diff --git a/finetune_csv/fetch_india_data.py b/finetune_csv/fetch_india_data.py
new file mode 100644
index 00000000..13b026be
--- /dev/null
+++ b/finetune_csv/fetch_india_data.py
@@ -0,0 +1,146 @@
+"""
+Fetch 5-minute OHLCV data for one or more NSE/BSE instruments via yfinance
+and save in the format expected by CustomKlineDataset.
+
+Required columns: timestamps, open, high, low, close, volume, amount
+
+Ticker conventions
+------------------
+  Equities  : RELIANCE, TCS, INFY        -> appended with .NS automatically
+  Indices   : NIFTY50, BANKNIFTY         -> mapped to ^NSEI, ^NSEBANK
+  BSE       : RELIANCE.BO, TCS.BO        -> passed through as-is
+  Raw       : ^NSEI, RELIANCE.NS, etc.   -> passed through as-is
+
+Usage:
+  python fetch_india_data.py --tickers RELIANCE TCS INFY --output data/india_5min.csv
+  python fetch_india_data.py --tickers NIFTY50 BANKNIFTY --period 30d --output data/indices_5min.csv
+  python fetch_india_data.py --tickers RELIANCE.NS TCS.NS --period 60d --output data/equities_5min.csv
+
+Note: yfinance supports a maximum of 60 days of 5-min data.
+"""
+
+import argparse
+import os
+import warnings
+warnings.filterwarnings("ignore")
+
+import pandas as pd
+import yfinance as yf
+
+# ── Constants ─────────────────────────────────────────────────────────────────
+
+IST          = "Asia/Kolkata"
+MARKET_OPEN  = "09:15"
+MARKET_CLOSE = "15:30"
+
+NSE_INDEX_MAP = {
+    "NIFTY50":    "^NSEI",
+    "NIFTY 50":   "^NSEI",
+    "NIFTY":      "^NSEI",
+    "BANKNIFTY":  "^NSEBANK",
+    "BANK NIFTY": "^NSEBANK",
+    "FINNIFTY":   "NIFTY_FIN_SERVICE.NS",
+    "NIFTYIT":    "^CNXIT",
+    "MIDCAPNIFTY":"^NSEMDCP50",
+    "SENSEX":     "^BSESN",
+}
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+def normalise_ticker(raw: str) -> str:
+    """Map a human-friendly name to a yfinance-compatible symbol."""
+    upper = raw.upper().strip()
+    if upper in NSE_INDEX_MAP:
+        return NSE_INDEX_MAP[upper]
+    if "." in upper or upper.startswith("^"):
+        return upper          # already has suffix or is an index
+    return upper + ".NS"      # default: NSE equity
+
+
+def fetch_ticker(ticker: str, period: str) -> pd.DataFrame:
+    sym = normalise_ticker(ticker)
+    raw = yf.download(sym, period=period, interval="5m", progress=False, auto_adjust=True)
+
+    if raw.empty:
+        print(f"  WARNING: no data returned for {ticker} ({sym}), skipping.")
+        return pd.DataFrame()
+
+    # Flatten MultiIndex columns produced by yfinance >= 0.2
+    if isinstance(raw.columns, pd.MultiIndex):
+        raw.columns = raw.columns.get_level_values(0)
+
+    raw = raw.rename(columns={
+        "Open": "open", "High": "high", "Low": "low",
+        "Close": "close", "Volume": "volume",
+    })
+
+    # Convert to IST and keep only NSE market hours
+    if raw.index.tz is None:
+        raw.index = raw.index.tz_localize("UTC")
+    raw.index = raw.index.tz_convert(IST)
+    raw = raw.between_time(MARKET_OPEN, MARKET_CLOSE)
+
+    raw = raw[["open", "high", "low", "close", "volume"]].dropna()
+
+    # amount = volume × avg(OHLC) — proxy for turnover
+    raw["amount"] = raw["volume"] * (
+        raw["open"] + raw["high"] + raw["low"] + raw["close"]
+    ) / 4
+
+    raw = (raw.reset_index()
+              .rename(columns={"Datetime": "timestamps", "index": "timestamps"}))
+    raw["timestamps"] = pd.to_datetime(raw["timestamps"]).dt.tz_localize(None)
+    raw = raw[["timestamps", "open", "high", "low", "close", "volume", "amount"]]
+    raw = raw.sort_values("timestamps").reset_index(drop=True)
+
+    print(f"  {ticker} ({sym}): {len(raw)} bars  "
+          f"[{raw['timestamps'].iloc[0]} → {raw['timestamps'].iloc[-1]}]")
+    return raw
+
+# ── Entry point ───────────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Fetch Indian equity/index 5-min data for Kronos fine-tuning"
+    )
+    parser.add_argument(
+        "--tickers", nargs="+", required=True,
+        help="One or more symbols: RELIANCE, TCS, NIFTY50, BANKNIFTY, RELIANCE.NS, …"
+    )
+    parser.add_argument(
+        "--period", type=str, default="60d",
+        help="yfinance period string (max 60d for 5-min). Default: 60d"
+    )
+    parser.add_argument(
+        "--output", type=str, default="data/india_5min.csv",
+        help="Output CSV path. Default: data/india_5min.csv"
+    )
+    args = parser.parse_args()
+
+    print(f"Fetching {len(args.tickers)} ticker(s) via yfinance …")
+    all_frames = []
+    for ticker in args.tickers:
+        df = fetch_ticker(ticker, args.period)
+        if not df.empty:
+            all_frames.append(df)
+
+    if not all_frames:
+        raise RuntimeError(
+            "No data fetched — check ticker symbols and internet connection.\n"
+            "Tip: NSE equities use the .NS suffix (e.g. RELIANCE.NS)."
