NexQuant/rdagent/components/coder/kronos_adapter.py

"""
Kronos Foundation Model Adapter for Predix.

Wraps the Kronos-mini OHLCV foundation model (4.1M params, AAAI 2026, MIT)
for use as:
  - Factor (Option A): predicted next-day return signal
  - Model alongside LightGBM (Option B): IC/Sharpe evaluation

Kronos repo: https://github.com/shiyu-coder/Kronos
HuggingFace:  NeoQuasar/Kronos-mini  |  NeoQuasar/Kronos-Tokenizer-2k
"""

from __future__ import annotations

import sys
from pathlib import Path
from typing import Optional

import numpy as np
import pandas as pd

from rdagent.log import rdagent_logger as logger


def _cuda_available() -> bool:
    try:
        import torch
        return torch.cuda.is_available()
    except ImportError:
        return False

KRONOS_REPO = Path.home() / "Kronos"
_KRONOS_AVAILABLE: Optional[bool] = None


def _ensure_kronos() -> bool:
    global _KRONOS_AVAILABLE
    if _KRONOS_AVAILABLE is not None:
        return _KRONOS_AVAILABLE
    if not KRONOS_REPO.exists():
        logger.warning(f"Kronos repo not found at {KRONOS_REPO}. Clone with: git clone https://github.com/shiyu-coder/Kronos ~/Kronos")
        _KRONOS_AVAILABLE = False
        return False
    repo_str = str(KRONOS_REPO)
    if repo_str not in sys.path:
        sys.path.insert(0, repo_str)
    try:
        import model as _  # noqa: F401
        _KRONOS_AVAILABLE = True
    except ImportError as e:
        logger.warning(f"Failed to import Kronos model: {e}")
        _KRONOS_AVAILABLE = False
    return _KRONOS_AVAILABLE


def _ohlcv_from_predix(df: pd.DataFrame) -> pd.DataFrame:
    """Convert Predix HDF5 format ($open/$close/...) to Kronos format (open/close/...)."""
    col_map = {"$open": "open", "$high": "high", "$low": "low", "$close": "close", "$volume": "volume"}
    renamed = df.rename(columns=col_map)
    cols = [c for c in ["open", "high", "low", "close", "volume"] if c in renamed.columns]
    return renamed[cols].astype(float)


class KronosAdapter:
    """
    Loads Kronos-mini once and provides rolling-window OHLCV inference.

    Usage:
        adapter = KronosAdapter(device="cuda")
        adapter.load()
        pred_return = adapter.predict_return(ohlcv_df, context_bars=512, pred_bars=96)
    """

    MODEL_ID = "NeoQuasar/Kronos-mini"
    TOKENIZER_ID = "NeoQuasar/Kronos-Tokenizer-2k"

    def __init__(self, device: Optional[str] = None, max_context: int = 512):
        self.device = device or ("cuda" if _cuda_available() else "cpu")
        self.max_context = max_context
        self._predictor = None

    def load(self) -> "KronosAdapter":
        if self._predictor is not None:
            return self
        if not _ensure_kronos():
            raise RuntimeError("Kronos not available — see warning above.")
        from model import Kronos, KronosTokenizer, KronosPredictor  # type: ignore

        logger.info(f"Loading Kronos-mini from HuggingFace ({self.MODEL_ID})...")
        tokenizer = KronosTokenizer.from_pretrained(self.TOKENIZER_ID)
        model = Kronos.from_pretrained(self.MODEL_ID)
        self._predictor = KronosPredictor(model, tokenizer, device=self.device, max_context=self.max_context)
        logger.info("Kronos-mini loaded.")
        return self

    def predict_next_bars(
        self,
        ohlcv_df: pd.DataFrame,
        context_bars: int,
        pred_bars: int,
        temperature: float = 1.0,
        top_p: float = 0.9,
    ) -> pd.DataFrame:
        """
        Run Kronos on `context_bars` of OHLCV data, returning `pred_bars` predicted bars.

        Args:
            ohlcv_df: DataFrame with columns open/high/low/close[/volume], DatetimeIndex.
            context_bars: Number of history bars to feed as context.
            pred_bars: Number of future bars to predict.

        Returns:
            DataFrame with predicted open/high/low/close/volume, indexed by future timestamps.
        """
        if self._predictor is None:
            raise RuntimeError("Call .load() first.")
        if len(ohlcv_df) < context_bars:
            raise ValueError(f"Need at least {context_bars} bars, got {len(ohlcv_df)}")

        ctx = ohlcv_df.iloc[-context_bars:].copy().reset_index(drop=True)
        freq = ohlcv_df.index.freq or pd.infer_freq(ohlcv_df.index[:100])
        last_ts = ohlcv_df.index[-1]
        future_idx = pd.date_range(start=last_ts, periods=pred_bars + 1, freq=freq or "1min")[1:]

        x_timestamp = pd.Series(ctx.index)
        y_timestamp = pd.Series(range(len(ctx), len(ctx) + pred_bars))

        pred_df = self._predictor.predict(
            df=ctx,
            x_timestamp=x_timestamp,
            y_timestamp=y_timestamp,
            pred_len=pred_bars,
            T=temperature,
            top_p=top_p,
            sample_count=1,
            verbose=False,
        )
        pred_df.index = future_idx
        return pred_df

    def predict_return(
        self,
        ohlcv_df: pd.DataFrame,
        context_bars: int = 512,
        pred_bars: int = 1,
    ) -> float:
        """
        Predict the average return over the next `pred_bars` using the last `context_bars`.
        Returns the predicted log-return (predicted_close / last_close - 1).
        """
        pred = self.predict_next_bars(ohlcv_df, context_bars=context_bars, pred_bars=pred_bars)
        last_close = float(ohlcv_df["close"].iloc[-1])
        pred_close = float(pred["close"].iloc[-1])
        return pred_close / last_close - 1.0


def build_kronos_factor(
    hdf5_path: str | Path,
    context_bars: int = 512,
    pred_bars: int = 96,
    stride_bars: int = 96,
    device: Optional[str] = None,
) -> pd.DataFrame:
    """
    Generate the Kronos predicted-return factor for all EUR/USD 1-min bars.

