "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": 8,
   "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:        $1,506,886\n",
      "Net Profit:          $1,406,886 (1,406.9%)\n",
      "Total Trades:        6898\n",
      "Win Rate:            48.7%\n",
      "Avg Win:             $3,934\n",
      "Avg Loss:            $-3,341\n",
      "Profit Factor:       1.12\n",
      "Max Drawdown:        $237,908\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:        ${-max_dd:,.0f}\")\n",
    "print(f\"Sharpe Ratio:        {sharpe:.2f}\")\n",
    "print(\"=\"*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39278772",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 8. PLOT EQUITY CURVE\n",
    "plt.figure(figsize=(14, 8))\n",
    "plt.plot(df['timestamp'], df['equity'], label='Equity Curve', color='blue')\n",
    "plt.fill_between(df['timestamp'], df['equity'], df['peak'], where=df['drawdown'] < 0, color='red', alpha=0.3, label='Drawdown')\n",
    "plt.title('Equity Curve - Long-Only + 10-Min Cooldown (20 Contracts)')\n",
    "plt.ylabel('Equity ($)')\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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