simulation_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import get_data\n",
    "import statsmodels\n",
    "from simulate import cholesky\n",
    "from simulate import simulatedata\n",
    "from simulate import simulate_multiple_time_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "syms = ['BTCUSDT']\n",
    "freq = '10min'\n",
    "start_date = '2020-01-01'\n",
    "end_date = '2020-01-03'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BTCUSDT_2019-12-26.json\n",
      "Your browser has been opened to visit:\n",
      "\n",
      "    https://accounts.google.com/o/oauth2/auth?client_id=695704595703-kho1v4q5lhtoqq268ckb4it0n08facat.apps.googleusercontent.com&redirect_uri=http%3A%2F%2Flocalhost%3A8080%2F&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&access_type=offline&response_type=code\n",
      "\n",
      "Authentication successful.\n",
      "BTCUSDT_2020-01-02.json\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Documents\\GitHub\\TradingBot\\get_data.py:52: FutureWarning: Indexing a timezone-aware DatetimeIndex with a timezone-naive datetime is deprecated and will raise KeyError in a future version.  Use a timezone-aware object instead.\n",
      "  data[symbol] = _fit_data_to_df(data[symbol], frequency)[start_time:end_time]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'BTCUSDT':                               open     high      low    close      volume  \\\n",
       " time                                                                        \n",
       " 2020-01-01 00:00:00+00:00  7195.24  7196.25  7178.20  7191.07  154.874358   \n",
       " 2020-01-01 00:10:00+00:00  7193.15  7193.53  7177.35  7178.29   80.261439   \n",
       " 2020-01-01 00:20:00+00:00  7177.71  7185.56  7175.47  7178.45   96.049725   \n",
       " 2020-01-01 00:30:00+00:00  7178.19  7185.44  7176.23  7177.53   59.973326   \n",
       " 2020-01-01 00:40:00+00:00  7177.14  7182.99  7176.34  7182.94   44.872982   \n",
       " ...                            ...      ...      ...      ...         ...   \n",
       " 2020-01-02 23:20:00+00:00  6959.16  6962.39  6950.00  6962.34  217.262815   \n",
       " 2020-01-02 23:30:00+00:00  6962.47  6974.75  6957.66  6965.37  145.757951   \n",
       " 2020-01-02 23:40:00+00:00  6965.09  6969.46  6959.87  6967.41   75.157310   \n",
       " 2020-01-02 23:50:00+00:00  6967.40  6968.82  6961.24  6965.71   82.459841   \n",
       " 2020-01-03 00:00:00+00:00  6965.49  6965.71  6948.45  6963.38  180.492276   \n",
       " \n",
       "                                   VWAP  \n",
       " time                                    \n",
       " 2020-01-01 00:00:00+00:00  7185.131532  \n",
       " 2020-01-01 00:10:00+00:00  7183.130112  \n",
       " 2020-01-01 00:20:00+00:00  7179.184768  \n",
       " 2020-01-01 00:30:00+00:00  7181.326228  \n",
       " 2020-01-01 00:40:00+00:00  7181.218046  \n",
       " ...                                ...  \n",
       " 2020-01-02 23:20:00+00:00  6956.932068  \n",
       " 2020-01-02 23:30:00+00:00  6967.519428  \n",
       " 2020-01-02 23:40:00+00:00  6963.849599  \n",
       " 2020-01-02 23:50:00+00:00  6965.270329  \n",
       " 2020-01-03 00:00:00+00:00  6956.339162  \n",
       " \n",
       " [289 rows x 6 columns]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = get_data.get_data(syms, freq, start_date, end_date)\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01 00:00:00+00:00</td>\n",
       "      <td>7195.24</td>\n",
       "      <td>7196.25</td>\n",
       "      <td>7178.20</td>\n",
       "      <td>7191.07</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01 00:10:00+00:00</td>\n",
       "      <td>7193.15</td>\n",
       "      <td>7193.53</td>\n",
       "      <td>7177.35</td>\n",
       "      <td>7178.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-01 00:20:00+00:00</td>\n",
       "      <td>7177.71</td>\n",
       "      <td>7185.56</td>\n",
       "      <td>7175.47</td>\n",
       "      <td>7178.45</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01 00:30:00+00:00</td>\n",
       "      <td>7178.19</td>\n",
       "      <td>7185.44</td>\n",
       "      <td>7176.23</td>\n",
       "      <td>7177.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-01 00:40:00+00:00</td>\n",
       "      <td>7177.14</td>\n",
       "      <td>7182.99</td>\n",
       "      <td>7176.34</td>\n",
       "      <td>7182.94</td>\n",
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       "      <th>284</th>\n",
       "      <td>2020-01-02 23:20:00+00:00</td>\n",
       "      <td>6959.16</td>\n",
       "      <td>6962.39</td>\n",
       "      <td>6950.00</td>\n",
       "      <td>6962.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>2020-01-02 23:30:00+00:00</td>\n",
       "      <td>6962.47</td>\n",
       "      <td>6974.75</td>\n",
       "      <td>6957.66</td>\n",
       "      <td>6965.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>2020-01-02 23:40:00+00:00</td>\n",
       "      <td>6965.09</td>\n",
       "      <td>6969.46</td>\n",
       "      <td>6959.87</td>\n",
       "      <td>6967.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>2020-01-02 23:50:00+00:00</td>\n",
       "      <td>6967.40</td>\n",
       "      <td>6968.82</td>\n",
       "      <td>6961.24</td>\n",
       "      <td>6965.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>2020-01-03 00:00:00+00:00</td>\n",
       "      <td>6965.49</td>\n",
       "      <td>6965.71</td>\n",
       "      <td>6948.45</td>\n",
