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 = pd.read_csv(
        "/Users/aszer/Documents/vscode/etf/data/index_all_stock.csv",
        encoding="utf-8-sig",
    )
    name = df.loc[df["代码"] == code, "名称"].values[0]

    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("name", name)
    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)