{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scan All Symbols for Cointegration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Found 2061 symbols in MT5\n",
"✅ Cointegrated Pair: EURUSD & AUDNZD (p=0.0059)\n",
"✅ Cointegrated Pair: USDCHF & EURAUD (p=0.0083)\n",
"✅ Cointegrated Pair: USDCHF & EURCAD (p=0.0279)\n",
"✅ Cointegrated Pair: USDCHF & GBPAUD (p=0.0099)\n",
"✅ Cointegrated Pair: USDCHF & GBPNOK (p=0.0316)\n",
"✅ Cointegrated Pair: USDCHF & NOKSEK (p=0.0437)\n",
"✅ Cointegrated Pair: USDJPY & AUDJPY (p=0.0037)\n",
"✅ Cointegrated Pair: AUDUSD & AUDJPY (p=0.0134)\n",
"✅ Cointegrated Pair: AUDUSD & CHFJPY (p=0.0103)\n",
"✅ Cointegrated Pair: AUDUSD & EURJPY (p=0.0171)\n",
"✅ Cointegrated Pair: AUDUSD & EURCHF (p=0.0032)\n",
"✅ Cointegrated Pair: AUDUSD & GBPCHF (p=0.0097)\n",
"✅ Cointegrated Pair: AUDUSD & GBPJPY (p=0.0270)\n",
"✅ Cointegrated Pair: AUDUSD & CADJPY (p=0.0240)\n",
"✅ Cointegrated Pair: AUDUSD & NZDCHF (p=0.0100)\n",
"✅ Cointegrated Pair: AUDUSD & NZDJPY (p=0.0260)\n",
"✅ Cointegrated Pair: AUDUSD & NZDUSD (p=0.0004)\n",
"✅ Cointegrated Pair: AUDUSD & GBPSEK (p=0.0420)\n",
"✅ Cointegrated Pair: AUDUSD & SEKJPY (p=0.0312)\n",
"✅ Cointegrated Pair: AUDUSD & SGDJPY (p=0.0049)\n",
"✅ Cointegrated Pair: AUDUSD & USDCNH (p=0.0080)\n",
"✅ Cointegrated Pair: AUDNZD & NZDCAD (p=0.0092)\n",
"✅ Cointegrated Pair: AUDNZD & NZDUSD (p=0.0275)\n",
"✅ Cointegrated Pair: AUDNZD & EURHKD (p=0.0037)\n",
"✅ Cointegrated Pair: AUDNZD & EURSGD (p=0.0096)\n",
"✅ Cointegrated Pair: AUDNZD & USDCZK (p=0.0074)\n",
"✅ Cointegrated Pair: AUDCAD & AUDJPY (p=0.0259)\n",
"✅ Cointegrated Pair: AUDCAD & CHFJPY (p=0.0413)\n",
"✅ Cointegrated Pair: AUDCAD & EURJPY (p=0.0406)\n",
"✅ Cointegrated Pair: AUDCAD & EURCHF (p=0.0325)\n",
"✅ Cointegrated Pair: AUDCAD & GBPCHF (p=0.0235)\n",
"✅ Cointegrated Pair: AUDCAD & GBPJPY (p=0.0477)\n",
"✅ Cointegrated Pair: AUDCAD & CADJPY (p=0.0228)\n",
"✅ Cointegrated Pair: AUDCAD & NZDCAD (p=0.0103)\n",
"✅ Cointegrated Pair: AUDCAD & NZDJPY (p=0.0308)\n",
"✅ Cointegrated Pair: AUDCAD & CHFSGD (p=0.0473)\n",
"✅ Cointegrated Pair: AUDCAD & EURSEK (p=0.0467)\n",
"✅ Cointegrated Pair: AUDCAD & EURZAR (p=0.0404)\n",
"✅ Cointegrated Pair: AUDCAD & GBPSGD (p=0.0495)\n",
"✅ Cointegrated Pair: AUDCAD & NOKJPY (p=0.0103)\n",
"✅ Cointegrated Pair: AUDCAD & SEKJPY (p=0.0440)\n",
"✅ Cointegrated Pair: AUDCAD & SGDJPY (p=0.0443)\n",
"✅ Cointegrated Pair: AUDCAD & USDCNH (p=0.0238)\n",
"✅ Cointegrated Pair: AUDCHF & EURJPY (p=0.0447)\n",
"✅ Cointegrated Pair: AUDCHF & EURNZD (p=0.0127)\n",
"✅ Cointegrated Pair: AUDCHF & EURDKK (p=0.0204)\n",
"✅ Cointegrated Pair: AUDCHF & EURNOK (p=0.0409)\n",
"✅ Cointegrated Pair: AUDCHF & EURZAR (p=0.0306)\n",
"✅ Cointegrated Pair: AUDCHF & GBPSEK (p=0.0232)\n",
"✅ Cointegrated Pair: AUDJPY & EURCHF (p=0.0391)\n",
"✅ Cointegrated Pair: AUDJPY & CADJPY (p=0.0318)\n",
"✅ Cointegrated Pair: AUDJPY & USDCNH (p=0.0480)\n",
"✅ Cointegrated Pair: CHFJPY & EURJPY (p=0.0242)\n",
"✅ Cointegrated Pair: CHFJPY & EURDKK (p=0.0232)\n",
"✅ Cointegrated Pair: CHFJPY & GBPSEK (p=0.0284)\n",
"✅ Cointegrated Pair: EURAUD & CHFSGD (p=0.0284)\n",
"✅ Cointegrated Pair: EURJPY & NZDCHF (p=0.0353)\n",
"✅ Cointegrated Pair: EURJPY & EURDKK (p=0.0029)\n",
"✅ Cointegrated Pair: EURJPY & GBPSEK (p=0.0155)\n",
"✅ Cointegrated Pair: EURJPY & USDCNH (p=0.0398)\n",
"✅ Cointegrated Pair: EURCHF & CADJPY (p=0.0243)\n",
"✅ Cointegrated Pair: EURCHF & NZDJPY (p=0.0450)\n",
"✅ Cointegrated Pair: EURCHF & NZDUSD (p=0.0040)\n",
"✅ Cointegrated Pair: EURCHF & AUDSGD (p=0.0366)\n",
"✅ Cointegrated Pair: EURCHF & SGDJPY (p=0.0137)\n",
"✅ Cointegrated Pair: EURCHF & USDCNH (p=0.0272)\n",
"✅ Cointegrated Pair: EURNZD & GBPJPY (p=0.0498)\n",
"✅ Cointegrated Pair: EURNZD & CADCHF (p=0.0067)\n",
"✅ Cointegrated Pair: EURNZD & EURDKK (p=0.0234)\n",
"✅ Cointegrated Pair: EURNZD & EURNOK (p=0.0157)\n",
"✅ Cointegrated Pair: EURNZD & GBPNOK (p=0.0008)\n",
"✅ Cointegrated Pair: EURNZD & GBPSEK (p=0.0343)\n",
"✅ Cointegrated Pair: EURCAD & NOKSEK (p=0.0463)\n",
"✅ Cointegrated Pair: GBPCHF & CADJPY (p=0.0391)\n",
"✅ Cointegrated Pair: GBPCHF & NZDUSD (p=0.0135)\n",
"✅ Cointegrated Pair: GBPCHF & USDCNH (p=0.0358)\n",
"✅ Cointegrated Pair: GBPJPY & EURDKK (p=0.0006)\n",
"✅ Cointegrated Pair: GBPJPY & EURTRY (p=0.0008)\n",
"✅ Cointegrated Pair: GBPJPY & GBPSEK (p=0.0307)\n",
"✅ Cointegrated Pair: GBPJPY & GBPTRY (p=0.0044)\n",
"✅ Cointegrated Pair: GBPJPY & SEKJPY (p=0.0242)\n",
"✅ Cointegrated Pair: CADCHF & EURDKK (p=0.0390)\n",
"✅ Cointegrated Pair: CADCHF & EURNOK (p=0.0225)\n",
"✅ Cointegrated Pair: CADCHF & GBPNOK (p=0.0044)\n",
"✅ Cointegrated Pair: CADJPY & NZDJPY (p=0.0275)\n",
"✅ Cointegrated Pair: GBPAUD & CHFSGD (p=0.0319)\n",
