xbtusd-standardization-market-making-backtest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from numba import njit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# last_price = pd.read_csv(\"last_price.csv\", index_col='timestamp', converters={'timestamp': pd.to_datetime})\n",
    "# ohlc = last_price.resample('5s', closed='right', label='right').ohlc().ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mid price = (best bid + best ask) / 2\n",
    "# buy = sum of bid volume up to (1 - x%) * mid price\n",
    "# sell = sum of ask volume up to (1 + x%) * mid price\n",
    "# imbalance = buy - sell\n",
    "\n",
    "# imbalance = pd.read_pickle('imbalance')\n",
    "# imbalance = imbalance.resample('5s', closed='right', label='right').last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = ohlc.merge(imbalance, left_on='timestamp', right_on='timestamp', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('data')\n",
    "df.iloc[:, 4:] = (df.iloc[:, 4:] - df.iloc[:, 4:].rolling(17280).mean()) / df.iloc[:, 4:].rolling(17280).std()  # 1-day rolling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2019-04-01 00:00:05</th>\n",
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       "      <th>2019-04-01 00:00:10</th>\n",
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       "      <th>2019-04-01 00:00:15</th>\n",
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       "      <th>2019-04-01 00:00:20</th>\n",
       "      <td>4090.0</td>\n",
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       "      <th>2021-03-16 23:59:40</th>\n",
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       "      <th>2021-03-16 23:59:45</th>\n",
       "      <td>56984.5</td>\n",
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       "      <th>2021-03-17 00:00:00</th>\n",
       "      <td>57009.5</td>\n",
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       "<p>12372482 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        open     high      low    close      0.5%        1%  \\\n",
       "timestamp                                                                     \n",
       "2019-04-01 00:00:00   4091.0   4091.0   4091.0   4091.0       NaN       NaN   \n",
       "2019-04-01 00:00:05   4091.0   4091.0   4091.0   4091.0       NaN       NaN   \n",
       "2019-04-01 00:00:10   4091.0   4091.5   4089.0   4091.5       NaN       NaN   \n",
       "2019-04-01 00:00:15   4091.0   4091.0   4090.0   4090.0       NaN       NaN   \n",
       "2019-04-01 00:00:20   4090.0   4090.0   4089.5   4089.5       NaN       NaN   \n",
       "...                      ...      ...      ...      ...       ...       ...   \n",
       "2021-03-16 23:59:40  56982.5  56984.0  56982.0  56984.0 -0.641302 -1.944758   \n",
       "2021-03-16 23:59:45  56984.5  57000.5  56984.5  56999.5 -0.638589 -2.232586   \n",
       "2021-03-16 23:59:50  57000.0  57003.5  57000.0  57003.5 -0.567310 -1.988590   \n",
       "2021-03-16 23:59:55  57003.0  57010.0  57003.0  57009.5 -0.702699 -2.087740   \n",
       "2021-03-17 00:00:00  57009.5  57010.0  57009.5  57009.5 -1.021331 -2.314683   \n",
       "\n",
       "                         2.5%        5%      7.5%       10%       15%  \\\n",
       "timestamp                                                               \n",
       "2019-04-01 00:00:00       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-04-01 00:00:05       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-04-01 00:00:10       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-04-01 00:00:15       NaN       NaN       NaN       NaN       NaN   \n",
       "2019-04-01 00:00:20       NaN       NaN       NaN       NaN       NaN   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2021-03-16 23:59:40 -1.908128 -1.918236 -1.701966 -1.225537  0.850241   \n",
       "2021-03-16 23:59:45 -2.149210 -1.916851 -1.702910 -1.236304  0.841622   \n",
       "2021-03-16 23:59:50 -2.226625 -1.958867 -1.756149 -1.313760  0.762580   \n",
       "2021-03-16 23:59:55 -2.274030 -1.997214 -1.792209 -1.355855  0.719538   \n",
       "2021-03-17 00:00:00 -2.346292 -2.037626 -1.840539 -1.425143  0.647195   \n",
       "\n",
       "                          20%       25%  \n",
       "timestamp                                \n",
       "2019-04-01 00:00:00       NaN       NaN  \n",
       "2019-04-01 00:00:05       NaN       NaN  \n",
       "2019-04-01 00:00:10       NaN       NaN  \n",
       "2019-04-01 00:00:15       NaN       NaN  \n",
       "2019-04-01 00:00:20       NaN       NaN  \n",
       "...                       ...       ...  \n",
       "2021-03-16 23:59:40  1.065207  0.226303  \n",
       "2021-03-16 23:59:45  1.064555  0.225722  \n",
       "2021-03-16 23:59:50  0.991849  0.146631  \n",
       "2021-03-16 23:59:55  0.967213  0.105804  \n",
       "2021-03-17 00:00:00  0.902332  0.037789  \n",
       "\n",
       "[12372482 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit\n",
    "def predict_njit(start_equity, A, B, half_spread, max_position, X, imbalance):\n",
