{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7fa3479a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Per‑day GDELT processing (paper §3.1 style) with your latest rules:\n",
    "\n",
    "1. Process EACH CSV separately; no global concat.\n",
    "2. Keep events whose *first 3 digits* of EventCode are 100–199 (numeric).\n",
    "3. Overwrite all SQLDATE values with the filename date (e.g., 20200101.csv → 20200101).\n",
    "4. Save one CSV and (optionally) Parquet per date directly under cleaned_news/\n",
    "   • Filenames: 20200101.csv  /  20200101.parquet\n",
    "5. Async headline fetching; run with:  await main()\n",
    "6. No cumulative/total prints.\n",
    "\"\"\"\n",
    "\n",
    "import asyncio, httpx, re, numpy as np\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "# ===================== CONFIG ===================== #\n",
    "NEWS_DIR      = Path(\"News\")          # input CSVs\n",
    "OUT_ROOT      = Path(\"cleaned_news\")  # flat output folder\n",
    "MAX_PER_DAY   = 100                   # top-N per day by NumArticles\n",
    "CODE_MIN, CODE_MAX = 100, 199         # first-3-digit EventCode range\n",
    "CONCURRENCY   = 64\n",
    "TIMEOUT_SEC   = 10\n",
    "HEADERS       = {\"User-Agent\": \"Mozilla/5.0\"}\n",
    "SAVE_PARQUET  = True                  # False to skip parquet\n",
    "# ================================================== #\n",
    "\n",
    "\n",
    "def first3_digits_int(series: pd.Series) -> pd.Series:\n",
    "    \"\"\"First 3 digits of positive ints, vectorized, no string ops.\"\"\"\n",
    "    s = pd.to_numeric(series, errors=\"coerce\").astype(\"Int64\")\n",
    "    mask = s.notna()\n",
    "    out = pd.Series(pd.NA, index=series.index, dtype=\"Int64\")\n",
    "    if mask.any():\n",
    "        vals = s[mask].astype(int).to_numpy()\n",
    "        # avoid log10(0)\n",
    "        safe = np.where(vals == 0, 1, vals)\n",
    "        digits = (np.floor(np.log10(safe)).astype(int) + 1)\n",
    "        div = np.power(10, np.clip(digits - 3, 0, None))\n",
    "        out_vals = (vals // div).astype(int)\n",
    "        out[mask] = out_vals\n",
    "    return out\n",
    "\n",
    "\n",
    "async def fetch_title(client: httpx.AsyncClient, url: str) -> str | None:\n",
    "    try:\n",
    "        r = await client.get(url, timeout=TIMEOUT_SEC, headers=HEADERS)\n",
    "        if r.status_code != 200 or not r.text:\n",
    "            return None\n",
    "        soup = BeautifulSoup(r.text, \"html.parser\")\n",
    "        title = soup.title.string.strip() if soup.title and soup.title.string else None\n",
    "        if not title:\n",
    "            og = soup.find(\"meta\", property=\"og:title\")\n",
    "            tw = soup.find(\"meta\", attrs={\"name\": \"twitter:title\"})\n",
    "            title = (og.get(\"content\") if og else None) or (tw.get(\"content\") if tw else None)\n",
    "            if title:\n",
    "                title = title.strip()\n",
    "        return re.sub(r\"\\s+\", \" \", title) if title else None\n",
    "    except Exception:\n",
    "        return None\n",
    "\n",
    "\n",
    "async def gather_titles(urls: list[str]) -> list[str | None]:\n",
    "    results = [None] * len(urls)\n",
    "    sem = asyncio.Semaphore(CONCURRENCY)\n",
    "    async with httpx.AsyncClient(follow_redirects=True) as client:\n",
    "        async def worker(i, u):\n",
    "            async with sem:\n",
    "                results[i] = await fetch_title(client, u)\n",
    "        tasks = [asyncio.create_task(worker(i, u)) for i, u in enumerate(urls)]\n",
    "        for t in asyncio.as_completed(tasks):\n",
    "            await t\n",
    "    return results\n",
    "\n",
    "\n",
    "def process_one_file(path: Path) -> pd.DataFrame:\n",
    "    date_str = path.stem  # e.g. \"20200101\"\n",
    "    df = pd.read_csv(path, low_memory=False)\n",
    "\n",
    "    # overwrite SQLDATE with filename date\n",
    "    df[\"SQLDATE\"] = int(date_str)\n",
    "\n",
    "    # numeric EventCode & first-3-digit filter\n",
    "    df[\"EventCode\"] = pd.to_numeric(df[\"EventCode\"], errors=\"coerce\").astype(\"Int64\")\n",
    "    df = df.dropna(subset=[\"EventCode\"])\n",
    "    df[\"EventCode\"] = df[\"EventCode\"].astype(int)\n",
    "\n",
    "    df[\"code3\"] = first3_digits_int(df[\"EventCode\"])\n",
    "    df = df[df[\"code3\"].between(CODE_MIN, CODE_MAX)]\n",
    "    if df.empty:\n",
    "        return df\n",
    "\n",
    "    # standardize\n",
    "    rename_map = {\n",
    "        \"SQLDATE\":        \"date\",\n",
    "        \"EventCode\":      \"event_code_full\",\n",
    "        \"GoldsteinScale\": \"goldstein_scale\",\n",
    "        \"NumArticles\":    \"num_articles\",\n",
    "        \"SOURCEURL\":      \"url\"\n",
    "    }\n",
    "    df = df.rename(columns=rename_map)\n",
    "    df[\"date\"] = pd.to_datetime(df[\"date\"], format=\"%Y%m%d\", errors=\"coerce\")\n",
    "\n",
    "    # final event_code = 3-digit version\n",
    "    df[\"event_code\"] = df[\"code3\"].astype(int)\n",
    "\n",
    "    keep_cols = [\n",
    "        \"date\", \"event_code\", \"goldstein_scale\", \"num_articles\", \"url\",\n",
    "        \"GLOBALEVENTID\", \"CAMEOCodeDescription\"\n",
    "    ]\n",
    "    df = df[keep_cols].dropna(subset=[\"date\", \"url\"])\n",
    "\n",
    "    # rank within the day\n",
    "    df[\"rank_num_articles\"] = df[\"num_articles\"].rank(method=\"first\", ascending=False)\n",
    "    df = (df[df[\"rank_num_articles\"] <= MAX_PER_DAY]\n",
    "            .sort_values(\"num_articles\", ascending=False)\n",
    "            .drop_duplicates(\"url\", keep=\"first\"))\n",
    "    return df\n",
    "\n",
    "\n",
    "def save_day(df: pd.DataFrame, date_str: str):\n",
    "    OUT_ROOT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "    csv_path = OUT_ROOT / f\"{date_str}.csv\"\n",
    "    df.to_csv(csv_path, index=False)\n",
    "\n",
    "    if SAVE_PARQUET:\n",
    "        try:\n",
    "            parquet_path = OUT_ROOT / f\"{date_str}.parquet\"\n",
    "            df.to_parquet(parquet_path, index=False)\n",
    "        except Exception:\n",
    "            df.to_pickle(OUT_ROOT / f\"{date_str}.pkl\")\n",
    "\n",
    "\n",
    "async def main():\n",
    "    files = sorted(NEWS_DIR.glob(\"*.csv\"))\n",
    "    if not files:\n",
    "        raise FileNotFoundError(f\"No CSVs in {NEWS_DIR.resolve()}\")\n",
    "\n",
    "    for f in files:\n",
    "        date_str = f.stem\n",
    "        day_df = process_one_file(f)\n",
    "        if day_df.empty:\n",
    "            continue\n",
    "\n",
    "        titles = await gather_titles(day_df[\"url\"].tolist())\n",
    "        day_df[\"headline\"] = titles\n",
    "        day_df = day_df.dropna(subset=[\"headline\"])\n",
    "        day_df = day_df[day_df[\"headline\"].str.len() > 0]\n",
    "\n",
    "        if day_df.empty:\n",
    "            continue\n",
    "\n",
    "        save_day(day_df, date_str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0cb1daeb",
   "metadata": {},
   "outputs": [
    {
     "ename": "CancelledError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mCancelledError\u001b[39m                            Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m main()\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 147\u001b[39m, in \u001b[36mmain\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m    144\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m day_df.empty:\n\u001b[32m    145\u001b[39m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m147\u001b[39m titles = \u001b[38;5;28;01mawait\u001b[39;00m gather_titles(day_df[\u001b[33m\"\u001b[39m\u001b[33murl\u001b[39m\u001b[33m\"\u001b[39m].tolist())\n\u001b[32m    148\u001b[39m day_df[\u001b[33m\"\u001b[39m\u001b[33mheadline\u001b[39m\u001b[33m\"\u001b[39m] = titles\n\u001b[32m    149\u001b[39m day_df = day_df.dropna(subset=[\u001b[33m\"\u001b[39m\u001b[33mheadline\u001b[39m\u001b[33m\"\u001b[39m])\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 73\u001b[39m, in \u001b[36mgather_titles\u001b[39m\u001b[34m(urls)\u001b[39m\n\u001b[32m     71\u001b[39m     tasks = [asyncio.create_task(worker(i, u)) \u001b[38;5;28;01mfor\u001b[39;00m i, u \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(urls)]\n\u001b[32m     72\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m asyncio.as_completed(tasks):\n\u001b[32m---> \u001b[39m\u001b[32m73\u001b[39m         \u001b[38;5;28;01mawait\u001b[39;00m t\n\u001b[32m     74\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m results\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.12/3.12.11/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/tasks.py:627\u001b[39m, in \u001b[36mas_completed.<locals>._wait_for_one\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m    626\u001b[39m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_wait_for_one\u001b[39m():\n\u001b[32m--> \u001b[39m\u001b[32m627\u001b[39m     f = \u001b[38;5;28;01mawait\u001b[39;00m done.get()\n\u001b[32m    628\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m f \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    629\u001b[39m         \u001b[38;5;66;03m# Dummy value from _on_timeout().\u001b[39;00m\n\u001b[32m    630\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m exceptions.TimeoutError\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.12/3.12.11/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/queues.py:158\u001b[39m, in \u001b[36mQueue.get\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    156\u001b[39m \u001b[38;5;28mself\u001b[39m._getters.append(getter)\n\u001b[32m    157\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m158\u001b[39m     \u001b[38;5;28;01mawait\u001b[39;00m getter\n\u001b[32m    159\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[32m    160\u001b[39m     getter.cancel()  \u001b[38;5;66;03m# Just in case getter is not done yet.\u001b[39;00m\n",
      "\u001b[31mCancelledError\u001b[39m: "
     ]
    }
   ],
   "source": [
    "await main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1536f112",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "68058b4b841d42bd8bad4b4460df7fe8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Files:   0%|          | 0/1988 [00:00<?, ?it/s]"
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    {
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      "text/plain": [
       "FinBERT 20200101:   0%|          | 0/1 [00:00<?, ?it/s]"
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       "FinBERT 20200102:   0%|          | 0/1 [00:00<?, ?it/s]"
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       "FinBERT 20200103:   0%|          | 0/1 [00:00<?, ?it/s]"
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       "FinBERT 20200104:   0%|          | 0/1 [00:00<?, ?it/s]"
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       "FinBERT 20200105:   0%|          | 0/1 [00:00<?, ?it/s]"
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       "FinBERT 20200107:   0%|          | 0/2 [00:00<?, ?it/s]"
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       "FinBERT 20200110:   0%|          | 0/2 [00:00<?, ?it/s]"
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   ],
   "source": [
    "\"\"\"\n",
    "Paper §3.2 – FinBERT Sentiment Scoring\n",
    "--------------------------------------\n",
    "Reads daily headline files from cleaned_news/*.csv\n",
    "Adds columns: p_neg, p_neu, p_pos, finbert_score (= p_pos - p_neg)\n",
    "Over‑writes each CSV (and optional Parquet/Pickle)\n",
    "\n",
    "Real‑time progress:\n",
    "  • Outer tqdm bar: per‑day file\n",
    "  • Inner tqdm bar: per‑batch headlines\n",
    "\n",
    "Run (in notebook/IPython):\n",
    "    await run_finbert_scoring()\n",
    "\n",
    "Dependencies:\n",
    "    pip install transformers torch pandas tqdm\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "from typing import List, Dict\n",
    "\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# ---------- CONFIG ---------- #\n",
    "IN_DIR        = Path(\"cleaned_news\")\n",
    "BATCH_SIZE    = 32\n",
    "MODEL_NAME    = \"ProsusAI/finbert\"\n",
    "MAX_LEN       = 512\n",
    "SAVE_PARQUET  = True          # disable if no pyarrow/fastparquet\n",
    "# ----------------------------- #\n",
    "\n",
    "# ---------- tqdm helper ---------- #\n",
    "try:\n",
    "    from tqdm.auto import tqdm\n",
    "    _USE_TQDM = True\n",
    "except ImportError:\n",
    "    tqdm = None\n",
    "    _USE_TQDM = False\n",
    "\n",
    "from contextlib import contextmanager\n",
    "@contextmanager\n",
    "def _maybe_tqdm(iterable, **kwargs):\n",
    "    \"\"\"Yield tqdm‑wrapped iterable if tqdm is installed, else raw iterable.\"\"\"\n",
    "    if _USE_TQDM:\n",
    "        yield tqdm(iterable, **kwargs)\n",
    "    else:\n",
    "        yield iterable\n",
    "\n",
    "# ---------- Dataset wrapper ---------- #\n",
    "class HeadlineDataset(Dataset):\n",
    "    def __init__(self, texts: List[str], tokenizer, max_len: int):\n",
    "        self.texts = texts\n",
    "        self.tok   = tokenizer\n",
    "        self.max_len = max_len\n",
    "\n",
    "    def __len__(self): return len(self.texts)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        enc = self.tok(\n",
    "            self.texts[idx],\n",
