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global.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Global.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/firmai/b9021274b40221ae0251daa491bccb3c/global.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "x7YsNWuZZSYO",
"outputId": "f03a4a4a-bb76-4b97-c90b-1ba3dba96480",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"!pip install scipy==1.2.2"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting scipy==1.2.2\n",
" Downloading scipy-1.2.2-cp37-cp37m-manylinux1_x86_64.whl (24.8 MB)\n",
"\u001b[K |████████████████████████████████| 24.8 MB 1.5 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.7/dist-packages (from scipy==1.2.2) (1.19.5)\n",
"Installing collected packages: scipy\n",
" Attempting uninstall: scipy\n",
" Found existing installation: scipy 1.4.1\n",
" Uninstalling scipy-1.4.1:\n",
" Successfully uninstalled scipy-1.4.1\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"Successfully installed scipy-1.2.2\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "JeQs2e0DHBhO",
"outputId": "8c224f2f-377f-48ea-9b65-4c4fc3f94b93",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"from sklearn.linear_model import ElasticNetCV as en \n",
"from statsmodels.tsa.stattools import adfuller as adf\n",
"import os\n",
"\n",
"## Save future files to your drive\n",
"import numpy as np\n",
"# from google.colab import drive\n",
"# drive.mount('/content/drive',force_remount=True)\n",
"# %cd \"/content/drive/My Drive/FirmAI/FinML/Data/Commodity\"\n",
"\n"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
" import pandas.util.testing as tm\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4DTi0fVBZNy1"
},
"source": [
"\n",
"df=pd.read_csv('input/brent crude nokjpy.csv')\n",
"df.set_index(pd.to_datetime(df[list(df.columns)[0]]),inplace=True)\n",
"del df[list(df.columns)[0]]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "p9a_U0YjaDpX",
"outputId": "41fddf8a-e904-442e-f14c-cdc977f1a284",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 233
}
},
"source": [
"df.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>nok</th>\n",
" <th>usd</th>\n",
" <th>eur</th>\n",
" <th>gbp</th>\n",
" <th>brent</th>\n",
" <th>gdp yoy</th>\n",
" <th>interest rate</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-04-25</th>\n",
" <td>16.901885</td>\n",
" <td>99.255002</td>\n",
" <td>129.165590</td>\n",
" <td>153.186275</td>\n",
" <td>10263.95955</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-04-26</th>\n",
" <td>16.760666</td>\n",
" <td>98.050001</td>\n",
" <td>127.746551</td>\n",
" <td>151.768098</td>\n",
" <td>10114.83800</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-04-29</th>\n",
" <td>16.818312</td>\n",
" <td>97.765004</td>\n",
" <td>128.073770</td>\n",
" <td>151.538112</td>\n",
" <td>10148.98465</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-04-30</th>\n",
" <td>16.882766</td>\n",
" <td>97.424999</td>\n",
" <td>128.287364</td>\n",
" <td>151.331719</td>\n",
" <td>9973.39725</td>\n",
" <td>NaN</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-05-01</th>\n",
" <td>16.883051</td>\n",
" <td>97.389997</td>\n",
" <td>128.353228</td>\n",
" <td>151.492198</td>\n",
" <td>9734.13050</td>\n",
" <td>NaN</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" nok usd ... gdp yoy interest rate\n",