    "    tick_size = 0.5\n",
    "    running_qty = 0\n",
    "    static_equity = start_equity\n",
    "    fee = 0\n",
    "    equity = []\n",
    "    running_qty_ = []\n",
    "    order_qty = 100\n",
    "    new_bid = np.nan\n",
    "    new_ask = np.nan\n",
    "    high = 1\n",
    "    low = 2\n",
    "    close = 3\n",
    "    #imbalance = 7\n",
    "    for row in X:\n",
    "        if new_bid > row[low]:\n",
    "            running_qty += order_qty\n",
    "            static_equity += order_qty / new_bid\n",
    "            fee += order_qty / new_bid * -0.00025    \n",
    "        if new_ask < row[high]:\n",
    "            running_qty -= order_qty\n",
    "            static_equity -= order_qty / new_ask\n",
    "            fee += order_qty / new_ask * -0.00025    \n",
    "        equity.append(static_equity - running_qty / row[close] - fee)\n",
    "        running_qty_.append(running_qty)\n",
    "        skew = B * running_qty / max_position * -1\n",
    "        quote_mid_price = row[close] + A * row[imbalance] + skew\n",
    "        new_bid = np.minimum(np.round(quote_mid_price * (1 - half_spread) / tick_size) * tick_size, row[close] - tick_size)\n",
    "        new_ask = np.maximum(np.round(quote_mid_price * (1 + half_spread) / tick_size) * tick_size, row[close] + tick_size)\n",
    "        if running_qty > max_position:\n",
    "            new_bid = np.nan\n",
    "        if running_qty < -max_position:\n",
    "            new_ask = np.nan\n",
    "    return equity, running_qty_\n",
    "\n",
    "class Backtest:\n",
    "    def __init__(self, equity=None, max_position=None, A=None, B=None, half_spread=None, imbalance=None):\n",
    "        self.equity = equity\n",
    "        self.max_position = max_position\n",
    "        self.A = A\n",
    "        self.B = B\n",
    "        self.half_spread = half_spread\n",
    "        self.imbalance = imbalance\n",
    "        \n",
    "    def set_params(self, A, B, half_spread, imbalance):\n",
    "        self.A = A\n",
    "        self.B = B\n",
    "        self.half_spread = half_spread\n",
    "        self.imbalance = imbalance\n",
    "        return self\n",
    "        \n",
    "    def get_params(self, deep=True):\n",
    "        return { 'equity': self.equity, 'max_position': self.max_position, 'A': self.A, 'B': self.B, 'half_spread': self.half_spread, 'imbalance': self.imbalance }\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def predict(self, X):\n",
    "        equity, running_qty = predict_njit(self.equity, self.A, self.B, self.half_spread, self.max_position, X, self.imbalance)\n",
    "        return equity, running_qty\n",
    "    \n",
    "    def score(self, X):\n",
    "        equity, _ = self.predict(X)\n",
    "        returns = pd.Series(equity).pct_change().fillna(0)\n",
    "#         bm_returns = pd.Series(X[:, 3]).pct_change().fillna(0)\n",
    "#         returns_ = returns - bm_returns\n",
    "        return np.divide(returns.mean(), returns.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[(df.index >= '2019-7-1') & (df.index < '2020-1-1')]\n",
    "valid = df[df.index >= '2020-1-1']\n",
    "# valid = df[df.index < '2020-4-1']\n",
    "# train = df[df.index >= '2020-4-1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 1 folds for each of 1000 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=8)]: Done  34 tasks      | elapsed:   35.2s\n",
      "[Parallel(n_jobs=8)]: Done 184 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=8)]: Done 434 tasks      | elapsed:  5.1min\n",
      "[Parallel(n_jobs=8)]: Done 784 tasks      | elapsed:  9.1min\n",
      "[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 11.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=[(array([      0,       1,       2, ..., 3179517, 3179518, 3179519]),\n",
       "                        array([      0,       1,       2, ..., 3179517, 3179518, 3179519]))],\n",
       "                   estimator=<__main__.Backtest object at 0x7fe04f9885d0>,\n",
       "                   n_iter=1000, n_jobs=8,\n",
       "                   param_distributions={'A': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe04f988590>,\n",
       "                                        'B': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe04f988690>,\n",
       "                                        'half_spread': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7fe084e7afd0>,\n",
       "                                        'imbalance': array([ 4,  5,  6,  7,  8,  9, 10, 11, 12])},\n",
       "                   verbose=1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#parameters = { 'A': np.linspace(0.01, 1, 100), 'B': np.linspace(0.01, 1, 100), 'half_spread': np.linspace(0.001, 0.01, 10) }\n",
    "#search = GridSearchCV(Backtest(10000, 10000), parameters)\n",
    "#search.fit(df)\n",
    "\n",
    "param_dist = { 'A': stats.uniform(50, 500), 'B': stats.uniform(1, 50), 'half_spread': stats.uniform(0, 0.001), 'imbalance': np.arange(4, 13) }\n",