    "            padding=\"max_length\",\n",
    "            truncation=True,\n",
    "            max_length=self.max_len,\n",
    "            return_tensors=\"pt\"\n",
    "        )\n",
    "        return {k: v.squeeze(0) for k, v in enc.items()}\n",
    "\n",
    "# ---------- Main runner ---------- #\n",
    "async def run_finbert_scoring(show_progress: bool = True):\n",
    "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "    model     = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME).to(device)\n",
    "\n",
    "    files = sorted(IN_DIR.glob(\"*.csv\"))\n",
    "    if not files:\n",
    "        raise FileNotFoundError(f\"No CSVs found in {IN_DIR.resolve()}\")\n",
    "\n",
    "    with _maybe_tqdm(files, desc=\"Files\", disable=not (show_progress and _USE_TQDM)) as file_bar:\n",
    "        for f in file_bar:\n",
    "            df = pd.read_csv(f)\n",
    "            if \"headline\" not in df.columns or df.empty:\n",
    "                continue\n",
    "\n",
    "            texts = df[\"headline\"].astype(str).str.strip().str.lower().tolist()\n",
    "            n_rows = len(texts)\n",
    "            if _USE_TQDM:\n",
    "                file_bar.set_description(f\"{f.name} ({n_rows} hl)\")\n",
    "\n",
    "            ds = HeadlineDataset(texts, tokenizer, MAX_LEN)\n",
    "            dl = DataLoader(ds, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "            p_neg, p_neu, p_pos = [], [], []\n",
    "            model.eval()\n",
    "            with torch.no_grad(), _maybe_tqdm(\n",
    "                dl,\n",
    "                total=len(dl),\n",
    "                disable=not (show_progress and _USE_TQDM),\n",
    "                leave=False,\n",
    "                desc=f\"FinBERT {f.stem}\"\n",
    "            ) as batch_bar:\n",
    "                for batch in batch_bar:\n",
    "                    batch = {k: v.to(device) for k, v in batch.items()}\n",
    "                    probs = torch.softmax(model(**batch).logits, dim=1).cpu().numpy()\n",
    "                    p_neg.extend(probs[:, 0]); p_neu.extend(probs[:, 1]); p_pos.extend(probs[:, 2])\n",
    "\n",
    "            df[\"p_neg\"]         = p_neg\n",
    "            df[\"p_neu\"]         = p_neu\n",
    "            df[\"p_pos\"]         = p_pos\n",
    "            df[\"finbert_score\"] = np.array(p_pos) - np.array(p_neg)\n",
    "\n",
    "            df.to_csv(f, index=False)\n",
    "            if SAVE_PARQUET:\n",
    "                try:\n",
    "                    df.to_parquet(f.with_suffix(\".parquet\"), index=False)\n",
    "                except Exception:\n",
    "                    df.to_pickle(f.with_suffix(\".pkl\"))\n",
    "\n",
    "            if not _USE_TQDM:\n",
    "                print(f\"Processed {f.name}: {n_rows} headlines ✔\")\n",
    "\n",
    "# Example call in notebook / IPython\n",
    "await run_finbert_scoring(show_progress=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0a3af4bb",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Usecols do not match columns, columns expected but not found: ['finbert_score']",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 109\u001b[39m\n\u001b[32m    106\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m daily_all\n\u001b[32m    108\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[34m__name__\u001b[39m == \u001b[33m\"\u001b[39m\u001b[33m__main__\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m109\u001b[39m     features = \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 94\u001b[39m, in \u001b[36mmain\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m     93\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mmain\u001b[39m():\n\u001b[32m---> \u001b[39m\u001b[32m94\u001b[39m     per_headline = \u001b[43mload_all_days\u001b[49m\u001b[43m(\u001b[49m\u001b[43mIN_DIR\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     95\u001b[39m     daily_primary = aggregate_daily(per_headline)\n\u001b[32m     96\u001b[39m     daily_all = add_temporal_features(daily_primary)\n",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 26\u001b[39m, in \u001b[36mload_all_days\u001b[39m\u001b[34m(in_dir)\u001b[39m\n\u001b[32m     24\u001b[39m usecols = [\u001b[33m\"\u001b[39m\u001b[33mdate\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mheadline\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mfinbert_score\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mgoldstein_scale\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m     25\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m files:\n\u001b[32m---> \u001b[39m\u001b[32m26\u001b[39m     df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[43m=\u001b[49m\u001b[43musecols\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     27\u001b[39m     dfs.append(df)\n\u001b[32m     28\u001b[39m out = pd.concat(dfs, ignore_index=\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m   1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m   1014\u001b[39m     dialect,\n\u001b[32m   1015\u001b[39m     delimiter,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1022\u001b[39m     dtype_backend=dtype_backend,\n\u001b[32m   1023\u001b[39m )\n\u001b[32m   1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m    617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m    619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m    623\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m   1617\u001b[39m     \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/io/parsers/readers.py:1898\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m   1895\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[32m   1897\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1898\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmapping\u001b[49m\u001b[43m[\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1899\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[32m   1900\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/io/parsers/c_parser_wrapper.py:140\u001b[39m, in \u001b[36mCParserWrapper.__init__\u001b[39m\u001b[34m(self, src, **kwds)\u001b[39m\n\u001b[32m    136\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.orig_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    137\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.usecols_dtype == \u001b[33m\"\u001b[39m\u001b[33mstring\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mset\u001b[39m(usecols).issubset(\n\u001b[32m    138\u001b[39m     \u001b[38;5;28mself\u001b[39m.orig_names\n\u001b[32m    139\u001b[39m ):\n\u001b[32m--> \u001b[39m\u001b[32m140\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_validate_usecols_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43musecols\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43morig_names\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    142\u001b[39m \u001b[38;5;66;03m# error: Cannot determine type of 'names'\u001b[39;00m\n\u001b[32m    143\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m.names) > \u001b[38;5;28mlen\u001b[39m(usecols):  \u001b[38;5;66;03m# type: ignore[has-type]\u001b[39;00m\n\u001b[32m    144\u001b[39m     \u001b[38;5;66;03m# error: Cannot determine type of 'names'\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/io/parsers/base_parser.py:988\u001b[39m, in \u001b[36mParserBase._validate_usecols_names\u001b[39m\u001b[34m(self, usecols, names)\u001b[39m\n\u001b[32m    986\u001b[39m missing = [c \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m usecols \u001b[38;5;28;01mif\u001b[39;00m c \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m names]\n\u001b[32m    987\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(missing) > \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m988\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    989\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUsecols do not match columns, columns expected but not found: \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    990\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmissing\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    991\u001b[39m     )\n\u001b[32m    993\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m usecols\n",
      "\u001b[31mValueError\u001b[39m: Usecols do not match columns, columns expected but not found: ['finbert_score']"
     ]
    }
   ],
   "source": [
    "# ================================================================\n",
    "#  Daily Sentiment Index Construction & Feature Engineering (§3.3)\n",
    "#  Input  : cleaned_news/YYYYMMDD.csv produced by your §3.1–3.2 code\n",
    "#  Requires columns per row: date, headline, finbert_score, goldstein_scale\n",
    "#  Output : daily_features.csv / daily_features.parquet\n",
    "# ================================================================\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# -------------------- CONFIG -------------------- #\n",
    "IN_DIR        = Path(\"cleaned_news\")\n",
    "OUT_CSV       = Path(\"daily_features.csv\")\n",
    "OUT_PARQUET   = Path(\"daily_features.parquet\")\n",
    "STRICT_WINDOWS = True      # rolling windows require full length (NaN at start)\n",
    "SAVE_PARQUET   = True\n",
    "# ------------------------------------------------ #\n",
    "\n",
    "def load_all_days(in_dir: Path) -> pd.DataFrame:\n",
    "    files = sorted(in_dir.glob(\"*.csv\"))\n",
    "    if not files:\n",
    "        raise FileNotFoundError(f\"No CSVs found in {in_dir.resolve()}\")\n",
    "    dfs = []\n",
    "    usecols = [\"date\", \"headline\", \"finbert_score\", \"goldstein_scale\"]\n",
    "    for f in files:\n",
    "        df = pd.read_csv(f, usecols=usecols)\n",
    "        dfs.append(df)\n",
    "    out = pd.concat(dfs, ignore_index=True)\n",
    "    # Ensure dtypes\n",
    "    out[\"date\"] = pd.to_datetime(out[\"date\"])\n",
    "    out[\"finbert_score\"] = pd.to_numeric(out[\"finbert_score\"], errors=\"coerce\")\n",
    "    out[\"goldstein_scale\"] = pd.to_numeric(out[\"goldstein_scale\"], errors=\"coerce\")\n",
    "    # \"Valid headlines\" = have a non-empty headline + non-null finbert_score\n",
    "    out[\"headline\"] = out[\"headline\"].astype(str).str.strip()\n",
    "    out = out[(out[\"headline\"].str.len() > 0) & out[\"finbert_score\"].notna()]\n",
    "    return out\n",
    "\n",
    "def aggregate_daily(df: pd.DataFrame) -> pd.DataFrame:\n",
    "    # Group by trading date t\n",
    "    g = df.groupby(\"date\", as_index=True)\n",
    "\n",
    "    # N_t (news volume), S_t (mean sentiment), σ_t (population std)\n",
    "    V_t  = g.size().rename(\"V_t\")\n",
    "    S_t  = g[\"finbert_score\"].mean().rename(\"S_t\")\n",
    "    s2_t = g[\"finbert_score\"].var(ddof=0)  # population variance (1/N)\n",
    "    sigma_t = np.sqrt(s2_t).rename(\"sigma_t\")\n",
    "\n",
    "    # Mean Goldstein and dispersion (population std)\n",
    "    Gbar_t = g[\"goldstein_scale\"].mean().rename(\"Gbar_t\")\n",
    "    s2G_t  = g[\"goldstein_scale\"].var(ddof=0)\n",
    "    sigmaG_t = np.sqrt(s2G_t).rename(\"sigmaG_t\")\n",
    "\n",
    "    # Join primary features\n",
    "    daily = pd.concat([V_t, S_t, sigma_t, Gbar_t, sigmaG_t], axis=1).sort_index()\n",
    "\n",
    "    # Log volume and Article Impact\n",
    "    daily[\"log_vol\"] = np.log1p(daily[\"V_t\"])\n",
    "    daily[\"AI_t\"]    = daily[\"S_t\"] * daily[\"log_vol\"]\n",
    "\n",
    "    return daily\n",
    "\n",
    "def add_temporal_features(daily: pd.DataFrame) -> pd.DataFrame:\n",
    "    df = daily.copy()\n",
    "\n",
    "    # Lags (1,2,3) for S_t, σ_t, V_t, Gbar_t\n",
    "    for k in (1, 2, 3):\n",
    "        df[f\"S_t_lag{k}\"]      = df[\"S_t\"].shift(k)\n",
    "        df[f\"sigma_t_lag{k}\"]  = df[\"sigma_t\"].shift(k)\n",
    "        df[f\"V_t_lag{k}\"]      = df[\"V_t\"].shift(k)\n",
    "        df[f\"Gbar_t_lag{k}\"]   = df[\"Gbar_t\"].shift(k)\n",
    "\n",
    "    # Moving averages (MA5, MA20) of S_t\n",
    "    min5  = 5  if STRICT_WINDOWS else 1\n",
    "    min10 = 10 if STRICT_WINDOWS else 1\n",
    "    min20 = 20 if STRICT_WINDOWS else 1\n",
    "\n",
    "    df[\"S_MA5\"]  = df[\"S_t\"].rolling(5,  min_periods=min5 ).mean()\n",
    "    df[\"S_MA20\"] = df[\"S_t\"].rolling(20, min_periods=min20).mean()\n",
    "\n",
    "    # Sentiment acceleration: ΔS_t = MA5(S)_t − MA20(S)_t\n",
    "    df[\"DeltaS_t\"] = df[\"S_MA5\"] - df[\"S_MA20\"]\n",
    "\n",
    "    # Rolling standard deviations of daily mean sentiment S_t (population std)\n",
    "    df[\"S_RollStd5\"]  = df[\"S_t\"].rolling(5,  min_periods=min5 ).std(ddof=0)\n",
    "    df[\"S_RollStd10\"] = df[\"S_t\"].rolling(10, min_periods=min10).std(ddof=0)\n",
    "\n",
    "    # Rolling sums of news volume V_t\n",
    "    df[\"V_RollSum5\"]  = df[\"V_t\"].rolling(5,  min_periods=min5 ).sum()\n",
    "    df[\"V_RollSum10\"] = df[\"V_t\"].rolling(10, min_periods=min10).sum()\n",
    "\n",
    "    return df\n",
    "\n",
    "def main():\n",
    "    per_headline = load_all_days(IN_DIR)\n",
    "    daily_primary = aggregate_daily(per_headline)\n",
    "    daily_all = add_temporal_features(daily_primary)\n",
    "\n",
    "    # Save\n",
    "    daily_all.reset_index().to_csv(OUT_CSV, index=False)\n",
    "    if SAVE_PARQUET:\n",
    "        try:\n",
    "            daily_all.reset_index().to_parquet(OUT_PARQUET, index=False)\n",
    "        except Exception:\n",
