AFML 5.4.ipynb

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    "# Fractional differentiate\n",
    "\n",
    "This notebook will cover exercise answer.\n",
    "\n",
    "* Exercise 5.6\n",
    "\n",
    "As we go along, there will be some explanations.\n",
    "\n",
    "Stationarity is a key concept in time-series, by now the idea itself has been demostrated in previous notebooks (Feat Importance).\n",
    "\n",
    "Most of the functions below can be found under research/Features and /Ensemble"
   ]
  },
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     "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\\statsmodels\\tools\\_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\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 matplotlib.pyplot as plt\n",
    "import research as rs\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.stattools import coint\n",
    "\n",
    "p = print\n",
    "\n",
    "#pls take note of version\n",
    "#numba 0.49.1 #https://github.com/numba/numba/issues/4255\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",
    "\n",
    "# For most part of the func we only use 'close'\n",
    "\n",
    "close = dollar['close'].to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ADF pVal: 0.04165 with critical value: 5%\n"
     ]
    }
   ],
   "source": [
    "ffd_series = close.apply(np.log).cumsum()\n",
    "ffd_series = rs.fracDiff_FFD(ffd_series, \n",
    "                    d = 1.99999889, \n",
    "                    thres=1e-5\n",
    "                   ).dropna()\n",
    "\n",
    "adf_pval = adfuller(ffd_series.squeeze(), \n",
    "                    maxlag = 1,\n",
    "                    regression='c', \n",
    "                    autolag=None)[1]\n",
    "\n",
    "p(\"\\nADF pVal: {0:.5f} with critical value: {1}%\".format(adf_pval, 5)) #confirm stationarity"
   ]
  },
  {
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       "    <tr>\n",
       "      <th>2015-01-02 09:35:57.204</th>\n",
       "      <td>-0.001693</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-02 12:59:42.176</th>\n",
       "      <td>0.000243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02 14:19:33.847</th>\n",
       "      <td>0.001329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02 14:33:39.311</th>\n",
       "      <td>0.000355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02 14:42:28.315</th>\n",
       "      <td>0.000714</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                            close\n",
       "date_time                        \n",
       "2015-01-02 09:35:57.204 -0.001693\n",
       "2015-01-02 12:59:42.176  0.000243\n",
       "2015-01-02 14:19:33.847  0.001329\n",
       "2015-01-02 14:33:39.311  0.000355\n",
       "2015-01-02 14:42:28.315  0.000714"
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "ffd_series.head()"
   ]
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-30 20:59:58.848</th>\n",
       "      <td>-0.101709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 21:00:00.352</th>\n",
       "      <td>-0.102272</td>\n",
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       "    <tr>\n",
       "      <th>2016-12-30 21:00:24.294</th>\n",
       "      <td>-0.101718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 21:03:03.027</th>\n",
       "      <td>-0.102393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30 21:13:31.990</th>\n",
       "      <td>-0.102062</td>\n",
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      "text/plain": [
       "                            close\n",
       "date_time                        \n",
       "2016-12-30 20:59:58.848 -0.101709\n",
       "2016-12-30 21:00:00.352 -0.102272\n",
       "2016-12-30 21:00:24.294 -0.101718\n",
       "2016-12-30 21:03:03.027 -0.102393\n",
       "2016-12-30 21:13:31.990 -0.102062"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ffd_series.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "839\n"
     ]
    }
   ],
   "source": [
    "cs_event = rs.cs_filter(data = ffd_series, limit=(ffd_series.std() * 0.2))\n",
    "\n",
    "p(len(cs_event))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "\n",
    "This is the real reason why cs_filter was created, it was meant to be applied to stationary series.\n",
    "\n",
    "Our stationary series can act as our anchor for our mean-reversion strategy, that is why we are using cs_filter on this series to sample feature matrix.\n",
    "\n",
    "However, due to the lack of data sample/ resource, I cannot use 2 x std. (In fact, we would have been able to increase our F1 score in previous chapters, if we had enough data samples.)\n",