"✅ Cointegrated Pair: GBPAUD & EURPLN (p=0.0082)\n",
"✅ Cointegrated Pair: GBPCAD & EURPLN (p=0.0464)\n",
"✅ Cointegrated Pair: GBPNZD & EURPLN (p=0.0043)\n",
"✅ Cointegrated Pair: NZDCAD & NZDJPY (p=0.0267)\n",
"✅ Cointegrated Pair: NZDCAD & EURHKD (p=0.0409)\n",
"✅ Cointegrated Pair: NZDCAD & NOKJPY (p=0.0145)\n",
"✅ Cointegrated Pair: NZDCHF & AUDSGD (p=0.0109)\n",
"✅ Cointegrated Pair: NZDCHF & GBPSEK (p=0.0124)\n",
"✅ Cointegrated Pair: NZDCHF & USDCNH (p=0.0489)\n",
"✅ Cointegrated Pair: NZDJPY & NZDUSD (p=0.0184)\n",
"✅ Cointegrated Pair: NZDJPY & EURDKK (p=0.0263)\n",
"✅ Cointegrated Pair: NZDJPY & EURSEK (p=0.0455)\n",
"✅ Cointegrated Pair: NZDJPY & GBPSEK (p=0.0484)\n",
"✅ Cointegrated Pair: NZDJPY & USDCNH (p=0.0337)\n",
"✅ Cointegrated Pair: NZDUSD & SGDJPY (p=0.0207)\n",
"✅ Cointegrated Pair: USDSGD & EURNOK (p=0.0378)\n",
"✅ Cointegrated Pair: USDSGD & NOKSEK (p=0.0244)\n",
"✅ Cointegrated Pair: AUDSGD & GBPSEK (p=0.0438)\n",
"✅ Cointegrated Pair: CHFSGD & EURDKK (p=0.0184)\n",
"✅ Cointegrated Pair: CHFSGD & EURNOK (p=0.0406)\n",
"✅ Cointegrated Pair: CHFSGD & EURPLN (p=0.0145)\n",
"✅ Cointegrated Pair: CHFSGD & EURZAR (p=0.0343)\n",
"✅ Cointegrated Pair: CHFSGD & GBPNOK (p=0.0194)\n",
"✅ Cointegrated Pair: CHFSGD & GBPSEK (p=0.0473)\n",
"✅ Cointegrated Pair: EURDKK & EURNOK (p=0.0176)\n",
"✅ Cointegrated Pair: EURDKK & EURPLN (p=0.0300)\n",
"✅ Cointegrated Pair: EURDKK & EURTRY (p=0.0016)\n",
"✅ Cointegrated Pair: EURDKK & GBPNOK (p=0.0010)\n",
"✅ Cointegrated Pair: EURDKK & GBPSEK (p=0.0109)\n",
"✅ Cointegrated Pair: EURDKK & GBPTRY (p=0.0025)\n",
"✅ Cointegrated Pair: EURDKK & SEKJPY (p=0.0025)\n",
"✅ Cointegrated Pair: EURPLN & GBPNOK (p=0.0148)\n",
"✅ Cointegrated Pair: EURSEK & EURZAR (p=0.0334)\n",
"✅ Cointegrated Pair: EURTRY & SEKJPY (p=0.0320)\n",
"✅ Cointegrated Pair: GBPSEK & GBPTRY (p=0.0201)\n",
"✅ Cointegrated Pair: GBPSEK & SEKJPY (p=0.0292)\n",
"✅ Cointegrated Pair: GBPSEK & SGDJPY (p=0.0222)\n",
"✅ Cointegrated Pair: GBPTRY & SEKJPY (p=0.0315)\n",
"✅ Cointegrated Pair: NOKJPY & SGDJPY (p=0.0357)\n",
"✅ Cointegrated Pair: NOKJPY & USDCNH (p=0.0328)\n",
"✅ Cointegrated Pair: NOKJPY & USDCZK (p=0.0034)\n",
"✅ Cointegrated Pair: SEKJPY & SGDJPY (p=0.0484)\n",
"\n",
"🔹 Best Cointegrated Pairs:\n",
"⚖️ AUDUSD & NZDUSD (p=0.0004)\n",
"⚖️ GBPJPY & EURDKK (p=0.0006)\n",
"⚖️ EURNZD & GBPNOK (p=0.0008)\n",
"⚖️ GBPJPY & EURTRY (p=0.0008)\n",
"⚖️ EURDKK & GBPNOK (p=0.0010)\n",
"⚖️ EURDKK & EURTRY (p=0.0016)\n",
"⚖️ EURDKK & SEKJPY (p=0.0025)\n",
"⚖️ EURDKK & GBPTRY (p=0.0025)\n",
"⚖️ EURJPY & EURDKK (p=0.0029)\n",
"⚖️ AUDUSD & EURCHF (p=0.0032)\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import MetaTrader5 as mt5\n",
"import pandas as pd\n",
"import itertools\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 available symbols\n",
"symbols = [s.name for s in mt5.symbols_get()]\n",
"print(f\"✅ Found {len(symbols)} symbols in MT5\")\n",
"\n",
"# Limit to top N symbols for testing (remove this for full scan)\n",
"symbols = symbols[:50] # Adjust as needed for performance\n",
"\n",
"# Function to fetch price data\n",
"def get_data(symbol, n_bars=1000, timeframe=mt5.TIMEFRAME_D1):\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\n",
"\n",
"# Load data for selected symbols\n",
"price_data = {symbol: get_data(symbol) for symbol in symbols if get_data(symbol) is not None}\n",
"\n",
"# Find cointegrated pairs\n",
"cointegrated_pairs = []\n",
"for sym1, sym2 in itertools.combinations(price_data.keys(), 2):\n",
" try:\n",
" p_value = coint(price_data[sym1][\"close\"], price_data[sym2][\"close\"])[1]\n",
" if p_value < 0.05: # Cointegration threshold\n",
" cointegrated_pairs.append((sym1, sym2, p_value))\n",
" print(f\"✅ Cointegrated Pair: {sym1} & {sym2} (p={p_value:.4f})\")\n",
" except Exception as e:\n",
" print(f\"⚠️ Error processing {sym1} & {sym2}: {e}\")\n",
"\n",
"# Sort pairs by strongest cointegration (smallest p-value)\n",
"cointegrated_pairs.sort(key=lambda x: x[2])\n",
"\n",
"# Display best pairs\n",
"print(\"\\n🔹 Best Cointegrated Pairs:\")\n",
"for pair in cointegrated_pairs[:10]: # Show top 10 pairs\n",
" print(f\"⚖️ {pair[0]} & {pair[1]} (p={pair[2]:.4f})\")\n",
"\n",
"# Shutdown MT5 connection\n",
"mt5.shutdown()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Feature Engineering for Pair Trading \n",
"We need to compute additional statistical indicators to enhance our entry and exit signals. \n",
"\n",
"🛠 Key Features: \n",
"1️⃣ Price Spread: Difference between two stock prices \n",
"2️⃣ Z-score: Standardized spread for signal generation \n",
"3️⃣ Rolling Mean & Std: For mean-reversion detection \n",
"4️⃣ Correlation & Beta Hedging: Adjust position sizes \n",
