non_stationary_dgp_rl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "import gymnasium as gym\n",
    "import gym_trading_env\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from RLTrade.agent import DQNAgent, XGBoostAgent\n",
    "from RLTrade.utils import (\n",
    "    nonstationaryDGP, \n",
    "    FeatureEngineering\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = nonstationaryDGP()\n",
    "spread = y - x\n",
    "df = pd.DataFrame({'x': x, 'y': y, 'close': spread}) # close price of portfolio is the spread\n",
    "df.plot(subplots=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.077\n",
      "Model:                            OLS   Adj. R-squared:                  0.077\n",
      "Method:                 Least Squares   F-statistic:                     830.6\n",
      "Date:                Wed, 05 Jun 2024   Prob (F-statistic):          1.58e-175\n",
      "Time:                        23:13:01   Log-Likelihood:                -25609.\n",
      "No. Observations:               10000   AIC:                         5.122e+04\n",
      "Df Residuals:                    9998   BIC:                         5.124e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        105.4116      0.363    290.444      0.000     104.700     106.123\n",
      "x1             0.9375      0.033     28.820      0.000       0.874       1.001\n",
      "==============================================================================\n",
      "Omnibus:                      289.378   Durbin-Watson:                   0.001\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              221.458\n",
      "Skew:                           0.276   Prob(JB):                     8.15e-49\n",
      "Kurtosis:                       2.524   Cond. No.                         130.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# confirm the x, y pair is not cointegrated\n",
    "x_ols = sm.add_constant(x)\n",
    "ols = sm.OLS(y, x_ols).fit()\n",
    "print(ols.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -1.7700605855263838\n",
      "p-value: 0.3954237850453966\n",
      "Is the spread stationary? No\n"
     ]
    }
   ],
   "source": [
    "adfuller_test = adfuller(spread)\n",
    "print(f'ADF Statistic: {adfuller_test[0]}')\n",
    "print(f'p-value: {adfuller_test[1]}')\n",
    "print('Is the spread stationary? {}'.format('Yes' if adfuller_test[1] < 0.05 else 'No'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>close</th>\n",
       "      <th>feature_rolling_0</th>\n",
       "      <th>feature_rolling_1</th>\n",
       "      <th>feature_rolling_2</th>\n",
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       "      <th>feature_rolling_5</th>\n",
       "      <th>feature_rolling_6</th>\n",
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       "      <th>0</th>\n",
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       "      <td>99.871923</td>\n",
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       "      <td>9.971701</td>\n",
       "      <td>109.963545</td>\n",
       "      <td>99.991843</td>\n",
       "      <td>0.059960</td>\n",
       "      <td>-0.059960</td>\n",
       "      <td>-0.059960</td>\n",
       "      <td>-0.059960</td>\n",
       "      <td>-0.059960</td>\n",
       "      <td>-0.059960</td>\n",
       "      <td>-0.059960</td>\n",
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       "      <th>2</th>\n",
       "      <td>10.002110</td>\n",
       "      <td>110.068336</td>\n",
       "      <td>100.066226</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>0.055153</td>\n",
       "      <td>-0.064768</td>\n",
       "      <td>-0.064768</td>\n",
       "      <td>-0.064768</td>\n",
       "      <td>-0.064768</td>\n",
       "      <td>-0.064768</td>\n",
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       "      <th>3</th>\n",
       "      <td>9.925394</td>\n",
       "      <td>110.042870</td>\n",
       "      <td>100.117476</td>\n",
       "      <td>-0.010139</td>\n",
       "      <td>0.012995</td>\n",
       "      <td>0.058532</td>\n",
       "      <td>-0.061388</td>\n",
       "      <td>-0.061388</td>\n",
       "      <td>-0.061388</td>\n",
       "      <td>-0.061388</td>\n",
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       "      <th>4</th>\n",
       "      <td>9.851801</td>\n",
       "      <td>110.025521</td>\n",
       "      <td>100.173720</td>\n",
       "      <td>-0.004116</td>\n",
       "      <td>-0.009110</td>\n",
       "      <td>0.014024</td>\n",
       "      <td>0.059561</td>\n",
       "      <td>-0.060359</td>\n",
       "      <td>-0.060359</td>\n",
       "      <td>-0.060359</td>\n",
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       "      <td>...</td>\n",
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       "      <th>9995</th>\n",
       "      <td>12.529054</td>\n",
       "      <td>111.690175</td>\n",
       "      <td>99.161120</td>\n",
       "      <td>-0.056986</td>\n",
       "      <td>-0.049103</td>\n",
       "      <td>0.204824</td>\n",
       "      <td>0.039934</td>\n",
       "      <td>0.012933</td>\n",
       "      <td>-0.111210</td>\n",
       "      <td>-0.040393</td>\n",
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       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>12.518737</td>\n",
       "      <td>111.480368</td>\n",
       "      <td>98.961631</td>\n",
       "      <td>-0.200355</td>\n",
       "      <td>-0.030326</td>\n",
       "      <td>-0.022442</td>\n",
       "      <td>0.231484</td>\n",
       "      <td>0.066594</td>\n",
       "      <td>0.039594</td>\n",
       "      <td>-0.084549</td>\n",
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       "      <th>9997</th>\n",
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       "      <td>0.062513</td>\n",
       "      <td>-0.224865</td>\n",
       "      <td>-0.054836</td>\n",
       "      <td>-0.046953</td>\n",
       "      <td>0.206974</td>\n",
       "      <td>0.042084</td>\n",
       "      <td>0.015083</td>\n",
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       "      <td>111.490170</td>\n",
       "      <td>99.005326</td>\n",
       "      <td>-0.057475</td>\n",
       "      <td>0.074606</td>\n",
       "      <td>-0.212772</td>\n",
       "      <td>-0.042743</td>\n",
       "      <td>-0.034860</td>\n",
       "      <td>0.219067</td>\n",
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       "      <td>-0.023570</td>\n",
       "      <td>-0.044517</td>\n",
       "      <td>0.087564</td>\n",
       "      <td>-0.199814</td>\n",
       "      <td>-0.029785</td>\n",
       "      <td>-0.021902</td>\n",
       "      <td>0.232024</td>\n",
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      ],
      "text/plain": [
       "              x           y       close  feature_rolling_0  feature_rolling_1  \\\n",
       "0      9.965975  109.837898   99.871923           0.000000           0.000000   \n",
       "1      9.971701  109.963545   99.991843           0.059960          -0.059960   \n",
       "2     10.002110  110.068336  100.066226           0.009615           0.055153   \n",