    "search = RandomizedSearchCV(Backtest(1, 10000),\n",
    "                            cv=[(np.arange(len(train)), np.arange(len(train)))],\n",
    "                            param_distributions=param_dist,\n",
    "                            verbose=1,\n",
    "                            n_iter=1000,\n",
    "                            n_jobs=8)\n",
    "search.fit(train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Backtest at 0x7fe04e653950>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# v1.4 params\n",
    "search.best_params_ = { 'A': 73.58892434077225, 'B': 48.80294748244207, 'half_spread': 0.0009172743599659284, 'imbalance': 11 }\n",
    "search.best_estimator_.set_params(**{ 'A': 73.58892434077225, 'B': 48.80294748244207, 'half_spread': 0.0009172743599659284, 'imbalance': 11 })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 73.58892434077225,\n",
       " 'B': 48.80294748244207,\n",
       " 'half_spread': 0.0009172743599659284,\n",
       " 'imbalance': 11}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0032099488689764926"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_estimator_.score(train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "equity, running_qty = search.best_estimator_.predict(train.values)\n",
    "equity = pd.Series(equity, index=train.index)\n",
    "running_qty = pd.Series(running_qty, index=train.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "equity.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "train[\"close\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 16,
     "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": [
    "running_qty.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date            2019-07-01\n",
      "End date              2019-12-31\n",
      "Time period (days)           183\n",
      "Sharpe Ratio            5.746339\n",
      "CAGR                   10.790324\n",
      "Max Daily Drawdown      0.061427\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "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": [
    "returns = equity.resample('1d').last().pct_change() \n",
    "bm_returns = train['close'].resample('1d').last().pct_change()\n",
    "# returns_ = returns - bm_returns\n",
    "returns_ = returns\n",
    "sr = np.divide(returns_.mean(), returns_.std()) * np.sqrt(252)\n",
    "\n",
    "equity_1d = equity.resample('1d').last()\n",
    "Roll_Max = equity_1d.cummax()\n",
    "Daily_Drawdown = np.divide(equity_1d, Roll_Max) - 1.0\n",
    "Max_Daily_Drawdown = Daily_Drawdown.cummin()\n",
    "\n",
    "period = (equity.index[-1] - equity.index[0]).days\n",
    "\n",
    "print(pd.Series({\n",
    "    'Start date': equity.index[0].strftime('%Y-%m-%d'),\n",
    "    'End date': equity.index[-1].strftime('%Y-%m-%d'),\n",
    "    'Time period (days)': period,\n",
    "    'Sharpe Ratio': sr,\n",
    "    'CAGR': (equity[-1] / equity[0]) ** (365 / period) - 1,\n",
    "    'Max Daily Drawdown': -Max_Daily_Drawdown.min(),\n",
    "}))\n",
    "\n",
    "equity.resample('1d').last().pct_change().hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "equity, running_qty = search.best_estimator_.predict(valid.values)\n",
    "equity = pd.Series(equity, index=valid.index)\n",
    "running_qty = pd.Series(running_qty, index=valid.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 19,
     "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": [
    "equity.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WlR1yYuzB+1RYZ+zB+3DVuAHMWbaZuV9s5vG50R1/Y7fXzAZKCCKSdW4/b0iFslbN8ujbtQ19u7ahT+dWpQkhm6jJSESyTqKL0vbvsufeykfs34l92javdjvXZPDd0RJRQhARAfp0blVmfs2WwmrrnD648fQwAiUEERFg704ON7YL1ZQQRCTrXV3LrqNnH9aTAfu0oVsNmpUyiU4qi0jW69S65lctA/z+nIonpRsDHSGISNZrTNcS1IUSgohkvWG92qc6hLRQbUIws+ZmNtvM5pnZx2b2m1De0cxeNbNF4blDXJ0rzWyxmS00s1Piyg8zs/lh2V1m0SkZM2tmZo+F8llm1jsJ71VEpIzYSeHqRj790Ul9GyCa1KvJEcJO4CR3HwIMBcaa2ZHAJGCau/cDpoV5zGwgMAE4GBgL3GNmsU/7XmAi0C88xobyi4HN7t4XuAO4te5vTUSkamcO6wFU3WQ0YJ82/GxM/4YKKaWqTQgeid1Zukl4ODAemBLKpwBnhOnxwKPuvtPdlwKLgRFm1h1o6+4z3d2Bh8rViW3rCWBU7OhBRKQuiopLKl12y1mDmXXVqCoTQmVjHDVGNTqHYGa5ZvYBsA541d1nAd3cfTVAeI59aj2AFXHVV4ayHmG6fHmZOu6+G8gHOiWIY6KZzTGzOevXr6/RGxSR7Nbv6pfLzD952cjS6aZ5OZV2HT2xfxcADujSukz5zWcdUs8Rpo8aJQR3L3b3oUBPol/7g6pYPdEve6+ivKo65eO4z92Hu/vwLl26VBO1iGS73pNerFB28L7talS3bbjxTU65b8lxh3Svc1zpqlbXIbj7V2b2OlHb/1oz6+7uq0NzUOxmpSuB/eKq9QRWhfKeCcrj66w0szygHbCplu9FRKRaNe1iWhJ+ksbuo/z2pJOYtWRjo7pDWnk16WXUxczah+kWwGhgAfAccEFY7QLg2TD9HDAh9BzqQ3TyeHZoViowsyPD+YHzy9WJbetsYHo4zyAiUmu7i0soLqnbV8hZ4YTzsP2iDpQ92rfgrEN7VlUl49XkCKE7MCX0FMoBprr7C2Y2E5hqZhcDy4FzANz9YzObCnwC7AYud/fisK3LgMlAC+Dl8AB4AHjYzBYTHRlMqI83JyLZqe/VLzP6oMT3QK6pEwd0Zdktp9VTRJmh2oTg7h8CwxKUbwRGVVLnJuCmBOVzgArnH9y9kJBQRETqoiQcGbz26drSshP7d+H1z9bjHi3PyVEnxkR0pbKINCrrCnZWKPvjecMYPyQaqlpt0ZVTQhCRRsUTfOW3a9mE284ZwnvXnkyujg4qpYQgIo3KkvXbysx3bxddZ9AkN4eOrWo3qmm2UUIQkUbl2/fPKjP/20Z8IVl9U0IQkUatd6dW1a8kgBKCiDQyncvd7KZH+xYpiiTzKCGISKOyYeuu0ulj+3WmaZ6+5mpKt9AUkUZp6c3j0KDJtaPUKSKNkpJB7SkhiEij8c+P16Q6hIymhCAiGWvn7mLO/fNMzv+/2ezaXcLEh+emOqSMpnMIIpKRCgqLOOS6f5bOH3jNy1WsLTWhIwQRyUjxyaC8n44+sAEjaTyUEESk0fn+8funOoSMpIQgIo1OTe+KJmUpIYiICKCEICIZqKSK22PO+/WYBoykcVFCEJGM8/6KzaXTd04YWjq97JbTaNeiSQoiahyUEEQk46zbsueuaCf07wrA7ecOSVU4jYauQxCRjNMt3PTmzglDadeiCctuOS3FETUOOkIQkYyTv70IgO7tNLR1fao2IZjZfmY2w8w+NbOPzezHobyjmb1qZovCc4e4Olea2WIzW2hmp8SVH2Zm88OyuyyMPmVmzczssVA+y8x6J+G9ikgjceHkdwHYvH1XNWtKbdTkCGE38HN3Pwg4ErjczAYCk4Bp7t4PmBbmCcsmAAcDY4F7zCzWKfheYCLQLzzGhvKLgc3u3he4A7i1Ht6biDRyPTvoCKE+VZsQ3H21u78XpguAT4EewHhgSlhtCnBGmB4PPOruO919KbAYGGFm3YG27j7T3R14qFyd2LaeAEaZxq4VySpbCot4dPZy1hfsrH7l4MBubZIYUfap1Unl0JQzDJgFdHP31RAlDTPrGlbrAfw7rtrKUFYUpsuXx+qsCNvabWb5QCdgQ7nXn0h0hEGvXr1qE7qIpKHComKa5uaQk2OcdtebrNi0A5jP7ecO4axDeyass3nbnmaivBz9bqxPNU4IZtYaeBL4ibtvqeIHfKIFXkV5VXXKFrjfB9wHMHz48MqvTBGRtHfR5HeZvmAdxx3YhYcuGhGSQeRnU+fxs6nzSudjdz/77gOzeHPRnt+JakioXzXqZWRmTYiSwd/c/alQvDY0AxGe14XylcB+cdV7AqtCec8E5WXqmFke0A7YVNs3IyLp78m5K/lsbQHTF0RfGW98tr7aOufdFzU6xCcDqX816WVkwAPAp+5+e9yi54ALwvQFwLNx5RNCz6E+RCePZ4fmpQIzOzJs8/xydWLbOhuYHs4ziEgj8tuXPuXnj89jzB1v1Kre7KWb+Kpcj6J3rx5dn6EJNWsyOhr4LjDfzD4IZVcBtwBTzexiYDlwDoC7f2xmU4FPiHooXe7uxaHeZcBkoAXwcnhAlHAeNrPFREcGE+r2tkQknezYVcxBv/pHreuNH7ovz34QNSQMvf7V0nJdiJYc1SYEd3+LxG38AKMqqXMTcFOC8jnAoATlhYSEIiKNz5othVUuP+K3r5WZf+GKY3j5o9X81ykDShNCzK9OH1jv8UlEQ1eISNJd9dT8KpevjRub6IlLRzKoRzsG9WiXcN2LjulTr7HJHhq6QkSSbuaSjRXKfjyqX4WyT64/heG9O5YpO+vQHhXWk+TQEYKINKhHJx7JiN4dyckx7py2qMyylk0rfiXddvYQrh8/iOfnreLkgd0aKsyspIQgIklVHHczm0+uPyXhl35VcnOM1s3y+I8Ruhg12ZQQRCQpRt48jdX5hWWafKpKBu9MOqkhwpIq6ByCiNTZjl3FfPRlfun82i2FrM6PehY99d6Xldbbv3Or0ul922ugulTTEYKI1FnsGoNhvdrz/vKvEq6z4IaxFcqm/fx4Hnt3Bd88LPG4RdKwlBBEpN5UlgwAmjfJrVBmZkzQuYG0oYQgIknz6fVjadG0YiKQ9KRzCCKSFE9cOlLJIMPoCEFE6t1zPzyawT3bpzoMqSUdIYhIreVvL+IvbyyhpMQZcVPZcYhuO3uwkkGG0hGCiNRY70kvlpm/6aVPS6fbNM9jzjWjaZanZqJMpYQgItVau6WQgsLdVa4z71djyNEtLTOaEoKIVGrwda+wpZpEANCjfQslg0ZACUFEEtpSWFRlMhjWqz0D9mnDorVbeeKyoxowMkkWJQSRBnL5395j4L5tufzEvqkOpUZWxt30Pt55w/fje0f35qDubRs4Ikk2JQSROpi/Mp81WworDMs8Y+E6Ru7fieZNcikqLqHEnRfnr+bF+asZM7Ab/bq1qXSba7cU8u8lGxk/NLX3ARh315sAdGvbjDd/eRJN89QpsbFTQhDZSyUlztfvfguI7vFbvgcOwN3fGsYPH3m/TNnJ4QbzT/3gKM665x2euHRk6U1hZi3ZyHn3/bt03YZKCkvWb6V3p1al5wEWrNlSuuzmsw5RMsgS5u7Vr5WGhg8f7nPmzEl1GJJl3J13l21mUI+2DPzVK0l/vWTfTP7422bwxcbtKY1BGpaZzXX34YmWKe2L1MJ9byzh3P+dWetk8NPRB3LjGYMYU8s7fs38vOKtJ2PcnaffX8mXX+2g96QXWZNf9Y3sy3tr0YZqk8GH142p1TYls6nJSKQWbn55QZXLp/38ePJyjF4dW9LnypeAqNno9MH7AvCdI78GwPKN22nXognXPf8xT7+/534BC24YS5PcHA64Kqr7woerGHlApwqvs2LTdo793YwyZUfePI0T+nfhuq8fzAm/f71CnXMO68m1Xx/Iode/yreP6MWUmV9U+V4W3jhWF5llmWqbjMzs/4DTgXXuPiiUdQQeA3oDy4Bz3X1zWHYlcDFQDPzI3V8J5YcBk4EWwEvAj93dzawZ8BBwGLAROM/dl1UXeCY1GW3buZuDfx39onz6B0cxrFeHFEeU/hav20q7Fk3o0qZZqkMpI9F5AoBbzjqEQT3aMahHu1pvc9fukgpt9PGv89wPj2bphm10atWMW/7xKR1aNuXNRRtq/TqVWfLbcbqGIItU1WRUkyOEycDdRF/aMZOAae5+i5lNCvP/bWYDgQnAwcC+wGtmdqC7FwP3AhOBfxMlhLHAy0TJY7O79zWzCcCtwHm1f5vpJ9GvuDPveYfTB3fnhQ9XA/DLsf257PgDKCr2RnPibvG6rfTp3IrcOnzJjL79X0DF9uvlG7fTNC+Hfdo1r1OMe2PVV3u6Yd5+7hDOOrQnL364moVrC+o0pn+iv/sjlxzBt+6fBcA37n670rrjDtmHq8YdRPd2LUqPKmrjiUtHKhlIqRqdVDaz3sALcUcIC4ET3H21mXUHXnf3/uHoAHe/Oaz3CnAd0VHEDHcfEMr/I9T/fmwdd59pZnnAGqCLVxNYOh8hLN+4neNum1H9iuV0bNWU9649OQkRNZzn563iir9HvWre/OWJ7NexJRdNfpfpC9Zxy1mHMOmp+aXrVnayMlEivfb0gdzwwiel80tvHodZxS+ybTt389naAobu1z7h8roYffu/WLxuK5D8E627i0voe/XLVa4z7pB9uOfbh5XOT3xoDv/8ZG3pfHyMG7fu5OF/f8FPRh9Y/8FKRqnrEUIi3dx9NUBICl1DeQ+iI4CYlaGsKEyXL4/VWRG2tdvM8oFOQIVjYjObSHSUQa9e6XGXpRc/XM3lj7xXo3UfvngE331gdqXLN23bxdn3vpMxV30WFBaxYE0Bh4cuk0BpMgA49nczWHbLaUxfsA6gTDIA+PGj73PnhGGl85U1xwBlkgFQ2j4f07Z5Hv/3vcM5+88zS8s+/+24So9S3J3ZSzdxzTMf8fwVxyS8mxfAhq07Oen3r5e5Yrd1s+SfesvLzaFLm2asL9hZpryqRHTf+dE+XlziFd53p9bNlAykWvX9n51o7/MqyquqU7HQ/T7gPoiOEPYmwL0x4qbXuGJUP74bTgjGXP30fP42a3mVdf/+n0dy9dPzmXzhCHp1aslrPzu+tDkkkTlfbK6wQ1/68Fz+8fEaoPJfxqkw5Df/pMThycuO4uqn5zOsV/sK67y5aH2l9Z/9YBXPfrAKgA4tm9Qpli2Fu8skA6DGTSgDrv1HrV5rfgP1vHn36tF7Va8uTXWS3fY2Iaw1s+5xTUbrQvlKYL+49XoCq0J5zwTl8XVWhiajdsCmvYyr3pT/tXrtMx/Ro31zBuzTln3btwCoMhk0b5LDghtOBWD6L04oLe/btXXpr7zbX/0Md+fnY/rj7qW/eg+46iXOPqwnT8xdWWG7L81fw2mDu9fpvdWXkpCSv3nvOwAsWFNQYZ3yR0Qn9u/C+SN7c+Hkd8uUb95eVDo9ondHZi+L/gWevGwkh/bqwIyF6xjcsz0ffZnP9x4sW7e8lk1z2b6ruNbvpyrnDd+Pm84cRF5u4zjPI5LI3iaE54ALgFvC87Nx5Y+Y2e1EJ5X7AbPdvdjMCszsSGAWcD7wP+W2NRM4G5he3fmD+ralsIg1+YUcGIYTmL00cT66aHLicxYPXng4I3p3xAwKCnfzxcbtjOjTMeG68X528p5D+PK/+hMlA4DLH3mP0wan94VCT//gKM68550yZc9cfjS9OrakY6umVdZdfNOp5OXmcPPLn/K//1rCgd3aYGacNCDqv39C/678+TuHcuKArlz55Hxe+3QtH153ChC1u8e+sJ95/0sembW8NLEA9O/WhoVr9yStz387jhJ3vtpeRJc2zSgoLGLtlp386tmPGHdIdw7q3pbu7ZqX/gAQaexq0u3078AJQGdgLfBr4BlgKtALWA6c4+6bwvpXAxcBu4GfuPvLoXw4e7qdvgxcEbqdNgceBoYRHRlMcPcl1QVenyeVq2q7rs4jlxzBUX0710sc81fmlw6FUN7Sm8eVHkFMvvBwTujfNeF6DSnR5/bxb06hVbO8MsuO6duZv15yRJn1iopLKCouYePWXRz7uxnk5hgf/noMrUL7fEmJs72ouM7t9bH/73RpZhNJtapOKmf90BVvLlpf6YneBTeMpXmTXDZt28W2nbtp3iSXw+NuF3j+yK9x/fhBdY6hvC82bqNVszwWring6LhkU/4LuKH7jy/fuJ3u7ZuTv6OIB99eyp9mfA7AZzeeWmU/eg19IJI+ktHLKOO5O6vzCytNBr8Yc2Bpz5OOrZqWNnU0xJfb1zq1AqBz36ovyprzxeYaNU1VZufuYuYu28yhX+tQaS+bmILCooRdaft1bZ2wH/3ca0ZT7E7XNg1/vYCI7J2sSwibt+1i2A2vVii///zhXPLQHO799qGcekh6nLQt771rT+bQuNgn3DeTJTfXLkEtWlvACx+u5iej+9H/mrK9a1780TG8uWgDlx5/APe/uYQbX9xzv9w7JwxNuL1XfnJcwvJOrdPrCmMRqV7WNRl9/X/eYv6X+WXKbjt7MOcM36+SGunnncUb+Nb9s7jw6N78+usHl5bP/WIzyzdt48xhPXly7kp+/vg8jujTkT+cO4SeHVoCdTtfEvPCFcfQt2vrao8qRCT9qMkoztM/OIpDrvsnO4qibomZ2L7dtW3067v8mEix7p/N83L5+ePzAJi1dBPH3DqDl350bOkNT8rbt11zVtVwpMwPfnUy7VtW3VNIRDJT1iWEvNwcPr1hLLt2l9AkN1N7nkRxL9uwrbRk7ZY9X+iX/a3ildOVJYPYifPpC9ZW6Fb7nSN7ceMZh/Dg20t59oNVPHXZURr3RqQRy7qEENMYBpK7/dXPGNSjLYbx/LxVFZaPH7pv6ZXA5ZU/MjppQLfSMndn0bqtpddlXHh0Hy48uk89Ry8i6SZrE0Imy9+xq3S6sovlAO6cMIxbvzm4zNAMj008sszYQ4mYWWkyEJHsoYSQgfp2rfzLep+2zXGc/Tu3BqB5k1yW3XIaq77aQbe2zTXOjYhUSgkhA7VrUflAcO9MOilhO7+GXxCR6mR+Q7qUOrF/F530FZG9piOERuKSY/pwzekDUx2GiGQwHSFkuHevHs2J/btw9WkHpToUEclwOkLIcF3aNOPBC0ekOgwRaQR0hJChRvTpSA+dKBaReqQjhAw19fsjUx2CiDQyOkIQERFACUFERAIlBBERAZQQREQkUEIQERFACUFERAIlBBERATL4nspmth74Yi+rdwY21GM4yaI461emxAmZE6virD8NFePX3L1LogUZmxDqwszmVHaT6XSiOOtXpsQJmROr4qw/6RCjmoxERARQQhARkSBbE8J9qQ6ghhRn/cqUOCFzYlWc9SflMWblOQQREakoW48QRESkHCUEEREBlBBEUsLMLNUxiJTXaBNCJuxwZtYxbjpt4zWzE8ws4YUs6cTMfm5mY8J02n6eQZvYRDrHms6xxcuEODNhf290CcHMxpvZFGBIqmOpjJmNNbM3gD+a2R8APA3P7sfF+W1gZ6rjqYyZjTGzV4D/Bs6H9Pw8AczsZDN7C/i9mf0S0jPWTNiPIDPizJT9HRrJLTTNzNzdzexE4AagCBhpZl+4++YUhweU/iLIAS4GLgJuBt4HHjKzU9395VTGFxPiNOA84H+Bi9398dRGVVGIswnwK+B4os+zKXC4mTUBdqfbTmdmPYHrgFuA14FHzayTu/937H84lfHFpPN+BNrfkynjjxDK7UhLgVOA/wKOAAanLLA4sRjdvRh4CzjG3Z8FCoF1wMdmlhNbNw3iLAFWAQ8Bi8Oyc8ysZ/iyTZc4dwHPuvux7v4SsBmY4O5FafTlGv85DQDmu/vz7l4A/An4qZn1C19w6dKMsBQYQ5rtR6D9PdkyOiGY2Q+Bp8zsp2a2j7svc/fV7j4dWAscb2Y90ijG7u7+ibvvNrNDgWeA3kRNHbfHqqQ4zp+ZWWeif+QPgXvNbAFwLvA/wD1pEmfs83w3lDdx938BS8zs1FTEVl65WNsCnwHHmNnIsEpX4GPgmlTFCGBmPzCzb4ZpA1a4+5p02o9A+3uDcPeMfABnAu8CJwIPAncDQ+OWDwb+CpxVrp6lQ4xAH6BXmG4FfAUMT5PP8k9Af2BfokPdYWG9jsB64LA0ifNuYEjs7xriux8Yk4b/n/cC3YiaECYDbwOPhP+DeUDvFMTYBvgzsAbYCuSF8pzYfpIO+1EVf/uhcctTHmem7O9VPTL5COEI4F53n0HULrsU+FFsobt/SPTHGWRmJ5nZf4fyhmxKSBTjj0McS919eZjeBkwF2jZgbFXFuQz4L3dfBfzG3d8PcW4i+pXTOjVhVvl5eoivBdEOSeywPEUSxfobd38A+E/gp+7+LWA5MBvY0tABetRs9S933wd4geiHAERfoh7WSYf9CLS/N4i0Twjl29ji5pcA3wJw9y+AF4FWZvaNuNX/DlwCPEY01nhS2uxqGWPLcjFiZtcABwOf1Hdsexnn80AbM/uGuxfGrX9tiHNBmsSZ6G/+V2CEmTX36FxIUtUi1ueADmZ2pkfnOGaH9W4g+sVYkKI4nwvPPwH+I5zPKDazvLh1GmQ/SiQd9/daxpiy/X1vpH1CIOpJUiou4z8BbDez8WF+NVHPjYEWaQ3cCcwHBrv7f5Wrn9IYAczsVIu6IB4InO3ua5IQW33EeayZzQhxftPd16ZbnHE7ZQvgUaA4yTHG1DbW/gBm1s/MngUGER0tFKUiTnffZmY54X/vHqImN9x9t7u7mbUC7qJh9iMAzCy3fJyk1/5e6xhDnYbe32stbROCmY00s8eB28xsYOwPYGaxrrKbgaeBy8zM3D2fqCmjefgDFQI/dvfT3H11msXYIiz/FLjU3c9PVoz1FOcy4HJ3/26axtksbqd81t3/kuwv2Lr8f4bla4g+028kM8FWEWdu+SY1d58E9Al1upnZ4aF540fJ3I/i4rw+xFEcVx5L9Omyv+9NjA26v9dFWiYEM+tKdELmJWAjUTvcRRD9cgmrtQBeIcrC95nZvsAwoj7JsV8469I0xl1hvWXu/lGyYqzHOFe4e7Kbs+oSZ2x5mR01TWON/X8WuPvKFMZZ7O4l4Zd1u7hqtxKd8H4TaBnWTdp+FOK8AJgCXGNm54ayvPDasUSf6v29LjE22P5eZ54GZ7bLP4CTgb/7njPypxCd9BoQym4k+uCHEfUsuZHo0OweIFcxKk7FWqM4bwD+ARwb5k8lOj/0e6BJA8Y5GuhJdO3D8rjy3PB8Xao/z0yIsV7eZ6oDCB/mGcBVwGlhvguwCDggzHcEfk3066UlUXe9A8pto2W2x6g4szvWusZJ1Na9XwPGeXqYzyUkIKLrX26IW7drivf3tI0xKe87pS8e/cM+A7wBXEp0Fd/ZYdktwB/DdA5wDPAXoGNc/RzFqDizPdZ6iLOhfmUnivPMsKxpeD4YyAe6Jaifqv09rWJM6vtP6YvDkUT93WPz3wXeCdNDiA5nR4f5g4i6yLVqyA8+E2JUnNkda2OIM8zHml/uBx4M06emy988XWJM5qPBTyqb2fkWDafcEphLNF5OrBvXJ0SX8kPUfexRohEC+wKjiK5GbQLgSexjngkxKs7sjrURxTk/zBvgIaZLgAvMbDMwpHxvqGyMsaE0yGin4YPch6idrQT4nOhqzR+7+1ozy/XoYpiDCD0ewj/q5NBTYhLRwGD/6e5fZWuMijO7Y22kcXYIcTrgZvY14A6iXk6Xe5J65WRCjCmR7EMQ9hxiHQj8NUznEQ2U9lS5dR4Czg3T+8Rto2m2x6g4szvWLIizS3huD4zI9hhT9UjaEULoo3s9kGtmLxGN21EMUZ9hM/sRsMrMjvdolEqIBthaGi7+OMvMxrr7So+GOc7KGBVndseaZXGO82i8n9kJXiIrYky1pLR7mdnxRG1xHYjG04/dxOJEMxsBpYdf1xP13421111EdPl3W+BET+KFO5kQo+LM7lizMM7l2RxjWkjGYQdwLPDduPl7gMuA7wFzQ1kOURveVOBrwAHAH4FDG+LQKBNiVJzZHavizK4Y0+GRrA+/JdCMPe1w3wZuDtMfAFeE6eHAoyl54xkQo+LM7lgVZ3bFmA6PpDQZuft2d9/pe8aVOZnoxioAFwIHmdkLRMPVzoWGG043k2JUnMmRKbEqzuyKMR0ktdtpaINzojtFxcZdLyC6JHwQsNTdv4SU3HAjY2IExZkMmRKr4syuGFMp2RdTlBBdALMBGBwy8LVAibu/FfvgUywTYgTFmQyZEqvirD+ZEGPqJLtNiuhS8BKiAaEuTnUbWabGqDizO1bFmV0xpuoRu5F20phZT6LxQG53951JfbG9lAkxguJMhkyJVXHWn0yIMVWSnhBERCQzNIoBmUREpO6UEEREBFBCEBGRQAlBREQAJQQREQmUECRrmFl7M/tBmN7XzJ5I4msNNbNxydq+SDIoIUg2aQ/8AMDdV7n72Ul8raGAEoJkFF2HIFnDzB4FxgMLgUXAQe4+yMy+B5wB5BKNZ/MHoCnRxUs7gXHuvsnMDgD+BHQBthPdinKBmZ0D/JroZiv5wGiiMfdbAF8CNwNLiYZSbgHsAC5094W1eO3XiUblHEE0Nv9F7t4ob9IiKZTqS6X10KOhHkBv4KME098j+gJvQ/Rlnw9cGpbdAfwkTE8D+oXpI4DpYXo+0CNMt4/b5t1xr90WyAvTo4Ena/narwN/CdPHxWLXQ4/6fCR1tFORDDLD3QuAAjPLB54P5fOJBkFrDRwFPB43KnKz8Pw20Y3spwJPVbL9dsAUM+tHNNpmk5q+dtx6fwdw9zfMrK2ZtXf3r/bu7YpUpIQgEokf06Ykbr6EaD/JAb5y96HlK7r7pWZ2BHAa8IGZVViH6JaNM9z9TDPrTfSLv6avXfpS5V+68rcjUns6qSzZpICoaabW3H0L0c3Wz4Ho5ilmNiRMH+Dus9z9V0TDKu+X4LXaEZ1PgKiZaG+cF17vGCDf3fP3cjsiCSkhSNZw943A22b2EXDbXmzi28DFZjYP+JjoBDXAbWY2P2z3DWAeMAMYaGYfmNl5wO+Am83sbaITyHtjs5m9A/wZuHgvtyFSKfUyEskAoZfRL9x9TqpjkcZLRwgiIgLoCEFERAIdIYiICKCEICIigRKCiIgASggiIhIoIYiICAD/D9eN2KmLmLSRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid[\"close\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x8iFD5uxynIbCDaOOaJTBBxvlOh2n1oh7PoyZku6S9DdJj0n6RAi/UNJzkh4Jn+Oz9rlA0lJJSyQdkxU+V9KisO5yxfzWVRKrd5KouZrke4JyBUs+uq3jxN1/sA/4VzN7KEyktFDS78O6y8zs69kbS5oNzAf2B3YDbpe0d5gT40pgAXAfcCtwLDHOibHm1a1xHdqpcfIZycsb/ZlwGptYSxhmtsrMHgrLG4C/AYUqgk8ArjezLWa2DFgKzAvTuE42s3stGv/iOuDEOLW3tXptnTOQ8W3+fobT2FQsVZTUCRwM3B+CPibpUUk/kDQthM0AVmTt1hPCZoTlweGDz7FAUrek7t7e3lHpHdvSPKr9q8EDy9ZUW0KiyF+FN7SIIWBsi2cinMamIjFA0kTgRuCTZraeqHppL2AOsAq4NLNpjt2tQPjAALOrzKzLzLra