Forecasting_Method.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "70953e40",
   "metadata": {},
   "source": [
    "## 讀取資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "40778334",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/david/.pyenv/versions/3.8.10/lib/python3.8/site-packages/pandas/compat/__init__.py:124: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-04</th>\n",
       "      <td>43.500000</td>\n",
       "      <td>43.049999</td>\n",
       "      <td>43.200001</td>\n",
       "      <td>43.200001</td>\n",
       "      <td>13705760.0</td>\n",
       "      <td>40.352589</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-05</th>\n",
       "      <td>43.400002</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.250000</td>\n",
       "      <td>43.099998</td>\n",
       "      <td>16601334.0</td>\n",
       "      <td>40.259178</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-06</th>\n",
       "      <td>43.299999</td>\n",
       "      <td>42.799999</td>\n",
       "      <td>43.099998</td>\n",
       "      <td>42.950001</td>\n",
       "      <td>18966159.0</td>\n",
       "      <td>40.119068</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-07</th>\n",
       "      <td>43.150002</td>\n",
       "      <td>42.849998</td>\n",
       "      <td>42.950001</td>\n",
       "      <td>42.849998</td>\n",
       "      <td>21021508.0</td>\n",
       "      <td>40.025658</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-08</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>42.799999</td>\n",
       "      <td>42.900002</td>\n",
       "      <td>42.950001</td>\n",
       "      <td>17371239.0</td>\n",
       "      <td>40.119068</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                 High        Low       Open      Close      Volume  Adj Close  \\\n",
       "Date                                                                            \n",
       "2021-01-04  43.500000  43.049999  43.200001  43.200001  13705760.0  40.352589   \n",
       "2021-01-05  43.400002  43.000000  43.250000  43.099998  16601334.0  40.259178   \n",
       "2021-01-06  43.299999  42.799999  43.099998  42.950001  18966159.0  40.119068   \n",
       "2021-01-07  43.150002  42.849998  42.950001  42.849998  21021508.0  40.025658   \n",
       "2021-01-08  43.000000  42.799999  42.900002  42.950001  17371239.0  40.119068   \n",
       "\n",
       "           symbol  \n",
       "Date               \n",
       "2021-01-04   1101  \n",
       "2021-01-05   1101  \n",
       "2021-01-06   1101  \n",
       "2021-01-07   1101  \n",
       "2021-01-08   1101  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv('data/20211109_top150_tw_stocks.csv')\n",
    "df.set_index('Date', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43cb2cb",
   "metadata": {},
   "source": [
    "## 建立環境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95b57eec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Volumes/Macintosh HD - Data/Users/davidchiu/project/Deep-Portfolio-Management/utils.py:13: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. This has been deprecated since 3.3 and in 3.6, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = mpl.cm.get_cmap(\"viridis\").copy()\n",
      "  current_cmap.set_bad(color='red')\n"
     ]
    }
   ],
   "source": [
    "from environment import ETFEnvironment\n",
    "from agent import SmoothingAgent\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e45aa7ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_ASSETS = 138 # 138 隻股票\n",
    "WINDOW_FIT = 20 # 20 天週期\n",
    "WINDOW_HOLD = 20 # 20 天週期\n",
    "env = ETFEnvironment()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745317e0",
   "metadata": {},
   "source": [
    "## Forecasting 方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bd5f79e",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent_smooth = SmoothingAgent(N_ASSETS, allow_short=True, forecast_horizon = WINDOW_HOLD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40102f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "actions_equal, actions_smooth = [], []\n",
    "result_equal, result_smooth = [], []\n",
    "\n",
    "for i in range(WINDOW_FIT, len(env.data), WINDOW_HOLD):\n",
    "    \n",
    "    state = env.get_state(i, WINDOW_FIT, is_cov_matrix=False, is_raw_time_series=True)\n",
    "    \n",
    "    action_equal = np.ones(N_ASSETS) / N_ASSETS\n",
    "    action_smooth = agent_smooth.act(state)\n",
    "\n",
    "    state_action = env.get_state(i+WINDOW_HOLD, WINDOW_HOLD, is_cov_matrix=False)\n",
    "    \n",
    "    r = np.dot(state_action, action_equal)\n",
    "    result_equal.append(r.tolist())\n",
    "    actions_equal.append(action_equal)\n",
    "    \n",
    "    r = np.dot(state_action, action_smooth)\n",
    "    result_smooth.append(r.tolist())\n",
    "    actions_smooth.append(action_smooth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30193758",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_equal_vis = [item for sublist in result_equal for item in sublist]\n",
    "result_smooth_vis = [item for sublist in result_smooth for item in sublist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5617de5",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result_equal_vis</th>\n",
       "      <th>result_smooth_vis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001805</td>\n",
       "      <td>0.000460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.002488</td>\n",
       "      <td>-0.008560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004718</td>\n",
       "      <td>0.003842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.021762</td>\n",
       "      <td>-0.000912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.008143</td>\n",
       "      <td>-0.004950</td>\n",
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      "text/plain": [
       "   result_equal_vis  result_smooth_vis\n",
       "0          0.001805           0.000460\n",
       "1         -0.002488          -0.008560\n",
       "2          0.004718           0.003842\n",
       "3          0.021762          -0.000912\n",
       "4          0.008143          -0.004950"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pandas.DataFrame({'result_equal_vis':result_equal_vis, \n",
    "                  'result_smooth_vis': result_smooth_vis})\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a92af95",
   "metadata": {},
   "source": [
    "## 匯出運算結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "906cbd32",
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('data/forecasting_result.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39956ecd",
   "metadata": {},
   "source": [
    "## 評估資產配置效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b389c6f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>result_equal_vis</th>\n",
       "      <th>result_smooth_vis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001805</td>\n",
       "      <td>0.000460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.002488</td>\n",
       "      <td>-0.008560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004718</td>\n",
       "      <td>0.003842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.021762</td>\n",
       "      <td>-0.000912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.008143</td>\n",
       "      <td>-0.004950</td>\n",
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      "text/plain": [
       "   result_equal_vis  result_smooth_vis\n",
       "0          0.001805           0.000460\n",
       "1         -0.002488          -0.008560\n",
       "2          0.004718           0.003842\n",
       "3          0.021762          -0.000912\n",
       "4          0.008143          -0.004950"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv('data/forecasting_result.csv', index_col = 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1f11b7ae",
   "metadata": {},
   "outputs": [
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       "<AxesSubplot:>"
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df.cumsum() + 1).plot(kind = 'line', figsize = [20,10])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}