       "      <td>6963.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>289 rows × 5 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                         time     open     high      low    close\n",
       "0   2020-01-01 00:00:00+00:00  7195.24  7196.25  7178.20  7191.07\n",
       "1   2020-01-01 00:10:00+00:00  7193.15  7193.53  7177.35  7178.29\n",
       "2   2020-01-01 00:20:00+00:00  7177.71  7185.56  7175.47  7178.45\n",
       "3   2020-01-01 00:30:00+00:00  7178.19  7185.44  7176.23  7177.53\n",
       "4   2020-01-01 00:40:00+00:00  7177.14  7182.99  7176.34  7182.94\n",
       "..                        ...      ...      ...      ...      ...\n",
       "284 2020-01-02 23:20:00+00:00  6959.16  6962.39  6950.00  6962.34\n",
       "285 2020-01-02 23:30:00+00:00  6962.47  6974.75  6957.66  6965.37\n",
       "286 2020-01-02 23:40:00+00:00  6965.09  6969.46  6959.87  6967.41\n",
       "287 2020-01-02 23:50:00+00:00  6967.40  6968.82  6961.24  6965.71\n",
       "288 2020-01-03 00:00:00+00:00  6965.49  6965.71  6948.45  6963.38\n",
       "\n",
       "[289 rows x 5 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.statespace.varmax import VARMAX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "varmax = VARMAX(dt1, order = (0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.18880288e-04, -1.21012422e-04, -3.85045835e-01,  3.06049127e-03,\n",
       "        8.76273782e-01,  3.67354401e-03, -1.50726841e-03,  5.16874961e-04,\n",
       "        1.68595030e-03])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = varmax.fit(return_params=True)\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_check_kwargs',\n",
       " '_clone_from_init_kwds',\n",
       " '_data_attr',\n",
       " '_final_exog',\n",
       " '_final_trend',\n",
       " '_fit_collinear',\n",
       " '_fit_zeros',\n",
       " '_fixed_params',\n",
       " '_fixed_params_index',\n",
       " '_forecasts_error_partial_derivatives',\n",
       " '_formula_max_endog',\n",
       " '_free_params_index',\n",
       " '_get_exog_names',\n",
       " '_get_extension_time_varying_matrices',\n",
       " '_get_index_label_loc',\n",
       " '_get_index_loc',\n",
       " '_get_index_with_final_state',\n",
       " '_get_init_kwds',\n",
       " '_get_prediction_index',\n",
       " '_handle_data',\n",
       " '_has_fixed_params',\n",
       " '_hessian_complex_step',\n",
       " '_hessian_finite_difference',\n",
       " '_hessian_oim',\n",
       " '_hessian_opg',\n",
       " '_hessian_param_defaults',\n",
       " '_hessian_param_names',\n",
       " '_idx_lower_state_cov',\n",
       " '_idx_state_intercept',\n",
       " '_idx_transition',\n",
       " '_index',\n",
       " '_index_dates',\n",
       " '_index_freq',\n",
       " '_index_generated',\n",
       " '_index_inferred_freq',\n",
       " '_index_int64',\n",
       " '_index_none',\n",
       " '_init_dates',\n",
       " '_init_keys',\n",
       " '_init_kwargs',\n",
       " '_k_order',\n",
       " '_kwargs_allowed',\n",
       " '_loglike_param_defaults',\n",
       " '_loglike_param_names',\n",
       " '_params_ar',\n",
       " '_params_index',\n",
       " '_params_ma',\n",
       " '_params_obs_cov',\n",
       " '_params_regression',\n",
       " '_params_state_cov',\n",
       " '_params_trend',\n",
       " '_res_classes',\n",
       " '_score_complex_step',\n",
       " '_score_finite_difference',\n",
       " '_score_harvey',\n",
       " '_score_obs_harvey',\n",
       " '_score_param_defaults',\n",
       " '_score_param_names',\n",
       " '_set_exog_names',\n",
       " '_set_final_exog',\n",
       " '_trend_data',\n",
       " '_trend_is_const',\n",
       " '_validate_can_fix_params',\n",
       " '_validate_out_of_sample_exog',\n",
       " '_wrap_results',\n",
       " 'clone',\n",
       " 'data',\n",
       " 'endog',\n",
       " 'endog_names',\n",
       " 'enforce_invertibility',\n",
       " 'enforce_stationarity',\n",
       " 'error_cov_type',\n",
       " 'exog',\n",
       " 'exog_names',\n",
       " 'filter',\n",
       " 'fit',\n",
       " 'fit_constrained',\n",
       " 'fix_params',\n",
       " 'from_formula',\n",
       " 'handle_params',\n",
       " 'hessian',\n",
       " 'impulse_responses',\n",
       " 'information',\n",
       " 'initial_variance',\n",
       " 'initialization',\n",
       " 'initialize',\n",
       " 'initialize_approximate_diffuse',\n",
       " 'initialize_known',\n",
       " 'initialize_statespace',\n",
       " 'initialize_stationary',\n",
       " 'k_ar',\n",
       " 'k_constant',\n",
       " 'k_endog',\n",
       " 'k_exog',\n",
       " 'k_ma',\n",
       " 'k_params',\n",
       " 'k_states',\n",
       " 'k_trend',\n",
       " 'loglike',\n",
       " 'loglikelihood_burn',\n",
       " 'loglikeobs',\n",
       " 'measurement_error',\n",
       " 'mle_regression',\n",
       " 'nobs',\n",
       " 'observed_information_matrix',\n",
       " 'opg_information_matrix',\n",
       " 'order',\n",
       " 'param_names',\n",
       " 'parameters',\n",
       " 'polynomial_trend',\n",
       " 'predict',\n",
       " 'prepare_data',\n",
       " 'score',\n",
       " 'score_obs',\n",
       " 'set_conserve_memory',\n",
       " 'set_filter_method',\n",
       " 'set_inversion_method',\n",
       " 'set_smoother_output',\n",
       " 'set_stability_method',\n",
       " 'simulate',\n",
       " 'simulation_smoother',\n",
       " 'smooth',\n",
       " 'ssm',\n",
       " 'start_params',\n",
       " 'state_names',\n",
