項目地址：https://github.com/shidenggui/easyquotation
代碼范例：

# -*- coding: utf-8 -*-
"""
Created on Sun Mar  9 12:15:55 2025
@author: John Cheng
https://github.com/shidenggui/easyquotation
"""

import easyquotation
import pandas as pd

#指定數據源： 新浪 ['sina'] 騰訊 ['tencent', 'qq'] 
quotation = easyquotation.use('sina') 

# 股票單支查詢， 支持直接指定前綴，如 'sh000001'
data = quotation.real('162411') 

# 多只股票查詢
data2 = quotation.real(['000001','000700'])
df = pd.DataFrame(data2)

#同時獲取指數和股票
data3 = quotation.stocks(['sh000001', 'sz000001'], prefix=True) 

 # prefix 參數指定返回的行情字典中的股票代碼 key 是否帶 sz/sh 前綴
data4 = quotation.market_snapshot(prefix=True)

#騰訊分時圖
quotation = easyquotation.use("timekline")
data = quotation.real(['603828'], prefix=True)

# -*- coding: utf-8 -*-
"""
Created on Sat Mar 15 19:13:01 2025
@author: CHENGJUN

代碼說明（感謝deepseek)： 
運行代碼后，會生成一個名為 stock_database_design.xlsx 的 Excel 文件，包含以下內容：
索引頁：包含所有 Sheet 的中文名和英文名。英文名帶有超鏈接，點擊可以跳轉到對應的 Sheet。列寬自適應。
數據表 Sheet：每個 Sheet 的列寬自適應，同時考慮列名和列內容的寬度，顯示清晰。

優點
代碼簡化：通過遍歷和動態生成，減少了重復代碼。

通用性增強：如果需要新增表，只需在 index_data 和 table_structures 中添加對應的信息即可。
索引頁優先：索引頁在最前面創建，方便后續動態生成數據表結構。
動態生成數據表結構：使用索引頁的英文名稱從 table_structures 字典中動態獲取表結構。
遍歷寫入 Excel 文件：使用 for 循環遍歷索引頁的英文名稱，動態寫入對應的 Sheet。
列寬自適應：遍歷每個 Sheet 的列，計算列名和列內容的最大長度，并設置列寬為 max(列名長度, 列內容長度) + 2。
超鏈接：在索引頁中為英文名添加超鏈接，點擊可以跳轉到對應的 Sheet。


"""

import pandas as pd

# 1. 創建索引頁
index_data = {
    "序號": [1, 2, 3, 4, 5],
    "中文名": ["股票基本信息表", "每日交易數據表", "分時數據表", "K線數據表", "數據庫變動信息表"],
    "英文名": ["stock_info", "daily_trade_data", "intraday_trade_data", "kline_data", "database_change_log"]
}
df_index = pd.DataFrame(index_data)

