From 38a4465e29968f3af4473a025bff0fcde745bd3f Mon Sep 17 00:00:00 2001 From: blade <8019068@qq.com> Date: Fri, 8 Aug 2025 18:02:37 +0800 Subject: [PATCH] support statistic volume-hour distribution --- core/statistics/price_volume_stats.py | 222 +++++++++++++++++++++++++- 1 file changed, 214 insertions(+), 8 deletions(-) diff --git a/core/statistics/price_volume_stats.py b/core/statistics/price_volume_stats.py index 490799d..7f8489e 100644 --- a/core/statistics/price_volume_stats.py +++ b/core/statistics/price_volume_stats.py @@ -51,6 +51,8 @@ class PriceVolumeStats: os.makedirs(self.stats_chart_dir, exist_ok=True) def batch_price_volume_statistics(self): + high_volume_hours_list = [] + huge_high_volume_hours_list = [] price_stats_list = [] pct_change_stats_list = [] peak_valley_data_list = [] @@ -83,6 +85,12 @@ class PriceVolumeStats: else: if data["timestamp"].iloc[-1] > latest_market_timestamp: latest_market_timestamp = data["timestamp"].iloc[-1] + # 统计高成交量小时分布 + logging.info(f"统计{symbol} {bar} 巨量小时分布数据") + high_volume_hours_data = self.stats_high_volume_hours(data) + high_volume_hours_list.append(high_volume_hours_data) + huge_high_volume_hours_data = self.stats_high_volume_hours(data, 4) + huge_high_volume_hours_list.append(huge_high_volume_hours_data) logging.info(f"统计{symbol} {bar} 价格数据") price_stats_data = self.calculate_price_statistics(data) logging.info(f"统计{symbol} {bar} 涨跌百分比数据") @@ -103,6 +111,10 @@ class PriceVolumeStats: peak_valley_stats_list.append(peak_valley_stats_data) volume_stats_list.append(volume_stats_data) price_volume_stats_list.append(price_volume_stats_data) + high_volume_hours_df = pd.concat(high_volume_hours_list) + high_volume_hours_df.sort_values(by=["symbol", "bar", "hour"], inplace=True) + huge_high_volume_hours_df = pd.concat(huge_high_volume_hours_list) + huge_high_volume_hours_df.sort_values(by=["symbol", "bar", "hour"], inplace=True) price_stats_df = pd.DataFrame(price_stats_list) price_stats_df.sort_values(by=["symbol", "bar"], inplace=True) pct_change_stats_df = pd.DataFrame(pct_change_stats_list) @@ -126,22 +138,32 @@ class PriceVolumeStats: output_file_path = os.path.join(self.stats_output_dir, output_file_name) logging.info(f"导出{output_file_path}") with pd.ExcelWriter(output_file_path) as writer: - price_stats_df.to_excel(writer, sheet_name="price_stats", index=False) + price_stats_df.to_excel(writer, sheet_name="价格统计", index=False) pct_change_stats_df.to_excel( - writer, sheet_name="pct_change_stats", index=False + writer, sheet_name="涨跌百分比统计", index=False ) peak_valley_data_df.to_excel( - writer, sheet_name="peak_valley_data", index=False + writer, sheet_name="波峰波谷明细", index=False ) peak_valley_stats_df.to_excel( - writer, sheet_name="peak_valley_stats", index=False + writer, sheet_name="波峰波谷统计", index=False ) - volume_stats_df.to_excel(writer, sheet_name="volume_stats", index=False) + volume_stats_df.to_excel(writer, sheet_name="量能统计", index=False) price_volume_stats_df.to_excel( - writer, sheet_name="price_volume_stats", index=False + writer, sheet_name="量价统计", index=False + ) + high_volume_hours_df.to_excel( + writer, sheet_name="放量小时分布", index=False + ) + huge_high_volume_hours_df.to_excel( + writer, sheet_name="4倍放量小时分布", index=False ) chart_dict = self.draw_price_change_peak_valley_chart(peak_valley_stats_df) self.output_chart_to_excel(output_file_path, chart_dict) + chart_dict = self.draw_high_volume_hours_chart(high_volume_hours_df, normal=True) + self.output_chart_to_excel(output_file_path, chart_dict) + chart_dict = self.draw_high_volume_hours_chart(huge_high_volume_hours_df, normal=False) + self.output_chart_to_excel(output_file_path, chart_dict) return price_stats_df, volume_stats_df, price_volume_stats_df def calculate_price_statistics(self, data: pd.DataFrame): @@ -395,6 +417,99 @@ class PriceVolumeStats: stats_data = self.base_statistics(peak_valley_data, "price_change_ratio") return peak_valley_data, stats_data + + def draw_high_volume_hours_chart(self, data: pd.DataFrame, normal: bool = True): + """ + 绘制高成交量小时分布图表（美观，保存到self.stats_chart_dir） + :param data: 高成交量小时分布数据（如high_volume_hours_df） + :return: None + """ + if data is None or data.empty: + return None + # seaborn风格设置 + sns.set_theme(style="whitegrid") + # plt.rcParams['font.family'] = "SimHei" + plt.rcParams["font.sans-serif"] = ["SimHei"] # 也可直接用字体名 + plt.rcParams["font.size"] = 11 # 设置字体大小 + plt.rcParams["axes.unicode_minus"] = False # 解决负号显示问题 + chart_dict = {} + + for symbol in data["symbol"].unique(): + symbol_data = data[data["symbol"] == symbol] + if normal: + sheet_name = f"{symbol}_量时分布图表" + else: + sheet_name = f"{symbol}_4倍量时分布图表" + chart_dict[sheet_name] = {} + for bar in symbol_data["bar"].unique(): + bar_data = symbol_data[symbol_data["bar"] == bar].copy() + # 将hour改名为小时 + bar_data.rename(columns={"hour": "小时"}, inplace=True) + # huge_volume_count改名为巨量次数 + bar_data.rename(columns={"huge_volume_count": "巨量次数"}, inplace=True) + # huge_volume_ratio改名为巨量次数占比 + bar_data.rename(columns={"huge_volume_ratio": "巨量次数占比"}, inplace=True) + # huge_volume_rise_count改名为巨量上涨次数 + bar_data.rename(columns={"huge_volume_rise_count": "巨量上涨次数"}, inplace=True) + # huge_volume_fall_count改名为巨量下跌次数 + bar_data.rename(columns={"huge_volume_fall_count": "巨量下跌次数"}, inplace=True) + bar_data.reset_index(drop=True, inplace=True) + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + fig.suptitle(f"巨量小时分布 - {symbol} {bar}", fontsize=18) + # huge_volume_count + # 柱状图使用不同颜色，巨量次数使用渐变蓝色 + palette = sns.color_palette("Blues_d", 2) + palette[0] = sns.color_palette("Blues_d", 2)[1] + palette[1] = sns.color_palette("Reds_d", 2)[1] + sns.barplot( + ax=axes[0], + x="小时", + y="巨量次数", + data=bar_data, + hue="symbol", + palette=palette, + legend=False, + ) + axes[0].set_title("巨量小时分布") + axes[0].set_ylabel("巨量次数") + # huge_volume_rise_count与huge_volume_fall_count + # 创建一个图表，都位于axes[1, 0]，包含两个柱状图: + # huge_volume_rise_count与huge_volume_fall_count并列放置， + # 并使用不同的颜色 + + df_long = pd.melt(bar_data, id_vars=['小时'], value_vars=['巨量上涨次数', '巨量下跌次数'], + var_name='类别', value_name='次数') + # 柱状图使用不同颜色，巨量上涨次数使用渐变红色，巨量下跌次数使用渐变绿色 + palette = sns.color_palette("Blues_d", 2) + palette[0] = sns.color_palette("Reds_d", 2)[1] + palette[1] = sns.color_palette("Greens_d", 2)[1] + sns.barplot( + ax=axes[1], + x="小时", + y="次数", + data=df_long, + hue="类别", + palette=palette, + legend=False, + ) + axes[1].set_title("巨量小时上涨下跌分布") + axes[1].set_ylabel("次数") + # 旋转x轴标签 + for ax in axes.flat: + for label in ax.get_xticklabels(): + label.set_rotation(45) + plt.tight_layout(rect=[0, 0, 1, 0.96]) + if normal: + save_path = os.path.join(self.stats_chart_dir, f"{symbol}_{bar}_high_volume_hours.png") + else: + save_path = os.path.join(self.stats_chart_dir, f"{symbol}_{bar}_4_high_volume_hours.png") + plt.savefig(save_path, dpi=150) + plt.close(fig) + chart_dict[sheet_name][ + f"巨量小时分布 - {bar}" + ] = save_path + return chart_dict + def draw_price_change_peak_valley_chart(self, data: pd.DataFrame): """ 绘制价格变化峰值和谷值图表（美观，保存到self.stats_chart_dir） @@ -409,7 +524,7 @@ class PriceVolumeStats: plt.rcParams["font.sans-serif"] = ["SimHei"] # 也可直接用字体名 plt.rcParams["font.size"] = 11 # 设置字体大小 plt.rcParams["axes.unicode_minus"] = False # 解决负号显示问题 - chart_dict = {"bar_peak_valley_chart": {}} + chart_dict = {"波峰波谷图表": {}} for bar in data["bar"].unique(): bar_data = data[data["bar"] == bar] fig, axes = plt.subplots(2, 2, figsize=(14, 10)) @@ -470,7 +585,7 @@ class PriceVolumeStats: save_path = os.path.join(self.stats_chart_dir, f"peak_valley_{bar}.png") plt.savefig(save_path, dpi=150) plt.close(fig) - chart_dict["bar_peak_valley_chart"][ + chart_dict["波峰波谷图表"][ f"波段变化峰值和谷值统计 - {bar}" ] = save_path return chart_dict @@ -530,6 +645,97 @@ class PriceVolumeStats: wb.save(excel_file_path) print(f"Chart saved as {excel_file_path}") + def stats_high_volume_hours(self, data: pd.DataFrame, volume_ratio_threshold: int = None): + """ + 统计巨量小时分布 + 小时包括0-23点，每小时一个数据 + 首先不区分价格涨跌，统计每个小时，满足huge_volume == 1的次数 + 然后区分价格涨跌，统计每个小时，满足huge_volume == 1的次数 + 最后统计每个小时，满足huge_volume == 1的次数，与满足huge_volume == 0的次数的比率 + :param data: 市场数据 + :return: 巨量小时分布 + """ + if data is None: + return None + if volume_ratio_threshold is not None and volume_ratio_threshold > 0: + data = data[data["volume_ratio"] >= volume_ratio_threshold] + # 将date_time转换为datetime类型 + data["date_time"] = pd.to_datetime(data["date_time"]) + # 通过pandas自带的功能，计算pct_chg + data["pct_chg"] = data["close"].pct_change() + # 统计每个小时，满足huge_volume == 1的次数 + huge_volume_hours = data.groupby(data["date_time"].dt.hour)["huge_volume"].sum() + # 统计每个小时，满足huge_volume == 0的次数 + # no_huge_volume_hours = ( + # data.groupby(data["date_time"].dt.hour)["huge_volume"].count() + # - huge_volume_hours + # ) + # 统计每个小时，满足huge_volume == 1的次数，与满足huge_volume == 0的次数的比率 + # huge_volume_ratio_hours = huge_volume_hours / no_huge_volume_hours + # 将huge_volume_ratio_hours转换为百分比 + # huge_volume_ratio_hours = huge_volume_ratio_hours * 100 + # 统计每个小时，满足huge_volume == 1且上涨的次数 + huge_volume_rise_hours_df = ( + data[(data["huge_volume"] == 1) & (data["pct_chg"] > 0)] + .groupby(data["date_time"].dt.hour)["huge_volume"] + .sum() + ) + # 统计每个小时，满足huge_volume == 1且下跌的次数 + huge_volume_fall_hours_df = ( + data[(data["huge_volume"] == 1) & (data["pct_chg"] < 0)] + .groupby(data["date_time"].dt.hour)["huge_volume"] + .sum() + ) + + # 将huge_volume_hours, no_huge_volume_hours, huge_volume_ratio_hours转换为DataFrame + huge_volume_hours_df = pd.DataFrame(huge_volume_hours) + # no_huge_volume_hours_df = pd.DataFrame(no_huge_volume_hours) + # huge_volume_ratio_hours_df = pd.DataFrame(huge_volume_ratio_hours) + huge_volume_rise_hours_df = pd.DataFrame(huge_volume_rise_hours_df) + huge_volume_fall_hours_df = pd.DataFrame(huge_volume_fall_hours_df) + # 将hour index作为列名: hour,将sum与count后的列名改为huge_volume_count, no_huge_volume_count + huge_volume_hours_df.index.name = "hour" + # no_huge_volume_hours_df.index.name = "hour" + # huge_volume_ratio_hours_df.index.name = "hour" + huge_volume_rise_hours_df.index.name = "hour" + huge_volume_fall_hours_df.index.name = "hour" + huge_volume_hours_df.columns = ["huge_volume_count"] + # no_huge_volume_hours_df.columns = ["no_huge_volume_count"] + # huge_volume_ratio_hours_df.columns = ["huge_volume_ratio"] + huge_volume_rise_hours_df.columns = ["huge_volume_rise_count"] + huge_volume_fall_hours_df.columns = ["huge_volume_fall_count"] + # 将huge_volume_hours_df, no_huge_volume_hours_df, huge_volume_ratio_hours_df, huge_volume_rise_hours_df, huge_volume_fall_hours_df合并为DataFrame + result_df = pd.concat( + [ + huge_volume_hours_df, + # no_huge_volume_hours_df, + # huge_volume_ratio_hours_df, + huge_volume_rise_hours_df, + huge_volume_fall_hours_df, + ], + axis=1, + ) + # 将hour index作为列名: hour + result_df.index.name = "hour" + result_df = result_df.reset_index() + # 将hour index转换为列名: hour, huge_volume_count, no_huge_volume_count, huge_volume_ratio + result_df["symbol"] = data.iloc[0]["symbol"] + result_df["bar"] = data.iloc[0]["bar"] + result_df = result_df[ + [ + "symbol", + "bar", + "hour", + "huge_volume_count", + # "no_huge_volume_count", + # "huge_volume_ratio", + "huge_volume_rise_count", + "huge_volume_fall_count", + ] + ] + result_df.reset_index(drop=True, inplace=True) + return result_df + def find_peaks_valleys(self, data: pd.DataFrame, window=10): """ 识别K线数据的波峰和波谷