AFML 3.1.ipynb

{
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  {
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   "source": [
    "# Triple Barrier Method\n",
    "\n",
    "This notebook will cover partial exercise answers:\n",
    "* Exercise 3.1\n",
    "* Exercise 3.2\n",
    "* Exercise 3.3\n",
    "\n",
    "As we go along, there will be some explanations. \n",
    "\n",
    "More importantly, this method can be applied not just within mean-reversion strategy but also other strategies as well. Most of the functions below can be found under research/Labels.\n",
    "\n",
    "Contact: boyboi86@gmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of CPU core:  4\n",
      "Machine info:  Windows-10-10.0.18362-SP0\n",
      "Python 3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]\n",
      "Numpy 1.17.3\n",
      "Pandas 1.0.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wei_X\\Anaconda3\\lib\\site-packages\\pandas_datareader\\compat\\__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  from pandas.util.testing import assert_frame_equal\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import research as rs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "p = print\n",
    "\n",
    "#pls take note of version\n",
    "#numpy 1.17.3\n",
    "#pandas 1.0.3\n",
    "#sklearn 0.21.3\n",
    "\n",
    "dollar = pd.read_csv('./research/Sample_data/dollar_bars.txt', \n",
    "                 sep=',', \n",
    "                 header=0, \n",
    "                 parse_dates = True, \n",
    "                 index_col=['date_time'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_vol = rs.vol(dollar['close'], span0 = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-02 07:07:35.156000', '2015-01-02 09:35:57.204000',\n",
       "               '2015-01-02 12:59:42.176000', '2015-01-02 14:19:33.847000',\n",
       "               '2015-01-02 14:33:39.311000', '2015-01-02 14:42:28.315000',\n",
       "               '2015-01-02 14:51:59.300000', '2015-01-02 15:01:45.497000',\n",
       "               '2015-01-02 15:14:31.569000', '2015-01-02 15:22:54.187000',\n",
       "               ...\n",
       "               '2016-12-30 20:57:19.151000', '2016-12-30 20:58:34.724000',\n",
       "               '2016-12-30 20:59:16.663000', '2016-12-30 20:59:34.157000',\n",
       "               '2016-12-30 20:59:50.345000', '2016-12-30 20:59:58.848000',\n",
       "               '2016-12-30 21:00:00.352000', '2016-12-30 21:00:24.294000',\n",
       "               '2016-12-30 21:03:03.027000', '2016-12-30 21:13:31.990000'],\n",
       "              dtype='datetime64[ns]', length=22890, freq=None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events = rs.cs_filter(dollar['close'], \n",
    "                    limit = d_vol.mean())\n",
    "\n",
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-02 07:07:35.156   2015-01-04 23:20:12.567\n",
       "2015-01-02 09:35:57.204   2015-01-04 23:20:12.567\n",
       "2015-01-02 12:59:42.176   2015-01-04 23:20:12.567\n",
       "2015-01-02 14:19:33.847   2015-01-04 23:20:12.567\n",
       "2015-01-02 14:33:39.311   2015-01-04 23:20:12.567\n",
       "                                    ...          \n",
       "2016-12-29 19:50:32.702   2016-12-30 19:55:31.030\n",
       "2016-12-29 20:43:20.886   2016-12-30 20:44:21.481\n",
       "2016-12-29 20:56:54.013   2016-12-30 20:57:19.151\n",
       "2016-12-29 21:00:00.349   2016-12-30 21:00:00.352\n",
       "2016-12-29 21:13:14.022   2016-12-30 21:13:31.990\n",
       "Name: date_time, Length: 22850, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vb = rs.vert_barrier(data = dollar['close'], \n",
    "                 events = events, \n",
    "                 period = 'days', \n",
    "                 freq = 1)\n",
    "\n",
    "vb # Show some example output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wei_X\\Desktop\\Python\\research\\Labels\\triple_barrier_method.py:75: UserWarning: Data and events index shape must be same, reindex data to fit events\n",
      "  warnings.warn('Data and events index shape must be same, reindex data to fit events')\n",
      "C:\\Users\\Wei_X\\Desktop\\Python\\research\\Labels\\triple_barrier_method.py:112: UserWarning: Not Recommended: No side prediction provided\n",
      "  warnings.warn('Not Recommended: No side prediction provided')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[                                             t1                      sl  \\\n",