"5️⃣ ML Features: Apply PCA, Clustering, Deep Learning "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" spread z_score rolling_mean rolling_std\n",
"time \n",
"2025-02-27 -0.31186 0.274287 -0.313533 0.006098\n",
"2025-02-28 -0.31641 -0.503723 -0.313397 0.005982\n",
"2025-03-03 -0.31801 -0.762773 -0.313429 0.006005\n",
"2025-03-04 -0.31793 -0.739662 -0.313469 0.006032\n",
"2025-03-05 -0.31743 -0.648396 -0.313505 0.006053\n"
]
}
],
"source": [
"import sys\n",
"import os\n",
"import warnings\n",
"from pathlib import Path\n",
"\n",
"# ---------------------------------------------------------------------------\n",
"# 1) SET PROJECT ROOT AND UPDATE PATH/WORKING DIRECTORY\n",
"# ---------------------------------------------------------------------------\n",
"project_root = Path.cwd().parent.parent # Adjust if your notebook is in notebooks/time_series\n",
"sys.path.append(str(project_root))\n",
"os.chdir(str(project_root))\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import MetaTrader5 as mt5\n",
"import vectorbt as vbt\n",
"\n",
"from data.data_loader import get_data_mt5\n",
"from features.feature_engineering import add_all_ta_features\n",
"\n",
"import MetaTrader5 as mt5\n",
"\n",
"\n",
"\n",
"# Initialize MetaTrader 5 connection\n",
"if not mt5.initialize():\n",
" print(\"MT5 Initialization Failed\")\n",
" mt5.shutdown()\n",
"\n",
"def compute_spread_features(df1, df2):\n",
" \"\"\"\n",
" Compute spread and statistical features for pair trading.\n",
" \"\"\"\n",
" spread = df1[\"close\"] - df2[\"close\"]\n",
" z_score = (spread - spread.rolling(window=30).mean()) / spread.rolling(window=30).std()\n",
"\n",
" features = pd.DataFrame({\n",
" \"spread\": spread,\n",
" \"z_score\": z_score,\n",
" \"rolling_mean\": spread.rolling(window=30).mean(),\n",
" \"rolling_std\": spread.rolling(window=30).std(),\n",
" })\n",
" \n",
" return features.dropna()\n",
"\n",
"# Example usage\n",
"df_AUDUSD = get_data_mt5(\"AUDUSD\", n_bars=1000, timeframe=mt5.TIMEFRAME_D1)\n",
"df_NZDUSD = get_data_mt5(\"EURCHF\", n_bars=1000, timeframe=mt5.TIMEFRAME_D1)\n",
"\n",
"spread_features = compute_spread_features(df_AUDUSD, df_NZDUSD)\n",
"print(spread_features.tail())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Machine Learning for Signal Generation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" spread z_score rolling_mean rolling_std target signal\n",
"time \n",
"2025-02-27 -0.31186 0.274287 -0.313533 0.006098 1 0\n",
"2025-02-28 -0.31641 -0.503723 -0.313397 0.005982 1 1\n",
"2025-03-03 -0.31801 -0.762773 -0.313429 0.006005 1 1\n",
"2025-03-04 -0.31793 -0.739662 -0.313469 0.006032 1 1\n",
"2025-03-05 -0.31743 -0.648396 -0.313505 0.006053 0 0\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"import numpy as np\n",
"\n",
"\n",
"# Prepare dataset\n",
"spread_features[\"target\"] = np.where(spread_features[\"z_score\"].shift(-1) < 0, 1, 0) # Mean Reversion Signal\n",
"X = spread_features.drop(\"target\", axis=1)\n",
"y = spread_features[\"target\"]\n",
"\n",
"# Train ML Model\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Predict signals\n",
"spread_features[\"signal\"] = model.predict(X)\n",
"\n",
"print(spread_features.tail())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trade Execution Logic"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"📉 Enter SHORT AUDUSD & LONG EURCHF\n"
]
}
],
"source": [
"import MetaTrader5 as mt5\n",
"\n",
"def execute_trade(symbol1, symbol2, signal):\n",
" if signal == 1:\n",
" print(f\"📈 Enter LONG {symbol1} & SHORT {symbol2}\")\n",
" mt5.order_send(symbol1, mt5.ORDER_TYPE_BUY, 0.1)\n",
" mt5.order_send(symbol2, mt5.ORDER_TYPE_SELL, 0.1)\n",
" elif signal == 0:\n",
" print(f\"📉 Enter SHORT {symbol1} & LONG {symbol2}\")\n",
" mt5.order_send(symbol1, mt5.ORDER_TYPE_SELL, 0.1)\n",
" mt5.order_send(symbol2, mt5.ORDER_TYPE_BUY, 0.1)\n",
"\n",
"# Execute trade for top cointegrated pair\n",
"latest_signal = spread_features[\"signal\"].iloc[-1]\n",
"execute_trade(\"AUDUSD\", \"EURCHF\", latest_signal)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Risk Management"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"SL_PIPS = 50\n",
"TP_PIPS = 100\n",
"\n",
"def execute_trade(symbol1, symbol2, signal):\n",
" price1 = mt5.symbol_info_tick(symbol1).ask\n",
" price2 = mt5.symbol_info_tick(symbol2).bid\n",
"\n",
" stop_loss1 = price1 - (SL_PIPS * 0.0001)\n",
" take_profit1 = price1 + (TP_PIPS * 0.0001)\n",
"\n",
" stop_loss2 = price2 + (SL_PIPS * 0.0001)\n",
" take_profit2 = price2 - (TP_PIPS * 0.0001)\n",
"\n",
" if signal == 1:\n",
" mt5.order_send(symbol1, mt5.ORDER_TYPE_BUY, 0.1, price1, sl=stop_loss1, tp=take_profit1)\n",
" mt5.order_send(symbol2, mt5.ORDER_TYPE_SELL, 0.1, price2, sl=stop_loss2, tp=take_profit2)\n",
