Article-24649-GrangerMTF-Causality-Detection/GrangerMTF_Causality.ipynb

{
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   "source": [
    "# Part 11 — Multi-Timeframe Causality Detection using Granger Analysis\n",
    "\n",
    "**Goal:** test whether returns of a *leading* (higher) timeframe Granger-cause returns of a *lagging* (lower) timeframe,\n",
    "then export a calibrated causality table that the MQL5 **GrangerCausalityMonitor** EA reads and re-tests live.\n",
    "\n",
    "Pairs tested: `H1 → M15`, `M30 → M15`, `M15 → M5`, `H1 → M5`\n",
    "\n",
    "**Pipeline**\n",
    "1. Load OHLC for every timeframe (MetaTrader5 package, CSV, or synthetic data)\n",
    "2. Convert prices to log returns (bps) and confirm stationarity (ADF)\n",
    "3. Align timeframes without look-ahead (only *completed* higher-TF bars are used)\n",
    "4. Select lags by BIC, run the Granger F-test, cross-check with statsmodels + HC3 robust test\n",
    "5. Rolling-window stability, out-of-sample check, Benjamini–Hochberg correction\n",
    "6. Export `granger_mtf_causality.csv` to the MT5 **Common\\Files** folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1b11aaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 0 — install once (uncomment and run if needed)\n",
    "# %pip install MetaTrader5 pandas numpy scipy statsmodels matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "261f2ab0-046f-4877-bb57-23a6e7bad76e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected  |  Euro vs US Dollar  |  digits=5\n",
      "Server: FBS MetaTrader 5\n",
      "\n",
      "    M5:  30,524 bars  2026-04-24 → 2026-09-21  | close [1.13254, 1.17941]  → ./data\\EURUSD.m_M5.csv\n",
      "   M15:  10,175 bars  2026-04-24 → 2026-09-21  | close [1.13254, 1.17885]  → ./data\\EURUSD.m_M15.csv\n",
      "   M30:   5,087 bars  2026-04-24 → 2026-09-21  | close [1.13286, 1.17885]  → ./data\\EURUSD.m_M30.csv\n",
      "    H1:   2,543 bars  2026-04-24 → 2026-09-21  | close [1.13286, 1.17885]  → ./data\\EURUSD.m_H1.csv\n",
      "\n",
      "Done. 4/4 timeframes loaded.\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30521</th>\n",
       "      <td>2026-09-21 10:15:00+00:00</td>\n",
       "      <td>1.14723</td>\n",
       "      <td>1.14730</td>\n",
       "      <td>1.14715</td>\n",
       "      <td>1.14724</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30522</th>\n",
       "      <td>2026-09-21 10:20:00+00:00</td>\n",
       "      <td>1.14724</td>\n",
       "      <td>1.14758</td>\n",
       "      <td>1.14723</td>\n",
       "      <td>1.14740</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30523</th>\n",
       "      <td>2026-09-21 10:25:00+00:00</td>\n",
       "      <td>1.14740</td>\n",
       "      <td>1.14760</td>\n",
       "      <td>1.14740</td>\n",
       "      <td>1.14755</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                           time     open     high      low    close  volume\n",
       "30521 2026-09-21 10:15:00+00:00  1.14723  1.14730  1.14715  1.14724     308\n",
       "30522 2026-09-21 10:20:00+00:00  1.14724  1.14758  1.14723  1.14740     234\n",
       "30523 2026-09-21 10:25:00+00:00  1.14740  1.14760  1.14740  1.14755     155"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ---------------------------------------------------------------------\n",
    "# CELL: Download EURUSD OHLC from MT5 for all required timeframes\n",
    "# ---------------------------------------------------------------------\n",
    "import MetaTrader5 as mt5\n",
    "import pandas as pd\n",
    "import os\n",
    "from datetime import datetime, timedelta, timezone\n",
    "\n",
    "SYMBOL   = \"EURUSD.m\"\n",
    "DAYS     = 150         # ← reduced so M5 stays under MT5's copy limit\n",
    "SAVE     = True\n",
    "DATA_DIR = \"./data\"\n",
    "\n",
    "TF_MAP = {\n",
    "    \"M5\":  mt5.TIMEFRAME_M5,\n",
    "    \"M15\": mt5.TIMEFRAME_M15,\n",
    "    \"M30\": mt5.TIMEFRAME_M30,\n",
    "    \"H1\":  mt5.TIMEFRAME_H1,\n",
    "}\n",
    "\n",
    "# --- connect \n",
    "if not mt5.initialize():\n",