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      "...                                         ...                     ...   \n",
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      "2015-09-16 19:54:03.737 2015-09-17 19:55:09.135                     NaT   \n",
      "\n",
      "                                             pt  \n",
      "2015-01-05 14:54:26.286                     NaT  \n",
      "2015-01-05 14:57:13.616                     NaT  \n",
      "2015-01-05 15:01:57.494                     NaT  \n",
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      "2015-01-05 15:13:09.655                     NaT  \n",
      "...                                         ...  \n",
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      "\n",
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      "...                                         ...                     ...   \n",
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      "2015-09-16 19:22:06.172 2015-09-17 19:22:59.160                     NaT   \n",
      "2015-09-16 19:32:47.172 2015-09-17 19:36:50.249                     NaT   \n",
      "2015-09-16 19:45:01.362 2015-09-17 19:47:12.228                     NaT   \n",
      "2015-09-16 19:54:03.737 2015-09-17 19:55:09.135                     NaT   \n",
      "\n",
      "                                             pt  \n",
      "2015-01-05 14:54:26.286                     NaT  \n",
      "2015-01-05 14:57:13.616                     NaT  \n",
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      "2015-01-05 15:13:09.655                     NaT  \n",
      "...                                         ...  \n",
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      "\n",
      "[7408 rows x 3 columns],                                              t1                      sl  \\\n",
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      "2016-04-28 08:58:32.457 2016-04-29 10:02:20.933                     NaT   \n",
      "2016-04-28 10:52:03.623 2016-04-29 11:47:49.541 2016-04-28 19:31:00.850   \n",
      "2016-04-28 12:01:23.295 2016-04-29 12:37:34.150 2016-04-28 19:25:12.672   \n",
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      "...                                         ...                     ...   \n",
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      "2016-12-30 21:03:03.027                     NaT                     NaT   \n",
      "2016-12-30 21:13:31.990                     NaT                     NaT   \n",
      "\n",
      "                                             pt  \n",
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      "...                                         ...  \n",
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      "\n",
      "[7408 rows x 3 columns]] this out\n"
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      "2020-05-21 15:58:35.187171 66.67% _pt_sl_t1 done after 0.47 minutes. Remaining 0.24 minutes.\r"
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      "[                                             t1                      sl  \\\n",
      "2015-01-05 14:54:26.286 2015-01-06 15:01:01.702 2015-01-05 15:40:45.114   \n",
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      "...                                         ...                     ...   \n",
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      "2015-09-16 19:22:06.172 2015-09-17 19:22:59.160                     NaT   \n",
      "2015-09-16 19:32:47.172 2015-09-17 19:36:50.249                     NaT   \n",
      "2015-09-16 19:45:01.362 2015-09-17 19:47:12.228                     NaT   \n",
      "2015-09-16 19:54:03.737 2015-09-17 19:55:09.135                     NaT   \n",
      "\n",
      "                                             pt  \n",
      "2015-01-05 14:54:26.286                     NaT  \n",
      "2015-01-05 14:57:13.616                     NaT  \n",
      "2015-01-05 15:01:57.494                     NaT  \n",
      "2015-01-05 15:07:29.012                     NaT  \n",
      "2015-01-05 15:13:09.655                     NaT  \n",
      "...                                         ...  \n",
      "2015-09-16 19:10:49.674 2015-09-17 18:13:04.358  \n",
      "2015-09-16 19:22:06.172 2015-09-17 18:44:39.366  \n",
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      "\n",
      "[7408 rows x 3 columns],                                              t1                      sl  \\\n",
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      "2016-04-28 08:58:32.457 2016-04-29 10:02:20.933                     NaT   \n",
