beader/buy_fund_a_or_c.ipynb

## buy_fund_a_or_c.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from jqdata import finance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>main_code</th>\n",
       "      <th>name</th>\n",
       "      <th>advisor</th>\n",
       "      <th>trustee</th>\n",
       "      <th>operate_mode_id</th>\n",
       "      <th>operate_mode</th>\n",
       "      <th>underlying_asset_type_id</th>\n",
       "      <th>underlying_asset_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11067</td>\n",
       "      <td>007119</td>\n",
       "      <td>睿远成长价值混合</td>\n",
       "      <td>睿远基金管理有限公司</td>\n",
       "      <td>招商银行股份有限公司</td>\n",
       "      <td>401001</td>\n",
       "      <td>开放式基金</td>\n",
       "      <td>402004</td>\n",
       "      <td>混合型</td>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11068</td>\n",
       "      <td>007120</td>\n",
       "      <td>睿远成长价值混合C</td>\n",
       "      <td>睿远基金管理有限公司</td>\n",
       "      <td>招商银行股份有限公司</td>\n",
       "      <td>401001</td>\n",
       "      <td>开放式基金</td>\n",
       "      <td>402004</td>\n",
       "      <td>混合型</td>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14473</td>\n",
       "      <td>008969</td>\n",
       "      <td>睿远均衡价值三年持有混合A</td>\n",
       "      <td>睿远基金管理有限公司</td>\n",
       "      <td>招商银行股份有限公司</td>\n",
       "      <td>401003</td>\n",
       "      <td>QDII</td>\n",
       "      <td>402004</td>\n",
       "      <td>混合型</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14474</td>\n",
       "      <td>008970</td>\n",
       "      <td>睿远均衡价值三年持有混合C</td>\n",
       "      <td>睿远基金管理有限公司</td>\n",
       "      <td>招商银行股份有限公司</td>\n",
       "      <td>401003</td>\n",
       "      <td>QDII</td>\n",
       "      <td>402004</td>\n",
       "      <td>混合型</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id main_code   ...     start_date end_date\n",
       "0  11067    007119   ...     2019-03-26     None\n",
       "1  11068    007120   ...     2019-03-26     None\n",
       "2  14473    008969   ...           None     None\n",
       "3  14474    008970   ...           None     None\n",
       "\n",
       "[4 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = query(finance.FUND_MAIN_INFO)\n",
    "q = q.filter(finance.FUND_MAIN_INFO.advisor == '睿远基金管理有限公司')\n",
    "finance.run_query(q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `007119` 是 睿远成长价值混合A\n",
    "- `007120` 是 睿远成长价值混合C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_a = finance.run_query(\n",
    "    query(finance.FUND_NET_VALUE)\n",
    "    .filter(finance.FUND_NET_VALUE.code == '007119')\n",
    ")\n",
    "\n",
    "df_c = finance.run_query(\n",
    "    query(finance.FUND_NET_VALUE)\n",
    "    .filter(finance.FUND_NET_VALUE.code == '007120')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.merge(\n",
    "    df_a, df_c[['day', 'net_value']], on='day', suffixes=('_a', '_c')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>code</th>\n",
       "      <th>day</th>\n",
       "      <th>net_value_a</th>\n",
       "      <th>sum_value</th>\n",
       "      <th>factor</th>\n",
       "      <th>acc_factor</th>\n",
       "      <th>refactor_net_value</th>\n",
       "      <th>net_value_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11414264</td>\n",
       "      <td>007119</td>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11432908</td>\n",
       "      <td>007119</td>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>1.0004</td>\n",
       "      <td>1.0004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0004</td>\n",
       "      <td>1.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11471449</td>\n",