    "    raise RuntimeError(f\"MT5 initialize() failed: {mt5.last_error()}\")\n",
    "\n",
    "mt5.symbol_select(SYMBOL, True)\n",
    "info = mt5.symbol_info(SYMBOL)\n",
    "if info is None:\n",
    "    mt5.shutdown()\n",
    "    raise RuntimeError(f\"{SYMBOL} not found in Market Watch\")\n",
    "\n",
    "print(f\"Connected  |  {info.description}  |  digits={info.digits}\")\n",
    "print(f\"Server: {mt5.terminal_info().name}\\n\")\n",
    "\n",
    "# --- date window (use copy_rates_range so MT5 handles chunking itself) ─\n",
    "date_to   = datetime.now(timezone.utc)\n",
    "date_from = date_to - timedelta(days=DAYS)\n",
    "\n",
    "if SAVE:\n",
    "    os.makedirs(DATA_DIR, exist_ok=True)\n",
    "\n",
    "data = {}\n",
    "for tf_name, tf_const in TF_MAP.items():\n",
    "    rates = mt5.copy_rates_range(SYMBOL, tf_const, date_from, date_to)\n",
    "\n",
    "    if rates is None or len(rates) == 0:\n",
    "        err = mt5.last_error()\n",
    "        print(f\"  {tf_name:>4}: NO DATA  ({err})\")\n",
    "        continue\n",
    "\n",
    "    df = pd.DataFrame(rates)\n",
    "    df[\"time\"] = pd.to_datetime(df[\"time\"], unit=\"s\", utc=True)\n",
    "    df = df.iloc[:-1].reset_index(drop=True)          # drop forming bar\n",
    "    df = df[[\"time\", \"open\", \"high\", \"low\", \"close\", \"tick_volume\"]].copy()\n",
    "    df.rename(columns={\"tick_volume\": \"volume\"}, inplace=True)\n",
    "\n",
    "    # sanity checks\n",
    "    assert (df[\"high\"] >= df[\"low\"]).all(),    f\"{tf_name}: high < low\"\n",
    "    assert (df[\"close\"] > 0).all(),             f\"{tf_name}: non-positive close\"\n",
    "    assert df[\"time\"].is_monotonic_increasing,  f\"{tf_name}: timestamps not sorted\"\n",
    "\n",
    "    data[tf_name] = df\n",
    "\n",
    "    path = os.path.join(DATA_DIR, f\"{SYMBOL}_{tf_name}.csv\") if SAVE else None\n",
    "    if SAVE:\n",
    "        df.to_csv(path, index=False)\n",
    "\n",
    "    print(f\"  {tf_name:>4}: {len(df):>7,} bars  \"\n",
    "          f\"{df['time'].iloc[0].strftime('%Y-%m-%d')} → \"\n",
    "          f\"{df['time'].iloc[-1].strftime('%Y-%m-%d')}  \"\n",
    "          f\"| close [{df['close'].min():.5f}, {df['close'].max():.5f}]\"\n",
    "          + (f\"  → {path}\" if SAVE else \"\"))\n",
    "\n",
    "mt5.shutdown()\n",
    "print(f\"\\nDone. {len(data)}/{len(TF_MAP)} timeframes loaded.\")\n",
    "\n",
    "# --- verify all four are present before continuing \n",
    "missing = [tf for tf in TF_MAP if tf not in data]\n",
    "if missing:\n",
    "    raise RuntimeError(f\"Missing timeframes: {missing}. \"\n",
    "                       \"Increase DAYS or check MT5 history depth.\")\n",
    "\n",
    "data[\"M5\"].tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e251ab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1 - Imports\n",
    "import os, warnings\n",
    "from datetime import datetime, timedelta, timezone\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option(\"display.width\", 180)\n",
    "pd.set_option(\"display.max_columns\", 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e431d3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timeframes needed: ['M5', 'M15', 'M30', 'H1']\n"
     ]
    }
   ],
   "source": [
    "# Step 2 — Configuration\n",
    "SYMBOL          = \"EURUSD.m\"\n",
    "DATA_SOURCE     = \"mt5\"      # \"mt5\", \"csv\" or \"synthetic\"\n",
    "CSV_DIR         = \"./data\"   # used when DATA_SOURCE == \"csv\": files named SYMBOL_TF.csv (time,open,high,low,close)\n",
    "DAYS_OF_HISTORY = 150\n",
    "\n",
    "PAIRS = [(\"H1\", \"M15\"), (\"M30\", \"M15\"), (\"M15\", \"M5\"), (\"H1\", \"M5\")]   # (lead, lag)\n",
    "\n",
    "ALPHA            = 0.05    # significance level (after BH correction)\n",
    "MAX_P            = 4       # max lags of the LEAD timeframe (in completed lead bars)\n",
    "MAX_Q            = 6       # max lags of the LAG timeframe (own returns)\n",
    "WINDOW           = 1500    # rolling window in lag-TF bars (the EA uses the same window live)\n",
    "STEP             = 250     # rolling step\n",
