修改为类；添加update_legend函数；添加实时鼠标y轴价格获取并计算该价格和可见范围内的最新收盘价之间的利润Crosshair Price Profit%

chart.py

@@ -9,115 +9,6 @@ from lightweight_charts import Chart
 import random
 from datetime import datetime
 
-horizontal_lines = {}
-
-
-def get_fixed_color_based_on_period(period):
-    # 使用周期值作为随机种子，确保相同周期生成相同颜色
-    random.seed(period)
-
-    # 生成随机的RGB值
-    r = random.randint(0, 255)
-    g = random.randint(0, 255)
-    b = random.randint(0, 255)
-
-    # 将RGB值转换为十六进制颜色代码
-    color_code = "#{:02x}{:02x}{:02x}".format(r, g, b)
-
-    # 重置随机种子，避免影响其他随机操作
-    random.seed(None)
-
-    return color_code
-
-
-def add_ema(df, chart, period: int = 50):
-    name = f"EMA_{period}"
-    df[name] = ta.EMA(df["close"], timeperiod=period)
-    color = get_fixed_color_based_on_period(period)
-    line = chart.create_line(
-        name, color=color, width=2, price_label=False, price_line=False
-    )
-    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[f"CCI_{period}"] = cci
-    cci_chart = chart.create_subchart(
-        position=position, width=1, height=height, sync=True
-    )
-    cci_chart.legend(
-        visible=True, font_size=14, color="#FFFFFF", font_family="Times New Roman"
-    )
-    cci_chart.time_scale(visible=False)
-    cci_line = cci_chart.create_line(name=f"CCI_{period}", color="#FF0000", width=2)
-    cci_line.set(df[["time", f"CCI_{period}"]])
-    df = df[["time"]].copy()
-    df["h"] = 100
-    df["l"] = -100
-    cci_line = cci_chart.create_line(
-        name="h",
-        color="#D4C21C",
-        width=1,
-        style="dashed",
-        price_label=False,
-        price_line=False,
-    )
-    cci_line.set(df[["time", "h"]])
-    cci_line = cci_chart.create_line(
-        name="l",
-        color="#D4C21C",
-        width=1,
-        style="dashed",
-        price_label=False,
-        price_line=False,
-    )
-    cci_line.set(df[["time", "l"]])
-
-
-def add_macd(
-    df,
-    chart,
-    fastperiod: int = 12,
-    slowperiod: int = 26,
-    signalperiod: int = 9,
-    height: float = 0.1,
-    position: str = "bottom",
-):
-    macd, signal, hist = ta.MACD(
-        df["close"],
-        fastperiod=fastperiod,
-        slowperiod=slowperiod,
-        signalperiod=signalperiod,
-    )
-    df["DIF"] = macd
-    df["DEA"] = signal
-    macd_name = f"MACD_{fastperiod}_{slowperiod}_{signalperiod}"
-    df[macd_name] = hist * 2
-    macd_chart = chart.create_subchart(
-        position=position, width=1, height=height, sync=True
-    )
-    macd_chart.legend(
-        visible=True, font_size=14, color="#FFFFFF", font_family="Times New Roman"
-    )
-    macd_chart.time_scale(visible=False)
-
-    histogram = macd_chart.create_histogram(name=macd_name)
-    hist_data = df[["time", macd_name]].copy()
-    hist_data["color"] = hist_data[macd_name].apply(
-        lambda x: "#00FF00" if x < 0 else "#ff0000"  # 绿色 : 红色
-    )
-    histogram.set(hist_data)
-
-    macd_line = macd_chart.create_line(
-        name="DIF", color="#2962FF", width=2, price_label=False, price_line=False
-    )
-    macd_line.set(df[["time", "DIF"]])
-    signal_line = macd_chart.create_line(
-        name="DEA", color="#FF0000", width=2, price_label=False, price_line=False
-    )
-    signal_line.set(df[["time", "DEA"]])
-
 