    "\n",
    "Based on my previous trials, when 1 x std is applied, the series itself will reduce heteroscedasticity even if it was not stationary.\n",
    "\n",
    "If you have the means to sample 5 - 10 years data, feel free to change to 2 x std."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>ffd_series</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-04 23:20:12.567</th>\n",
       "      <td>2040.75</td>\n",
       "      <td>-0.003825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07 10:55:23.194</th>\n",
       "      <td>2008.00</td>\n",
       "      <td>0.004166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07 15:10:50.900</th>\n",
       "      <td>2014.50</td>\n",
       "      <td>-0.002360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:48:57.964</th>\n",
       "      <td>2037.25</td>\n",
       "      <td>0.006857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 05:47:32.006</th>\n",
       "      <td>2032.75</td>\n",
       "      <td>-0.003119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-28 01:31:48.252</th>\n",
       "      <td>2205.75</td>\n",
       "      <td>-0.099969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-04 23:32:49.403</th>\n",
       "      <td>2183.50</td>\n",
       "      <td>-0.102717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-05 02:06:52.025</th>\n",
       "      <td>2189.00</td>\n",
       "      <td>-0.096321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-08 12:46:05.346</th>\n",
       "      <td>2233.00</td>\n",
       "      <td>-0.101778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-14 20:09:50.768</th>\n",
       "      <td>2248.25</td>\n",
       "      <td>-0.103682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>839 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           close  ffd_series\n",
       "2015-01-04 23:20:12.567  2040.75   -0.003825\n",
       "2015-01-07 10:55:23.194  2008.00    0.004166\n",
       "2015-01-07 15:10:50.900  2014.50   -0.002360\n",
       "2015-01-08 01:48:57.964  2037.25    0.006857\n",
       "2015-01-08 05:47:32.006  2032.75   -0.003119\n",
       "...                          ...         ...\n",
       "2016-11-28 01:31:48.252  2205.75   -0.099969\n",
       "2016-12-04 23:32:49.403  2183.50   -0.102717\n",
       "2016-12-05 02:06:52.025  2189.00   -0.096321\n",
       "2016-12-08 12:46:05.346  2233.00   -0.101778\n",
       "2016-12-14 20:09:50.768  2248.25   -0.103682\n",
       "\n",
       "[839 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This portion requires combining events based on ffd_series with original dollar close using filtered index\n",
    "# since filter is based on ffd, therefore index matrix must also be based on filtered ffd\n",
    "df_mtx = pd.DataFrame(index = cs_event).assign(close = close,\n",
    "                                                ffd_series = ffd_series).drop_duplicates().dropna()\n",
    "df_mtx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mtx['volatility'] = rs.vol(df_mtx.close, span0 = 50) #one of our features, since we do not have a side\n",
    "\n",
    "df_mtx.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vb = rs.vert_barrier(data = df_mtx.close, events = cs_event, period = 'days', freq = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
      "2015-01-07 15:10:50.900 2015-01-12 16:02:08.112                     NaT   \n",
      "2015-01-08 01:48:57.964 2015-01-13 09:38:58.103                     NaT   \n",
      "2015-01-08 05:47:32.006 2015-01-13 09:38:58.103                     NaT   \n",
      "2015-01-09 14:48:46.704 2015-01-14 19:14:20.771 2015-01-14 04:31:40.468   \n",
      "2015-01-12 14:36:34.243 2015-01-19 09:36:49.301 2015-01-14 19:14:20.771   \n",
      "...                                         ...                     ...   \n",
      "2015-09-01 19:46:17.742 2015-09-07 01:34:00.944                     NaT   \n",
      "2015-09-01 22:05:09.069 2015-09-07 01:34:00.944                     NaT   \n",
      "2015-09-02 00:20:04.277 2015-09-07 01:34:00.944                     NaT   \n",
      "2015-09-02 02:09:17.333 2015-09-07 04:40:25.376                     NaT   \n",
      "2015-09-02 08:08:50.931 2015-09-08 01:27:51.915                     NaT   \n",
      "\n",
      "                                             pt  \n",
      "2015-01-07 15:10:50.900 2015-01-08 01:48:57.964  \n",
      "2015-01-08 01:48:57.964                     NaT  \n",
      "2015-01-08 05:47:32.006                     NaT  \n",