      "2016-04-28 10:52:03.623 2016-04-29 11:47:49.541 2016-04-28 19:31:00.850   \n",
      "2016-04-28 12:01:23.295 2016-04-29 12:37:34.150 2016-04-28 19:25:12.672   \n",
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      "...                                         ...                     ...   \n",
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      "2016-12-30 21:03:03.027                     NaT                     NaT   \n",
      "2016-12-30 21:13:31.990                     NaT                     NaT   \n",
      "\n",
      "                                             pt  \n",
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      "...                                         ...  \n",
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      "2015-09-16 20:13:49.208 2015-09-17 20:49:02.616 2015-09-17 19:55:09.135   \n",
      "2015-09-17 02:31:29.158 2015-09-18 05:53:05.346 2015-09-17 20:03:58.960   \n",
      "2015-09-17 07:50:29.399 2015-09-18 08:36:51.929                     NaT   \n",
      "...                                         ...                     ...   \n",
      "2016-04-28 01:20:23.379 2016-04-29 04:43:02.149 2016-04-28 03:08:23.517   \n",
      "2016-04-28 03:08:23.517 2016-04-29 04:43:02.149 2016-04-28 08:11:31.935   \n",
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      "2016-04-28 07:22:07.437 2016-04-29 08:08:00.258 2016-04-28 08:58:32.457   \n",
      "\n",
      "                                             pt  \n",
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      "2015-09-17 07:50:29.399 2015-09-17 17:23:18.817  \n",
      "...                                         ...  \n",
      "2016-04-28 01:20:23.379                     NaT  \n",
      "2016-04-28 03:08:23.517 2016-04-28 13:51:37.515  \n",
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      "\n",
      "[7408 rows x 3 columns]] this out\n"
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22224 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             t1      trgt\n",
       "2015-01-05 14:54:26.286 2015-01-05 15:40:45.114  0.002244\n",
       "2015-01-05 14:57:13.616 2015-01-05 15:40:45.114  0.002469\n",
       "2015-01-05 15:01:57.494 2015-01-05 16:21:16.062  0.002787\n",
       "2015-01-05 15:07:29.012 2015-01-05 15:40:45.114  0.002827\n",
       "2015-01-05 15:13:09.655 2015-01-05 16:10:05.172  0.002882\n",
       "...                                         ...       ...\n",
       "2016-12-30 20:59:58.848                     NaT  0.002397\n",
       "2016-12-30 21:00:00.352                     NaT  0.002350\n",
       "2016-12-30 21:00:24.294                     NaT  0.002304\n",
       "2016-12-30 21:03:03.027                     NaT  0.002261\n",
       "2016-12-30 21:13:31.990                     NaT  0.002228\n",
       "\n",
       "[22224 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb = rs.tri_barrier(data = dollar['close'], \n",
    "                events = events, \n",
    "                trgt = d_vol, \n",
    "                min_req = 0.002, \n",
    "                num_threads = 3, \n",
    "                ptSl = [1,1], \n",
    "                t1 = vb, \n",
    "                side = None)\n",
    "\n",
    "tb # Show some example\n",
    "\n",
    "# the pandas obj will break the data up process it then stich it back into 1 piece again. (See below)\n",
    "# this will only happen when you use pandas obj multiprocess func using num_threads > 1.\n",
    "\n",
    "# if you scroll all the way to the bottom, that is your final dataframe output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\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>ret</th>\n",
       "      <th>bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-05 14:54:26.286</th>\n",
       "      <td>-0.003448</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 14:57:13.616</th>\n",
       "      <td>-0.002957</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 15:01:57.494</th>\n",
       "      <td>-0.003701</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 15:07:29.012</th>\n",
       "      <td>-0.002957</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 15:13:09.655</th>\n",
       "      <td>-0.003451</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 18:02:22.880</th>\n",
       "      <td>-0.003242</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 18:36:03.267</th>\n",
       "      <td>-0.002904</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:02:57.783</th>\n",
       "      <td>-0.002908</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:55:31.030</th>\n",
