ponggung/asset_prediction.ipynb

## asset_prediction.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用pandas預測你的人生財務曲線"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "起始資金 = 30\n",
    "每月薪水 = 3\n",
    "每月開銷 = 1 # 不含房租\n",
    "每月房租 = 1\n",
    "退休年齡 = 65\n",
    "預測時段 = range(25, 90, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 每年淨額 計算\n",
    "每年淨額就是每年銀行帳戶的變化值，也就是當年的**總收入** - **總支出**\n",
    "* 收入的部分有：薪水\n",
    "* 支出的部分有：開銷、房租\n",
    "\n",
    "同時必須考慮起始資金跟退休年齡！\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114acccf8>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114a38780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 每年淨額\n",
    "每年淨額 = pd.Series(0, index=預測時段)\n",
    "每年淨額.iloc[0] = 起始資金\n",
    "每年淨額.loc[:退休年齡] += 每月薪水 * 12\n",
    "每年淨額 -= (每月開銷 + 每月房租) * 12\n",
    "\n",
    "%matplotlib inline\n",
    "每年淨額.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 沒有投資的總資產變化情況"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11800c860>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10baa5470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 無投資總資產\n",
    "\n",
    "無投資總資產 = 每年淨額.cumsum()\n",
    "無投資總資產.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 有投資的總資產變化情況\n",
    "\n",
    "每年，我們都審視前年的帳戶，將資金分成兩份\n",
    "* 投資金額 = 總帳戶金額 * 投資部位\n",
    "* 存在帳戶裡不動錢 = 總帳戶金額 * (1 - 投資部位)\n",
    "\n",
    "所以今年底的帳戶餘額 = 投資金額 * 投資年利率 + 存在帳戶裡不動的錢\n",
    "\n",
    "於是我們可以寫一個函式代表每年的資產增加變化："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1181987f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11817a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "投資部位 = 0.7\n",
    "投資年利率 = 5 # 跟影片中不太一樣，影片中：1.05，改成 5% 來表示，比較好理解，也就是一年有5％的報酬率\n",
    "\n",
    "def compound_interest(arr, ratio, return_rate):\n",
    "    ret = [arr.iloc[0]]\n",
    "    for v in arr[1:]:\n",
    "        ret.append(ret[-1] * ratio * (return_rate/100+1) + ret[-1] * (1 - ratio) + v)\n",
    "    return pd.Series(ret, 預測時段)\n",
    "\n",
    "投資總資產 = compound_interest(每年淨額, 投資部位, 投資年利率)\n",
    "投資總資產.plot()\n",
    "無投資總資產.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 考慮買房的話\n",
    "* 假設買房總共要花「買房價格」\n",
    "* 然後一開始，我們會付「買房頭期款」\n",
    "* 在到達「買房年紀」以前，先租房；從「買房年紀」以後，就是負貸款\n",
    "* 設定「貸款年數」，設定究竟想要貸款幾年\n",
    "* 貸款就得付「房貸利率」（％）！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "買房價格 = 300\n",
    "買房頭期款 = 100\n",
    "買房年紀 = 35\n",
    "房貸利率 = 3\n",