      "2015-01-09 14:48:46.704                     NaT  \n",
      "2015-01-12 14:36:34.243                     NaT  \n",
      "...                                         ...  \n",
      "2015-09-01 19:46:17.742                     NaT  \n",
      "2015-09-01 22:05:09.069                     NaT  \n",
      "2015-09-02 00:20:04.277                     NaT  \n",
      "2015-09-02 02:09:17.333                     NaT  \n",
      "2015-09-02 08:08:50.931                     NaT  \n",
      "\n",
      "[279 rows x 3 columns]] this out\n",
      "[                                             t1                      sl  \\\n",
      "2015-01-07 15:10:50.900 2015-01-12 16:02:08.112                     NaT   \n",
      "2015-01-08 01:48:57.964 2015-01-13 09:38:58.103                     NaT   \n",
      "2015-01-08 05:47:32.006 2015-01-13 09:38:58.103                     NaT   \n",
      "2015-01-09 14:48:46.704 2015-01-14 19:14:20.771 2015-01-14 04:31:40.468   \n",
      "2015-01-12 14:36:34.243 2015-01-19 09:36:49.301 2015-01-14 19:14:20.771   \n",
      "...                                         ...                     ...   \n",
      "2015-09-01 19:46:17.742 2015-09-07 01:34:00.944                     NaT   \n",
      "2015-09-01 22:05:09.069 2015-09-07 01:34:00.944                     NaT   \n",
      "2015-09-02 00:20:04.277 2015-09-07 01:34:00.944                     NaT   \n",
      "2015-09-02 02:09:17.333 2015-09-07 04:40:25.376                     NaT   \n",
      "2015-09-02 08:08:50.931 2015-09-08 01:27:51.915                     NaT   \n",
      "\n",
      "                                             pt  \n",
      "2015-01-07 15:10:50.900 2015-01-08 01:48:57.964  \n",
      "2015-01-08 01:48:57.964                     NaT  \n",
      "2015-01-08 05:47:32.006                     NaT  \n",
      "2015-01-09 14:48:46.704                     NaT  \n",
      "2015-01-12 14:36:34.243                     NaT  \n",
      "...                                         ...  \n",
      "2015-09-01 19:46:17.742                     NaT  \n",
      "2015-09-01 22:05:09.069                     NaT  \n",
      "2015-09-02 00:20:04.277                     NaT  \n",
      "2015-09-02 02:09:17.333                     NaT  \n",
      "2015-09-02 08:08:50.931                     NaT  \n",
      "\n",
      "[279 rows x 3 columns],                                              t1  sl  pt\n",
      "2016-01-21 20:15:15.382 2016-01-27 00:04:03.812 NaT NaT\n",
      "2016-01-22 01:19:34.937 2016-01-27 13:41:54.306 NaT NaT\n",
      "2016-01-22 04:21:15.807 2016-01-27 13:41:54.306 NaT NaT\n",
      "2016-01-22 06:19:21.200 2016-01-27 13:41:54.306 NaT NaT\n",
      "2016-01-22 08:48:09.889 2016-01-27 13:41:54.306 NaT NaT\n",
      "...                                         ...  ..  ..\n",
      "2016-11-28 01:31:48.252 2016-12-04 23:32:49.403 NaT NaT\n",
      "2016-12-04 23:32:49.403 2016-12-14 20:09:50.768 NaT NaT\n",
      "2016-12-05 02:06:52.025 2016-12-14 20:09:50.768 NaT NaT\n",
      "2016-12-08 12:46:05.346 2016-12-14 20:09:50.768 NaT NaT\n",
      "2016-12-14 20:09:50.768                     NaT NaT NaT\n",
      "\n",
      "[279 rows x 3 columns]] this out\n",
      "[                                             t1                      sl  \\\n",
      "2015-01-07 15:10:50.900 2015-01-12 16:02:08.112                     NaT   \n",
      "2015-01-08 01:48:57.964 2015-01-13 09:38:58.103                     NaT   \n",
      "2015-01-08 05:47:32.006 2015-01-13 09:38:58.103                     NaT   \n",
      "2015-01-09 14:48:46.704 2015-01-14 19:14:20.771 2015-01-14 04:31:40.468   \n",
      "2015-01-12 14:36:34.243 2015-01-19 09:36:49.301 2015-01-14 19:14:20.771   \n",
      "...                                         ...                     ...   \n",
      "2015-09-01 19:46:17.742 2015-09-07 01:34:00.944                     NaT   \n",
      "2015-09-01 22:05:09.069 2015-09-07 01:34:00.944                     NaT   \n",
      "2015-09-02 00:20:04.277 2015-09-07 01:34:00.944                     NaT   \n",
      "2015-09-02 02:09:17.333 2015-09-07 04:40:25.376                     NaT   \n",
      "2015-09-02 08:08:50.931 2015-09-08 01:27:51.915                     NaT   \n",
      "\n",
      "                                             pt  \n",
      "2015-01-07 15:10:50.900 2015-01-08 01:48:57.964  \n",
      "2015-01-08 01:48:57.964                     NaT  \n",
      "2015-01-08 05:47:32.006                     NaT  \n",
      "2015-01-09 14:48:46.704                     NaT  \n",
      "2015-01-12 14:36:34.243                     NaT  \n",
