xbtusd-vanila-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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2021-03-16 23:59:40</th>\n",
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      "text/plain": [
       "                        open     high      low    close        0.5%  \\\n",
       "timestamp                                                             \n",
       "2019-04-01 00:00:00   4091.0   4091.0   4091.0   4091.0 -10765186.0   \n",
       "2019-04-01 00:00:05   4091.0   4091.0   4091.0   4091.0 -10812221.0   \n",
       "2019-04-01 00:00:10   4091.0   4091.5   4089.0   4091.5 -11479728.0   \n",
       "2019-04-01 00:00:15   4091.0   4091.0   4090.0   4090.0 -11424558.0   \n",
       "2019-04-01 00:00:20   4090.0   4090.0   4089.5   4089.5 -10676383.0   \n",
       "...                      ...      ...      ...      ...         ...   \n",
       "2021-03-16 23:59:40  56982.5  56984.0  56982.0  56984.0  -2180935.0   \n",
       "2021-03-16 23:59:45  56984.5  57000.5  56984.5  56999.5  -2169597.0   \n",
       "2021-03-16 23:59:50  57000.0  57003.5  57000.0  57003.5  -1862396.0   \n",
       "2021-03-16 23:59:55  57003.0  57010.0  57003.0  57009.5  -2446897.0   \n",
       "2021-03-17 00:00:00  57009.5  57010.0  57009.5  57009.5  -3821822.0   \n",
       "\n",
       "                             1%        2.5%          5%        7.5%  \\\n",
       "timestamp                                                             \n",
       "2019-04-01 00:00:00 -17422533.0 -10582776.0  21903241.0  41196764.0   \n",
       "2019-04-01 00:00:05 -17472053.0 -10632960.0  21856228.0  41149751.0   \n",
       "2019-04-01 00:00:10 -19943766.0 -13622060.0  18615715.0  37584853.0   \n",
       "2019-04-01 00:00:15 -19888596.0 -13566890.0  18670885.0  37640023.0   \n",
       "2019-04-01 00:00:20 -19284247.0 -13187497.0  19218544.0  37876052.0   \n",
       "...                         ...         ...         ...         ...   \n",
       "2021-03-16 23:59:40 -22850664.0 -32311913.0 -46057067.0 -13535863.0   \n",
       "2021-03-16 23:59:45 -26098949.0 -35400006.0 -46032707.0 -13558022.0   \n",
       "2021-03-16 23:59:50 -23349140.0 -36395095.0 -47009491.0 -14593029.0   \n",
       "2021-03-16 23:59:55 -24469653.0 -37006078.0 -47902065.0 -15295934.0   \n",
       "2021-03-17 00:00:00 -27033452.0 -37935653.0 -48842772.0 -16236641.0   \n",
       "\n",
       "                            10%         15%         20%          25%  \n",
       "timestamp                                                             \n",
       "2019-04-01 00:00:00  55648501.0  98500739.0  99959834.0  101851428.0  \n",