# 2. 定義數據表結構（通過索引頁的英文名稱動態生成）
table_structures = {
    "stock_info": {
        "中文列名": ["股票ID", "股票代碼", "股票名稱", "市場", "行業", "上市日期"],
        "英文列名": ["stock_id", "stock_code", "stock_name", "market", "industry", "listing_date"],
        "數據類型": ["INT", "VARCHAR", "VARCHAR", "VARCHAR", "VARCHAR", "DATE"],
        "數據長度": ["-", "10", "50", "10", "50", "-"],
        "是否主鍵": ["是", "否", "否", "否", "否", "否"],
        "是否外鍵": ["否", "否", "否", "否", "否", "否"],
        "默認值": ["-", "-", "-", "-", "-", "-"],
        "描述": ["股票的唯一標識", "股票代碼", "股票名稱", "所屬市場", "所屬行業", "股票上市日期"]
    },
    "daily_trade_data": {
        "中文列名": ["交易ID", "股票ID", "交易日期", "開盤價", "收盤價", "最高價", "最低價", "成交量", "成交額"],
        "英文列名": ["trade_id", "stock_id", "trade_date", "open_price", "close_price", "high_price", "low_price", "volume", "turnover"],
        "數據類型": ["INT", "INT", "DATE", "DECIMAL", "DECIMAL", "DECIMAL", "DECIMAL", "BIGINT", "DECIMAL"],
        "數據長度": ["-", "-", "-", "10,2", "10,2", "10,2", "10,2", "-", "15,2"],
        "是否主鍵": ["是", "否", "否", "否", "否", "否", "否", "否", "否"],
        "是否外鍵": ["否", "是", "否", "否", "否", "否", "否", "否", "否"],
        "默認值": ["-", "-", "-", "-", "-", "-", "-", "-", "-"],
        "描述": ["交易記錄的唯一標識", "關聯股票信息表", "交易日期", "開盤價", "收盤價", "最高價", "最低價", "成交量", "成交額"]
    },
    "intraday_trade_data": {
        "中文列名": ["分時數據ID", "股票ID", "交易日期", "交易時間", "價格", "成交量", "成交額"],
        "英文列名": ["intraday_id", "stock_id", "trade_date", "trade_time", "price", "volume", "turnover"],
        "數據類型": ["INT", "INT", "DATE", "TIME", "DECIMAL", "INT", "DECIMAL"],
        "數據長度": ["-", "-", "-", "-", "10,2", "-", "15,2"],
        "是否主鍵": ["是", "否", "否", "否", "否", "否", "否"],
        "是否外鍵": ["否", "是", "否", "否", "否", "否", "否"],
        "默認值": ["-", "-", "-", "-", "-", "-", "-"],
        "描述": ["分時數據的唯一標識", "關聯股票信息表", "交易日期", "交易時間", "交易價格", "成交量", "成交額"]
    },
    "kline_data": {
        "中文列名": ["K線數據ID", "股票ID", "K線類型", "開始日期", "結束日期", "開盤價", "收盤價", "最高價", "最低價", "成交量", "成交額"],
        "英文列名": ["kline_id", "stock_id", "kline_type", "start_date", "end_date", "open_price", "close_price", "high_price", "low_price", "volume", "turnover"],
        "數據類型": ["INT", "INT", "VARCHAR", "DATE", "DATE", "DECIMAL", "DECIMAL", "DECIMAL", "DECIMAL", "BIGINT", "DECIMAL"],
        "數據長度": ["-", "-", "10", "-", "-", "10,2", "10,2", "10,2", "10,2", "-", "15,2"],
        "是否主鍵": ["是", "否", "否", "否", "否", "否", "否", "否", "否", "否", "否"],
        "是否外鍵": ["否", "是", "否", "否", "否", "否", "否", "否", "否", "否", "否"],
        "默認值": ["-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-"],
        "描述": ["K線數據的唯一標識", "關聯股票信息表", "K線類型（日K、周K等）", "K線開始日期", "K線結束日期", "開盤價", "收盤價", "最高價", "最低價", "成交量", "成交額"]
    },
    "database_change_log": {
        "中文列名": ["日志ID", "變更時間", "變更類型", "表名", "記錄ID", "操作用戶", "舊數據", "新數據"],
        "英文列名": ["log_id", "change_time", "change_type", "table_name", "record_id", "user", "old_data", "new_data"],
        "數據類型": ["INT", "TIMESTAMP", "VARCHAR", "VARCHAR", "INT", "VARCHAR", "JSON", "JSON"],
        "數據長度": ["-", "-", "20", "50", "-", "50", "-", "-"],
        "是否主鍵": ["是", "否", "否", "否", "否", "否", "否", "否"],
        "是否外鍵": ["否", "否", "否", "否", "否", "否", "否", "否"],
        "默認值": ["-", "CURRENT_TIMESTAMP", "-", "-", "-", "-", "-", "-"],
        "描述": ["日志的唯一標識", "操作時間", "操作類型（INSERT/UPDATE/DELETE）", "操作的表名", "操作的記錄ID", "操作用戶", "舊數據（僅UPDATE/DELETE）", "新數據（僅INSERT/UPDATE）"]
    }
}

# 3. 將字典轉換為 DataFrame
dataframes = {}
for table_name in index_data["英文名"]:
    dataframes[table_name] = pd.DataFrame(table_structures[table_name])

# 4. 創建 Excel 文件
with pd.ExcelWriter("stock_database_design.xlsx", engine="xlsxwriter") as writer:
    # 寫入索引頁
    df_index.to_excel(writer, sheet_name="索引頁", index=False)
    
    # 遍歷寫入其他 Sheet
    for table_name, df in dataframes.items():
        df.to_excel(writer, sheet_name=table_name, index=False)