      "...                                         ...  \n",
      "2015-09-01 19:46:17.742                     NaT  \n",
      "2015-09-01 22:05:09.069                     NaT  \n",
      "2015-09-02 00:20:04.277                     NaT  \n",
      "2015-09-02 02:09:17.333                     NaT  \n",
      "2015-09-02 08:08:50.931                     NaT  \n",
      "\n",
      "[279 rows x 3 columns],                                              t1  sl  pt\n",
      "2016-01-21 20:15:15.382 2016-01-27 00:04:03.812 NaT NaT\n",
      "2016-01-22 01:19:34.937 2016-01-27 13:41:54.306 NaT NaT\n",
      "2016-01-22 04:21:15.807 2016-01-27 13:41:54.306 NaT NaT\n",
      "2016-01-22 06:19:21.200 2016-01-27 13:41:54.306 NaT NaT\n",
      "2016-01-22 08:48:09.889 2016-01-27 13:41:54.306 NaT NaT\n",
      "...                                         ...  ..  ..\n",
      "2016-11-28 01:31:48.252 2016-12-04 23:32:49.403 NaT NaT\n",
      "2016-12-04 23:32:49.403 2016-12-14 20:09:50.768 NaT NaT\n",
      "2016-12-05 02:06:52.025 2016-12-14 20:09:50.768 NaT NaT\n",
      "2016-12-08 12:46:05.346 2016-12-14 20:09:50.768 NaT NaT\n",
      "2016-12-14 20:09:50.768                     NaT NaT NaT\n",
      "\n",
      "[279 rows x 3 columns],                                              t1  sl  pt\n",
      "2015-09-03 00:05:02.346 2015-09-08 01:27:51.915 NaT NaT\n",
      "2015-09-03 06:00:48.819 2015-09-08 12:08:06.840 NaT NaT\n",
      "2015-09-03 07:52:14.021 2015-09-08 12:08:06.840 NaT NaT\n",
      "2015-09-03 09:57:43.822 2015-09-08 12:08:06.840 NaT NaT\n",
      "2015-09-03 12:33:59.011 2015-09-09 03:06:59.837 NaT NaT\n",
      "...                                         ...  ..  ..\n",
      "2016-01-21 16:00:11.522 2016-01-27 00:04:03.812 NaT NaT\n",
      "2016-01-21 18:20:55.061 2016-01-27 00:04:03.812 NaT NaT\n",
      "2016-01-21 19:14:12.535 2016-01-27 00:04:03.812 NaT NaT\n",
      "2016-01-21 19:36:37.429 2016-01-27 00:04:03.812 NaT NaT\n",
      "2016-01-21 20:07:23.306 2016-01-27 00:04:03.812 NaT NaT\n",
      "\n",
      "[279 rows x 3 columns]] this out\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-05-29 16:48:39.895072 33.33% _pt_sl_t1 done after 0.11 minutes. Remaining 0.23 minutes.\r",
      "2020-05-29 16:48:39.922997 66.67% _pt_sl_t1 done after 0.12 minutes. Remaining 0.06 minutes.\r",
      "2020-05-29 16:48:39.968388 100.0% _pt_sl_t1 done after 0.12 minutes. Remaining 0.0 minutes.\n"
     ]
    }
   ],
   "source": [
    "# triple barrier events based on filter while data is also based on filtered index\n",
    "tb = rs.tri_barrier(data = df_mtx.close, \n",
    "                    events = cs_event, \n",
    "                    trgt = df_mtx['volatility'], \n",
    "                    min_req = 0.0002, \n",
    "                    num_threads = 3, \n",
    "                    ptSl= [2,2], #2x barriers\n",
    "                    t1 = vb, \n",
    "                    side = None)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                             t1      trgt\n",
       "2015-01-07 15:10:50.900 2015-01-08 01:48:57.964  0.002252\n",
       "2015-01-08 01:48:57.964 2015-01-13 09:38:58.103  0.007576\n",
       "2015-01-08 05:47:32.006 2015-01-13 09:38:58.103  0.006867\n",
       "2015-01-09 14:48:46.704 2015-01-14 04:31:40.468  0.009708\n",
       "2015-01-12 14:36:34.243 2015-01-14 19:14:20.771  0.008815\n",
       "...                                         ...       ...\n",
       "2016-11-14 02:12:45.857 2016-11-28 01:31:48.252  0.022494\n",
       "2016-11-28 01:31:48.252 2016-12-04 23:32:49.403  0.022526\n",
       "2016-12-04 23:32:49.403 2016-12-14 20:09:50.768  0.022102\n",
       "2016-12-05 02:06:52.025 2016-12-14 20:09:50.768  0.021669\n",
       "2016-12-08 12:46:05.346 2016-12-14 20:09:50.768  0.021819\n",
       "\n",
       "[836 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlabel = rs.meta_label(data = df_mtx.close, \n",
    "                       events = tb, \n",
    "                       drop = 0.05) # because we do not have side, we need to drop rare labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    489\n",
       "-1.0    345\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlabel['bin'].value_counts() #834"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                         0    1    2    3    4    5    6    7    8    9    \\\n",
       "2015-01-07 15:10:50.900  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2015-01-08 01:48:57.964  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2015-01-08 05:47:32.006  0.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2015-01-09 14:48:46.704  0.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2015-01-12 14:36:34.243  0.0  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