"date ... \n",
"2013-04-25 16.901885 99.255002 ... NaN NaN\n",
"2013-04-26 16.760666 98.050001 ... NaN NaN\n",
"2013-04-29 16.818312 97.765004 ... NaN NaN\n",
"2013-04-30 16.882766 97.424999 ... NaN 1.5\n",
"2013-05-01 16.883051 97.389997 ... NaN 1.5\n",
"\n",
"[5 rows x 7 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ovMtE4iJrI-D"
},
"source": [
"## List of features that don't make sense but works\n",
"## Shift the target, never the features. \n",
"## Correlation catcher\n",
"## A hand engineered feature that weights N rolling averages of two variables and find\n",
"## .. the rolling correlations of the rolling averages\n",
"## If the bb correlation with response is bigger than the cc coefficient, probably causal \n",
"\n",
"\n",
"for fact in [40]:\n",
" b=2 \n",
" g=0\n",
" keep = []\n",
" for r in range(fact):\n",
" b = b**(1+((10/fact)/np.log(fact)))\n",
" keep.append(b)\n",
" g = g + b\n",
" df['brent_'+str(r)] = df['brent'].rolling(window=fact).mean().bfill()\n",
" df['nok'+str(r)] = df['nok'].rolling(window=fact).mean().bfill()\n",
" df['cc_'+str(r)] = df['brent_'+str(r)].rolling(fact).corr(df['nok'+str(r)]).bfill()\n",
" \n",
" del df['nok'+str(r)], df['brent_'+str(r)]\n",
" \n",
"\n",
" arr = np.log(np.array(keep))\n",
" arr = arr/arr.sum()\n",
" arr = arr[::-1]\n",
"\n",
" for it,ar in enumerate(arr):\n",
" df['cc_'+str(it)] = df['cc_'+str(it)]*ar\n",
" if it==0:\n",
" df['cc_weighted_'+str(fact)] = 0\n",
" df['cc_weighted_'+str(fact)] = df['cc_weighted_'+str(fact)] + df['cc_'+str(it)] \n",
" del df['cc_'+str(it)] \n",
" df['cc_weighted_'+str(fact)] = df['cc_weighted_'+str(fact)].replace(np.inf,np.nan).replace(-np.inf,np.nan)\n",
" df['cc_weighted_'+str(fact)] = df['cc_weighted_'+str(fact)].bfill()\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TLjuX1teft03"
},
"source": [
"## Shift the target, never the features. \n",
"## Correlation catcher\n",
"## A hand engineered feature that weights N rolling averages of two variables and find\n",
"## .. the rolling correlations of the rolling averages\n",
"## If the cc correlation with response is bigger than the bb coefficient, probably causal\n",
"## If bb is larger there is lagged causality\n",
"## the best techniques to confirm causality would in fact be to look at prediction success.\n",
"\n",
"\n",
"for fact in [2,3,5,10,20,40,60,80,120,150,200]:\n",
" b=2 \n",
" g=0\n",
" keep = []\n",
" for r in range(fact):\n",
" b = b**(1+((10/fact)/np.log(fact)))\n",
" keep.append(b)\n",
" g = g + b\n",
" df['brent_'+str(r)] = df['brent'].rolling(window=2+r).mean().bfill()\n",
" df['nok'+str(r)] = df['nok'].rolling(window=2+r).mean().bfill()\n",
" df['cc_'+str(r)] = df['brent_'+str(r)].rolling(2+r).corr(df['nok'+str(r)]).bfill()\n",
" df['bb_'+str(r)] = df['brent_'+str(r)].rolling(2+r).corr(df['nok'+str(r)]).bfill()\n",
" \n",
" del df['nok'+str(r)], df['brent_'+str(r)]\n",
" \n",
"\n",
" arr = np.log(np.array(keep))\n",
" arr = arr/arr.sum()\n",
"\n",
" for it,ar in enumerate(arr):\n",
" df['cc_'+str(it)] = df['cc_'+str(it)]*ar\n",
" if it==0:\n",
" df['cc_weighted_'+str(fact)] = 0\n",
" df['cc_weighted_'+str(fact)] = df['cc_weighted_'+str(fact)] + df['cc_'+str(it)] \n",
" del df['cc_'+str(it)] \n",
" df['cc_weighted_'+str(fact)] = df['cc_weighted_'+str(fact)].replace(np.inf,np.nan).replace(-np.inf,np.nan)\n",
