From a46b68041315cf72d1799b05d8a236e9011f9c98 Mon Sep 17 00:00:00 2001
From: phaoer <phaoer@sina.cn>
Date: Tue, 4 Nov 2025 17:51:54 +0800
Subject: [PATCH] feat: China A-share market

---
 examples/prediction_cn_markets_day.py | 208 ++++++++++++++++++++++++++
 1 file changed, 208 insertions(+)
 create mode 100644 examples/prediction_cn_markets_day.py

diff --git a/examples/prediction_cn_markets_day.py b/examples/prediction_cn_markets_day.py
new file mode 100644
index 0000000..93b28f9
--- /dev/null
+++ b/examples/prediction_cn_markets_day.py
@@ -0,0 +1,208 @@
+# -*- coding: utf-8 -*-
+"""
+prediction_cn_markets_day.py
+
+Description:
+    Predicts future daily K-line (1D) data for A-share markets using Kronos model and akshare.
+    The script automatically downloads the latest historical data, cleans it, and runs model inference.
+
+Usage:
+    python prediction_cn_markets_day.py --symbol 000001
+
+Arguments:
+    --symbol     Stock code (e.g. 002594 for BYD, 000001 for SSE Index)
+
+Output:
+    - Saves the prediction results to ./outputs/pred_<symbol>_data.csv and ./outputs/pred_<symbol>_chart.png
+    - Logs and progress are printed to console
+
+Example:
+    bash> python prediction_cn_markets_day.py --symbol 000001
+    python3 prediction_cn_markets_day.py --symbol 002594
+"""
+
+import os
+import argparse
+import time
+import pandas as pd
+import akshare as ak
+import matplotlib.pyplot as plt
+import sys
+sys.path.append("../")
+from model import Kronos, KronosTokenizer, KronosPredictor
+
+save_dir = "./outputs"
+os.makedirs(save_dir, exist_ok=True)
+
+# Setting
+TOKENIZER_PRETRAINED = "NeoQuasar/Kronos-Tokenizer-base"
+MODEL_PRETRAINED = "NeoQuasar/Kronos-base"
+DEVICE = "cpu"  # "cuda:0"
+MAX_CONTEXT = 512
+LOOKBACK = 400
+PRED_LEN = 120
+T = 1.0
+TOP_P = 0.9
+SAMPLE_COUNT = 1
+
+def load_data(symbol: str) -> pd.DataFrame:
+    print(f"📥 Fetching {symbol} daily data from akshare ...")
+
+    max_retries = 3
+    df = None
+
+    # Retry mechanism
+    for attempt in range(1, max_retries + 1):
+        try:
+            df = ak.stock_zh_a_hist(symbol=symbol, period="daily", adjust="")
+            if df is not None and not df.empty:
+                break
+        except Exception as e:
+            print(f"⚠️ Attempt {attempt}/{max_retries} failed: {e}")
+        time.sleep(1.5)
+
+    # If still empty after retries
+    if df is None or df.empty:
+        print(f"❌ Failed to fetch data for {symbol} after {max_retries} attempts. Exiting.")
+        sys.exit(1)
+    
+    df.rename(columns={
+        "日期": "date",
+        "开盘": "open",
+        "收盘": "close",
+        "最高": "high",
+        "最低": "low",
+        "成交量": "volume",
+        "成交额": "amount"
+    }, inplace=True)
+
+    df["date"] = pd.to_datetime(df["date"])
+    df = df.sort_values("date").reset_index(drop=True)
+
+    # Convert numeric columns
+    numeric_cols = ["open", "high", "low", "close", "volume", "amount"]
+    for col in numeric_cols:
+        df[col] = (
+            df[col]
+            .astype(str)
+            .str.replace(",", "", regex=False)
+            .replace({"--": None, "": None})
+        )
+        df[col] = pd.to_numeric(df[col], errors="coerce")
+
+    # Fix invalid open values
+    open_bad = (df["open"] == 0) | (df["open"].isna())
+    if open_bad.any():
+        print(f"⚠️  Fixed {open_bad.sum()} invalid open values.")