    "STABILITY_MIN    = 0.35    # share of rolling windows that must be significant\n",
    "TRAIN_FRAC       = 0.70    # chronological train/test split\n",
    "REQUIRE_OOS_EDGE = False   # if True, VALID also needs a positive out-of-sample directional edge\n",
    "SCALE            = 1e4     # returns expressed in basis points\n",
    "OUTPUT_FILE      = \"granger_mtf_causality.csv\"\n",
    "\n",
    "TF_MINUTES = {\"M1\": 1, \"M5\": 5, \"M15\": 15, \"M30\": 30, \"H1\": 60, \"H4\": 240, \"D1\": 1440}\n",
    "TFS = sorted({tf for pair in PAIRS for tf in pair}, key=lambda t: TF_MINUTES[t])\n",
    "print(\"Timeframes needed:\", TFS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eaf797c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  M5:   30560 bars  2026-04-24 10:50:00  ->  2026-09-21 13:40:00\n",
      " M15:   10187 bars  2026-04-24 11:00:00  ->  2026-09-21 13:30:00\n",
      " M30:    5093 bars  2026-04-24 11:00:00  ->  2026-09-21 13:00:00\n",
      "  H1:    2546 bars  2026-04-24 11:00:00  ->  2026-09-21 12:00:00\n"
     ]
    }
   ],
   "source": [
    "# Step 3 --- Data loaders\n",
    "def load_mt5(symbol, tf, days):\n",
    "    import MetaTrader5 as mt5\n",
    "    tf_map = {\"M1\": mt5.TIMEFRAME_M1, \"M5\": mt5.TIMEFRAME_M5, \"M15\": mt5.TIMEFRAME_M15,\n",
    "              \"M30\": mt5.TIMEFRAME_M30, \"H1\": mt5.TIMEFRAME_H1, \"H4\": mt5.TIMEFRAME_H4,\n",
    "              \"D1\": mt5.TIMEFRAME_D1}\n",
    "    date_to = datetime.now(timezone.utc) + timedelta(days=1)\n",
    "    date_from = date_to - timedelta(days=days + 1)\n",
    "    rates = mt5.copy_rates_range(symbol, tf_map[tf], date_from, date_to)\n",
    "    if rates is None or len(rates) == 0:\n",
    "        raise RuntimeError(f\"No {tf} data for {symbol}: {mt5.last_error()}\")\n",
    "    df = pd.DataFrame(rates)\n",
    "    df[\"time\"] = pd.to_datetime(df[\"time\"], unit=\"s\")\n",
    "    # drop the last (still forming) bar so every row is a closed bar\n",
    "    return df[[\"time\", \"open\", \"high\", \"low\", \"close\"]].iloc[:-1].reset_index(drop=True)\n",
    "\n",
    "\n",
    "def load_csv(symbol, tf):\n",
    "    df = pd.read_csv(os.path.join(CSV_DIR, f\"{symbol}_{tf}.csv\"), parse_dates=[\"time\"])\n",
    "    return df[[\"time\", \"open\", \"high\", \"low\", \"close\"]].sort_values(\"time\").reset_index(drop=True)\n",
    "\n",
    "\n",
    "def make_synthetic(days, seed=11):\n",
    "    # M5 random walk with a slow persistent drift -> real multi-timeframe structure for testing\n",
    "    rng = np.random.default_rng(seed)\n",
    "    n = days * 24 * 12\n",
    "    t = pd.date_range(\"2025-01-01\", periods=n, freq=\"5min\")\n",
    "    slow = np.zeros(n)\n",
    "    shocks = rng.standard_normal(n)\n",
    "    for i in range(1, n):\n",
    "        slow[i] = 0.995 * slow[i - 1] + 0.1 * shocks[i]\n",
    "    r = 0.6e-4 * np.r_[0, slow[:-1]] + 4e-4 * rng.standard_normal(n)\n",
    "    close = 2000 * np.exp(np.cumsum(r))\n",
    "    m5 = pd.DataFrame({\"time\": t, \"open\": np.r_[close[0], close[:-1]], \"close\": close})\n",
    "    m5[\"high\"] = m5[[\"open\", \"close\"]].max(axis=1)\n",
    "    m5[\"low\"] = m5[[\"open\", \"close\"]].min(axis=1)\n",
    "    out = {}\n",
    "    for tf in TFS:\n",
    "        if tf == \"M5\":\n",
    "            out[tf] = m5[[\"time\", \"open\", \"high\", \"low\", \"close\"]].copy()\n",
    "            continue\n",
    "        agg = (m5.set_index(\"time\")\n",
    "                 .resample(f\"{TF_MINUTES[tf]}min\", label=\"left\", closed=\"left\")\n",
    "                 .agg({\"open\": \"first\", \"high\": \"max\", \"low\": \"min\", \"close\": \"last\"})\n",
    "                 .dropna().reset_index())\n",
    "        out[tf] = agg\n",
    "    return out\n",
    "\n",
    "\n",
    "if DATA_SOURCE == \"mt5\":\n",
    "    import MetaTrader5 as mt5\n",
    "    if not mt5.initialize():\n",
    "        raise RuntimeError(f\"MT5 initialize() failed: {mt5.last_error()}\")\n",
    "    mt5.symbol_select(SYMBOL, True)\n",
    "    data = {tf: load_mt5(SYMBOL, tf, DAYS_OF_HISTORY) for tf in TFS}\n",