" df['cc_weighted_'+str(fact)] = df['cc_weighted_'+str(fact)].bfill()\n",
" \n",
" dar = arr[::-1].copy()\n",
" for it,ar in enumerate(dar):\n",
" df['bb_'+str(it)] = df['bb_'+str(it)]*ar\n",
" if it==0:\n",
" df['bb_weighted_'+str(fact)] = 0\n",
" df['bb_weighted_'+str(fact)] = df['bb_weighted_'+str(fact)] + df['bb_'+str(it)] \n",
" del df['bb_'+str(it)] \n",
" df['bb_weighted_'+str(fact)] = df['bb_weighted_'+str(fact)].replace(np.inf,np.nan).replace(-np.inf,np.nan)\n",
" df['bb_weighted_'+str(fact)] = df['bb_weighted_'+str(fact)].bfill()\n",
" df['causal_size_step'+str(fact)] = df['bb_weighted_'+str(fact)] - df['cc_weighted_'+str(fact)]\n",
" \n",
" \n",
" "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qgiTME0VudGa",
"outputId": "24485e5e-da36-434c-8faf-99a79a89e6f9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 728
}
},
"source": [
"df.sum()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"nok 1.911090e+04\n",
"usd 1.417756e+05\n",
"eur 1.687475e+05\n",
"gbp 2.078402e+05\n",
"brent 9.708692e+06\n",
"gdp yoy 3.372000e+01\n",
"interest rate 1.235250e+03\n",
"cc_weighted_40 4.692687e+02\n",
"cc_weighted_2 4.166537e+02\n",
"bb_weighted_2 3.554804e+02\n",
"causal_size_step2 -6.117328e+01\n",
"cc_weighted_3 4.121278e+02\n",
"bb_weighted_3 3.648854e+02\n",
"causal_size_step3 -4.724238e+01\n",
"cc_weighted_5 4.331537e+02\n",
"bb_weighted_5 3.801708e+02\n",
"causal_size_step5 -5.298283e+01\n",
"cc_weighted_10 4.217326e+02\n",
"bb_weighted_10 3.985647e+02\n",
"causal_size_step10 -2.316794e+01\n",
"cc_weighted_20 4.603348e+02\n",
"bb_weighted_20 4.207841e+02\n",
"causal_size_step20 -3.955065e+01\n",
"bb_weighted_40 4.398241e+02\n",
"causal_size_step40 -2.944460e+01\n",
"cc_weighted_60 3.607571e+02\n",
"bb_weighted_60 4.126314e+02\n",
"causal_size_step60 5.187432e+01\n",
"cc_weighted_80 3.517289e+02\n",
"bb_weighted_80 4.066224e+02\n",
"causal_size_step80 5.489353e+01\n",
"cc_weighted_120 4.033036e+02\n",
"bb_weighted_120 4.147532e+02\n",
"causal_size_step120 1.144967e+01\n",
"cc_weighted_150 4.375767e+02\n",
"bb_weighted_150 4.432083e+02\n",
"causal_size_step150 5.631628e+00\n",
"cc_weighted_200 5.503299e+02\n",
"bb_weighted_200 5.629468e+02\n",
"causal_size_step200 1.261697e+01\n",
"dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_7NQE1tTs0Lj",
"outputId": "2ce61b84-2cca-4432-829d-e5408e3b6979",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"### This to me shows that in the short run the NOKs exchange rate change in expectation of a change\n",
"### of the oil price, oil is a lagging indicator, but oil becomes a leading indicator from 60 time steps\n",
"### onwards. Can be seen in the middle chart a few code blocks below. \n",
"df.corr()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>nok</th>\n",
" <th>usd</th>\n",
" <th>eur</th>\n",
" <th>gbp</th>\n",
" <th>brent</th>\n",
" <th>gdp yoy</th>\n",
" <th>interest rate</th>\n",
" <th>cc_weighted_40</th>\n",
" <th>cc_weighted_2</th>\n",
" <th>bb_weighted_2</th>\n",
" <th>causal_size_step2</th>\n",
" <th>cc_weighted_3</th>\n",
" <th>bb_weighted_3</th>\n",
" <th>causal_size_step3</th>\n",
" <th>cc_weighted_5</th>\n",
" <th>bb_weighted_5</th>\n",
" <th>causal_size_step5</th>\n",
" <th>cc_weighted_10</th>\n",
" <th>bb_weighted_10</th>\n",
" <th>causal_size_step10</th>\n",
" <th>cc_weighted_20</th>\n",