       " 'tolerance',\n",
       " 'transform_jacobian',\n",
       " 'transform_params',\n",
       " 'trend',\n",
       " 'trend_offset',\n",
       " 'untransform_params',\n",
       " 'update']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(varmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00+00:00</th>\n",
       "      <td>0.001064</td>\n",
       "      <td>0.004048</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 00:10:00+00:00</th>\n",
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       "      <td>0.003106</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 00:20:00+00:00</th>\n",
       "      <td>-0.002110</td>\n",
       "      <td>-0.001739</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 00:30:00+00:00</th>\n",
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       "      <td>-0.002545</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 00:40:00+00:00</th>\n",
       "      <td>-0.000831</td>\n",
       "      <td>-0.001819</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>0.001228</td>\n",
       "      <td>0.000759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>-0.003780</td>\n",
       "      <td>0.000669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>0.000507</td>\n",
       "      <td>-0.000556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>0.002015</td>\n",
       "      <td>-0.001486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>-0.001418</td>\n",
       "      <td>0.002757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               open     close\n",
       "time                                         \n",
       "2020-01-01 00:00:00+00:00  0.001064  0.004048\n",
       "2020-01-01 00:10:00+00:00 -0.001429  0.003106\n",
       "2020-01-01 00:20:00+00:00 -0.002110 -0.001739\n",
       "2020-01-01 00:30:00+00:00  0.001962 -0.002545\n",
       "2020-01-01 00:40:00+00:00 -0.000831 -0.001819\n",
       "...                             ...       ...\n",
       "2020-01-02 23:20:00+00:00  0.001228  0.000759\n",
       "2020-01-02 23:30:00+00:00 -0.003780  0.000669\n",
       "2020-01-02 23:40:00+00:00  0.000507 -0.000556\n",
       "2020-01-02 23:50:00+00:00  0.002015 -0.001486\n",
       "2020-01-03 00:00:00+00:00 -0.001418  0.002757\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varmax.simulate(params, len(dt1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
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       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2020-01-01 00:00:00+00:00</th>\n",
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       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>-0.001664</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.000435</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.000293</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>0.000332</td>\n",
       "      <td>-0.000244</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>-0.000274</td>\n",
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       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               open     close\n",
       "time                                         \n",
       "2020-01-01 00:00:00+00:00       NaN       NaN\n",
       "2020-01-01 00:10:00+00:00 -0.000290 -0.001777\n",
       "2020-01-01 00:20:00+00:00 -0.002146  0.000022\n",
       "2020-01-01 00:30:00+00:00  0.000067 -0.000128\n",
       "2020-01-01 00:40:00+00:00 -0.000146  0.000754\n",
       "...                             ...       ...\n",
       "2020-01-02 23:20:00+00:00 -0.001664  0.000457\n",
       "2020-01-02 23:30:00+00:00  0.000476  0.000435\n",
       "2020-01-02 23:40:00+00:00  0.000376  0.000293\n",
       "2020-01-02 23:50:00+00:00  0.000332 -0.000244\n",
       "2020-01-03 00:00:00+00:00 -0.000274 -0.000334\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
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       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00+00:00</th>\n",
       "      <td>7195.24</td>\n",
       "      <td>7196.25</td>\n",
       "      <td>7178.20</td>\n",
       "      <td>7191.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:10:00+00:00</th>\n",
       "      <td>7193.15</td>\n",
       "      <td>7193.53</td>\n",
       "      <td>7177.35</td>\n",
       "      <td>7178.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:20:00+00:00</th>\n",
       "      <td>7177.71</td>\n",
       "      <td>7185.56</td>\n",
       "      <td>7175.47</td>\n",
       "      <td>7178.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:30:00+00:00</th>\n",
       "      <td>7178.19</td>\n",
       "      <td>7185.44</td>\n",
       "      <td>7176.23</td>\n",
       "      <td>7177.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:40:00+00:00</th>\n",
       "      <td>7177.14</td>\n",
       "      <td>7182.99</td>\n",
       "      <td>7176.34</td>\n",
       "      <td>7182.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>6959.16</td>\n",
       "      <td>6962.39</td>\n",
       "      <td>6950.00</td>\n",
       "      <td>6962.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>6962.47</td>\n",
       "      <td>6974.75</td>\n",
       "      <td>6957.66</td>\n",
       "      <td>6965.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>6965.09</td>\n",
       "      <td>6969.46</td>\n",
       "      <td>6959.87</td>\n",
       "      <td>6967.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>6967.40</td>\n",
       "      <td>6968.82</td>\n",