    # 獲取 workbook 和 worksheet 對象
    workbook = writer.book
    worksheet_index = writer.sheets["索引頁"]

    # 為英文名添加超鏈接
    for i, row in enumerate(index_data["英文名"], start=1):
        worksheet_index.write_url(f"C{i + 1}", f"internal:'{row}'!A1", string=row)

    # 設置所有 Sheet 的列寬為自適應
    for sheet_name in writer.sheets:
        worksheet = writer.sheets[sheet_name]
        # 獲取當前 Sheet 的 DataFrame
        if sheet_name == "索引頁":
            df = df_index
        else:
            df = dataframes[sheet_name]

        # 遍歷每一列，設置列寬
        for col_num, column in enumerate(df.columns):
            # 計算列名的長度
            col_name_len = len(str(column))
            # 計算列內容的最大長度
            col_content_len = df[column].astype(str).map(len).max()
            # 取最大值，并增加 2 個字符的緩沖
            max_len = max(col_name_len, col_content_len) + 6
            # 設置列寬
            worksheet.set_column(col_num, col_num, max_len)

print("Excel 文件已生成：stock_database_design.xlsx")

# -*- coding: utf-8 -*-
"""
Created on Sat Mar 15 20:55:25 2025
@author: CHENGJUN
"""

import pandas as pd
import mysql.connector

# 讀取 Excel 文件
excel_file = "stock_database_design.xlsx"
sheets = pd.read_excel(excel_file, sheet_name=None)

# 數據庫連接配置
db_config = {
    "host": "localhost",
    "user": "root",
    "password": "password",
    "database": "stock_db"
}

# 生成 SQL 命令
sql_commands = []
for sheet_name, df in sheets.items():
    if sheet_name == "索引頁":
        continue  # 跳過索引頁

    # 提取表名
    table_name = sheet_name

    # 提取列信息
    columns = []
    for _, row in df.iterrows():
        column_name = row["英文列名"]
        data_type = row["數據類型"]
        data_length = row["數據長度"]
        is_primary_key = row["是否主鍵"] == "是"
        is_foreign_key = row["是否外鍵"] == "是"
        default_value = row["默認值"]
        description = row["描述"]  # 獲取描述信息

        # 處理數據長度
        if pd.isna(data_length) or data_length == "-":
            column_def = f"{column_name} {data_type}"
        else:
            column_def = f"{column_name} {data_type}({data_length})"

        # 處理主鍵
        if is_primary_key:
            column_def += " PRIMARY KEY"

        # 處理外鍵
        if is_foreign_key:
            referenced_table = "stock_info"  # 假設外鍵都關聯到 stock_info 表
            column_def += f", FOREIGN KEY ({column_name}) REFERENCES {referenced_table}(stock_id)"

        # 處理默認值
        if not pd.isna(default_value) and default_value != "-":
            column_def += f" DEFAULT {default_value}"

        # 添加備注
        if not pd.isna(description) and description != "-":
            column_def += f" COMMENT '{description}'"

        columns.append(column_def)

    # 生成 CREATE TABLE 語句
    create_table_sql = f"CREATE TABLE {table_name} (\n"
    create_table_sql += ",\n".join(columns)
    create_table_sql += "\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;"
    sql_commands.append(create_table_sql)

# 打印生成的 SQL 命令
print("生成的 SQL 命令：")
for sql in sql_commands:
    print(sql)
    print("-" * 80)

# 用戶選擇是否執行 SQL 命令
execute = input("是否立即執行以上 SQL 命令？(y/n): ").strip().lower()
if execute == "y":
    # 連接到數據庫
    conn = mysql.connector.connect(**db_config)
    cursor = conn.cursor()

    # 執行 SQL 命令
    for sql in sql_commands:
        try:
            cursor.execute(f"DROP TABLE IF EXISTS {sql.split()[2]};")  # 如果表已存在，先刪除
            cursor.execute(sql)
            print(f"表 {sql.split()[2]} 創建成功！")
        except mysql.connector.Error as err:
            print(f"創建表 {sql.split()[2]} 時出錯: {err}")

    # 提交更改并關閉連接
    conn.commit()
    cursor.close()
    conn.close()
else:
    print("SQL 命令未執行。")

-- 交易所信息表

CREATE TABLE exchange_info (
    exchange_id INT PRIMARY KEY,
    exchange_code VARCHAR(10) NOT NULL UNIQUE,
    exchange_name VARCHAR(50) NOT NULL,
    country VARCHAR(50),
    timezone VARCHAR(30),
    trading_hours JSON,  -- 存儲交易時間段
    status TINYINT DEFAULT 1,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
);