    "            daily_all.reset_index().to_pickle(OUT_PARQUET.with_suffix(\".pkl\"))\n",
    "\n",
    "    return daily_all\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    features = main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "743ad433",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved market_features.csv with shape (1379, 6)\n",
      "           date   close  volume_t       r_t   vol20_t  y_t\n",
      "1376 2025-07-23  5.8195     37881  0.016983  0.482091    0\n",
      "1377 2025-07-24  5.8015     30222 -0.003098  0.483863    0\n",
      "1378 2025-07-25  5.7830     20318 -0.003194  0.478829    0\n"
     ]
    }
   ],
   "source": [
    "# ================================================================\n",
    "# Part §3.4 — Market target & features from copper_prices.csv\n",
    "# Input : columns = [\"time\",\"close\",\"Volume\"]; time is YYYY-MM-DD\n",
    "# Output: market_features.csv with r_t, vol20_t, y_t (target uses r_{t+1})\n",
    "# Notes :\n",
    "#   r_t         = log(P_t) - log(P_{t-1})\n",
    "#   y_t         = 1{ r_{t+1} > 0 }  (predict t→t+1 direction; no look-ahead)\n",
    "#   vol20_t     = 20-day annualized std of daily log returns (uses data ≤ t)\n",
    "# ================================================================\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "IN_FILE   = Path(\"copper_prices.csv\")\n",
    "OUT_FILE  = Path(\"market_features.csv\")\n",
    "TRADING_DAYS = 252  # annualization factor\n",
    "\n",
    "def build_market_features(in_file: Path) -> pd.DataFrame:\n",
    "    # --- load & clean\n",
    "    df = pd.read_csv(in_file)\n",
    "    df = df.rename(columns={\"time\": \"date\"})\n",
    "    df[\"date\"]  = pd.to_datetime(df[\"date\"])\n",
    "    df[\"close\"] = pd.to_numeric(df[\"close\"], errors=\"coerce\")\n",
    "    df[\"Volume\"] = pd.to_numeric(df[\"Volume\"], errors=\"coerce\")\n",
    "    df = df.dropna(subset=[\"date\", \"close\"]).drop_duplicates(subset=[\"date\"])\n",
    "    df = df.sort_values(\"date\").reset_index(drop=True)\n",
    "\n",
    "    # guard against nonpositive prices\n",
    "    df = df[df[\"close\"] > 0].copy()\n",
    "\n",
    "    # --- returns\n",
    "    logp = np.log(df[\"close\"])\n",
    "    df[\"r_t\"] = logp.diff()                     # from t-1 close to t close\n",
    "\n",
    "    # next-day return aligned to day t (target driver)\n",
    "    df[\"r_t_plus_1\"] = df[\"r_t\"].shift(-1)\n",
    "\n",
    "    # --- target variable: y_t = 1{ r_{t+1} > 0 }\n",
    "    df[\"y_t\"] = (df[\"r_t_plus_1\"] > 0).astype(\"Int64\")\n",
    "\n",
    "    # --- additional market features\n",
    "    # 20-day historical volatility (annualized); uses info available up to t\n",
    "    df[\"vol20_t\"] = df[\"r_t\"].rolling(20, min_periods=20).std(ddof=0) * np.sqrt(TRADING_DAYS)\n",
    "\n",
    "    # optional: keep today’s volume as a contextual feature\n",
    "    # (not required by §3.4, but often useful and does not cause look-ahead)\n",
    "    df[\"volume_t\"] = df[\"Volume\"]\n",
    "\n",
    "    # --- no look-ahead: drop rows that don’t have all features known at t\n",
    "    # (need r_t for today, vol20_t for today, and r_{t+1} for target)\n",
    "    out = df.dropna(subset=[\"r_t\", \"vol20_t\", \"y_t\"]).copy()\n",
    "\n",
    "    # tidy columns\n",
    "    cols = [\"date\", \"close\", \"volume_t\", \"r_t\", \"vol20_t\", \"y_t\"]\n",
    "    out = out[cols].reset_index(drop=True)\n",
    "\n",
    "    return out\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    features = build_market_features(IN_FILE)\n",
    "    features.to_csv(OUT_FILE, index=False)\n",
    "    print(f\"Saved {OUT_FILE} with shape {features.shape}\")\n",
    "    print(features.tail(3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c76798e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved combined dataset: combined_dataset.csv  shape=(1350, 33)\n"
     ]
    }
   ],
   "source": [
    "# ================================================================\n",
    "# §3.5 — Part 1: Build combined_dataset from daily_features & market_features\n",
    "# Keeps ONLY trading dates (market calendar). Saves CSV (+ optional Parquet).\n",
    "# Range: 2020-01-01 ~ 2025-06-14\n",
    "# ================================================================\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# ---------- CONFIG ----------\n",
    "DAILY_FILE   = Path(\"daily_features.csv\")\n",
    "MKT_FILE     = Path(\"market_features.csv\")\n",
    "OUT_CSV      = Path(\"combined_dataset.csv\")\n",
    "OUT_PARQUET  = Path(\"combined_dataset.parquet\")\n",
    "SAVE_PARQUET = True\n",
    "\n",
    "START_DATE   = pd.Timestamp(\"2020-01-01\")\n",
    "END_DATE     = pd.Timestamp(\"2025-06-14\")\n",
    "# ----------------------------\n",
    "\n",
    "def build_combined_dataset() -> pd.DataFrame:\n",
    "    df_sent = pd.read_csv(DAILY_FILE, parse_dates=[\"date\"])\n",
    "    df_mkt  = pd.read_csv(MKT_FILE,   parse_dates=[\"date\"])\n",
    "\n",
    "    df_sent = (df_sent.sort_values(\"date\")\n",
    "                        .drop_duplicates(subset=[\"date\"])\n",
    "                        .reset_index(drop=True))\n",
    "    df_mkt  = (df_mkt.sort_values(\"date\")\n",
    "                       .drop_duplicates(subset=[\"date\"])\n",
    "                       .reset_index(drop=True))\n",
    "\n",
    "    # Keep ONLY trading dates by left-joining FROM market to sentiment\n",
    "    df = (df_mkt.merge(df_sent, on=\"date\", how=\"left\", suffixes=(\"\", \"_sent\"))\n",
    "                 .query(\"@START_DATE <= date <= @END_DATE\")\n",
    "                 .reset_index(drop=True))\n",
    "\n",
    "    # Ensure r_{t+1} exists (if §3.4 didn’t write it)\n",
    "    if \"r_t_plus_1\" not in df.columns and \"r_t\" in df.columns:\n",
    "        df[\"r_t_plus_1\"] = df[\"r_t\"].shift(-1)\n",
    "\n",
    "    # Minimal integrity: target and next-day return must exist to backtest\n",
    "    df = df.dropna(subset=[\"y_t\", \"r_t_plus_1\"]).copy()\n",
    "\n",
    "    # Save\n",
    "    df.to_csv(OUT_CSV, index=False)\n",
    "    if SAVE_PARQUET:\n",
    "        try:\n",
    "            df.to_parquet(OUT_PARQUET, index=False)\n",
    "        except Exception:\n",
    "            df.to_pickle(OUT_PARQUET.with_suffix(\".pkl\"))\n",
    "\n",
    "    print(f\"Saved combined dataset: {OUT_CSV}  shape={df.shape}\")\n",
    "    return df\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    build_combined_dataset()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36deda6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LOGISTIC | Fold 1] Test: 2021-01-20 → 2021-12-02 | n=221\n",
      "[LOGISTIC | Fold 2] Test: 2021-12-03 → 2022-10-19 | n=221\n",
      "[LOGISTIC | Fold 3] Test: 2022-10-20 → 2023-09-07 | n=221\n",
      "[LOGISTIC | Fold 4] Test: 2023-09-08 → 2024-07-26 | n=221\n",
      "[LOGISTIC | Fold 5] Test: 2024-07-29 → 2025-06-12 | n=221\n",
      "[XGB | Fold 1] Test: 2021-01-20 → 2021-12-02 | n=221\n",
      "[XGB | Fold 2] Test: 2021-12-03 → 2022-10-19 | n=221\n",
      "[XGB | Fold 3] Test: 2022-10-20 → 2023-09-07 | n=221\n",
      "[XGB | Fold 4] Test: 2023-09-08 → 2024-07-26 | n=221\n",
      "[XGB | Fold 5] Test: 2024-07-29 → 2025-06-12 | n=221\n",
      "\n",
      "=== Aggregated OOS (2020-01-01 ~ 2025-06-14) ===\n",
      "LOGISTIC | AUC=0.512 | ACC=0.508 | Sharpe=-0.05 | CAGR=-4.27% | n=1105\n",
      "     XGB | AUC=0.515 | ACC=0.495 | Sharpe=0.20 | CAGR=1.95% | n=1105\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ================================================================\n",
    "# §3.5 — Part 2: Predictive Modeling & Expanding-Window Backtest\n",
    "# Inputs : combined_dataset.csv  (from Part 1)\n",
    "# Models : Logistic (L2, scaled) and XGBoost (binary:logistic)\n",
    "# CV     : 5-fold expanding window, 20-day warm-up\n",
    "# Trading: position_t = +1 if p_hat_t > 0.5 else -1; apply to r_{t+1}\n",
    "# Costs  : 2 bps per position change\n",
    "# Outputs: oos_logistic.csv, oos_xgb.csv + equity-curve plot\n",
    "# ================================================================\n",
    "from __future__ import annotations\n",
    "from pathlib import Path\n",
    "import itertools, warnings, numpy as np, pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# ---------- CONFIG ----------\n",
    "COMBINED_FILE = Path(\"combined_dataset.csv\")\n",
    "WARMUP_DAYS   = 20\n",
    "N_SPLITS      = 5\n",
    "THRESH        = 0.50\n",
    "TCOST         = 0.0002   # 2 bps per change\n",
    "ANNUAL_DAYS   = 252\n",
    "RANDOM_STATE  = 7\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# ---------------------------\n",
    "\n",
    "# ---------- Feature selection ----------\n",
    "def feature_columns(df: pd.DataFrame) -> list[str]:\n",
    "    non_features = {\n",
    "        \"date\", \"y_t\", \"close\", \"r_t\", \"r_t_plus_1\",\n",
    "        \"p_hat\", \"y_true\", \"position\", \"ret_strategy\", \"equity\"\n",
    "    }\n",
    "    num_cols = df.select_dtypes(include=[np.number]).columns\n",
    "    return [c for c in num_cols if c not in non_features]\n",
    "\n",
    "# ---------- Metrics / trading ----------\n",
    "def sharpe(returns: pd.Series, annualize=True):\n",
    "    mu, sd = returns.mean(), returns.std(ddof=0)\n",
    "    if sd == 0 or np.isnan(sd): return np.nan\n",
    "    s = mu / sd\n",
    "    return s * np.sqrt(ANNUAL_DAYS) if annualize else s\n",
    "\n",
    "def cagr(returns: pd.Series, dates: pd.Series):\n",
    "    if len(returns) == 0: return np.nan\n",
    "    equity = (1.0 + returns).prod()\n",
    "    years = (dates.iloc[-1] - dates.iloc[0]).days / 365.25\n",
    "    return equity ** (1 / years) - 1 if years > 0 else np.nan\n",
    "\n",
    "def trade_from_proba(proba: pd.Series, r_tp1: pd.Series,\n",
    "                     thr: float = THRESH, tcost: float = TCOST):\n",
    "    pos = np.where(proba > thr, 1, -1)\n",
    "    prev_pos = np.concatenate([[0], pos[:-1]])\n",
    "    cost = tcost * np.abs(pos - prev_pos)\n",
    "    ret = pos * r_tp1.to_numpy() - cost\n",
    "    return (pd.Series(ret, index=proba.index, name=\"ret_strategy\"),\n",
    "            pd.Series(pos, index=proba.index, name=\"position\"))\n",
    "\n",
    "# ---------- XGB inner CV (constructor ES for xgboost>=2.1) ----------\n",
    "def xgb_inner_cv(X_tr: pd.DataFrame, y_tr: pd.Series, param_grid: dict) -> dict:\n",
    "    best_params, best_auc = None, -np.inf\n",
    "    tss = TimeSeriesSplit(n_splits=3)\n",
    "    keys = list(param_grid.keys())\n",
    "\n",
    "    for values in itertools.product(*param_grid.values()):\n",
    "        params = dict(zip(keys, values))\n",
    "        aucs = []\n",
    "        for tr_idx, va_idx in tss.split(X_tr):\n",
    "            Xtr, Xva = X_tr.iloc[tr_idx], X_tr.iloc[va_idx]\n",
    "            ytr, yva = y_tr.iloc[tr_idx], y_tr.iloc[va_idx]\n",
    "            model = XGBClassifier(\n",
    "                objective=\"binary:logistic\",\n",
    "                eval_metric=\"logloss\",\n",
    "                n_estimators=2000,\n",
    "                early_stopping_rounds=50,   # set in constructor\n",
    "                tree_method=\"hist\",\n",
    "                random_state=RANDOM_STATE,\n",
    "                **params\n",
    "            )\n",
    "            model.fit(Xtr, ytr, eval_set=[(Xva, yva)], verbose=False)\n",
    "            p = model.predict_proba(Xva)[:, 1]\n",
    "            aucs.append(roc_auc_score(yva, p))\n",
    "        mauc = float(np.mean(aucs))\n",
    "        if mauc > best_auc:\n",
    "            best_auc, best_params = mauc, params\n",
    "    return best_params\n",
    "\n",
    "# ---------- Expanding-window CV + OOS assembly ----------\n",
    "def expanding_cv_predict(df: pd.DataFrame, model_name: str):\n",
    "    feats = feature_columns(df)\n",
    "    X_all = df[feats].copy()\n",
    "    y_all = df[\"y_t\"].astype(int).copy()\n",
    "    dates = df[\"date\"].copy()\n",
    "    r_tp1 = df[\"r_t_plus_1\"].copy()\n",
    "\n",
    "    # (1) Warm-up\n",
    "    X_all = X_all.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    y_all = y_all.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    dates = dates.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    r_tp1 = r_tp1.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "\n",
    "    # (2) Drop any NaNs in features/target/next return\n",
    "    mask_valid = X_all.notna().all(axis=1) & y_all.notna() & r_tp1.notna()\n",
    "    X_all = X_all.loc[mask_valid].reset_index(drop=True)\n",
    "    y_all = y_all.loc[mask_valid].reset_index(drop=True)\n",
    "    dates = dates.loc[mask_valid].reset_index(drop=True)\n",
    "    r_tp1 = r_tp1.loc[mask_valid].reset_index(drop=True)\n",
    "\n",
    "    # (3) Index by date for label-based alignment later\n",
    "    X_all.index = dates\n",
    "    y_all.index = dates\n",
    "    r_tp1.index = dates\n",
    "\n",
    "    tss = TimeSeriesSplit(n_splits=N_SPLITS)\n",
    "    oos_dates, oos_prob, oos_true = [], [], []\n",
    "\n",
    "    for k, (tr_idx, te_idx) in enumerate(tss.split(X_all), start=1):\n",
    "        X_tr, X_te = X_all.iloc[tr_idx], X_all.iloc[te_idx]\n",
    "        y_tr, y_te = y_all.iloc[tr_idx], y_all.iloc[te_idx]\n",
    "\n",