       "...                      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "2016-11-28 01:31:48.252  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2016-12-04 23:32:49.403  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2016-12-05 02:06:52.025  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2016-12-08 12:46:05.346  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2016-12-14 20:09:50.768  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "                         ...  826  827  828  829  830  831  832  833  834  835  \n",
       "2015-01-07 15:10:50.900  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2015-01-08 01:48:57.964  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2015-01-08 05:47:32.006  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2015-01-09 14:48:46.704  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "2015-01-12 14:36:34.243  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "...                      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "2016-11-28 01:31:48.252  ...  1.0  1.0  1.0  1.0  1.0  1.0  1.0  0.0  0.0  0.0  \n",
       "2016-12-04 23:32:49.403  ...  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  \n",
       "2016-12-05 02:06:52.025  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  \n",
       "2016-12-08 12:46:05.346  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  1.0  \n",
       "2016-12-14 20:09:50.768  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  1.0  \n",
       "\n",
       "[837 rows x 836 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "troo = rs.mp_idx_matrix(data = df_mtx.close, events = tb)\n",
    "#np.arange(troo.shape[1])\n",
    "troo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Quick Reference Guide/ Summary to func usage**\n",
    "\n",
    "1. Apply cs_filter using stationary series (FFD)\n",
    "\n",
    "2. Apply bband_as_side using close price series\n",
    "\n",
    "3. Create a new dataframe, using both outputs with index = cs_filter output.\n",
    "\n",
    "4. Apply func tri_barrier, Vert_barrier using new dataframe, before applying meta_label func.\n",
    "\n",
    "5. Feed these meta lables and features to random forest.\n",
    "\n",
    "Always include indicators, stationary series, volatility as part of your feature matrix if possible, since these are the features on how you derive meta-labels. \n",
    "\n",
    "As seen in previous exercises, ML models are smart enough to know what was the key parameters which meta-labels was labelled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Wei_X\\Desktop\\Python\\research\\Sampling\\sample_unique.py:491: RuntimeWarning: invalid value encountered in log\n",
      "  rtn = np.log(data).diff().fillna(0) # Log-returns, so that they are additive\n"
     ]
    },
    {
     "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>close</th>\n",
       "      <th>ffd_series</th>\n",
       "      <th>volatility</th>\n",
       "      <th>wr</th>\n",
       "      <th>wt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-07 15:10:50.900</th>\n",
       "      <td>2014.50</td>\n",
       "      <td>-0.002360</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>3.004931</td>\n",
       "      <td>0.503742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 01:48:57.964</th>\n",
       "      <td>2037.25</td>\n",
       "      <td>0.006857</td>\n",
       "      <td>0.007576</td>\n",
       "      <td>2.548574</td>\n",
       "      <td>0.505364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08 05:47:32.006</th>\n",
       "      <td>2032.75</td>\n",
       "      <td>-0.003119</td>\n",
       "      <td>0.006867</td>\n",
       "      <td>0.456357</td>\n",
       "      <td>0.506811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09 14:48:46.704</th>\n",
       "      <td>2049.75</td>\n",
       "      <td>-0.003951</td>\n",
       "      <td>0.009708</td>\n",
       "      <td>0.651985</td>\n",
       "      <td>0.507852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12 14:36:34.243</th>\n",
       "      <td>2030.50</td>\n",
       "      <td>-0.004798</td>\n",
       "      <td>0.008815</td>\n",
       "      <td>2.571428</td>\n",
       "      <td>0.508686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-14 02:12:45.857</th>\n",
       "      <td>2167.75</td>\n",
       "      <td>-0.097722</td>\n",