-- 股票基礎信息表

CREATE TABLE stock_info (
    stock_id BIGINT PRIMARY KEY,
    exchange_id INT,
    stock_code VARCHAR(10) NOT NULL,
    stock_name VARCHAR(100) NOT NULL,
    listing_date DATE,
    delisting_date DATE,
    is_index TINYINT DEFAULT 0,  -- 是否為指數
    sector_code VARCHAR(20),     -- 行業代碼
    status TINYINT DEFAULT 1,    -- 狀態：1-正常交易 2-停牌 3-退市
    FOREIGN KEY (exchange_id) REFERENCES exchange_info(exchange_id),
    UNIQUE KEY `idx_stock_code` (exchange_id, stock_code)
);



-- 行業分類表

CREATE TABLE sector_info (
    sector_id INT PRIMARY KEY,
    sector_code VARCHAR(20) NOT NULL UNIQUE,
    sector_name VARCHAR(100) NOT NULL,
    parent_id INT,
    level TINYINT,  -- 行業層級
    standard VARCHAR(50)  -- 分類標準（例如：證監會、申萬）
);



-- 日線數據表（按年份分區）

CREATE TABLE daily_quotes (
    id BIGINT PRIMARY KEY AUTO_INCREMENT,
    stock_id BIGINT NOT NULL,
    trade_date DATE NOT NULL,
    open_price DECIMAL(18,4),
    high_price DECIMAL(18,4),
    low_price DECIMAL(18,4),
    close_price DECIMAL(18,4),
    adj_factor DECIMAL(18,8),    -- 復權因子
    volume BIGINT,               -- 成交量
    amount DECIMAL(20,2),        -- 成交額
    turnover_rate DECIMAL(10,4), -- 換手率
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    UNIQUE KEY `idx_stock_date` (stock_id, trade_date),
    INDEX `idx_trade_date` (trade_date)
) PARTITION BY RANGE (YEAR(trade_date)) (
    PARTITION p2020 VALUES LESS THAN (2021),
    PARTITION p2021 VALUES LESS THAN (2022),
    PARTITION p2022 VALUES LESS THAN (2023),
    PARTITION p2023 VALUES LESS THAN (2024),
    PARTITION p2024 VALUES LESS THAN (2025)
);



-- 分鐘線數據表（按日期分區）
CREATE TABLE minute_quotes (
    id BIGINT PRIMARY KEY AUTO_INCREMENT,
    stock_id BIGINT NOT NULL,
    trade_time DATETIME NOT NULL,
    open_price DECIMAL(18,4),
    high_price DECIMAL(18,4),
    low_price DECIMAL(18,4),
    close_price DECIMAL(18,4),
    volume BIGINT,
    amount DECIMAL(20,2),
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    UNIQUE KEY `idx_stock_time` (stock_id, trade_time)
) PARTITION BY RANGE (TO_DAYS(trade_time)) (
    PARTITION pmax VALUES LESS THAN MAXVALUE
);

1. 創建數據庫
首先，我們需要創建一個名為 stock_database 的數據庫：
sql
CREATE DATABASE IF NOT EXISTS stock_database;
USE stock_database;

2. 股票交易數據表
每日交易數據表（daily_trading_data）
該表用于存儲股票的每日交易數據，包括股票代碼、交易日期、開盤價、最高價、最低價、收盤價、成交量等信息。
sql
CREATE TABLE IF NOT EXISTS daily_trading_data (
    id INT AUTO_INCREMENT PRIMARY KEY,
    stock_code VARCHAR(20) NOT NULL,
    trading_date DATE NOT NULL,
    open_price DECIMAL(10, 2) NOT NULL,
    high_price DECIMAL(10, 2) NOT NULL,
    low_price DECIMAL(10, 2) NOT NULL,
    close_price DECIMAL(10, 2) NOT NULL,
    volume BIGINT NOT NULL,
    UNIQUE KEY unique_stock_date (stock_code, trading_date)
);