    "elif DATA_SOURCE == \"csv\":\n",
    "    data = {tf: load_csv(SYMBOL, tf) for tf in TFS}\n",
    "else:\n",
    "    data = make_synthetic(DAYS_OF_HISTORY)\n",
    "\n",
    "for tf in TFS:\n",
    "    d = data[tf]\n",
    "    print(f\"{tf:>4}: {len(d):>7} bars  {d['time'].iloc[0]}  ->  {d['time'].iloc[-1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7a4ea9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADF p (price)</th>\n",
       "      <th>ADF p (returns)</th>\n",
       "      <th>ret std (bps)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tf</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M5</th>\n",
       "      <td>0.814968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.576934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M15</th>\n",
       "      <td>0.550736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.895177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M30</th>\n",
       "      <td>0.381284</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.459110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H1</th>\n",
       "      <td>0.382926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.354726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ADF p (price)  ADF p (returns)  ret std (bps)\n",
       "tf                                                \n",
       "M5        0.814968              0.0       1.576934\n",
       "M15       0.550736              0.0       2.895177\n",
       "M30       0.381284              0.0       4.459110\n",
       "H1        0.382926              0.0       6.354726"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 4 — Log returns (bps) and stationarity check\n",
    "def add_returns(df):\n",
    "    df = df.copy()\n",
    "    df[\"ret\"] = SCALE * np.log(df[\"close\"] / df[\"close\"].shift(1))\n",
    "    return df\n",
    "\n",
    "data = {tf: add_returns(df) for tf, df in data.items()}\n",
    "\n",
    "rows = []\n",
    "for tf in TFS:\n",
    "    px = data[tf][\"close\"].tail(5000)\n",
    "    rt = data[tf][\"ret\"].dropna().tail(5000)\n",
    "    rows.append({\"tf\": tf,\n",
    "                 \"ADF p (price)\": adfuller(px, autolag=\"AIC\")[1],\n",
    "                 \"ADF p (returns)\": adfuller(rt, autolag=\"AIC\")[1],\n",
    "                 \"ret std (bps)\": rt.std()})\n",
    "adf_table = pd.DataFrame(rows).set_index(\"tf\")\n",
    "adf_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9610bd95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  H1 -> M15  rows: 10167\n",
      " M30 -> M15  rows: 10177\n",
      " M15 -> M5   rows: 30543\n",
      "  H1 -> M5   rows: 30498\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>ylag1</th>\n",
       "      <th>ylag2</th>\n",
       "      <th>ylag3</th>\n",
       "      <th>ylag4</th>\n",
       "      <th>ylag5</th>\n",
       "      <th>ylag6</th>\n",
       "      <th>xlag1</th>\n",
       "      <th>xlag2</th>\n",
       "      <th>xlag3</th>\n",
       "      <th>xlag4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2026-04-24 16:00:00</th>\n",
       "      <td>-0.597884</td>\n",
       "      <td>1.110385</td>\n",
       "      <td>-5.294575</td>\n",
       "      <td>-1.536610</td>\n",
       "      <td>0.768275</td>\n",
       "      <td>-1.792551</td>\n",
       "      <td>-1.194855</td>\n",
       "      <td>-4.952524</td>\n",
       "      <td>9.651150</td>\n",
       "      <td>14.365853</td>\n",
       "      <td>-2.909041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-04-24 16:15:00</th>\n",
       "      <td>-4.955274</td>\n",
       "      <td>-0.597884</td>\n",
       "      <td>1.110385</td>\n",
       "      <td>-5.294575</td>\n",
       "      <td>-1.536610</td>\n",
       "      <td>0.768275</td>\n",
       "      <td>-1.792551</td>\n",
       "      <td>-4.952524</td>\n",
       "      <td>9.651150</td>\n",
       "      <td>14.365853</td>\n",
       "      <td>-2.909041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-04-24 16:30:00</th>\n",
       "      <td>1.367218</td>\n",
       "      <td>-4.955274</td>\n",