    "        if model_name.lower() == \"logistic\":\n",
    "            pipe = Pipeline([\n",
    "                (\"scale\", StandardScaler(with_mean=True, with_std=True)),\n",
    "                (\"clf\", LogisticRegression(\n",
    "                    penalty=\"l2\", solver=\"lbfgs\", max_iter=5000,\n",
    "                    C=1.0, random_state=RANDOM_STATE\n",
    "                ))\n",
    "            ])\n",
    "            # simple inner CV for C\n",
    "            Cs = [0.01, 0.1, 1.0, 3.0, 10.0]\n",
    "            best_auc, best_C = -np.inf, None\n",
    "            inner_tss = TimeSeriesSplit(n_splits=3)\n",
    "            for C in Cs:\n",
    "                pipe.set_params(clf__C=C)\n",
    "                aucs = []\n",
    "                for tr2, va2 in inner_tss.split(X_tr):\n",
    "                    Xtr, Xva = X_tr.iloc[tr2], X_tr.iloc[va2]\n",
    "                    ytr, yva = y_tr.iloc[tr2], y_tr.iloc[va2]\n",
    "                    pipe.fit(Xtr, ytr)\n",
    "                    p = pipe.predict_proba(Xva)[:, 1]\n",
    "                    aucs.append(roc_auc_score(yva, p))\n",
    "                mauc = float(np.mean(aucs))\n",
    "                if mauc > best_auc:\n",
    "                    best_auc, best_C = mauc, C\n",
    "            pipe.set_params(clf__C=best_C)\n",
    "            pipe.fit(X_tr, y_tr)\n",
    "            p_te = pipe.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        elif model_name.lower() == \"xgb\":\n",
    "            param_grid = {\n",
    "                \"max_depth\": [2, 3, 4],\n",
    "                \"learning_rate\": [0.03, 0.05, 0.1],\n",
    "                \"subsample\": [0.7, 1.0],\n",
    "                \"colsample_bytree\": [0.7, 1.0],\n",
    "                \"min_child_weight\": [1, 5],\n",
    "                \"reg_alpha\": [0.0, 0.5],\n",
    "                \"reg_lambda\": [1.0, 2.0],\n",
    "            }\n",
    "            best_params = xgb_inner_cv(X_tr, y_tr, param_grid)\n",
    "\n",
    "            # final fit with early stopping on last 20% of training\n",
    "            split_pt = int(len(X_tr) * 0.8)\n",
    "            Xtr, Xva = X_tr.iloc[:split_pt], X_tr.iloc[split_pt:]\n",
    "            ytr, yva = y_tr.iloc[:split_pt], y_tr.iloc[split_pt:]\n",
    "\n",
    "            model = XGBClassifier(\n",
    "                objective=\"binary:logistic\",\n",
    "                eval_metric=\"logloss\",\n",
    "                n_estimators=3000,\n",
    "                early_stopping_rounds=100,  # constructor\n",
    "                tree_method=\"hist\",\n",
    "                random_state=RANDOM_STATE,\n",
    "                **best_params\n",
    "            )\n",
    "            model.fit(Xtr, ytr, eval_set=[(Xva, yva)], verbose=False)\n",
    "            p_te = model.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        else:\n",
    "            raise ValueError(\"model_name must be 'logistic' or 'xgb'\")\n",
    "\n",
    "        oos_dates.append(X_te.index.values)\n",
    "        oos_prob.append(p_te)\n",
    "        oos_true.append(y_te.values)\n",
    "\n",
    "        print(f\"[{model_name.upper()} | Fold {k}] Test: \"\n",
    "              f\"{X_te.index[0].date()} → {X_te.index[-1].date()} | n={len(X_te)}\")\n",
    "\n",
    "    # Stitch OOS in chronological order (dates already)\n",
    "    oos_dates = np.concatenate(oos_dates)\n",
    "    oos_prob  = np.concatenate(oos_prob)\n",
    "    oos_true  = np.concatenate(oos_true)\n",
    "\n",
    "    order = np.argsort(oos_dates)\n",
    "    dates_oos = oos_dates[order]\n",
    "    prob  = pd.Series(oos_prob[order], index=dates_oos, name=\"p_hat\")\n",
    "    truth = pd.Series(oos_true[order], index=dates_oos, name=\"y_true\")\n",
    "    r_tp1_oos = r_tp1.loc[prob.index]\n",
    "\n",
    "    # Metrics\n",
    "    auc = roc_auc_score(truth.values, prob.values)\n",
    "    acc = accuracy_score(truth.values, (prob.values > THRESH).astype(int))\n",
    "\n",
    "    # Strategy performance\n",
    "    ret_strategy, position = trade_from_proba(prob, r_tp1_oos, thr=THRESH, tcost=TCOST)\n",
    "    equity = (1.0 + ret_strategy).cumprod()\n",
    "\n",
    "    oos_df = pd.DataFrame({\n",
    "        \"date\": prob.index,\n",
    "        \"p_hat\": prob.values.astype(float),\n",
    "        \"y_true\": truth.values.astype(int),\n",
    "        \"r_t_plus_1\": r_tp1_oos.values.astype(float),\n",
    "        \"position\": position.values.astype(int),\n",
    "        \"ret_strategy\": ret_strategy.values.astype(float),\n",
    "        \"equity\": equity.values.astype(float),\n",
    "    }).reset_index(drop=True)\n",
    "\n",
    "    summary = {\n",
    "        \"model\": model_name.upper(),\n",
    "        \"AUC\": float(auc),\n",
    "        \"Accuracy\": float(acc),\n",
    "        \"Sharpe\": float(sharpe(ret_strategy)),\n",
    "        \"CAGR\": float(cagr(ret_strategy, prob.index.to_series())),\n",
    "        \"n_oos\": int(len(prob))\n",
    "    }\n",
    "    return oos_df, summary\n",
    "\n",
    "# ---------- Main ----------\n",
    "if __name__ == \"__main__\":\n",
    "    df = pd.read_csv(COMBINED_FILE, parse_dates=[\"date\"]).sort_values(\"date\").reset_index(drop=True)\n",
    "\n",
    "    # Train & evaluate both models\n",
    "    oos_log, sum_log = expanding_cv_predict(df, \"logistic\")\n",
    "    oos_xgb, sum_xgb = expanding_cv_predict(df, \"xgb\")\n",
    "\n",
    "    # Print summary\n",
    "    print(\"\\n=== Aggregated OOS (2020-01-01 ~ 2025-06-14) ===\")\n",
    "    for s in (sum_log, sum_xgb):\n",
    "        print(f\"{s['model']:>8} | AUC={s['AUC']:.3f} | ACC={s['Accuracy']:.3f} | \"\n",
    "              f\"Sharpe={s['Sharpe']:.2f} | CAGR={s['CAGR']:.2%} | n={s['n_oos']}\")\n",
    "\n",
    "    # Save OOS\n",
    "    oos_log.to_csv(\"oos_logistic.csv\", index=False)\n",
    "    oos_xgb.to_csv(\"oos_xgb.csv\", index=False)\n",
    "\n",
    "    # Equity curves\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.plot(oos_log[\"date\"], oos_log[\"equity\"], label=\"Logistic (L2, scaled)\")\n",
    "    plt.plot(oos_xgb[\"date\"], oos_xgb[\"equity\"], label=\"XGBoost\")\n",
    "    plt.title(\"Equity Curve — Aggregated OOS (5-fold Expanding, TC=2bps/change)\")\n",
    "    plt.xlabel(\"Date\"); plt.ylabel(\"Equity (Start=1.0)\")\n",
    "    plt.grid(True); plt.legend(); plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e6a6971",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LOGISTIC | Fold 1] Test: 2021-01-20 → 2021-12-02 | n=221\n",
      "[LOGISTIC | Fold 2] Test: 2021-12-03 → 2022-10-19 | n=221\n",
      "[LOGISTIC | Fold 3] Test: 2022-10-20 → 2023-09-07 | n=221\n",
      "[LOGISTIC | Fold 4] Test: 2023-09-08 → 2024-07-26 | n=221\n",
      "[LOGISTIC | Fold 5] Test: 2024-07-29 → 2025-06-12 | n=221\n",
      "[XGB | Fold 1] Test: 2021-01-20 → 2021-12-02 | n=221\n",
      "[XGB | Fold 2] Test: 2021-12-03 → 2022-10-19 | n=221\n",
      "[XGB | Fold 3] Test: 2022-10-20 → 2023-09-07 | n=221\n",
      "[XGB | Fold 4] Test: 2023-09-08 → 2024-07-26 | n=221\n",
      "[XGB | Fold 5] Test: 2024-07-29 → 2025-06-12 | n=221\n",
      "[RF | Fold 1] Test: 2021-01-20 → 2021-12-02 | n=221\n",
      "[RF | Fold 2] Test: 2021-12-03 → 2022-10-19 | n=221\n",
      "[RF | Fold 3] Test: 2022-10-20 → 2023-09-07 | n=221\n",
      "[RF | Fold 4] Test: 2023-09-08 → 2024-07-26 | n=221\n",
      "[RF | Fold 5] Test: 2024-07-29 → 2025-06-12 | n=221\n",
      "[SVM | Fold 1] Test: 2021-01-20 → 2021-12-02 | n=221\n",
      "[SVM | Fold 2] Test: 2021-12-03 → 2022-10-19 | n=221\n",
      "[SVM | Fold 3] Test: 2022-10-20 → 2023-09-07 | n=221\n",
      "[SVM | Fold 4] Test: 2023-09-08 → 2024-07-26 | n=221\n",
      "[SVM | Fold 5] Test: 2024-07-29 → 2025-06-12 | n=221\n",
      "\n",
      "=== Aggregated OOS Prediction Accuracy ===\n",
      "LOGISTIC | ACC=0.508\n",
      "     XGB | ACC=0.495\n",
      "      RF | ACC=0.513\n",
      "     SVM | ACC=0.513\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Aggregated OOS (2021-01-20 ~ 2025-06-12) ===\n",
      "LOGISTIC |Sharpe=-0.05 | CAGR=-4.27% | n=1105\n",
      "     XGB |Sharpe=0.20 | CAGR=1.95% | n=1105\n",
      "      RF |Sharpe=0.58 | CAGR=12.01% | n=1105\n",
      "     SVM |Sharpe=0.24 | CAGR=2.95% | n=1105\n",
      "    Hold |Sharpe=0.26 | CAGR=3.32% | n=1105\n"
     ]
    }
   ],
   "source": [
    "# ================================================================\n",
    "# §3.5 — Part 2: Predictive Modeling & Expanding-Window Backtest\n",
    "# Inputs : combined_dataset.csv  (from Part 1)\n",
    "# Models : Logistic (scaled), XGBoost, RandomForest, SVM (RBF, scaled)\n",
    "# CV     : 5-fold expanding window, 20-day warm-up\n",
    "# Trading: position_t = +1 if p_hat_t > 0.5 else -1; apply to r_{t+1}\n",
    "# Costs  : 2 bps per position change\n",
    "# Outputs:\n",
    "#   • oos_logistic.csv, oos_xgb.csv, oos_rf.csv, oos_svm.csv\n",
    "#   • oos_buyhold.csv  (buy & hold equity on the same OOS dates)\n",
    "#   • accuracy_summary.csv (aggregated OOS prediction accuracy per model)\n",
    "#   • Equity-curve plot: four ML strategies + Buy & Hold\n",
    "#   • Printed summary block with Sharpe/CAGR/n for all strategies incl. Hold\n",
    "# ================================================================\n",
    "from __future__ import annotations\n",
    "from pathlib import Path\n",
    "import itertools, warnings, numpy as np, pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# ---------- CONFIG ----------\n",
    "COMBINED_FILE = Path(\"combined_dataset.csv\")\n",
    "WARMUP_DAYS   = 20\n",
    "N_SPLITS      = 5\n",
    "THRESH        = 0.50\n",
    "TCOST         = 0.0002   # 2 bps per change\n",
    "ANNUAL_DAYS   = 252\n",
    "RANDOM_STATE  = 7\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# ---------------------------\n",
    "\n",
    "# ---------- Feature selection ----------\n",
    "def feature_columns(df: pd.DataFrame) -> list[str]:\n",
    "    non_features = {\n",
    "        \"date\", \"y_t\", \"close\", \"r_t\", \"r_t_plus_1\",\n",
    "        \"p_hat\", \"y_true\", \"position\", \"ret_strategy\", \"equity\"\n",
    "    }\n",
    "    num_cols = df.select_dtypes(include=[np.number]).columns\n",
    "    return [c for c in num_cols if c not in non_features]\n",
    "\n",
    "# ---------- Metrics / trading ----------\n",
    "def sharpe(returns: pd.Series, annualize=True):\n",
    "    mu, sd = returns.mean(), returns.std(ddof=0)\n",
    "    if sd == 0 or np.isnan(sd): return np.nan\n",
    "    s = mu / sd\n",
    "    return s * np.sqrt(ANNUAL_DAYS) if annualize else s\n",
    "\n",
    "def cagr(returns: pd.Series, dates: pd.Series):\n",
    "    if len(returns) == 0: return np.nan\n",
    "    equity = (1.0 + returns).prod()\n",
    "    years = (dates.iloc[-1] - dates.iloc[0]).days / 365.25\n",
    "    return equity ** (1 / years) - 1 if years > 0 else np.nan\n",
    "\n",
    "def trade_from_proba(proba: pd.Series, r_tp1: pd.Series,\n",
    "                     thr: float = THRESH, tcost: float = TCOST):\n",
    "    pos = np.where(proba > thr, 1, -1)\n",
    "    prev_pos = np.concatenate([[0], pos[:-1]])\n",
    "    cost = tcost * np.abs(pos - prev_pos)\n",
    "    ret = pos * r_tp1.to_numpy() - cost\n",
    "    return (pd.Series(ret, index=proba.index, name=\"ret_strategy\"),\n",
    "            pd.Series(pos, index=proba.index, name=\"position\"))\n",
    "\n",
    "# ---------- XGB inner CV (constructor ES for xgboost>=2.1) ----------\n",
    "def xgb_inner_cv(X_tr: pd.DataFrame, y_tr: pd.Series, param_grid: dict) -> dict:\n",
    "    best_params, best_auc = None, -np.inf\n",
    "    tss = TimeSeriesSplit(n_splits=3)\n",
    "    keys = list(param_grid.keys())\n",
    "\n",
    "    for values in itertools.product(*param_grid.values()):\n",
    "        params = dict(zip(keys, values))\n",
    "        aucs = []\n",
    "        for tr_idx, va_idx in tss.split(X_tr):\n",
    "            Xtr, Xva = X_tr.iloc[tr_idx], X_tr.iloc[va_idx]\n",
    "            ytr, yva = y_tr.iloc[tr_idx], y_tr.iloc[va_idx]\n",
    "            model = XGBClassifier(\n",
    "                objective=\"binary:logistic\",\n",
    "                eval_metric=\"logloss\",\n",
    "                n_estimators=2000,\n",
    "                early_stopping_rounds=50,   # set in constructor\n",
    "                tree_method=\"hist\",\n",
    "                random_state=RANDOM_STATE,\n",
    "                **params\n",
    "            )\n",
    "            model.fit(Xtr, ytr, eval_set=[(Xva, yva)], verbose=False)\n",
    "            p = model.predict_proba(Xva)[:, 1]\n",
    "            aucs.append(roc_auc_score(yva, p))\n",
    "        mauc = float(np.mean(aucs))\n",
    "        if mauc > best_auc:\n",
    "            best_auc, best_params = mauc, params\n",
    "    return best_params\n",
    "\n",
    "# ---------- RF inner CV ----------\n",
    "def rf_inner_cv(X_tr: pd.DataFrame, y_tr: pd.Series, param_grid: dict) -> dict:\n",
    "    best_params, best_auc = None, -np.inf\n",
    "    tss = TimeSeriesSplit(n_splits=3)\n",
    "    keys = list(param_grid.keys())\n",
    "\n",
    "    for values in itertools.product(*param_grid.values()):\n",
    "        params = dict(zip(keys, values))\n",
    "        aucs = []\n",
    "        for tr_idx, va_idx in tss.split(X_tr):\n",