" elif signal == 0:\n",
" mt5.order_send(symbol1, mt5.ORDER_TYPE_SELL, 0.1, price1, sl=stop_loss1, tp=take_profit1)\n",
" mt5.order_send(symbol2, mt5.ORDER_TYPE_BUY, 0.1, price2, sl=stop_loss2, tp=take_profit2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run Backtest on Historical Data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔄 2021-06-22 00:00:00: Signal 1.0\n",
"🔄 2021-06-23 00:00:00: Signal 1.0\n",
"🔄 2021-06-24 00:00:00: Signal 1.0\n",
"🔄 2021-06-25 00:00:00: Signal 1.0\n",
"🔄 2021-06-28 00:00:00: Signal 1.0\n",
"🔄 2021-06-29 00:00:00: Signal 1.0\n",
"🔄 2021-06-30 00:00:00: Signal 1.0\n",
"🔄 2021-07-01 00:00:00: Signal 1.0\n",
"🔄 2021-07-02 00:00:00: Signal 1.0\n",
"🔄 2021-07-05 00:00:00: Signal 1.0\n",
"🔄 2021-07-06 00:00:00: Signal 1.0\n",
"🔄 2021-07-07 00:00:00: Signal 1.0\n",
"🔄 2021-07-08 00:00:00: Signal 1.0\n",
"🔄 2021-07-09 00:00:00: Signal 1.0\n",
"🔄 2021-07-12 00:00:00: Signal 1.0\n",
"🔄 2021-07-13 00:00:00: Signal 1.0\n",
"🔄 2021-07-14 00:00:00: Signal 1.0\n",
"🔄 2021-07-15 00:00:00: Signal 1.0\n",
"🔄 2021-07-16 00:00:00: Signal 1.0\n",
"🔄 2021-07-19 00:00:00: Signal 1.0\n",
"🔄 2021-07-20 00:00:00: Signal 1.0\n",
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}
],
"source": [
"for time, row in spread_features.iterrows():\n",
" signal = row[\"signal\"]\n",
" if signal != 0: # Only execute when signal exists\n",
" print(f\"🔄 {time}: Signal {signal}\")\n",
" execute_trade(\"AUDUSD\", \"NZDUSD\", signal)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Full Pair Trading Backtest with MT5 + VectorBT"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Connected to MT5\n",
"\n",
"✅ Data Loaded (2000 bars)\n",
" AUDUSD NZDUSD\n",
"time \n",
"2017-06-19 0.75973 0.72291\n",
"2017-06-20 0.75798 0.72397\n",
"2017-06-21 0.75521 0.72160\n",
"2017-06-22 0.75405 0.72642\n",
"2017-06-23 0.75691 0.72800\n",
"✅ AUDUSD & NZDUSD are cointegrated (p=0.0182)\n"
]
}
],
"source": [
"import sys\n",
"import os\n",
"import warnings\n",
"from pathlib import Path\n",
"import numpy as np\n",
"import pandas as pd\n",
"import MetaTrader5 as mt5\n",
"import vectorbt as vbt\n",
"import statsmodels.api as sm\n",
"from statsmodels.tsa.stattools import coint\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"\n",
"import sys\n",
"import os\n",
"import warnings\n",
"from pathlib import Path\n",
"\n",
"# ---------------------------------------------------------------------------\n",
"# 1) SET PROJECT ROOT AND UPDATE PATH/WORKING DIRECTORY\n",
"# ---------------------------------------------------------------------------\n",
"project_root = Path.cwd().parent.parent # Adjust if your notebook is in notebooks/time_series\n",
"sys.path.append(str(project_root))\n",
"os.chdir(str(project_root))\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# Disable warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# ✅ Load our modules\n",
"from data.data_loader import get_data_mt5\n",
"\n",
"# ✅ Initialize MetaTrader 5 connection\n",
"if not mt5.initialize():\n",
" print(\"Failed to initialize MT5\")\n",
"else:\n",
" print(\"✅ Connected to MT5\")\n",
"\n",
"# ✅ Select Trading Pair\n",
"pair1, pair2 = \"AUDUSD\", \"NZDUSD\"\n",
"n_bars = 2000\n",
"timeframe = mt5.TIMEFRAME_D1 # Use 4-hour data\n",
"\n",
"# ✅ Load historical price data\n",
"df1 = get_data_mt5(pair1, n_bars, timeframe)\n",
"df2 = get_data_mt5(pair2, n_bars, timeframe)\n",
"\n",
"# ✅ Align timestamps\n",
"df = pd.DataFrame({\n",
" \"time\": df1.index,\n",
" f\"{pair1}\": df1[\"close\"],\n",
" f\"{pair2}\": df2[\"close\"]\n",
"}).dropna().set_index(\"time\")\n",
"\n",
"print(f\"\\n✅ Data Loaded ({len(df)} bars)\")\n",
"print(df.head())\n",
"\n",
"# ✅ Check for Cointegration\n",
"coint_p_value = coint(df[pair1], df[pair2])[1]\n",
"if coint_p_value > 0.05:\n",
" print(f\"⚠️ {pair1} & {pair2} are NOT cointegrated (p={coint_p_value:.4f})\")\n",
"else:\n",
" print(f\"✅ {pair1} & {pair2} are cointegrated (p={coint_p_value:.4f})\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"✅ Spread & Z-score Calculated\n",
" AUDUSD NZDUSD spread spread_mean spread_std z_score\n",
"time \n",
"2025-02-27 0.62356 0.56311 0.076056 0.077058 0.002399 -0.417721\n",
"2025-02-28 0.62039 0.55958 0.076318 0.076999 0.002342 -0.290614\n",
"2025-03-03 0.62247 0.56164 0.076396 0.076947 0.002296 -0.240165\n",
"2025-03-04 0.62707 0.56642 0.076348 0.076899 0.002256 -0.243992\n",
"2025-03-05 0.62652 0.56594 0.076265 0.076862 0.002236 -0.267003\n"
]
}
],
"source": [
"# ✅ Compute Spread (OLS Regression)\n",
"X = sm.add_constant(df[pair2]) # Add intercept\n",
"model = sm.OLS(df[pair1], X).fit()\n",
"hedge_ratio = model.params[pair2]\n",
"df[\"spread\"] = df[pair1] - hedge_ratio * df[pair2]\n",