    "貸款年數 = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 先計算每年的買房花費"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11824a2b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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grKCPiDn6If97mfnl4vDzEXFJ8f1LgGOjfjYz78jMPZm5Z2FhYZwypoYXTEmqwzirbgK4CziUmZ8Z+tZXgH3F1/uAezdf3mzZ2AVTWWFFks4GrTF+di/w94HvRcTDxbF/BvwmcHdE3Ao8Ddw8XomzY729bhzRSyrDpoM+M78NrNZwvn6zv+8sa2+kddPtkZn0/8EkSePzytgKrTcZu6XlDcIlTZ5BX6H1tkAY7INjn17SJBn0Fep0c+119MVfAvbpJU2SQV+RzNxAj74JGPSSJsugr8hyr9+O2bLOqhsw6CVNlkFfkUF4r7cfPUC7262kJklnB4O+IoPNytbe1Kz/l0B72clYSZNj0FekvZGgd3mlpBIY9BUZLJlccz/6ppOxkibPoK9IZ9Cjb22gR2/QS5ogg74igx79YNQ+yokLpgx6SZNj0Ffk+GtYdePtBCVN0ji7V+o1WFl1s8Y6+sEa+3/5Px7j333jcCV1nS3e/NOv5zN/5+q6y5BqYdBXZCOTsZddeC43/9xOfvSTdlVlnRWef+U4X37wB3z8+r/CZdu31l2OVDmDviIbWkffavBvbr6qqpLOGkeO/SXv+cy3+M4TL3DZ9svqLkeqnD36ipxYR+8+81W7YuE8Lj5/C98+8kLdpUi1MOgrsrK8co0RvcoREey9Ygf/64m/oNfzqmOdfUydigxG9Gttaqby7L1yBy/+uM2hH75SdylS5UydimykR6/y7L1yBwDfsX2js5CpU5FOsVHZWssrVZ6f/qlzuPKi8/jOkb+ouxSpcqZORZyMrd/eK7bzJ99/kePLbgOts4tBX5ETWyD4ltdl75U7+H+dLg89/VLdpUiVMnUqYo++ftddsZ1GwB/bp9dZxtSpyGBHynl79LU5/5w5rnrjNtfT66xj6lSkXWyB0GrYo6/Tz1+5g+8efZlXXu3UXYpUGYO+Ip1uj/lmgwiDvk7vvGIH3V7ywJMv1l2KVBmDviKd5Z4rbs4A1/zMNs6Za7ieXmeVUoI+It4bEY9HxJGIuL2M15g2nW7PNfRngC2tJtdevt2g11ll4skTEU3g3wM3AG8FPhQRb53060ybdjddcXOG+Pkrt3P42P/l+VderbsUqRJlJM+1wJHMfDIz28AXgRtLeJ2pMujRq37vvKK/HcIfP+GoXmeHMvajvxR4Zuj5UeCvlfA63P2nz/Cf/ujJMn7rifvhy6+y/bz5ussQ8NZLzueCrXP8xn9/jP9w/xN1l6Oz3Eev383fuuoNpb5GGUE/asbxtL1hI+I24DaAyy7b3M0gtm2dY/fF523qZ6u2++LzVjbWUr0ajeCT73sL33z8WN2lSPzU6+ZKf43InOz+3BHxDuBfZOYvFs8/CZCZ/3q1n9mzZ08uLi5OtA5JmnURcTAz96x3XhlN4z8FdkfE5RExD9wCfKWE15EkbcDEWzeZuRwR/xj4Q6AJ/E5mPjrp15EkbUwpNwfPzK8BXyvj95YkvTau95OkGWfQS9KMM+glacYZ9JI04wx6SZpxE79galNFRCwBf153HavYAUzzpijTXP801w7TXf801w7TXf9rqf1nMnNhvZPOiKA/k0XE4kauPDtTTXP901w7THf901w7THf9ZdRu60aSZpxBL0kzzqBf3x11FzCmaa5/mmuH6a5/mmuH6a5/4rXbo5ekGeeIXpJmnEE/JCLOiYg/iYjvRsSjEfHrxfHLI+KBiDgcEV8qtl8+I0VEMyIeioivFs+nqfanIuJ7EfFwRCwWxy6MiPuK+u+LiAvqrnOUiNgWEfdExJ9FxKGIeMcU1f7m4j0f/HolIj4+RfX/cvHn9ZGI+ELx53iaPvcfK2p/NCI+Xhyb6Htv0J/sOPDuzLwKuBp4b0RcB3wa+Gxm7gZ+BNxaY43r+RhwaOj5NNUO8Ncz8+qh5WW3AweK+g8Uz89Evw18PTN/FriK/v+Dqag9Mx8v3vOrgZ8DfgL8AVNQf0RcCnwU2JOZb6O/NfotTMnnPiLeBvwj+vfavgp4f0TsZtLvfWb6a8QvYCvwIP373b4AtIrj7wD+sO76Vql5Z/GheDfwVfq3dZyK2ov6ngJ2nHLsceCS4utLgMfrrnNE3ecD36eY85qm2kf8t/wN4DvTUj8n7lF9If1t178K/OK0fO6Bm4E7h57/c+BXJ/3eO6I/RdH6eBg4BtwHPAG8lJnLxSlH6X+4zkSfo/8h6RXPtzM9tUP/3sL/MyIOFvcUBrg4M58DKB4vqq261b0JWAJ+t2ib3RkR5zIdtZ/qFuALxddnfP2Z+QPgt4CngeeAl4GDTM/n/hHgXRGxPSK2Au8D3siE33uD/hSZ2c3+P2F30v/n1FtGnVZtVeuLiPcDxzLz4PDhEaeecbUP2ZuZ1wA3AB+JiHfVXdAGtYBrgM9n5tuBH3MGtjnWU/SxPwD8ft21bFTRu74RuBx4A3Au/c/Pqc7Iz31mHqLfZroP+DrwXWB5zR/aBIN+FZn5EvBN4DpgW0QM7sa1E3i2rrrWsBf4QEQ8BXyRfvvmc0xH7QBk5rPF4zH6PeJrgecj4hKA4vFYfRWu6ihwNDMfKJ7fQz/4p6H2YTcAD2bm88Xzaaj/PcD3M3MpMzvAl4F3Ml2f+7sy85rMfBfwInCYCb/3Bv2QiFiIiG3F16+j/yE6BNwPfLA4bR9wbz0Vri4zP5mZOzNzF/1/fn8jM3+JKagdICLOjYjXD76m3yt+hP6N5fcVp52R9WfmD4FnIuLNxaHrgceYgtpP8SFOtG1gOup/GrguIrZGRHDivZ+Kzz1ARFxUPF4G/G36/w8m+t57wdSQiPirwH76M/cN4O7M/I2IeBP9UfKFwEPA38vM4/VVuraI+AXgn2bm+6el9qLOPyietoD/mpn/KiK2A3cDl9H/Q31zZr5YU5mrioirgTuBeeBJ4MMUnyHO8NoBiv7wM8CbMvPl4ti0vPe/Dvxd+i2Ph4B/SL8nf8Z/7gEi4o/oz6d1gE9k5oFJv/cGvSTNOFs3kjTjDHpJmnEGvSTNOINekmacQS9JM86gl6QZZ9BL0owz6CVpxv1/OfN0wcdmki8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180faa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "買房花費 = pd.Series(0, index=預測時段)\n",
    "買房花費[買房年紀] = 買房頭期款\n",
    "買房花費.loc[買房年紀:買房年紀+貸款年數-1] += (買房價格 - 買房頭期款) / 貸款年數\n",
    "買房花費.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 接下來計算貸款的利息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11824ac88>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180cafd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 先計算有多少欠款\n",
    "欠款 = pd.Series(0, index=預測時段)\n",
    "欠款[買房年紀] = 買房價格\n",
    "欠款 = 欠款.cumsum()\n",
    "欠款 = 欠款 - 買房花費.cumsum()\n",
    "#欠款.plot()\n",
    "#\n",
    "利息 = 欠款.shift().fillna(0) * 房貸利率 / 100\n",
    "利息.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 計算繳房租"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118330588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11816a940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "房租年繳 = pd.Series(每月房租*12, index=預測時段)\n",
    "房租年繳.loc[買房年紀:] = 0\n",
    "房租年繳.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1184ae2b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KXqQMhmbnUTM+hn6nNvU7ihxCjbgY3ryxK+0zkrl9zCy+mL/e70ghRUUvUopF63byzwUb6HdqU5Kqa5oPVTXiYnizXxfapidx+5iZfLlAZX+Ail6kFEOz86gZF0N/TfMhr2Z8LCNu7EqbhkkMGjOTiQs3+B0pJKjoRX7F4vU7+Xz+em44tQlJCZrmw0Gt+FhG9u/KSQ2SuG30DLIXqexV9CK/4rnsfGrExdD/NE3z4aRWfCwjb+zKifVrcetbM5m0uGqXvYpe5DCWbviJz+avo+8pjUlOqOZ3HDlCSdVjGXVjN1odV5NbRs1k8pKNfkfyjYpe5DCGZueREBtN/9Oa+R1FjlJSQixv9e/G8cfV4OZRM5hSRcteRS9yCHkbfuLTeeu4/pQmpCRqmg9nB8q+RVoNBo6awTdLN/kdqdKp6EUO4blJ+VSPjWbAbzTNR4LkhGqMvqkbzdNqMGBkLt/mVa2yV9GLHCR/4898PHct153cWNN8BKmdGCz7pnUSuWlELt/nb/Y7UqVR0Ysc5PlJecTHRDNQ03zESUmsxpgBWTStk0j/ETn8UEXKXkUvUsLyTT8zYU5wmk+tEed3HKkAKd5k3zglkRtH5PDjsi1+R6pwKnqREp6flE+1mCitzUe41BpxjB7QjYzaCdz4Zg5Tl0d22avoRTwrNu/iw9lruLZbY9JqapqPdHVqxDFmQBYNa1en3xs5TIvgslfRi3ien5RPbHQUA0/XNF9VpNWMY8yAbjRIjqffmznkrNzqd6QKoaIXAVZtCU7zfbo1pm7NeL/jSCWqWzOesQOyOC4pnhten05uBJa9il6E4DQfE2Xcomm+SqpbK563B2RRr1Y8fV+fzoxVkVX2Knqp8gq27OaDWWvo3bURdWtpmq+q6taKZ+zALOrWiqfv6znMWLXN70jlRkUvVd4Lk/OJjjJuPaO531HEZ/VqBZdx6tSoRt/XpzOzIDLKXkUvVdrqrbt5f2YhvbtkUE/TvADHJQUn+9Qa1eg7fDqzV2/3O9IxK7XozSzezKab2RwzW2Bmj3jbm5rZNDPLM7N3zKyatz3Ou57v3d6kYr8FkaP34pR8osy4RdO8lFA/qTpjB2RRO7Ea1w2fxpwwL/uyTPR7gbOcc+2BDsD5ZpYFPAkMds61BLYB/b39+wPbnHMtgMHefiIhp3Dbbt7NLeTqLhnUT6rudxwJMQ2SqzN2YBbJCbFcN3wa8wp3+B3pqJVa9C7oZ+9qrPfmgLOA97ztI4BLvcs9vOt4t59tZlZuiUXKyYtTlmGG1ublsBomByf7WtVjuXb4NOavCc+yL9MavZlFm9lsYCMwEVgGbHfOFXm7FAINvcsNgdUA3u07gNTyDC1yrNZs/4V3c1fTMzODBsma5uXw0msnMHZAFjXiYujzWniWfZmK3jlX7JzrAKQDXYETD7Wb9/5Q07s7eIOZDTSzXDPL3bSpap0bWvz30pR8AG47s4XPSSQcZKQk8PbAYNlfO3waC9fu9DvSETmio26cc9uBKUAWkGxmMd5N6cBa73IhkAHg3Z4E/NezD5xzw5xzmc65zLS0tKNLL3IU1u34hXE5hVzZOYOGmualjDJSgpN9Qmw0fV6bGlZlX5ajbtLMLNm7XB04B1gETAau9HbrC3zkXZ7gXce7fZJz7r8mehG/vDRlGQHnGHSm1ublyDRKTWDswCzivbJftC48yr4sE319YLKZzQVygInOuU+AB4B7zCyf4Br8cG//4UCqt/0e4MHyjy1ydNbv2MPb01dzVWY66bUT/I4jYahxaiJjB2QRFxNNn9emsWT9T35HKpWFwrCdmZnpcnNz/Y4hVcBfJizgramrmHzfGWSkqOjl6K3YvItew36kqNgxdmAWx9erWekZzGyGcy6ztP30zFipMjbs3MOY6QVc0SldJS/HrGmd4GQfHWVc8+pU8jaE7mSvopcq46UpyygOOAbpSBspJ83SajB2YBZRZvR+dRr5G0Oz7FX0UiVs3LmHsdMLuLxjQxqlapqX8tM8rQZjBmQB0GvYNPI3/lzKR1Q+Fb1UCS9/vZyigOP2szTNS/lrUbcGbw/sBjh6vzqVZZtCq+xV9BLxNv60h9HTVnFph4Y0Tk30O45EqBZ1azJ2QBaBgKP3sKksD6GyV9FLRHPO8fgni9hfHNA0LxWuZb2ajBmQRXEgONmv2LzL70iAil4i3D8mLmXCnLXce24rmtbRNC8Vr9VxwbLfXxyc7FeGQNmr6CVijctZzXOT8unVJYPbdIZKqUStjqvJ6Ju6sbeomN6vTmXVFn/LXkUvEenbvE08NH4ev2lZh8cubYPOlC2V7cT6tRh9Uxa/7C+m97CpFGzZ7VsWFb1EnMXrd3LrWzNpUbcGL/bpRGy07ubij5Ma1GL0Td3YtS842a/e6k/Z6zdAIsr6HXvo90YOiXHRvNGvCzXjY/2OJFVc6wZJjL6pGz/vLaLXsKkUbqv8slfRS8TYuWc/N7wxnZ2/7Of1G7ro5QElZLRpGCz7n/bs96XsVfQSEfYVBbhl1AzyN/7My9d1pnWDJL8jifyHYNlnsfOX/fR+dSprtv9SaV9bRS9hLxBw/P69OfywbAtPXdmO37TUC9lIaGqbnsSo/t3Yvns/vYdNZW0llb2KXsLek18s5qPZa7n//FZc3ind7zgiv6p9RjKj+ndj26599H51Kut2VHzZq+glrL3x/Qpe+WY515/cmFtP17HyEh46ZCQzsn9Xtv68j+xFGyv868WUvotIaPpk7loe/WQh57Wux8O/a61j5SWsdGxUm+x7T6durfgK/1qa6CUsfZe3mbvfmU2XxikM6dWR6CiVvISfyih5UNFLGJpXuIObR+XSPK0Gr/bNJD422u9IIiFNRS9hZcXmXdzwxnSSE6ox4sauJFXXE6JESqOil7Cxcecerhs+DQeM6t+VepX0Z69IuFPRS1jYsXs/178+na279vHGDV1ollbD70giYUNFLyFv974ibhyRw/JNu3jlus60z0j2O5JIWFHRS0jbVxTglrdmMqtgG0N7d9CzXkWOgo6jl5BVHHDcPW423yzdxFNXtOP8NvX9jiQSljTRS0hyzvG/H87n07nr+OOFJ9KzS4bfkUTClopeQo5zjie+WMzY6QUMOrM5A7o38zuSSFhT0UvIeX5SPq98vZzrshpz37mt/I4jEvZU9BJSXvt2Oc9MXMoVndJ55BKdv0akPKjoJWSMmVbA458u4sK2x/HkFW2J0vlrRMqFil5Cwoez1vDHD+dxZqs0nr26IzF6QW+RclPqb5OZZZjZZDNbZGYLzOxOb3uKmU00szzvfW1vu5nZUDPLN7O5Ztapor8JCW9fzF/Hve/OIatpKi9d25lqMSp5kfJUlt+oIuBe59yJQBYwyMxOAh4Esp1zLYFs7zrABUBL720g8FK5p5aI8dXCDdw+Zhbt05N4TWeiFKkQpRa9c26dc26md/knYBHQEOgBjPB2GwFc6l3uAYx0QVOBZDPTM13kv0xespHbRs+kdYNavHljVxLj9Pw9kYpwRH8jm1kToCMwDajnnFsHwf8MgLrebg2B1SU+rNDbJvIv3+Vt5uZRM2hZrwYjb+xGrXidblikopS56M2sBvA+cJdzbuev7XqIbe4Qn2+gmeWaWe6mTZvKGkMiwI/LtnDTyBya1Unkrf7dSEpQyYtUpDIVvZnFEiz50c65D7zNGw4syXjvD7zCbSFQ8vnq6cDagz+nc26Ycy7TOZeZlqYTVVUV05Zvof+IHDJqJ/DWTd2onVjN70giEa8sR90YMBxY5Jz7R4mbJgB9vct9gY9KbL/eO/omC9hxYIlHqrZpy7fQ780c6ifFM3pAN+rUiPM7kkiVUJZHv04FrgPmmdlsb9tDwBPAODPrDxQAV3m3fQZcCOQDu4F+5ZpYwlLJkh87MIu6NfXqUCKVpdSid859x6HX3QHOPsT+Dhh0jLkkgkxbvoUb3sihQbJKXsQPemaKVCiVvIj/VPRSYb7P36ySFwkBKnqpEF8v3cSNb+bQKCWBtweerJIX8ZGeiijlLnvRBm59ayYt6tbgrZu6kaJDKEV8pYleytUX89dzy1szOKF+TcYMUMmLhAJN9FJuPpm7ljvfnk279CRG3NhVpzUQCRGa6KVcjMtZzR1jZ9G5UW1G9de5a0RCiSZ6OWZvfL+CRz5eSPfj03jl2s5Ur6ZTDYuEEhW9HDXnHC9MzufvXy7l/NbHMaR3B+JiVPIioUZFL0fFOceTXyzh5a+XcXnHhjx1ZTu9/J9IiFLRyxErDjj+9NF8xkwr4NqsRjx6SRu9kLdICFPRyxHZVxTg7nGz+XTuOm49ozn3n9eK4AlORSRUqeilzHbtLeKWt2bwbd5mHrrwBAZ2b+53JBEpAxW9lMn23fvo92YOc1Zv56kr29EzM6P0DxKRkKCirwT7iwM88P5c9hYFeOGaTn7HOWLrdvxC39ens3Lzbl7s05nz2xzndyQROQIq+gpWVBzgrneCa9qJ1aJxzoXVmnbehp+4/vXp/LSniDf7deGUFnX8jiQiR0jHw1WgouIAd4+bw6dz19EuPYld+4rZsmuf37HKLHflVq58+UeKAo53bs5SyYuEKRV9BSkqDnDPuDl8PGctf7jgBO4+53gAVm3Z5XOysvlywXr6vDaNlMRqfHDrKbRukOR3JBE5Sir6ClAccNz77hwmzFnLA+efwM2nN6dRagIAq7bs9jld6cZMK/DOQFmL9245mYyUBL8jicgx0Bp9OSsOOO57dw4fzV7L789rxa1nBA9BTK9dHTNYGcJF75zj718u4YXJyzijVRov9ulEQjXdRUTCnX6Ly1FxwPH79+YwftYa7jv3eAad2eJft8XFRNMgqToFIbp0s7eomPvfm8tHs9fSu2sGj/Voo1MaiEQIFX05CQQcD7w/lw9mruGe3x7P7We1/K99GqcmhOREv2P3fgaOymXaiq38/rxW3HZG87A6MkhEfp2KvhwEAo4HP5jLezMKueucltxx9n+XPEDj1ET+uWB9Jaf7dau37qbfmzkUbNnNkF4d6NGhod+RRKScqeiPUSDgeGj8PMblFnLH2S25yzu65lAapyawddc+du7ZHxIvzDGzYBsDR+ayryjAyP5dyWqW6nckEakAWoQ9BoGA448fzuPtnNXcfmYL7j7n0JP8AU28I28KQmD5ZsKctfQaNpWEajF8cNupKnmRCKaiP0qBgON/P5rP2OmrGXRmc+499/hS17UbpSQC/h5i6ZxjaHYed4ydRfv0JD4cdCot6tbwLY+IVDwt3RwF5xx/nhA8H/utZzTnvnPLdqrext5Ev9KnI2/2FhXz4PvzGD9rDZd3bMj/XdFWrwglUgWo6I+Qc46HJyzgrakF3HL6kZ2PPTEuhjo14nxZutn40x5uHjWDWQXb/3Xop46sEakaVPRHwDnHIx8vZOSPq7i5ezMeOP/IX3QjeIhl5U7089fsYMDIXLbv3s+LfTpxYdv6lfr1RcRfWqMvI+ccj36ykDd/WMlNpzXlwQtOOKqJuHFqAgVbK2+i/3jOWq58+QcMeO/Wk1XyIlWQir4MnHM89ski3vh+Jf1Pa8ofLzrxqJc9Gqcksm7HHvbsLy7nlP8pEHA88+US/mfsLNo0SOKj20/TiclEqqhSi97MXjezjWY2v8S2FDObaGZ53vva3nYzs6Fmlm9mc80s/F5l4yDOOR7/dBGvf7+Cfqc24X+PoeQBmtQJPiC7ugKn+p179jNgZC7PTcqnZ2Y6owd0I61mXIV9PREJbWWZ6N8Ezj9o24NAtnOuJZDtXQe4AGjpvQ0EXiqfmP5wzvG3zxYx/LsV3HBKE/588UnH/ABmo5QDR95UTNHnbfiJS5//nq+XbuLRHq158op2OrJGpIorteidc98AWw/a3AMY4V0eAVxaYvtIFzQVSDazsFwUds7xxOeLefXbFVx/cmMe/t2xlzxAk9QDx9KX/wOyX8xfz6UvfM/OPfsZMyCL609uoiNrROSoj7qp55xbB+CcW2dmdb3tDYHVJfYr9LatO/qIlc85x5NfLOGVb5ZzbVYjHrmkdbkVZnJCLDXjY8r1SVPFAcfgiUt5fnI+7TOSefnaTtRPql5un19Ewlt5H155qDZ0h9zRbCDB5R0aNWpVY41bAAAJI0lEQVRUzjGOnnOOp/+5hJe/Xkafbo149JI25ToVmxmNUxNYVU5r9Ft+3std78zm27zNXJ2ZwSM9WhMfq6UaEfm3oy36DWZW35vm6wMbve2FQEaJ/dKBtYf6BM65YcAwgMzMzEP+Z1DZnHM88+VSXpyyjN5dG/FYjzZERZX/0kfj1EQWrNlxzJ9nZsE2Bo2eyZZd+3jyirZc3SV0/sMUkdBxtIdXTgD6epf7Ah+V2H69d/RNFrDjwBJPOBj8VR7PT86nV5cM/nppxZQ8QOOUBAq3/UJRceCoPt45x8gfV3L1Kz8SE218cOspKnkROaxSJ3ozGwucAdQxs0LgYeAJYJyZ9QcKgKu83T8DLgTygd1AvwrIXCGe/WopQ7Pz6JmZzt8ua1thJQ/BB2SLAo612/f867Vky+qnPft5aPx8Pp6zlrNOqMvgnh1ISvD/lMciErpKLXrnXO/D3HT2IfZ1wKBjDVXZhnyVx7Nf5XFl53SeuLxdhZY88K9yX7ll1xEV/fw1O7h9zEwKtu4Ovh7t6c0rPKuIhL8qf66b57LzGPzVUq7olM6TV1R8yUOJQyzL+ICsc463phXw2CcLqZ0Qy9gBWXTT+eNFpIyqdNG/MDmfZyYu5fKODXnqynZEV9J0XLdmHHExUazaXPqx9Dv37OcPH8zj07nrOP34NP7Rsz2pNfQsVxEpuypb9C9Oyefpfy7h0g4NePqq9pVW8gBRUWU7xHJmwTbufHsWa7fv4YHzT+Dm7s20VCMiR6xKFv3LXy/jqS+W0KNDA57p2aFSS/6ARimJh312bHHA8dKUfAZ/lUf9pHjG3XwynRvXruSEIhIpqlzRD/tmGU98vpjftW/AM5U8yZfUODWB7/I3EQi4/5jS1+34hbvens20FVv5XfsG/PWyNiHxQuIiEr6qVNG/9u1y/vbZYi5qV5/BPdsTE+3fWZqbpCawZ3+AjT/t5bikeAA+nbuOh8bPY39xgL9f1Z4rOjXUuWpE5JhVmaIf/t0KHv90ERe1rc+Qqzv4WvIAjUqc3CwhLpqHP1rA+FlraJ+exOCrO9AsTS/YLSLlo0oU/Rvfr+CxTxZyYdvjeLaX/yUPwYke4MPZa7hn3BzW79zDnWe35PazWhAbAvlEJHJEfNGP+GElj3y8kPNbH8eQXh1DpkQbJFcnOsoYO301Tesk8t4tJ9OxkR5wFZHyF9FFP/LHlTw8YQHnta7Hc9eETskDxEZH0TMzg7iYKO4/vxUJ1SL6n0JEfBSx7TJq6ir+/NECfntSPZ7r3SmkSv6A/7u8rd8RRKQKCL32Kwejp63iTx/O55wT6/LCNZ2oFhOR36aISJlEXAOOmVbAH8fP5+wT6vJCH5W8iEhEteDb0wt4aPw8zmyVxovXdtKLYouIEEFFPy5nNQ9+MI8zWqXx0rWdVfIiIp6IKPp3c1fzwAdz6X58Gi9f21mvmSoiUkLYF/37Mwq5//25nNaiDsOuU8mLiBwsrIv+w1lruO+9OZzavA6vXp+pkhcROYSwLvqGtavz2xPrqeRFRH5FWD9hqkuTFLo0SfE7hohISAvriV5EREqnohcRiXAqehGRCKeiFxGJcCp6EZEIp6IXEYlwKnoRkQinohcRiXDmnPM7A2a2CVjld47DqANs9jvEMQjn/OGcHcI7fzhnh/DOfyTZGzvn0krbKSSKPpSZWa5zLtPvHEcrnPOHc3YI7/zhnB3CO39FZNfSjYhIhFPRi4hEOBV96Yb5HeAYhXP+cM4O4Z0/nLNDeOcv9+xaoxcRiXCa6EVEIpyKvgQzizez6WY2x8wWmNkj3vamZjbNzPLM7B0zq+Z31sMxs2gzm2Vmn3jXwyn7SjObZ2azzSzX25ZiZhO9/BPNrLbfOQ/FzJLN7D0zW2xmi8zs5DDK3sr7mR9422lmd4VR/ru939f5ZjbW+z0Op/v9nV72BWZ2l7etXH/2Kvr/tBc4yznXHugAnG9mWcCTwGDnXEtgG9Dfx4yluRNYVOJ6OGUHONM516HE4WUPAtle/mzveigaAnzhnDsBaE/w3yAssjvnlng/8w5AZ2A3MJ4wyG9mDYE7gEznXBsgGuhFmNzvzawNMADoSvB+c7GZtaS8f/bOOb0d4g1IAGYC3Qg+eSHG234y8E+/8x0mc7p3pzgL+ASwcMnu5VsJ1Dlo2xKgvne5PrDE75yHyF0LWIH3mFc4ZT/E93Iu8H245AcaAquBFIKvmPcJcF643O+Bq4DXSlz/E3B/ef/sNdEfxFv6mA1sBCYCy4Dtzrkib5dCgneuUPQswTtJwLueSvhkB3DAl2Y2w8wGetvqOefWAXjv6/qW7vCaAZuAN7xls9fMLJHwyH6wXsBY73LI53fOrQH+DhQA64AdwAzC534/H+huZqlmlgBcCGRQzj97Ff1BnHPFLvgnbDrBP6dOPNRulZuqdGZ2MbDROTej5OZD7Bpy2Us41TnXCbgAGGRm3f0OVEYxQCfgJedcR2AXIbjMURpvHfsS4F2/s5SVt3bdA2gKNAASCd5/DhaS93vn3CKCy0wTgS+AOUDRr37QUVDRH4ZzbjswBcgCks3swAuppwNr/cr1K04FLjGzlcDbBJdvniU8sgPgnFvrvd9IcI24K7DBzOoDeO83+pfwsAqBQufcNO/6ewSLPxyyl3QBMNM5t8G7Hg75zwFWOOc2Oef2Ax8ApxBe9/vhzrlOzrnuwFYgj3L+2avoSzCzNDNL9i5XJ3gnWgRMBq70dusLfORPwsNzzv3BOZfunGtC8M/vSc65PoRBdgAzSzSzmgcuE1wrng9MIJgbQjS/c249sNrMWnmbzgYWEgbZD9Kbfy/bQHjkLwCyzCzBzIx//+zD4n4PYGZ1vfeNgMsJ/huU689eT5gqwczaASMIPnIfBYxzzj1qZs0ITskpwCzgWufcXv+S/jozOwO4zzl3cbhk93KO967GAGOcc381s1RgHNCI4C/1Vc65rT7FPCwz6wC8BlQDlgP98O5DhHh2AG99eDXQzDm3w9sWLj/7R4CrCS55zAJuIrgmH/L3ewAz+5bg42n7gXucc9nl/bNX0YuIRDgt3YiIRDgVvYhIhFPRi4hEOBW9iEiEU9GLiEQ4Fb2ISIRT0YuIRDgVvYhIhPt/57RzdTp+nXkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f60b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "每年淨額_買房 = pd.Series(0, index=預測時段)\n",
    "每年淨額_買房.iloc[0] = 起始資金\n",
    "每年淨額_買房.loc[:退休年齡] += 每月薪水 * 12\n",
    "每年淨額_買房 -= (每月開銷*12 + 房租年繳 + 利息 + 買房花費)\n",
    "每年淨額_買房.cumsum().plot()\n",
    "#每年淨額_買房.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1185322b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118561cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "投資_買房_總資產 = compound_interest(每年淨額_買房, 投資部位, 投資年利率)\n",
    "#\n",
    "投資總資產.plot(color='green')\n",
    "投資_買房_總資產.plot(color='blue')\n",
    "每年淨額_買房.cumsum().plot(color='red')\n",
    "無投資總資產.plot(color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8dff092f5e654deb9a7e12823b6f95d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(FloatSlider(value=20.0, description='起始資金', step=10.0), IntSlider(value=30, description='起始年紀'), FloatSlider(value=3.0, description='每月薪水', max=20.0), FloatSlider(value=1.0, description='每月開銷', max=20.0, step=0.2), FloatSlider(value=1.0, description='每月房租', max=20.0, step=0.5), IntSlider(value=60, description='退休年齡'), FloatSlider(value=0.7, description='投資部位', max=1.0), FloatSlider(value=5.0, description='投資年利率', max=20.0, step=0.5), IntSlider(value=300, description='買房價格', max=2000, min=100, step=50), IntSlider(value=100, description='買房頭期款', max=2000, min=100, step=50), IntSlider(value=40, description='買房年紀', min=20), FloatSlider(value=2.4, description='房貸利率', max=5.0, min=1.0), IntSlider(value=20, description='貸款年數', max=40), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.asset_prediction>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "%matplotlib inline\n",
    "def asset_prediction(起始資金 ,起始年紀,\n",
    "    每月薪水 ,\n",
    "    每月開銷 ,\n",
    "    每月房租 ,\n",
    "    退休年齡 ,\n",
    "    投資部位,\n",
    "    投資年利率,\n",
    "    買房價格,\n",
    "    買房頭期款,\n",
    "    買房年紀,\n",
    "    房貸利率,\n",
    "    貸款年數,):\n",
    "\n",
    "    預測時段 = range(起始年紀, 100)\n",
    "    每年淨額 = pd.Series(0, index=預測時段)\n",
    "    每年淨額.iloc[0] = 起始資金\n",
    "    每年淨額.loc[:退休年齡] += 每月薪水 * 12\n",
    "    每年淨額 -= (每月開銷 + 每月房租) * 12\n",
    "    \n",
    "    \n",
    "    def compound_interest(arr, ratio, return_rate):\n",
    "        ret = [arr.iloc[0]]\n",
    "        for v in arr[1:]:\n",