" <th>bb_weighted_20</th>\n",
" <th>causal_size_step20</th>\n",
" <th>bb_weighted_40</th>\n",
" <th>causal_size_step40</th>\n",
" <th>cc_weighted_60</th>\n",
" <th>bb_weighted_60</th>\n",
" <th>causal_size_step60</th>\n",
" <th>cc_weighted_80</th>\n",
" <th>bb_weighted_80</th>\n",
" <th>causal_size_step80</th>\n",
" <th>cc_weighted_120</th>\n",
" <th>bb_weighted_120</th>\n",
" <th>causal_size_step120</th>\n",
" <th>cc_weighted_150</th>\n",
" <th>bb_weighted_150</th>\n",
" <th>causal_size_step150</th>\n",
" <th>cc_weighted_200</th>\n",
" <th>bb_weighted_200</th>\n",
" <th>causal_size_step200</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>nok</th>\n",
" <td>1.000000</td>\n",
" <td>-0.259508</td>\n",
" <td>0.803596</td>\n",
" <td>0.582291</td>\n",
" <td>0.896812</td>\n",
" <td>0.258060</td>\n",
" <td>0.948522</td>\n",
" <td>-0.141402</td>\n",
" <td>-0.056319</td>\n",
" <td>-0.064972</td>\n",
" <td>-0.020026</td>\n",
" <td>-0.021618</td>\n",
" <td>-0.067087</td>\n",
" <td>-0.047840</td>\n",
" <td>0.005980</td>\n",
" <td>-0.065978</td>\n",
" <td>-0.065444</td>\n",
" <td>-0.041579</td>\n",
" <td>-0.060965</td>\n",
" <td>-0.009961</td>\n",
" <td>-0.064844</td>\n",
" <td>-0.053142</td>\n",
" <td>0.023753</td>\n",
" <td>-0.097234</td>\n",
" <td>0.089757</td>\n",
" <td>-0.350755</td>\n",
" <td>-0.246233</td>\n",
" <td>0.261053</td>\n",
" <td>-0.334209</td>\n",
" <td>-0.290207</td>\n",
" <td>0.216959</td>\n",
" <td>-0.271450</td>\n",
" <td>-0.320824</td>\n",
" <td>0.074887</td>\n",
" <td>-0.302172</td>\n",
" <td>-0.313927</td>\n",
" <td>0.142523</td>\n",
" <td>-0.422151</td>\n",
" <td>-0.141308</td>\n",
" <td>0.372049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>usd</th>\n",
" <td>-0.259508</td>\n",
" <td>1.000000</td>\n",
" <td>0.122163</td>\n",
" <td>0.502560</td>\n",
" <td>-0.550984</td>\n",
" <td>0.330808</td>\n",
" <td>-0.284277</td>\n",
" <td>0.237304</td>\n",
" <td>0.026735</td>\n",
" <td>0.021255</td>\n",
" <td>-0.000384</td>\n",
" <td>0.014072</td>\n",
" <td>0.023031</td>\n",
" <td>0.011031</td>\n",
" <td>-0.005367</td>\n",
" <td>0.024029</td>\n",
" <td>0.026596</td>\n",
" <td>0.023814</td>\n",
" <td>0.025707</td>\n",
" <td>-0.002068</td>\n",
" <td>0.193440</td>\n",
" <td>0.053981</td>\n",
" <td>-0.161100</td>\n",
" <td>0.167541</td>\n",
" <td>-0.146911</td>\n",
" <td>0.503233</td>\n",
" <td>0.365989</td>\n",
" <td>-0.363736</td>\n",
" <td>0.619542</td>\n",
" <td>0.468312</td>\n",
" <td>-0.466680</td>\n",
" <td>0.745436</td>\n",
" <td>0.644580</td>\n",
" <td>-0.420112</td>\n",
" <td>0.719586</td>\n",
" <td>0.711037</td>\n",
" <td>-0.369146</td>\n",
" <td>0.705825</td>\n",
" <td>0.602888</td>\n",
" <td>-0.390721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>eur</th>\n",
" <td>0.803596</td>\n",
" <td>0.122163</td>\n",
" <td>1.000000</td>\n",
" <td>0.758971</td>\n",
" <td>0.633535</td>\n",
" <td>0.550526</td>\n",
" <td>0.695060</td>\n",
" <td>-0.138216</td>\n",
" <td>-0.059551</td>\n",
" <td>-0.057299</td>\n",
" <td>-0.009414</td>\n",
" <td>-0.060483</td>\n",
" <td>-0.062147</td>\n",
" <td>-0.011515</td>\n",
" <td>-0.052460</td>\n",
" <td>-0.070466</td>\n",
" <td>-0.018944</td>\n",
" <td>-0.079864</td>\n",
" <td>-0.084028</td>\n",
" <td>0.008779</td>\n",
" <td>-0.045029</td>\n",
" <td>-0.092966</td>\n",
" <td>-0.031896</td>\n",