分時交易數據表（minute_trading_data）
該表用于存儲股票的分時交易數據，與每日交易數據表類似，但時間粒度更細，精確到分鐘。
sql
CREATE TABLE IF NOT EXISTS minute_trading_data (
    id INT AUTO_INCREMENT PRIMARY KEY,
    stock_code VARCHAR(20) NOT NULL,
    trading_time DATETIME NOT NULL,
    open_price DECIMAL(10, 2) NOT NULL,
    high_price DECIMAL(10, 2) NOT NULL,
    low_price DECIMAL(10, 2) NOT NULL,
    close_price DECIMAL(10, 2) NOT NULL,
    volume BIGINT NOT NULL,
    UNIQUE KEY unique_stock_time (stock_code, trading_time)
);
K 線交易數據表（k_line_trading_data）
該表用于存儲股票的 K 線交易數據，可根據不同的 K 線周期（如 5 分鐘、15 分鐘、30 分鐘等）進行存儲。
sql
CREATE TABLE IF NOT EXISTS k_line_trading_data (
    id INT AUTO_INCREMENT PRIMARY KEY,
    stock_code VARCHAR(20) NOT NULL,
    k_line_period VARCHAR(10) NOT NULL,  -- 如 '5min', '15min', '30min' 等
    start_time DATETIME NOT NULL,
    open_price DECIMAL(10, 2) NOT NULL,
    high_price DECIMAL(10, 2) NOT NULL,
    low_price DECIMAL(10, 2) NOT NULL,
    close_price DECIMAL(10, 2) NOT NULL,
    volume BIGINT NOT NULL,
    UNIQUE KEY unique_stock_period_time (stock_code, k_line_period, start_time)
);
3. 數據庫變動信息記錄表（database_change_log）
該表用于記錄每次數據庫調用或修改的情況，包括操作類型（插入、更新、刪除等）、操作時間、操作的表名等信息。
sql
CREATE TABLE IF NOT EXISTS database_change_log (
    id INT AUTO_INCREMENT PRIMARY KEY,
    operation_type VARCHAR(20) NOT NULL,  -- 如 'INSERT', 'UPDATE', 'DELETE'
    operation_time DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP,
    table_name VARCHAR(50) NOT NULL,
    description TEXT
);
4. 數據表結構信息記錄表（table_structure_info）
該表用于保存不同數據表的結構信息，包括表名、字段名、字段個數、字段類型等。
sql
CREATE TABLE IF NOT EXISTS table_structure_info (
    id INT AUTO_INCREMENT PRIMARY KEY,
    table_name VARCHAR(50) NOT NULL,
    column_name VARCHAR(50) NOT NULL,
    column_position INT NOT NULL,
    column_type VARCHAR(50) NOT NULL
);
5. 插入數據表結構信息示例
為了方便管理，我們可以插入各個表的結構信息到 table_structure_info 表中。以下是插入 daily_trading_data 表結構信息的示例：
sql
INSERT INTO table_structure_info (table_name, column_name, column_position, column_type)
VALUES 
('daily_trading_data', 'id', 1, 'INT AUTO_INCREMENT PRIMARY KEY'),
('daily_trading_data', 'stock_code', 2, 'VARCHAR(20) NOT NULL'),
('daily_trading_data', 'trading_date', 3, 'DATE NOT NULL'),
('daily_trading_data', 'open_price', 4, 'DECIMAL(10, 2) NOT NULL'),
('daily_trading_data', 'high_price', 5, 'DECIMAL(10, 2) NOT NULL'),
('daily_trading_data', 'low_price', 6, 'DECIMAL(10, 2) NOT NULL'),
('daily_trading_data', 'close_price', 7, 'DECIMAL(10, 2) NOT NULL'),
('daily_trading_data', 'volume', 8, 'BIGINT NOT NULL');
你可以根據以上代碼創建數據庫和表結構，然后根據實際需求進行數據的插入、更新和查詢操作。同時，每次對數據庫進行操作時，可以在 database_change_log 表中記錄相應的變動信息。