       "      <td>-0.597884</td>\n",
       "      <td>1.110385</td>\n",
       "      <td>-5.294575</td>\n",
       "      <td>-1.536610</td>\n",
       "      <td>0.768275</td>\n",
       "      <td>-4.952524</td>\n",
       "      <td>9.651150</td>\n",
       "      <td>14.365853</td>\n",
       "      <td>-2.909041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-04-24 16:45:00</th>\n",
       "      <td>2.563029</td>\n",
       "      <td>1.367218</td>\n",
       "      <td>-4.955274</td>\n",
       "      <td>-0.597884</td>\n",
       "      <td>1.110385</td>\n",
       "      <td>-5.294575</td>\n",
       "      <td>-1.536610</td>\n",
       "      <td>-4.952524</td>\n",
       "      <td>9.651150</td>\n",
       "      <td>14.365853</td>\n",
       "      <td>-2.909041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-04-24 17:00:00</th>\n",
       "      <td>3.416351</td>\n",
       "      <td>2.563029</td>\n",
       "      <td>1.367218</td>\n",
       "      <td>-4.955274</td>\n",
       "      <td>-0.597884</td>\n",
       "      <td>1.110385</td>\n",
       "      <td>-5.294575</td>\n",
       "      <td>-1.622912</td>\n",
       "      <td>-4.952524</td>\n",
       "      <td>9.651150</td>\n",
       "      <td>14.365853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            y     ylag1     ylag2     ylag3     ylag4     ylag5     ylag6     xlag1     xlag2      xlag3      xlag4\n",
       "2026-04-24 16:00:00 -0.597884  1.110385 -5.294575 -1.536610  0.768275 -1.792551 -1.194855 -4.952524  9.651150  14.365853  -2.909041\n",
       "2026-04-24 16:15:00 -4.955274 -0.597884  1.110385 -5.294575 -1.536610  0.768275 -1.792551 -4.952524  9.651150  14.365853  -2.909041\n",
       "2026-04-24 16:30:00  1.367218 -4.955274 -0.597884  1.110385 -5.294575 -1.536610  0.768275 -4.952524  9.651150  14.365853  -2.909041\n",
       "2026-04-24 16:45:00  2.563029  1.367218 -4.955274 -0.597884  1.110385 -5.294575 -1.536610 -4.952524  9.651150  14.365853  -2.909041\n",
       "2026-04-24 17:00:00  3.416351  2.563029  1.367218 -4.955274 -0.597884  1.110385 -5.294575 -1.622912 -4.952524   9.651150  14.365853"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 5 — Look-ahead-free alignment\n",
    "# For every lag-TF bar opening at time t we only use lead-TF bars that CLOSED at or before t,\n",
    "# i.e. lead bars whose open time <= t - lead_period.\n",
    "def build_design(tf_lead, tf_lag, p_max=MAX_P, q_max=MAX_Q):\n",
    "    lo, hi = data[tf_lag], data[tf_lead]\n",
    "    per_h = pd.Timedelta(minutes=TF_MINUTES[tf_lead])\n",
    "    y = lo[\"ret\"].to_numpy()\n",
    "    hret = hi[\"ret\"].to_numpy()\n",
    "    hpos = np.searchsorted(hi[\"time\"].to_numpy(), (lo[\"time\"] - per_h).to_numpy(), side=\"right\") - 1\n",
    "    i = np.arange(len(lo))\n",
    "    ok = (i - q_max >= 1) & (hpos - (p_max - 1) >= 1)\n",
    "    idx, hp = i[ok], hpos[ok]\n",
    "    cols = {\"y\": y[idx]}\n",
    "    for j in range(1, q_max + 1):\n",
    "        cols[f\"ylag{j}\"] = y[idx - j]                 # own lags (lag-TF bars)\n",
    "    for k in range(1, p_max + 1):\n",
    "        cols[f\"xlag{k}\"] = hret[hp - (k - 1)]         # last k completed lead-TF bars\n",
    "    return pd.DataFrame(cols, index=lo[\"time\"].to_numpy()[idx]).dropna()\n",
    "\n",
    "designs = {pair: build_design(*pair) for pair in PAIRS}\n",
    "for pair, D in designs.items():\n",
    "    print(f\"{pair[0]:>4} -> {pair[1]:<4} rows: {len(D)}\")\n",
    "designs[PAIRS[0]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f05cc7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 6 — Granger F-test (restricted vs unrestricted OLS) and BIC lag selection\n",
    "def design_matrices(D, p, q):\n",
    "    Xr = np.column_stack([np.ones(len(D))] + [D[f\"ylag{j}\"].to_numpy() for j in range(1, q + 1)])\n",
    "    Xu = np.column_stack([Xr] + [D[f\"xlag{k}\"].to_numpy() for k in range(1, p + 1)])\n",
    "    return Xr, Xu, D[\"y\"].to_numpy()\n",
    "\n",
    "\n",
    "def ols(X, y):\n",
    "    beta, *_ = np.linalg.lstsq(X, y, rcond=None)\n",