       "      <td>6961.24</td>\n",
       "      <td>6965.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>6965.49</td>\n",
       "      <td>6965.71</td>\n",
       "      <td>6948.45</td>\n",
       "      <td>6963.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              open     high      low    close\n",
       "time                                                         \n",
       "2020-01-01 00:00:00+00:00  7195.24  7196.25  7178.20  7191.07\n",
       "2020-01-01 00:10:00+00:00  7193.15  7193.53  7177.35  7178.29\n",
       "2020-01-01 00:20:00+00:00  7177.71  7185.56  7175.47  7178.45\n",
       "2020-01-01 00:30:00+00:00  7178.19  7185.44  7176.23  7177.53\n",
       "2020-01-01 00:40:00+00:00  7177.14  7182.99  7176.34  7182.94\n",
       "...                            ...      ...      ...      ...\n",
       "2020-01-02 23:20:00+00:00  6959.16  6962.39  6950.00  6962.34\n",
       "2020-01-02 23:30:00+00:00  6962.47  6974.75  6957.66  6965.37\n",
       "2020-01-02 23:40:00+00:00  6965.09  6969.46  6959.87  6967.41\n",
       "2020-01-02 23:50:00+00:00  6967.40  6968.82  6961.24  6965.71\n",
       "2020-01-03 00:00:00+00:00  6965.49  6965.71  6948.45  6963.38\n",
       "\n",
       "[289 rows x 4 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = raw_data['BTCUSDT'][['open', 'high', 'low','close']].copy()\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_close, seasonal_order_close = simulatedata.get_order(dt['close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if sum(seasonal_order_close) == 1: seasonal_order_close = (0, 0, 0, 0)\n",
    "model_params_close = simulatedata.fit_sarima(dt['close'], order_close, seasonal_order_close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa27b12fd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "simulatedata.simulate_sarima(dt['close'], order_close, seasonal_order_close, model_params_close, len(dt), 1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa27a55640>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dt['close'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "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>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:10:00+00:00</th>\n",
       "      <td>-0.000290</td>\n",
       "      <td>-0.001777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:20:00+00:00</th>\n",
       "      <td>-0.002146</td>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:30:00+00:00</th>\n",
       "      <td>0.000067</td>\n",
       "      <td>-0.000128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:40:00+00:00</th>\n",
       "      <td>-0.000146</td>\n",
       "      <td>0.000754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>-0.001664</td>\n",
       "      <td>0.000457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>0.000476</td>\n",
       "      <td>0.000435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.000293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>0.000332</td>\n",
       "      <td>-0.000244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>-0.000274</td>\n",
       "      <td>-0.000334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               open     close\n",
       "time                                         \n",
       "2020-01-01 00:00:00+00:00       NaN       NaN\n",
       "2020-01-01 00:10:00+00:00 -0.000290 -0.001777\n",
       "2020-01-01 00:20:00+00:00 -0.002146  0.000022\n",
       "2020-01-01 00:30:00+00:00  0.000067 -0.000128\n",
       "2020-01-01 00:40:00+00:00 -0.000146  0.000754\n",
       "...                             ...       ...\n",
       "2020-01-02 23:20:00+00:00 -0.001664  0.000457\n",
       "2020-01-02 23:30:00+00:00  0.000476  0.000435\n",
       "2020-01-02 23:40:00+00:00  0.000376  0.000293\n",
       "2020-01-02 23:50:00+00:00  0.000332 -0.000244\n",
       "2020-01-03 00:00:00+00:00 -0.000274 -0.000334\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt1 = pd.DataFrame()\n",
    "dt1['open'] = dt['open'].pct_change()\n",
    "dt1['close'] = dt['close'].pct_change()\n",
    "dt1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.00011071658288543273,\n",
       " 0.0014098873357997598,\n",
       " -0.00011168703848396907,\n",
       " 0.0014030332655430606,\n",
       " -1.8471211971044486e-07,\n",
       " -0.09337766601688922,\n",
       "                open         close\n",
       " open   1.975361e-06 -1.847121e-07\n",
       " close -1.847121e-07  1.994708e-06)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_close = simulate_multiple_time_series.compute_std(dt1['close'])\n",
    "mean_close = dt1['close'].mean()\n",
    "std_open = simulate_multiple_time_series.compute_std(dt1['open'])\n",
    "mean_open = dt1['open'].mean()\n",
    "\n",
    "cov_open_close = dt1.cov()\n",
    "cov = cov_open_close.iloc[0, 1]\n",
    "corr = cov / (std_close*std_open)\n",
    "\n",
    "mean_close, std_close, mean_open, std_open, cov, corr, cov_open_close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:10:00+00:00</th>\n",
       "      <td>-0.127426</td>\n",
       "      <td>0.220745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:20:00+00:00</th>\n",
       "      <td>-1.450286</td>\n",
       "      <td>-0.017618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:30:00+00:00</th>\n",
       "      <td>0.127268</td>\n",
       "      <td>0.002311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:40:00+00:00</th>\n",
       "      <td>-0.024653</td>\n",
       "      <td>-0.114507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>-1.106465</td>\n",