 def TD(dataframe: pd.DataFrame):
     close = dataframe["close"].to_list()
@@ -136,77 +27,6 @@ def TD(dataframe: pd.DataFrame):
     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"] in [9, 13]:
-            markers.append(
-                {
-                    "time": item["time"].strftime("%Y-%m-%d"),
-                    "position": "above",
-                    "shape": "arrow_down",
-                    "color": "#00FF00",
-                    "text": f"{item['TD']}",
-                }
-            )
-        elif item["TD"] in [-9, -13]:
-            markers.append(
-                {
-                    "time": item["time"].strftime("%Y-%m-%d"),
-                    "position": "below",
-                    "shape": "arrow_up",
-                    "color": "#FF0000",
-                    "text": f"{item['TD']}",
-                }
-            )
-
-    chart.marker_list(markers)
-
-
-def add_buy_sell_signal_markers(df, chart):
-
-    if "buy" not in df.columns or "sell" not in df.columns:
-        return
-
-    markers = []
-    signals = df[["time", "buy", "sell"]].to_dict(orient="records")
-    for item in signals:
-        if item["buy"] == 1:
-            markers.append(
-                {
-                    "time": item["time"].strftime("%Y-%m-%d"),
-                    "position": "below",
-                    "shape": "arrow_up",
-                    "color": "#00FF00",
-                    "text": f"B",
-                }
-            )
-        elif item["sell"] == 1:
-            markers.append(
-                {
-                    "time": item["time"].strftime("%Y-%m-%d"),
-                    "position": "above",
-                    "shape": "arrow_down",
-                    "color": "#FF0000",
-                    "text": f"S",
-                }
-            )
-
-    chart.marker_list(markers)
-
-
 def resample_data(df: pd.DataFrame, timeframe: str) -> pd.DataFrame:
     """
     对日线数据进行重采样
@@ -296,7 +116,196 @@ def POC(df, bins: int = 50):
     return poc
 