    "    e = y - X @ beta\n",
    "    return beta, float(e @ e)\n",
    "\n",
    "\n",
    "def granger_test(D, p, q):\n",
    "    Xr, Xu, y = design_matrices(D, p, q)\n",
    "    beta_u, rss_u = ols(Xu, y)\n",
    "    beta_r, rss_r = ols(Xr, y)\n",
    "    n, k = Xu.shape\n",
    "    df2 = n - k\n",
    "    F = max(((rss_r - rss_u) / p) / (rss_u / df2), 0.0)\n",
    "    return {\"F\": F, \"p_value\": float(stats.f.sf(F, p, df2)), \"df2\": df2,\n",
    "            \"beta_u\": beta_u, \"beta_r\": beta_r, \"sigma_y\": float(np.std(y))}\n",
    "\n",
    "\n",
    "def select_lags(D):\n",
    "    best = None\n",
    "    for q in range(1, MAX_Q + 1):\n",
    "        for p in range(1, MAX_P + 1):\n",
    "            _, Xu, y = design_matrices(D, p, q)\n",
    "            _, rss = ols(Xu, y)\n",
    "            n, k = Xu.shape\n",
    "            bic = n * np.log(rss / n) + k * np.log(n)\n",
    "            if best is None or bic < best[0]:\n",
    "                best = (bic, p, q)\n",
    "    return best[1], best[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b8d984e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>lead_tf</th>\n",
       "      <th>lag_tf</th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>window</th>\n",
       "      <th>alpha</th>\n",
       "      <th>f_stat</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_statsmodels</th>\n",
       "      <th>p_hc3</th>\n",
       "      <th>stability</th>\n",
       "      <th>oos_hit</th>\n",
       "      <th>oos_hit_base</th>\n",
       "      <th>oos_edge_bps</th>\n",
       "      <th>oos_r2_gain</th>\n",
       "      <th>n_train</th>\n",
       "      <th>p_adj</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>H1</td>\n",
       "      <td>M15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00119</td>\n",
       "      <td>0.97252</td>\n",
       "      <td>0.97252</td>\n",
       "      <td>0.98082</td>\n",
       "      <td>0.08571</td>\n",
       "      <td>0.52521</td>\n",
       "      <td>0.52254</td>\n",
       "      <td>0.14268</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>7116</td>\n",
       "      <td>0.97252</td>\n",
       "      <td>INVALID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M30</td>\n",
       "      <td>M15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.05</td>\n",
       "      <td>9.64243</td>\n",
       "      <td>0.00191</td>\n",
       "      <td>0.00191</td>\n",
       "      <td>0.11002</td>\n",
       "      <td>0.42857</td>\n",
       "      <td>0.49967</td>\n",
       "      <td>0.51901</td>\n",
       "      <td>-0.03522</td>\n",
       "      <td>-0.00575</td>\n",
       "      <td>7123</td>\n",
       "      <td>0.00764</td>\n",
       "      <td>VALID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M15</td>\n",
       "      <td>M5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.05</td>\n",
       "      <td>4.84478</td>\n",
       "      <td>0.02774</td>\n",
       "      <td>0.02774</td>\n",
       "      <td>0.21685</td>\n",
       "      <td>0.19658</td>\n",
       "      <td>0.50703</td>\n",
       "      <td>0.50657</td>\n",
       "      <td>0.04416</td>\n",
       "      <td>0.00066</td>\n",
       "      <td>21380</td>\n",
       "      <td>0.05548</td>\n",
       "      <td>INVALID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H1</td>\n",
       "      <td>M5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1500</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.09290</td>\n",
       "      <td>0.76053</td>\n",
       "      <td>0.76053</td>\n",
       "      <td>0.83955</td>\n",
       "      <td>0.06034</td>\n",
       "      <td>0.50492</td>\n",
       "      <td>0.50412</td>\n",
       "      <td>0.00523</td>\n",
       "      <td>-0.00000</td>\n",
       "      <td>21348</td>\n",
       "      <td>0.97252</td>\n",
       "      <td>INVALID</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lead_tf lag_tf  p  q  window  alpha   f_stat  p_value  p_statsmodels    p_hc3  stability  oos_hit  oos_hit_base  oos_edge_bps  oos_r2_gain  n_train    p_adj   status\n",