       "      <td>-0.075194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>0.418607</td>\n",
       "      <td>-0.072313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>0.347811</td>\n",
       "      <td>-0.053461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>0.315988</td>\n",
       "      <td>0.017654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>-0.115783</td>\n",
       "      <td>0.029642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               open     close\n",
       "time                                         \n",
       "2020-01-01 00:00:00+00:00       NaN       NaN\n",
       "2020-01-01 00:10:00+00:00 -0.127426  0.220745\n",
       "2020-01-01 00:20:00+00:00 -1.450286 -0.017618\n",
       "2020-01-01 00:30:00+00:00  0.127268  0.002311\n",
       "2020-01-01 00:40:00+00:00 -0.024653 -0.114507\n",
       "...                             ...       ...\n",
       "2020-01-02 23:20:00+00:00 -1.106465 -0.075194\n",
       "2020-01-02 23:30:00+00:00  0.418607 -0.072313\n",
       "2020-01-02 23:40:00+00:00  0.347811 -0.053461\n",
       "2020-01-02 23:50:00+00:00  0.315988  0.017654\n",
       "2020-01-03 00:00:00+00:00 -0.115783  0.029642\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt2 = pd.DataFrame()\n",
    "dt2['open'] = (dt1['open'] - mean_open) / std_open\n",
    "dt2['close'] = (dt1['close'] - mean_close) * 2*corr / std_close\n",
    "dt2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:10:00+00:00</th>\n",
       "      <td>-0.127426</td>\n",
       "      <td>-0.348171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:20:00+00:00</th>\n",
       "      <td>-1.450286</td>\n",
       "      <td>-1.432668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:30:00+00:00</th>\n",
       "      <td>0.127268</td>\n",
       "      <td>0.124957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:40:00+00:00</th>\n",
       "      <td>-0.024653</td>\n",
       "      <td>0.089854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>-1.106465</td>\n",
       "      <td>-1.031271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>0.418607</td>\n",
       "      <td>0.490919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>0.347811</td>\n",
       "      <td>0.401271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>0.315988</td>\n",
       "      <td>0.298334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>-0.115783</td>\n",
       "      <td>-0.145425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               open     close\n",
       "time                                         \n",
       "2020-01-01 00:00:00+00:00       NaN       NaN\n",
       "2020-01-01 00:10:00+00:00 -0.127426 -0.348171\n",
       "2020-01-01 00:20:00+00:00 -1.450286 -1.432668\n",
       "2020-01-01 00:30:00+00:00  0.127268  0.124957\n",
       "2020-01-01 00:40:00+00:00 -0.024653  0.089854\n",
       "...                             ...       ...\n",
       "2020-01-02 23:20:00+00:00 -1.106465 -1.031271\n",
       "2020-01-02 23:30:00+00:00  0.418607  0.490919\n",
       "2020-01-02 23:40:00+00:00  0.347811  0.401271\n",
       "2020-01-02 23:50:00+00:00  0.315988  0.298334\n",
       "2020-01-03 00:00:00+00:00 -0.115783 -0.145425\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt3 = dt2.copy()\n",
    "dt3['close'] = dt3['open'] - dt3['close']\n",
    "dt3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "chol = np.linalg.cholesky(dt3.corr())\n",
    "inverse_chol = np.linalg.inv(chol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:10:00+00:00</th>\n",
       "      <td>-0.127426</td>\n",
       "      <td>-1.199174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:20:00+00:00</th>\n",
       "      <td>-1.450286</td>\n",
       "      <td>-0.041260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:30:00+00:00</th>\n",
       "      <td>0.127268</td>\n",
       "      <td>-0.000492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:40:00+00:00</th>\n",
       "      <td>-0.024653</td>\n",
       "      <td>0.613537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>-1.106465</td>\n",
       "      <td>0.300644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>0.418607</td>\n",
       "      <td>0.428175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>0.347811</td>\n",
       "      <td>0.320144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>0.315988</td>\n",
       "      <td>-0.065313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>-0.115783</td>\n",
       "      <td>-0.170281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               open     close\n",
       "time                                         \n",
       "2020-01-01 00:00:00+00:00       NaN       NaN\n",
       "2020-01-01 00:10:00+00:00 -0.127426 -1.199174\n",
       "2020-01-01 00:20:00+00:00 -1.450286 -0.041260\n",
       "2020-01-01 00:30:00+00:00  0.127268 -0.000492\n",
       "2020-01-01 00:40:00+00:00 -0.024653  0.613537\n",
       "...                             ...       ...\n",
       "2020-01-02 23:20:00+00:00 -1.106465  0.300644\n",
       "2020-01-02 23:30:00+00:00  0.418607  0.428175\n",
       "2020-01-02 23:40:00+00:00  0.347811  0.320144\n",
       "2020-01-02 23:50:00+00:00  0.315988 -0.065313\n",
       "2020-01-03 00:00:00+00:00 -0.115783 -0.170281\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt5 = np.matmul(inverse_chol, dt3.T).T # or np.matmul(dt3, inverse_chol.T)\n",
    "dt5.columns = ['open', 'close']\n",