       "2019-04-01 00:00:05  55601488.0  98453726.0  99912821.0  101804414.0  \n",
       "2019-04-01 00:00:10  54453685.0  94850145.0  96235710.0   98146467.0  \n",
       "2019-04-01 00:00:15  54508855.0  94905315.0  96290880.0   98201637.0  \n",
       "2019-04-01 00:00:20  54774020.0  95132450.0  96515130.0   98426662.0  \n",
       "...                         ...         ...         ...          ...  \n",
       "2021-03-16 23:59:40  27913682.0  58666132.0  61756488.0   52110835.0  \n",
       "2021-03-16 23:59:45  27766287.0  58555030.0  61748493.0   52102840.0  \n",
       "2021-03-16 23:59:50  26715022.0  57526876.0  60694031.0   51008150.0  \n",
       "2021-03-16 23:59:55  26142627.0  56967326.0  60337439.0   50442895.0  \n",
       "2021-03-17 00:00:00  25201430.0  56026129.0  59396242.0   49501698.0  \n",
       "\n",
       "[12372482 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "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] / 100000000 + 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",
    "#         #orders = []\n",
    "#         tick_size = 0.5\n",
    "#         running_qty = 0\n",
    "#         static_equity = self.equity\n",
    "#         fee = 0\n",
    "#         equity = []\n",
    "#         order_qty = 100\n",
    "#         new_bid = np.nan\n",
    "#         new_ask = np.nan\n",
    "#         for _, row in X.iterrows():\n",
    "#             #for price, qty in orders:\n",
    "#             #    if qty > 0 and price >= row['low']:\n",
    "#             #        running_qty += qty\n",
    "#             #        static_equity -= qty / price\n",
    "#             #        fee += qty / price * -0.00025    \n",
    "#             #    if qty < 0 and price <= row['high']:\n",
    "#             #        running_qty -= qty\n",
    "#             #        static_equity += qty / price\n",
    "#             #        fee += qty / price * -0.00025    \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",
    "#             skew = self.B * running_qty / self.max_position * -1\n",
    "#             quote_mid_price = row['close'] + self.A * row['fairBasis'] + skew\n",
    "#             new_bid = np.minimum(np.round(quote_mid_price * (1 - self.half_spread) / tick_size) * tick_size, row['close'] - tick_size)\n",
    "#             new_ask = np.maximum(np.round(quote_mid_price * (1 + self.half_spread) / tick_size) * tick_size, row['close'] + tick_size)\n",
    "#             #if running_qty < self.max_position:\n",
    "#             #    orders.append((new_bid, 100))\n",
    "#             #if running_qty > -self.max_position:\n",