" <td>-0.109861</td>\n",
" <td>0.075088</td>\n",
" <td>-0.176562</td>\n",
" <td>-0.143334</td>\n",
" <td>0.114942</td>\n",
" <td>-0.140039</td>\n",
" <td>-0.160778</td>\n",
" <td>0.054639</td>\n",
" <td>0.040982</td>\n",
" <td>-0.123307</td>\n",
" <td>-0.167086</td>\n",
" <td>0.044275</td>\n",
" <td>-0.098405</td>\n",
" <td>-0.138423</td>\n",
" <td>-0.068797</td>\n",
" <td>-0.063644</td>\n",
" <td>0.035004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gbp</th>\n",
" <td>0.582291</td>\n",
" <td>0.502560</td>\n",
" <td>0.758971</td>\n",
" <td>1.000000</td>\n",
" <td>0.275338</td>\n",
" <td>0.378526</td>\n",
" <td>0.591654</td>\n",
" <td>0.011280</td>\n",
" <td>-0.005235</td>\n",
" <td>-0.010576</td>\n",
" <td>-0.006541</td>\n",
" <td>0.009679</td>\n",
" <td>-0.009902</td>\n",
" <td>-0.017496</td>\n",
" <td>-0.015093</td>\n",
" <td>-0.008871</td>\n",
" <td>0.004966</td>\n",
" <td>-0.042187</td>\n",
" <td>-0.014716</td>\n",
" <td>0.029678</td>\n",
" <td>0.068271</td>\n",
" <td>-0.011837</td>\n",
" <td>-0.083518</td>\n",
" <td>0.020098</td>\n",
" <td>0.003373</td>\n",
" <td>0.092282</td>\n",
" <td>0.066808</td>\n",
" <td>-0.066962</td>\n",
" <td>0.178726</td>\n",
" <td>0.099995</td>\n",
" <td>-0.167128</td>\n",
" <td>0.371613</td>\n",
" <td>0.211026</td>\n",
" <td>-0.309489</td>\n",
" <td>0.413040</td>\n",
" <td>0.288933</td>\n",
" <td>-0.308915</td>\n",
" <td>0.352685</td>\n",
" <td>0.356759</td>\n",
" <td>-0.160210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>brent</th>\n",
" <td>0.896812</td>\n",
" <td>-0.550984</td>\n",
" <td>0.633535</td>\n",
" <td>0.275338</td>\n",
" <td>1.000000</td>\n",
" <td>0.157756</td>\n",
" <td>0.837671</td>\n",
" <td>-0.247128</td>\n",
" <td>-0.091345</td>\n",
" <td>-0.087462</td>\n",
" <td>-0.013998</td>\n",
" <td>-0.075629</td>\n",
" <td>-0.094047</td>\n",
" <td>-0.030317</td>\n",
" <td>-0.041948</td>\n",
" <td>-0.102366</td>\n",
" <td>-0.057185</td>\n",
" <td>-0.110637</td>\n",
" <td>-0.114513</td>\n",
" <td>0.013759</td>\n",
" <td>-0.196904</td>\n",
" <td>-0.144189</td>\n",
" <td>0.086957</td>\n",
" <td>-0.228803</td>\n",
" <td>0.106623</td>\n",
" <td>-0.452798</td>\n",
" <td>-0.386232</td>\n",
" <td>0.278934</td>\n",
" <td>-0.474177</td>\n",
" <td>-0.442606</td>\n",
" <td>0.279251</td>\n",
" <td>-0.472386</td>\n",
" <td>-0.509751</td>\n",
" <td>0.174362</td>\n",
" <td>-0.516936</td>\n",
" <td>-0.537918</td>\n",
" <td>0.243109</td>\n",
" <td>-0.641033</td>\n",
" <td>-0.404657</td>\n",
" <td>0.445014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gdp yoy</th>\n",
" <td>0.258060</td>\n",
" <td>0.330808</td>\n",
" <td>0.550526</td>\n",
" <td>0.378526</td>\n",
" <td>0.157756</td>\n",
" <td>1.000000</td>\n",
" <td>0.100783</td>\n",
" <td>-0.152517</td>\n",
" <td>0.114226</td>\n",
" <td>0.107809</td>\n",
" <td>-0.001082</td>\n",
" <td>-0.095895</td>\n",
" <td>0.100718</td>\n",
" <td>0.213975</td>\n",
" <td>-0.141884</td>\n",
" <td>0.072137</td>\n",
" <td>0.209196</td>\n",
" <td>-0.293206</td>\n",
" <td>-0.030928</td>\n",
" <td>0.208870</td>\n",
" <td>0.048541</td>\n",
" <td>-0.162178</td>\n",
" <td>-0.139372</td>\n",
" <td>-0.174560</td>\n",
" <td>0.085000</td>\n",
" <td>0.014738</td>\n",
" <td>-0.039319</td>\n",
" <td>-0.061609</td>\n",
" <td>-0.035928</td>\n",
" <td>-0.046444</td>\n",
" <td>0.004799</td>\n",
" <td>0.124796</td>\n",