# coding=utf-8
# inter_candle.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import mplfinance as mpf

# 讀取示例數據
data = pd.read_csv('test_data.csv', index_col=0)
data.index = pd.to_datetime(data.index)

my_color = mpf.make_marketcolors(up='r',
                                 down='g',
                                 edge='inherit',
                                 wick='inherit',
                                 volume='inherit')
my_style = mpf.make_mpf_style(marketcolors=my_color,
                                  figcolor='(0.82, 0.83, 0.85)',
                                  gridcolor='(0.82, 0.83, 0.85)')
# 定義各種字體
title_font = {'fontname': 'pingfang HK',
              'size':     '16',
              'color':    'black',
              'weight':   'bold',
              'va':       'bottom',
              'ha':       'center'}
large_red_font = {'fontname': 'Arial',
                  'size':     '24',
                  'color':    'red',
                  'weight':   'bold',
                  'va':       'bottom'}
large_green_font = {'fontname': 'Arial',
                    'size':     '24',
                    'color':    'green',
                    'weight':   'bold',
                    'va':       'bottom'}
small_red_font = {'fontname': 'Arial',
                  'size':     '12',
                  'color':    'red',
                  'weight':   'bold',
                  'va':       'bottom'}
small_green_font = {'fontname': 'Arial',
                    'size':     '12',
                    'color':    'green',
                    'weight':   'bold',
                    'va':       'bottom'}
normal_label_font = {'fontname': 'pingfang HK',
                     'size':     '12',
                     'color':    'black',
                     'weight':   'normal',
                     'va':       'bottom',
                     'ha':       'right'}
normal_font = {'fontname': 'Arial',
               'size':     '12',
               'color':    'black',
               'weight':   'normal',
               'va':       'bottom',
               'ha':       'left'}

class InterCandle:
    def __init__(self, data, my_style):
        self.pressed = False
        self.xpress = None

        # 初始化交互式K線圖對象，歷史數據作為唯一的參數用于初始化對象
        self.data = data
        self.style = my_style
        # 設置初始化的K線圖顯示區間起點為0，即顯示第0到第99個交易日的數據（前100個數據）
        self.idx_start = 0
        self.idx_range = 100
        # 設置ax1圖表中顯示的均線類型
        self.avg_type = 'ma'
        self.indicator = 'macd'

        # 初始化figure對象，在figure上建立三個Axes對象并分別設置好它們的位置和基本屬性
        self.fig = mpf.figure(style=my_style, figsize=(12, 8), facecolor=(0.82, 0.83, 0.85))
        fig = self.fig
        self.ax1 = fig.add_axes([0.08, 0.25, 0.88, 0.60])
        self.ax2 = fig.add_axes([0.08, 0.15, 0.88, 0.10], sharex=self.ax1)
        self.ax2.set_ylabel('volume')
        self.ax3 = fig.add_axes([0.08, 0.05, 0.88, 0.10], sharex=self.ax1)
        self.ax3.set_ylabel('macd')
        # 初始化figure對象，在figure上預先放置文本并設置格式，文本內容根據需要顯示的數據實時更新
        self.t1 = fig.text(0.50, 0.94, '513100.SH - 納斯達克指數ETF基金', **title_font)
        self.t2 = fig.text(0.12, 0.90, '開/收: ', **normal_label_font)
        self.t3 = fig.text(0.14, 0.89, f'', **large_red_font)
        self.t4 = fig.text(0.14, 0.86, f'', **small_red_font)
        self.t5 = fig.text(0.22, 0.86, f'', **small_red_font)
        self.t6 = fig.text(0.12, 0.86, f'', **normal_label_font)
        self.t7 = fig.text(0.40, 0.90, '高: ', **normal_label_font)
        self.t8 = fig.text(0.40, 0.90, f'', **small_red_font)
        self.t9 = fig.text(0.40, 0.86, '低: ', **normal_label_font)
        self.t10 = fig.text(0.40, 0.86, f'', **small_green_font)
        self.t11 = fig.text(0.55, 0.90, '量(萬手): ', **normal_label_font)
        self.t12 = fig.text(0.55, 0.90, f'', **normal_font)
        self.t13 = fig.text(0.55, 0.86, '額(億元): ', **normal_label_font)
        self.t14 = fig.text(0.55, 0.86, f'', **normal_font)
        self.t15 = fig.text(0.70, 0.90, '漲停: ', **normal_label_font)
        self.t16 = fig.text(0.70, 0.90, f'', **small_red_font)
        self.t17 = fig.text(0.70, 0.86, '跌停: ', **normal_label_font)
        self.t18 = fig.text(0.70, 0.86, f'', **small_green_font)
        self.t19 = fig.text(0.85, 0.90, '均價: ', **normal_label_font)
        self.t20 = fig.text(0.85, 0.90, f'', **normal_font)
        self.t21 = fig.text(0.85, 0.86, '昨收: ', **normal_label_font)
        self.t22 = fig.text(0.85, 0.86, f'', **normal_font)