    "#             #    orders.append((new_ask, -100))\n",
    "#             if running_qty > self.max_position:\n",
    "#                 new_bid = np.nan\n",
    "#             if running_qty < -self.max_position:\n",
    "#                 new_ask = np.nan\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') & (df.index < '2021-1-8')]"
   ]
  },
  {
   "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:   28.2s\n",
      "[Parallel(n_jobs=8)]: Done 184 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=8)]: Done 434 tasks      | elapsed:  5.0min\n",
      "[Parallel(n_jobs=8)]: Done 784 tasks      | elapsed:  8.9min\n",
      "[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 11.1min 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 0x7f08f016d990>,\n",
       "                   n_iter=1000, n_jobs=8,\n",
       "                   param_distributions={'A': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f08f016d9d0>,\n",
       "                                        'B': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f08f016d310>,\n",
       "                                        'half_spread': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f08f0392fd0>,\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 0x7f08eede3310>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# v1.3 params\n",
    "search.best_params_ = { 'A': 53.69024193790823, 'B': 44.26080387202236, 'half_spread': 0.00091530679386619, 'imbalance': 7 }\n",
    "search.best_estimator_.set_params(**{ 'A': 53.69024193790823, 'B': 44.26080387202236, 'half_spread': 0.00091530679386619, 'imbalance': 7 })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 53.69024193790823,\n",
       " 'B': 44.26080387202236,\n",
       " 'half_spread': 0.00091530679386619,\n",
       " 'imbalance': 7}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001928838331132949"
      ]
     },
     "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             3.76095\n",
      "CAGR                    1.979518\n",
      "Max Daily Drawdown      0.067246\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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hvrMHY4yJJcUlyqqt+9mVl1/lfVs1rh+BGtWcKt2H4JpyBgPLgPaquh28pCEi7dxmnYCPA3bLcWWFbrlsuW+fre5YRSKSC7QBvi3z+pPxzjDo2rVrVapujDE14s9vrOff720CIHv6mCrt26RBbN/6FfZFZRFpCrwI3KCqB0JtGqRMQ5SH2qd0gepMVU1X1fTU1NTKqmyMMcfsUEERm3Yf9D//z4fZ/uXb53/Bc8u3hNw/a9fRfVs2iu1rCGGlKxGph5cMnlHVl1zxThHp6M4OOgK7XHkO0CVg987ANlfeOUh54D45IpICtAD2ViMeY4ypMSu37OOCGR8BR88Gvisq8a+ftdTrNdSiUT2ufeZT7vhhPy4f1t2/Pm3Kq6WO16pJbDcZhdPLSIDHgXWqen/AqgXARLc8EZgfUD7e9RzqjnfxeLlrXsoTkaHumBPK7OM71kXAO+46gzHGRI0vGQAcLCiqcLtrn/kUgDv+t5Z/v7cRgLz8wshWLgLCaTIaBlwGnC0iq9xjNDAdOEdEvgLOcc9R1TXAPGAt8DpwnaoWu2NdAzyGd6F5I/CaK38caCMiWcBNuB5LxhhT24qKS7h+zkqWbdpTqnz20uyw9r/3tfUAfLkzr1T5pafE/nVPidcv4unp6ZqZmRntahhjEszIB5ewfkde0HXZ08dw1//W8sSHm0MeI3v6mFLNRe2aNWD5rSNqtJ7VJSIrVDU92Dq7U9kYY4Aj3xWzbf+RCpOBT0XJYN5Vp/qXA5PBFcPSYiYZVCa2+0AZY0wtGf3Q+2z+9lCF60f27xBy/4rmNrj9h/2PqV61yRKCMcZAyGQA8PqaHdw0d1WpsicuT+fUHm1JSpC2FksIxhgTppdWfuNfHtilJWcf3z6Ktal5CZLXjDEmMh6dUP766y2jj2f+dcPKlddLLn2P7RXD0iJVrYiwhGCMMWVcdWYPAEaf0IE+7ZuWW39lwM1ngaaO6lvqeTxdPwBrMjLGmHKmjurLxFPTaNu0ATsPlB/ELiU5+HfpK4al0f+45mzYmcdTS2N77oNgLCEYY+q8/MJi//JP0r2Rd45r2QiAemU+/Du58mBEhFN6tOGUHm2YcGpazVc0wqzJyBhT5017dZ1/+b6LTiy1rkOLhqWef7P/SK3UKRosIRhj6jzf1Jahvv37/LlMwkgklhCMMXXSo0s2cfcra0uVFZdUPpTPJeldKt0mXtk1BGNMnTRtoddM9PgHR4eiWDr17JD7nFLB3ciJws4QjDEJTVUpKfPN/56F64JuW9nMvXMDxitKRHaGYIxJaN2nLgRgxW0jyN5zmB25+cxcsqlKx9h4z+iwmpPinSUEY0ydMORPb4dcv+g3Z1a4LjlJSE4KffaQCMKZMe0JEdklIl8ElN0hIt+UmTDHt26qiGSJyAYROS+gfIiIrHbrHnKzpuFmVpvrypeJSFoNx2iMMUFNHXU8Z/RJ5Y0bzqBnavk7kuuacK4hPAmMDFL+gKoOco+FACLSDxgP9Hf7zBCRZLf9I8BkvCk1ewcccxKwT1V7AQ8A91UzFmOMqZKrzuzJ7Csz+F6HZtGuSkyoNCGo6hLCn/B+LDBHVQtUdTPeVJkZItIRaK6qS91cybOBcQH7zHLLLwDDpbIrO8YYc4yypo2KdhVizrH0MvqliHzumpRaubJOwNaAbXJcWSe3XLa81D6qWgTkAm2CvaCITBaRTBHJ3L179zFU3RhTl/3x/H4VjkdUl1X3HXkE6AkMArYDf3Plwb7Za4jyUPuUL1SdqarpqpqemppapQobYwzAiL7tufL04KOV1nXVSgiqulNVi1W1BHgUyHCrcoDA2/g6A9tceecg5aX2EZEUoAXhN1EZY0yF8vILy5U9NjHo/PKGaiYEd03A5wLA1wNpATDe9RzqjnfxeLmqbgfyRGSouz4wAZgfsM9Et3wR8I67zmCMMVWWe6TQf8/ACXe8WWrdVWf0iEaV4kal9yGIyHPAD4C2IpID3A78QEQG4TXtZANXAajqGhGZB6wFioDrVNU3ruw1eD2WGgGvuQfA48BTIpKFd2YwvgbiMsbUQXn5hQy8800uGtKZKaOO95d3atmID6eEHpbCgMTrl/H09HTNzMyMdjWMMTEgbcqrALzzmzM5+2/vlVv/xZ3n0bSB3YcLICIrVDVou5ldZjfGxLUduUdnNAuWDABLBmGyd8kYE5dUledX5LBg1bbKNzZhsYRgjIkrN81bxUuffhP29pvuGV35RgawJiNjTJwJJxlk3jaCs76Xynu/+wFJdWBQuppiZwjGmLiR/e2hSrfZeM9okpOE/1yRUem2pjRLCMYksHXbD9AjtQkNUpIr3zgOZO8pnRCe/cUpPLw4ixF923Ph4M60aFwvSjVLDJYQjElQh78rYtTf3wcge/qYKNemZgSeIfhiOq1n22hVJ+HYNQRj4sTba3fywoqcoOse/2AzhcUlpcrWbjvgX/7im9ywXiNr10FWbd1f7TpG2h3/WxvtKiQ0O0MwJk78fLZ3I+ZFQ7xhwdKmvMrgri05rWcbHl68kbtfWcvme0fz5tqdXPXUilL7nv+PDwD4atooUpKE/MISGtUv3YxUUqKMuN/rx7/53tGVzi9c2wLnRX7k0pOiWJPEZQnBmDjw7/c2lnruuzN35Zb9rNyy31/umz+4Ir1vfc2/fPbx7Xji8pMBWLZpDz+Z+bF/3dPLtnDZ0G7HWu1qKS5R/r7oKyYN606TBskkJwkiQo9bjsY26oSOIY5gqssSgjExKvdIIY3rJ1MvOYl7X1vvL79hzsoaOf4763f5E0tZMxZn1XpC2HfoO+av+sbfLPTQoq/8624eeXxFu5kaZAnBmBg18M43g5b/N4w7c/9vaFf+NO4EsnblUaLw6+dWsn5HXtivvT03n32HvqNVk/ph73OsBt/9VoXr7nv9aEJMlAvkscguKhsTg8IZdLJji4aMG3QcH9x8VrkPyT+NOwGAXu2a0ad9M16/4Qze+c2ZnNOvfdAP1MtPS2PzvaN58oqT/WW7DxZUWof3v9pN2pRXeXbZFtKmvMrPZ2WGVffqWnbL8Igd29