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kumrzz/backtest_es_LO_CD_1min15minxover_06nov2025.ipynb

Created November 6, 2025 21:15
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backtest_es_LO_CD_1min15minxover_06nov2025.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"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": 6,
"id": "f06ff118",
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" <th>Volume</th>\n",
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" <th>4</th>\n",
" <td>2022-11-05 00:04:00+00:00</td>\n",
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" <td>6766.339</td>\n",
" <td>6766.339</td>\n",
" <td>6765.136</td>\n",
" <td>6765.499</td>\n",
" <td>0.02</td>\n",
" </tr>\n",
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"</table>\n",
"<p>1578240 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp open high low close \\\n",
"0 2022-11-05 00:00:00+00:00 3766.331 3766.331 3766.331 3766.331 \n",
"1 2022-11-05 00:01:00+00:00 3766.331 3766.331 3766.331 3766.331 \n",
"2 2022-11-05 00:02:00+00:00 3766.331 3766.331 3766.331 3766.331 \n",
"3 2022-11-05 00:03:00+00:00 3766.331 3766.331 3766.331 3766.331 \n",
"4 2022-11-05 00:04:00+00:00 3766.331 3766.331 3766.331 3766.331 \n",
"... ... ... ... ... ... \n",
"1578235 2025-11-04 23:55:00+00:00 6766.648 6766.648 6765.942 6766.145 \n",
"1578236 2025-11-04 23:56:00+00:00 6765.942 6766.445 6765.654 6765.951 \n",
"1578237 2025-11-04 23:57:00+00:00 6765.639 6766.499 6765.639 6766.345 \n",
"1578238 2025-11-04 23:58:00+00:00 6766.445 6766.654 6766.142 6766.445 \n",
"1578239 2025-11-04 23:59:00+00:00 6766.339 6766.339 6765.136 6765.499 \n",
"\n",
" Volume \n",
"0 0.00 \n",
"1 0.00 \n",
"2 0.00 \n",
"3 0.00 \n",
"4 0.00 \n",
"... ... \n",
"1578235 0.02 \n",
"1578236 0.03 \n",
"1578237 0.02 \n",
"1578238 0.02 \n",
"1578239 0.02 \n",
"\n",
"[1578240 rows x 6 columns]"
]
},
"execution_count": 6,
"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": 15,
"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\n",
"ema_timeframe = '30min'"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "11d0b57a",
"metadata": {},
"outputs": [],
"source": [
"# 3. INDICATOR CALCULATIONS\n",
"# 1-minute EMAs (short-term trend)\n",
"df['ema_fast_1m'] = df['close'].ewm(span=ema_fast, adjust=False).mean()\n",
"df['ema_slow_1m'] = df['close'].ewm(span=ema_slow, adjust=False).mean()\n",
"\n",
"# 30-minute EMAs (long-term trend)\n",
"df_30m = df.set_index('timestamp').resample(ema_timeframe).agg({'close': 'last'}).dropna()\n",
"df_30m['ema_fast_30m'] = df_30m['close'].ewm(span=ema_fast, adjust=False).mean()\n",
"df_30m['ema_slow_30m'] = df_30m['close'].ewm(span=ema_slow, adjust=False).mean()\n",
"\n",
"# Map the 30-minute EMAs back to the 1-minute dataframe\n",
"df = df.set_index('timestamp')\n",
"df['ema_fast_30m'] = df_30m['ema_fast_30m'].reindex(df.index, method='ffill')\n",
"df['ema_slow_30m'] = df_30m['ema_slow_30m'].reindex(df.index, method='ffill')\n",
"df = df.reset_index()\n",
"\n",
"\n",
"# --- Original 1-minute indicators ---\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": 17,
"id": "6a98f0cd",
"metadata": {},
"outputs": [],
"source": [
"# Drop NaN\n",
"df = df.dropna().reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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",
" # Dual timeframe trend check\n",
" trend_up_1m = row['ema_fast_1m'] > row['ema_slow_1m']\n",
" trend_up_30m = row['ema_fast_30m'] > row['ema_slow_30m']\n",
" trend_up = trend_up_1m and trend_up_30m\n",
"\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": 19,
"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": 21,
"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: $1,569,689\n",
"Net Profit: $1,469,689 (1,469.7%)\n",
"Total Trades: 4107\n",
"Win Rate: 48.9%\n",
"Avg Win: $3,900\n",
"Avg Loss: $-3,034\n",
"Profit Factor: 1.23\n",
"Max Drawdown: $306,828\n",
"Sharpe Ratio: 2.84\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: ${-max_dd:,.0f}\")\n",
"print(f\"Sharpe Ratio: {sharpe:.2f}\")\n",
"print(\"=\"*60)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "39278772",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 8. PLOT EQUITY CURVE\n",
"plt.figure(figsize=(14, 8))\n",
"plt.plot(df['timestamp'], df['equity'], label='Equity Curve', color='blue')\n",
"plt.fill_between(df['timestamp'], df['equity'], df['peak'], where=df['drawdown'] < 0, color='red', alpha=0.3, label='Drawdown')\n",
"plt.title('Equity Curve - Long-Only + 10-Min Cooldown (20 Contracts)')\n",
"plt.ylabel('Equity ($)')\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": "pyvenv_lemaske",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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