    "dt5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = dt5.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2, 0, 0), (0, 0, 0, 1), (2, 0, 0), (0, 0, 0, 1))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_close, seasonal_order_close = simulatedata.get_order(transform['close'][1:])\n",
    "order_open, seasonal_order_open = simulatedata.get_order(transform['open'][1:])\n",
    "order_close, seasonal_order_close, order_open, seasonal_order_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if sum(seasonal_order_close) == 1: seasonal_order_close = (0, 0, 0, 0)\n",
    "model_params_close = simulatedata.fit_sarima(transform['close'][1:], order_close, seasonal_order_close)\n",
    "if sum(seasonal_order_open) == 1: seasonal_order_open = (0, 0, 0, 0)\n",
    "model_params_open = simulatedata.fit_sarima(transform['open'][1:], order_open, seasonal_order_open)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(            0\n",
       " 0   -0.137662\n",
       " 1    0.704559\n",
       " 2   -0.470258\n",
       " 3   -0.516564\n",
       " 4   -0.576349\n",
       " ..        ...\n",
       " 284 -0.026299\n",
       " 285 -0.700252\n",
       " 286  0.154434\n",
       " 287 -0.130136\n",
       " 288 -0.107519\n",
       " \n",
       " [289 rows x 1 columns],\n",
       "             0\n",
       " 0    1.171152\n",
       " 1    0.271017\n",
       " 2   -1.282887\n",
       " 3    1.094195\n",
       " 4   -0.017147\n",
       " ..        ...\n",
       " 284  0.312110\n",
       " 285  0.032312\n",
       " 286  1.762012\n",
       " 287 -0.601912\n",
       " 288 -0.629714\n",
       " \n",
       " [289 rows x 1 columns])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_close = simulatedata.simulate_sarima(transform['close'][1:], order_close, seasonal_order_close, model_params_close, len(transform), 1)\n",
    "t_open = simulatedata.simulate_sarima(transform['open'][1:], order_open, seasonal_order_open, model_params_open, len(transform), 1)\n",
    "t_open, t_close"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.137662</td>\n",
       "      <td>0.082495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.704559</td>\n",
       "      <td>0.742664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.470258</td>\n",
       "      <td>-0.700590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.516564</td>\n",
       "      <td>-0.304108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.576349</td>\n",
       "      <td>-0.569487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>-0.026299</td>\n",
       "      <td>0.032192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>-0.700252</td>\n",
       "      <td>-0.682033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>0.154434</td>\n",
       "      <td>0.479359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>-0.130136</td>\n",
       "      <td>-0.239782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>-0.107519</td>\n",
       "      <td>-0.222729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1\n",
       "0   -0.137662  0.082495\n",
       "1    0.704559  0.742664\n",
       "2   -0.470258 -0.700590\n",
       "3   -0.516564 -0.304108\n",
       "4   -0.576349 -0.569487\n",
       "..        ...       ...\n",
       "284 -0.026299  0.032192\n",
       "285 -0.700252 -0.682033\n",
       "286  0.154434  0.479359\n",
       "287 -0.130136 -0.239782\n",
       "288 -0.107519 -0.222729\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt7 = np.concatenate([t_open, t_close], axis = 1)\n",
    "dt7 = np.matmul(chol, dt7.T).T # or np.matmul(dt7, chol.T)\n",
    "dt7 = pd.DataFrame(dt7)\n",
    "dt7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00+00:00</th>\n",
       "      <td>-0.137662</td>\n",
       "      <td>-0.220157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:10:00+00:00</th>\n",
       "      <td>0.704559</td>\n",
       "      <td>-0.038105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:20:00+00:00</th>\n",
       "      <td>-0.470258</td>\n",
       "      <td>0.230332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:30:00+00:00</th>\n",
       "      <td>-0.516564</td>\n",
       "      <td>-0.212456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:40:00+00:00</th>\n",
       "      <td>-0.576349</td>\n",
       "      <td>-0.006862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>-0.026299</td>\n",
       "      <td>-0.058490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>-0.700252</td>\n",
       "      <td>-0.018219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>0.154434</td>\n",
       "      <td>-0.324925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>-0.130136</td>\n",
       "      <td>0.109646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>-0.107519</td>\n",
       "      <td>0.115210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               open     close\n",
       "time                                         \n",
       "2020-01-01 00:00:00+00:00 -0.137662 -0.220157\n",
       "2020-01-01 00:10:00+00:00  0.704559 -0.038105\n",
       "2020-01-01 00:20:00+00:00 -0.470258  0.230332\n",
       "2020-01-01 00:30:00+00:00 -0.516564 -0.212456\n",
       "2020-01-01 00:40:00+00:00 -0.576349 -0.006862\n",
       "...                             ...       ...\n",
       "2020-01-02 23:20:00+00:00 -0.026299 -0.058490\n",
       "2020-01-02 23:30:00+00:00 -0.700252 -0.018219\n",
       "2020-01-02 23:40:00+00:00  0.154434 -0.324925\n",