    Strategy:
        Every `stride_bars` bars, run Kronos on the previous `context_bars` and
        predict the next `pred_bars`. The predicted log-return is forward-filled
        across the predicted window. Default: daily stride (96 bars/day at 1-min).

    Returns:
        MultiIndex (datetime, instrument) DataFrame with column "KronosPredReturn".
    """
    device = device or ("cuda" if _cuda_available() else "cpu")
    logger.info(f"Loading data from {hdf5_path}...")
    raw = pd.read_hdf(hdf5_path, key="data")

    # Flatten to single-instrument 1-min series
    instrument = raw.index.get_level_values("instrument").unique()[0]
    df = raw.xs(instrument, level="instrument")
    ohlcv = _ohlcv_from_predix(df)

    adapter = KronosAdapter(device=device, max_context=min(context_bars, 512))
    adapter.load()

    factor_values: dict[pd.Timestamp, float] = {}
    n = len(ohlcv)
    starts = range(context_bars, n, stride_bars)

    logger.info(f"Running Kronos inference: {len(starts)} windows (stride={stride_bars}, ctx={context_bars}, pred={pred_bars})")
    for i, bar_idx in enumerate(starts):
        ctx_df = ohlcv.iloc[bar_idx - context_bars:bar_idx]
        try:
            pred = adapter.predict_next_bars(ctx_df, context_bars=context_bars, pred_bars=pred_bars)
            last_close = float(ctx_df["close"].iloc[-1])
            # Store predicted return for each predicted bar's timestamp
            for ts, row in pred.iterrows():
                ret = float(row["close"]) / last_close - 1.0
                factor_values[ts] = ret
        except Exception as e:
            logger.warning(f"Kronos inference failed at bar {bar_idx}: {e}")
            continue

        if (i + 1) % 100 == 0:
            logger.info(f"  {i+1}/{len(starts)} windows done")

    if not factor_values:
        raise RuntimeError("No Kronos predictions were generated.")

    factor_series = pd.Series(factor_values, name="KronosPredReturn")
    factor_series = factor_series.reindex(ohlcv.index, method="ffill")

    result = factor_series.to_frame()
    result.index = pd.MultiIndex.from_arrays(
        [ohlcv.index, [instrument] * len(ohlcv)],
        names=["datetime", "instrument"],
    )
    logger.info(f"Kronos factor built: {len(result)} bars, {result['KronosPredReturn'].notna().sum()} non-NaN")
    return result


def evaluate_kronos_model(
    hdf5_path: str | Path,
    context_bars: int = 512,
    pred_bars: int = 30,
    stride_bars: int = 30,
    device: Optional[str] = None,
) -> dict:
    """
    Evaluate Kronos as a standalone model (Option B, alongside LightGBM).

    Computes IC (Information Coefficient) between Kronos predicted returns and
    actual realized returns on the test set.

    Returns:
        dict with keys: IC_mean, IC_std, IC_IR (IC / std), hit_rate, n_predictions
    """
    device = device or ("cuda" if _cuda_available() else "cpu")
    raw = pd.read_hdf(hdf5_path, key="data")
    instrument = raw.index.get_level_values("instrument").unique()[0]
    df = raw.xs(instrument, level="instrument")
    ohlcv = _ohlcv_from_predix(df)

    adapter = KronosAdapter(device=device, max_context=min(context_bars, 512))
    adapter.load()

    predicted_returns = []
    actual_returns = []
    n = len(ohlcv)

    for bar_idx in range(context_bars, n - pred_bars, stride_bars):
        ctx_df = ohlcv.iloc[bar_idx - context_bars:bar_idx]
        try:
            pred = adapter.predict_next_bars(ctx_df, context_bars=context_bars, pred_bars=pred_bars)
            last_close = float(ctx_df["close"].iloc[-1])
            pred_ret = float(pred["close"].iloc[-1]) / last_close - 1.0

            actual_future = ohlcv.iloc[bar_idx:bar_idx + pred_bars]
            actual_ret = float(actual_future["close"].iloc[-1]) / last_close - 1.0

            predicted_returns.append(pred_ret)
            actual_returns.append(actual_ret)
        except Exception:
            continue

    pred_arr = np.array(predicted_returns)
    actual_arr = np.array(actual_returns)

    ic = np.corrcoef(pred_arr, actual_arr)[0, 1] if len(pred_arr) > 1 else float("nan")
    ic_std = float(np.std([
        np.corrcoef(pred_arr[i:i+50], actual_arr[i:i+50])[0, 1]
        for i in range(0, len(pred_arr) - 50, 10)
    ])) if len(pred_arr) > 60 else float("nan")
    hit_rate = float(np.mean(np.sign(pred_arr) == np.sign(actual_arr)))

    return {
        "IC_mean": float(ic),
        "IC_std": ic_std,
        "IC_IR": float(ic / ic_std) if ic_std and ic_std > 0 else float("nan"),
        "hit_rate": hit_rate,
        "n_predictions": len(pred_arr),
    }