20250314修改:增加dingdanliu_nb_mflow

999.账户相关/test/akshare_find_option.ipynb

@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import akshare as ak\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# option_df = ak.option_finance_board(symbol=\"中证1000股指期权\", end_month=\"2503\")\n",
+    "# option_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# option_cffex_df = ak.option_cffex_zz1000_spot_sina(symbol='ag2504')\n",
+    "option_commodity_contract_table_sina_df = ak.option_commodity_contract_table_sina(symbol=\"白银期权\", contract=\"ag2504\")\n",
+    "option_commodity_contract_table_sina_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#根据中证l000指数获取点位\n",
+    "\n",
+    "def get_format_option_gap(value:float, deviation:int=0):\n",
+    "    \"\"\"\n",
+    "    根据标准的行权价，生成不同档位的期权列表，适合中金所\n",
+    "    :param value:当前行权价\n",
+    "    :param deviation:相较于行权价向哪个方向偏移，>0表示较行权价向上调整，<-表示较行权价向下调整\n",
+    "    return:\n",
+    "    \"\"\"\n",
+    "    def _gap_value(_mark_value):\n",
+    "        if _mark_value < 500:\n",
+    "            return 25\n",
+    "        elif _mark_value < 5000:\n",
+    "            return 50\n",
+    "        elif _mark_value < 10000:\n",
+    "            return 100\n",
+    "        elif _mark_value >10000:\n",
+    "            return 200\n",
+    "        \n",
+    "    for i in range(abs(deviation)):\n",
+    "        _gap =_gap_value(value)\n",
+    "        #option a ex price=int(value/gap)*gap#买入看跌期权\n",
+    "        if deviation >=0:\n",
+    "            value +=_gap\n",
+    "        elif deviation<0:\n",
+    "            value -=_gap\n",
+    "    return value\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def future_to_option_code(symbol):\n",
+    "    \"\"\"\n",
+    "    转换期权代码格式\n",
+    "    \n",
+    "    Args:\n",
+    "        symbol (str): 原始期权代码\n",
+    "        \n",
+    "    Returns:\n",
+    "        str: 转换后的期权代码\n",
+    "    \"\"\"\n",
+    "    symbol_upper = symbol.upper()\n",
+    "    \n",
+    "    if 'IM' in symbol_upper:\n",
+    "        # 将IM替换为MO\n",
+    "        return symbol_upper.replace('IM', 'mo')\n",
+    "    elif 'IF' in symbol_upper:\n",
+    "        # 将IF替换为IO\n",
+    "        return symbol_upper.replace('IF', 'io')\n",
+    "    elif 'IH' in symbol_upper:\n",
+    "        # 将IH替换为HO\n",
+    "        return symbol_upper.replace('IH', 'ho')\n",
+    "    else:\n",
+    "        raise ValueError(f\"不支持的期权代码格式: {symbol}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "future_to_option_code('im2503')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def get_atm_option_code(symbol:str, current_price:float, call_level:int, put_level:int):\n",
+    "    symbol_upper = symbol.upper()\n",
+    "\n",
+    "    # if 'IM' in symbol_upper:\n",
+    "    #     # 将IM替换为MO\n",
+    "    #     return symbol_upper.replace('IM', 'mo')\n",
+    "    #     # print(symbol_upper)\n",
+    "    # elif 'IF' in symbol_upper:\n",
+    "    #     # 将IF替换为IO\n",
+    "    #     return symbol_upper.replace('IF', 'io')\n",
+    "    # elif 'IH' in symbol_upper:\n",
+    "    #     # 将IH替换为HO\n",
+    "    #     return symbol_upper.replace('IH', 'ho')\n",
+    "    # else:\n",
+    "    #     raise ValueError(f\"不支持的期权代码格式: {symbol}\")\n",
+    "\n",
+    "    # # symbol_upper = symbol_upper.lower()\n",