 
-def on_range_change_poc(chart, bars_before, bars_after):
+def get_fixed_color_based_on_period(num: int):
+    # 使用周期值作为随机种子，确保相同周期生成相同颜色
+    random.seed(num)
+
+    # 生成随机的RGB值
+    r = random.randint(0, 255)
+    g = random.randint(0, 255)
+    b = random.randint(0, 255)
+
+    # 将RGB值转换为十六进制颜色代码
+    color_code = "#{:02x}{:02x}{:02x}".format(r, g, b)
+
+    # 重置随机种子，避免影响其他随机操作
+    random.seed(None)
+
+    return color_code
+
+
+class QuantChart:
+
+    def __init__(self):
+        self.horizontal_lines = {}
+        self.legend_data = {}
+        self.visible_latest_close = None
+
+    def update_legend(self, chart, key, text):
+        self.legend_data[key] = text
+        sorted_dict = dict(sorted(self.legend_data.items()))
+        full_text = ", ".join(sorted_dict.values())
+        chart.legend(visible=True, text=full_text)
+
+    def add_ema(self, df, chart, period: int = 50):
+        name = f"EMA_{period}"
+        df[name] = ta.EMA(df["close"], timeperiod=period)
+        color = get_fixed_color_based_on_period(num=period)
+        line = chart.create_line(
+            name, color=color, width=2, price_label=False, price_line=False
+        )
+        line.set(df[["time", name]])
+
+    def add_cci(
+        self, df, chart, period: int = 14, height: float = 0.1, position: str = "bottom"
+    ):
+        cci = ta.CCI(df["high"], df["low"], df["close"], timeperiod=period)
+        df[f"CCI_{period}"] = cci
+        cci_chart = chart.create_subchart(
+            position=position, width=1, height=height, sync=True
+        )
+        cci_chart.legend(
+            visible=True, font_size=14, color="#FFFFFF", font_family="Times New Roman"
+        )
+        cci_chart.time_scale(visible=False)
+        cci_line = cci_chart.create_line(name=f"CCI_{period}", color="#FF0000", width=2)
+        cci_line.set(df[["time", f"CCI_{period}"]])
+        df = df[["time"]].copy()
+        df["h"] = 100
+        df["l"] = -100
+        cci_line = cci_chart.create_line(
+            name="h",
+            color="#D4C21C",
+            width=1,
+            style="dashed",
+            price_label=False,
+            price_line=False,
+        )
+        cci_line.set(df[["time", "h"]])
+        cci_line = cci_chart.create_line(
+            name="l",
+            color="#D4C21C",
+            width=1,
+            style="dashed",
+            price_label=False,
+            price_line=False,
+        )
+        cci_line.set(df[["time", "l"]])
+
+    def add_macd(
+        self,
+        df,
+        chart,
+        fastperiod: int = 12,
+        slowperiod: int = 26,
+        signalperiod: int = 9,
+        height: float = 0.1,
+        position: str = "bottom",
+    ):
+        macd, signal, hist = ta.MACD(
+            df["close"],
+            fastperiod=fastperiod,
+            slowperiod=slowperiod,
+            signalperiod=signalperiod,
+        )
+        df["DIF"] = macd
+        df["DEA"] = signal
+        macd_name = f"MACD_{fastperiod}_{slowperiod}_{signalperiod}"
+        df[macd_name] = hist * 2
+        macd_chart = chart.create_subchart(
+            position=position, width=1, height=height, sync=True
+        )
+        macd_chart.legend(
+            visible=True, font_size=14, color="#FFFFFF", font_family="Times New Roman"
+        )
+        macd_chart.time_scale(visible=False)
+
+        histogram = macd_chart.create_histogram(name=macd_name)
+        hist_data = df[["time", macd_name]].copy()
+        hist_data["color"] = hist_data[macd_name].apply(
+            lambda x: "#00FF00" if x < 0 else "#ff0000"  # 绿色 : 红色
+        )
+        histogram.set(hist_data)
+
+        macd_line = macd_chart.create_line(
+            name="DIF", color="#2962FF", width=2, price_label=False, price_line=False
+        )
+        macd_line.set(df[["time", "DIF"]])
+        signal_line = macd_chart.create_line(
+            name="DEA", color="#FF0000", width=2, price_label=False, price_line=False
+        )
+        signal_line.set(df[["time", "DEA"]])
+
+    def add_TD(self, 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"] in [9, 13]:
+                markers.append(
+                    {
+                        "time": item["time"].strftime("%Y-%m-%d"),
+                        "position": "above",
+                        "shape": "arrow_down",
+                        "color": "#00FF00",
+                        "text": f"{item['TD']}",
+                    }
+                )
+            elif item["TD"] in [-9, -13]:
+                markers.append(
+                    {
+                        "time": item["time"].strftime("%Y-%m-%d"),
+                        "position": "below",
+                        "shape": "arrow_up",
+                        "color": "#FF0000",
+                        "text": f"{item['TD']}",
+                    }
+                )
+
+        chart.marker_list(markers)
+
+    def add_buy_sell_signal_markers(self, df, chart):
+
+        if "buy" not in df.columns or "sell" not in df.columns:
+            return
+
+        markers = []
+        signals = df[["time", "buy", "sell"]].to_dict(orient="records")
+        for item in signals:
+            if item["buy"] == 1:
+                markers.append(
+                    {
+                        "time": item["time"].strftime("%Y-%m-%d"),
+                        "position": "below",
+                        "shape": "arrow_up",
+                        "color": "#00FF00",
+                        "text": f"B",
+                    }
+                )
+            elif item["sell"] == 1:
+                markers.append(
+                    {
+                        "time": item["time"].strftime("%Y-%m-%d"),
+                        "position": "above",
+                        "shape": "arrow_down",
+                        "color": "#FF0000",
+                        "text": f"S",
+                    }
+                )
+
+        chart.marker_list(markers)
+
+    def on_range_change_poc(self, chart, bars_before, bars_after):
         df = chart.candle_data
         if df is None or df.empty:
             return
@@ -316,23 +325,24 @@ def on_range_change_poc(chart, bars_before, bars_after):
         # logger.info(f"df_range_len: {len(df_range)}")
         poc = POC(df_range)
         poc_line_name = "POC"
-    if poc_line_name in horizontal_lines:
+        if poc_line_name in self.horizontal_lines:
-        horizontal_lines[poc_line_name].delete()
+            self.horizontal_lines[poc_line_name].delete()
         # 添加新的 POC 水平线
-    latest_close = df_range["close"].iloc[-1]
+        self.visible_latest_close = df_range["close"].iloc[-1]
-    profit = (latest_close - poc) / poc * 100
+        profit = (self.visible_latest_close - poc) / poc * 100
         poc_line = chart.horizontal_line(
             price=poc, color="#FF0000", width=4, style="solid", text=f"{poc:.2f}"
         )
-    chart.legend(
+        # chart.legend(
-        visible=True,
+        #     visible=True,
-        text=f"POC: {poc:.2f}, poc_range_profit%: {profit:.1f}%, tf_cnt: {len(df_range)}",
+        #     text=f"POC: {poc:.2f}, poc_range_profit%: {profit:.1f}%, visible_tf_cnt: {len(df_range)}",
-    )
+        # )
+        legend_text = f"POC: {poc:.2f}, poc_range_profit%: {profit:.1f}%, visible_tf_cnt: {len(df_range)}"
+        self.update_legend(chart=chart, key=poc_line_name, text=legend_text)
 