" <td>0.009955</td>\n",
" <td>-0.143363</td>\n",
" <td>0.117644</td>\n",
" <td>0.029261</td>\n",
" <td>-0.120829</td>\n",
" <td>0.060222</td>\n",
" <td>0.000712</td>\n",
" <td>-0.063900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>interest rate</th>\n",
" <td>0.948522</td>\n",
" <td>-0.284277</td>\n",
" <td>0.695060</td>\n",
" <td>0.591654</td>\n",
" <td>0.837671</td>\n",
" <td>0.100783</td>\n",
" <td>1.000000</td>\n",
" <td>-0.140215</td>\n",
" <td>-0.023259</td>\n",
" <td>-0.035662</td>\n",
" <td>-0.017370</td>\n",
" <td>0.019710</td>\n",
" <td>-0.034545</td>\n",
" <td>-0.049587</td>\n",
" <td>0.032019</td>\n",
" <td>-0.027943</td>\n",
" <td>-0.053177</td>\n",
" <td>-0.007743</td>\n",
" <td>-0.012240</td>\n",
" <td>-0.002616</td>\n",
" <td>-0.036317</td>\n",
" <td>-0.004077</td>\n",
" <td>0.035419</td>\n",
" <td>-0.052167</td>\n",
" <td>0.126652</td>\n",
" <td>-0.317493</td>\n",
" <td>-0.195803</td>\n",
" <td>0.259093</td>\n",
" <td>-0.311229</td>\n",
" <td>-0.244178</td>\n",
" <td>0.225678</td>\n",
" <td>-0.276848</td>\n",
" <td>-0.285256</td>\n",
" <td>0.113570</td>\n",
" <td>-0.278183</td>\n",
" <td>-0.270781</td>\n",
" <td>0.145253</td>\n",
" <td>-0.379649</td>\n",
" <td>-0.066175</td>\n",
" <td>0.372963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_40</th>\n",
" <td>-0.141402</td>\n",
" <td>0.237304</td>\n",
" <td>-0.138216</td>\n",
" <td>0.011280</td>\n",
" <td>-0.247128</td>\n",
" <td>-0.152517</td>\n",
" <td>-0.140215</td>\n",
" <td>1.000000</td>\n",
" <td>0.019477</td>\n",
" <td>0.004681</td>\n",
" <td>-0.011425</td>\n",
" <td>0.031125</td>\n",
" <td>0.007694</td>\n",
" <td>-0.017739</td>\n",
" <td>0.072217</td>\n",
" <td>0.016940</td>\n",
" <td>-0.047054</td>\n",
" <td>0.180516</td>\n",
" <td>0.053522</td>\n",
" <td>-0.134962</td>\n",
" <td>0.495968</td>\n",
" <td>0.195253</td>\n",
" <td>-0.363982</td>\n",
" <td>0.585515</td>\n",
" <td>-0.721943</td>\n",
" <td>0.664622</td>\n",
" <td>0.759728</td>\n",
" <td>-0.245657</td>\n",
" <td>0.435070</td>\n",
" <td>0.774564</td>\n",
" <td>0.084900</td>\n",
" <td>0.067352</td>\n",
" <td>0.652547</td>\n",
" <td>0.501109</td>\n",
" <td>-0.010402</td>\n",
" <td>0.576002</td>\n",
" <td>0.482557</td>\n",
" <td>0.004544</td>\n",
" <td>0.546441</td>\n",
" <td>0.339829</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_2</th>\n",
" <td>-0.056319</td>\n",
" <td>0.026735</td>\n",
" <td>-0.059551</td>\n",
" <td>-0.005235</td>\n",
" <td>-0.091345</td>\n",
" <td>0.114226</td>\n",
" <td>-0.023259</td>\n",
" <td>0.019477</td>\n",
" <td>1.000000</td>\n",
" <td>0.441720</td>\n",
" <td>-0.378815</td>\n",
" <td>0.681645</td>\n",
" <td>0.537411</td>\n",
" <td>-0.029027</td>\n",
" <td>0.346351</td>\n",
" <td>0.670538</td>\n",
" <td>0.312608</td>\n",
" <td>0.116856</td>\n",
" <td>0.718559</td>\n",
" <td>0.489816</td>\n",
" <td>0.062246</td>\n",
" <td>0.545786</td>\n",
" <td>0.404263</td>\n",
" <td>0.329986</td>\n",
" <td>0.257869</td>\n",
" <td>0.016281</td>\n",
" <td>0.217205</td>\n",
" <td>0.162657</td>\n",
" <td>0.019825</td>\n",
" <td>0.159568</td>\n",
" <td>0.118920</td>\n",
" <td>0.000581</td>\n",
" <td>0.094481</td>\n",
" <td>0.084916</td>\n",
" <td>0.001302</td>\n",
" <td>0.083766</td>\n",
" <td>0.066469</td>\n",
" <td>-0.001095</td>\n",
" <td>0.100864</td>\n",
" <td>0.064839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_2</th>\n",