"\n",
"# ✅ Compute Z-Score for Trading\n",
"lookback = 100 # Moving window for Z-score\n",
"df[\"spread_mean\"] = df[\"spread\"].rolling(lookback).mean()\n",
"df[\"spread_std\"] = df[\"spread\"].rolling(lookback).std()\n",
"df[\"z_score\"] = (df[\"spread\"] - df[\"spread_mean\"]) / df[\"spread_std\"]\n",
"\n",
"print(\"\\n✅ Spread & Z-score Calculated\")\n",
"print(df.tail())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"✅ Trading Signals Generated\n",
" spread z_score long_signal short_signal exit_signal\n",
"time \n",
"2025-02-27 0.076056 -0.417721 False False True\n",
"2025-02-28 0.076318 -0.290614 False False True\n",
"2025-03-03 0.076396 -0.240165 False False True\n",
"2025-03-04 0.076348 -0.243992 False False True\n",
"2025-03-05 0.076265 -0.267003 False False True\n"
]
}
],
"source": [
"# ✅ Define Trading Thresholds\n",
"entry_threshold = 1.5 # Enter when Z-score > 1.5 or < -1.5\n",
"exit_threshold = 0.5 # Exit when Z-score returns to 0.5\n",
"\n",
"# ✅ Generate Trading Signals\n",
"df[\"long_signal\"] = df[\"z_score\"] < -entry_threshold\n",
"df[\"short_signal\"] = df[\"z_score\"] > entry_threshold\n",
"df[\"exit_signal\"] = df[\"z_score\"].abs() < exit_threshold\n",
"\n",
"# ✅ Convert to VectorBT Signals\n",
"entries = df[\"long_signal\"].astype(int) - df[\"short_signal\"].astype(int) # 1 for long, -1 for short\n",
"exits = df[\"exit_signal\"]\n",
"\n",
"print(\"\\n✅ Trading Signals Generated\")\n",
"print(df[[\"spread\", \"z_score\", \"long_signal\", \"short_signal\", \"exit_signal\"]].tail())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"✅ Backtest Completed\n",
"Start 2017-06-19 00:00:00\n",
"End 2025-03-05 00:00:00\n",
"Period 333 days 08:00:00\n",
"Start Value 10000.0\n",
"End Value 12095.674086\n",
"Total Return [%] 20.956741\n",
"Benchmark Return [%] -17.533861\n",
"Max Gross Exposure [%] 100.0\n",
"Total Fees Paid 107.953045\n",
"Max Drawdown [%] 16.732506\n",
"Max Drawdown Duration 118 days 04:00:00\n",
"Total Trades 27\n",
"Total Closed Trades 27\n",
"Total Open Trades 0\n",
"Open Trade PnL 0.0\n",
"Win Rate [%] 51.851852\n",
"Best Trade [%] 9.07005\n",
"Worst Trade [%] -9.965733\n",
"Avg Winning Trade [%] 3.459788\n",
"Avg Losing Trade [%] -2.101339\n",
"Avg Winning Trade Duration 4 days 00:17:08.571428571\n",
"Avg Losing Trade Duration 7 days 13:50:46.153846153\n",
"Profit Factor 1.775186\n",
"Expectancy 77.617559\n",
"Sharpe Ratio 1.092797\n",
"Calmar Ratio 1.384306\n",
"Omega Ratio 1.096542\n",
"Sortino Ratio 1.630103\n",
"dtype: object\n"
]
},
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"source": [
"# ✅ Define Initial Cash and Trading Fees\n",
"init_cash = 10000\n",
"fees = 0.0002 # 0.02% transaction cost\n",
"\n",
"# ✅ Run Portfolio Backtest\n",
"portfolio = vbt.Portfolio.from_signals(\n",
" close=df[pair1], \n",
" entries=entries > 0,\n",
" exits=exits,\n",
" short_entries=entries < 0,\n",
" short_exits=exits,\n",
" size=1, \n",
" size_type=\"percent\", \n",
" init_cash=init_cash,\n",
" fees=fees,\n",
" freq=\"4H\"\n",
")\n",
"\n",
"# ✅ Print Final Results\n",
"print(\"\\n✅ Backtest Completed\")\n",
"print(portfolio.stats())\n",
"\n",
"# ✅ Plot Performance\n",
"portfolio.plot().show()\n",
"\n"
]
},
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"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Portfolio' object has no attribute 'win_rate'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[11], line 4\u001b[0m\n\u001b[0;32m 2\u001b[0m total_return \u001b[38;5;241m=\u001b[39m portfolio\u001b[38;5;241m.\u001b[39mtotal_return()\n\u001b[0;32m 3\u001b[0m sharpe_ratio \u001b[38;5;241m=\u001b[39m portfolio\u001b[38;5;241m.\u001b[39msharpe_ratio()\n\u001b[1;32m----> 4\u001b[0m win_rate \u001b[38;5;241m=\u001b[39m portfolio\u001b[38;5;241m.\u001b[39mwin_rate()\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m🔹 **Final Performance Metrics:**\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m🔹 Total Return: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtotal_return\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2%\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[1;31mAttributeError\u001b[0m: 'Portfolio' object has no attribute 'win_rate'"
]
}
],
"source": [
"# ✅ Extract Performance Metrics\n",
"total_return = portfolio.total_return()\n",
"sharpe_ratio = portfolio.sharpe_ratio()\n",
"win_rate = portfolio.win_rate()\n",
"\n",
"print(\"\\n🔹 **Final Performance Metrics:**\")\n",
"print(f\"🔹 Total Return: {total_return:.2%}\")\n",
"print(f\"🔹 Sharpe Ratio: {sharpe_ratio:.2f}\")\n",
"print(f\"🔹 Win Rate: {win_rate:.2%}\")\n",
"\n",
"# ✅ Display Trade Log\n",
"print(\"\\n🔹 **Trade Log:**\")\n",
"print(portfolio.orders.records_readable.tail())\n",