29tHpfnxVetHtX81SKLmkVCutprmptx1T3nbNrwJw2lwYjcMSa1EZvF/ZvZzADNbbWYpM0sD3wPmhc17gJlZu3cAK0N4R45wxyk7uRu93S0cJ+5eUgKuBv5mZt/ICt81a7OTgMVh+RZgvqQ2SXsAs4AHzGwVsEHSoeGYZwA3x6ndqR/23WVStSU4Tl0Qdyvem4HTgUWSHglhnwVOkzSHqFppOXA2gJk9JukG4HGiHlbnhR5SAOcC1wDjiHpHxdZDyqkfmpvEjKnjeOL5DUPW5avaylWYEN6t1nFiNQwz+xO5S/i3FtjnYuDiHOHdwAHlU+c0AiNJ4t0YHCc33u3DSRzVeD9R8kZvx3HDcBzHcYrCDcOpa8pXKvDiheO4YTh1Tz7TyDeIob+H4Ti5ccNwGpZ0uvht3Swcxw3DSSClvOld3h5P7hpOY+OG4TiDyPmmd8VVOE7t4YbhOEXi1VJOo+OG4dQ3BRL5fDVbucaNcrNwksT5x+0by3HdMJzEUfoUrfGk9nkGu3WcqjOmOZ6k3Q3DaVishNZz5Wg+9xFsnUbDDcOpa+JM0t0unEbDDcNximRwgcILGE6tEtezmSjDkHSspCWSlko6P67zvPjKlrgO7SSUnEOeu2M4NUpcT2ZiDENSM/Bt4DhgNtGcGrPjOFfXl2+P47Cx0zATBZXy4t4IRpktdnu3C6fRSIxhEE3jutTMnjazrcD1wAnlPsmmranhN6pR2ie1VVtC3SINfWu8yUsYToORJMOYAazI+t4TwvqRtEBSt6Tu3t7eEZ3khQ2bR66wypQyZIaTn6JLGO4XToORJMPIFT0HJJFmdpWZdZlZV3t7+4hOMrEt7llrndHy84efq/g5hYYYhJcwnFolrva1JBlGDzAz63sHsLLcJ0lyIlD6C231T5ylrgQ/Ko4zIpJkGA8CsyTtIWkMMB+4pcqanBpnWyr/GOYlmYmGFnHdL5xaJa7MTGLqX8ysT9LHgN8CzcAPzOyxcp8nyblGb8MYStpKT9iLHRK92ccGcWqUuJ7MxBgGgJndCtxabR1OcmhrKU8h2q3BcZJVJeUMg5cwhjKSW5J/ilYV/O449Y4bxiA80W0cSukkkHPI83KKcZwE4IZRR3gvqRwUuCWeOXCc0nDDcOqeUmuO1m/aNvQYZdLiOEnGDWMQnul0po4fU9R23oTh1Cz+4p4zHF7FMpSRVNPlimq5BjHclvIb7tQmDT9arePUGpu3JXegSqe+8fkwKkQp03bWGslVXh3y3q9cc18w9IW+nSb76MBOY+GG4dQ9xb65XSpJHnfMqW/iyve6YdQTXsQYQjkjzmB/aHbDcBoMN4xBTBnXWm0JTpXJ3eg9NPTlHN1vHaeeccMYREtzEx3TxlVbxojwF/eGUuiOjLa9aseJxXW/dZx6wQ0jBwlu93bKQL5hQAaHehuGU6skrpeUpK9JekLSo5JukjQ1hHdK2iTpkfD5TtY+cyUtkrRU0uUKMVdSm6SfhPD7JXXGpTvJuNENpVApYrS3a++dJ43yCI6TLOIsYfweOMDMDgT+DlyQte4pM5sTPudkhV8JLABmhc+xIfwsYK2ZvRa4DLgkRt2kPeWtK0rNbeXcPMeLe5e898CRSnKcWElcLykz+52Z9YWv9xFNqZoXSbsCk83sXouyhdcBJ4bVJwDXhuWfAUcqxrGlk2oYyVRdexR7H8eNaY5Vh+OMlFe39A2/0QioVBvGh4Hbsr7vIelhSX+QdFgIm0E0b3eGnhCWWbcCopn3gHXA9MEnkbRAUrek7t7e3hGLTahf1AXlmvCo3MT1LofjxEFrczzxaFQz7km6Hdglx6rPmdnNYZvPAX3A/4V1q4DXmNlLkuYCv5C0P7lrAjJJd6F12wPMrgKuAujq6hpxsr9hczzuHDdJfks9Lsp5R3zCJKfRGZVhmNlRhdZLOhN4F3BkqGbCzLYAW8LyQklPAXsTlSiyq606gJVhuQeYCfRIagGmAGtGo70Qm3yMoIagFH+tFa/479MO5uM/frjaMpwGJc5eUscCnwHeY2Ybs8LbJTWH5T2JGrefNrNVwAZJh4b2iTOAm8NutwBnhuVTgDvNs9NDqIcbUu6E2ax8x6wFz3j3QbtVW4LTwIyqhDEM3wLagN+Hovx9oUfUW4EvSeoDUsA5ZpYpLZwLXAOMI2rzyLR7XA38UNJSopLF/Bh1O84QasEsHKdY4so8xmYYoQtsrvAbgRvzrOsGDsgRvhk4tawC6xAvc8WMu4bT4NRmlxTHqTFqpQ3DcaqJG0Yd4QWM3OQreXkzmFOvxPVsu2HUGZ4THj25IpvCn+M0Mm4Y9YSZJ2mO48SGG4ZT95SjdL5q/WYvvTmJYf6818RyXDcMp6aoZLVPKT4yY+rY2HQ4TrmZ2BZPB1g3jDqiHppw45gEqjzH9OKF47hhOA1Lvqqq9TnGEpPcMhzHDaOOiIbBSHayVgs9kSb4sOWOkxM3jDqj+sltcshXVZVraGjhXZYdxw2jjoij/r8eyFf11Jcq/n65WTiOG4bTAOSzhaYSXGC4F/c+9Y69S1TlOMnDDaOO8JEuSsOHBnGc0ohzPowLJT0n6ZHwOT5r3QWSlkpaIumYrPC5khaFdZdn5u2W1CbpJyH8fkmdcel2God8dpGr4OFVUo4TfwnjMjObEz63AkiaTTSfxf7AscAVmQmVgCuBBUSTKs0K6wHOAtaGIdMvAy6JWfcAWpuTkVp4hrk0Sppxj8KmkfTeaY5TDNWokjoBuN7MtpjZMmApME/SrsBkM7s3zKZ3HXBi1j7XhuWfAUfKY+gQHl+1nr60u8Zg8o5Wm6eMkautwh83x4nfMD4m6VFJP5A0LYTNAFZkbdMTwmaE5cHhA/Yxsz5gHTB98MkkLZDULam7t7e3vFfiVIRKpsuleqtbhtPojMowJN0uaXGOzwlE1Ut7AXOAVcClmd1yHMoKhBfaZ2CA2VVm1mVmXe3t7aVejlO35HYGr8JznNIY1QhVZnZUMdtJ+h7wq/C1B5iZtboDWBnCO3KEZ+/TI6kFmEI0t3dF8ISlclT2XuepksrT6N37ytaY9ThObRNnL6lds76eBCwOy7cA80PPpz2IGrcfMLNVwAZJh4b2iTOAm7P2OTMsnwLcad4n0hklpVRJCbHrFB+x1mls4hkDN+I/Jc0hysYtB84GMLPHJN0APA70AeeZWSrscy5wDTAOuC18AK4GfihpKVHJYn6Mup0iOWKfdu5aUt62ojjaMAZnLWbuMI4VazblfQ8jlcdJCkl7dcvQAQsdp96IzTDM7PQC6y4GLs4R3g0ckCN8M3BqWQUWwfGv24WTDu7gvP97iPoYPLy8JOWOvDIoMf/AG3bnq7c9kbeEkesNcAmamvJbxrTxY0al0XGSgL/pXYCdJ4/l6Nk7V1uGM0q2pdIDvjcHQ8hXwsjZhjHMObzXrdMIuGEUINMf3wf1SzYTx7YO+J5J3Ev5VSU3BcdxwyiAJxD1weCShEbiGJQ2WKHj1CNuGAXYtC1qi6/2pD61mk4ltZ9a5naWVsIo/BT4m+BOI+CGUYAxYSIdr5LKzfiEzkyXabtOl+B4w44lNTpJjpMI3DCKIKk56bg58eAZw29UInEkvIN/v0xvp3yGkbO0oMJVUl7AcBoBN4wCZBIUTwxyM2FMnK/xxIf6e0mV8ZjlO5Tj1CxuGAXwkkVhkmqkI2rDQN5O4TQ8bhgFKKWO20kOTSPoJSUVLkW4mTiNgBtGAXxqifpgcKcFjbDRu8CL3oktbTlOKbhhFCRKUKpd0KjVtKhWdQ1mSKP3MAWMfNflpQin0XHDKEC1jcKJBw0zNEjufbxbreO4YRTg347dt9oSAM/ZlptMG0apw5v77+A0OnHOh/ETSY+Ez3JJj4TwTkmbstZ9J2ufuZIWSVoq6fLMvN1h7oyfhPD7JXXGpTubHSb4CKSVJo5EeXBBYrgzLFm9YUjYcI3e3ojhNAJxDm/+/syypEuJ5uHO8JSZzcmx25XAAuA+4FbgWKI5Mc4C1prZayXNBy4B3p9j/1jwmqn6YvDotYPJOx+GV0k5DU7sVVKhlPA+4MfDbLcrMNnM7g2z6V0HnBhWnwBcG5Z/Bhwprx+oS+KYSHFwL6lxIxjSJOol5Y9chgkJHRbGGR2VaMM4DFhtZk9mhe0h6WFJf5B0WAibQTR3d4aeEJZZtwLAzPqISivTB59I0gJJ3ZK6e3vLOxNcNanZZKpmhRWmpWkEj32ewQdPmdvBjKnjGq5GyvNrjcmoqqQk3Q7skmPV58wsMx/3aQwsXawCXmNmL0maC/xC0v7kTn4yWcNC67YHmF0FXAXQ1dXlNUkJpCJtGCM4Rb7BBy9574Fs7Utz08PPjUhbUomjJOjUPqMyDDM7qtB6SS3AycDcrH22AFvC8kJJTwF7E5UoOrJ27wBWhuUeYCbQE445hWhu74bAM3PlpdALeIVYt2nbkLDmJo2oiqtSnHzIDH7+UPnNzEsYjUncVVJHAU+YWX9Vk6R2Sc1heU9gFvC0ma0CNkg6NLRPnAFkSim3AGeG5VOAO62CWRzPTeUmjnlCXt3aN/xGo2S4toixrUOjhQQ7TGjLu0+tpp9v27s9luPW6OU6MRP3cKPzGdrY/VbgS5L6gBRwjpllSgvnAtcA44h6R90Wwq8GfihpKVHJYn7Mup0qEYc3Dz7kSHLH6bR5Iuk0PLEahpl9MEfYjcCNebbvBg7IEb4ZOLXc+orFyxcJJ8/QIKWwfnMfhdrK3UycRsDf9E4A1Z4iNh+1Wg2TTa7un8Pp3m/XyUPC2ie1FfwdavVexNbWUKPX68SLG0YReNxILu+ZM4MXX90yIGy4RPS9h3Rw2ycOGxDWJNWsKThOpXDDyMFR++084p40sVBLWhKGBFPHtQ4IG67RWxpayogGHyxQwvAfyWkAkjnHZsx8/8yuAd+T0IYh+ei6uRDQ2jwwXzRcZiDXfWwadiypUpUlmwa7XCfgJYw64Y17Dnnx3SH3sOTDlQY2b0sNPQ5eJeXUJpPGVi7f74aRAKYMqlLJRTUSsySkn0JDqqCGK2HkehFPKlyV1VJTdZjbiesdIn9xrzFxw0gAV50+d9htfGC8/Ay