    "            Xtr, Xva = X_tr.iloc[tr_idx], X_tr.iloc[va_idx]\n",
    "            ytr, yva = y_tr.iloc[tr_idx], y_tr.iloc[va_idx]\n",
    "            model = RandomForestClassifier(\n",
    "                n_estimators=params[\"n_estimators\"],\n",
    "                max_depth=params[\"max_depth\"],\n",
    "                min_samples_leaf=params[\"min_samples_leaf\"],\n",
    "                max_features=params[\"max_features\"],\n",
    "                random_state=RANDOM_STATE,\n",
    "                n_jobs=-1,\n",
    "            )\n",
    "            model.fit(Xtr, ytr)\n",
    "            p = model.predict_proba(Xva)[:, 1]\n",
    "            aucs.append(roc_auc_score(yva, p))\n",
    "        mauc = float(np.mean(aucs))\n",
    "        if mauc > best_auc:\n",
    "            best_auc, best_params = mauc, params\n",
    "    return best_params\n",
    "\n",
    "# ---------- SVM inner CV (RBF, scaled, probability=True) ----------\n",
    "def svm_inner_cv(X_tr: pd.DataFrame, y_tr: pd.Series, param_grid: dict) -> dict:\n",
    "    best_params, best_auc = None, -np.inf\n",
    "    tss = TimeSeriesSplit(n_splits=3)\n",
    "    keys = list(param_grid.keys())\n",
    "\n",
    "    for values in itertools.product(*param_grid.values()):\n",
    "        params = dict(zip(keys, values))\n",
    "        aucs = []\n",
    "        for tr_idx, va_idx in tss.split(X_tr):\n",
    "            Xtr, Xva = X_tr.iloc[tr_idx], X_tr.iloc[va_idx]\n",
    "            ytr, yva = y_tr.iloc[tr_idx], y_tr.iloc[va_idx]\n",
    "            pipe = Pipeline([\n",
    "                (\"scale\", StandardScaler(with_mean=True, with_std=True)),\n",
    "                (\"svc\", SVC(kernel=\"rbf\", probability=True, **params))\n",
    "            ])\n",
    "            pipe.fit(Xtr, ytr)\n",
    "            p = pipe.predict_proba(Xva)[:, 1]\n",
    "            aucs.append(roc_auc_score(yva, p))\n",
    "        mauc = float(np.mean(aucs))\n",
    "        if mauc > best_auc:\n",
    "            best_auc, best_params = mauc, params\n",
    "    return best_params\n",
    "\n",
    "# ---------- Expanding-window CV + OOS assembly ----------\n",
    "def expanding_cv_predict(df: pd.DataFrame, model_name: str):\n",
    "    feats = feature_columns(df)\n",
    "    X_all = df[feats].copy()\n",
    "    y_all = df[\"y_t\"].astype(int).copy()\n",
    "    dates = df[\"date\"].copy()\n",
    "    r_tp1 = df[\"r_t_plus_1\"].copy()\n",
    "\n",
    "    # (1) Warm-up\n",
    "    X_all = X_all.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    y_all = y_all.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    dates = dates.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    r_tp1 = r_tp1.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "\n",
    "    # (2) Drop any NaNs\n",
    "    mask_valid = X_all.notna().all(axis=1) & y_all.notna() & r_tp1.notna()\n",
    "    X_all = X_all.loc[mask_valid].reset_index(drop=True)\n",
    "    y_all = y_all.loc[mask_valid].reset_index(drop=True)\n",
    "    dates = dates.loc[mask_valid].reset_index(drop=True)\n",
    "    r_tp1 = r_tp1.loc[mask_valid].reset_index(drop=True)\n",
    "\n",
    "    # (3) Index by date\n",
    "    X_all.index = dates\n",
    "    y_all.index = dates\n",
    "    r_tp1.index = dates\n",
    "\n",
    "    tss = TimeSeriesSplit(n_splits=N_SPLITS)\n",
    "    oos_dates, oos_prob, oos_true = [], [], []\n",
    "\n",
    "    for k, (tr_idx, te_idx) in enumerate(tss.split(X_all), start=1):\n",
    "        X_tr, X_te = X_all.iloc[tr_idx], X_all.iloc[te_idx]\n",
    "        y_tr, y_te = y_all.iloc[tr_idx], y_all.iloc[te_idx]\n",
    "\n",
    "        name = model_name.lower()\n",
    "        if name == \"logistic\":\n",
    "            pipe = Pipeline([\n",
    "                (\"scale\", StandardScaler(with_mean=True, with_std=True)),\n",
    "                (\"clf\", LogisticRegression(\n",
    "                    penalty=\"l2\", solver=\"lbfgs\", max_iter=5000,\n",
    "                    C=1.0, random_state=RANDOM_STATE\n",
    "                ))\n",
    "            ])\n",
    "            # inner CV for C\n",
    "            Cs = [0.01, 0.1, 1.0, 3.0, 10.0]\n",
    "            best_auc, best_C = -np.inf, None\n",
    "            inner_tss = TimeSeriesSplit(n_splits=3)\n",
    "            for C in Cs:\n",
    "                pipe.set_params(clf__C=C)\n",
    "                aucs = []\n",
    "                for tr2, va2 in inner_tss.split(X_tr):\n",
    "                    Xtr, Xva = X_tr.iloc[tr2], X_tr.iloc[va2]\n",
    "                    ytr, yva = y_tr.iloc[tr2], y_tr.iloc[va2]\n",
    "                    pipe.fit(Xtr, ytr)\n",
    "                    p = pipe.predict_proba(Xva)[:, 1]\n",
    "                    aucs.append(roc_auc_score(yva, p))\n",
    "                mauc = float(np.mean(aucs))\n",
    "                if mauc > best_auc: best_auc, best_C = mauc, C\n",
    "            pipe.set_params(clf__C=best_C)\n",
    "            pipe.fit(X_tr, y_tr)\n",
    "            p_te = pipe.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        elif name == \"xgb\":\n",
    "            param_grid = {\n",
    "                \"max_depth\": [2, 3, 4],\n",
    "                \"learning_rate\": [0.03, 0.05, 0.1],\n",
    "                \"subsample\": [0.7, 1.0],\n",
    "                \"colsample_bytree\": [0.7, 1.0],\n",
    "                \"min_child_weight\": [1, 5],\n",
    "                \"reg_alpha\": [0.0, 0.5],\n",
    "                \"reg_lambda\": [1.0, 2.0],\n",
    "            }\n",
    "            best_params = xgb_inner_cv(X_tr, y_tr, param_grid)\n",
    "            split_pt = int(len(X_tr) * 0.8)\n",
    "            Xtr, Xva = X_tr.iloc[:split_pt], X_tr.iloc[split_pt:]\n",
    "            ytr, yva = y_tr.iloc[:split_pt], y_tr.iloc[split_pt:]\n",
    "            model = XGBClassifier(\n",
    "                objective=\"binary:logistic\",\n",
    "                eval_metric=\"logloss\",\n",
    "                n_estimators=3000,\n",
    "                early_stopping_rounds=100,\n",
    "                tree_method=\"hist\",\n",
    "                random_state=RANDOM_STATE,\n",
    "                **best_params\n",
    "            )\n",
    "            model.fit(Xtr, ytr, eval_set=[(Xva, yva)], verbose=False)\n",
    "            p_te = model.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        elif name == \"rf\":\n",
    "            param_grid = {\n",
    "                \"n_estimators\": [300, 600, 1000],\n",
    "                \"max_depth\": [None, 5, 8],\n",
    "                \"min_samples_leaf\": [1, 5],\n",
    "                \"max_features\": [\"sqrt\", 0.5],\n",
    "            }\n",
    "            best_params = rf_inner_cv(X_tr, y_tr, param_grid)\n",
    "            model = RandomForestClassifier(\n",
    "                random_state=RANDOM_STATE, n_jobs=-1, **best_params\n",
    "            )\n",
    "            model.fit(X_tr, y_tr)\n",
    "            p_te = model.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        elif name == \"svm\":\n",
    "            param_grid = {\n",
    "                \"C\": [0.1, 1.0, 3.0, 10.0],\n",
    "                \"gamma\": [\"scale\", 0.1, 0.01],\n",
    "            }\n",
    "            best_params = svm_inner_cv(X_tr, y_tr, param_grid)\n",
    "            pipe = Pipeline([\n",
    "                (\"scale\", StandardScaler(with_mean=True, with_std=True)),\n",
    "                (\"svc\", SVC(kernel=\"rbf\", probability=True, **best_params))\n",
    "            ])\n",
    "            pipe.fit(X_tr, y_tr)\n",
    "            p_te = pipe.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        else:\n",
    "            raise ValueError(\"model_name must be 'logistic' | 'xgb' | 'rf' | 'svm'\")\n",
    "\n",
    "        oos_dates.append(X_te.index.values)\n",
    "        oos_prob.append(p_te)\n",
    "        oos_true.append(y_te.values)\n",
    "\n",
    "        print(f\"[{model_name.upper()} | Fold {k}] Test: \"\n",
    "              f\"{X_te.index[0].date()} → {X_te.index[-1].date()} | n={len(X_te)}\")\n",
    "\n",
    "    # Stitch OOS chronologically\n",
    "    oos_dates = np.concatenate(oos_dates)\n",
    "    oos_prob  = np.concatenate(oos_prob)\n",
    "    oos_true  = np.concatenate(oos_true)\n",
    "\n",
    "    order = np.argsort(oos_dates)\n",
    "    dates_oos = oos_dates[order]\n",
    "    prob  = pd.Series(oos_prob[order], index=dates_oos, name=\"p_hat\")\n",
    "    truth = pd.Series(oos_true[order], index=dates_oos, name=\"y_true\")\n",
    "    r_tp1_oos = r_tp1.loc[prob.index]\n",
    "\n",
    "    # Metrics\n",
    "    auc = roc_auc_score(truth.values, prob.values)\n",
    "    acc = accuracy_score(truth.values, (prob.values > THRESH).astype(int))\n",
    "\n",
    "    # Strategy\n",
    "    ret_strategy, position = trade_from_proba(prob, r_tp1_oos, thr=THRESH, tcost=TCOST)\n",
    "    equity = (1.0 + ret_strategy).cumprod()\n",
    "\n",
    "    oos_df = pd.DataFrame({\n",
    "        \"date\": prob.index,\n",
    "        \"p_hat\": prob.values.astype(float),\n",
    "        \"y_true\": truth.values.astype(int),\n",
    "        \"r_t_plus_1\": r_tp1_oos.values.astype(float),\n",
    "        \"position\": position.values.astype(int),\n",
    "        \"ret_strategy\": ret_strategy.values.astype(float),\n",
    "        \"equity\": equity.values.astype(float),\n",
    "    }).reset_index(drop=True)\n",
    "\n",
    "    summary = {\n",
    "        \"model\": model_name.upper(),\n",
    "        \"AUC\": float(auc),\n",
    "        \"Accuracy\": float(acc),\n",
    "        \"Sharpe\": float(sharpe(ret_strategy)),\n",
    "        \"CAGR\": float(cagr(ret_strategy, prob.index.to_series())),\n",
    "        \"n_oos\": int(len(prob))\n",
    "    }\n",
    "    return oos_df, summary\n",
    "\n",
    "# ---------- Main ----------\n",
    "if __name__ == \"__main__\":\n",
    "    df = pd.read_csv(COMBINED_FILE, parse_dates=[\"date\"]).sort_values(\"date\").reset_index(drop=True)\n",
    "\n",
    "    # Train & evaluate models\n",
    "    oos_log, sum_log = expanding_cv_predict(df, \"logistic\")\n",
    "    oos_xgb, sum_xgb = expanding_cv_predict(df, \"xgb\")\n",
    "    oos_rf,  sum_rf  = expanding_cv_predict(df, \"rf\")\n",
    "    oos_svm, sum_svm = expanding_cv_predict(df, \"svm\")\n",
    "\n",
    "    # === (1) Aggregated OOS prediction accuracy for each ML model ===\n",
    "    acc_df = pd.DataFrame([\n",
    "        {\"model\": s[\"model\"], \"Accuracy\": s[\"Accuracy\"]}\n",
    "        for s in (sum_log, sum_xgb, sum_rf, sum_svm)\n",
    "    ])\n",
    "    acc_df.to_csv(\"accuracy_summary.csv\", index=False)\n",
    "    print(\"\\n=== Aggregated OOS Prediction Accuracy ===\")\n",
    "    for _, row in acc_df.iterrows():\n",
    "        print(f\"{row['model']:>8} | ACC={row['Accuracy']:.3f}\")\n",
    "\n",
    "    # Save OOS files\n",
    "    oos_log.to_csv(\"oos_logistic.csv\", index=False)\n",
    "    oos_xgb.to_csv(\"oos_xgb.csv\", index=False)\n",
    "    oos_rf.to_csv(\"oos_rf.csv\", index=False)\n",
    "    oos_svm.to_csv(\"oos_svm.csv\", index=False)\n",
    "\n",
    "    # === (2) Buy & Hold equity curve and metrics on the SAME OOS dates ===\n",
    "    # Use Logistic's OOS calendar; all models share the same splits here.\n",
    "    bh = oos_log[[\"date\", \"r_t_plus_1\"]].copy()\n",
    "    bh[\"equity\"] = (1.0 + bh[\"r_t_plus_1\"]).cumprod()\n",
    "    bh.to_csv(\"oos_buyhold.csv\", index=False)\n",
    "\n",
    "    hold_sharpe = sharpe(bh[\"r_t_plus_1\"])\n",
    "    hold_cagr   = cagr(bh[\"r_t_plus_1\"], bh[\"date\"])\n",
    "    hold_n      = len(bh)\n",
    "\n",
    "    # === (3) Equity curves plot ===\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(oos_log[\"date\"], oos_log[\"equity\"], label=\"Logistic (L2)\")\n",
    "    plt.plot(oos_xgb[\"date\"], oos_xgb[\"equity\"], label=\"XGBoost\")\n",
    "    plt.plot(oos_rf[\"date\"],  oos_rf[\"equity\"],  label=\"RandomForest\")\n",
    "    plt.plot(oos_svm[\"date\"], oos_svm[\"equity\"], label=\"SVM (RBF)\")\n",
    "    plt.plot(bh[\"date\"],      bh[\"equity\"],      label=\"Buy & Hold\", linestyle=\"--\")\n",
    "    plt.title(\"Equity Curve — OOS (5-fold Expanding, TC=2bps/change)\")\n",
    "    plt.xlabel(\"Date\"); plt.ylabel(\"Equity (Start=1.0)\")\n",
    "    plt.grid(True); plt.legend(); plt.tight_layout(); plt.show()\n",
    "\n",
    "    # === (4) Final summary block in requested format ===\n",
    "    start_str = pd.to_datetime(oos_log[\"date\"].iloc[0]).date()\n",
    "    end_str   = pd.to_datetime(oos_log[\"date\"].iloc[-1]).date()\n",
    "    print(f\"\\n=== Aggregated OOS ({start_str} ~ {end_str}) ===\")\n",
    "    print(f\"{'LOGISTIC':>8} |Sharpe={sum_log['Sharpe']:.2f} | CAGR={sum_log['CAGR']:.2%} | n={sum_log['n_oos']}\")\n",
    "    print(f\"{'XGB':>8} |Sharpe={sum_xgb['Sharpe']:.2f} | CAGR={sum_xgb['CAGR']:.2%} | n={sum_xgb['n_oos']}\")\n",
    "    print(f\"{'RF':>8} |Sharpe={sum_rf['Sharpe']:.2f} | CAGR={sum_rf['CAGR']:.2%} | n={sum_rf['n_oos']}\")\n",
    "    print(f\"{'SVM':>8} |Sharpe={sum_svm['Sharpe']:.2f} | CAGR={sum_svm['CAGR']:.2%} | n={sum_svm['n_oos']}\")\n",