"\n",
"# ✅ Plot Equity Curve\n",
"portfolio.plot().show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define Trade Execution Function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import MetaTrader5 as mt5\n",
"\n",
"def execute_trade(pair1, pair2, signal, lot_size=0.1):\n",
" \"\"\"\n",
" Execute trades in MT5 for a pair trading strategy.\n",
" \n",
" - pair1: First currency pair (e.g., \"AUDUSD\")\n",
" - pair2: Second currency pair (e.g., \"NZDUSD\")\n",
" - signal: 1 for LONG pair1 & SHORT pair2, -1 for SHORT pair1 & LONG pair2, 0 to close\n",
" - lot_size: Position size in lots\n",
" \"\"\"\n",
" if not mt5.initialize():\n",
" print(\"MT5 Initialization Failed\")\n",
" return\n",
" \n",
" # Define order types\n",
" if signal == 1:\n",
" print(f\"📈 Entering LONG {pair1} & SHORT {pair2}\")\n",
" order1 = mt5.ORDER_TYPE_BUY\n",
" order2 = mt5.ORDER_TYPE_SELL\n",
" elif signal == -1:\n",
" print(f\"📉 Entering SHORT {pair1} & LONG {pair2}\")\n",
" order1 = mt5.ORDER_TYPE_SELL\n",
" order2 = mt5.ORDER_TYPE_BUY\n",
" else:\n",
" print(f\"🔄 Closing all positions for {pair1} & {pair2}\")\n",
" close_all_positions(pair1, pair2)\n",
" return\n",
"\n",
" # Place trades\n",
" request1 = {\n",
" \"action\": mt5.TRADE_ACTION_DEAL,\n",
" \"symbol\": pair1,\n",
" \"volume\": lot_size,\n",
" \"type\": order1,\n",
" \"price\": mt5.symbol_info_tick(pair1).ask if order1 == mt5.ORDER_TYPE_BUY else mt5.symbol_info_tick(pair1).bid,\n",
" \"deviation\": 10,\n",
" \"magic\": 123456,\n",
" \"comment\": \"Pair Trading\",\n",
" \"type_time\": mt5.ORDER_TIME_GTC,\n",
" \"type_filling\": mt5.ORDER_FILLING_IOC,\n",
" }\n",
" \n",
" request2 = {\n",
" \"action\": mt5.TRADE_ACTION_DEAL,\n",
" \"symbol\": pair2,\n",
" \"volume\": lot_size,\n",
" \"type\": order2,\n",
" \"price\": mt5.symbol_info_tick(pair2).ask if order2 == mt5.ORDER_TYPE_BUY else mt5.symbol_info_tick(pair2).bid,\n",
" \"deviation\": 10,\n",
" \"magic\": 123456,\n",
" \"comment\": \"Pair Trading\",\n",
" \"type_time\": mt5.ORDER_TIME_GTC,\n",
" \"type_filling\": mt5.ORDER_FILLING_IOC,\n",
" }\n",
"\n",
" # Send orders\n",
" result1 = mt5.order_send(request1)\n",
" result2 = mt5.order_send(request2)\n",
"\n",
" if result1.retcode == mt5.TRADE_RETCODE_DONE and result2.retcode == mt5.TRADE_RETCODE_DONE:\n",
" print(f\"✅ Trade executed: {pair1} {order1}, {pair2} {order2}\")\n",
" else:\n",
" print(f\"❌ Trade failed: {result1.comment}, {result2.comment}\")\n",
"\n",
" mt5.shutdown()\n",
" \n",
"def close_all_positions(pair1, pair2):\n",
" \"\"\" Closes all open positions for a given pair. \"\"\"\n",
" open_positions = mt5.positions_get()\n",
" if open_positions is None:\n",
" print(\"No open positions found\")\n",
" return\n",
"\n",
" for pos in open_positions:\n",
" if pos.symbol in [pair1, pair2]:\n",
" close_request = {\n",
" \"action\": mt5.TRADE_ACTION_DEAL,\n",
" \"symbol\": pos.symbol,\n",
" \"volume\": pos.volume,\n",
" \"type\": mt5.ORDER_TYPE_SELL if pos.type == mt5.ORDER_TYPE_BUY else mt5.ORDER_TYPE_BUY,\n",
" \"position\": pos.ticket,\n",
" \"price\": mt5.symbol_info_tick(pos.symbol).bid if pos.type == mt5.ORDER_TYPE_BUY else mt5.symbol_info_tick(pos.symbol).ask,\n",
" \"deviation\": 10,\n",
" \"magic\": 123456,\n",
" \"comment\": \"Closing Pair Trading\",\n",
" \"type_time\": mt5.ORDER_TIME_GTC,\n",
" \"type_filling\": mt5.ORDER_FILLING_IOC,\n",
" }\n",
" result = mt5.order_send(close_request)\n",
" if result.retcode == mt5.TRADE_RETCODE_DONE:\n",
" print(f\"✅ Closed {pos.symbol} position\")\n",
" else:\n",
" print(f\"❌ Failed to close {pos.symbol} position: {result.comment}\")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scan two Symbols for Cointegration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Successfully loaded data for AAPL.NAS\n",
"✅ Successfully loaded data for MSFT.NAS\n",
"\n",
"📊 Data for AAPL.NAS:\n",
" open high low close tick_volume spread real_volume\n",
"time \n",
"2021-03-11 122.76 123.20 121.25 121.97 62184 10 0\n",
"2021-03-12 119.90 121.16 119.15 120.77 61027 10 0\n",
"2021-03-15 120.88 123.90 120.74 123.87 52819 10 0\n",
"2021-03-16 126.01 127.21 124.71 125.65 68656 0 0\n",
"2021-03-17 123.51 125.85 122.33 124.46 102649 0 0\n",
"\n",
"📊 Data for MSFT.NAS:\n",
" open high low close tick_volume spread real_volume\n",
"time \n",
"2021-03-11 236.13 239.17 234.52 237.24 169798 1 0\n",
"2021-03-12 234.37 235.72 233.22 235.32 197211 1 0\n",
"2021-03-15 233.62 234.47 231.81 234.36 151859 1 0\n",
"2021-03-16 236.24 240.05 236.06 238.26 163664 0 0\n",
"2021-03-17 235.24 238.54 233.22 236.75 293709 0 0\n"
]
}
],
"source": [
"import sys\n",
"import os\n",
"import warnings\n",
"from pathlib import Path\n",
"\n",