+        )
+
+    combined = pd.concat(all_frames, ignore_index=True)
+    combined = combined.sort_values("timestamps").reset_index(drop=True)
+
+    os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True)
+    combined.to_csv(args.output, index=False)
+
+    print(f"\nSaved {len(combined):,} rows → {args.output}")
+    print(f"Date range  : {combined['timestamps'].min()} → {combined['timestamps'].max()}")
+    print(f"Market hours: {MARKET_OPEN}–{MARKET_CLOSE} IST")
+    print(f"Columns     : {list(combined.columns)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/finetune_csv/run_india_finetune.py b/finetune_csv/run_india_finetune.py
new file mode 100644
index 00000000..b57f3f27
--- /dev/null
+++ b/finetune_csv/run_india_finetune.py
@@ -0,0 +1,189 @@
+"""
+End-to-end fine-tuning runner for Indian equity/index 5-min data (NSE/BSE).
+
+Steps:
+  1. Fetch data from yfinance  (skipped if CSV already exists)
+  2. Fine-tune KronosTokenizer
+  3. Fine-tune Kronos predictor
+
+Usage:
+  # Full pipeline — fetch NIFTY50 + top stocks, then train
+  python run_india_finetune.py --tickers NIFTY50 RELIANCE TCS INFY HDFCBANK
+
+  # Use an existing CSV
+  python run_india_finetune.py --data_path data/india_5min.csv
+
+  # Skip tokenizer (already done), only re-train predictor
+  python run_india_finetune.py --tickers NIFTY50 --skip_tokenizer
+
+  # Custom config
+  python run_india_finetune.py --data_path data/india_5min.csv --config configs/config_india_5min.yaml
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), ".."))
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+def run(cmd: list, desc: str):
+    print(f"\n{'='*60}")
+    print(f"  {desc}")
+    print(f"{'='*60}")
+    print(f"  CMD: {' '.join(cmd)}\n")
+    subprocess.run(cmd, check=True)
+
+
+def patch_config_data_path(config_path: str, data_path: str, out_path: str):
+    """Write a copy of config_path with data.data_path replaced."""
+    import yaml
+    with open(config_path) as f:
+        cfg = yaml.safe_load(f)
+    cfg["data"]["data_path"] = os.path.abspath(data_path)
+    with open(out_path, "w") as f:
+        yaml.dump(cfg, f, default_flow_style=False, allow_unicode=True, indent=2)
+
+
+def ensure_config(script_dir: str, config_arg: str) -> str:
+    """Return a config path, generating a minimal one if none exists."""