       "3      9.925394  110.042870  100.117476          -0.010139           0.012995   \n",
       "4      9.851801  110.025521  100.173720          -0.004116          -0.009110   \n",
       "...         ...         ...         ...                ...                ...   \n",
       "9995  12.529054  111.690175   99.161120          -0.056986          -0.049103   \n",
       "9996  12.518737  111.480368   98.961631          -0.200355          -0.030326   \n",
       "9997  12.514282  111.563801   99.049519           0.062513          -0.224865   \n",
       "9998  12.484843  111.490170   99.005326          -0.057475           0.074606   \n",
       "9999  12.446778  111.428859   98.982081          -0.023570          -0.044517   \n",
       "\n",
       "      feature_rolling_2  feature_rolling_3  feature_rolling_4  \\\n",
       "0              0.000000           0.000000           0.000000   \n",
       "1             -0.059960          -0.059960          -0.059960   \n",
       "2             -0.064768          -0.064768          -0.064768   \n",
       "3              0.058532          -0.061388          -0.061388   \n",
       "4              0.014024           0.059561          -0.060359   \n",
       "...                 ...                ...                ...   \n",
       "9995           0.204824           0.039934           0.012933   \n",
       "9996          -0.022442           0.231484           0.066594   \n",
       "9997          -0.054836          -0.046953           0.206974   \n",
       "9998          -0.212772          -0.042743          -0.034860   \n",
       "9999           0.087564          -0.199814          -0.029785   \n",
       "\n",
       "      feature_rolling_5  feature_rolling_6  \n",
       "0              0.000000           0.000000  \n",
       "1             -0.059960          -0.059960  \n",
       "2             -0.064768          -0.064768  \n",
       "3             -0.061388          -0.061388  \n",
       "4             -0.060359          -0.060359  \n",
       "...                 ...                ...  \n",
       "9995          -0.111210          -0.040393  \n",
       "9996           0.039594          -0.084549  \n",
       "9997           0.042084           0.015083  \n",
       "9998           0.219067           0.054177  \n",
       "9999          -0.021902           0.232024  \n",
       "\n",
       "[10000 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build features\n",
    "df_train = df.copy()\n",
    "\n",
    "feature_config = {\n",
    "    \"window\": 7, # 7 days window, pad with first value when window is not available\n",
    "    \"mode\": \"diff\", # difference between current and previous close price\n",
    "    \"subtract_mean\": True,\n",
    "}\n",
    "\n",
    "fe = FeatureEngineering(df_train, main_feature_col=\"close\")\n",
    "fe.rolling_feature(**feature_config)\n",
    "df_train = fe.df.copy()\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "Market Return : -0.89%   |   Portfolio Return :  0.88%   |   Position Changes : 1   |   Episode Length : 10000   |   \n"
     ]
    }
   ],
   "source": [
    "# Define the environment\n",
    "env_action_space = [-1, 0, 1] # Positions : [-1=SHORT, 0=OUT, 1=LONG]\n",
    "# env_action_space = [i/10 for i in range(-10, 11)] # Positions : [-1, -0.9, -0.8, ..., 0, 0.1, 0.2, ..., 1]\n",
    "\n",
    "env = gym.make(\"TradingEnv\",\n",
    "        name= \"stationaryDGP\",\n",
    "        df = df_train, # Your dataset with your custom features\n",
    "        positions = env_action_space, # -1 (=SHORT), 0(=OUT), +1 (=LONG)\n",
    "        trading_fees = 0.01/100, # 0.01% per stock buy / sell (Binance fees)\n",
    "        borrow_interest_rate= 0.0003/100, # 0.0003% per timestep (one timestep = 1h here)\n",
    "        # max_episode_duration=180\n",
    "    )\n",
    "env.unwrapped.add_metric('Position Changes', lambda history : np.sum(np.diff(history['position']) != 0) )\n",
    "env.unwrapped.add_metric('Episode Length', lambda history : len(history['position']) )\n",
    "\n",
    "# Run an episode until it ends :\n",
    "done, truncated = False, False\n",
    "observation, info = env.reset()\n",
    "print(observation)\n",
    "while not done and not truncated:\n",
    "    # Pick a position by its index in your position list (=[-1, 0, 1])....usually something like : position_index = your_policy(observation)\n",
    "    position_index = env.action_space.sample() # At every timestep, pick a random position index from your position list (=[-1, 0, 1])\n",
    "    observation, reward, done, truncated, info = env.step(0) # always short position\n",
    "\n",
    "n_actions = env.action_space.n\n",
    "n_observations = len(env.reset()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Market Return : -0.89%   |   Portfolio Return : -56.47%   |   Position Changes : 5726   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -44.04%   |   Position Changes : 5271   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -36.11%   |   Position Changes : 5137   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -33.11%   |   Position Changes : 5078   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -42.34%   |   Position Changes : 4889   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -36.18%   |   Position Changes : 4853   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -38.19%   |   Position Changes : 4914   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -34.08%   |   Position Changes : 4705   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -21.32%   |   Position Changes : 4771   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -32.99%   |   Position Changes : 4577   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -22.02%   |   Position Changes : 4510   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -23.43%   |   Position Changes : 4447   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -27.36%   |   Position Changes : 4389   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -8.02%   |   Position Changes : 4341   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -12.63%   |   Position Changes : 4163   |   Episode Length : 10000   |   \n"
     ]
    }
   ],
   "source": [
    "# Train a DQN agent using training data from a stationary data generating process\n",
    "agent = DQNAgent(n_observations=n_observations, n_actions=n_actions)\n",