       "0      H1    M15  1  1    1500   0.05  0.00119  0.97252        0.97252  0.98082    0.08571  0.52521       0.52254       0.14268     -0.00000     7116  0.97252  INVALID\n",
       "1     M30    M15  1  2    1500   0.05  9.64243  0.00191        0.00191  0.11002    0.42857  0.49967       0.51901      -0.03522     -0.00575     7123  0.00764    VALID\n",
       "2     M15     M5  1  1    1500   0.05  4.84478  0.02774        0.02774  0.21685    0.19658  0.50703       0.50657       0.04416      0.00066    21380  0.05548  INVALID\n",
       "3      H1     M5  1  1    1500   0.05  0.09290  0.76053        0.76053  0.83955    0.06034  0.50492       0.50412       0.00523     -0.00000    21348  0.97252  INVALID"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 7 — Full evaluation per pair: train test, cross-checks, rolling stability, out-of-sample\n",
    "results, rolling = [], {}\n",
    "\n",
    "for (lead, lag), D in designs.items():\n",
    "    cut = int(len(D) * TRAIN_FRAC)\n",
    "    D_tr, D_te = D.iloc[:cut], D.iloc[cut:]\n",
    "    p, q = select_lags(D_tr)\n",
    "    g = granger_test(D_tr, p, q)\n",
    "\n",
    "    # cross-check 1: statsmodels nested F-test must match ours\n",
    "    Xr, Xu, y = design_matrices(D_tr, p, q)\n",
    "    fit_u, fit_r = sm.OLS(y, Xu).fit(), sm.OLS(y, Xr).fit()\n",
    "    F_sm, p_sm, _ = fit_u.compare_f_test(fit_r)\n",
    "\n",
    "    # cross-check 2: heteroskedasticity-robust (HC3) Wald test on the lead-TF coefficients\n",
    "    R = np.zeros((p, Xu.shape[1]))\n",
    "    R[:, 1 + q:] = np.eye(p)\n",
    "    p_hc3 = float(sm.OLS(y, Xu).fit(cov_type=\"HC3\").f_test(R).pvalue)\n",
    "\n",
    "    # rolling stability over the whole sample, same window the EA uses live\n",
    "    pv, ts = [], []\n",
    "    for s in range(0, len(D) - WINDOW + 1, STEP):\n",
    "        w = D.iloc[s:s + WINDOW]\n",
    "        pv.append(granger_test(w, p, q)[\"p_value\"])\n",
    "        ts.append(w.index[-1])\n",
    "    rolling[(lead, lag)] = pd.Series(pv, index=pd.to_datetime(ts))\n",
    "    stability = float(np.mean(np.array(pv) < ALPHA)) if pv else 0.0\n",
    "\n",
    "    # out-of-sample: train coefficients applied to unseen data\n",
    "    Xr_te, Xu_te, y_te = design_matrices(D_te, p, q)\n",
    "    pred_u, pred_r = Xu_te @ g[\"beta_u\"], Xr_te @ g[\"beta_r\"]\n",
    "    nz = y_te != 0\n",
    "    oos_hit = float(np.mean(np.sign(pred_u[nz]) == np.sign(y_te[nz])))\n",
    "    oos_hit_base = float(np.mean(np.sign(pred_r[nz]) == np.sign(y_te[nz])))\n",
    "    oos_edge = float(np.mean(np.sign(pred_u) * y_te))\n",
    "    oos_r2_gain = 1 - np.sum((y_te - pred_u) ** 2) / np.sum((y_te - pred_r) ** 2)\n",
    "\n",
    "    results.append({\"lead_tf\": lead, \"lag_tf\": lag, \"p\": p, \"q\": q, \"window\": WINDOW, \"alpha\": ALPHA,\n",
    "                    \"f_stat\": g[\"F\"], \"p_value\": g[\"p_value\"], \"p_statsmodels\": p_sm, \"p_hc3\": p_hc3,\n",
    "                    \"stability\": stability, \"oos_hit\": oos_hit, \"oos_hit_base\": oos_hit_base,\n",
    "                    \"oos_edge_bps\": oos_edge, \"oos_r2_gain\": oos_r2_gain, \"n_train\": len(D_tr)})\n",
    "\n",
    "res = pd.DataFrame(results)\n",
    "res[\"p_adj\"] = multipletests(res[\"p_value\"], alpha=ALPHA, method=\"fdr_bh\")[1]\n",
    "ok = (res[\"p_adj\"] < ALPHA) & (res[\"stability\"] >= STABILITY_MIN)\n",
    "if REQUIRE_OOS_EDGE:\n",
    "    ok &= res[\"oos_edge_bps\"] > 0\n",
    "res[\"status\"] = np.where(ok, \"VALID\", \"INVALID\")\n",
    "\n",
    "assert np.allclose(res[\"p_value\"], res[\"p_statsmodels\"], atol=1e-8), \"F-test mismatch with statsmodels\"\n",
    "res.round(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "73f27e8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MULTI-TIMEFRAME CAUSALITY\n",
      "  H1 → M15  INVALID  p_adj=0.9725  F=  0.00  stab=9%  lags(p=1, q=1)  OOS hit=52.5% vs 52.3%\n",
      " M30 → M15  VALID    p_adj=0.0076  F=  9.64  stab=43%  lags(p=1, q=2)  OOS hit=50.0% vs 51.9%\n",
      " M15 → M5   INVALID  p_adj=0.0555  F=  4.84  stab=20%  lags(p=1, q=1)  OOS hit=50.7% vs 50.7%\n",