" <td>-0.064972</td>\n",
" <td>0.021255</td>\n",
" <td>-0.057299</td>\n",
" <td>-0.010576</td>\n",
" <td>-0.087462</td>\n",
" <td>0.107809</td>\n",
" <td>-0.035662</td>\n",
" <td>0.004681</td>\n",
" <td>0.441720</td>\n",
" <td>1.000000</td>\n",
" <td>0.662961</td>\n",
" <td>0.290797</td>\n",
" <td>0.993228</td>\n",
" <td>0.731994</td>\n",
" <td>0.167121</td>\n",
" <td>0.945600</td>\n",
" <td>0.719044</td>\n",
" <td>0.092802</td>\n",
" <td>0.761850</td>\n",
" <td>0.550353</td>\n",
" <td>0.061562</td>\n",
" <td>0.500361</td>\n",
" <td>0.365789</td>\n",
" <td>0.286184</td>\n",
" <td>0.238558</td>\n",
" <td>0.019176</td>\n",
" <td>0.192245</td>\n",
" <td>0.137577</td>\n",
" <td>0.017496</td>\n",
" <td>0.141574</td>\n",
" <td>0.105645</td>\n",
" <td>0.008441</td>\n",
" <td>0.088304</td>\n",
" <td>0.068719</td>\n",
" <td>0.016434</td>\n",
" <td>0.078391</td>\n",
" <td>0.042160</td>\n",
" <td>0.020298</td>\n",
" <td>0.088914</td>\n",
" <td>0.033928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step2</th>\n",
" <td>-0.020026</td>\n",
" <td>-0.000384</td>\n",
" <td>-0.009414</td>\n",
" <td>-0.006541</td>\n",
" <td>-0.013998</td>\n",
" <td>-0.001082</td>\n",
" <td>-0.017370</td>\n",
" <td>-0.011425</td>\n",
" <td>-0.378815</td>\n",
" <td>0.662961</td>\n",
" <td>1.000000</td>\n",
" <td>-0.268841</td>\n",
" <td>0.576123</td>\n",
" <td>0.779323</td>\n",
" <td>-0.116626</td>\n",
" <td>0.415900</td>\n",
" <td>0.480877</td>\n",
" <td>-0.001782</td>\n",
" <td>0.186277</td>\n",
" <td>0.158985</td>\n",
" <td>0.011563</td>\n",
" <td>0.060710</td>\n",
" <td>0.039987</td>\n",
" <td>0.019851</td>\n",
" <td>0.030902</td>\n",
" <td>0.006195</td>\n",
" <td>0.017061</td>\n",
" <td>0.006186</td>\n",
" <td>0.001505</td>\n",
" <td>0.012887</td>\n",
" <td>0.009744</td>\n",
" <td>0.008223</td>\n",
" <td>0.012249</td>\n",
" <td>0.000027</td>\n",
" <td>0.015866</td>\n",
" <td>0.010965</td>\n",
" <td>-0.011976</td>\n",
" <td>0.021852</td>\n",
" <td>0.007552</td>\n",
" <td>-0.019108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_3</th>\n",
" <td>-0.021618</td>\n",
" <td>0.014072</td>\n",
" <td>-0.060483</td>\n",
" <td>0.009679</td>\n",
" <td>-0.075629</td>\n",
" <td>-0.095895</td>\n",
" <td>0.019710</td>\n",
" <td>0.031125</td>\n",
" <td>0.681645</td>\n",
" <td>0.290797</td>\n",
" <td>-0.268841</td>\n",
" <td>1.000000</td>\n",
" <td>0.384019</td>\n",
" <td>-0.436602</td>\n",
" <td>0.640177</td>\n",
" <td>0.560061</td>\n",
" <td>-0.042713</td>\n",
" <td>0.235220</td>\n",
" <td>0.748908</td>\n",
" <td>0.397316</td>\n",
" <td>0.109206</td>\n",
" <td>0.670883</td>\n",
" <td>0.461821</td>\n",
" <td>0.441560</td>\n",
" <td>0.338874</td>\n",
" <td>0.000491</td>\n",
" <td>0.282819</td>\n",
" <td>0.239554</td>\n",
" <td>0.016462</td>\n",
" <td>0.205261</td>\n",
" <td>0.166109</td>\n",
" <td>-0.018795</td>\n",
" <td>0.116565</td>\n",
" <td>0.131064</td>\n",
" <td>-0.022937</td>\n",
" <td>0.099332</td>\n",
" <td>0.111069</td>\n",
" <td>-0.022974</td>\n",
" <td>0.127136</td>\n",
" <td>0.105320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_3</th>\n",
" <td>-0.067087</td>\n",
" <td>0.023031</td>\n",
" <td>-0.062147</td>\n",
" <td>-0.009902</td>\n",
" <td>-0.094047</td>\n",
" <td>0.100718</td>\n",
" <td>-0.034545</td>\n",
" <td>0.007694</td>\n",
" <td>0.537411</td>\n",
" <td>0.993228</td>\n",