       "      <td>007119</td>\n",
       "      <td>2019-04-04</td>\n",
       "      <td>1.0015</td>\n",
       "      <td>1.0015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0015</td>\n",
       "      <td>1.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11514715</td>\n",
       "      <td>007119</td>\n",
       "      <td>2019-04-12</td>\n",
       "      <td>0.9901</td>\n",
       "      <td>0.9901</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9901</td>\n",
       "      <td>0.9899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11555507</td>\n",
       "      <td>007119</td>\n",
       "      <td>2019-04-19</td>\n",
       "      <td>0.9991</td>\n",
       "      <td>0.9991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9991</td>\n",
       "      <td>0.9989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id    code     ...      refactor_net_value  net_value_c\n",
       "0  11414264  007119     ...                  1.0000       1.0000\n",
       "1  11432908  007119     ...                  1.0004       1.0004\n",
       "2  11471449  007119     ...                  1.0015       1.0014\n",
       "3  11514715  007119     ...                  0.9901       0.9899\n",
       "4  11555507  007119     ...                  0.9991       0.9989\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "净值数据从 2019-03-26 到 2020-02-13\n"
     ]
    }
   ],
   "source": [
    "df['day'] = pd.to_datetime(df['day'])\n",
    "df['num_days_since_last_eval'] = df['day'].diff().dt.days.fillna(0)\n",
    "\n",
    "print('净值数据从 {} 到 {}'.format(\n",
    "    df['day'].min().strftime('%Y-%m-%d'),\n",
    "    df['day'].max().strftime('%Y-%m-%d'),\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_net_value_c_based_on_a(init_net_value_a, \n",
    "                                    init_net_value_c, \n",
    "                                    net_value_df,\n",
    "                                    annual_op_fee_rate,\n",
    "                                    annual_sales_fee_rate):\n",
    "    \"\"\"\n",
    "    每天从a类基金的净值，反推出当天基金的净值变动情况，估算出c类基金的净值\n",
    "    之后，拿估算出的c类基金净值与真实的c类基金净值做比较，验证计算的准确性\n",
    "    \"\"\"\n",
    "    # 设置两份基金的初始净值\n",
    "    net_value_a = init_net_value_a\n",
    "    net_value_c = init_net_value_c\n",
    "    \n",
    "    # 每日管理费与托管费费率 (a 和 c 都要扣)\n",
    "    daily_op_fee_rate = annual_op_fee_rate / 365\n",
    "    # 每日销售服务费率 (c 要扣，a 不需要扣)\n",
    "    daily_sales_fee_rate = annual_sales_fee_rate / 365\n",
    "    \n",
    "    net_value_c_estimated = []\n",
    "    # 每日基金扣费前净值变动系数，存下来是为了模拟以后更长时间的两份基金净值变动\n",
    "    net_value_multipliers = []\n",
    "    \n",
    "    for _, row in df.iterrows():\n",
    "        # 计算当天 a 类基金的管理费，上一日净值 * 管理费率 * 距离上一净值公布日过去的天数\n",
    "        op_fee_a = net_value_a * daily_op_fee_rate * row['num_days_since_last_eval']\n",
    "        # 计算当天扣除管理费之前 a 类基金的净值\n",
    "        net_value_a_before_fee = row['net_value_a'] + op_fee_a\n",
    "        # 计算基金当天的实际涨幅，扣费前净值 / 上一交易日净值\n",
    "        net_value_multiplier = net_value_a_before_fee / net_value_a\n",
    "        net_value_multipliers.append(net_value_multiplier)\n",
    "        \n",
    "        # 计算 c 类基金扣费前净值\n",
    "        net_value_c_before_fee = net_value_c * net_value_multiplier\n",
    "        # 计算 c 类基金当天要扣除的管理费和销售服务费\n",
    "        op_fee_c = net_value_c * daily_op_fee_rate * row['num_days_since_last_eval']\n",
    "        sales_fee_c = net_value_c * daily_sales_fee_rate * row['num_days_since_last_eval']\n",
    "        # 计算扣除管理费和销售服务费后 c 的净值\n",
    "        net_value_c = net_value_c_before_fee - op_fee_c - sales_fee_c\n",
    "        \n",
    "        net_value_a = row['net_value_a']\n",
    "        # 将估算后的 c 类基金净值按照 6 位小数向下取整\n",