      "  H1 → M5   INVALID  p_adj=0.9725  F=  0.09  stab=6%  lags(p=1, q=1)  OOS hit=50.5% vs 50.4%\n"
     ]
    }
   ],
   "source": [
    "# Step 8 — Monitor-style summary (this is exactly what the EA panel will show)\n",
    "print(\"MULTI-TIMEFRAME CAUSALITY\")\n",
    "for _, r in res.iterrows():\n",
    "    print(f\"{r.lead_tf:>4} \\u2192 {r.lag_tf:<4} {r.status:<8} p_adj={r.p_adj:.4f}  F={r.f_stat:6.2f}  \"\n",
    "          f\"stab={r.stability:.0%}  lags(p={r.p}, q={r.q})  OOS hit={r.oos_hit:.1%} vs {r.oos_hit_base:.1%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "94b5a898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 9 — Visuals for the article\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 4.5))\n",
    "labels = [f\"{a}\\u2192{b}\" for a, b in zip(res.lead_tf, res.lag_tf)]\n",
    "colors = [\"tab:green\" if s == \"VALID\" else \"tab:red\" for s in res.status]\n",
    "axes[0].bar(labels, -np.log10(res[\"p_adj\"].clip(lower=1e-300)), color=colors)\n",
    "axes[0].axhline(-np.log10(ALPHA), ls=\"--\", c=\"k\", lw=1, label=f\"alpha = {ALPHA}\")\n",
    "axes[0].set_title(\"Granger significance  (-log10 adjusted p)\")\n",
    "axes[0].legend()\n",
    "\n",
    "for (lead, lag), s in rolling.items():\n",
    "    axes[1].plot(s.index, -np.log10(s.clip(lower=1e-300)), lw=1.2, label=f\"{lead}\\u2192{lag}\")\n",
    "axes[1].axhline(-np.log10(ALPHA), ls=\"--\", c=\"k\", lw=1)\n",
    "axes[1].set_title(f\"Rolling causality strength (window = {WINDOW} bars)\")\n",
    "axes[1].legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1bd4ace7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported -> C:\\Users\\wyt_coal\\AppData\\Roaming\\MetaQuotes\\Terminal\\Common\\Files\\granger_mtf_causality.csv\n",
      "lead_tf,lag_tf,p,q,window,alpha,f_stat,p_value,p_adj,stability,oos_hit,oos_edge_bps,status\n",
      "H1,M15,1,1,1500,0.0500,0.001187,0.97251713,0.97251713,0.0857,0.5252,0.142677,INVALID\n",
      "M30,M15,1,2,1500,0.0500,9.642431,0.00190880,0.00763521,0.4286,0.4997,-0.035219,VALID\n",
      "M15,M5,1,1,1500,0.0500,4.844780,0.02774058,0.05548117,0.1966,0.5070,0.044158,INVALID\n",
      "H1,M5,1,1,1500,0.0500,0.092896,0.76053027,0.97251713,0.0603,0.5049,0.005228,INVALID\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Step 10 — Export to MT5 Common\\\\Files (the EA reads it with FILE_COMMON)\n",
    "def common_files_dir():\n",
    "    if DATA_SOURCE == \"mt5\":\n",
    "        info = mt5.terminal_info()\n",
    "        if info is not None:\n",
    "            return os.path.join(info.commondata_path, \"Files\")\n",
    "    return os.path.abspath(\"./outputs\")\n",
    "\n",
    "export_cols = [\"lead_tf\", \"lag_tf\", \"p\", \"q\", \"window\", \"alpha\", \"f_stat\", \"p_value\", \"p_adj\",\n",
    "               \"stability\", \"oos_hit\", \"oos_edge_bps\", \"status\"]\n",
    "\n",
    "def write_table(path):\n",
    "    os.makedirs(os.path.dirname(path), exist_ok=True)\n",
    "    with open(path, \"w\", newline=\"\", encoding=\"ascii\") as f:\n",
    "        f.write(\",\".join(export_cols) + \"\\r\\n\")\n",
    "        for _, r in res[export_cols].iterrows():\n",
    "            f.write(\",\".join([r.lead_tf, r.lag_tf, str(int(r.p)), str(int(r.q)), str(int(r.window)),\n",
    "                              f\"{r.alpha:.4f}\", f\"{r.f_stat:.6f}\", f\"{r.p_value:.8f}\", f\"{r.p_adj:.8f}\",\n",
    "                              f\"{r.stability:.4f}\", f\"{r.oos_hit:.4f}\", f\"{r.oos_edge_bps:.6f}\",\n",
    "                              r.status]) + \"\\r\\n\")\n",
    "\n",
    "target = os.path.join(common_files_dir(), OUTPUT_FILE)\n",
    "write_table(target)\n",
    "write_table(os.path.abspath(os.path.join(\"./outputs\", OUTPUT_FILE)))\n",
    "print(\"Exported ->\", target)\n",
    "print(open(target).read())\n",
    "\n",
    "if DATA_SOURCE == \"mt5\":\n",
    "    mt5.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5e26503-5f32-48a0-9712-f871f6f01414",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.14.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}