       "      <td>0.022494</td>\n",
       "      <td>1.194957</td>\n",
       "      <td>0.992436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-28 01:31:48.252</th>\n",
       "      <td>2205.75</td>\n",
       "      <td>-0.099969</td>\n",
       "      <td>0.022526</td>\n",
       "      <td>1.384323</td>\n",
       "      <td>0.994040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-04 23:32:49.403</th>\n",
       "      <td>2183.50</td>\n",
       "      <td>-0.102717</td>\n",
       "      <td>0.022102</td>\n",
       "      <td>1.510426</td>\n",
       "      <td>0.996258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-05 02:06:52.025</th>\n",
       "      <td>2189.00</td>\n",
       "      <td>-0.096321</td>\n",
       "      <td>0.021669</td>\n",
       "      <td>4.223331</td>\n",
       "      <td>0.998337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-08 12:46:05.346</th>\n",
       "      <td>2233.00</td>\n",
       "      <td>-0.101778</td>\n",
       "      <td>0.021819</td>\n",
       "      <td>3.550162</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>834 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           close  ffd_series  volatility        wr        wt\n",
       "2015-01-07 15:10:50.900  2014.50   -0.002360    0.002252  3.004931  0.503742\n",
       "2015-01-08 01:48:57.964  2037.25    0.006857    0.007576  2.548574  0.505364\n",
       "2015-01-08 05:47:32.006  2032.75   -0.003119    0.006867  0.456357  0.506811\n",
       "2015-01-09 14:48:46.704  2049.75   -0.003951    0.009708  0.651985  0.507852\n",
       "2015-01-12 14:36:34.243  2030.50   -0.004798    0.008815  2.571428  0.508686\n",
       "...                          ...         ...         ...       ...       ...\n",
       "2016-11-14 02:12:45.857  2167.75   -0.097722    0.022494  1.194957  0.992436\n",
       "2016-11-28 01:31:48.252  2205.75   -0.099969    0.022526  1.384323  0.994040\n",
       "2016-12-04 23:32:49.403  2183.50   -0.102717    0.022102  1.510426  0.996258\n",
       "2016-12-05 02:06:52.025  2189.00   -0.096321    0.021669  4.223331  0.998337\n",
       "2016-12-08 12:46:05.346  2233.00   -0.101778    0.021819  3.550162  1.000000\n",
       "\n",
       "[834 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df_mtx.reindex(mlabel.index)\n",
    "Z = tb.reindex(mlabel.index)\n",
    "y = mlabel['bin']\n",
    "\n",
    "X['wr'] = rs.wght_by_rtn(data = X, events = Z, num_threads = 1)\n",
    "X['wt'] = rs.wght_by_td(data = X, events = Z, num_threads = 1, td = 0.5)\n",
    "\n",
    "X #show example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-08-26 10:34:40.144    0.394145\n",
       "2015-08-25 11:37:16.214    0.488536\n",
       "2016-01-08 10:53:07.196    0.817534\n",
       "2015-09-23 13:11:44.559    0.736790\n",
       "2015-07-01 10:25:32.469    0.581751\n",
       "                             ...   \n",
       "2015-03-23 08:14:01.389    1.331041\n",
       "2015-05-29 10:32:41.319    0.509290\n",
       "2015-09-01 13:48:58.967    0.310314\n",
       "2015-12-22 09:01:07.048    0.770506\n",
       "2015-05-12 14:41:49.319    2.245505\n",
       "Name: wr, Length: 583, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into train and test sets, earlier on we combine sample weights with X because we need to match during fit\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, shuffle=True, stratify=None)\n",
    "\n",
    "wght_td = X_train['wt']\n",
    "wght_rtn = X_train['wr']\n",
    "\n",
    "#weights are not feature so have to remove\n",
    "X_train = X_train.drop(['wr','wt'], axis = 1)\n",
    "X_test = X_test.drop(['wr','wt'], axis = 1)\n",
    "\n",
    "wght_rtn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_estimator = DecisionTreeClassifier()\n",
    "\n",
    "# common parameters to use, this ensemble method mimics monte-carlos as it randomly selects samples to form \"forest\"\n",
    "n_estimate = 10\n",
    "random_state = 42\n",
    "max_samples = 35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ave uniqueness for grp sample 1: 0.647674\n",
      "Ave uniqueness for grp sample 2: 0.854561\n",
      "Ave uniqueness for grp sample 3: 0.849644\n",
      "Ave uniqueness for grp sample 4: 0.856805\n",
      "Ave uniqueness for grp sample 5: 0.867614\n",
      "Ave uniqueness for grp sample 6: 0.857027\n",
      "Ave uniqueness for grp sample 7: 0.804856\n",
      "Ave uniqueness for grp sample 8: 0.812000\n",
      "Ave uniqueness for grp sample 9: 0.904695\n",
      "Ave uniqueness for grp sample 10: 0.800973\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(),\n",