-    horizontal_lines[poc_line_name] = poc_line
+        self.horizontal_lines[poc_line_name] = poc_line
 
-
+    def check_df(self, df: pd.DataFrame):
-def check_df(df: pd.DataFrame):
         # basic type check
         if not isinstance(df, pd.DataFrame):
             raise TypeError("df must be a pandas DataFrame")
@@ -348,7 +358,9 @@ def check_df(df: pd.DataFrame):
         time_series = df["time"]
         if not (
             pd.api.types.is_datetime64_any_dtype(time_series)
-        or time_series.apply(lambda x: isinstance(x, (pd.Timestamp, datetime))).all()
+            or time_series.apply(
+                lambda x: isinstance(x, (pd.Timestamp, datetime))
+            ).all()
         ):
             raise TypeError(
                 "Column 'time' must contain datetime values (python datetime.datetime or pandas.Timestamp) "
@@ -360,12 +372,43 @@ def check_df(df: pd.DataFrame):
             if not pd.api.types.is_numeric_dtype(df[col]):
                 raise TypeError(f"Column '{col}' must be numeric (int or float)")
 
+    def setup_crosshair_tracking(self, chart):
+        chart.run_script(
+            f"""  
+            {chart.id}.chart.subscribeCrosshairMove((param) => {{  
+                if (!param.point) return;  
+                const price = {chart.id}.series.coordinateToPrice(param.point.y);  
+                if (price !== null) {{  
+                    window.callbackFunction(`crosshair_price_~_${{price}}`);  
+                }}  
+            }})  
+        """
+        )
 
-def plot_chart(
+        # 注册回调处理
-    df, symbol: str, name: str, timeframe: str, init_visible_num_bars: int = 90
+        def on_crosshair_price(price_str):
-):
+            # 实时获取鼠标位置y轴的价格
+            price = float(price_str)
+            if self.visible_latest_close is not None:
+                profit = (self.visible_latest_close - price) / price * 100
+                self.update_legend(
+                    chart=chart,
+                    key="Crosshair Price Profit%",
+                    text=f"Crosshair Price Profit%: {profit:.2f}%",
+                )
+
+        chart.win.handlers["crosshair_price"] = lambda p: on_crosshair_price(p)
+
+    def plot_chart(
+        self,
+        df,
+        symbol: str,
+        name: str,
+        timeframe: str,
+        init_visible_num_bars: int = 90,
+    ):
         # 校验数据是否满足
-    check_df(df)
+        self.check_df(df)
 
         chart = Chart(toolbox=True, inner_height=0.8, maximize=True)
         chart.topbar.textbox("symbol", symbol)
@@ -382,17 +425,19 @@ def plot_chart(
         chart.set_visible_range(start_time, end_time)
 
         # 设置每次放缩k线范围时的回调函数计算实时计算poc
-    chart.events.range_change += on_range_change_poc
+        chart.events.range_change += self.on_range_change_poc
+
+        self.setup_crosshair_tracking(chart)
 
         # 添加技术指标
         # add_ema(df, chart, period=10)
         # add_ema(df, chart, period=20)
-    add_ema(df, chart, period=30)
+        self.add_ema(df, chart, period=30)
         # add_ema(df, chart, period=60)
-    add_macd(df, chart)
+        self.add_macd(df, chart)
-    add_cci(df, chart, period=14)
+        self.add_cci(df, chart, period=14)
-    add_TD(df, chart)
+        self.add_TD(df, chart)
-    add_buy_sell_signal_markers(df, chart)
+        self.add_buy_sell_signal_markers(df, chart)
 
         chart.show(block=True)