{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f20c8e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hanyueju/miniconda3/envs/kronos/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from model import Kronos, KronosTokenizer, KronosPredictor\n",
    "\n",
    "# Load from Hugging Face Hub\n",
    "tokenizer = KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")\n",
    "model = Kronos.from_pretrained(\"NeoQuasar/Kronos-base\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72dc9ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the predictor\n",
    "predictor = KronosPredictor(model, tokenizer, max_context=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b07f8d0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X 的行数: 400\n",
      "Y 的行数: 120\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load your data\n",
    "df = pd.read_csv(\"/home/hanyueju/MinModel/data/qlib_ready_csv/sz002709.csv\")\n",
    "df['timestamps'] = pd.to_datetime(df['date'])\n",
    "df.sort_values('timestamps', inplace=True, ascending=True)\n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "# Define context window and prediction length\n",
    "lookback = 400\n",
    "pred_len = 120\n",
    "\n",
    "# Prepare inputs for the predictor\n",
    "x_df = df.iloc[-(lookback + pred_len):-pred_len][['open', 'high', 'low', 'close', 'volume', 'amount']]\n",
    "\n",
    "x_timestamp = df.iloc[-(lookback + pred_len):-pred_len]['timestamps']\n",
    "\n",
    "y_timestamp = df.iloc[-pred_len:]['timestamps']\n",
    "# 可以打印一下长度检查是否绝对正确：\n",
    "print(f\"X 的行数: {len(x_df)}\")  # 应该输出 400\n",
    "print(f\"Y 的行数: {len(y_timestamp)}\")  # 应该输出 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a6466d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/120 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 120/120 [00:01<00:00, 80.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecasted Data Head:\n",
      "                 open       high        low      close         volume  \\\n",
      "timestamps                                                              \n",
      "2026-04-03  17.217230  17.387726  17.004833  17.084772  218270.890625   \n",
      "2026-04-07  17.034378  17.179306  16.864655  16.910782  211789.062500   \n",
      "2026-04-08  16.875727  17.017885  16.738979  16.775610  209235.468750   \n",
      "2026-04-09  16.813190  16.977127  16.632999  16.682169  252907.796875   \n",
      "2026-04-10  16.673990  16.577105  16.020086  15.899137  270104.250000   \n",
      "\n",
      "                  amount  \n",
      "timestamps                \n",
      "2026-04-03  310970.37500  \n",
      "2026-04-07  290761.37500  \n",
      "2026-04-08  282911.37500  \n",
      "2026-04-09  410148.81250  \n",
      "2026-04-10  450768.71875  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Generate predictions\n",
    "pred_df = predictor.predict(\n",
    "    df=x_df,\n",
    "    x_timestamp=x_timestamp,\n",
    "    y_timestamp=y_timestamp,\n",
    "    pred_len=pred_len,\n",
    "    T=1.0,          # Temperature for sampling\n",
    "    top_p=0.9,      # Nucleus sampling probability\n",
    "    sample_count=1  # Number of forecast paths to generate and average\n",
    ")\n",
    "\n",
    "print(\"Forecasted Data Head:\")\n",
    "print(pred_df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d30a9976",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "kronos",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.25"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}