    "    print(f\"{'Hold':>8} |Sharpe={hold_sharpe:.2f} | CAGR={hold_cagr:.2%} | n={hold_n}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "310b3358",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Trimmed] 2020-03-02 → 2025-06-12 | n=1330\n",
      "[Trimmed] Wrote: combined_dataset copy.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "SRC = \"combined_dataset.csv\"\n",
    "DST = \"combined_dataset copy.csv\"\n",
    "START_DATE = pd.Timestamp(\"2020-03-02\")\n",
    "\n",
    "# Load\n",
    "df = pd.read_csv(SRC, parse_dates=[\"date\"])\n",
    "\n",
    "# Keep only rows >= 2020-03-02\n",
    "df = df[df[\"date\"] >= START_DATE]\n",
    "\n",
    "# Save trimmed dataset\n",
    "df.to_csv(DST, index=False)\n",
    "\n",
    "print(f\"[Trimmed] {df['date'].iloc[0].date()} → {df['date'].iloc[-1].date()} | n={len(df)}\")\n",
    "print(f\"[Trimmed] Wrote: {DST}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "83d78894",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LOGISTIC | Fold 1] Test: 2021-02-18 → 2021-12-27 | n=217\n",
      "[LOGISTIC | Fold 2] Test: 2021-12-28 → 2022-11-04 | n=217\n",
      "[LOGISTIC | Fold 3] Test: 2022-11-07 → 2023-09-19 | n=217\n",
      "[LOGISTIC | Fold 4] Test: 2023-09-20 → 2024-08-01 | n=217\n",
      "[LOGISTIC | Fold 5] Test: 2024-08-02 → 2025-06-12 | n=217\n",
      "[XGB | Fold 1] Test: 2021-02-18 → 2021-12-27 | n=217\n",
      "[XGB | Fold 2] Test: 2021-12-28 → 2022-11-04 | n=217\n",
      "[XGB | Fold 3] Test: 2022-11-07 → 2023-09-19 | n=217\n",
      "[XGB | Fold 4] Test: 2023-09-20 → 2024-08-01 | n=217\n",
      "[XGB | Fold 5] Test: 2024-08-02 → 2025-06-12 | n=217\n",
      "[RF | Fold 1] Test: 2021-02-18 → 2021-12-27 | n=217\n",
      "[RF | Fold 2] Test: 2021-12-28 → 2022-11-04 | n=217\n",
      "[RF | Fold 3] Test: 2022-11-07 → 2023-09-19 | n=217\n",
      "[RF | Fold 4] Test: 2023-09-20 → 2024-08-01 | n=217\n",
      "[RF | Fold 5] Test: 2024-08-02 → 2025-06-12 | n=217\n",
      "[SVM | Fold 1] Test: 2021-02-18 → 2021-12-27 | n=217\n",
      "[SVM | Fold 2] Test: 2021-12-28 → 2022-11-04 | n=217\n",
      "[SVM | Fold 3] Test: 2022-11-07 → 2023-09-19 | n=217\n",
      "[SVM | Fold 4] Test: 2023-09-20 → 2024-08-01 | n=217\n",
      "[SVM | Fold 5] Test: 2024-08-02 → 2025-06-12 | n=217\n",
      "\n",
      "=== Aggregated OOS Prediction Accuracy ===\n",
      "LOGISTIC | ACC=0.505\n",
      "     XGB | ACC=0.491\n",
      "      RF | ACC=0.520\n",
      "     SVM | ACC=0.504\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Aggregated OOS (2021-02-18 ~ 2025-03-21) ===\n",
      "LOGISTIC |Sharpe=0.24 | CAGR=2.08% | n=1028\n",
      "     XGB |Sharpe=0.44 | CAGR=5.41% | n=1028\n",
      "      RF |Sharpe=1.26 | CAGR=20.82% | n=1028\n",
      "     SVM |Sharpe=0.38 | CAGR=4.39% | n=1028\n",
      "    Hold |Sharpe=0.27 | CAGR=2.51% | n=1028\n"
     ]
    }
   ],
   "source": [
    "# ================================================================\n",
    "# §3.5 — Part 2: Predictive Modeling & Expanding-Window Backtest\n",
    "# Inputs : combined_dataset.csv  (from Part 1)\n",
    "# Models : Logistic (scaled), XGBoost, RandomForest, SVM (RBF, scaled)\n",
    "# CV     : 5-fold expanding window, 20-day warm-up\n",
    "# Trading: position_t = +1 if p_hat_t > 0.5 else -1; apply to r_{t+1}\n",
    "# Costs  : 2 bps per position change\n",
    "# Outputs:\n",
    "#   • oos_logistic.csv, oos_xgb.csv, oos_rf.csv, oos_svm.csv\n",
    "#   • oos_buyhold.csv  (buy & hold equity on the same OOS dates)\n",
    "#   • accuracy_summary.csv (aggregated OOS prediction accuracy per model)\n",
    "#   • Equity-curve plot: four ML strategies + Buy & Hold\n",
    "#   • Printed summary block with Sharpe/CAGR/n for all strategies incl. Hold\n",
    "# ================================================================\n",
    "from __future__ import annotations\n",
    "from pathlib import Path\n",
    "import itertools, warnings, numpy as np, pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# ---------- CONFIG ----------\n",
    "COMBINED_FILE = Path(\"combined_dataset copy.csv\")\n",
    "WARMUP_DAYS   = 20\n",
    "N_SPLITS      = 5\n",
    "THRESH        = 0.50\n",
    "TCOST         = 0.0001   # 2 bps per position change\n",
    "ANNUAL_DAYS   = 252\n",
    "RANDOM_STATE  = 7\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Optional: cap the plot & printed summary at a chosen end date.\n",
    "# Examples: END_PLOT_DATE = \"2024-12-31\"  or  None for full range\n",
    "END_PLOT_DATE = \"2025-03-21\"\n",
    "# ---------------------------\n",
    "\n",
    "# ---------- Feature selection ----------\n",
    "def feature_columns(df: pd.DataFrame) -> list[str]:\n",
    "    non_features = {\n",
    "        \"date\", \"y_t\", \"close\", \"r_t\", \"r_t_plus_1\",\n",
    "        \"p_hat\", \"y_true\", \"position\", \"ret_strategy\", \"equity\"\n",
    "    }\n",
    "    num_cols = df.select_dtypes(include=[np.number]).columns\n",
    "    return [c for c in num_cols if c not in non_features]\n",
    "\n",
    "# ---------- Metrics / trading ----------\n",
    "def sharpe(returns: pd.Series, annualize=True):\n",
    "    mu, sd = returns.mean(), returns.std(ddof=0)\n",
    "    if sd == 0 or np.isnan(sd): return np.nan\n",
    "    s = mu / sd\n",
    "    return s * np.sqrt(ANNUAL_DAYS) if annualize else s\n",
    "\n",
    "def cagr(returns: pd.Series, dates: pd.Series):\n",
    "    if len(returns) == 0: return np.nan\n",
    "    equity = (1.0 + returns).prod()\n",
    "    years = (dates.iloc[-1] - dates.iloc[0]).days / 252\n",
    "    return equity ** (1 / years) - 1 if years > 0 else np.nan\n",
    "\n",
    "def trade_from_proba(proba: pd.Series, r_tp1: pd.Series,\n",
    "                     thr: float = THRESH, tcost: float = TCOST):\n",
    "    pos = np.where(proba > thr, 1, -1)\n",
    "    prev_pos = np.concatenate([[0], pos[:-1]])\n",
    "    cost = tcost * np.abs(pos - prev_pos)\n",
    "    ret = pos * r_tp1.to_numpy() - cost\n",
    "    return (pd.Series(ret, index=proba.index, name=\"ret_strategy\"),\n",
    "            pd.Series(pos, index=proba.index, name=\"position\"))\n",
    "\n",
    "# ---------- XGB inner CV (constructor ES for xgboost>=2.1) ----------\n",
    "def xgb_inner_cv(X_tr: pd.DataFrame, y_tr: pd.Series, param_grid: dict) -> dict:\n",
    "    best_params, best_auc = None, -np.inf\n",
    "    tss = TimeSeriesSplit(n_splits=3)\n",
    "    keys = list(param_grid.keys())\n",
    "\n",
    "    for values in itertools.product(*param_grid.values()):\n",
    "        params = dict(zip(keys, values))\n",
    "        aucs = []\n",
    "        for tr_idx, va_idx in tss.split(X_tr):\n",
    "            Xtr, Xva = X_tr.iloc[tr_idx], X_tr.iloc[va_idx]\n",
    "            ytr, yva = y_tr.iloc[tr_idx], y_tr.iloc[va_idx]\n",
    "            model = XGBClassifier(\n",
    "                objective=\"binary:logistic\",\n",
    "                eval_metric=\"logloss\",\n",
    "                n_estimators=2000,\n",
    "                early_stopping_rounds=50,   # set in constructor\n",
    "                tree_method=\"hist\",\n",
    "                random_state=RANDOM_STATE,\n",
    "                **params\n",
    "            )\n",
    "            model.fit(Xtr, ytr, eval_set=[(Xva, yva)], verbose=False)\n",
    "            p = model.predict_proba(Xva)[:, 1]\n",
    "            aucs.append(roc_auc_score(yva, p))\n",
    "        mauc = float(np.mean(aucs))\n",
    "        if mauc > best_auc:\n",
    "            best_auc, best_params = mauc, params\n",
    "    return best_params\n",
    "\n",
    "# ---------- RF inner CV ----------\n",
    "def rf_inner_cv(X_tr: pd.DataFrame, y_tr: pd.Series, param_grid: dict) -> dict:\n",
    "    best_params, best_auc = None, -np.inf\n",
    "    tss = TimeSeriesSplit(n_splits=3)\n",
    "    keys = list(param_grid.keys())\n",
    "\n",
    "    for values in itertools.product(*param_grid.values()):\n",
    "        params = dict(zip(keys, values))\n",
    "        aucs = []\n",
    "        for tr_idx, va_idx in tss.split(X_tr):\n",
    "            Xtr, Xva = X_tr.iloc[tr_idx], X_tr.iloc[va_idx]\n",
    "            ytr, yva = y_tr.iloc[tr_idx], y_tr.iloc[va_idx]\n",
    "            model = RandomForestClassifier(\n",
    "                n_estimators=params[\"n_estimators\"],\n",
    "                max_depth=params[\"max_depth\"],\n",
    "                min_samples_leaf=params[\"min_samples_leaf\"],\n",
    "                max_features=params[\"max_features\"],\n",
    "                random_state=RANDOM_STATE,\n",
    "                n_jobs=-1,\n",
    "            )\n",
    "            model.fit(Xtr, ytr)\n",
    "            p = model.predict_proba(Xva)[:, 1]\n",
    "            aucs.append(roc_auc_score(yva, p))\n",
    "        mauc = float(np.mean(aucs))\n",
    "        if mauc > best_auc:\n",
    "            best_auc, best_params = mauc, params\n",
    "    return best_params\n",
    "\n",
    "# ---------- SVM inner CV (RBF, scaled, probability=True) ----------\n",
    "def svm_inner_cv(X_tr: pd.DataFrame, y_tr: pd.Series, param_grid: dict) -> dict:\n",
    "    best_params, best_auc = None, -np.inf\n",
    "    tss = TimeSeriesSplit(n_splits=3)\n",
    "    keys = list(param_grid.keys())\n",
    "\n",
    "    for values in itertools.product(*param_grid.values()):\n",
    "        params = dict(zip(keys, values))\n",
    "        aucs = []\n",
    "        for tr_idx, va_idx in tss.split(X_tr):\n",
    "            Xtr, Xva = X_tr.iloc[tr_idx], X_tr.iloc[va_idx]\n",
    "            ytr, yva = y_tr.iloc[tr_idx], y_tr.iloc[va_idx]\n",
    "            pipe = Pipeline([\n",
    "                (\"scale\", StandardScaler(with_mean=True, with_std=True)),\n",
    "                (\"svc\", SVC(kernel=\"rbf\", probability=True, **params))\n",
    "            ])\n",
    "            pipe.fit(Xtr, ytr)\n",
    "            p = pipe.predict_proba(Xva)[:, 1]\n",
    "            aucs.append(roc_auc_score(yva, p))\n",
    "        mauc = float(np.mean(aucs))\n",
    "        if mauc > best_auc:\n",
    "            best_auc, best_params = mauc, params\n",
    "    return best_params\n",
    "\n",
    "# ---------- Expanding-window CV + OOS assembly ----------\n",
    "def expanding_cv_predict(df: pd.DataFrame, model_name: str):\n",
    "    feats = feature_columns(df)\n",
    "    X_all = df[feats].copy()\n",
    "    y_all = df[\"y_t\"].astype(int).copy()\n",
    "    dates = df[\"date\"].copy()\n",
    "    r_tp1 = df[\"r_t_plus_1\"].copy()\n",
    "\n",
    "    # (1) Warm-up\n",
    "    X_all = X_all.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    y_all = y_all.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    dates = dates.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    r_tp1 = r_tp1.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "\n",
    "    # (2) Drop any NaNs\n",
    "    mask_valid = X_all.notna().all(axis=1) & y_all.notna() & r_tp1.notna()\n",
    "    X_all = X_all.loc[mask_valid].reset_index(drop=True)\n",
    "    y_all = y_all.loc[mask_valid].reset_index(drop=True)\n",
    "    dates = dates.loc[mask_valid].reset_index(drop=True)\n",
    "    r_tp1 = r_tp1.loc[mask_valid].reset_index(drop=True)\n",
    "\n",
    "    # (3) Index by date\n",
    "    X_all.index = dates\n",
    "    y_all.index = dates\n",
    "    r_tp1.index = dates\n",
    "\n",
    "    tss = TimeSeriesSplit(n_splits=N_SPLITS)\n",
    "    oos_dates, oos_prob, oos_true = [], [], []\n",
    "\n",
    "    for k, (tr_idx, te_idx) in enumerate(tss.split(X_all), start=1):\n",
    "        X_tr, X_te = X_all.iloc[tr_idx], X_all.iloc[te_idx]\n",
    "        y_tr, y_te = y_all.iloc[tr_idx], y_all.iloc[te_idx]\n",
    "\n",
    "        name = model_name.lower()\n",
    "        if name == \"logistic\":\n",
    "            pipe = Pipeline([\n",
    "                (\"scale\", StandardScaler(with_mean=True, with_std=True)),\n",
    "                (\"clf\", LogisticRegression(\n",
    "                    penalty=\"l2\", solver=\"lbfgs\", max_iter=5000,\n",
    "                    C=1.0, random_state=RANDOM_STATE\n",
    "                ))\n",
    "            ])\n",
    "            # inner CV for C\n",
    "            Cs = [0.01, 0.1, 1.0, 3.0, 10.0]\n",
    "            best_auc, best_C = -np.inf, None\n",
    "            inner_tss = TimeSeriesSplit(n_splits=3)\n",
    "            for C in Cs:\n",
    "                pipe.set_params(clf__C=C)\n",
    "                aucs = []\n",
    "                for tr2, va2 in inner_tss.split(X_tr):\n",
    "                    Xtr, Xva = X_tr.iloc[tr2], X_tr.iloc[va2]\n",
    "                    ytr, yva = y_tr.iloc[tr2], y_tr.iloc[va2]\n",
    "                    pipe.fit(Xtr, ytr)\n",
    "                    p = pipe.predict_proba(Xva)[:, 1]\n",
    "                    aucs.append(roc_auc_score(yva, p))\n",
    "                mauc = float(np.mean(aucs))\n",
    "                if mauc > best_auc: best_auc, best_C = mauc, C\n",
    "            pipe.set_params(clf__C=best_C)\n",
    "            pipe.fit(X_tr, y_tr)\n",
    "            p_te = pipe.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        elif name == \"xgb\":\n",
    "            param_grid = {\n",
    "                \"max_depth\": [2, 3, 4],\n",
    "                \"learning_rate\": [0.03, 0.05, 0.1],\n",
    "                \"subsample\": [0.7, 1.0],\n",
    "                \"colsample_bytree\": [0.7, 1.0],\n",
    "                \"min_child_weight\": [1, 5],\n",