+    "    # print(symbol_upper)\n",
+    "    if 'MO' in symbol_upper:\n",
+    "        option_cffex_df = ak.option_cffex_zz1000_spot_sina(symbol=symbol)\n",
+    "    elif 'IO' in symbol_upper:\n",
+    "        option_cffex_df = ak.option_cffex_hs300_spot_sina(symbol=symbol)\n",
+    "    elif 'HO' in symbol_upper:\n",
+    "        option_cffex_df = ak.option_cffex_sz50_spot_sina(symbol=symbol)\n",
+    "    else:\n",
+    "        raise ValueError(f\"不支持的期权代码格式: {symbol_upper}\")\n",
+    "    # 提取所有行权价并转换为数值\n",
+    "    strike_prices = option_cffex_df['行权价'].astype(float).unique()\n",
+    "    find_call_prices = get_format_option_gap(current_price,call_level)\n",
+    "    find_put_prices = get_format_option_gap(current_price,-put_level)\n",
+    "    closest_call_strike = min(strike_prices, key=lambda x: abs(x - find_call_prices))\n",
+    "    closest_put_strike = min(strike_prices, key=lambda x: abs(x - find_put_prices))\n",
+    "    # 生成平值期权代码（如MO2503-C-6250）\n",
+    "    call_atm_option_code = f\"{symbol_upper}-C-{int(closest_call_strike)}\"\n",
+    "    put_atm_option_code = f\"{symbol_upper}-P-{int(closest_put_strike)}\"\n",
+    "    # print(f\"平值看涨期权：{call_atm_option_code}\")\n",
+    "    # print(f\"平值看跌期权：{put_atm_option_code}\")\n",
+    "    return call_atm_option_code, put_atm_option_code\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_atm_option_code(future_to_option_code('im2503'),6273,0,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_atm_option_code(future_to_option_code('ih2503'),2628,0,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_atm_option_code(future_to_option_code('if2503'),3386,0,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "option_cffex_df = ak.option_cffex_zz1000_spot_sina(symbol='mo2503')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# stock_individual_spot_xq_df = ak.stock_individual_spot_xq(symbol='000852')\n",
+    "# print(stock_individual_spot_xq_df.dtypes)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

999.账户相关/test/akshare_view.py

@@ -0,0 +1,165 @@
+import akshare as ak
+import pandas as pd
+import numpy as np
+import os
+import ast
+import time
+import mplfinance as mpf
+import matplotlib.pyplot as plt
+
+# from flask import Flask, render_template, jsonify
+
+# # 读取数据（注意替换实际文件路径）
+file_path = r"C:\Users\zhouj\Desktop\IM888_5MIN_2024_ofdata.csv"
+df = pd.read_csv(file_path, parse_dates=["datetime"], index_col="datetime").sort_index()
+
+# 使用akshare获取数据 
+symbol_name = 'IM2503'
+time_period = 30
+# df = ak.futures_zh_minute_sina(symbol=symbol_name, period="15")
+
+# ultimate_smoother函数
+def ultimate_smoother(price, period):
+    # 初始化变量（修正角度单位为弧度）
+    a1 = np.exp(-1.414 * np.pi / period)
+    b1 = 2 * a1 * np.cos(1.414 * np.pi / period)  # 将180改为np.pi
+    c2 = b1
+    c3 = -a1**2
+    c1 = (1 + c2 - c3) / 4
+
+    # 准备输出序列
+    us = np.zeros(len(price))
+    us_new = np.zeros(len(price))
+    trend = [None] * (len(price))
+    ma_close = np.zeros(len(price))
+
+    # 前4个点用原始价格初始化
+    for i in range(len(price)):
+        if i < 4:
+            us[i] = price[i]
+        else:
+            # 应用递归公式
+            us[i] = (1 - c1) * price[i] + (2 * c1 - c2) * price[i-1] \
+                    - (c1 + c3) * price[i-2] + c2 * us[i-1] + c3 * us[i-2]
+