+    if config_arg and os.path.exists(config_arg):
+        return config_arg
+
+    candidates = [
+        os.path.join(script_dir, "configs", "config_india_5min.yaml"),
+        os.path.join(script_dir, "configs", "config_us_5min.yaml"),
+    ]
+    for p in candidates:
+        if os.path.exists(p):
+            return p
+
+    # Generate minimal YAML
+    import yaml
+    path = os.path.join(script_dir, "configs", "config_india_5min.yaml")
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    cfg = {
+        "data": {
+            "data_path": "data/india_5min.csv",
+            "lookback": 512,
+            "pred_len": 20,
+            "train_ratio": 0.7,
+            "val_ratio": 0.15,
+        },
+        "tokenizer": {
+            "pretrained": "NeoQuasar/Kronos-Tokenizer-base",
+            "save_path": "finetuned/india_5min/tokenizer",
+            "epochs": 10,
+            "batch_size": 32,
+            "lr": 1e-4,
+        },
+        "predictor": {
+            "pretrained": "NeoQuasar/Kronos-base",
+            "tokenizer_path": "finetuned/india_5min/tokenizer/best_model",
+            "save_path": "finetuned/india_5min/basemodel",
+            "epochs": 10,
+            "batch_size": 16,
+            "lr": 5e-5,
+        },
+    }
+    with open(path, "w") as f:
+        yaml.dump(cfg, f, default_flow_style=False, allow_unicode=True, indent=2)
+    print(f"  Generated minimal config → {path}")
+    return path
+
+
+# ── Main ──────────────────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Kronos Indian market (NSE/BSE) fine-tuning pipeline"
+    )
+    parser.add_argument(
+        "--tickers", nargs="+", default=[],
+        help="NSE symbols to fetch: RELIANCE, TCS, NIFTY50, BANKNIFTY, …"
+    )
+    parser.add_argument(
+        "--period", default="60d",
+        help="yfinance period (max 60d for 5-min interval). Default: 60d"
+    )
+    parser.add_argument(
+        "--data_path", default="data/india_5min.csv",
+        help="Path to existing or target CSV. Default: data/india_5min.csv"
+    )
+    parser.add_argument(
+        "--config", default="",
+        help="YAML config path. Auto-detected if omitted."
+    )
+    parser.add_argument("--skip_tokenizer", action="store_true",
+                        help="Skip tokenizer training")
+    parser.add_argument("--skip_predictor", action="store_true",
+                        help="Skip predictor training")
+    args = parser.parse_args()
+
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    data_path  = args.data_path if os.path.isabs(args.data_path) \
+                 else os.path.join(script_dir, args.data_path)
+
+    # ── 1. Fetch data ─────────────────────────────────────────────────────────
+    if args.tickers:
+        run(
+            [sys.executable, os.path.join(script_dir, "fetch_india_data.py"),
+             "--tickers", *args.tickers,
+             "--period", args.period,
+             "--output", data_path],
+            f"Fetching 5-min data for: {', '.join(args.tickers)}"
+        )
+    else:
+        if not os.path.exists(data_path):
+            raise FileNotFoundError(
+                f"No tickers provided and data file not found: {data_path}\n"
+                "Pass --tickers RELIANCE TCS … to fetch data first."
+            )
+        print(f"Using existing data file: {data_path}")
+
+    # ── 2. Resolve + patch config ─────────────────────────────────────────────
+    config_src = ensure_config(script_dir, args.config)
+    config_run = os.path.join(script_dir, "configs", "_run_india_config.yaml")
+    os.makedirs(os.path.dirname(config_run), exist_ok=True)
+    patch_config_data_path(config_src, data_path, config_run)
+    print(f"Active config → {config_run}")
+
+    finetune_dir      = os.path.join(script_dir, "..", "finetune")
+    tokenizer_script  = os.path.join(finetune_dir, "train_tokenizer.py")
+    predictor_script  = os.path.join(finetune_dir, "train_predictor.py")
+
+    # ── 3. Fine-tune tokenizer ────────────────────────────────────────────────
+    if not args.skip_tokenizer:
+        if not os.path.exists(tokenizer_script):
+            print(f"\n  WARNING: {tokenizer_script} not found.")
+            print("  Run from inside the cloned Kronos repo.")
+        else:
+            run([sys.executable, tokenizer_script, "--config", config_run],
+                "Stage 1 / 2 — Fine-tuning KronosTokenizer on Indian data")
+    else:
+        print("\nSkipping tokenizer training (--skip_tokenizer set).")
+
+    # ── 4. Fine-tune predictor ────────────────────────────────────────────────
+    if not args.skip_predictor:
+        if not os.path.exists(predictor_script):
+            print(f"\n  WARNING: {predictor_script} not found.")
+        else:
+            run([sys.executable, predictor_script, "--config", config_run],
+                "Stage 2 / 2 — Fine-tuning Kronos predictor on Indian data")
+    else:
+        print("\nSkipping predictor training (--skip_predictor set).")
+
+    print("\n" + "=" * 60)
+    print("  Fine-tuning complete!")
+    print("  Models saved under: finetuned/india_5min/")
+    print("  Inference paths:")
+    print("    tokenizer : finetuned/india_5min/tokenizer/best_model")
+    print("    model     : finetuned/india_5min/basemodel/best_model")
+    print("=" * 60 + "\n")
+
+
+if __name__ == "__main__":
+    main()