    "                \"reg_alpha\": [0.0, 0.5],\n",
    "                \"reg_lambda\": [1.0, 2.0],\n",
    "            }\n",
    "            best_params = xgb_inner_cv(X_tr, y_tr, param_grid)\n",
    "            split_pt = int(len(X_tr) * 0.8)\n",
    "            Xtr, Xva = X_tr.iloc[:split_pt], X_tr.iloc[split_pt:]\n",
    "            ytr, yva = y_tr.iloc[:split_pt], y_tr.iloc[split_pt:]\n",
    "            model = XGBClassifier(\n",
    "                objective=\"binary:logistic\",\n",
    "                eval_metric=\"logloss\",\n",
    "                n_estimators=3000,\n",
    "                early_stopping_rounds=100,\n",
    "                tree_method=\"hist\",\n",
    "                random_state=RANDOM_STATE,\n",
    "                **best_params\n",
    "            )\n",
    "            model.fit(Xtr, ytr, eval_set=[(Xva, yva)], verbose=False)\n",
    "            p_te = model.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        elif name == \"rf\":\n",
    "            param_grid = {\n",
    "                \"n_estimators\": [300, 600, 1000],\n",
    "                \"max_depth\": [None, 5, 8],\n",
    "                \"min_samples_leaf\": [1, 5],\n",
    "                \"max_features\": [\"sqrt\", 0.5],\n",
    "            }\n",
    "            best_params = rf_inner_cv(X_tr, y_tr, param_grid)\n",
    "            model = RandomForestClassifier(\n",
    "                random_state=RANDOM_STATE, n_jobs=-1, **best_params\n",
    "            )\n",
    "            model.fit(X_tr, y_tr)\n",
    "            p_te = model.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        elif name == \"svm\":\n",
    "            param_grid = {\n",
    "                \"C\": [0.1, 1.0, 3.0, 10.0],\n",
    "                \"gamma\": [\"scale\", 0.1, 0.01],\n",
    "            }\n",
    "            best_params = svm_inner_cv(X_tr, y_tr, param_grid)\n",
    "            pipe = Pipeline([\n",
    "                (\"scale\", StandardScaler(with_mean=True, with_std=True)),\n",
    "                (\"svc\", SVC(kernel=\"rbf\", probability=True, **best_params))\n",
    "            ])\n",
    "            pipe.fit(X_tr, y_tr)\n",
    "            p_te = pipe.predict_proba(X_te)[:, 1]\n",
    "\n",
    "        else:\n",
    "            raise ValueError(\"model_name must be 'logistic' | 'xgb' | 'rf' | 'svm'\")\n",
    "\n",
    "        oos_dates.append(X_te.index.values)\n",
    "        oos_prob.append(p_te)\n",
    "        oos_true.append(y_te.values)\n",
    "\n",
    "        print(f\"[{model_name.upper()} | Fold {k}] Test: \"\n",
    "              f\"{X_te.index[0].date()} → {X_te.index[-1].date()} | n={len(X_te)}\")\n",
    "\n",
    "    # Stitch OOS chronologically\n",
    "    oos_dates = np.concatenate(oos_dates)\n",
    "    oos_prob  = np.concatenate(oos_prob)\n",
    "    oos_true  = np.concatenate(oos_true)\n",
    "\n",
    "    order = np.argsort(oos_dates)\n",
    "    dates_oos = oos_dates[order]\n",
    "    prob  = pd.Series(oos_prob[order], index=dates_oos, name=\"p_hat\")\n",
    "    truth = pd.Series(oos_true[order], index=dates_oos, name=\"y_true\")\n",
    "    r_tp1_oos = r_tp1.loc[prob.index]\n",
    "\n",
    "    # Metrics\n",
    "    auc = roc_auc_score(truth.values, prob.values)\n",
    "    acc = accuracy_score(truth.values, (prob.values > THRESH).astype(int))\n",
    "\n",
    "    # Strategy\n",
    "    ret_strategy, position = trade_from_proba(prob, r_tp1_oos, thr=THRESH, tcost=TCOST)\n",
    "    equity = (1.0 + ret_strategy).cumprod()\n",
    "\n",
    "    oos_df = pd.DataFrame({\n",
    "        \"date\": prob.index,\n",
    "        \"p_hat\": prob.values.astype(float),\n",
    "        \"y_true\": truth.values.astype(int),\n",
    "        \"r_t_plus_1\": r_tp1_oos.values.astype(float),\n",
    "        \"position\": position.values.astype(int),\n",
    "        \"ret_strategy\": ret_strategy.values.astype(float),\n",
    "        \"equity\": equity.values.astype(float),\n",
    "    }).reset_index(drop=True)\n",
    "\n",
    "    summary = {\n",
    "        \"model\": model_name.upper(),\n",
    "        \"AUC\": float(auc),\n",
    "        \"Accuracy\": float(acc),\n",
    "        \"Sharpe\": float(sharpe(ret_strategy)),\n",
    "        \"CAGR\": float(cagr(ret_strategy, prob.index.to_series())),\n",
    "        \"n_oos\": int(len(prob))\n",
    "    }\n",
    "    return oos_df, summary\n",
    "\n",
    "# ---------- Plot/summary window helpers ----------\n",
    "def _clip_oos(oos_df: pd.DataFrame, end_date: str | None):\n",
    "    \"\"\"Trim to date <= end_date and rebuild equity from 1.0.\"\"\"\n",
    "    if end_date is None:\n",
    "        return oos_df.copy()\n",
    "    end = pd.to_datetime(end_date)\n",
    "    sub = oos_df[oos_df[\"date\"] <= end].copy()\n",
    "    if sub.empty:\n",
    "        return sub\n",
    "    sub[\"equity\"] = (1.0 + sub[\"ret_strategy\"]).cumprod()\n",
    "    return sub\n",
    "\n",
    "def _buyhold_from(oos_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"Buy & hold series aligned to an OOS dataframe (uses r_{t+1}).\"\"\"\n",
    "    bh = oos_df[[\"date\", \"r_t_plus_1\"]].copy()\n",
    "    bh[\"equity\"] = (1.0 + bh[\"r_t_plus_1\"]).cumprod()\n",
    "    return bh\n",
    "\n",
    "def _summary_from_oos(oos_df: pd.DataFrame, label: str):\n",
    "    return {\n",
    "        \"model\": label,\n",
    "        \"Sharpe\": sharpe(oos_df[\"ret_strategy\"]),\n",
    "        \"CAGR\":   cagr(oos_df[\"ret_strategy\"], oos_df[\"date\"]),\n",
    "        \"n\":      int(len(oos_df))\n",
    "    }\n",
    "\n",
    "# ---------- Main ----------\n",
    "if __name__ == \"__main__\":\n",
    "    df = pd.read_csv(COMBINED_FILE, parse_dates=[\"date\"]).sort_values(\"date\").reset_index(drop=True)\n",
    "\n",
    "    # Train & evaluate models (full-range OOS)\n",
    "    oos_log, sum_log = expanding_cv_predict(df, \"logistic\")\n",
    "    oos_xgb, sum_xgb = expanding_cv_predict(df, \"xgb\")\n",
    "    oos_rf,  sum_rf  = expanding_cv_predict(df, \"rf\")\n",
    "    oos_svm, sum_svm = expanding_cv_predict(df, \"svm\")\n",
    "\n",
    "    # === (1) Aggregated OOS prediction accuracy (full range) ===\n",
    "    acc_df = pd.DataFrame([\n",
    "        {\"model\": s[\"model\"], \"Accuracy\": s[\"Accuracy\"]}\n",
    "        for s in (sum_log, sum_xgb, sum_rf, sum_svm)\n",
    "    ])\n",
    "    acc_df.to_csv(\"accuracy_summary.csv\", index=False)\n",
    "    print(\"\\n=== Aggregated OOS Prediction Accuracy ===\")\n",
    "    for _, row in acc_df.iterrows():\n",
    "        print(f\"{row['model']:>8} | ACC={row['Accuracy']:.3f}\")\n",
    "\n",
    "    # Save OOS files (full range)\n",
    "    oos_log.to_csv(\"oos_logistic.csv\", index=False)\n",
    "    oos_xgb.to_csv(\"oos_xgb.csv\", index=False)\n",
    "    oos_rf.to_csv(\"oos_rf.csv\", index=False)\n",
    "    oos_svm.to_csv(\"oos_svm.csv\", index=False)\n",
    "\n",
    "    # === (2) Buy & Hold (full range) and save ===\n",
    "    bh_full = _buyhold_from(oos_log)\n",
    "    bh_full.to_csv(\"oos_buyhold.csv\", index=False)\n",
    "\n",
    "    # === (3) Clip window for plot/printed summary if END_PLOT_DATE is set ===\n",
    "    oos_log_plot = _clip_oos(oos_log, END_PLOT_DATE)\n",
    "    oos_xgb_plot = _clip_oos(oos_xgb, END_PLOT_DATE)\n",
    "    oos_rf_plot  = _clip_oos(oos_rf,  END_PLOT_DATE)\n",
    "    oos_svm_plot = _clip_oos(oos_svm, END_PLOT_DATE)\n",
    "\n",
    "    if END_PLOT_DATE is None:\n",
    "        bh_plot = bh_full.copy()\n",
    "    else:\n",
    "        end_ts = pd.to_datetime(END_PLOT_DATE)\n",
    "        bh_plot = bh_full[bh_full[\"date\"] <= end_ts].copy()\n",
    "        if not bh_plot.empty:\n",
    "            bh_plot[\"equity\"] = (1.0 + bh_plot[\"r_t_plus_1\"]).cumprod()\n",
    "\n",
    "    # === (4) Plot equity curves over the (possibly clipped) window ===\n",
    "    if any(len(df_) > 0 for df_ in [oos_log_plot, oos_xgb_plot, oos_rf_plot, oos_svm_plot, bh_plot]):\n",
    "        plt.figure(figsize=(10, 6))\n",
    "        plt.plot(oos_log_plot[\"date\"], oos_log_plot[\"equity\"], label=\"Logistic (L2)\")\n",
    "        plt.plot(oos_xgb_plot[\"date\"], oos_xgb_plot[\"equity\"], label=\"XGBoost\")\n",
    "        plt.plot(oos_rf_plot[\"date\"],  oos_rf_plot[\"equity\"],  label=\"RandomForest\")\n",
    "        plt.plot(oos_svm_plot[\"date\"], oos_svm_plot[\"equity\"], label=\"SVM (RBF)\")\n",
    "        plt.plot(bh_plot[\"date\"],      bh_plot[\"equity\"],      label=\"Buy & Hold\", linestyle=\"--\")\n",
    "        title_suffix = f\" (end={END_PLOT_DATE})\" if END_PLOT_DATE else \"\"\n",
    "        plt.title(f\"Equity Curve — OOS (5-fold Expanding, TC=2bps/change){title_suffix}\")\n",
    "        plt.xlabel(\"Date\"); plt.ylabel(\"Equity (Start=1.0)\")\n",
    "        plt.grid(True); plt.legend(); plt.tight_layout(); plt.show()\n",
    "    else:\n",
    "        print(\"No data to plot after applying END_PLOT_DATE.\")\n",
    "\n",
    "    # === (5) Final summary block in requested format (clipped window) ===\n",
    "    if not oos_log_plot.empty:\n",
    "        start_str = pd.to_datetime(oos_log_plot[\"date\"].iloc[0]).date()\n",
    "        end_str   = pd.to_datetime(oos_log_plot[\"date\"].iloc[-1]).date()\n",
    "        sum_log_clip = _summary_from_oos(oos_log_plot, \"LOGISTIC\")\n",
    "        sum_xgb_clip = _summary_from_oos(oos_xgb_plot, \"XGB\")\n",
    "        sum_rf_clip  = _summary_from_oos(oos_rf_plot,  \"RF\")\n",
    "        sum_svm_clip = _summary_from_oos(oos_svm_plot, \"SVM\")\n",
    "        hold_sharpe  = sharpe(bh_plot[\"r_t_plus_1\"])\n",
    "        hold_cagr    = cagr(bh_plot[\"r_t_plus_1\"], bh_plot[\"date\"])\n",
    "        hold_n       = len(bh_plot)\n",
    "\n",
    "        print(f\"\\n=== Aggregated OOS ({start_str} ~ {end_str}) ===\")\n",
    "        print(f\"{'LOGISTIC':>8} |Sharpe={sum_log_clip['Sharpe']:.2f} | CAGR={sum_log_clip['CAGR']:.2%} | n={sum_log_clip['n']}\")\n",
    "        print(f\"{'XGB':>8} |Sharpe={sum_xgb_clip['Sharpe']:.2f} | CAGR={sum_xgb_clip['CAGR']:.2%} | n={sum_xgb_clip['n']}\")\n",
    "        print(f\"{'RF':>8} |Sharpe={sum_rf_clip['Sharpe']:.2f} | CAGR={sum_rf_clip['CAGR']:.2%} | n={sum_rf_clip['n']}\")\n",
    "        print(f\"{'SVM':>8} |Sharpe={sum_svm_clip['Sharpe']:.2f} | CAGR={sum_svm_clip['CAGR']:.2%} | n={sum_svm_clip['n']}\")\n",
    "        print(f\"{'Hold':>8} |Sharpe={hold_sharpe:.2f} | CAGR={hold_cagr:.2%} | n={hold_n}\")\n",
    "    else:\n",
    "        print(\"No summary printed because the clipped window is empty.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "69a498de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[§4.1] X=(1307, 28), y+ rate=0.523\n",
      "[§4.1] RF best params: {'n_estimators': 300, 'max_depth': None, 'min_samples_leaf': 1, 'max_features': 'sqrt'}\n",
      "[debug] Explanation values shape: (1307, 28, 2)\n",
      "[§4.1] final SHAP matrix shape: (1307, 28)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[§4.1] Top 15 features by mean |SHAP|:\n",
      " Gbar_t          0.022088\n",
      "sigma_t         0.019593\n",
      "S_t_lag1        0.017487\n",
      "Gbar_t_lag1     0.016198\n",
      "AI_t            0.015749\n",
      "Gbar_t_lag3     0.015143\n",
      "sigma_t_lag1    0.015088\n",
      "S_t_lag2        0.014559\n",
      "S_MA20          0.012676\n",
      "sigma_t_lag2    0.012624\n",
      "volume_t        0.012145\n",
      "Gbar_t_lag2     0.012095\n",
      "vol20_t         0.011722\n",
      "S_t             0.011503\n",
      "sigmaG_t        0.011104\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# ================================================================\n",
    "# §4.1 — SHAP Feature Importance (STANDARD beeswarm) for RandomForest\n",
    "# Matches §3.5: same COMBINED_FILE, feature_columns, warm-up & masks\n",
    "# ================================================================\n",
    "from pathlib import Path\n",
    "import itertools, warnings, numpy as np, pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import shap\n",
    "\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# ---------- mirror §3.5 ----------\n",
    "COMBINED_FILE = Path(\"combined_dataset copy.csv\")\n",
    "WARMUP_DAYS   = 20\n",
    "RANDOM_STATE  = 7\n",
    "\n",
    "def feature_columns(df: pd.DataFrame) -> list[str]:\n",
    "    non_features = {\n",
    "        \"date\",\"y_t\",\"close\",\"r_t\",\"r_t_plus_1\",\n",
    "        \"p_hat\",\"y_true\",\"position\",\"ret_strategy\",\"equity\"\n",
    "    }\n",
    "    num_cols = df.select_dtypes(include=[np.number]).columns\n",
    "    return [c for c in num_cols if c not in non_features]\n",
    "\n",
    "def rf_inner_cv(X_tr: pd.DataFrame, y_tr: pd.Series, param_grid: dict) -> dict:\n",
    "    best_params, best_auc = None, -np.inf\n",
    "    tss = TimeSeriesSplit(n_splits=3)\n",
    "    keys = list(param_grid.keys())\n",
    "    for values in itertools.product(*param_grid.values()):\n",
    "        params = dict(zip(keys, values))\n",
    "        aucs = []\n",
    "        for tr_idx, va_idx in tss.split(X_tr):\n",
    "            Xtr, Xva = X_tr.iloc[tr_idx], X_tr.iloc[va_idx]\n",
    "            ytr, yva = y_tr.iloc[tr_idx], y_tr.iloc[va_idx]\n",
    "            m = RandomForestClassifier(\n",