    "history_metrics, _ = agent.train(env, num_episodes=15) # Beware of overfitting when num_episodes is too high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XDTaSLDs7W4GBgUc8HxgYqOzs7Hp7nenTp+uuu+6q/bmwsFBhYWH1dn8AAAAAAND0zfp2i3YfLFWI1UOPXNSzwV7HYrGoT5if+oT56b7REdq2v1hxKdn6bnO2UvYVKmFnnhJ25unRrzerd3vroYX/o4PVpW2rBsuE+lHnkuxYI7f+as2aNZKOvsPf0XZNPB3u7u5yd2coY3PXs2fPYy6SP3v2bF1zzTWNnAgAAAAA0Fys2JqrufGHvlM+d3kfWT0bZ40wi8WibkE+6hbko/87t5sy8kr13aZsfb8pR2t25ylpr01Je2167rs0dQ1spTF/FGY9Q33rtRtB/ahzSTZt2jRNnDjxuOeEh4crKSlJOTk5RxzLzc09bL0o1I8RI0aogWbONopvv/1WVVVVRz3G5wUAAAAAcCy2sird+3mSJGlyTEed2S3AtCxh/l66+azOuvmszsotqtAPm3P03aZsrdx+QNv2F+u1/dv02i/b1M7Ps3aE2YCOreXsRGHWFNS5JAsICFBAwIk/cDExMbLZbFq9erUGDx4sSUpISJDNZtOwYcPqnhQt2p+7cwIAAAAAUBePLt6k7MJyhbfx0gNjI82OU6utj7smDemgSUM6yFZWpV+27Nd3m7K1LC1X+wrK9O7vO/Xu7zsV0MpN50cFaXTPYA3rEiA3FyezozusBluTrEePHhozZoymTJmi2bNnS5KmTp2qcePGHbazZWRkpGbNmqUJEyZIkvLy8rRnzx5lZmZKktLS0iRJwcHBCg4Orpdsf+4ECZiFzyAAAAAAnL64lGwt3LBPThbphSv7ysutwWqO02L1dNUl/drpkn7tVFZZo1+35uq7Tdn6cXOODhRX6pPVGfpkdYZ83F10To9AjekZrOERbZvs+2mpGvRXe968ebr99ts1atQoSdL48eP12muvHXZOWlqabDZb7c+LFy8+bOfLP6d2PvLII5o5c+Zp5XFzc5OTk5MyMzPVtm1bubm5MQcYjcowDFVWVio3N1dOTk5yc3MzOxIAAAAANEsHiiv00KJkSdI/hnfRgI6tTU50cjzdnDW656HdL6tq7IrfcVDfbcrWd5tylFtUoa8SM/VVYqbcXZx0dve2GtMzWOf2CJSfF98fG5rFaM4LWR3Fibb1rKysVFZWlkpLS01IBxzi5eWlkJAQSjIAAAAAOAWGYeiWuev0/eYcRQb76KtpZ8jdxdnsWKfFbje0ISNf323KUVxKtvbk/a+3cHGyaGjnNhodHazRUUEK9PUwMWnzc6Ku6E8OV5JJh/5lqq6uVk1NTSOnAyRnZ2e5uLgwihEAAAAATtGCdXt19+cb5eps0eJpZ6pHyLGLj+bIMAylZhX9McIsW1uyi2qPWSxSvzA/jYk+NBqtYxtvE5M2D5RkJ3jjAAAAAACg+cksKNPol35VUUW17h0doX+O7Gp2pAa360CJvtuUrbhN2dqwp+CwY5HBPhoTfWinzIggHwZkHAUlGSUZAAAAAAAtit1uKPbdBP2+7aD6dfDT57fEyMXZsXaDzLaV64fNhwqz+B15qrH/r9bp2MZLY3oGa3R0sPq295OTE4WZRElGSQYAAAAAQAvzwcpdemTxJnm4Omnpv85WpwDHnmqYX1Kpn7bsV1xKtn7dmqvKanvtsSBfd42KOjTCbHAnf7k6WJn4V5RklGQAAAAAALQYO3KLdcGrK1ReZdej43vqumHhZkdqUkoqqrUsLVffbcrWz1v2q7iiuvaY1dNV5/UI0pjoYJ3VLUAers17k4O6oiSjJAMAAAAAoEWorrHritmrtGFPgc7o2kZzbxzCVMLjqKiu0cptB/Xdpmx9vzlHeSWVtce83Jw1IqKtRvcM1jmRgfLxcDUxaeOgJKMkAwAAAACgRfjvL9v03Hdp8nF30Xd3nq1QP0+zIzUbNXZDa3blHdopMyVbmbby2mNuzk4a1rWNxvQM1nlRQQpo5W5i0oZDSUZJBgAAAABAs7c5s1AX//c3VdUYev6KPrp8QHuzIzVbhmEoeZ/t0E6ZKdnanltSe8zJIg0M969d+L9dCyoiKckoyQAAAAAAaNYqqmt08Wu/a0t2kc6PCtJbsQNksTDNsr5s21+k7zblKC4lW8n7bIcde++GQRoZEWhSsvp1sl2RSyNmAgAAAAAAOGmv/LhVW7KL5O/tplmX9qIgq2ddA33UNdBH/xzZVXvzS/X9phzFbcpW0t4CDezY2ux4jY6SDAAAAAAANDnrdufrzeXbJUlPTYhusetlNRXtW3vpxjM76cYzO6movMohFvT/OyezAwAAAAAAAPxVaWW17vl8o+yGNKFfO42JDjE7kkNxxIJMoiQDAAAAAABNzDNLt2jngRIF+3po5vieZseBg6AkAwAAAAAATcbv2w7og1W7JUnPXt5bVk/HHNWExkdJBgAAAAAAmoTC8ird+/lGSdK1Qzvo7O5tTU4ER0JJBgAAAAAAmoRHF29Wpq1cHdt46cELepgdBw6GkgwAAAAAAJju+03ZWrB+rywW6YUr+sjLzcXsSHAwlGQAAAAAAMBUB4sr9OCiZEnS1LM7a2C4v8mJ4IgoyQAAAAAAgGkMw9BDi1J0oLhSEUE+uuv87mZHgoOiJAMAAAAAAKb5MnGf4jZly8XJoheu7CN3F2ezI8FBUZIBAAAAAABTZNnK9PBXmyRJ/zq3m6LbWU1OBEdGSQYAAAAAABqdYRi674skFZVXq0+Yn24d0cXsSHBwlGQAAAAAAKDRfZSwRyu2HpC7i5NeuKKPXJypKGAuPoEAAAAAAKBR7TpQoqeWpEqS7h8Tqa6BrUxOBFCSAQAAAACARlRjN3TP5xtVVlWjmM5tdP2wcLMjAZIoyQAAAAAAQCOas2KH1u7OVyt3Fz13RW85OVnMjgRIoiQDAAAAAACNZEt2oV78Pl2S9PC4KLVv7WVyIuB/KMkAAAAAAECDq6y26675G1VZY9e5kYG6YmB7syMBh6EkAwAAAAAADe4/P2/V5qxCtfZy1azLesliYZolmhZKMgAAAAAA0KASMwr0+rLtkqQnLumlQB8PkxMBR6IkAwAAAAAADaa8qkZ3fZaoGruh8X1CdWHvELMjAUdFSQYAAAAAABrMM3FbtCO3REG+7nrs4p5mxwGOiZIMAAAAAAA0iJXbD+i933dJkp65rLf8vNzMDQQcByUZAAAAAACod0XlVbr38yRJ0qQhHTQiItDkRMDxUZIBAAAAAIB69/g3m7WvoEwd/L300AU9zI4DnFCDlmT5+fmKjY2V1WqV1WpVbGysCgoKjnl+VVWV7r//fvXq1Uve3t4KDQ3V5MmTlZmZ2ZAxAQAAAABAPfpxc44+W7tXFov0/BV95O3uYnYk4IQatCSbNGmSEhMTFRcXp7i4OCUmJio2NvaY55eWlmr9+vWaMWOG1q9fr4ULFyo9PV3jx49vyJgAAAAAAKCe5JVU6oGFyZKkKWd11uBO/iYnAk6OxTAMoyFunJqaqqioKMXHx2vIkCGSpPj4eMXExGjLli2KiIg4qfusWbNGgwcP1u7du9WhQ4cTnl9YWCir1SqbzSZfX9/Teg8AAAAAAODkGYahaR9v0JLkLHUPaqXF086Uh6uz2bHg4E62K2qwkWSrVq2S1WqtLcgkaejQobJarVq5cuVJ38dms8liscjPz++oxysqKlRYWHjYAwAAAAAANL7FGzO1JDlLLk4WvXhlXwoyNCsNVpJlZ2crMPDInSsCAwOVnZ19UvcoLy/XAw88oEmTJh2z6Zs1a1btmmdWq1VhYWGnlRsAAAAAANRdtq1cM75MkST93zndFN3OanIioG7qXJLNnDlTFovluI+1a9dKkiwWyxHXG4Zx1Of/rqqqShMnTpTdbtfrr79+zPOmT58um81W+8jIyKjrWwIAAAAAAKfBMAzdvyBJheXV6t3eqttGdjE7ElBndd5eYtq0aZo4ceJxzwkPD1dSUpJycnKOOJabm6ugoKDjXl9VVaUrr7xSO3fu1M8//3zc+aLu7u5yd3c/ufAAAAAAAKDefbx6j5an58rNxUkvXtlHrs4Nuk8g0CDqXJIFBAQoICDghOfFxMTIZrNp9erVGjx4sCQpISFBNptNw4YNO+Z1fxZkW7du1S+//KI2bdrUNSIAAAAAAGgkuw+W6MklqZKk+0ZHqGugj8mJgFPTYNVujx49NGbMGE2ZMkXx8fGKj4/XlClTNG7cuMN2toyMjNSiRYskSdXV1br88su1du1azZs3TzU1NcrOzlZ2drYqKysbKioAAAAAADgFNXZD93y+UaWVNRrSyV83ntHJ7EjAKWvQ8Y/z5s1Tr169NGrUKI0aNUq9e/fW3LlzDzsnLS1NNptNkrR3714tXrxYe/fuVd++fRUSElL7qMuOmAAAAAAAoOG989sOrdmVL283Zz1/RR85OZ14DXKgqarzdMu68Pf310cffXTccwzDqP378PDww34GAAAAAABNU3pOkZ7/Ll2SNGNclML8vUxOBJweVtIDAAAAAAB1UlVj112fJaqyxq5zIgN11aAwsyMBp42SDAAAAAAA1Ml/ft6mlH2F8vNy1dOX9pLFwjRLNH+UZAAAAAAA4KRtzCjQf3/ZJkl6/OJoBfp6mJwIqB+UZAAAAAAA4KSUV9Xors8SVWM3NK53iC7qE2p2JKDeUJIBAAAAAICT8tx3adqeW6K2Pu56/OJos+MA9YqSDAAAAAAAnFD8joN69/edkqRnL+ut1t5uJicC6hclGQAAAAAAOK7iimrd8/lGGYY0cVCYRkYGmh0JqHeUZAAAAAAA4Lie+Gaz9uaXqX1rT/17XJTZcYAGQUkGAAAAAACO6ectOfp0TYYsFun5K/qolbuL2ZGABkFJBgAAAAAAjiq/pFL3L0iWJN14RicN7dzG5ERAw6EkAwAAgMOrrLZrf1G52TEAoMmZ8VWKcosq1DWwle4dHWF2HKBBUZIBAADAoeWXVGrcf1Zo6FM/6fO1GWbHAYAm4+uNmfomKUvOTha9eGUfebg6mx0JaFBMJAYAAIDDKq2s1g3vr1F6TrEk6b4FSTIkXTkwzNxgAGCy/YXlmvFViiTpnyO7qnd7P3MDAY2AkWQAAABwSFU1dt360XolZhTI6umqi/uGyjCk+xck6bM1jCgD4LgMw9D9C5JUUFql6Ha++r9zupodCWgUjCQDAACAw7HbDd37+UYtT8+Vp6uz3r1+kPp38JPV01Ufrtqt+xcmSZKuHMSIMpw+u92Qk5PF7BjASZu/JkO/pOXKzcVJL17ZV67OjK+BY6AkAwAAgEMxDENPfpuqLxMz5eJk0evX9teAjq0lSY+O7ymLpA9W7f5j6qWhqwZ1MDcwmq2i8ir969NELU/PVRtvN4X4eSrU6qEQq6dC/Q79NcTPQ6FWT7X1cZczRRqagIy8Uj3+zWZJ0j2juqt7kI/JiYDGQ0kGAAAAh/Lm8h1657edkqTnruitkRGBtccsFotmju8pi8Wi91fu0v0LkmUY0sTBFGWom7ySSl337mol77NJkvYXVWh/UYU2HmMmr4uTRUG+HgqxetSWacF/K9QCWrnJYqFIQ8Ox2w3d8/lGlVTWaHC4v246s7PZkYBGRUkGAAAAh/HZmgw9E7dFkvTvC3toQr/2R5xjsVj0yEVRslik937fpQcWJsuQdDVFGU5Stq1cse8kaOv+Yvl7u+n1a/rLy81ZmQXlyrKVKctWfuhRcOjvswvLVW03tK+gTPsKyqTd+Ue9r5uz0x/FmYdC/TwPK9T+LNOsnq4UaThl7/6+Uwk78+Tl5qznr+jD6EY4HEoyAAAAOIQfNufogT/WGrt1RBfdfNaxR0hYLBY9PC5KFln07u87NX3hoRFlk4ZQlOH49hws1TXvxCsjr0zBvh766ObB6hp4aLpa7yM7WUlSjd1QblGFMm1lyvqjSPuzUMv8o0zLLa5QZY1de/JKtSev9Jiv7+nqXDuF87AS7S9/beXO10Acadv+Ij37XZok6d8XRqlDGy+TEwGNj98dAQAA0OKt3pmnaR+vl92QrhzYXveNjjjhNRaLRTPG9ZDFIr3z2049uChZhgxdM6RjIyRGc5SeU6Rr307Q/qIKdWzjpY9uGqIw/xMXDc5OFgX/Mb1Sx+hhK6vtyin8YwTaX0u0v4xOyyupVFlVjXbklmhHbskxX8/Hw+VQifbHNM6/l2ghVg95uDqf6i8DmqGqGrvu+myjKqvtGhHRVlcPZtMSOCZKMgAAALRoqVmFuumDNaqotuu8HkF6akKvk56OZrFY9O8Le8gi6e3fduqhRSmyG1LsUIoyHG5jRoGue2+1Ckqr1D2olT66aYgCfT3q7f5uLk4K8/c6bulWXlVTO40z869//WOEWqatTEXl1Soqr1ZaeZHScoqOeS9/bzcF+3ocscHAn1M9g3w95ObCjoctxeu/bFfSXpusnq565rLeTNmFw6IkAwAAQIuVkVeqye+uVlF5tQaFt9Zrk/rJxbluX+wtFoseuvDQiLI5K3ZqxpcpkmEoNia8YUKj2YnfcVA3f7BWxRXV6tPeqvdvGKzW3m6NnsPD1VmdArzVKcD7mOcUV1QfWaIVlCm7sFyZf6yRVlpZo7ySSuWVVGpzVuFR72OxSAGt3GvXQ6st0fz+tz5aoI8Ha1o1A8l7bfrPz1slSY9d3FNB9VjuAs0NJRkAAABapAPFFYp9J0G5RRWKDPbR29cNOuUpZBaLRQ9e0EMWi0Vv/bpDM77aJEPSZIoyh/fzlhzd+tF6VVTbNbSzv96+blCTXvOrlbuLugX5qFuQz1GPG4ahwrLqQ+uj/XU65x8j0f7cdKCy2q7cogrlFlVo417bUe/l7GRRkI977RTO2s0G/rJjZxtvNzlRpJmmvKpGd32WqGq7oQt7hWh8n1CzIwGmarq/ewMAAACnqKi8Ste/t1q7DpaqfWtPfXDjYFk9XU/rnhaLRdPHRsoiafavO/TwV5skUZQ5sq83ZurO+YcKhnMjA/Xfa/o3+7W8LBaLrF6usnq5qkeI71HPMQxDB0sq/1ec/TECLfNvO3bW2A1l/vH8sfx9x85QPw8N7tRGw7q0kWsdR32i7l78IV1b9xcroJW7Hr8kmmmWcHiUZAAAAGhRKqprdMvcdUrZV6g23m6ae9OQeps+ZLFY9MDYSMkizV5+qCgzDOm6YeH1cn80H5+s3nNoMwdDuqhPqF68so/DlDoWi0UBrdwV0Mpdvdpbj3rO6ezY+d9ftsvXw0XnRwXrgl7BOrNbgNxdmnf52BSt3pmnOSt2SJKeuayX/E2YIgw0NZRkAAAAaDFq7IbunJ+oldsPytvNWe/fMPi46zOdCovFogfGRMrJYtEby7brkcWbZBiGrj+jU72+DpquOb/u0JPfpkqSrh7cQU9cEs3aW39zKjt2ZtnKtTO3RD9t2a8DxRVasH6vFqzfq1buLjq3R6DGRgdrePdAebpRmJ2ukopq3fP5Rhl/7Ph7bo8gsyMBTQIlGQAAAFoEwzD0yOIUfZucLTdnJ701eeAxR7mcLovFovtGR8gi6fVl2zXz680yJN1AUdaiGYahl35I16s/b5Mk3XJ2Zz0wNpIpaqfoWDt21tgNrdudr2+TsxSXkq3swnJ9lZiprxIz5enqrHMiAzUmOljnRAbKuwmv/9aUPfltqvbklaqdn6dmjIsyOw7QZFgMwzDMDlGfCgsLZbVaZbPZ5Ot79Dn0AAAAaHle/jFdL/+4VRaL9NrV/XVh75AGf03DMPT892n67y/bJUkzxkXppjMpyloiu93QY99s1vsrd0mS7h0dodtGdKEga2B2u6HEvQWKS8nWt8lZ2ptfVnvM3cVJZ3dvqwt6BevcHkHy9Ti9dQcdxbK0/br+vTWSpI+nDNGwLgEmJwIa3sl2RZRkAAAAaPbmxu/WjC9TJEmPXxKt2KEdG+21DcPQC9+n67VfDo0u+veFPXTzWZ0b7fXR8Kpr7HpgYbK+WLdXkvTo+J6sQ2cCwzCUsq9QS1OytDQlWzsPlNQec3W26MyuARobHaLzo4LUmvW1jspWWqVRLy9XTmGFbjgjXI9c1NPsSECjoCSjJAMAAHAIS5KyNO2T9TIM6V/ndtOd53dv9AyGYejFH9L1n58pylqaiuoa3fFpopamZMvZyaJnL+utywa0NzuWwzMMQ2k5Rfo2OVtLk7O0dX9x7TFnJ4uGdWmjMdHBGhUVrLY+7iYmbVr+9ekGfZWYqc5tvfXt7Wc1+91YgZNFSUZJBgAA0OL9vu2Arn9vtapqDF07tIMevzjatOlvf1+v6qELemjK2RRlzVlpZbVumbtOK7YekJuzk169up/GRAebHQtHsW1/kZYmZ2tpSrY2ZxXWPu9kkQaF++uCXiEa3TP40EYCDmpJUpb++fF6OTtZtODWYeob5md2JKDRUJJRkgEAALRoyXttmvjWKpVU1uiCXsH6z9X9Td9h0DAMvfTjVr3601ZJ0oMXRGrq2V1MzYRTYyur0k3vr9Ha3fnydHXWW5MH6Kxubc2OhZOw+2CJlqYcGmG2ca/tsGMDOrbW2OhgjYkOVvvWXse4Q8uzv6hco1/6VfmlVfq/c7rq7lERZkcCGhUlGSUZAABAi7XzQIkuf2OlDpZUaliXNnrvhkFyd2k604Ze+iFdr/xRlE0fG6lbhlOUNScHiis0+Z3V2pxVKB8PF71/wyAN6Ohvdiycgr35pYpLOTTCbN3u/MOO9Wlv1ZjoEI2NDlZ4gLdJCRueYRi6+YO1+mnLfvUM9dWi286Qm4uT2bGARkVJRkkGAADQIuUUluuyN1Zqb36Zotv56tOpMWrl7mJ2rCP8udumJD0wNlL/oChrFrJsZbrm7QTtyC1RQCs3fXDjYPUMtZodC/Ug21au7zZla2lKllbvzJP9L9+Ee4T46oLoYI3tFayugT7mhWwAn63J0H0LkuTm7KSv/+9MRQS3rPcHnAxKMkoyAACAFsdWVqWrZq/Sluwihbfx0he3DlNAq6a7KPcrP27VSz+mS5LuHxOpW0dQlDVlOw+U6Nq3E7SvoEyhVg99dPMQdW7byuxYaAC5RRX6fnO24lKytXL7QdX8pTHrFthKY6ODNbZXiCKDfUxb57A+ZOSVauwrK1RcUU1ZD4dGSUZJBgAA0KKUV9Uo9p0ErdmVr7Y+7lp46zCF+Tf9NYVe/WmrXvzhUFF235gI3Taiq8mJcDRbsgt17durdaC4Qp0CvPXRzUPUzs/T7FhoBPkllfohNUdLk7P027YDqqr531fkTgHeGhMdrAuiQxTdzrdZFWZ2u6FJb8crfkeeBnZsrfm3xJi+biNglpPtihp0InJ+fr5iY2NltVpltVoVGxurgoKC414zc+ZMRUZGytvbW61bt9Z5552nhISEhowJAACAJq66xq5pH2/Qml358vFw0Yc3Dm4WBZkk3X5uN919fndJ0rNxafrvL9tMToS/27AnX1fNjteB4gpFBvvos1tiKMgcSGtvN105MEzv3TBYa/99vl66qo9GRQXJzcVJOw+U6I1l23XRa7/prGd/0ZNLNmvd7nzZ7U1/rMn7K3cpfkeePF2d9fwVfSjIgJPQoCPJxo4dq7179+qtt96SJE2dOlXh4eH6+uuvj3nNxx9/rMDAQHXu3FllZWV66aWX9Pnnn2vbtm1q2/bEu8kwkgwAAKBlMQxD9y9I0mdr98rdxUlzbxqiwZ2a3yLqr/28Vc9/f2hE2T2jumvaOd1MTgRJWrn9gG7+YK1KK2vUr4Of3r9+sKxermbHQhNQXFGtX7bsV1xKtn7esl9lVTW1x4J9PTQmOlhjo4M1MNy/yRVQ2/YX68JXV6ii2q7HL4lW7NCOZkcCTGX6dMvU1FRFRUUpPj5eQ4YMkSTFx8crJiZGW7ZsUUTEyW05++cb+fHHH3Xuueee9PmUZAAAAC3DM3Fb9May7XKySLNjB+r8qCCzI52y//6yTc99lyZJuvv87vq/cynKzPTj5hzd9vF6VVbbdUbXNnordqC8m+AmEDBfWWWNlqfnamlKln5K3a/iiuraYwGt3DW6Z5Au6BWiIZ385eJs7s6R1TV2XfbmKm3MKNBZ3QL04Y2Dm9U0UaAhnGxX1GD/BVi1apWsVmttQSZJQ4cOldVq1cqVK0+qJKusrNRbb70lq9WqPn36HPWciooKVVRU1P5cWFh4+uEBAADQJLy9YofeWLZdkvT0pb2bdUEmSf8ceWg9sue+S9MLP6TL0KHpmGh8XyXu012fbVSN3dD5UUH6z9X95OHqbHYsNFGebs4aEx2sMdHBKq+q0e/bDujb5Gz9sDlbB4orNC9hj+Yl7FFrL1eNigrWmF7BOqNLgNxcGr8we2PZdm3MKJCPh4uevbw3BRlQBw1WkmVnZyswMPCI5wMDA5WdnX3ca7/55htNnDhRpaWlCgkJ0Q8//KCAgICjnjtr1iw9+uij9ZIZAAAATceiDXv1xJJUSYcWvL9yUJjJierHP0d2lcVyaH2yF39Il2FI/zqPoqwxfRS/WzO+SpFhSBP6tdOzl/eWq8mjf9B8eLg669weQTq3R5Aqq3tp1Y6DikvJ0nebcpRXUqn5azM0f22GfDxcdH5UkMZGh+isbgGNUsKm7LPplZ+2SpIeu7inQqysrQfURZ3/SzBz5kxZLJbjPtauXStJR22sDcM4YZM9cuRIJSYmauXKlRozZoyuvPJK7d+//6jnTp8+XTabrfaRkZFR17cEAACAJuaXtP269/MkSdKNZ3TSrcO7mJyoft02oqvuHxMpSXrpx3S9/GO6yYkcxxvLtuvfXx4qyGKHdtQLV/ShIMMpc3Nx0vDubTXr0t5a/eC5+njKEMUO7ai2Pu4qKq/WwvX7NOXDtRrw+A/6v082aGlylkorq09841NQUV2juz/bqGq7oTE9g3VJ33YN8jpAS1bnNckOHDigAwcOHPec8PBwffzxx7rrrruO2M3Sz89PL730km644YaTfs1u3brpxhtv1PTp0094LmuSAQAANG/r9+TrmjkJKquq0SV9Q/XilX3l1MQWxa4vby7frqeXbpEk/evcbrrzj10wUf8Mw9Bz36Xp9T+m7942oovuHR3BVDQ0iBq7ofV78vVtcpbiUrKVZSuvPebh6qSREYEaEx2sc3sEqVU9rYM3a2mqZi/foYBWbvrujrPVppV7vdwXaAkabE2ygICAY059/KuYmBjZbDatXr1agwcPliQlJCTIZrNp2LBhdXpNwzAOW3cMAAAALdPWnCLd+P4alVXVaHj3tnruij4ttiCTpH8M7yIni/TUt1v0yk9bZUi687xuFDf1zG439MjiTZobv1uSdP+YSN06omWNTkTT4uxk0aBwfw0K99eMC6O0cW+B4lKy9W1KljLyyrQ0JVtLU7Ll5uKks7u11djoYJ0XFSSr56ntrLp2V57e+nWHJOmpCb0oyIBT1GC7W0rS2LFjlZmZqdmzZ0uSpk6dqo4dO+rrr7+uPScyMlKzZs3ShAkTVFJSoieffFLjx49XSEiIDh48qNdff10fffSR1q1bp549e57wNRlJBgAA0DxlFpTpsjdWKstWrr5hfvp4yhB5uTnGToNzft2hJ789tP7a7ed01Z3nd6coqyfVNXbd+0WSFm3YJ4tFevziaF07tKPZseCgDMPQpsxCLU3J0tLkbO04UFJ7zNXZomFdAnRBr2CdHxUsf2+3k7pnSUW1Lnh1hXYfLNVl/dvrhSuPvukd4MhM391SkubNm6fbb79do0aNkiSNHz9er7322mHnpKWlyWazSZKcnZ21ZcsWffDBBzpw4IDatGmjQYMGacWKFSdVkAEAAKB5yi+pVOw7CcqylatrYCu9d/0ghynIJGnK2Z1lsUhPLEnVqz9vkyHpLoqy01ZeVaP/+2SDfticI2cni168so8uZp0mmMhisSi6nVXR7ay6Z1SE0nOKa6dkpuUUaXl6rpan5+rBRSka2tlfY6NDNKpnkAJ9PI55z1lLU7X7YKlCrR56ZHxUI74boOVp0JFkZmAkGQAAQPNSWlmtSXMSlJhRoBCrhxbcOkyhfo65I9vbK3bU7ug5bWRX3T2KouxUlVRUa+rctfp920G5uTjp9Un9dV5UkNmxgGPanlt8aEpmcpY2ZRbWPm+xSIPC/TU2OlhjooMP27Hy1/RcTX53tSRp3s1DdEbXEy+NBDiik+2KKMkAAABgmqoau27+YK2Wp+fKz8tVX/wjRl0DfcyOZaq/FmX/HNlF94xicfm6spVW6fr3V2vDngJ5uTnr7ckDNYzyAM3InoOlWpqSpW9TsrUxo+CwY/06+OmC6BCd0TVAN76/RtmF5boupqMevTjanLBAM0BJRkkGAADQpNnthu76LFFfJmbK09VZ86YMUf8Orc2O1SS889tOPf7NZknswlhXuUUVin0nQVuyi2T1dNX7NwxSPz5XaMb2FZQpLiVbS5OztG5Pvv7+Db5TgLe+vf0sebo5mxMQaAaaxJpkAAAAwNEYhqEnlqTqy8RMuThZ9Ma1/SnI/uKmMzvJIumxbzbr9WXbZUi6j6LshPYVlOnatxO080CJAlq566ObBysymD84R/PWzs9TN53ZSTed2Uk5heX6blO2liZnK2HnQbk4OemFK/tQkAH1hJIMAAAAje6N5dv17u87JUnPX9FHIyICTU7U9Nx4Zic5WaSZX2/WG8u2yzCk+8dQlB3LjtxiXft2gjJt5Wrn56l5Nw9ReIC32bGAehXk66HJMeGaHBOug8UVqqyxH7ZGGYDTQ0kGAACARjV/zR49G5cmSZoxLkqX9GO3wWO5/oxOslgsemTxJr25fLsMw9ADYyMpyv5mU6ZN1727WgeKK9W5rbc+ummIw27+AMfRppW72RGAFoeSDAAAAI3mu03Zmr4wWZJ064guuunMTiYnavquGxYui0V6+KtNmv3rDhmSplOU1Vq3O0/Xv7dGReXV6hnqqw9vHEx5AAA4JZRkAAAAaBQJOw7q/z7ZILshXTmwve4bHWF2pGZjcky4LJJmfLVJb/26Q4Zh6MELejh8Ufbb1gOa8uFalVXVaGDH1nrn+kGyerqaHQsA0ExRkgEAAKDBpWYV6uYP16qy2q7zo4L01IReDl/w1FVsTLhksWjGlymas2KnDEN66ELHLcq+25St//t4gypr7DqrW4Bmxw6QlxtfbwAAp47/igAAAKBBZeSVavK7q1VUXq3B4f76z9X95OLsZHasZil2aEdZJP37yxS9/dtOGZL+7YBF2cL1e3XvF0mqsRsaGx2slyf2lbsLu/sBAE4PJRkAAAAazIHiCsW+k6DcogpFBvtoznUD5eFKmXE6rh3aURaL9NCiFL3z26ERZTPGOU5R9uGqXXr4q02SpMsHtNfTl/aidAUA1AtKMgAAADSIovIqXf/eau06WKr2rT314Y2DWS+qnlwzpKMssujBRcl69/edMmTo4XFRLbooMwxDry/brue+O7Qz6vXDwvXwuCg5ObXc9wwAaFyUZAAAAKh3FdU1umXuOqXsK1QbbzfNvWmIAn09zI7Vokwa0kGS9OCiZL33+y4ZhvTIRS2zKDMMQ0/HbdHs5TskSbef2013ntetRb5XAIB5KMkAAABQr2rshu6cn6iV2w/K281Z798wWJ0CvM2O1SJNGtJBThbpgYXJen/lLkktryirsRua8VWKPk7YI+nQGmw3n9XZ5FQAgJaIkgwAAAD1xjAMPfxVir5Nzpabs5PmTB6oXu2tZsdq0SYO7iDLX4oywzA0c3zPFlGUVdXYdfdnG7V4Y6YsFmnWhF6aOLiD2bEAAC0UJRkAAADqzcs/btW8hD2yWKSXJ/bVsK4BZkdyCFcN6iCLLLp/YZI+WLVbhqRHm3lRVl5Vo3/OW6+ftuyXi5NFL13VVxf1CTU7FgCgBaMkAwAAQL2Yu2qXXvlpqyTpsYujdUGvEJMTOZYrB4VJFun+BUn6cNVuGYb02MXNsygrrqjWlA/WatWOg3J3cdKb1w7QyMhAs2MBAFo4SjIAAACctm+SMvXw4k2SpDvO66bYoR1NTuSYrhwYJouk+xYkaW78bhky9Nj46Ga1A2RBaaWue2+NNmYUqJW7i96+bqCGdm5jdiwAgAOgJAMAAMBp+W3rAd05P1GGIcUO7ah/ndvN7EgO7YqBYbJYLLr3i436KH6PDEN6/OLmUZTtLyxX7DurlZZTJD8vV31442D1bu9ndiwAgIOgJAMAAMApS9pboFvmrlVVjaELe4W0mAXjm7vLB7SXJN37xUbNS9gjQ9ITTbwoy8gr1bXvJGj3wVIF+rjro5uHqHuQj9mxAAAOhJIMAAAAp2RHbrFueG+NSiprdEbXNnrxqj5ybsIljKO5fEB7WSTd88VGfZxwaETZk5c0zaJs2/5iXft2grILyxXm76l5Nw1VhzZeZscCADgYSjIAAADUWc4f0+IOllSqVzurZscOlLuLs9mx8DeXDWgvi0W6+/ON+mT1HhmGoacm9GpSRVnKPpsmv7taeSWV6hbYSnNvGqJgq4fZsQAADoiSDAAAAHViK63S5HdWa19BmToFeOu9GwaplTv/W9lUXdr/j6Lss436dE2GJDWZomzNrjzd+N4aFVVUq1c7qz64cbD8vd3MjgUAcFD83wwAAABOWnlVjW7+cI3ScooU6OOuD28crIBW7mbHwglM6NdeThaL7pyfqE/XZMgwpFmXmluULU/P1S1z16q8yq7Bnfz1znUD5ePhaloeAAAoyQAAAHBSqmvsmvbxeq3ZlS8fDxd9cONghfmzblRzcXHfdpKkO+cnav7aDBky9PSlvU0pypYmZ+n2TzeoqsbQyIi2euPaAfJwZbouAMBclGQAAAA4IcMwNH1hsn5M3S93Fye9c90g9QjxNTsW6uivRdlna/fKMKRnLmvcouyztRl6YEGS7IZ0Ye8QvXRlX7m5ODXa6wMAcCyUZAAAADihZ+LS9Pm6vXJ2sui1Sf01uJO/2ZFwii7u204Wi0V3fLpBn6/bK0OHirLG2Jn03d926rFvNkuSJg4K05MTerEjKgCgyaAkAwAAwHG9vWKH3ly+XZI0a0IvnR8VZHIinK7xfUJlkXTH/ER9se7QiLJnL2+4oswwDL360za99GO6JGnKWZ304AU9ZLFQkAEAmg5KMgAAABzTwvV79cSSVEnS/WMideWgMJMTob5c1CdUFov0r08TtWD9Xhky9Nzlfeq9KDMMQ08uSdXbv+2UJN11fnf93zldKcgAAE0OJRkAAACO6pct+3XfF0mSpJvO7KR/DO9sciLUt3G9Q2WRRbd/ukEL1++TDOm5K+qvKKuxG3pwYbLmr82QJD08Lko3ntmpXu4NAEB9oyQDAADAEdbtztet89ap2m5oQr92eoipcS3Whb1DZLFI//fJBi3csE+GpOfroSirrLbrzs8StSQpS06WQ+ueXTGQkYgAgKaLkgwAAACH2ZpTpBvfX6PyKrtGRLTVs5c37u6HaHwX9AqRRYeKskUb9skwDL1wZd9TLsrKKmt067x1WpaWK1dni16d2E9je4XUb2gAAOoZey0DAACg1r6CMk1+d7VsZVXq18FPr1/TX67O/C+jIxjbK0SvTeonFyeLvkzM1F2fJaq6xl7n+xSVV+m691ZrWVquPFyd9PZ1gyjIAADNAv/HAwAAAElSXkmlJr+ToCxbuboGttK71w2SlxsTDxzJmOgQvTapv1ycLPoqMVN3fbaxTkVZXkmlJs1J0OqdefJxd9Hcm4ZoePe2DZgYAID6Q0kGAAAAlVRU68b312h7bolCrB768MbBau3tZnYsmGBMdHBtUbZ4Y6buPMmiLNtWrqtmr1LyPpv8vd30ydShGhTu3wiJAQCoH5RkAAAADq6y2q5b561XYkaB/LxcNfemwQr18zQ7Fkw0JjpY/73mUFH29cZM3TH/+FMv9xws1RWzV2rr/mIF+3ros1tiFN3O2oiJAQA4fZRkAAAADsxuN3TvFxv1a3quPF2d9d71g9Q10MfsWGgCRvcM/mNNOou+ScrSv45RlKXnFOnyN1cqI69MHdt46fN/xKhrYCsTEgMAcHoatCTLz89XbGysrFarrFarYmNjVVBQcNLX33LLLbJYLHr55ZcbLCMAAICjMgxDjy/ZrK8SM+XiZNEb1/ZXvw6tzY6FJmRUz2C9fs0AuTpbtCQpS//6NFFVfynKkvYW6KrZq7S/qEIRQT76/JYYhfl7mZgYAIBT16Al2aRJk5SYmKi4uDjFxcUpMTFRsbGxJ3Xtl19+qYSEBIWGhjZkRAAAAIf1+rLteu/3XZKk56/ooxERgeYGQpN0flSQ3vizKEvO0r8+3aCqGrvidxzUpDkJyi+tUp8wP82/ZagCfT3MjgsAwClrsO2KUlNTFRcXp/j4eA0ZMkSSNGfOHMXExCgtLU0RERHHvHbfvn2aNm2avvvuO1144YUNFREAAMBhfbp6j577Lk2S9PC4KF3Sr53JidCUnRcVpDevHaBbP1qvb5OzlVeSoA17ClRRbVdM5zaac91AtXJnJ1QAQPPWYCPJVq1aJavVWluQSdLQoUNltVq1cuXKY15nt9sVGxure++9Vz179jzh61RUVKiwsPCwBwDUN7vd0JbsQpVUVJsdBQBO23ebsvXgomRJ0m0juujGMzuZnAjNwbk9gvRmbH+5OTspfkeeKqrtOq9HoN67YRAFGQCgRWiw/5plZ2crMPDIIfuBgYHKzs4+5nXPPPOMXFxcdPvtt5/U68yaNUuPPvroKecEgGM5WFyhX7fmanlarn7dekB5JZXq2MZLc28cog5tWG8FQPOUsOOg/u+TDbIb0lUDw3Tv6GOP7gf+7pzIIM2OHaD7FiTpnIhAPTEhWq7O7AUGAGgZ6lySzZw584Sl1Jo1ayRJFovliGOGYRz1eUlat26dXnnlFa1fv/6Y5/zd9OnTddddd9X+XFhYqLCwsJO6FgD+qsZuKDGjQMvTc7U8bb+S9tlkGIefs/tgqS57c6U+vHGweoT4mhMUAE7R5sxC3fzBWlVW23V+VJCenBB90v/PBfxpZGSgVj94Lp8dAECLU+eSbNq0aZo4ceJxzwkPD1dSUpJycnKOOJabm6ugoKCjXrdixQrt379fHTp0qH2upqZGd999t15++WXt2rXriGvc3d3l7u5etzcBAH/ILarQr+m5WpaeqxVbc1VQWnXY8R4hvhoR0VYjurdVe38v3fjeGqXlFOmq2av07vWDNDDc36TkAFA3ew6W6rr3VquoolqDw/31n6v7yYURQDhFFGQAgJbIYhh/HydRP1JTUxUVFaWEhAQNHjxYkpSQkKChQ4dqy5YtR124/+DBg8rKyjrsudGjRys2NlY33HDDcRf7/1NhYaGsVqtsNpt8fRnlAeBw1TV2JWYUaFlarpal71fKvsPXMfTxcNHZ3dpqeERbDe/eVkF/26XLVlqlGz9Yo3W78+Xh6qQ3rhmgkZHsBgegacstqtDlb67U7oOligz20fxbYmT1dDU7FgAAQKM42a6owUoySRo7dqwyMzM1e/ZsSdLUqVPVsWNHff3117XnREZGatasWZowYcJR7xEeHq477rhDd9xxx0m9JiUZgL/bX1iuZemH1hZbsTVXheWHL74f3c5XI7oHanhEW/UL8zvhyIrSymrdNm+9lqXlysXJouev6MOucACarKLyKk18K16bMgsV5u+pBf8YpsC//QEAAABAS3ayXVGDbkMzb9483X777Ro1apQkafz48XrttdcOOyctLU02m60hYwBwMFU1dq3fna/l6blalparzVmHjxazerrqrG4BGhERqLO7ByjQp25fFr3cXDRn8kDd8/lGfZWYqTvmJyq/tFI3nMHucACalvKqGk39cJ02ZRYqoJWbPrxxCAUZAADAMTToSDIzMJIMcEzZtnItT9+vZWm5+m3rARVV/G+0mMUi9W5n1fDubTU8IlB9w/zk7HT6a6nY7YYe/XqTPli1W5J0+zlddef53VmnBUCTYCut0v0LkhS3KVut3F306dShim5nNTsWAABAo2sSI8kAoKFUVtu1bne+lqXv1/K0XG3JLjrseGsvV53dva1GRLTVWd3aKqBV/W/w4eRk0czxPeXv7a6XfkzXqz9vU15ppR4dH10vJRwA1JVhGFq3O18fr96jJUlZqqi2y83ZSW/FDqAgAwAAOAFKMgDNRmZB2aEF99P2a+X2gyr+22ixPu39Du1EGRGoXu2sjVJUWSwW/eu8bvL3dtXDizfpo/g9Kiit0otX9pWbC7vGAWgcttIqLdywV5+s3qP0nOLa5yODffTA2EgN6xpgYjoAAIDmgZIMQJNVUV2jtbvytSzt0DTKrfuLDzvextvtjymUh0aL+Xu7mZRUio0Jl5+Xm+76LFHfJGXJVlal2bED5OXGb7MAGsbRRo1Jkqersy7qE6KrB3dQ3zA/poADAACcJL69AWhSMvJKa3eiXLn9gEora2qPOVmkfh1aa/gf0yijQ61yakLTGi/qEypfT1f9Y+46rdh6QJPmJOi96weptYnlHYCW53ijxq4Z0kEX92snXw9XExMCAAA0TyzcD8BU5VU1Wr0z74+dKPdre27JYccDWrnXlmJndQuQn1fTL5zW78nXDe+tka2sSt0CW2nuTUMUbGU3OQCnjlFjAAAAp+5kuyJKMgCNbs/BUi37YyfKVdsPqqzqf6PFnJ0s6t/BTyMiAjW8e1tFhfg2qdFiJys9p0ix7yQop7BC7fw8NfemwerctpXZsQA0M4waAwAAOH2UZJRkQJNRXlWj+B0HtSwtV8vTc7XzwOGjxYJ8/xwtFqgzugbI6tkyvvBl5JVq8rurtfNAidp4u+mDGwezuxyAE6odNZawR0uSGTUGAABwuijJKMkAU+08UFK74H78joO1X/IkycXJogEdW9eOFusR4tNiv+wdKK7Qde+u1qbMQrVyd9GcyQMV06WN2bEANEEFpZVauH6fPlm957CNSnqE+GrSkA66uG8oo8YAAABOASUZJRnQqMoqa7RqxwEtT8vVsvRc7T5YetjxEKuHRkS01fDubTWsa4BDfdErKq/SzR+sVcLOPLm5OOk/V/fT6J7BZscC0AQYhqG1u/P1yTFGjU0a0lF92ltb7B8kAAAANAZKMkoyoEEZhqHtuSW1C+4n7MxT5V9Gi7k6WzQo3L92GmX3oFYO/SWvvKpGt3+yQd9vzpGTRXr6st66cmCY2bEAmIRRYwAAAI3nZLsil0bMBKCZK6mo1qrtB2sX3d+bX3bY8XZ+nhoe0VYj/hgt1sqd32L+5OHqrNev6a/pC5P1+bq9uu+LJOWXVOqW4V3Mjgagkfw5auzPtcYq/zJqbHyfUF09pAOjxgAAAEzEN1gAx2QYhrbtL9aytFwtS9+vNTvzVVnzv9Fibs5OGtzJXyMi2mpERFt1aevYo8VOxMXZSc9e3lv+3m6a/esOzVq6RXmllXpgTCS/bkALVlBaqQV/jBrbxqgxAACAJouSDMBhiiuq9fu2A1qWlqtf03O1r+Dw0WJh/p4a0T1QIyLaamjnNvJmtFidWCwWTb+gh1p7u+nppVs0e/kOFZRU6ckJ0XJxdjI7HoB6YhiG1uzK1yerGTUGAADQXPDtFnBwhmEoLadIy9JytTwtV2t356mq5n9LFbq5OGlo5zZ/rC3WVp0DvPlSVw/+MbyLWnu5avrCZM1fm6GCskq9MrGfPFydzY4G4DScaNTYJX1D5cOoMQAAgCaJkgxwUIZh6P2Vu/TWrzuUZSs/7FjHNl4a8ceC+0M7t5GnG8VNQ7hqUAdZPd10+ycb9N2mHN3w3hq9NXkAX6Ad0Lb9RXJ2clJHfy85OVFCNzfHGjXm5fbHqLHBHdSbUWMAAABNHrtbAg6ovKpGDy5K1sL1+yRJ7i5OiunSprYYCw/wNjmhY1m5/YCmfrhOxRXVim7nq/dvGKyAVu5mx0IjyLaV65HFKfpuU44kycfdRT3b+apXO6ui/3h0auNNcdZE5ZdUauGGI0eNRf1lrTFKbwAAAPOdbFdESQY4mP2F5brlo3XasKdAzk4WTR8bqWuHdmSan8mS99p03XurlVdSqc4B3vrwpsFq39rL7FhoIDV2Q/MSduvZuDQVV1TL2ckiZydL7Qikv2rl7qKo0EPF2Z/lWecAijOz/Dlq7OOE3fo2JZtRYwAAAM0AJRklGXCEpL0FmvrhOmUXlsvXw0WvXzNAZ3YLMDsW/rA9t1iT31mtfQVlCvb10NybBqtbkI/ZsVDPUrMKNX1hshIzCiRJfcP8NOvSXuoa2Epbc4qVss+m5H02pWTatDmzUBVHKc683ZzVM/RQYdar/aECrVNAKzlTnDWY/JJKLVi/V5+s3qPtuSW1zzNqDAAAoOmjJKMkAw6zeGOm7v18oyqq7erS1ltvXzdInZhW2eRk2coU+85qbdtfLD8vV713/SD169Da7FioB2WVNXrlp616e8UOVdsNtXJ30X1jInTNkI7HLLeqa+zallus5L222vJsc1ahyquOLM683JwVFeJ7qDhrZ1Wv9lZ1aUtxdjoMw9DqnXn6ZPUeRo0BAAA0Y5RklGSAJMluN/TCD2n67y/bJUkjI9rqlav7yZcRD01Wfkmlbnh/jRIzCuTl5qw3rx2gs7u3NTsWTsOv6bn695cp2pNXKkka3TNIj46PVrDVo873qq6xa3tuyaHRZn8WZ5mFKquqOeJcT1fn2qmaf5ZnXdp6y8XZ6bTfU0t2rFFjPUMPjRob34dRYwAAAM0JJRklGaDiimrd8Wmifkw9tCj4LWd31n1jIhlZ0gyUVFTrHx+t04qtB+TqbNFLV/XVuN6hZsdCHR0ortAT32zWl4mZkqQQq4ceHd9To3oG1+vr1NgN7cgtVvKfUzX32bQps1CllUcWZx6uTuoRcnhx1i2wlcMXZycaNTZpSAf1aseoMQAAgOaIkoySDA5uz8FS3fzhGqXnFMvNxUlPX9pLl/Zvb3Ys1EFFdY3u+myjliRlyWKRHrs4WrFDO5odCyfBMAx9vnavnlqaqoLSKlks0nUx4bpndIRaubs0SoYau6GdB/4ozvYW/lGc2VRylOLM3eV/xdmf5Vm3oFZydYDijFFjAAAALR8lGSUZHNiq7Qd127x1yi+tUlsfd70VO4B1rZqpGruhh79K0byEPZKku8/vrmnndGU0SxO2PbdYDy1KVvyOPElSjxBfPX1pL/UJ8zM3mA5Nv955sOTQNM29h0adbcosVHFF9RHnurk4qUewT+1os+h2VnUP8pGbS/MvzgzDUMIfo8aWJmersuZ/o8Yu7ntorTFGjQEAALQclGSUZHBQc+N369HFm1RtN9S7vVVvxQ48pXWP0HQYhqGXfkjXqz9vkyTdcEa4ZlwYJSemzTYpFdU1enPZDv33l22qrLHLw9VJd53fXTee0alJT2W02w3tOnj4Gmeb9hWq6GjFmbOTIkP+V5z1ambF2Z+jxj5evUc7GDUGAADgMCjJKMngYKpq7Hr06036KP7QiKPxfUL17OW95eHqbHIy1Jd3f9upx77ZLEma0K+dnr28t0NMh2sOVu/M0/SFSbXT9YZ3b6snLolWmL+XyclOjd1uaHdeqVL+Upwl77OpqPzI4szV2aKIYJ/D1jiLCPaRu0vT+L3nZEaN9W7vZ25IAAAANChKMkoyOJC8kkrdNm+d4nfkyWKR7hkVodtGdGGqUAu0aMNe3fN5kmrshs6JDNR/J/WXp1vTKCMcka20Sk/HpeqT1RmSpIBWbnr4op66qHdIi/v3zzAM7ckrPWxzgJR9hbKVVR1xrquzRd2DjizOGrO0zyup1MLjjBq7uG+7RlsfDgAAAOaiJKMkg4NIyy7SzR+uUUZembzdnPXyxH46PyrI7FhoQD9vydGtH61XRbVdAzu21jvXD5LVkylijckwDH2dlKXHvt6sA8UVkqSJg8L0wNhI+Xm5mZyu8RiGoYy8MqVk2g6brllQemRx5uJkUbcgH/Vq97+dNXuE+NZrcfbnqLGPE/YoLuV/o8a83Zw1vm87TRrcQb3aW+vt9QAAANA8UJJRksEBfL8pW3fOT1RJZY06+Hvp7esGqnuQj9mx0AjW7MrTje+vUVF5tSKDffThjYMV6Mvac40hI69U//4yRcvTcyVJXdp6a9alvTW4k7/JyZoGwzC0N7/ssGmaKftsyj9KcebsZFG3wFaH1jdr/0dxFuxb59GReSWVWrDu0A6VOw78b9RYdDtfTRrcUeP7hjJqDAAAwIFRklGSoQUzDEOvL9uu579Pk2FIMZ3b6PVr+qu1t+OMYIGUmlWoye+uVm5RhTr4e2nuTYPVsY232bFarOoau975bade+jFd5VV2uTk76Z8ju+ofIzo3mfW3mirDMLSv4H/FWcq+QqXss+lgSeUR5zo7WdS1bas/pmn6qld7q6JCrEcUZ4ZhKH7HobXGGDUGAACA46EkoyRDC1VWWaP7FiTp642ZkqTYoR318EVRLODuoHYfLFHsO6u1J69UAa3c9eGNgxUVyu999W1jRoGmL0zW5qxCSdKQTv566tJe6tK2lcnJmi/DMJRlKz9smmbKPpsOFB9ZnDlZpK6BrWrXN6uqsevT1RmHjRrr1c6qqwd3YNQYAAAAjkBJRkmGFijLVqapH65T8j6bXJwsmjm+p64d2tHsWDDZ/sJyTX53tbZkF8nHw0XvXj9Ig8KZ+lcfiiuq9fx3afpw1S7ZDcnq6aqHLuihKwa2b3EL8zcFhmEou7BcyXv/2Bggs1DJ+2zKLao46vmMGgMAAMDJoCSjJEMLs35Pvm6Zu065RRVq7eWq168ZoJgubcyOhSbCVlalmz9YozW78uXu4qQ3ru2vcyLZwOF0/LA5Rw9/laIsW7kk6ZK+ofr3uCgFtHI3OZnjyfmjOPtztFlpZY0u6hPKqDEAAACcFEoySjK0IAvW7dX0RcmqrLYrIshHb183UGH+XmbHQhNTVlmjf368Xj9v2S9nJ4ueu7y3Lu3f3uxYzU62rVwzF29S3KZsSVKYv6eevKSXzu7e1uRkAAAAAE7FyXZF/PEr0ITV2A09E7dFb/26Q5J0flSQXrqqLyMncFSebs6aHTtA932RpEUb9umuzzaqoLRKN57ZyexozUKN3dC8hN16Ni5NxRXVcnayaMpZnfWvc7vVebdFAAAAAM0P37SBJqqwvEq3f7JBy9JyJUnTRnbVXed3l5MT6yDh2FydnfTCFX3k5+Wq937fpce+2az80krddX531tA6jtSsQk1fmKzEjAJJUt8wP826tJd6hDAiGQAAAHAUlGRAE7TzQIlu+mCNduSWyN3FSc9f0UcX9Qk1OxaaCScnix4eF6U23m56/vt0/efnbTpYUqnHL46WMyXrYcqravTKT1s159cdqrYbauXuontHR+jaoR35tQIAAAAcjFND3jw/P1+xsbGyWq2yWq2KjY1VQUHBca+5/vrrZbFYDnsMHTq0IWMCTcqKrbm6+LXftCO3RMG+HvriH8MoyFBnFotF087ppicuiZbFIn2csEe3f7JBFdU1ZkdrMlZszdWol37VG8u2q9puaHTPIP1413BdNyycggwAAABwQA06kmzSpEnau3ev4uLiJElTp05VbGysvv766+NeN2bMGL333nu1P7u5uTVkTKBJMAxD76/cpSeWpKrGbqhfBz/Njh2gQB8Ps6OhGbt2aEf5ebnqzvmJWpKcpcLyKr157QB5O/C6dgeLK/TEklQt2rBPkhTs66HHLu6pUT2DTU4GAAAAwEwN9i0pNTVVcXFxio+P15AhQyRJc+bMUUxMjNLS0hQREXHMa93d3RUczJcVOI6K6ho9/OUmzV+bIUm6tH87PTWhlzxcWSwcp29c71BZPV11y9x1WrH1gCa9naD3rx+k1t6O9QcQhmHo83V79dS3qSoorZLFIl0XE657RkewGQYAAACAhptuuWrVKlmt1tqCTJKGDh0qq9WqlStXHvfaZcuWKTAwUN27d9eUKVO0f//+Y55bUVGhwsLCwx5Ac3KguELXzEnQ/LUZcrJI/76wh164og8FGerVWd3aat7NQ+Tn5aqNGQW6YvYqZdnKzI7VaLbnFuvqOfG674skFZRWqUeIrxbddoZmju9JQQYAAABAUgOWZNnZ2QoMDDzi+cDAQGVnZx/zurFjx2revHn6+eef9cILL2jNmjU655xzVFFRcdTzZ82aVbvmmdVqVVhYWL29B6Chbcq06eLXftfa3fnycXfRO9cP0s1ndWYXQjSIfh1a6/NbYhTs66Ft+4t1+RurtD232OxYDaqiukav/LhVY19eofgdefJwddL0sZFaPO0M9Q3zMzseAAAAgCakziXZzJkzj1hY/++PtWvXStJRv+gbhnHcAuCqq67ShRdeqOjoaF100UVaunSp0tPTtWTJkqOeP336dNlsttpHRkZGXd8SYIqlyVm6/I1V2ldQpk4B3lr0zzM0MuLIYhmoT92CfPTFrTHqHOCtfQVluuLNVUreazM7VoNYvTNPF776m176MV2VNXYN795WP9w5XLcM7yJX5wbdtwYAAABAM1TnOSbTpk3TxIkTj3tOeHi4kpKSlJOTc8Sx3NxcBQUFnfTrhYSEqGPHjtq6detRj7u7u8vd3f2k7weYzW439MpPW/XKT4c+02d1C9BrV/eX1cvV5GRwFO1be+nzf8To+vfWKHmfTRPfWqU51w3UsC4BZkerF7bSKj0dl6pPVh/6Q5OAVm6aMS5K4/uEMkoTAAAAwDHVuSQLCAhQQMCJv0jFxMTIZrNp9erVGjx4sCQpISFBNptNw4YNO+nXO3jwoDIyMhQSElLXqECTU1pZrbs/26ilKYemHN94Ric9eEGkXBjVgkbWppW7Pp4yRFM/XKdVOw7q+nfX6NWr+2pMdPP9vdYwDH2dlKXHvt6sA8WHpuhPHBSmB8ZGys/LsTYpAAAAAFB3FsMwjIa6+dixY5WZmanZs2dLkqZOnaqOHTvq66+/rj0nMjJSs2bN0oQJE1RcXKyZM2fqsssuU0hIiHbt2qUHH3xQe/bsUWpqqnx8fE74moWFhbJarbLZbPL19W2otwbU2d78Uk35cJ1Sswrl6mzRk5f00pWDWEMP5iqvqtG/Pt2g7zblyMkizbq0l64a1MHsWHWWkVeqGV+laFlariSpS1tvPTWhl4Z0bmNyMgAAAABmO9muqEG39Jo3b55uv/12jRo1SpI0fvx4vfbaa4edk5aWJpvt0Ho4zs7OSk5O1ocffqiCggKFhIRo5MiRmj9//kkVZEBTtWZXnv4xd50OllQqoJWb3rx2gAaG+5sdC5CHq7P+O6m/HlqUovlrM3T/gmTll1bpH8O7mB3tpFTX2PXu7zv10g9bVVZVIzdnJ902sotuHdFF7i7sEAsAAADg5DXoSDIzMJIMTc2nq/doxlcpqqoxFBXiqznXDVQ7P0+zYwGHMQxDz8Sl6c3l2yVJU8/urOljI5v0Gl4bMwo0fWGyNmcVSpKGdPLXkxN6qWtgK5OTAQAAAGhKmsRIMsCRVdfY9cSSVL2/cpck6cJeIXruit7ycuNfOzQ9FotFD4yNlL+3q576dove+nWH8ksqNevSXk1uzbziimq98H2aPli5S3ZDsnq66qELeuiKge2bdKkHAAAAoGnj2zrQAApKKzXt4w36bdsBSdKd53XX7ed25Qs8mrypZ3dRay83PbAwWZ+v26uCsir95+p+8nBtGlMXf9ico4e/SlGWrVySdHHfUM0YF6WAVuxyDAAAAOD0UJIB9Wzb/iLd/MFa7TpYKk9XZ710VZ9mvWMgHM8VA8Nk9XTVtE826IfNObru3dWac91A+Xq4mpYp21aumYs3KW7ToZ1hw/w99cQlvTS8e1vTMgEAAABoWViTDKhHv2zZr9s/2aCiimq18/PUnMkDFRXK5xDNU/yOg7r5g7UqrqhWz1BffXDj4EYfsVVjNzQvYbeejUtTcUW1nJ0smnJWZ/3r3G7ydGsao9sAAAAANG0n2xVRkgH1wDAMvfXrDj0dt0WGIQ0O99fr1/ZnChiavZR9Nl3/3modKK5UpwBvfXjjYIX5ezXKa2/JLtT0hcnasKdAktQnzE+zJvSieAYAAABQJ5RklGRoJOVVNXpwYbIWbtgnSZo4KEyPXRwtN5emtdg5cKp2HijRtW8naF9BmYJ83TX3piHqHuTTYK9XXlWjV37aqjm/7lC13VArdxfdOzpC1w7tKGcn1vUDAAAAUDeUZJRkaAT7C8s1de46JWYUyNnJohkX9tB1w8JZoB8tTratXJPfTVB6TrGsnq569/pBGtCxdb2/zoqtuXpoUYr25JVKkkb3DNLM8T0VYvWs99cCAAAA4BgoySjJ0MCS9hZoyodrlVNYIaunq/47qb/O7BZgdiygwRSUVuqG99dow54Cebo6683YAfW2cP7B4go9sSRVi/4YkRns66FHL+6p0T2D6+X+AAAAABwXJRklGRrQV4n7dN8XSaqotqtrYCu9PXmgwgO8zY4FNLjSymrd+tF6LU/PlauzRS9c2Vfj+4Se8v0Mw9Dn6/bqqW9TVVBaJYtFui4mXHeP6i4fE3fTBAAAANBynGxX5NKImYBmz2439Pz3aXp92XZJ0siItnrl6n7y5cs8HISXm4vmTB6ouz/fqK83Zupfn26QrbRSsTHhdb7XjtxiPbgoWfE78iRJPUJ8NevSXuob5le/oQEAAADgJFCSASepuKJad3y6QT+m7pck3TK8s+4bHclC4nA4bi5OeuWqvmrt5aoPV+3WjK82Ka+kSref2/Wk1uOrrLbrzeXb9dov21RZbZeHq5PuOK+7bjqzk1yd2fACAAAAgDkoyYCTsOdgqW7+cI3Sc4rl5uKkZy7rpQn92psdCzCNk5NFj47vqdZebnrlp6166cd05ZdW6uFxUXI6TnG8Zleepi9M1rb9xZKks7u31ZOXRCvM36uxogMAAADAUVGSASewcvsB3TZvvQpKqxTo4663Jg9kOhggyWKx6M7zu8vf202PLN6k91fuUn5ppZ6/os8RI8JspVV6Om6LPlm9R5IU0MpNM8ZFaXyfUHaDBQAAANAkUJIBxzE3frceXbxJ1XZDvdtb9VbsQAVbPcyOBTQp1w0Ll5+Xq+7+bKO+SsyUraxKb1wzQJ5uzjIMQ98kZenRrzfrQHGFJGnioDA9MDZSfl5uJicHAAAAgP+hJAOOoqrGrpmLN2lewqFRLxf3DdUzl/WWh6uzycmApunivu3k6+mqWz9ap2Vpubr2nQQ9Or6nnv8+TcvSciVJndt6a9aEXhrSuY3JaQEAAADgSBbDMAyzQ9Snk93WEziWvJJK3frROiXszJPFIt03OlL/GN6ZKWHASVi3O083vLdGheXVtc+5OTvptpFddOuILnJ3oWgGAAAA0LhOtitiJBnwF1uyC3XzB2u1N79M3m7OemViP50XFWR2LKDZGNDRX5/9I0aT31mt/UUVGtzJX09N6KWuga3MjgYAAAAAx0VJBvzh+03ZunN+okoqa9TB30tvXzdQ3YN8zI4FNDuRwb5a+q+zlJ5TrCGd/I+72yUAAAAANBWUZHB4hmHov79s0/Pfp0uSYjq30evX9FdrbxYVB05Vm1buimnlbnYMAAAAADhplGRwaGWVNbr3i436JilLkjQ5pqNmjIuSq7OTyckAAAAAAEBjoiSDw8qylWnqh+uUvM8mFyeLHr24p64Z0tHsWAAAAAAAwASUZHBI63bn65a563SguEKtvVz1xrUDNLRzG7NjAQAAAAAAk1CSweF8sW6vHlyYrMoauyKDfTRn8kCF+XuZHQsAAAAAAJiIkgwOo8Zu6OmlqZqzYqckaVRUkF66qq+83fnXAAAAAAAAR0c7AIdgK6vS7Z9s0PL0XEnS/53TVXee111OThaTkwEAAAAAgKaAkgwt3o7cYt384VrtyC2Rh6uTnru8jy7qE2p2LAAAAAAA0IRQkqFF+zU9V9M+Xq/C8mqFWD00Z/JARbezmh0LAAAAAAA0MZRkaJEMw9B7v+/SE0s2y25I/Tv46c3YAQr08TA7GgAAAAAAaIIoydDiVFTXaMaXKfps7V5J0uUD2uvJCdFyd3E2ORkAAAAAAGiqKMnQopRWVuu6d1drza58OVmkBy/ooZvO7CSLhQX6AQAAAADAsVGSoUV5/rt0rdmVLx8PF/3n6n4aERFodiQAAAAAANAMOJkdAKgvG/bk672VOyWJggwAAAAAANQJJRlahMpqux5YkCzDkCb0a0dBBgAAAAAA6oSSDC3Cm8u3Ky2nSP7ebpoxLsrsOAAAAAAAoJmhJEOzt21/kV77eZsk6ZGLouTv7WZyIgAAAAAA0NxQkqFZs9sNPbAgWZU1do2MaKvxfULNjgQAAAAAAJqhBi3J8vPzFRsbK6vVKqvVqtjYWBUUFJzwutTUVI0fP15Wq1U+Pj4aOnSo9uzZ05BR0UzNS9ittbvz5e3mrCcm9JLFYjE7EgAAAAAAaIYatCSbNGmSEhMTFRcXp7i4OCUmJio2Nva412zfvl1nnnmmIiMjtWzZMm3cuFEzZsyQh4dHQ0ZFM5RZUKanl26RJN03JlLt/DxNTgQAAAAAAJori2EYRkPcODU1VVFRUYqPj9eQIUMkSfHx8YqJidGWLVsUERFx1OsmTpwoV1dXzZ0796Rep6KiQhUVFbU/FxYWKiwsTDabTb6+vqf/RtAkGYahmz9Yq5+27Ff/Dn764h/D5OTEKDIAAAAAAHC4wsJCWa3WE3ZFDTaSbNWqVbJarbUFmSQNHTpUVqtVK1euPOo1drtdS5YsUffu3TV69GgFBgZqyJAh+vLLL4/5OrNmzaqdzmm1WhUWFlbfbwVN0NdJWfppy365OTvpmct6U5ABAAAAAIDT0mAlWXZ2tgIDA494PjAwUNnZ2Ue9Zv/+/SouLtbTTz+tMWPG6Pvvv9eECRN06aWXavny5Ue9Zvr06bLZbLWPjIyMen0faHrySyr16OJNkqR/juyqbkE+JicCAAAAAADNnUtdL5g5c6YeffTR456zZs0aSTrqIuqGYRxzcXW73S5Juvjii3XnnXdKkvr27auVK1fqzTff1PDhw4+4xt3dXe7u7nV6D2jeHl+yWQdLKtU9qJVuHdHF7DgAAAAAAKAFqHNJNm3aNE2cOPG454SHhyspKUk5OTlHHMvNzVVQUNBRrwsICJCLi4uioqIOe75Hjx767bff6hoVLdCv6blauH6fLBbp6ct6y82lQfeeAAAAAAAADqLOJVlAQIACAgJOeF5MTIxsNptWr16twYMHS5ISEhJks9k0bNiwo17j5uamQYMGKS0t7bDn09PT1bFjx7pGRQtTUlGtBxclS5KuiwlX/w6tTU4EAAAAAABaigYbhtOjRw+NGTNGU6ZMUXx8vOLj4zVlyhSNGzfusJ0tIyMjtWjRotqf7733Xs2fP19z5szRtm3b9Nprr+nrr7/Wbbfd1lBR0Uy8+EO69uaXqZ2fp+4dffTdUQEAAAAAAE5Fg85Vmzdvnnr16qVRo0Zp1KhR6t27t+bOnXvYOWlpabLZbLU/T5gwQW+++aaeffZZ9erVS2+//bYWLFigM888syGjoolLzCjQe7/vlCQ9OSFa3u51HgQJAAAAAABwTBbDMAyzQ9SnwsJCWa1W2Ww2+fr6mh0H9aCy2q7xr/2mLdlFmtCvnV66qq/ZkQAAAAAAQDNxsl0Rq56jyZu9fLu2ZBfJ39tNM8ZFnfgCAAAAAACAOqIkQ5O2bX+x/vPzNknSIxdFyd/bzeREAAAAAACgJaIkQ5NltxuavjBJlTV2jYhoq/F9Qs2OBAAAAAAAWihKMjRZ8xJ2a82ufHm5OevJCb1ksVjMjgQAAAAAAFooSjI0SZkFZXomLk2SdN/oCLXz8zQ5EQAAAAAAaMkoydDkGIahGV+mqLiiWv07+Ck2JtzsSAAAAAAAoIWjJEOT801Sln7asl+uzhY9fVlvOTsxzRIAAAAAADQsSjI0KfkllZq5eJMk6Z8ju6p7kI/JiQAAAAAAgCOgJEOT8sSSVB0sqVS3wFa6dUQXs+MAAAAAAAAHQUmGJuPX9FwtWL9XFov09GW95e7ibHYkAAAAAADgICjJ0CSUVlbrwUXJkqTrYsI1oGNrkxMBAAAAAABHQkmGJuGF79O1N79M7fw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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_metrics = pd.DataFrame(history_metrics)\n",
