diff --git a/chart.py b/chart.py
new file mode 100644
index 0000000..80a498e
--- /dev/null
+++ b/chart.py
@@ -0,0 +1,203 @@
+import pandas as pd
+from db_config import DatabaseManager, DatabaseConfig
+from loguru import logger
+import talib as ta
+from lightweight_charts import Chart
+
+
+def add_ema(df, chart, period: int = 50):
+    ema = ta.EMA(df["close"], timeperiod=period)
+    name = f"EMA {period}"
+    df[name] = ema
+    PERIOD_COLORS = {
+        10: "#E42C2A",
+        20: "#E49D30",
+        30: "#3CFF0A",
+        60: "#3275E4",
+        200: "#B06CE3",
+    }
+    line = chart.create_line(name, color=PERIOD_COLORS[period], width=2)
+    line.set(df[["time", name]])
+
+
+def add_cci(df, chart, period: int = 14, height: float = 0.1, position: str = "bottom"):
+    cci = ta.CCI(df["high"], df["low"], df["close"], timeperiod=period)
+    df["cci"] = cci
+    cci_chart = chart.create_subchart(
+        position=position, width=1, height=height, sync=True
+    )
+    cci_chart.legend(visible=True)
+    cci_chart.time_scale(visible=False)
+    cci_line = cci_chart.create_line(name="cci", color="#FF5722", width=2)
+    cci_line.set(df[["time", "cci"]])
+
+
+def add_macd(df, chart, height: float = 0.2, position: str = "bottom"):
+    macd, signal, hist = ta.MACD(
+        df["close"], fastperiod=12, slowperiod=26, signalperiod=9
+    )
+    df["macd"] = macd
+    df["signal"] = signal
+    df["histogram"] = hist
+    macd_chart = chart.create_subchart(
+        position=position, width=1, height=height, sync=True
+    )
+    macd_chart.legend(visible=True)
+    macd_chart.time_scale(visible=False)
+
+    histogram = macd_chart.create_histogram(name="histogram")
+    hist_data = df[["time", "histogram"]].copy()
+    hist_data["color"] = hist_data["histogram"].apply(
+        lambda x: "#26a69a" if x < 0 else "#ef5350"  # 绿色 : 红色
+    )
+    histogram.set(hist_data)
+
+    macd_line = macd_chart.create_line(name="macd", color="#2962FF", width=2)
+    macd_line.set(df[["time", "macd"]])
+    signal_line = macd_chart.create_line(name="signal", color="#FF6D00", width=2)
+    signal_line.set(df[["time", "signal"]])
+
+
+def TD(dataframe: pd.DataFrame):
+    close = dataframe["close"].to_list()
+    td = [0, 0, 0, 0]
+    up = 0
+    down = 0
+    for i in range(4, len(close)):
+        if close[i] > close[i - 4]:
+            up += 1
+            down = 0
+            td.append(up)
+        else:
+            down -= 1
+            up = 0
+            td.append(down)
+    return td
+
+
+def add_TD(df, chart):
+    df["TD"] = TD(df)
+    td_line = chart.create_line(
+        name="TD",
+        color="rgba(0, 0, 0, 0)",  # 透明色,不在图表上显示线条
+        width=0,
+        price_line=False,
+        price_label=False,
+        price_scale_id="td_scale",
+    )
+    td_line.precision(0)
+    td_line.set(df[["time", "TD"]])
+    TDs = df[["time", "TD"]].to_dict(orient="records")
+    markers = []
+    for item in TDs:
+        if item["TD"] == 9:
+            markers.append(
+                {
+                    "time": item["time"].strftime("%Y-%m-%d"),
+                    "position": "below",
+                    "shape": "arrow_up",
+                    "color": "#4CAF50",
+                    "text": f"{item['TD']}",
+                }
+            )
+        elif item["TD"] == -9:
+            markers.append(
+                {
+                    "time": item["time"].strftime("%Y-%m-%d"),
+                    "position": "above",
+                    "shape": "arrow_down",
+                    "color": "#F44336",
+                    "text": f"{item['TD']}",
+                }
+            )
+
+    chart.marker_list(markers)
+
+
+def get_kline(code: str) -> list:
+    """
+    获取所有指数代码
+    :return:
+    """
+    env = "online"
+    env = "daily"
+    db_config = DatabaseConfig(env=env)
+    logger.info(f"数据库连接: {db_config.connection_string}")
+
+    db_manager = DatabaseManager(db_config)
+    sql = f"SELECT date as time, open, high, low, close, volume FROM public.index_kline where code='{code}' order by date;"
+    res = db_manager.execute_query(sql)
+    data_list = [dict(item) for item in res]
+    df = pd.DataFrame(data_list)
+    df["time"] = pd.to_datetime(df["time"])
+    num_cols = ["open", "high", "low", "close", "volume"]
+    for col in num_cols:
+        if col in df.columns:
+            df[col] = pd.to_numeric(df[col], errors="coerce").astype(float)
+    return df
+
+
+def resample_data(df: pd.DataFrame, timeframe: str) -> pd.DataFrame:
+    """
+    对日线数据进行重采样
+    :param df: 原始日线数据(time 作为索引)
+    :param timeframe: 目标周期 ('1D', '1W', '1M', '3M', '1Y')
+    :return: 重采样后的 DataFrame
+    """
+    # 映射周期到 pandas resample 规则
+    timeframe_map = {
+        "1D": "D",  # 日线
+        "1W": "W",  # 周线
+        "1M": "M",  # 月线
+        "3M": "3M",  # 季线
+        "1Y": "Y",  # 年线
+    }
+
+    if timeframe not in timeframe_map:
+        return df
+    df = df.set_index("time")
+
+    rule = timeframe_map[timeframe]
+
+    # 对 OHLCV 数据进行重采样
+    resampled = (
+        df.resample(rule)
+        .agg(
+            {
+                "open": "first",  # 开盘价取第一个
+                "high": "max",  # 最高价取最大值
+                "low": "min",  # 最低价取最小值
+                "close": "last",  # 收盘价取最后一个
+                "volume": "sum",  # 成交量求和
+            }
+        )
+        .dropna()
+    )
+
+    # 重置索引,将 time 变回列
+    resampled = resampled.reset_index()
+    return resampled
+
+
+if __name__ == "__main__":
+    code = "399986"
+    timeframe = "1W"
+    df = get_kline(code=code)
+    df = resample_data(df, timeframe)
+    chart = Chart(toolbox=True, inner_height=0.7, maximize=True)
+
+    chart.topbar.textbox("symbol", code)
+    chart.topbar.textbox("timeframe", timeframe)
+    # chart.time_scale(visible=False) # 将主图的时间轴隐藏
+    chart.legend(visible=True)
+
+    chart.set(df)
+    add_ema(df, chart, period=10)
+    add_ema(df, chart, period=20)
+    add_ema(df, chart, period=30)
+    add_ema(df, chart, period=60)
+    add_macd(df, chart)
+    add_cci(df, chart, period=14)
+    add_TD(df, chart)
+
+    chart.show(block=True)