       "      <td>0.003028</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:50:57.567</th>\n",
       "      <td>0.002915</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22207 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              ret  bin\n",
       "2015-01-05 14:54:26.286 -0.003448 -1.0\n",
       "2015-01-05 14:57:13.616 -0.002957 -1.0\n",
       "2015-01-05 15:01:57.494 -0.003701 -1.0\n",
       "2015-01-05 15:07:29.012 -0.002957 -1.0\n",
       "2015-01-05 15:13:09.655 -0.003451 -1.0\n",
       "...                           ...  ...\n",
       "2016-12-30 18:02:22.880 -0.003242 -1.0\n",
       "2016-12-30 18:36:03.267 -0.002904 -1.0\n",
       "2016-12-30 19:02:57.783 -0.002908 -1.0\n",
       "2016-12-30 19:55:31.030  0.003028  1.0\n",
       "2016-12-30 20:50:57.567  0.002915  1.0\n",
       "\n",
       "[22207 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_label = rs.meta_label(data = dollar['close'],\n",
    "                      events = tb,\n",
    "                      drop = False)\n",
    "\n",
    "m_label # Show some example\n",
    "\n",
    "# previously when we run tri_bar func, NaT is present. However once func is passed to labels, these NaTs will be dropped.\n",
    "# There is an in-built drop func that will trigger the below drop_label func as well.\n",
    "# change drop = False to float value i.e. 0.05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> AFML page 54 section 3.9\n",
    ">\n",
    "> \"Some ML classifiers do not perform well when data samples are too imbalanced. \n",
    ">  In those circumstances, it is preferably to drop those rare labels and focus on more common outcomes.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    11343\n",
       "-1.0    10784\n",
       " 0.0       80\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_label['bin'].value_counts()\n",
    "\n",
    "# Here is a quick look at our 'bin' values.\n",
    "# Apparently we have a rare label, bin = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    0.510785\n",
       "-1.0    0.485613\n",
       " 0.0    0.003602\n",
       "Name: bin, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_label['bin'].value_counts(normalize = True)\n",
    "\n",
    "# basically it's 0.003602 of all our metalabels. Max is 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>ret</th>\n",
       "      <th>bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-05 14:54:26.286</th>\n",
       "      <td>-0.003448</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 14:57:13.616</th>\n",
       "      <td>-0.002957</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 15:01:57.494</th>\n",
       "      <td>-0.003701</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 15:07:29.012</th>\n",
       "      <td>-0.002957</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05 15:13:09.655</th>\n",
       "      <td>-0.003451</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 18:02:22.880</th>\n",
       "      <td>-0.003242</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 18:36:03.267</th>\n",
       "      <td>-0.002904</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:02:57.783</th>\n",
       "      <td>-0.002908</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 19:55:31.030</th>\n",
       "      <td>0.003028</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:50:57.567</th>\n",
       "      <td>0.002915</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22127 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              ret  bin\n",
       "2015-01-05 14:54:26.286 -0.003448 -1.0\n",
       "2015-01-05 14:57:13.616 -0.002957 -1.0\n",
       "2015-01-05 15:01:57.494 -0.003701 -1.0\n",
       "2015-01-05 15:07:29.012 -0.002957 -1.0\n",
       "2015-01-05 15:13:09.655 -0.003451 -1.0\n",
       "...                           ...  ...\n",
       "2016-12-30 18:02:22.880 -0.003242 -1.0\n",
       "2016-12-30 18:36:03.267 -0.002904 -1.0\n",
       "2016-12-30 19:02:57.783 -0.002908 -1.0\n",
       "2016-12-30 19:55:31.030  0.003028  1.0\n",
       "2016-12-30 20:50:57.567  0.002915  1.0\n",
       "\n",
       "[22127 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_meta_label = rs.drop_label(events = m_label, \n",
    "                                min_pct = 0.05)\n",
    "\n",
    "drop_meta_label # Show some example\n",
    "\n",
    "# In the below case we dropped all bin = 0, while keeping only 1 & -1"
   ]
  }
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