    "                n_estimators=params[\"n_estimators\"],\n",
    "                max_depth=params[\"max_depth\"],\n",
    "                min_samples_leaf=params[\"min_samples_leaf\"],\n",
    "                max_features=params[\"max_features\"],\n",
    "                random_state=RANDOM_STATE,\n",
    "                n_jobs=-1,\n",
    "            )\n",
    "            m.fit(Xtr, ytr)\n",
    "            p = m.predict_proba(Xva)[:, 1]\n",
    "            aucs.append(roc_auc_score(yva, p))\n",
    "        mauc = float(np.mean(aucs))\n",
    "        if mauc > best_auc:\n",
    "            best_auc, best_params = mauc, params\n",
    "    return best_params\n",
    "# ----------------------------------\n",
    "\n",
    "def build_training_matrix(df: pd.DataFrame):\n",
    "    feats = feature_columns(df)\n",
    "    X = df[feats].copy()\n",
    "    y = df[\"y_t\"].astype(int).copy()\n",
    "    dates = df[\"date\"].copy()\n",
    "\n",
    "    # warm-up\n",
    "    X = X.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    y = y.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "    dates = dates.iloc[WARMUP_DAYS:].reset_index(drop=True)\n",
    "\n",
    "    # drop NaN rows (same as §3.5)\n",
    "    mask = X.notna().all(axis=1) & y.notna()\n",
    "    X = X.loc[mask].reset_index(drop=True)\n",
    "    y = y.loc[mask].reset_index(drop=True)\n",
    "    dates = dates.loc[mask].reset_index(drop=True)\n",
    "\n",
    "    X.index = dates\n",
    "    y.index = dates\n",
    "    return X, y\n",
    "\n",
    "def force_2d_shap_values(rf, X) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Return a (n_samples, n_features) SHAP matrix.\n",
    "    Handles SHAP versions that return:\n",
    "      - list of arrays (per class)\n",
    "      - Explanation with multi-output (n, p, n_classes)\n",
    "      - accidental interaction tensors\n",
    "    We *do not* compute interactions; we keep standard SHAP only.\n",
    "    \"\"\"\n",
    "    # Try the new Explanation API first (non-interaction)\n",
    "    try:\n",
    "        ex = shap.TreeExplainer(rf, model_output=\"raw\")(X)  # log-odds; stable\n",
    "        vals = ex.values\n",
    "        print(f\"[debug] Explanation values shape: {np.array(vals).shape}\")\n",
    "        if vals.ndim == 3:\n",
    "            # pick positive class (index 1) → (n, p)\n",
    "            if vals.shape[-1] == 2:\n",
    "                vals = vals[..., 1]\n",
    "            else:\n",
    "                # Unusual multi-output; take the last axis argmax variance\n",
    "                variances = vals.reshape(-1, vals.shape[-1]).var(axis=0)\n",
    "                cls = int(np.argmax(variances))\n",
    "                vals = vals[..., cls]\n",
    "        if vals.ndim != 2:\n",
    "            raise ValueError(f\"Expected 2D SHAP values, got {vals.shape}\")\n",
    "        return np.array(vals)\n",
    "    except Exception as e:\n",
    "        print(f\"[debug] Falling back to legacy shap_values(): {e}\")\n",
    "\n",
    "    # Legacy path\n",
    "    sv = shap.TreeExplainer(rf, model_output=\"raw\").shap_values(X)\n",
    "    if isinstance(sv, list):\n",
    "        vals = sv[1]  # positive class\n",
    "    else:\n",
    "        vals = sv\n",
    "\n",
    "    vals = np.array(vals)\n",
    "    print(f\"[debug] legacy shap_values shape: {vals.shape}\")\n",
    "\n",
    "    # Some builds append bias column → drop\n",
    "    if vals.ndim == 2 and vals.shape[1] == X.shape[1] + 1:\n",
    "        vals = vals[:, :-1]\n",
    "\n",
    "    # If for some reason it's 3D (e.g., (n,p,2)), select class-1\n",
    "    if vals.ndim == 3 and vals.shape[-1] == 2:\n",
    "        vals = vals[..., 1]\n",
    "\n",
    "    if vals.ndim != 2 or vals.shape[1] != X.shape[1]:\n",
    "        raise ValueError(f\"Could not coerce SHAP values to (n,p). Got {vals.shape} vs X {X.shape}\")\n",
    "\n",
    "    return vals\n",
    "\n",
    "def main():\n",
    "    # 1) load & build training matrix\n",
    "    df = pd.read_csv(COMBINED_FILE, parse_dates=[\"date\"]).sort_values(\"date\").reset_index(drop=True)\n",
    "    X, y = build_training_matrix(df)\n",
    "    print(f\"[§4.1] X={X.shape}, y+ rate={y.mean():.3f}\")\n",
    "\n",
    "    # 2) tune & fit RF (same style as §3.5)\n",
    "    grid = {\n",
    "        \"n_estimators\": [300, 600, 1000],\n",
    "        \"max_depth\": [None, 5, 8],\n",
    "        \"min_samples_leaf\": [1, 5],\n",
    "        \"max_features\": [\"sqrt\", 0.5],\n",
    "    }\n",
    "    best = rf_inner_cv(X, y, grid)\n",
    "    print(f\"[§4.1] RF best params: {best}\")\n",
    "    rf = RandomForestClassifier(random_state=RANDOM_STATE, n_jobs=-1, **best)\n",
    "    rf.fit(X, y)\n",
    "\n",
    "    # 3) get STANDARD (2-D) SHAP values for positive class\n",
    "    shap_vals = force_2d_shap_values(rf, X)\n",
    "    print(f\"[§4.1] final SHAP matrix shape: {shap_vals.shape}\")  # should be (n, p)\n",
    "\n",
    "    # 4) plots\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    shap.summary_plot(shap_vals, X, plot_type=\"dot\", show=False,\n",
    "                      max_display=min(25, X.shape[1]), plot_size=(10, 6))\n",
    "    plt.title(\"SHAP Summary — RandomForest (impact on model output)\", fontsize=13)\n",
    "    plt.tight_layout(); plt.savefig(\"shap_rf_beeswarm.png\", dpi=300); plt.show()\n",
    "\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    shap.summary_plot(shap_vals, X, plot_type=\"bar\", show=False,\n",
    "                      max_display=min(25, X.shape[1]), plot_size=(10, 5))\n",
    "    plt.title(\"SHAP Feature Importance (mean |SHAP|) — RandomForest\", fontsize=13)\n",
    "    plt.tight_layout(); plt.savefig(\"shap_rf_bar.png\", dpi=300); plt.show()\n",
    "\n",
    "    # 5) console top features\n",
    "    imp = pd.Series(np.abs(shap_vals).mean(axis=0), index=X.columns).sort_values(ascending=False)\n",
    "    print(\"\\n[§4.1] Top 15 features by mean |SHAP|:\\n\", imp.head(15))\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4f3018ce",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['target'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 17\u001b[39m\n\u001b[32m     14\u001b[39m df = df.sort_values(\u001b[33m\"\u001b[39m\u001b[33mdate\u001b[39m\u001b[33m\"\u001b[39m).reset_index(drop=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m     16\u001b[39m \u001b[38;5;66;03m# --- Features & target ---\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m X = \u001b[43mdf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdate\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtarget\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     18\u001b[39m y = df[\u001b[33m\"\u001b[39m\u001b[33mtarget\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m     20\u001b[39m \u001b[38;5;66;03m# --- Split into train/test (last 20% as test set for evaluation) ---\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/core/frame.py:5588\u001b[39m, in \u001b[36mDataFrame.drop\u001b[39m\u001b[34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[39m\n\u001b[32m   5440\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdrop\u001b[39m(\n\u001b[32m   5441\u001b[39m     \u001b[38;5;28mself\u001b[39m,\n\u001b[32m   5442\u001b[39m     labels: IndexLabel | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[32m   (...)\u001b[39m\u001b[32m   5449\u001b[39m     errors: IgnoreRaise = \u001b[33m\"\u001b[39m\u001b[33mraise\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m   5450\u001b[39m ) -> DataFrame | \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m   5451\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m   5452\u001b[39m \u001b[33;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[32m   5453\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m   5586\u001b[39m \u001b[33;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[32m   5587\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m5588\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   5589\u001b[39m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5590\u001b[39m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5591\u001b[39m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m=\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5592\u001b[39m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5593\u001b[39m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5594\u001b[39m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[43m=\u001b[49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5595\u001b[39m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   5596\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/core/generic.py:4807\u001b[39m, in \u001b[36mNDFrame.drop\u001b[39m\u001b[34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[39m\n\u001b[32m   4805\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes.items():\n\u001b[32m   4806\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m4807\u001b[39m         obj = \u001b[43mobj\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   4809\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[32m   4810\u001b[39m     \u001b[38;5;28mself\u001b[39m._update_inplace(obj)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/core/generic.py:4849\u001b[39m, in \u001b[36mNDFrame._drop_axis\u001b[39m\u001b[34m(self, labels, axis, level, errors, only_slice)\u001b[39m\n\u001b[32m   4847\u001b[39m         new_axis = axis.drop(labels, level=level, errors=errors)\n\u001b[32m   4848\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m4849\u001b[39m         new_axis = \u001b[43maxis\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   4850\u001b[39m     indexer = axis.get_indexer(new_axis)\n\u001b[32m   4852\u001b[39m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[32m   4853\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Desktop/GDELT Alpha/.venv/lib/python3.12/site-packages/pandas/core/indexes/base.py:7136\u001b[39m, in \u001b[36mIndex.drop\u001b[39m\u001b[34m(self, labels, errors)\u001b[39m\n\u001b[32m   7134\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m mask.any():\n\u001b[32m   7135\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m errors != \u001b[33m\"\u001b[39m\u001b[33mignore\u001b[39m\u001b[33m\"\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m7136\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask].tolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not found in axis\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m   7137\u001b[39m     indexer = indexer[~mask]\n\u001b[32m   7138\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.delete(indexer)\n",
      "\u001b[31mKeyError\u001b[39m: \"['target'] not found in axis\""
     ]
    }
   ],
   "source": [
    "# ============================\n",
    "# Part 3.5 — Model Training & Prediction\n",
    "# ============================\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, classification_report\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from joblib import dump  # for saving the model\n",
    "\n",
    "# --- Load combined dataset (already prepared from previous parts) ---\n",
    "df = pd.read_csv(\"combined_dataset copy.csv\", parse_dates=[\"date\"])\n",
    "df = df.sort_values(\"date\").reset_index(drop=True)\n",
    "\n",
    "# --- Features & target ---\n",
    "X = df.drop(columns=[\"date\", \"target\"])\n",
    "y = df[\"target\"]\n",
    "\n",
    "# --- Split into train/test (last 20% as test set for evaluation) ---\n",
    "split_idx = int(len(df) * 0.8)\n",
    "X_train, X_test = X.iloc[:split_idx], X.iloc[split_idx:]\n",
    "y_train, y_test = y.iloc[:split_idx], y.iloc[split_idx:]\n",
    "\n",
    "# --- Train Random Forest ---\n",
    "rf = RandomForestClassifier(\n",
    "    n_estimators=500,\n",
    "    max_depth=6,\n",
    "    min_samples_leaf=20,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# --- Save trained model ---\n",
    "dump(rf, \"rf_model.joblib\")\n",
    "print(\"✅ Random Forest model saved as rf_model.joblib\")\n",
    "\n",
    "# --- Predictions ---\n",
    "y_pred = rf.predict(X_test)\n",
    "y_proba = rf.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# --- Evaluation ---\n",
    "print(\"\\n[Part 3.5] Random Forest Performance\")\n",
    "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "print(\"ROC-AUC:\", roc_auc_score(y_test, y_proba))\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
    "\n",
    "# --- Equity curve (simple strategy: long if prob > 0.5, else flat) ---\n",
    "df.loc[df.index >= split_idx, \"proba\"] = y_proba\n",
    "df.loc[df.index >= split_idx, \"signal\"] = (df.loc[df.index >= split_idx, \"proba\"] > 0.5).astype(int)\n",
    "df[\"ret\"] = df[\"target\"].shift(1)  # assume target is 1 = up, 0 = down → ret proxy\n",
    "df[\"strategy_ret\"] = df[\"signal\"] * df[\"ret\"]\n",
    "\n",
    "equity = (1 + df[\"strategy_ret\"].fillna(0)).cumprod()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(df[\"date\"], equity, label=\"RF Strategy\")\n",
    "plt.axvline(df[\"date\"].iloc[split_idx], color=\"red\", linestyle=\"--\", label=\"Train/Test Split\")\n",
    "plt.legend()\n",
    "plt.title(\"Random Forest Strategy Equity Curve\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"rf_equity_curve.png\", dpi=300)\n",
    "plt.show()\n"
   ]
  }
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