       "                  data=2015-01-07 15:10:50.900    2014.50\n",
       "2015-01-08 01:48:57.964    2037.25\n",
       "2015-01-08 05:47:32.006    2032.75\n",
       "2015-01-09 14:48:46.704    2049.75\n",
       "2015-01-12 14:36:34.243    2030.50\n",
       "                            ...   \n",
       "2016-11-28 01:31:48.252    2205.75\n",
       "2016-12-04 23:32:49.403    2183.50\n",
       "2016-12-05 02:06:52.025    2189.00\n",
       "2016-12-08 12:46:05.346    2233.00\n",
       "2016-12-14 20:09:50.768    2...\n",
       "2015-01-12 14:36:34.243 2015-01-14 19:14:20.771  0.008815\n",
       "...                                         ...       ...\n",
       "2016-11-14 02:12:45.857 2016-11-28 01:31:48.252  0.022494\n",
       "2016-11-28 01:31:48.252 2016-12-04 23:32:49.403  0.022526\n",
       "2016-12-04 23:32:49.403 2016-12-14 20:09:50.768  0.022102\n",
       "2016-12-05 02:06:52.025 2016-12-14 20:09:50.768  0.021669\n",
       "2016-12-08 12:46:05.346 2016-12-14 20:09:50.768  0.021819\n",
       "\n",
       "[836 rows x 2 columns],\n",
       "                  max_samples=35, n_jobs=1, oob_score=True, random_state=42)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#warning, this is a heavy algo, will take up to 15 mins to complete with current parameter\n",
    "\n",
    "rtn_bag_cls=rs.BaggingClassifier(data = df_mtx.close,\n",
    "                                events = tb,\n",
    "                                base_estimator = base_estimator,\n",
    "                                n_estimators = n_estimate, # the number of trees as a estimate\n",
    "                                max_samples = max_samples, #if you put max_sample = 1.0, pls mentally get ready to wait\n",
    "                                max_features = 1.0,\n",
    "                                bootstrap = True, #must always be True\n",
    "                                bootstrap_features=False,\n",
    "                                oob_score = True, # You will need to have bootstrap = True\n",
    "                                warm_start = False,\n",
    "                                n_jobs = 1, # depending your machine. might become slower if you use all processors\n",
    "                                random_state = random_state, #seed figure\n",
    "                                verbose = 0)\n",
    "\n",
    "\n",
    "\n",
    "# Fit with absolute return weights\n",
    "rtn_bag_cls.fit(X_train, y_train, sample_weight = wght_rtn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-bag Score: 0.668954\n"
     ]
    }
   ],
   "source": [
    "p(\"Out-of-bag Score: {0:.6f}\".format(rtn_bag_cls.oob_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Classification Report\n",
      "=======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.58      0.70      0.63       102\n",
      "         1.0       0.76      0.66      0.71       149\n",
      "\n",
      "    accuracy                           0.67       251\n",
      "   macro avg       0.67      0.68      0.67       251\n",
      "weighted avg       0.69      0.67      0.68       251\n",
      "\n",
      "\n",
      " Confusion Matrix\n",
      "=======================================================\n",
      "[[TN, FP]\n",
      " [FN, TP]]\n",
      "\n",
      "[[71 31]\n",
      " [51 98]]\n",
      "\n",
      " Accuracy Score\n",
      "=======================================================\n",
      "0.6733067729083665\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = rtn_bag_cls.predict(X_test)\n",
    "y_prob = rtn_bag_cls.predict_proba(X_test)[:,1] #we only want true positive probability\n",
    "\n",
    "rs.report_matrix(y_test, y_pred, y_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ave uniqueness for grp sample 1: 0.647674\n",
      "Ave uniqueness for grp sample 2: 0.854561\n",
      "Ave uniqueness for grp sample 3: 0.849644\n",
      "Ave uniqueness for grp sample 4: 0.856805\n",
      "Ave uniqueness for grp sample 5: 0.867614\n",
      "Ave uniqueness for grp sample 6: 0.857027\n",
      "Ave uniqueness for grp sample 7: 0.804856\n",
      "Ave uniqueness for grp sample 8: 0.812000\n",
      "Ave uniqueness for grp sample 9: 0.904695\n",
      "Ave uniqueness for grp sample 10: 0.800973\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(),\n",
       "                  data=2015-01-07 15:10:50.900    2014.50\n",
       "2015-01-08 01:48:57.964    2037.25\n",
       "2015-01-08 05:47:32.006    2032.75\n",
       "2015-01-09 14:48:46.704    2049.75\n",
       "2015-01-12 14:36:34.243    2030.50\n",