    "        net_value_c = floor(net_value_c * 1000000) / 1000000\n",
    "        \n",
    "        net_value_c_estimated.append(net_value_c)\n",
    "        \n",
    "    return net_value_c_estimated, net_value_multipliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "睿远的年化管理费率为 1.5%，托管费率 0.15%，c类基金销售服务费为 0.4%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_value_c_estimated, net_value_multipliers = estimate_net_value_c_based_on_a(\n",
    "    init_net_value_a=1.0,\n",
    "    init_net_value_c=1.0,\n",
    "    net_value_df=df,\n",
    "    annual_op_fee_rate=(1.5 + 0.15) / 100,\n",
    "    annual_sales_fee_rate=0.4 / 100\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c类估计净值与实际净值的平均误差: 2.6602339181283453e-05\n",
      "2020-02-13 c类实际净值: 1.3259, 估计净值: 1.325876\n"
     ]
    }
   ],
   "source": [
    "diffs = df['net_value_c'] - pd.Series(net_value_c_estimated)\n",
    "print('c类估计净值与实际净值的平均误差: {}'.format(diffs.mean()))\n",
    "\n",
    "last_day = df['day'].max().strftime('%Y-%m-%d')\n",
    "print('{} c类实际净值: {}, 估计净值: {}'.format(\n",
    "    last_day, \n",
    "    df['net_value_c'].iloc[-1], \n",
    "    net_value_c_estimated[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "若在基金创建之初，分别投入 10000 元到两支基金。\n",
    "\n",
    "a 类基金销售收购费率 1.5%\n",
    "c 类基金申购费率 0.%\n",
    "\n",
    "那么，该投资者将持有 \n",
    "\n",
    "- a 类基金 10000 * (1 - 0.015) / 1 = 9850 份\n",
    "- c 类基金 10000 份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "基金运行171个交易日后\n",
      "a类基金价值: 13107.395\n",
      "c类基金价值: 13259.0\n",
      "a与c之间差异: -151.60499999999956\n"
     ]
    }
   ],
   "source": [
    "fund_a_shares = 10000 * (1 - 0.015)\n",
    "fund_c_shares = 10000\n",
    "fund_a_value = df['net_value_a'].iloc[-1] * fund_a_shares\n",
    "fund_c_value = df['net_value_c'].iloc[-1] * fund_c_shares\n",
    "print('基金运行{}个交易日后\\na类基金价值: {}\\nc类基金价值: {}'.format(\n",
    "    len(df), fund_a_value, fund_c_value\n",
    "))\n",
    "print('a与c之间差异: {}'.format(fund_a_value - fund_c_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们知道，运行一段时间之后 a 类的价值比 c 类少的原因是，购买 a 的时候收取了一笔一次性的申购费，导致确认的 a 类份额要比 c 类份额要少，因此，a类价值若想要超过c类价值，则:\n",
    "\n",
    "`a净值 * 10000 * (1 - 申购费率) >= c净值 * 10000`\n",
    "\n",
    "即当某一天 `c净值 / a净值 <= (1 - 申购费率)` 的时候，a的价值开始超过c\n",
    "\n",
    "我们来模拟一下这个过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_net_values(init_net_value_a,\n",
    "                        init_net_value_c,\n",
    "                        net_value_multipliers,\n",
    "                        annual_op_fee_rate,\n",
    "                        annual_sales_fee_rate):\n",
    "    \"\"\"\n",
    "    根据给定的每日净值变动系数，模拟 a, c 的净值走势\n",
    "    \"\"\"\n",
    "    \n",
    "    net_value_a = init_net_value_a\n",
    "    net_value_c = init_net_value_c\n",
    "    \n",
    "    # 一年平均 250 个交易日\n",
    "    daily_op_fee_rate = annual_op_fee_rate / 250\n",
    "    daily_sales_fee_rate = annual_sales_fee_rate / 250\n",
    "    \n",
    "    net_value_a_estimated = []\n",
    "    net_value_c_estimated = []\n",
    "    \n",
    "    for nav_multiplier in net_value_multipliers:\n",
    "        net_value_a_before_fee = net_value_a * nav_multiplier\n",
    "        op_fee_a = daily_op_fee_rate * net_value_a\n",
    "        net_value_a = net_value_a_before_fee - op_fee_a\n",
    "        \n",
    "        net_value_c_before_fee = net_value_c * nav_multiplier\n",
    "        op_fee_c = daily_op_fee_rate * net_value_c\n",
    "        sales_fee_c = daily_sales_fee_rate * net_value_c\n",
    "        net_value_c = net_value_c_before_fee - op_fee_c - sales_fee_c\n",
    "        \n",
    "        net_value_a_estimated.append(net_value_a)\n",
    "        net_value_c_estimated.append(net_value_c)\n",