    "            ret.append(ret[-1] * ratio * (return_rate/100 + 1) + ret[-1] * (1 - ratio) + v)\n",
    "        return pd.Series(ret, 預測時段)\n",
    "    \n",
    "    買房花費 = pd.Series(0, index=預測時段)\n",
    "    買房花費[買房年紀] = 買房頭期款\n",
    "    買房花費.loc[買房年紀:買房年紀+貸款年數-1] += (買房價格 - 買房頭期款) / 貸款年數\n",
    "    \n",
    "    欠款 = pd.Series(0, index=預測時段)\n",
    "    欠款[買房年紀] = 買房價格\n",
    "    欠款 = 欠款.cumsum()\n",
    "    欠款 = 欠款 - 買房花費.cumsum()\n",
    "    利息 = 欠款.shift().fillna(0) * 房貸利率 / 100\n",
    "\n",
    "\n",
    "    房租年繳 = pd.Series(每月房租*12, index=預測時段)\n",
    "    房租年繳.loc[買房年紀:] = 0\n",
    "    \n",
    "    每年淨額_買房 = pd.Series(0, index=預測時段)\n",
    "    每年淨額_買房.iloc[0] = 起始資金\n",
    "    每年淨額_買房.loc[:退休年齡] += 每月薪水 * 12\n",
    "    每年淨額_買房 -= (每月開銷*12 + 房租年繳 + 利息 + 買房花費)\n",
    "    \n",
    "    \n",
    "    \n",
    "    pd.DataFrame({\n",
    "        'no invest, no house': 每年淨額.cumsum(),\n",
    "        'invest, no house': compound_interest(每年淨額, 投資部位, 投資年利率),\n",
    "        'no invest, house': 每年淨額_買房.cumsum(),\n",
    "        'invest, house': compound_interest(每年淨額_買房, 投資部位, 投資年利率),\n",
    "        \n",
    "    }).plot()\n",
    "\n",
    "    \n",
    "    import matplotlib.pylab as plt\n",
    "    plt.ylim(0, None)\n",
    "    \n",
    "    print('月繳房貸', (買房價格 - 買房頭期款) / 貸款年數 / 12)\n",
    "    print('利息', 利息.sum() / 貸款年數)\n",
    "    print('')\n",
    "\n",
    "import ipywidgets as widgets\n",
    "widgets.interact(asset_prediction, \n",
    "    起始資金=widgets.FloatSlider(min=0, max=100, step=10, value=20),\n",
    "    起始年紀=widgets.IntSlider(min=0, max=100, step=1, value=30),\n",
    "    每月薪水=widgets.FloatSlider(min=0, max=20, step=0.1, value=3),\n",
    "    每月開銷=widgets.FloatSlider(min=0, max=20, step=0.2, value=1),\n",
    "    每月房租=widgets.FloatSlider(min=0, max=20, step=0.5, value=1),\n",
    "    退休年齡=widgets.IntSlider(min=0, max=100, step=1, value=60),\n",
    "    投資部位=widgets.FloatSlider(min=0, max=1, step=0.1, value=0.7),\n",
    "    投資年利率=widgets.FloatSlider(min=0, max=20, step=0.5, value=5),\n",
    "    買房價格=widgets.IntSlider(min=100, max=2000, step=50, value=300),\n",
    "    買房頭期款=widgets.IntSlider(min=100, max=2000, step=50, value=100),\n",
    "    買房年紀=widgets.IntSlider(min=20, max=100, step=1, value=40),\n",
    "    房貸利率=widgets.FloatSlider(min=1, max=5, step=0.1, value=2.4),\n",
    "    貸款年數=widgets.IntSlider(min=0, max=40, step=1, value=20)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 結論\n",
    "\n",
    "1. 絕對要開始投資，讓資產報酬率在每年5％就差很多了！\n",
    "2. 買不買房差很大！\n",
    "3. 投資部位很重要"
   ]
  }
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