        fig.canvas.mpl_connect('button_press_event', self.on_press)
        fig.canvas.mpl_connect('button_release_event', self.on_release)
        fig.canvas.mpl_connect('motion_notify_event', self.on_motion)
        fig.canvas.mpl_connect('key_press_event', self.on_key_press)
        fig.canvas.mpl_connect('scroll_event', self.on_scroll)

    def refresh_plot(self, idx_start, idx_range):
        """ 根據最新的參數，重新繪制整個圖表
        """
        all_data = self.data
        plot_data = all_data.iloc[idx_start: idx_start + idx_range]

        ap = []
        # 添加K線圖重疊均線，根據均線類型添加移動均線或布林帶線
        if self.avg_type == 'ma':
            ap.append(mpf.make_addplot(plot_data[['MA5', 'MA10', 'MA20', 'MA60']], ax=self.ax1))
        elif self.avg_type == 'bb':
            ap.append(mpf.make_addplot(plot_data[['bb-u', 'bb-m', 'bb-l']], ax=self.ax1))
        # 添加指標，根據指標類型添加MACD或RSI或DEMA
        if self.indicator == 'macd':
            ap.append(mpf.make_addplot(plot_data[['macd-m', 'macd-s']], ylabel='macd', ax=self.ax3))
            bar_r = np.where(plot_data['macd-h'] > 0, plot_data['macd-h'], 0)
            bar_g = np.where(plot_data['macd-h'] <= 0, plot_data['macd-h'], 0)
            ap.append(mpf.make_addplot(bar_r, type='bar', color='red', ax=self.ax3))
            ap.append(mpf.make_addplot(bar_g, type='bar', color='green', ax=self.ax3))
        elif self.indicator == 'rsi':
            ap.append(mpf.make_addplot([75] * len(plot_data), color=(0.75, 0.6, 0.6), ax=self.ax3))
            ap.append(mpf.make_addplot([30] * len(plot_data), color=(0.6, 0.75, 0.6), ax=self.ax3))
            ap.append(mpf.make_addplot(plot_data['rsi'], ylabel='rsi', ax=self.ax3))
        else:  # indicator == 'dema'
            ap.append(mpf.make_addplot(plot_data['dema'], ylabel='dema', ax=self.ax3))

        # 繪制圖表
        mpf.plot(plot_data,
                 ax=self.ax1,
                 volume=self.ax2,
                 addplot=ap,
                 type='candle',
                 style=self.style,
                 datetime_format='%Y-%m',
                 xrotation=0)

        plt.show()

    def refresh_texts(self, display_data):
        """ 更新K線圖上的價格文本
        """
        # display_data是一個交易日內的所有數據，將這些數據分別填入figure對象上的文本中
        self.t3.set_text(f'{np.round(display_data["open"], 3)} / {np.round(display_data["close"], 3)}')
        self.t4.set_text(f'{np.round(display_data["change"], 3)}')
        self.t5.set_text(f'[{np.round(display_data["pct_change"], 3)}%]')
        self.t6.set_text(f'{display_data.name.date()}')
        self.t8.set_text(f'{np.round(display_data["high"], 3)}')
        self.t10.set_text(f'{np.round(display_data["low"], 3)}')
        self.t12.set_text(f'{np.round(display_data["volume"] / 10000, 3)}')
        self.t14.set_text(f'{display_data["value"]}')
        self.t16.set_text(f'{np.round(display_data["upper_lim"], 3)}')
        self.t18.set_text(f'{np.round(display_data["lower_lim"], 3)}')
        self.t20.set_text(f'{np.round(display_data["average"], 3)}')
        self.t22.set_text(f'{np.round(display_data["last_close"], 3)}')
        # 根據本交易日的價格變動值確定開盤價、收盤價的顯示顏色
        if display_data['change'] > 0:  # 如果今日變動額大于0，即今天價格高于昨天，今天價格顯示為紅色
            close_number_color = 'red'
        elif display_data['change'] < 0:  # 如果今日變動額小于0，即今天價格低于昨天，今天價格顯示為綠色
            close_number_color = 'green'
        else:
            close_number_color = 'black'
        self.t3.set_color(close_number_color)
        self.t4.set_color(close_number_color)
        self.t5.set_color(close_number_color)

    def on_press(self, event):
        if not (event.inaxes == self.ax1) and (not event.inaxes == self.ax3):
            return
        if event.button != 1:
            return
        self.pressed = True
        self.xpress = event.xdata