"# ---------------------------------------------------------------------------\n",
"# 1) SET PROJECT ROOT AND UPDATE PATH/WORKING DIRECTORY\n",
"# ---------------------------------------------------------------------------\n",
"project_root = Path.cwd().parent.parent # Adjust if your notebook is in notebooks/time_series\n",
"sys.path.append(str(project_root))\n",
"os.chdir(str(project_root))\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import MetaTrader5 as mt5\n",
"import vectorbt as vbt\n",
"\n",
"from data.data_loader import get_data_mt5\n",
"from features.feature_engineering import add_all_ta_features\n",
"\n",
"import MetaTrader5 as mt5\n",
"\n",
"\n",
"\n",
"# Initialize MetaTrader 5 connection\n",
"if not mt5.initialize():\n",
" print(\"MT5 Initialization Failed\")\n",
" mt5.shutdown()\n",
"\n",
"# Define the symbols you want to load\n",
"tickers = [\"AAPL.NAS\", \"MSFT.NAS\"]\n",
"\n",
"# Define parameters for data retrieval\n",
"n_bars = 1000\n",
"timeframe = mt5.TIMEFRAME_D1 # Daily timeframe\n",
"\n",
"# Dictionary to store data\n",
"symbols_data = {}\n",
"\n",
"# Fetch and store data for each symbol\n",
"for ticker in tickers:\n",
" try:\n",
" df = get_data_mt5(ticker, n_bars, timeframe) # Fetch data\n",
" symbols_data[ticker] = df # Store in dictionary\n",
" print(f\"✅ Successfully loaded data for {ticker}\")\n",
" except ValueError as e:\n",
" print(f\"⚠️ Error loading {ticker}: {e}\")\n",
"\n",
"# Print sample data\n",
"for symbol, df in symbols_data.items():\n",
" print(f\"\\n📊 Data for {symbol}:\\n{df.head()}\")\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Feature Engineering"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"📊 Features for AAPL.NAS:\n",
" open high low close tick_volume spread \\\n",
"time \n",
"2021-04-16 133.881 134.565 133.275 134.086 36110 10 \n",
"2021-04-19 134.446 135.456 133.701 134.460 37393 10 \n",
"2021-04-20 134.755 135.515 131.798 133.118 37088 10 \n",
"2021-04-21 132.078 133.722 131.319 133.520 35153 10 \n",
"2021-04-22 132.814 134.136 131.390 132.057 37384 10 \n",
"\n",
" real_volume returns bb_upper bb_lower rsi macd \n",
"time \n",
"2021-04-16 0 -0.002730 136.783601 115.534699 68.157682 3.139965 \n",
"2021-04-19 0 0.002789 137.720914 116.012386 68.747924 3.249395 \n",
"2021-04-20 0 -0.009981 138.412049 116.286651 64.152705 3.191046 \n",
"2021-04-21 0 0.003020 139.043257 116.749343 64.909357 3.141035 \n",
"2021-04-22 0 -0.010957 139.207604 117.751596 59.949932 2.949350 \n",
"\n",
"📊 Features for MSFT.NAS:\n",
" open high low close tick_volume spread real_volume \\\n",
"time \n",
"2021-04-16 258.22 260.88 257.90 260.73 21851 1 0 \n",
"2021-04-19 260.89 261.44 257.81 258.22 23590 1 0 \n",
"2021-04-20 258.59 260.15 256.82 258.07 23187 1 0 \n",
"2021-04-21 258.20 260.63 257.22 260.52 21336 1 0 \n",
"2021-04-22 260.58 261.69 255.62 256.92 24417 1 0 \n",
"\n",
" returns bb_upper bb_lower rsi macd \n",
"time \n",
"2021-04-16 0.003773 265.619490 224.352510 73.256268 6.107722 \n",
"2021-04-19 -0.009627 266.696801 226.000199 67.802939 6.104296 \n",
"2021-04-20 -0.000581 267.836309 227.056691 67.479650 6.020081 \n",
"2021-04-21 0.009494 269.218841 227.959159 69.996059 6.080938 \n",
"2021-04-22 -0.013819 269.718922 229.552078 62.360251 5.772139 \n"
]
}
],
"source": [
"from ta.volatility import BollingerBands\n",
"from ta.momentum import RSIIndicator\n",
"from ta.trend import MACD\n",
"\n",
"# Function to add features\n",
"def add_features(df):\n",
" df[\"returns\"] = df[\"close\"].pct_change()\n",
" \n",
" # Bollinger Bands\n",
" bb = BollingerBands(df[\"close\"])\n",
" df[\"bb_upper\"] = bb.bollinger_hband()\n",
" df[\"bb_lower\"] = bb.bollinger_lband()\n",
"\n",
" # RSI\n",
" rsi = RSIIndicator(df[\"close\"])\n",
" df[\"rsi\"] = rsi.rsi()\n",
"\n",
" # MACD\n",
" macd = MACD(df[\"close\"])\n",
" df[\"macd\"] = macd.macd()\n",
" \n",
" return df.dropna()\n",
"\n",
"# Apply to both stocks\n",
"symbols_data = {ticker: add_features(df) for ticker, df in symbols_data.items()}\n",
"\n",
"# Print sample after feature addition\n",
"for symbol, df in symbols_data.items():\n",
" print(f\"\\n📊 Features for {symbol}:\\n\", df.head())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cointegration & Statistical Testing"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"⚖️ Cointegration Test p-value: 0.7008\n",
"⚠️ Not cointegrated. Consider other pairs.\n"
]
}
],
"source": [
"from statsmodels.tsa.stattools import coint\n",
"\n",
"# Extract closing prices\n",
"aapl = symbols_data[\"AAPL.NAS\"][\"close\"]\n",
"msft = symbols_data[\"MSFT.NAS\"][\"close\"]\n",
"\n",
"# Perform cointegration test\n",
"score, p_value, _ = coint(aapl, msft)\n",
"print(f\"\\n⚖️ Cointegration Test p-value: {p_value:.4f}\")\n",
"\n",