+        us_new = np.around(us, decimals=2)
+        ma_close = price.rolling(window=5 * period).mean()
+
+        if us_new[i] > price[i] and ma_close[i] > price[i]:
+            trend[i] = '空头趋势'
+        elif us_new[i] < price[i] and ma_close[i] < price[i]:
+            trend[i] = '多头趋势'
+        else:
+            trend[i] = '无趋势'
+
+    return us_new, trend
+
+def safe_literal_eval(x):
+    """带异常处理的安全转换"""
+    try:
+        return ast.literal_eval(x)
+    except:
+        return []  # 返回空列表作为占位符
+
+def add_poc_column(df):
+    # 安全转换列数据
+    df['price'] = df['price'].apply(safe_literal_eval)
+    df['Ask'] = df['Ask'].apply(lambda x: list(map(int, safe_literal_eval(x))))
+    df['Bid'] = df['Bid'].apply(lambda x: list(map(int, safe_literal_eval(x))))
+    
+    # 定义处理函数（带数据验证）
+    def find_poc(row):
+        # 验证三个列表长度一致且非空
+        if not (len(row['price']) == len(row['Ask']) == len(row['Bid']) > 0):
+            return '缺值'  # 返回空值标记异常数据
+        
+        sums = [a + b for a, b in zip(row['Ask'], row['Bid'])]
+        try:
+            max_index = sums.index(max(sums))
+            return row['price'][max_index]
+        except ValueError:
+            return '缺值'  # 处理空求和列表情况
+    
+    # 应用处理函数
+    df['POC'] = df.apply(find_poc, axis=1)
+    
+    # 可选：统计异常数据
+    error_count = df['POC'].isnull().sum()
+    if error_count > 0:
+        print(f"警告：发现 {error_count} 行异常数据（已标记为NaN）")
+    
+    return df['POC']
+df.reset_index()
+# df['datetime'] = pd.to_datetime(df['datetime'])
+df['终极平滑值'], df['趋势方向'] = ultimate_smoother(df["close"], time_period)
+df['POC'] = add_poc_column(df)
+df.index = pd.to_datetime(df.index)
+print(df.head(5))
+
+# 规范列名（兼容MA列）
+df = df.rename(columns={"close": "Close", "open": "Open", "high": "High", "low": "Low", "volume":"Volume"})
+
+# 创建一个新的DataFrame来存储有效的POC值
+valid_poc = pd.DataFrame(index=df.index)  # 创建与主数据相同索引的DataFrame
+valid_poc['POC'] = df['POC'].apply(lambda x: float(x) if x != '缺值' else np.nan)  # 将有效POC值转换为float，无效值设为NaN
+
+# 创建附加绘图对象（关键步骤）
+apd = [
+    mpf.make_addplot(
+        df["终极平滑值"],  # MA数据列
+        color="dodgerblue",  # 线条颜色
+        width=1,  # 线宽
+        panel=0,  # 显示在主图区域
+        ylabel="终极平滑值",  # 右侧Y轴标签
+        mav=(60),#绘制均线
+        secondary_y=True,  # 与主图共享左侧Y轴
+    ),
+    mpf.make_addplot(
+        valid_poc['POC'],  # 使用处理后的POC值
+        type='scatter',  # 使用散点图
+        marker='o',  # 圆形标记
+        markersize=100,  # 标记大小
+        color='yellow',  # 标记颜色
+        alpha=0.5,  # 透明度
+        panel=0,  # 显示在主图区域
+        ylabel="POC",  # 右侧Y轴标签
+        secondary_y=False,  # 与主图共享左侧Y轴
+    )
+]
+
+# 定义 mplfinance 的自定义风格
+mc = mpf.make_marketcolors(up='r', down='g', volume='inherit')
+s = mpf.make_mpf_style(base_mpf_style='charles', marketcolors=mc, rc={'font.sans-serif': ['Microsoft YaHei']})
+
+# 创建图形
+fig, axes = mpf.plot(
+    df,
+    type="candle",
+    style=s,
+    addplot=apd,  # 添加ultimate_smoother线和POC散点
+    title=f"{symbol_name} K-Line with ultimate_smoother and POC",
+    ylabel="Price",
+    show_nontrading=False,
+    returnfig=True  # 返回图形对象
+)
+
+# 获取主图轴对象
+ax = axes[0]
+
+# 添加POC值标注
+for idx, row in valid_poc.iterrows():
+    if pd.notnull(row['POC']):
+        ax.annotate(
+            f'{row["POC"]:.2f}',
+            xy=(idx, row['POC']),
+            xytext=(10, 10),