       "2020-01-02 23:50:00+00:00 -0.130136  0.109646\n",
       "2020-01-03 00:00:00+00:00 -0.107519  0.115210\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt8 = dt7.copy()\n",
    "dt8.index = dt1.index\n",
    "dt8.columns = ['open', 'close']\n",
    "dt8['close'] = dt8['open'] - dt8['close']\n",
    "dt8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00+00:00</th>\n",
       "      <td>-0.000305</td>\n",
       "      <td>0.001551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:10:00+00:00</th>\n",
       "      <td>0.000877</td>\n",
       "      <td>0.000177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:20:00+00:00</th>\n",
       "      <td>-0.000771</td>\n",
       "      <td>-0.001850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:30:00+00:00</th>\n",
       "      <td>-0.000836</td>\n",
       "      <td>0.001493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:40:00+00:00</th>\n",
       "      <td>-0.000920</td>\n",
       "      <td>-0.000059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>-0.000149</td>\n",
       "      <td>0.000331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>-0.001094</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.002342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>-0.000294</td>\n",
       "      <td>-0.000938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>-0.000263</td>\n",
       "      <td>-0.000980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               open     close\n",
       "time                                         \n",
       "2020-01-01 00:00:00+00:00 -0.000305  0.001551\n",
       "2020-01-01 00:10:00+00:00  0.000877  0.000177\n",
       "2020-01-01 00:20:00+00:00 -0.000771 -0.001850\n",
       "2020-01-01 00:30:00+00:00 -0.000836  0.001493\n",
       "2020-01-01 00:40:00+00:00 -0.000920 -0.000059\n",
       "...                             ...       ...\n",
       "2020-01-02 23:20:00+00:00 -0.000149  0.000331\n",
       "2020-01-02 23:30:00+00:00 -0.001094  0.000027\n",
       "2020-01-02 23:40:00+00:00  0.000105  0.002342\n",
       "2020-01-02 23:50:00+00:00 -0.000294 -0.000938\n",
       "2020-01-03 00:00:00+00:00 -0.000263 -0.000980\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt9 = dt8.copy()\n",
    "dt9['open'] = dt9['open'] * std_open + mean_open\n",
    "dt9['close'] = dt9['close'] * std_close / (2*corr) + mean_close\n",
    "dt9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>2.306320e-06</td>\n",
       "      <td>-1.977011e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>-1.977011e-07</td>\n",
       "      <td>1.848650e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open         close\n",
       "open   2.306320e-06 -1.977011e-07\n",
       "close -1.977011e-07  1.848650e-06"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt9.cov()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stage 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00+00:00</th>\n",
       "      <td>7195.240000</td>\n",
       "      <td>7191.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:10:00+00:00</th>\n",
       "      <td>7193.046666</td>\n",
       "      <td>7202.225757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:20:00+00:00</th>\n",
       "      <td>7199.353761</td>\n",
       "      <td>7203.500228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:30:00+00:00</th>\n",
       "      <td>7193.799639</td>\n",
       "      <td>7190.176787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:40:00+00:00</th>\n",
       "      <td>7187.782436</td>\n",
       "      <td>7200.913097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:20:00+00:00</th>\n",
       "      <td>7110.245403</td>\n",
       "      <td>7137.369992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:30:00+00:00</th>\n",
       "      <td>7109.188928</td>\n",
       "      <td>7139.731391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:40:00+00:00</th>\n",
       "      <td>7101.410312</td>\n",
       "      <td>7139.922903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:50:00+00:00</th>\n",
       "      <td>7102.155886</td>\n",
       "      <td>7156.646480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03 00:00:00+00:00</th>\n",
       "      <td>7100.065919</td>\n",
       "      <td>7149.930114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>289 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0            1\n",
       "2020-01-01 00:00:00+00:00  7195.240000  7191.070000\n",
       "2020-01-01 00:10:00+00:00  7193.046666  7202.225757\n",
       "2020-01-01 00:20:00+00:00  7199.353761  7203.500228\n",
       "2020-01-01 00:30:00+00:00  7193.799639  7190.176787\n",
       "2020-01-01 00:40:00+00:00  7187.782436  7200.913097\n",
       "...                                ...          ...\n",
       "2020-01-02 23:20:00+00:00  7110.245403  7137.369992\n",
       "2020-01-02 23:30:00+00:00  7109.188928  7139.731391\n",
       "2020-01-02 23:40:00+00:00  7101.410312  7139.922903\n",
       "2020-01-02 23:50:00+00:00  7102.155886  7156.646480\n",
       "2020-01-03 00:00:00+00:00  7100.065919  7149.930114\n",
       "\n",
       "[289 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulated_price_data = pd.DataFrame()\n",
    "simulated_price_data[0] = simulatedata.construct_price_series(dt9['open'], dt['open'][0], dt.index[0], freq)\n",
    "simulated_price_data[1] = simulatedata.construct_price_series(dt9['close'], dt['close'][0], dt.index[0], freq)\n",