5NSMZGiTPWFIZJrY1Vu3ucCP+OhWkglXRbhgJYKfJY4fdxnN8uYnmsRhcwij9XjUNM+NerRp2qc/F+CKHOdm4dWi1nVMdSpmbfrS4YRRBEhqTazO5qj65Bg0cSeIeHSf/frtPH1/yMWuRYo1g3h47xKzEKZZKJk9uGAmgmHroamRwS5nitJYYrrkhUyLJbkxsaipcJTVr50n85fy3l0FdMjioY0q1JTiBSmZo3TDqhGqUMFqaa79c8/KmbUNKBq8WmYv+6xfewWnzZgKhDWMYV95t6riRiYyRuH6hWq2Ca0S8SsoZQDHPQzUicI12DBrATpPahugsVnZT1o4qYb9GoCkJP75Tdtww6oRqZPiaRzJzXYXJVZXUXEJilzHrJqng4IONRrOXMGqGxLRhSDpV0mOS0pK6Bq27QNJSSUskHZMVPlfSorDu8szc3JLaJP0khN8vqTNrnzMlPRk+Z9JgFJObe+nVrRVQMpBZO02s+DlLpTlHVVIpSV3GMHL1tmpkvIBRO1Ryvp7R5pkWE82od092oKTZRHNW7A8cC1yRmTQJuBJYQDRx0qywHuAsYK2ZvRa4DLgkHGsH4IvAG4B5wBclTRul7kTRWkTsnDmt8r10kpDJbMnVWF2Cbgv5tyavkxqAd+POTVtL5Yuh21IJMQwz+5uZLcmx6gTgejPbYmbLgKXAPEm7ApPN7N4wY951wIlZ+1wbln8GHBlKH8cAvzezNWa2Fvg9202mIWhpHv5nGlOFBzUJNDWVPqRH9vaZnmC53hhvZEqp1mskxhQRV5NMXFc3A1iR9b0nhM0Iy4PDB+xjZn3AOmB6gWMNQdICSd2Sunt7e8twGbVBMZFz8AB7TkRLU64qqdLbMDTc4IMNhhtGblrrPOM27NVJul3S4hyfEwrtliPMCoSPdJ+BgWZXmVmXmXW1t5dvLuPD9ynuWK+bMbBv+tzdt9ecjaYEUMzbt9nnipOdJm2f13pwbqrWxlNqbRZvfu2OvGmvgQMz7tU+oeB+r+/c/lLaIbtPBWDG1HHsOmX7G/f/79DXjEjTRw7bY0T7wfYxxebMnDpk3bTxuccb23+3oZNBDccJc3brX873k+6z86SSj1sJqv0Ifvztr63o+Tqmjato1bDK0WAi6W7gU2GKVSRdAGBmXwnffwtcCCwH7jKzfUP4acDhZnZ2Zhszu1dSC/A80E7UFnK4mZ0d9vkucLeZDZ4rfABdXV3W3d096mvLsC2VpknCzPpzrE1hSPGtqTQtTaK5SWzpSzOmuYm0WX8VRl/a+hPTlBnNEqmwPpU2xrQ00ZdKk7YoketLG+mwXdQ7R/0atqXStLU097uoQf8xNm1NkTJjwphmtqYiHdtSRnOT2JZKM7Z1u/Fs6Uv157SbFOUYzaLjRedoIpU20rY9d93S3EQ6aJOi6+1LpelLGxK0tTSztS/dfw2tYfumcP7M29Ibt/YxfkwLfelI45a+NK3NTf3X1Je2oCc6TyrcPym61pRZpNWiOuO0Za7R+nO+mfuf+b6lL0VL6ObU3CS29qXZ0pdiYltLdD5FmnMZ+5a+FG0t0b3b2pemuUlhqJChMTWVNraF5yG6t2LroHu/tS9an/ntmrS92jGzf2tzdP8z96E5q6RkZmxLGUakO/PcpNJGS3P0LEkibdZ/HIX7mjZjTHMTEv33K5U2WpujZzejM3usKIXrGPzMpsLxMufIXEsqpCmtTU3915i53mz9mWNkftsoHjVhZtFv0dxEyqLjjm1tZktfKprIikh7S7P6fzcp6uDQFO7X5m1pWpq3PzuZuNekaJ+t4fqkKOPTl47WZceTzPlSQcu28Lym0pE+gLGtzfSl0hjR8zimpYnN21I0N6m/dJt9/6WoNqAvpCfb0pl0JcpwpbLS49bmpgHP29ZUuj/OZtKM7OrqrX1p0ma0tTSNqo1J0kIz68q5LibD2B/4EVEj9W7AHcAsM0tJehD4OHA/cCvw32Z2q6TzgNeZ2TmS5gMnm9n7QqP3QuCQcLqHgLlmtqaQpnIbhuM4TiNQyDBGNcSmpJOA/yYqCfxa0iNmdoyZPSbpBuBxoA84z8wyr9eeC1wDjANuCx+Aq4EfSloKrCEqWWBmayRdBDwYtvvScGbhOI7jlJ+ylDBqES9hOI7jlE6hEkZ9N+k7juM4ZcMNw3EcxykKNwzHcRynKNwwHMdxnKKo20ZvSb3AM6M4xI7Ai2WSExdJ0AiuMw6SotV1lo9KadzdzHK+rVy3hjFaJHXn6ylQKyRBI7jOOEiKVtdZPmpBo1dJOY7jOEXhhuE4juMUhRtGfq6qtoAiSIJGcJ1xkBStrrN8VF2jt2E4juM4ReElDMdxHKco3DAcx3GconDDcJwaRT5xtlNjNKxhJCUyhvlAMss1q1nS4ZLKN81hTEj6V0nvCMs1ez8D/dPa1bLWWtaWTa3rTEJcbzjDkHSCpGuBg6qtpRCSjpV0D/BNSZcCWA32UMjS+QFgS7X15EPSO8Ksjp8BzoDavJ8Ako6W9Cfg65L+DWpTa4LiUk3rTEpch1FOoJQUJMnMTNIRwEXANuCNkp4xs7VVltdPyFU0AWcBHwa+AjwMXCfpODO7rdD+lSLoFPB+4LvAWWb20+qqGkrQ2Qp8AXgb0f0cA7xeUivQV2sRU1IH0XTGXwXuBq6XNN3MPpN5jqupL0MS4lItx/mkxPXB1H0JY1AkWwYcA3waeANwYNWEDSKjM8xM+CfgLWZ2M7AZeAF4TFJTZtsa0JkGVgLXAUvDulMldYTEuFZ0bgVuNrPDzOxWYC0w38y21VDim32f9gUWmdkvzWwD8G3gnyXNCglgrVRVLAPeQQ3HpfC15uJ8UuJ6LuraMCR9DPi5pH+WtIuZLTezVWZ2J7AaeJukGVWWOVjnrmb2uJn1SToE+AXQSVSV8o3MLlXW+S+SdiR62B8FrpT0BPA+oil7r6gRnZn7+WAIbzWzPwBPSzquGtoGM0jrZODvwFskvTFsshPwGPD5amkEkPRRSe8NywJWmNnzNR6Xai7OJyWu58XM6vIDnEQ0D/gRwP8A3wLmZK0/EPhf4ORB+6lWdAJ7AK8JyxOAl4GuGrmf3wb2AXYjKk4fHLbbAegF5taIzm8BB2V+26Dv+8A7avAZvRLYmaia4hrgz8CPwnPwV6CzChonAd8BngdeAVpCeFMmriQhLtWCzqTE9UKfei5hvAG40szuIqoTXgb8U2almT1K9OMdIOntkj4TwitdTZFL5yeClmVm9mxYfhW4AZhcYX35dC4HPm1mK4H/MLOHg841RDmlidWRWfB+WtA3jijSkin6V4lcWv/DzK4GPgL8s5n9A/As8ACwvtICLaoW+4OZ7QL8iiijAFEia2GbWo5LtRTnkxLX85J4wxhcx5f1/WngHwDM7Bng18AESe/J2vzHwD8CPyEaaz62OsMSdY4fpBNJnwf2Bx6PQ98IdP4SmCTpPWa2OWv7fw86n6gRnbl+9/8F5kkaa1FbTKyUoPUWYJqkkyxqY3kgbHcRUa5zQ5V03hL+fxI4LbSnpCS1ZG1Tsbg0mFqN80Xqq1pcHwmJNwyiXjD9ZOUWfgZslHRC+L6KqNfJbEVMBP4LWAQcaGafHrR/1XUCSDpOURfLvYFTzOz5mPSNVudhku4KOt9rZqtrTWdWxB0HXA+kYtaYoVSt+wBImiXpZuAAotLGtmroNLNXJTWFZ+8Koio9zKzPzEzSBOByKheXkNQ8WCc1FOdL1Rf2qXRcL5nEGoakN0r6KfA1SbMzP5CkTFfhtcBNwLmSZGbriKpJxoYfcDPwCTN7p5mtqkGd48L6vwHnmNkZNa5zOXCemZ1eozrbsiLuzWb2vbgT4NE8o2H980T39D1xGnABnc2Dq+zM7Hxgj7DPzpJeH6pQ/qlCcelLQUcqKzyTEahqnB+FvorG9dGQSMOQtBNRg9GtwEtE9YAfhijXEzYbB/yWyMWvkrQbcDBRf+xM7uiFGta5NWy33MwWJ0DnCjOLu7psNDoz6wdE5hrVmnlGN5hZTxV1pswsHXLmU7J2u4SoQf6PwPiwbdxx6UzgWuDzkt4XwlrCuTMZgarF+VHqq1hcHzVWAy3vpX6Ao4Ef2/YeBccQNcjtG8K+TPTDHEzUK+bLREW/K4Bm1+k6XWvROi8CfgMcFr4fR9Q+9XWgtYI6jwI6iN79eDYrvDn8v7Ca97PW9ZXtOqstoMgf40Tgs8A7w/d24Elgr/B9B+CLRDmf8URdEfcadIzxrtN1NrrW0eokqm+fWUGd7wrfmwkGRfT+z0VZ2+5U6ftZ6/piu+5qCxjmR2kn6qJ5D3AO0VuQp4R1XwW+GZabgLcA3wN2yNq/yXW6TtdaFp0VyQXn0XlSWDcm/N8fWAfsnGP/WO9nreuL/feptoBhfpxDifr6Z76fDvwlLB9EVFQ+Knzfj6j734RK/zCuszF1JklrPegM3zNVPN8H/icsH+f6KvOpuUZvSWcoGip7PLCQaKyiTDe1x4mGSYCoa9z1RCM8vhY4kuhN3lYAi7l/vetsTJ1J0lpHOheF7wIsaPpH4ExJa4GDBvfmaiR9laQmRqsNN3oXonq+NPAU0ZuunzCz1ZKaLXpRaD9Cb43wEF8TenmcTzRo20fM7GXX6TobVWud6pwWdBpgknYHLiPqpXWexdCzqNb1VY1qF3HYXoTbG/jfsNxCNIjdzwdtcx3wvrC8S9YxxrhO19noWhtAZ3v4PxWY16j6qvmpWgkj9FH+EtAs6VaicVNSEPWXlvRPwEpJb7NohFGIBj9bFl6OOVnSsWbWY9EQ1q7TdTak1gbTebxFYy49kOMUda2vFqhKvZqktxHVBU4jmkshM8HJEZLmQX/x7ktE/Zcz9YUfJnq9fjJwhMX/UpPrbECdSdLagDqfbUR9NUM1ijXAYcDpWd+vAM4FPggsDGFNRHWINwC7A3sB3wQOcZ2u07W6zkbSVyuf6pw0eiGoje31gB8AvhKWHwE+Hpa7gOurdnNcZ0PqTJJW19kY+mrlU5UqKTPbaGZbbPuYPkcTTboD8CFgP0m/IhqKeCFUbqhk1+k6ITlaXWdj6KsVqtqtNtQBGtEsY5kx9zcQvXJ/ALDMzJ6