gZgjExKWffkUq3eXRCOgM6tfA/H358Oxat31XhUA09Upvy6ARvkMtnf3EKN839jB0H8klr05g7ftQfgDP7HJ2J8FBBUcjXn7lkI/cs9L653/LyagDeXreT7lMXkj19DIXFJRwqKOK9L3dz/ZxVpfbddM9okpKEgqJiBKHPbUevbSy/dTgbdx3i1J5tyMsv5I01OxlzQsdyF8FNzbOEYEwMmvFuVoXr5l11Km2b1qdHatNS5Y9ffnIFe5R3Ws+2fBzk23Zgz6ILZnxUYW+jL3fm+ZNBMKpa6gJ2WU8v+5o/zl9Trrxt0/q0a9aQds0aAtCsYT1/ryoTeeFMkPMEcD6wS1UHuLLWwFwgDW+CnEtUdZ9bNxWYBBQDv1bVN1z5EI5OkLMQuF5VVUQaALOBIcAe4Ceqml1jEcao4hLlpLvfIvdIId9r34ynf34Kqc0aRLtaCWnLnsPsPpjPkG6to12VsDWuf/RfM2vaKESEnq6XTUb3yMbRrU1jvt5zGPB6Lb3669PZlVfA0o17eGvtTnKPFLL30Hel9hk1oAO3junL6fct9u8XSrBkAPDJrSNqIAJTXeGcITwJ/BPvQ9tnCrBIVaeLyBT3/GYR6Yc341l/4DjgbRHp42ZNewSYDHyMlxBG4s2aNgnYp6q9RGQ8cB/wk5oILhapKmu2HeC3z39G7hFvvtcNO/M4edrbbPjTSN5au5NfPruSyWf04JbRfaNc28Rwxl+8D6lY7FsfTFFxCY9/sNn/PCXZu9T39k1ncCA/dDNOTZh1RQY/+Ou7/udjHvqgwm1nXjaEc/t3CHm8F685jQYpSTRtkMLWfYe57PHl/nV/Hz/I35x025i+cfH7SWSVJgRVXSIiaWWKx+JNqwkwC3gXuNmVz1HVAmCzmxYzQ0SygeaquhRARGYD4/ASwljgDnesF4B/iogk6rzKob45fe+21/3LM5ds4pbRfXn9ix28vDKHf19WNyYGV1X/e+RrZw5cJ3K03bl+StX6REx4YjlPTTqlVNni9bu44slPyJo2yv/BG22BvYj+8dPB/uVe7ZrVyuuntW0S9rZlk8FlQ7vx1Mdf+5//5/KTGdKtValjz74yg/opSQzt0QaAsYM6HWONTU2p7jWE9qq6HUBVt4tIO1feCe8MwCfHlRW65bLlvn22umMViUgu0Ab4tuyLishkvLMMunbtWs2q1z5V5Y/z15T6RwnH6pxcrn7au+P00SWb+EUdmCD8jTU7/cs9b13I5nu9HjG+/vLLbxlOxj2L/Nv88fx+XHl69wqPVxxwd+v7X31L2pRXuXFEH1KbNfBfCAXodetrIbsz5uUXsu9QIV3bNK56UFX0yea9/uUfDjwu4q9XFa9d/316pjYl90ghLYMMJHf3uAHcPW4A73+1m237j3DW8e3KbXNGwIVrE1tq+qJysPM9DVEeap/yhaozgZngzalcnQrWho82fsuQbq04VFDMmm25HCoorjAZZE8fw6yPsrl9Qfk21R/+8+ip+rSF6zjr+NRa+5YYLb+Zt8q/rAqFxSXUC/jmHpgMAO56ZW3QhPCXN9bz8OKN5coBHnj7y5B1uP/NDQzs0pLTerb192w54Y6j9wREqunpsfc38adX19X4cavjgZ8M5Ma5n5Ur79uxOUCl17u+39s+9ONRdRPCThHp6M4OOgK7XHkO0CVgu87ANlfeOUh54D45IpICtAD2Eoc+3rSH8TM/rnS7GZeexLXPfMoYd/v9hFO7sWn3QWYtDX0GMeL+JQlzU87G3QcZ/rf3WHvXef4LqCUlyqHvikttt31/Pl1aNwp5rJvmreL+Swb5n6/fcaDCZBBKRXftlrVl72G6tam4WUVVWfLVt0x8wmsrn3HpSZzbrz1b9h6mRaN6tGla+sM0v7CY4//wernjZE0bVYXa16wLBnfmgsFH/2V9zXUmsVU3ISwAJgLT3c/5AeXPisj9eBeVewPLVbVYRPJEZCiwDJgA/KPMsZYCFwHvxOr1g237j3Agv5DjOzQvt05VK00Gz/78FE7r5Q3VG/jBLiLcOXYAGd3bsOLrfTzx4dELikt+dxb/eOcrnnejXB7IL6R5w9Kn6t8eLOAvr29gzfZcHp94Mu2bN6x2jLVluJsM/Z6F60hr04Rz+3Xgr29uKLfdjgP5zFqaHfJYL336jX8WrU33jGbkg+9XuO3gri1Ljf2z/NbhZExbVOH2wZz5l3cBuGFEbx7/YDN5ARd6/3vdMH726MccDkhs1z7zadDjdG/bhM0VTPjy2e3nxsw1DcCSQR0hlX32ishzeBeQ2wI7gduB/wLzgK7AFuBiVd3rtr8VuBIoAm5Q1ddceTpHu52+BvzKdTttCDwFDMY7Mxivqpsqq3h6erpmZmZWLdpqUlX63/6G/5/8yz+NKndBM5xvl+F+u8/M3kvOviP8cOBxJLuLqr7j9+vYnIXXf9+/Xdc2jct9oMXDWURl79eff3wiv3/x86DrurVpzHu/O4tF63YyaVbov4G3bjyDnH1HuOLJTzipa0teunYYAHsOFtCycX2Sk8Rfl44tGrI9Nx+A9G6tyPx6X6ljzb4ygwlPLCeS1t89kob17AYsEzkiskJVg/ZSCaeX0U8rWBX0HnJVnQZMC1KeCQwIUp4PXFxZPaJh697DfP/Pi8uVf7xpD/e/9SVPXnEyLRuXH+vlxhF9uOrMHnz69T56tW9KxrRFvHjNaWG/bnpaa9LTSpc1qZ/Moe+KWbv9AGlTXuXKYd1LnUkkklEDOnAgv7Bc+cvXnsb+w4X+C5XD+7YPeRxfYtx32DtWUsC33MBmm9+e24edBwq4e9wAf3J4wf2+DhUU8e3BAn8TUUqSUFQS3gns5ntHs2bbAW5+8XOev/pUduTm89gHm/nim1w+z/HmJ0hOEprUT+bvPx3MWd8rfwHWmNpU6RlCrKqNM4RwvvWv/MM5ZO0+yMX/WgrAnMlD/d3patKNc1fx8sqKJxdvVC+ZI4XeGcysKzNKDUEQi0K9t9nTxzD2nx/wWU5uufKKjvPDgcfxv8+Odtd8//dn0aW11yMo93AhA+96k3/93xBGDgjdZz4zey/1U5I4sXPLoOtLShQRmPvJVqa8tNp/gdnXC6lL60Ys+GwbQ3u0iYumO1P3HNMZQl3h+2AJdcreqWUjvtlfeoyZsiM0RiIZAPzq7F4hE8Lau87jx498xKdb9tO0QXw1Odx0Th/uf8vr+eP70C+bDFbfcW7QfX3b5+UX+hPC3MlD/ckAoEXjemE3o6Wnhb4L2HdfxPiMrozPONr1uVnDejRz13asX72JV5YQygjW28P3LTD720Oc++ASbh55PO9u2MX7X5W+VSKSbfc9Upvy3u9+wL/e28QLK7ZSWKzlXnPsoE58umU/P35kKf/75emc0LlFRYeLGX/+8YlccnIXzj+xI10DPsTP+l4qizfs9j9v1rB8n/dAzRqG/6FvjAnOEgLw+hfbQ6739bBIa9uEL//kdQWcdHr3Us0er/zq9MhV0OnWpgn3XngC9154QtD1gXeE/vCfH3DW91L5zxUZEa9XVQU2U15ystdLuexAbX07NvcnhEtPiZ+bEI2JZ5YQgKufDt4tEOC5XwytcF2sfSMNHAoZYPGG3ZSUaKnhH6Lhlc+3kZIktG/ekAtmfBTWPjee04eRAzpU2JZvjKl5dT4hfJ6zP2h5rH3YV9fiDbsq7Y1TFb6JzquSZH75bPApH68+s2eF+9RLrvjCrjEmMupsQng+cyu/e6F0P/fs6WP4dMs+mjeM37dl5mVDuO2/X7Arz5vtatXW/ceUEPLyC0lOEhrXT+Hpj7/mtv9+4V/3yKUnBZ3sfHvuEQ5/V0zPMs1Agc7p154po2yeXGNiSZ3rdpqZvZeLXBfRQP/6v5MYOaD8h1u8GvngEtbvyKN+SpL/ukd1pE15lRaN6vHZ7ecG7So6om973l63k+zpY3hhRQ6/fb70+De/OrsX/3jn6GQv0y4YwM8yutqdr8ZEiXU7DRA4bAF4F4dvPKcPTRsk1lvR77jmrN+RR2rT8oOQZe06yK68fE7r2ZaSEuXNtTvZ9O1BLhzcmQ4tyvedzz1SWOF9A2+v80YnrWh9YDJIlGY4YxJVYn0KhuEXZ/Rg7ODj/MM9/OH8flGuUWQ0cwnuYJB5cUfc740jlD19DD1uOTo/w59f3+AvB28AumA+nHI2w6a/E/L1RbzRSn3mTK744rwxJjbUuYQA0K5ZQ96+6UwaVHGClXjSxCWEUBOlb3HTJJa199B3nFTmhjuf80/sSKeWjbj/koHcNK/88MjgJZSSEvUnmxevOa1Ul1hjTGyqkwkBoFe7ii94JgJfQig77k5gEvBNLVlWsGQwsn8HfnteH/98DBee1JkLT+rMyi37uGDGRyz45TB+9M8P/dsnJYk1ERkTZ+psQkh0zSroKTX24eDz4348dThD7w0+DPTGe0b7R10ta3DXVv4P/k//cA5N4mzYDGPMUYnbZlLHNal/NCFs3XuYK5/8hEMFRaWmlPQZ0q0VHVo0ZHiQ6Q6BCpNBWa2b1KdBiiUEY+KVnSEkqMD5GnxDeL+9bicH8stfU1jhxv1/bGI6a7Yd4Px/eGcR1uRjTN1yTAlBRLKBPKAYKFLVdBFpDcwF0oBs4BJV3ee2nwpMctv/WlXfcOVDODp5zkLg+lidNS1eBBt6+fo5q4Juu+K2EYA3ZtOATi3IvG2ETdJiTB1UE2cIZ6lq4LCfU4BFqjpdRKa45zeLSD9gPNAfb3rNt0Wkj6oWA48Ak4GP8RLCSLxZ1Uw1ZXQPPYxzqGG+2wa5d8EYk/gicQ1hLDDLLc8CxgWUz1HVAlXdDGQBGSLSEWiuqkvdWcHsgH1MhNgZgDGmrGNNCAq8KSIrRGSyK2uvqtsB3E/flcpOwNaAfXNcWSe3XLbcREhFPZCMMXXbsX4yDFPVbSLSDnhLRNaH2DZYVxUNUV7+AF7SmQzQtauNkR+uBilJFBSVADDvqlM5Oc1uEjPGlHdMZwiqus393AW8DGQAO10zEO7nLrd5DtAlYPfOwDZX3jlIebDXm6mq6aqanpoa23MGx5KlU4f7lzO6t7aB5YwxQVU7IYhIExFp5lsGzgW+ABYAE91mE4H5bnkBMF5EGohId6A3sNw1K+WJyFDxPqkmBOxjakDrJvWjXQVjTBw4liaj9sDL7ttmCvCsqr4uIp8A80RkErAFuBhAVdeIyDxgLVAEXOd6GAFcw9Fup69hPYxq3Bd3nke9ZDszMMZUrNoJQVU3AQODlO8BhpffA1R1GjAtSHkmMKC6dTGVS7ThvY0xNc+GrjDGGANYQkhoY07sWGoIC2OMCcXaERLYwz87KdpVMMbEEfv6aIwxBrCEYIwxxrGEYIwxBrCEYIwxxrGEYIwxBrCEYIwxxrGEYIwxBgCJ15kqRWQ38HU1d28LfFvpVrEtEWIAiyPWWByxJRJxdFPVoMNFx21COBYikqmq6dGux7FIhBjA4og1Fkdsqe04rMnIGGMMYAnBGGOMU1cTwsxoV6AGJEIMYHHEGosjttRqHHXyGoIxxpjy6uoZgjHGmDIsIRhjjAEsIRhT54ibCN2YshI2ISTCH72ItA5Yjtt4ROQHIhL0Rph4ISK/EZFz3XLc/i6cZr6FeI4lnuvuE2sxJFxCEJGxIjILGBjtulSXiIwUkSXAgyLyNwCNw6v/AXFcChREuz7VISLnisgbwM3ABIjP3wWAiJwjIh8AfxWR30N8xpIg/+MxGUNCTKEpIqKqKiJnAXcDhcCpIvK1qu6LcvXC4r4pJAGTgCuBe4GVwGwRGaWqr0WzfuFycQjwE+DfwCRVfT66taoaF0M94I/AmXi/i/rAySJSDyiKtw9SEekM3AFMB94F5ohIG1W92ff/E836hSve/8dj/XMq7s8QyvwxbwbOA34HnAKcGLWKVYEvBlUtBj4ATlfV+UA+sAtYIyJJvm2jWNWQAuIoAbYBs4Est+5iEensPlBjNo6AGL4D5qvq91V1IbAPGK+qhXH04Rn4Hh8PrFbV/6lqHvAwcKOI9HYfUjH5+whiM3Aucfo/7p7G7OdUXCcEEfkl8JKI3CgiHVQ1W1W3q+o7wE7gTBHpFOVqhlQmho6qulZVi0TkJOC/QBpec8X9vl2iU9PQAuK4SUTa4iW2z4FHRGQ9cAnwD2CGb5fo1LRiQX4Xn7jyeqr6HrBJREZFt5bhKRNLc+BL4HQROdVt0g5YA9wWrTqGQ0SuFZEfu2UBtqrqjjj+H4/pz6m4TQgicgEwEXgIL8PeJiKDAjZ5BuiDl4ED94uZD6IgMdwaEIPvG2kG8HvgchFJd9++Y0qZOE4A7gR6Aa8Ai4GfqurFeM1h40RkSKzFUcHvwte+W+Qu8H8NFEepimELEst9eNdwHgCuEpEP8b5lXwgMEpG0WDvrEZFmIvIvvGa7WSKS4uoYeDYTj//jMf05FbcJAe8NfERVF+O1jW4Gfu1bqaqfA58AA0TkbBG52ZXH0h9+sBiuB1DVzaq6xS0fAuYBzaNUz8qUjSMb+J2qbgPuVNWVAKq6F++sp2l0qhlSqN+Furo3As4C8DXhxahgsdypqo8DvwBuVNWfAVuA5cCBaFW0Iq5Z6z1V7YD3xeJht8rf9BLH/+Mx+zkVy3/UQPlMGfB8E/AzAFX9GngVaCIiPwrY/Dng58BcvHHFo5J5qxhD4zIxICK3Af2BtZGvbcWqEMf/gGYi8iNVzQ/Y/g94cayvnRqXd4x/T08DGSLSMBbOcKoQywKglYhc4K6BLHfb3Q00AfJqqcpBhYhjgft5A/BTd72jWERSAraJif/xsuLxcwriICHg9fbwC8icLwCHRWSse74dr/dEP/E0Bf4OrAZOVNXfldm/NlU5BgARGSVeN8E+wEWquqN2qluh6sbxfRFZjBfHj1V1Z+1UN6hq/T25skbAHGKn2aiqsXwPQER6i8h8YADe2UJh7VS3QkHjUNVDIpLk/u5nAI+58iJ3IbwJXlNMLPyPIyLJvuU4/ZyK3YQgIqeKyPPAX0Skn+/NFhFfV9l9wMvANSIiqpqL1xTR0L2Z+cD1qjpGVbfHWQyN3Pp1wNWqOiFaMUCNxJENXKeql8Xh76JBwD/nfFV9NNofoMfyv+HW78D7ffwomsk5RBzJZZvkVHUK0N3t015ETnZNqb+Ogf/xu1wdiwPKfV8iYv5zKlBMJgQRaQf8E1gI7MFry70SvG8HbrNGwBt4GXemiBwHDMbr2+v7FrGrlqvud4wxfOe2y1bVL2q56qXUUBxbVTVqzV3HGINvfal/+Gipof+NPFXNqeWql1JJHMWqWuK+PbcI2O0+4EPgfaCx2zaa/+MTgVl4F4ovcWUprl6+LxEx/TlVjqrG3AM4B3jOLTfB67P7CnC8K/sT3ps8GGjtnr+Ld1qZHO36J0oMiRJHIsSQaLGEEcfdwOvA993zUXjXnv4K1It2/V2dRgCd8XpsbQkoT3Y/74iH30WpmKJdAffGjQNuAca456nAV0BP97w1cDveN4TGwLO+dQHHaGwxWByJEkOixXKsceBdj+oSIzGc754n45IT3n03dwds2y5WfxchY4zyG5yK1w1xCXA13l25F7l104EH3XIScDrwKNA6YP+kqL+BCRBDosSRCDEkWiw1EEfUv0lXEMMFbl1997M/kAu0D7J/TPwuwoo1ym/0ULz+6r7nlwEfueWBeKeMI9zzvnjd0JrE0pucCDEkShyJEEOixZIIcYSKwT33NRE9BvzHLY+Kdr2r86j1we1EZAJHb4hZgXejhq/L1lq82+nB64Y1B2/Ez3HAcLzhDuoBaBT7gSdCDJAYcSRCDD6JEksixBFGDKvdcwEUQFV/LiIlLpb7ROSNaP8uqqpWEoJ70zrgtamVABvx7pi8XlV3ikiyejec9MX1KnBv5JOuN8IUvMG5fqGq+2ujzokYAyRGHIkQg0+ixJIIcVQxhlYuBsUbTqMb3tAg7+N16Y1q78Bqq4XTLd/pVB/gabecgjfQ2UtltpkNXOKWOwQco340T6MSIYZEiSMRYki0WBIhjmOIIdX9bAlkRPt3cayPiJ0huP64dwHJIrIQbxyeYvD63orIr4FtInKmeiNJAhwENrsbPS4UkZGqmqPeUMS1LhFigMSIIxFi8EmUWBIhjhqKYbR6444tD/IScSUiN6aJyJl47W6t8MbD900GcZaIZID/VOsuvL66vra5K/Fu9W4OnKVRvHkmEWJwdYr7OBIhBp9EiSUR4qjBGLbUeuUjJRKnHcD3gcsCns8ArgEuB1a4siS89rp5QDegJ/AgcFK0T5sSJYZEiSMRYki0WBIhjkSIocbfkwi90Y2BBhxtc7sUuNctrwJ+5ZbTgTnRfhMSNYZEiSMRYki0WBIhjkSIoaYfEWkyUtXDqlqgR8d+OQfY7ZavAPqKyCt4w76ugNgYsjZQIsQAiRFHIsTgkyixJEIciRBDTYtot1PX3qZAe46ObZ6Hd/v3AGCzqn4DMTephV8ixACJEUcixOCTKLEkQhyJEENNifRopyV4N5l8C5zosu0fgBJV/cD3Jse4RIgBEiOORIjBJ1FiSYQ4EiGGmhHpNim8275L8AZ/mhTtNrK6GkOixJEIMSRaLIkQRyLEUBMPcW9GxIhIZ7yxP+5X1YKIvliEJEIMkBhxJEIMPokSSyLEkQgx1ISIJwRjjDHxISZnTDPGGFP7LCEYY4wBLCEYY4xxLCEYY4wBLCEYY4xxLCGYOkNEWorItW75OBF5IYKvNUhERkfq+MZEgiUEU5e0BK4FUNVtqnpRBF9rEGAJwcQVuw/B1BkiMgcYC2wAvgL6quoAEbkcGAck441d8zegPt6NSgXAaFXdKyI9gYeBVOAw3nSP60XkYuB2vIlVcoEReOPrNwK+Ae7Fm5P3QVd2BLhCVTdU4bXfxRuBMwNvHP4rVTXuJ2QxMSbat0rbwx619QDSgC+CLF+O9wHeDO/DPhe42q17ALjBLS8CervlU4B33PJqoJNbbhlwzH8GvHZzIMUtjwBerOJrvws86pbP8NXdHvaoyUdERzs1Jo4sVtU8IE9EcoH/ufLVeAOeNQVOA54PGAG5gfv5Id5k8fOAlyo4fgtgloj0xhtZs164rx2w3XMAqrpERJqLSEuN0oT0JjFZQjDGEzh+TUnA8xK8/5MkYL+qDiq7o6peLSKnAGOAVSJSbhu86RkXq+oFIpKG940/3Nf2v1TZl644HGOqzi4qm7okD69ppspU9QDexOoXgzdRiogMdMs9VXWZqv4RbwjlLkFeqwXe9QTwmomq4yfu9U4HclU1t5rHMSYoSwimzlDVPcCHIvIF8JdqHOJSYJKIfAaswbtADfAXEVntjrsE+AxYDPQTkVUi8hPgz8C9IvIh3gXk6tgnIh8B/wImVfMYxlTIehkZEwdcL6PfqmpmtOtiEpedIRhjjAHsDMEYY4xjZwjGGGMASwjGGGMcSwjGGGMASwjGGGMcSwjGGGMA+H9r4PnCFOxXWgAAAABJRU5ErkJggg==\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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\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              2021-01-07\n",
      "Time period (days)           372\n",
      "Sharpe Ratio            3.098785\n",
      "CAGR                    0.864021\n",
      "Max Daily Drawdown      0.102859\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 - 2021-01-07\n",
      "Sharpe ratio                         3.526001\n",
      "CAGR                                 0.996891\n",
      "RRR                                 22.499692\n",
      "Maximum drawdown                     0.044307\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"
    },
    {
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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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 25,
     "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 / equity[0] - 1).plot()"
   ]
  },
  {
   "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
}