       "                            ...   \n",
       "2016-11-28 01:31:48.252    2205.75\n",
       "2016-12-04 23:32:49.403    2183.50\n",
       "2016-12-05 02:06:52.025    2189.00\n",
       "2016-12-08 12:46:05.346    2233.00\n",
       "2016-12-14 20:09:50.768    2...\n",
       "2015-01-12 14:36:34.243 2015-01-14 19:14:20.771  0.008815\n",
       "...                                         ...       ...\n",
       "2016-11-14 02:12:45.857 2016-11-28 01:31:48.252  0.022494\n",
       "2016-11-28 01:31:48.252 2016-12-04 23:32:49.403  0.022526\n",
       "2016-12-04 23:32:49.403 2016-12-14 20:09:50.768  0.022102\n",
       "2016-12-05 02:06:52.025 2016-12-14 20:09:50.768  0.021669\n",
       "2016-12-08 12:46:05.346 2016-12-14 20:09:50.768  0.021819\n",
       "\n",
       "[836 rows x 2 columns],\n",
       "                  max_samples=35, n_jobs=1, oob_score=True, random_state=42)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td_bag_cls=rs.BaggingClassifier(data = df_mtx.close,\n",
    "                                events = tb,\n",
    "                                base_estimator = base_estimator,\n",
    "                                n_estimators = n_estimate, # the number of trees as a estimate\n",
    "                                max_samples = max_samples, #if you put max_sample = 1.0, pls mentally get ready to wait\n",
    "                                max_features = 1.0,\n",
    "                                bootstrap = True, #must always be True\n",
    "                                bootstrap_features=False,\n",
    "                                oob_score = True, # You will need to have bootstrap = True\n",
    "                                warm_start = False,\n",
    "                                n_jobs = 1, # depending your machine. might become slower if you use all processors\n",
    "                                random_state = random_state, #seed figure\n",
    "                                verbose = 0)\n",
    "\n",
    "\n",
    "\n",
    "# Fit sample weight by time-decay\n",
    "td_bag_cls.fit(X_train, y_train, sample_weight = wght_td)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-bag Score: 0.662093\n"
     ]
    }
   ],
   "source": [
    "p(\"Out-of-bag Score: {0:.6f}\".format(td_bag_cls.oob_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Classification Report\n",
      "=======================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.57      0.61      0.59       102\n",
      "         1.0       0.72      0.68      0.70       149\n",
      "\n",
      "    accuracy                           0.65       251\n",
      "   macro avg       0.64      0.65      0.64       251\n",
      "weighted avg       0.66      0.65      0.65       251\n",
      "\n",
      "\n",
      " Confusion Matrix\n",
      "=======================================================\n",
      "[[TN, FP]\n",
      " [FN, TP]]\n",
      "\n",
      "[[ 62  40]\n",
      " [ 47 102]]\n",
      "\n",
      " Accuracy Score\n",
      "=======================================================\n",
      "0.6533864541832669\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred1 = td_bag_cls.predict(X_test)\n",
    "y_prob1 = td_bag_cls.predict_proba(X_test)[:,1] #we only want true positive probability\n",
    "\n",
    "rs.report_matrix(y_test, y_pred1, y_prob1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out-of-Bag score\n",
    "\n",
    "Original sklearn bagging method: Out-of-bag Score: 0.662093\n",
    "\n",
    "Sequential bootstrap bagging method:\n",
    "        \n",
    "1. without sample weight: Out-of-bag Score: 0.655232\n",
    "2. with abs return wght: Out-of-bag Score: 0.668954\n",
    "3. with time-decay wght: Out-of-bag Score: 0.662093\n",
    "\n",
    "Comparing the original sklearn bagging ensemble using decision tree classifier, our OOB score is lower.\n",
    "\n",
    "Once we included sample weights by either absolute returns or time-decay, our OOB score becomes higher or the equal with original sklearn OOB score.\n",
    "\n",
    "In fact, overall accuracy and f1-scores also increased with a better ROC graph. All of it with a less bias result.\n",
    "\n",
    "**Conclusion**\n",
    "\n",
    "Whenever we use bagging, we should try and include sample weights so that ML classification will improve the overall results.\n",
    "\n",
    "**Note**\n",
    "\n",
    "If you are wondering why I can get identical average uniqueness for all sample grps, just make sure you set random state."
   ]
  }
 ],
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