    "simulated_price_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulatedata.Evaluate_performance(dt['open'], simulated_price_data, lags=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10824/1630679231.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# thế nào để fix cho cái dt1 nó ko bị null mặc dù cho chạy thẳng hàm cholesky.cholesky2 thì cũng có đoạn tính corr dt1 mà nó ko bị null\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mcov_open_close\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdt1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mcov\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcov_open_close\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mvar_close\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mvar_open\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[0mcov\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvar_open\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3456\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3457\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3458\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3459\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "var_close = simulate_multiple_time_series.compute_std(dt1['close'])\n",
    "mean_close = dt1['close'].mean()\n",
    "var_open = simulate_multiple_time_series.compute_std(dt1['open'])\n",
    "mean_open = dt1['open'].mean()\n",
    "\n",
    "\n",
    "# dt1.corr bị thành null matrix nên lúc trước tôi để thành dt để test và dt thì nó hiển nhiên ko đúng but ko biết làm \n",
    "# thế nào để fix cho cái dt1 nó ko bị null mặc dù cho chạy thẳng hàm cholesky.cholesky2 thì cũng có đoạn tính corr dt1 mà nó ko bị null\n",
    "cov_open_close = dt1.corr()\n",
    "cov = cov_open_close[0][0] / var_close * var_open\n",
    "cov, var_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = []\n",
    "data1 = np.append(data1, simulate_multiple_time_series.normalize_or_standardize_data(dt1['open'], is_normalize= False))\n",
    "data2 = pd.DataFrame()\n",
    "data2['close'] = dt1['close']-mean_close\n",
    "for i in range(len(data2['close'])):\n",
    "    data2['close'][i] = data2['close'][i] * 2 * cov_open_close[0][0] * var_open/var_close\n",
    "data3 = pd.DataFrame()\n",
    "data3['open'] = dt1['open']\n",
    "data3['close'] = -data2['close']+ dt1['open']\n",
    "chol = np.linalg.cholesky(data3.corr())\n",
    "inverse_chol = np.linalg.inv(chol)\n",
    "data1 = np.append(data1, data3['close'])\n",
    "data1 = data1.reshape(2, len(dt1['close']))\n",
    "data1 = np.matmul(inverse_chol, data1)\n",
    "transform = pd.DataFrame(dt1).copy()\n",
    "transform['close'] = data1[1]\n",
    "transform['open'] = data1[0]\n",
    "transform, simulate_multiple_time_series.compute_std(transform['close']),simulate_multiple_time_series.compute_std(transform['open'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform_x_chol = pd.DataFrame()\n",
    "transform_x_chol['close'] = t_close[0]\n",
    "transform_x_chol['open'] = t_open[0]\n",
    "array = []\n",
    "array = np.append(array, transform_x_chol['open'])\n",
    "array = np.append(array, transform_x_chol['close'])\n",
    "array = array.reshape(2,len(transform_x_chol['open']))\n",
    "transform_back = np.matmul(chol, array)\n",
    "# simulate_corr_rets['close'] = simulate_corr_rets['open'] - simulate_corr_rets['close']\n",
    "simulate = pd.DataFrame()\n",
    "simulate['open'] =simulate_multiple_time_series.mulback_cholesky(transform_back[0],  False, mean_open, var_open)\n",
    "simulate['close'] = simulate_multiple_time_series.mulback_cholesky(transform_back[0] -transform_back[1],  False, mean_close, var_close/(2*cov))\n",
    "# simulate ['close'] = simulate['open']-simulate['close']\n",
    "simulate\n",
    "# transform_back = simulate_multiple_time_series.transform_back(transform_x_chol, chol, var_close/(2*cov), mean_close, var_open, mean_open)\n",
    "# transform_back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulate.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulated_price_data = pd.DataFrame()\n",
    "simulated_price_data[0] = simulatedata.construct_price_series(simulate['open'], dt['open'][0], dt.index[0], freq)\n",
    "simulated_price_data[1] = simulatedata.construct_price_series(simulate['close'], dt['close'][0], dt.index[0], freq)\n",
    "# for i in range(len(simulated_price_data[1])):\n",
    "#     simulated_price_data[1][i]=simulated_price_data[1][i] * simulated_price_data[0][i]\n",
    "simulated_price_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulatedata.Evaluate_performance(dt['open'], simulated_price_data, lags=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulate_multiple_time_series.simulate_open_and_close(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# order, seasonal_order = simulatedata.get_order(dt['open'][1:])\n",
    "# order, seasonal_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if sum(seasonal_order) == 1: seasonal_order = (0, 0, 0, 0)\n",
    "# model_params = simulatedata.fit_sarima(dt['open'][1:], order, seasonal_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# t = simulatedata.simulate_sarima(dt['open'][1:], order, seasonal_order, model_params, len(dt), 10)\n",
    "# t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulated_price_data = simulatedata.construct_price_series(t, dt['close'][0], dt.index[0], freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulatedata.Evaluate_performance(dt['close'], simulated_price_data, lags=30)"
   ]
  }
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