    "        \n",
    "    return net_value_a_estimated, net_value_c_estimated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "假设该基金按照原来的波动方式再运行1710个交易日\n",
      "模拟运行之后a类净值: 23.969585887528655, c类净值: 23.23955354523753\n",
      "a类基金价值: 236100.42099215725\n",
      "c类基金价值: 232395.5354523753\n",
      "a与c之间差异: 3704.8855397819425\n"
     ]
    }
   ],
   "source": [
    "# 设置一段较长的模拟区间，假设按照之前的波动重复 10 遍\n",
    "net_value_multipliers = net_value_multipliers * 10\n",
    "\n",
    "print('假设该基金按照原来的波动方式再运行{}个交易日'.format(len(net_value_multipliers)))\n",
    "\n",
    "simulate_net_values_a, simulate_net_values_c = simulate_net_values(\n",
    "    init_net_value_a=df.iloc[-1]['net_value_a'],\n",
    "    init_net_value_c=df.iloc[-1]['net_value_c'],\n",
    "    net_value_multipliers=net_value_multipliers,\n",
    "    annual_op_fee_rate=(1.5 + 0.15) / 100,\n",
    "    annual_sales_fee_rate=0.4 / 100\n",
    ")\n",
    "\n",
    "print('模拟运行之后a类净值: {}, c类净值: {}'.format(simulate_net_values_a[-1], simulate_net_values_c[-1]))\n",
    "\n",
    "fund_a_value = simulate_net_values_a[-1] * fund_a_shares\n",
    "fund_c_value = simulate_net_values_c[-1] * fund_c_shares\n",
    "print('a类基金价值: {}\\nc类基金价值: {}'.format(\n",
    "    fund_a_value, fund_c_value\n",
    "))\n",
    "print('a与c之间差异: {}'.format(fund_a_value - fund_c_value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_value_df = pd.DataFrame({\n",
    "    'net_value_a': df['net_value_a'].tolist() + simulate_net_values_a,\n",
    "    'net_value_c': df['net_value_c'].tolist() + simulate_net_values_c\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_value_df['value_a'] = net_value_df['net_value_a'] * fund_a_shares\n",
    "net_value_df['value_c'] = net_value_df['net_value_c'] * fund_c_shares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_value_df['c_a_value_diff'] = net_value_df['value_c'] - net_value_df['value_a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'total value difference')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net_value_df['c_a_value_diff'].head(900).plot(figsize=(8, 5))\n",
    "plt.title('total value of c minus total value of a')\n",
    "plt.xlabel('trade days')\n",
    "plt.ylabel('total value difference')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a 什么时候超过 c，取决于 c 与 a 的净值之比，什么时候开始小于 (1 - a申购手续费 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_value_df['c_to_a'] = net_value_df['net_value_c'] / net_value_df['net_value_a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'net_value')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net_value_df[['net_value_a', 'net_value_c']].plot(figsize=(8, 5))\n",
    "plt.xlabel('trade days')\n",
    "plt.ylabel('net_value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f49a8759d68>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net_value_df['c_to_a'].plot(figsize=(8, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "申购费率为1.5%时，从第一天买入开始算，大约经过890个交易日，a的价值开始超过c\n",
      "申购费率为0.12%时，从第一天买入开始算，大约经过26个交易日，a的价值开始超过c\n"
     ]
    }
   ],
   "source": [
    "print('申购费率为1.5%时，从第一天买入开始算，大约经过{}个交易日，a的价值开始超过c'.format(\n",
    "    net_value_df.loc[net_value_df['c_to_a'] <= (1 - 0.015)].index[0]\n",
    "))\n",
    "\n",
    "print('申购费率为0.12%时，从第一天买入开始算，大约经过{}个交易日，a的价值开始超过c'.format(\n",
    "    net_value_df.loc[net_value_df['c_to_a'] <= (1 - 0.0012)].index[0]\n",
    "))"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "base_numbering": 1,
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   "number_sections": false,
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   "title_cell": "MarkDown菜单",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
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