+        df.loc[open_bad, "open"] = df["close"].shift(1)
+        df["open"].fillna(df["close"], inplace=True)
+
+    # Fix missing amount
+    if df["amount"].isna().all() or (df["amount"] == 0).all():
+        df["amount"] = df["close"] * df["volume"]
+
+    print(f"✅ Data loaded: {len(df)} rows, range: {df['date'].min()} ~ {df['date'].max()}")
+
+    print("Data Head:")
+    print(df.head())
+
+    return df
+
+
+def prepare_inputs(df):
+    x_df = df.iloc[-LOOKBACK:][["open","high","low","close","volume","amount"]]
+    x_timestamp = df.iloc[-LOOKBACK:]["date"]
+    y_timestamp = pd.bdate_range(start=df["date"].iloc[-1] + pd.Timedelta(days=1), periods=PRED_LEN)
+    return x_df, pd.Series(x_timestamp), pd.Series(y_timestamp)
+
+def apply_price_limits(pred_df, last_close, limit_rate=0.1):
+    print(f"🔒 Applying ±{limit_rate*100:.0f}% price limit ...")
+
+    # Ensure integer index
+    pred_df = pred_df.reset_index(drop=True)
+
+    # Ensure float64 dtype for safe assignment
+    cols = ["open", "high", "low", "close"]
+    pred_df[cols] = pred_df[cols].astype("float64")
+
+    for i in range(len(pred_df)):
+        limit_up = last_close * (1 + limit_rate)
+        limit_down = last_close * (1 - limit_rate)
+
+        for col in cols:
+            value = pred_df.at[i, col]
+            if pd.notna(value):
+                clipped = max(min(value, limit_up), limit_down)
+                pred_df.at[i, col] = float(clipped)
+
+        last_close = float(pred_df.at[i, "close"])  # ensure float type
+
+    return pred_df
+
+
+def plot_result(df_hist, df_pred, symbol):
+    plt.figure(figsize=(12, 6))
+    plt.plot(df_hist["date"], df_hist["close"], label="Historical", color="blue")
+    plt.plot(df_pred["date"], df_pred["close"], label="Predicted", color="red", linestyle="--")
+    plt.title(f"Kronos Prediction for {symbol}")
+    plt.xlabel("Date")
+    plt.ylabel("Close Price")
+    plt.legend()
+    plt.grid(True)
+    plt.tight_layout()
+    plot_path = os.path.join(save_dir, f"pred_{symbol.replace('.', '_')}_chart.png")
+    plt.savefig(plot_path)
+    plt.close()
+    print(f"📊 Chart saved: {plot_path}")
+
+
+def predict_future(symbol):
+    print(f"🚀 Loading Kronos tokenizer:{TOKENIZER_PRETRAINED} model:{MODEL_PRETRAINED} ...")
+    tokenizer = KronosTokenizer.from_pretrained(TOKENIZER_PRETRAINED)
+    model = Kronos.from_pretrained(MODEL_PRETRAINED)
+    predictor = KronosPredictor(model, tokenizer, device=DEVICE, max_context=MAX_CONTEXT)
+
+    df = load_data(symbol)
+    x_df, x_timestamp, y_timestamp = prepare_inputs(df)
+
+    print("🔮 Generating predictions ...")
+
+    pred_df = predictor.predict(
+        df=x_df,
+        x_timestamp=x_timestamp,
+        y_timestamp=y_timestamp,
+        pred_len=PRED_LEN,
+        T=T,
+        top_p=TOP_P,
+        sample_count=SAMPLE_COUNT,
+    )
+
+    pred_df["date"] = y_timestamp.values
+
+    # Apply ±10% price limit
+    last_close = df["close"].iloc[-1]
+    pred_df = apply_price_limits(pred_df, last_close, limit_rate=0.1)
+
+    # Merge historical and predicted data
+    df_out = pd.concat([
+        df[["date", "open", "high", "low", "close", "volume", "amount"]],
+        pred_df[["date", "open", "high", "low", "close", "volume", "amount"]]
+    ]).reset_index(drop=True)
+
+    # Save CSV
+    out_file = os.path.join(save_dir, f"pred_{symbol.replace('.', '_')}_data.csv")
+    df_out.to_csv(out_file, index=False)
+    print(f"✅ Prediction completed and saved: {out_file}")
+
+    # Plot
+    plot_result(df, pred_df, symbol)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Kronos stock prediction script")
+    parser.add_argument("--symbol", type=str, default="000001", help="Stock code")
+    args = parser.parse_args()
+
+    predict_future(
+        symbol=args.symbol,
+    )