+            textcoords='offset points',
+            color='black',
+            fontsize=8,
+            bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5),
+            arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')
+        )
+
+# 显示图形
+plt.show()
+

999.账户相关/test/askshare_option.py

@@ -0,0 +1,119 @@
+import numpy as np
+from scipy.stats import norm
+from datetime import datetime
+
+# Black 期权定价模型（适用于期货期权）
+def black_model(F, K, T, r, sigma, option_type):
+    """
+    F: 期货价格
+    K: 执行价格
+    T: 剩余到期时间（年）
+    r: 无风险利率
+    sigma: 波动率
+    option_type: 'call' 或 'put'
+    """
+    d1 = (np.log(F/K) + (0.5 * sigma**2) * T) / (sigma * np.sqrt(T))
+    d2 = d1 - sigma * np.sqrt(T)
+    
+    if option_type == 'call':
+        price = np.exp(-r * T) * (F * norm.cdf(d1) - K * norm.cdf(d2))
+    elif option_type == 'put':
+        price = np.exp(-r * T) * (K * norm.cdf(-d2) - F * norm.cdf(-d1))
+    else:
+        raise ValueError("option_type must be 'call' or 'put'")
+    
+    return price
+
+# 计算平值期权价格
+def calculate_atm_options(F, T, r, sigma):
+    """
+    F: 期货价格（平值执行价 K=F）
+    T: 剩余时间（年）
+    r: 无风险利率
+    sigma: 波动率
+    返回：看涨/看跌期权价格元组
+    """
+    K = F  # 平值期权
+    call_price = black_model(F, K, T, r, sigma, 'call')
+    put_price = black_model(F, K, T, r, sigma, 'put')
+    return call_price, put_price
+
+# 计算到期时间（示例函数）
+def calculate_time_to_maturity(expiry_date):
+    """
+    expiry_date: 期权到期日（格式：'YYYY-MM-DD'）
+    返回：剩余时间（年）
+    """
+    today = datetime.now()
+    expiry = datetime.strptime(expiry_date, '%Y-%m-%d')
+    delta = expiry - today
+    return delta.days / 365.0
+
+# 合成期权策略
+def synthetic_position(F, call_price, put_price, option_type):
+    """
+    构建合成头寸（反向推导标的资产价格）
+    option_type: 要合成的标的类型（'future' 或 'option'）
+    """
+    if option_type == 'future':
+        # 合成期货：买入看涨 + 卖出看跌（相同执行价）
+        synthetic_future = call_price - put_price
+        return synthetic_future
+    elif option_type == 'option':
+        # 反向合成期权（需要已知期货价格）
+        pass  # 需要具体实现策略逻辑
+
+# 市场参数配置（示例数据）
+parameters = {
+    'IM2503': {
+        'expiry': '2025-03-21',  # 假设到期日
+        'r': 0.028,              # 无风险利率（2.8%）
+        'sigma': 0.22            # 隐含波动率（22%）
+    },
+    'IF2503': {
+        'expiry': '2025-03-21',
+        'r': 0.028,
+        'sigma': 0.18
+    },
+    'IH2503': {
+        'expiry': '2025-03-21',
+        'r': 0.028,
+        'sigma': 0.15
+    }
+}
+
+def main(F, contract_code):
+    """
+    F: 期货当前价格
+    contract_code: 合约代码（IM2503/IF2503/IH2503）
+    """
+    # 获取参数
+    params = parameters[contract_code]
+    T = calculate_time_to_maturity(params['expiry'])
+    r = params['r']
+    sigma = params['sigma']
+    
+    # 计算平值期权价格
+    call, put = calculate_atm_options(F, T, r, sigma)
+    
+    # 合成期货验证（看涨看跌平价关系）
+    synthetic_future = call - put
+    
+    # 输出结果
+    print(f"\n{contract_code} 定价结果（F={F}）：")
+    print(f"看涨期权价格：{call:.4f}")
+    print(f"看跌期权价格：{put:.4f}")
+    print(f"合成期货价值：{synthetic_future:.4f}（理论值应为0，实际误差：{synthetic_future:.2e}）")
+    
+    return call, put
+
+if __name__ == "__main__":
+    # 示例计算（假设当前期货价格）
+    im_price = 5000.0
+    if_price = 3500.0
+    ih_price = 2500.0
+    
+    # 计算各品种平值期权
+    main(im_price, 'IM2503')
+    main(if_price, 'IF2503')
+    main(ih_price, 'IH2503')