"# If p-value < 0.05, the pair is cointegrated\n",
"if p_value < 0.05:\n",
" print(\"✅ AAPL and MSFT are cointegrated. Suitable for pair trading.\")\n",
"else:\n",
" print(\"⚠️ Not cointegrated. Consider other pairs.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute Spread & Z-score"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"📊 Pair Trading Signals:\n",
" spread z_score\n",
"time \n",
"2025-02-26 -159.28 -0.090304\n",
"2025-02-27 -155.23 0.004369\n",
"2025-02-28 -155.00 0.009745\n",
"2025-03-03 -149.33 0.142288\n",
"2025-03-04 -153.16 0.052757\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# Compute spread\n",
"spread = aapl - msft\n",
"\n",
"# Compute Z-score\n",
"z_score = (spread - spread.mean()) / spread.std()\n",
"\n",
"# Store Z-score in a DataFrame\n",
"signals = pd.DataFrame(index=aapl.index)\n",
"signals[\"spread\"] = spread\n",
"signals[\"z_score\"] = z_score\n",
"\n",
"print(\"\\n📊 Pair Trading Signals:\\n\", signals.tail())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 5: Define Trading Rules \n",
"📌 Trading Strategy Rules: \n",
"\n",
"Go Long (Buy AAPL, Sell MSFT) → If Z-score < -1.5 \n",
"Go Short (Sell AAPL, Buy MSFT) → If Z-score > 1.5 \n",
"Exit Position → If |Z-score| < 0.5 "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"📊 Trade Signals:\n",
" spread z_score long short exit\n",
"time \n",
"2025-02-26 -159.28 -0.090304 False False True\n",
"2025-02-27 -155.23 0.004369 False False True\n",
"2025-02-28 -155.00 0.009745 False False True\n",
"2025-03-03 -149.33 0.142288 False False True\n",
"2025-03-04 -153.16 0.052757 False False True\n"
]
}
],
"source": [
"# Define trade signals\n",
"signals[\"long\"] = signals[\"z_score\"] < -1.5 # Buy AAPL, Sell MSFT\n",
"signals[\"short\"] = signals[\"z_score\"] > 1.5 # Sell AAPL, Buy MSFT\n",
"signals[\"exit\"] = abs(signals[\"z_score\"]) < 0.5 # Exit trade\n",
"\n",
"print(\"\\n📊 Trade Signals:\\n\", signals.tail())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Step 6: Backtesting Strategy"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"📊 Strategy Performance:\n",
" strategy_returns cumulative_returns\n",
"time \n",
"2025-02-26 0.0 0.921201\n",
"2025-02-27 0.0 0.921201\n",
"2025-02-28 0.0 0.921201\n",
"2025-03-03 0.0 0.921201\n",
"2025-03-04 0.0 0.921201\n"
]
}
],
"source": [
"# Define returns for AAPL and MSFT\n",
"signals[\"aapl_returns\"] = aapl.pct_change()\n",
"signals[\"msft_returns\"] = msft.pct_change()\n",
"\n",
"# Compute strategy returns\n",
"signals[\"strategy_returns\"] = (\n",
" signals[\"long\"] * signals[\"aapl_returns\"] -\n",
" signals[\"long\"] * signals[\"msft_returns\"] -\n",
" signals[\"short\"] * signals[\"aapl_returns\"] +\n",
" signals[\"short\"] * signals[\"msft_returns\"]\n",
")\n",
"\n",
"# Compute cumulative returns\n",
"signals[\"cumulative_returns\"] = (1 + signals[\"strategy_returns\"]).cumprod()\n",
"\n",
"print(\"\\n📊 Strategy Performance:\\n\", signals[[\"strategy_returns\", \"cumulative_returns\"]].tail())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualize Strategy Performance"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.figure(figsize=(12,6))\n",
"plt.plot(signals[\"cumulative_returns\"], label=\"Pair Trading Strategy\", color=\"blue\")\n",
"plt.axhline(1, color=\"red\", linestyle=\"--\")\n",
"plt.xlabel(\"Date\")\n",
"plt.ylabel(\"Cumulative Returns\")\n",
"plt.legend()\n",
"plt.title(\"Pair Trading Strategy Performance\")\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 8: Live Trading Execution in MT5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def execute_trade(signal, symbol1, symbol2):\n",
" \"\"\"\n",
" Executes trades in MetaTrader 5.\n",
" If 'long', buys symbol1 & sells symbol2.\n",
" If 'short', sells symbol1 & buys symbol2.\n",
" If 'exit', closes all positions.\n",
" \"\"\"\n",
" if signal == \"long\":\n",
" mt5.order_send(\n",
" mt5.ORDER_TYPE_BUY, symbol1, volume=1\n",
" )\n",
" mt5.order_send(\n",
" mt5.ORDER_TYPE_SELL, symbol2, volume=1\n",
" )\n",
" elif signal == \"short\":\n",
" mt5.order_send(\n",
" mt5.ORDER_TYPE_SELL, symbol1, volume=1\n",
" )\n",
" mt5.order_send(\n",
" mt5.ORDER_TYPE_BUY, symbol2, volume=1\n",
" )\n",
" elif signal == \"exit\":\n",
" mt5.positions_close(symbol1)\n",
" mt5.positions_close(symbol2)\n",
"\n",
"# Execute trade based on last signal\n",
"latest_signal = signals.iloc[-1]\n",
"\n",
"if latest_signal[\"long\"]:\n",
" execute_trade(\"long\", \"AAPL.NAS\", \"MSFT.NAS\")\n",
"elif latest_signal[\"short\"]:\n",
" execute_trade(\"short\", \"AAPL.NAS\", \"MSFT.NAS\")\n",
"elif latest_signal[\"exit\"]:\n",
" execute_trade(\"exit\", \"AAPL.NAS\", \"MSFT.NAS\")\n"
]
}
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