    "perc2float = lambda x: float(x.strip('%'))/100\n",
    "\n",
    "all_metrics[\"Market Return\"] = all_metrics[\"Market Return\"].apply(perc2float)\n",
    "all_metrics[\"Portfolio Return\"] = all_metrics[\"Portfolio Return\"].apply(perc2float)\n",
    "\n",
    "all_metrics.plot(y=[\"episodic_reward\"], figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Market Return :  2.98%   |   Portfolio Return : -57.47%   |   Position Changes : 4770   |   Episode Length : 10000   |   \n",
      "Market Return : -6.47%   |   Portfolio Return : -61.32%   |   Position Changes : 4693   |   Episode Length : 10000   |   \n",
      "Market Return : -12.00%   |   Portfolio Return : -60.93%   |   Position Changes : 4840   |   Episode Length : 10000   |   \n",
      "Market Return :  4.10%   |   Portfolio Return : -55.96%   |   Position Changes : 4790   |   Episode Length : 10000   |   \n",
      "Market Return :  2.64%   |   Portfolio Return : -62.67%   |   Position Changes : 4697   |   Episode Length : 10000   |   \n",
      "Market Return :  4.88%   |   Portfolio Return : -58.19%   |   Position Changes : 4731   |   Episode Length : 10000   |   \n",
      "Market Return : -13.30%   |   Portfolio Return : -62.51%   |   Position Changes : 4785   |   Episode Length : 10000   |   \n",
      "Market Return : -16.08%   |   Portfolio Return : -56.73%   |   Position Changes : 4740   |   Episode Length : 10000   |   \n",
      "Market Return : -30.69%   |   Portfolio Return : -64.00%   |   Position Changes : 4808   |   Episode Length : 10000   |   \n",
      "Market Return : -12.14%   |   Portfolio Return : -54.60%   |   Position Changes : 4710   |   Episode Length : 10000   |   \n",
      "Average Portfolio Return: -0.5943799999999999\n",
      "Std Portfolio Return: 0.03077813509620101\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the agent on a new dataset from the same data generating process\n",
    "returns = []\n",
    "for i in range(10):\n",
    "    x, y = nonstationaryDGP()\n",
    "    spread = y - x\n",
    "    _df = pd.DataFrame({'x': x, 'y': y, 'close': spread})\n",
    "    fe = FeatureEngineering(_df, main_feature_col=\"close\")\n",
    "    fe.rolling_feature(**feature_config)\n",
    "    _df = fe.df.copy()\n",
    "    \n",
    "    env_test = gym.make(\"TradingEnv\",\n",
    "            name= \"stationaryDGP\",\n",
    "            df = _df, # Your dataset with your custom features\n",
    "            positions = env_action_space, # -1 (=SHORT), 0(=OUT), +1 (=LONG)\n",
    "            trading_fees = 0.01/100, # 0.01% per stock buy / sell (Binance fees)\n",
    "            borrow_interest_rate= 0.0003/100, # 0.0003% per timestep (one timestep = 1h here)\n",
    "        )\n",
    "    env_test.unwrapped.add_metric('Position Changes', lambda history : np.sum(np.diff(history['position']) != 0) )\n",
    "    env_test.unwrapped.add_metric('Episode Length', lambda history : len(history['position']) )\n",
    "\n",
    "    env_test = agent.eval(env_test)\n",
    "    returns.append(perc2float(env_test.unwrapped.get_metrics()[\"Portfolio Return\"]))\n",
    "\n",
    "print(f\"Average Portfolio Return: {np.mean(returns)}\")\n",
    "print(f\"Std Portfolio Return: {np.std(returns)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>y</th>\n",
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       "      <th>feature_rolling_mean_corrected</th>\n",
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       "      <td>110.025521</td>\n",
       "      <td>100.173720</td>\n",
       "      <td>-0.004116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>12.529054</td>\n",
       "      <td>111.690175</td>\n",
       "      <td>99.161120</td>\n",
       "      <td>-0.025336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>12.518737</td>\n",
       "      <td>111.480368</td>\n",
       "      <td>98.961631</td>\n",
       "      <td>-0.192887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>12.514282</td>\n",
       "      <td>111.563801</td>\n",
       "      <td>99.049519</td>\n",
       "      <td>0.092151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>12.484843</td>\n",
       "      <td>111.490170</td>\n",
       "      <td>99.005326</td>\n",
       "      <td>-0.039321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>12.446778</td>\n",
       "      <td>111.428859</td>\n",
       "      <td>98.982081</td>\n",
       "      <td>-0.019947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              x           y       close  feature_rolling_mean_corrected\n",
       "0      9.965975  109.837898   99.871923                        0.000000\n",
       "1      9.971701  109.963545   99.991843                        0.059960\n",
       "2     10.002110  110.068336  100.066226                        0.009615\n",
       "3      9.925394  110.042870  100.117476                       -0.010139\n",
       "4      9.851801  110.025521  100.173720                       -0.004116\n",
       "...         ...         ...         ...                             ...\n",
       "9995  12.529054  111.690175   99.161120                       -0.025336\n",
       "9996  12.518737  111.480368   98.961631                       -0.192887\n",
       "9997  12.514282  111.563801   99.049519                        0.092151\n",
       "9998  12.484843  111.490170   99.005326                       -0.039321\n",
       "9999  12.446778  111.428859   98.982081                       -0.019947\n",
       "\n",
       "[10000 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build features\n",
    "df_train = df.copy()\n",
    "\n",
    "feature_config = {\n",
    "    \"window\": 100, # 7 days window, pad with first value when window is not available\n",
    "    \"mode\": \"diff\", # difference between current and previous close price\n",
    "}\n",
    "\n",
    "fe = FeatureEngineering(df_train, main_feature_col=\"close\")\n",
    "fe.rolling_mean_corrected_feature(**feature_config)\n",
    "df_train = fe.df.copy()\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 1. 1.]\n",
      "Market Return : -0.89%   |   Portfolio Return :  0.87%   |   Position Changes : 1   |   Episode Length : 10000   |   \n"
     ]
    }
   ],
   "source": [
    "# Define the environment\n",
    "env_action_space = [-1, 0, 1] # Positions : [-1=SHORT, 0=OUT, 1=LONG]\n",