        # 切換當前ma類型, 在ma、bb、none之間循環
        if event.inaxes == self.ax1 and event.dblclick == 1:
            if self.avg_type == 'ma':
                self.avg_type = 'bb'
            elif self.avg_type == 'bb':
                self.avg_type = 'none'
            else:
                self.avg_type = 'ma'
        # 切換當前indicator類型，在macd/dma/rsi/kdj之間循環
        if event.inaxes == self.ax3 and event.dblclick == 1:
            if self.indicator == 'macd':
                self.indicator = 'dma'
            elif self.indicator == 'dma':
                self.indicator = 'rsi'
            elif self.indicator == 'rsi':
                self.indicator = 'kdj'
            else:
                self.indicator = 'macd'

        self.ax1.clear()
        self.ax2.clear()
        self.ax3.clear()
        self.refresh_plot(self.idx_start, self.idx_range)

    def on_release(self, event):
        self.pressed = False
        dx = int(event.xdata - self.xpress)
        self.idx_start -= dx
        if self.idx_start <= 0:
            self.idx_start = 0
        if self.idx_start >= len(self.data) - 100:
            self.idx_start = len(self.data) - 100

    def on_motion(self, event):
        if not self.pressed:
            return
        if not event.inaxes == self.ax1:
            return
        dx = int(event.xdata - self.xpress)
        new_start = self.idx_start - dx
        # 設定平移的左右界限，如果平移后超出界限，則不再平移
        if new_start <= 0:
            new_start = 0
        if new_start >= len(self.data) - 100:
            new_start = len(self.data) - 100
        self.ax1.clear()
        self.ax2.clear()
        self.ax3.clear()

        self.refresh_texts(self.data.iloc[new_start])
        self.refresh_plot(new_start, self.idx_range)

    def on_scroll(self, event):
        # 僅當鼠標滾輪在axes1范圍內滾動時起作用
        scale_factor = 1.0
        if event.inaxes != self.ax1:
            return
        if event.button == 'down':
            # 縮小20%顯示范圍
            scale_factor = 0.8
        if event.button == 'up':
            # 放大20%顯示范圍
            scale_factor = 1.2
        # 設置K線的顯示范圍大小
        self.idx_range = int(self.idx_range * scale_factor)
        # 限定可以顯示的K線圖的范圍，最少不能少于30個交易日，最大不能超過當前位置與
        # K線數據總長度的差
        data_length = len(self.data)
        if self.idx_range >= data_length - self.idx_start:
            self.idx_range = data_length - self.idx_start
        if self.idx_range <= 30:
            self.idx_range = 30
            # 更新圖表（注意因為多了一個參數idx_range，refresh_plot函數也有所改動）
        self.ax1.clear()
        self.ax2.clear()
        self.ax3.clear()
        self.refresh_texts(self.data.iloc[self.idx_start])
        self.refresh_plot(self.idx_start, self.idx_range)

    # 鍵盤按下處理
    def on_key_press(self, event):
        data_length = len(self.data)
        if event.key == 'a':  # avg_type, 在ma,bb,none之間循環
            if self.avg_type == 'ma':
                self.avg_type = 'bb'
            elif self.avg_type == 'bb':
                self.avg_type = 'none'
            elif self.avg_type == 'none':
                self.avg_type = 'ma'
        elif event.key == 'up':  # 向上，看仔細1倍
            if self.idx_range > 30:
                self.idx_range = self.idx_range // 2
        elif event.key == 'down':  # 向下，看多1倍標的
            if self.idx_range <= data_length - self.idx_start:
                self.idx_range = self.idx_range * 2
        elif event.key == 'left':
            if self.idx_start > self.idx_range:
                self.idx_start = self.idx_start - self.idx_range // 2
        elif event.key == 'right':
            if self.idx_start < data_length - self.idx_range:
                self.idx_start = self.idx_start + self.idx_range //2
        self.ax1.clear()
        self.ax2.clear()
        self.ax3.clear()
        self.refresh_texts(self.data.iloc[self.idx_start])
        self.refresh_plot(self.idx_start, self.idx_range)

if __name__ == '__main__':
    candle = InterCandle(data, my_style)
    candle.idx_start = 150
    candle.idx_range = 100
    candle.refresh_texts(data.iloc[249])
    candle.refresh_plot(150, 100)