DqkzG0o/rLC9J0QnJ0eo6G0Nftan2yyRpopeDXgQODA7+70DazP6U+WFqANdZXpKiE5Kj1XWWh1rXV12qXSdG9Kp9mmjArrOqrcd1us6kanWdjaGvmp/MJO5VQ1IH0Xgs3zCzLVUVUwDXWV6SohOSo9V1loda11dNqm4YjuM4TjKodhuG4ziOkxDcMBzHcZyicMNwHMdxisINw3EcxykKNwzHcRynKNwwHCcgaaqkj4bl3ST9LMZzzZF0fFzHd5w4cMNwnO1MBT4KYGYrzeyUGM81B3DDcBKFv4fhOAFJ1wMnAEuAJ4H9zOwASR8ETgSaicYTuhQYQ/Ry1xbgeDNbI2kv4NtAO7CRaJrTJySdCnyRaDKedcBRRHMujAOeA74CLCMaKnscsAn4kJktKeHcdxONqjqPaG6GD5tZXU7i41SRar9q7h//1MoH6AQW51j+IFECP4nIDNYB54R1lwGfDMt3ALPC8huAO8PyImBGWJ6adcxvZZ17MtASlo8Cbizx3HcD3wvLb81o949/yvmp6mi1jpMg7jKzDcAGSeuAX4bwRUSD1E0E3gT8NGvU67bw/8/ANZJuAH6e5/hTgGslzSIaLbW12HNnbfdjADO7R9JkSVPN7OWRXa7jDMUNw3GKI3tMoXTW9zRRPGoCXjazOYN3NLNzJL0BeCfwiKQh2xBNCXqXmZ0kqZOoxFDsuftPNfjU+S/HcUrHG70dZzsbiKp+SsbM1gPLQnsFijgoLO9lZveb2ReIhs2emeNcU4jaMyCqhhoJ7w/newuwzszWjfA4jpMTNwzHCZjZS8CfJS0GvjaCQ3wAOEvSX4HHiBrQAb4maVE47j3AX4G7gNmSHpH0fuA/ga9I+jNRA/dIWCvpL8B3gLNGeAzHyYv3knKcOiD0kvqUmXVXW4tTv3gJw3EcxykKL2E4juM4ReElDMdxHKco3DAcx3GconDDcBzHcYrCDcNxHMcpCjcMx3Ecpyj+P9GDanpiHvcqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "running_qty.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date            2020-01-01\n",
      "End date              2021-03-17\n",
      "Time period (days)           441\n",
      "Sharpe Ratio            1.552092\n",
      "CAGR                    1.351208\n",
      "Max Daily Drawdown      0.234535\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "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": [
    "returns = equity.resample('1d').last().pct_change() \n",
    "bm_returns = valid['close'].resample('1d').last().pct_change()\n",
    "#returns_ = returns - bm_returns\n",
    "returns_ = returns\n",
    "sr = np.divide(returns_.mean(), returns_.std()) * np.sqrt(252)\n",
    "\n",
    "equity_1d = equity.resample('1d').last()\n",
    "Roll_Max = equity_1d.cummax()\n",
    "Daily_Drawdown = np.divide(equity_1d, Roll_Max) - 1.0\n",
    "Max_Daily_Drawdown = Daily_Drawdown.cummin()\n",
    "\n",
    "period = (equity.index[-1] - equity.index[0]).days\n",
    "\n",
    "print(pd.Series({\n",
    "    'Start date': equity.index[0].strftime('%Y-%m-%d'),\n",
    "    'End date': equity.index[-1].strftime('%Y-%m-%d'),\n",
    "    'Time period (days)': period,\n",
    "    'Sharpe Ratio': sr,\n",
    "    'CAGR': (equity[-1] / equity[0]) ** (365 / period) - 1,\n",
    "    'Max Daily Drawdown': -Max_Daily_Drawdown.min(),\n",
    "}))\n",
    "\n",
    "equity.resample('1d').last().pct_change().hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date                         2019-04-01\n",
      "End date                           2021-03-17\n",
      "Time period (days)                        716\n",
      "Train period          2019-07-01 - 2019-12-31\n",
      "Valid period          2020-01-01 - 2021-03-17\n",
      "Sharpe ratio                         3.174463\n",
      "CAGR                                 1.726663\n",
      "RRR                                 19.781955\n",
      "Maximum drawdown                     0.087285\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "equity, running_qty = search.best_estimator_.predict(df.values)\n",
    "equity = pd.Series(equity, index=df.index)\n",
    "running_qty = pd.Series(running_qty, index=df.index)\n",
    "\n",
    "\n",
    "returns = equity.resample('1d').last().pct_change() \n",
    "bm_returns = valid['close'].resample('1d').last().pct_change()\n",
    "#returns_ = returns - bm_returns\n",
    "returns_ = returns\n",
    "sr = np.divide(returns_.mean(), returns_.std()) * np.sqrt(252)\n",
    "\n",
    "equity_1d = equity.resample('1d').last()\n",
    "Roll_Max = equity_1d.cummax()\n",
    "Daily_Drawdown = np.divide(equity_1d, Roll_Max) - 1.0\n",
    "Max_Daily_Drawdown = Daily_Drawdown.cummin()\n",
    "\n",
    "period = (equity.index[-1] - equity.index[0]).days\n",
    "\n",
    "print(pd.Series({\n",
    "    'Start date': equity.index[0].strftime('%Y-%m-%d'),\n",
    "    'End date': equity.index[-1].strftime('%Y-%m-%d'),\n",
    "    'Time period (days)': period,\n",
    "    'Train period': '%s - %s' % (train.index[0].strftime('%Y-%m-%d'), train.index[-1].strftime('%Y-%m-%d')),\n",
    "    'Valid period': '%s - %s' % (valid.index[0].strftime('%Y-%m-%d'), valid.index[-1].strftime('%Y-%m-%d')),\n",
    "    'Sharpe ratio': sr,\n",
    "    'CAGR': (equity[-1] / equity[0]) ** (365 / period) - 1,\n",
    "    'RRR': ((equity[-1] / equity[0]) ** (365 / period) - 1) / -Max_Daily_Drawdown.min(),\n",
    "    'Maximum drawdown': -Max_Daily_Drawdown.min(),\n",
    "}))\n",
    "\n",
    "# equity.resample('1d').last().pct_change().hist(bins=20)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(8, 8))\n",
    "ax1 = ((equity / equity[0] - 1) * 100).plot()\n",
    "ax2 = ((df['close'] / df['close'][0] - 1) * 100).plot()\n",
    "ax1.set_ylabel('Cumulative Returns (%)')\n",
    "ax1.legend([\"Strategy's equity in BTC\", 'BitMEX XBTUSD perpetual'])\n",
    "ax1.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "Daily_Drawdown.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}