    "# env_action_space = [i/10 for i in range(-10, 11)] # Positions : [-1, -0.9, -0.8, ..., 0, 0.1, 0.2, ..., 1]\n",
    "\n",
    "env = gym.make(\"TradingEnv\",\n",
    "        name= \"stationaryDGP\",\n",
    "        df = df_train, # Your dataset with your custom features\n",
    "        positions = env_action_space, # -1 (=SHORT), 0(=OUT), +1 (=LONG)\n",
    "        trading_fees = 0.01/100, # 0.01% per stock buy / sell (Binance fees)\n",
    "        borrow_interest_rate= 0.0003/100, # 0.0003% per timestep (one timestep = 1h here)\n",
    "        # max_episode_duration=180\n",
    "    )\n",
    "env.unwrapped.add_metric('Position Changes', lambda history : np.sum(np.diff(history['position']) != 0) )\n",
    "env.unwrapped.add_metric('Episode Length', lambda history : len(history['position']) )\n",
    "\n",
    "# Run an episode until it ends :\n",
    "done, truncated = False, False\n",
    "observation, info = env.reset()\n",
    "print(observation)\n",
    "while not done and not truncated:\n",
    "    # Pick a position by its index in your position list (=[-1, 0, 1])....usually something like : position_index = your_policy(observation)\n",
    "    position_index = env.action_space.sample() # At every timestep, pick a random position index from your position list (=[-1, 0, 1])\n",
    "    observation, reward, done, truncated, info = env.step(0) # always short position\n",
    "\n",
    "n_actions = env.action_space.n\n",
    "n_observations = len(env.reset()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Market Return : -0.89%   |   Portfolio Return : -60.44%   |   Position Changes : 6653   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -58.74%   |   Position Changes : 6654   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -51.12%   |   Position Changes : 4926   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -45.95%   |   Position Changes : 4999   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -19.15%   |   Position Changes : 3236   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -41.01%   |   Position Changes : 3190   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -13.83%   |   Position Changes : 2013   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -16.76%   |   Position Changes : 1951   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -9.03%   |   Position Changes : 1319   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -4.01%   |   Position Changes : 1343   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -2.05%   |   Position Changes : 1056   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return : -3.34%   |   Position Changes : 1093   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return :  3.16%   |   Position Changes : 910   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return :  6.05%   |   Position Changes : 922   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return :  4.53%   |   Position Changes : 792   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return :  6.99%   |   Position Changes : 805   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return :  6.73%   |   Position Changes : 787   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return :  6.38%   |   Position Changes : 783   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return :  7.77%   |   Position Changes : 765   |   Episode Length : 10000   |   \n",
      "Market Return : -0.89%   |   Portfolio Return :  6.25%   |   Position Changes : 766   |   Episode Length : 10000   |   \n"
     ]
    }
   ],
   "source": [
    "agent = XGBoostAgent(n_observations, n_actions)\n",
    "history_metrics, _ = agent.train(env, num_episodes=20, replay_every=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_metrics = pd.DataFrame(history_metrics)\n",
    "perc2float = lambda x: float(x.strip('%'))/100\n",
    "\n",
    "all_metrics[\"Market Return\"] = all_metrics[\"Market Return\"].apply(perc2float)\n",
    "all_metrics[\"Portfolio Return\"] = all_metrics[\"Portfolio Return\"].apply(perc2float)\n",
    "\n",
    "all_metrics.plot(y=[\"episodic_reward\"], figsize=(15, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Market Return :  8.90%   |   Portfolio Return : -5.29%   |   Position Changes : 773   |   Episode Length : 10000   |   \n",
      "Market Return : -11.70%   |   Portfolio Return : -6.90%   |   Position Changes : 810   |   Episode Length : 10000   |   \n",
      "Market Return : -16.33%   |   Portfolio Return : -10.54%   |   Position Changes : 765   |   Episode Length : 10000   |   \n",
      "Market Return : -13.15%   |   Portfolio Return : -2.51%   |   Position Changes : 783   |   Episode Length : 10000   |   \n",
      "Market Return : -5.31%   |   Portfolio Return : -11.60%   |   Position Changes : 789   |   Episode Length : 10000   |   \n",
      "Market Return :  8.35%   |   Portfolio Return : -14.85%   |   Position Changes : 782   |   Episode Length : 10000   |   \n",
      "Market Return : 22.10%   |   Portfolio Return : -5.94%   |   Position Changes : 727   |   Episode Length : 10000   |   \n",
      "Market Return :  2.16%   |   Portfolio Return : -10.63%   |   Position Changes : 792   |   Episode Length : 10000   |   \n",
      "Market Return : 21.21%   |   Portfolio Return : -13.15%   |   Position Changes : 762   |   Episode Length : 10000   |   \n",
      "Market Return : -3.86%   |   Portfolio Return : -5.91%   |   Position Changes : 732   |   Episode Length : 10000   |   \n",
      "Average Portfolio Return: -0.08732000000000001\n",
      "Std Portfolio Return: 0.037645870955524456\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the agent on a new dataset from the same data generating process\n",
    "returns = []\n",
    "for i in range(10):\n",
    "    x, y = nonstationaryDGP()\n",
    "    spread = y - x\n",
    "    _df = pd.DataFrame({'x': x, 'y': y, 'close': spread})\n",
    "    fe = FeatureEngineering(_df, main_feature_col=\"close\")\n",
    "    fe.rolling_mean_corrected_feature(**feature_config)\n",
    "    _df = fe.df.copy()\n",
    "    \n",
    "    env_test = gym.make(\"TradingEnv\",\n",
    "            name= \"stationaryDGP\",\n",
    "            df = _df, # Your dataset with your custom features\n",
    "            positions = env_action_space, # -1 (=SHORT), 0(=OUT), +1 (=LONG)\n",
    "            trading_fees = 0.01/100, # 0.01% per stock buy / sell (Binance fees)\n",
    "            borrow_interest_rate= 0.0003/100, # 0.0003% per timestep (one timestep = 1h here)\n",
    "        )\n",
    "    env_test.unwrapped.add_metric('Position Changes', lambda history : np.sum(np.diff(history['position']) != 0) )\n",
    "    env_test.unwrapped.add_metric('Episode Length', lambda history : len(history['position']) )\n",
    "\n",
    "    env_test = agent.eval(env_test)\n",
    "    returns.append(perc2float(env_test.unwrapped.get_metrics()[\"Portfolio Return\"]))\n",
    "\n",
    "print(f\"Average Portfolio Return: {np.mean(returns)}\")\n",
    "print(f\"Std Portfolio Return: {np.std(returns)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_full_py3_9",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}