" <td>0.576123</td>\n",
" <td>0.384019</td>\n",
" <td>1.000000</td>\n",
" <td>0.663011</td>\n",
" <td>0.220073</td>\n",
" <td>0.975888</td>\n",
" <td>0.700861</td>\n",
" <td>0.109456</td>\n",
" <td>0.817069</td>\n",
" <td>0.580337</td>\n",
" <td>0.068879</td>\n",
" <td>0.552241</td>\n",
" <td>0.402713</td>\n",
" <td>0.320373</td>\n",
" <td>0.264060</td>\n",
" <td>0.019470</td>\n",
" <td>0.214115</td>\n",
" <td>0.155759</td>\n",
" <td>0.019018</td>\n",
" <td>0.157492</td>\n",
" <td>0.118172</td>\n",
" <td>0.007022</td>\n",
" <td>0.097205</td>\n",
" <td>0.078705</td>\n",
" <td>0.014384</td>\n",
" <td>0.086072</td>\n",
" <td>0.051112</td>\n",
" <td>0.017804</td>\n",
" <td>0.098878</td>\n",
" <td>0.042939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step3</th>\n",
" <td>-0.047840</td>\n",
" <td>0.011031</td>\n",
" <td>-0.011515</td>\n",
" <td>-0.017496</td>\n",
" <td>-0.030317</td>\n",
" <td>0.213975</td>\n",
" <td>-0.049587</td>\n",
" <td>-0.017739</td>\n",
" <td>-0.029027</td>\n",
" <td>0.731994</td>\n",
" <td>0.779323</td>\n",
" <td>-0.436602</td>\n",
" <td>0.663011</td>\n",
" <td>1.000000</td>\n",
" <td>-0.304609</td>\n",
" <td>0.496786</td>\n",
" <td>0.717524</td>\n",
" <td>-0.084061</td>\n",
" <td>0.188926</td>\n",
" <td>0.243324</td>\n",
" <td>-0.021428</td>\n",
" <td>-0.005853</td>\n",
" <td>0.017955</td>\n",
" <td>-0.045846</td>\n",
" <td>-0.017461</td>\n",
" <td>0.018573</td>\n",
" <td>-0.020677</td>\n",
" <td>-0.042458</td>\n",
" <td>0.005183</td>\n",
" <td>-0.012966</td>\n",
" <td>-0.019534</td>\n",
" <td>0.022080</td>\n",
" <td>0.000205</td>\n",
" <td>-0.029576</td>\n",
" <td>0.032612</td>\n",
" <td>0.003329</td>\n",
" <td>-0.040251</td>\n",
" <td>0.035974</td>\n",
" <td>-0.006736</td>\n",
" <td>-0.043553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_5</th>\n",
" <td>0.005980</td>\n",
" <td>-0.005367</td>\n",
" <td>-0.052460</td>\n",
" <td>-0.015093</td>\n",
" <td>-0.041948</td>\n",
" <td>-0.141884</td>\n",
" <td>0.032019</td>\n",
" <td>0.072217</td>\n",
" <td>0.346351</td>\n",
" <td>0.167121</td>\n",
" <td>-0.116626</td>\n",
" <td>0.640177</td>\n",
" <td>0.220073</td>\n",
" <td>-0.304609</td>\n",
" <td>1.000000</td>\n",
" <td>0.369233</td>\n",
" <td>-0.528572</td>\n",
" <td>0.520399</td>\n",
" <td>0.678629</td>\n",
" <td>0.053434</td>\n",
" <td>0.181165</td>\n",
" <td>0.778577</td>\n",
" <td>0.477515</td>\n",
" <td>0.582389</td>\n",
" <td>0.408879</td>\n",
" <td>-0.013380</td>\n",
" <td>0.384120</td>\n",
" <td>0.344189</td>\n",
" <td>0.005003</td>\n",
" <td>0.277689</td>\n",
" <td>0.249813</td>\n",
" <td>-0.042570</td>\n",
" <td>0.149458</td>\n",
" <td>0.192946</td>\n",
" <td>-0.050180</td>\n",
" <td>0.117863</td>\n",
" <td>0.162041</td>\n",
" <td>-0.049278</td>\n",
" <td>0.132915</td>\n",
" <td>0.137705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_5</th>\n",
" <td>-0.065978</td>\n",
" <td>0.024029</td>\n",
" <td>-0.070466</td>\n",
" <td>-0.008871</td>\n",
" <td>-0.102366</td>\n",
" <td>0.072137</td>\n",
" <td>-0.027943</td>\n",
" <td>0.016940</td>\n",
" <td>0.670538</td>\n",
" <td>0.945600</td>\n",
" <td>0.415900</td>\n",
" <td>0.560061</td>\n",
" <td>0.975888</td>\n",
" <td>0.496786</td>\n",
" <td>0.369233</td>\n",
" <td>1.000000</td>\n",
" <td>0.593738</td>\n",
" <td>0.166683</td>\n",
" <td>0.911254</td>\n",
" <td>0.602718</td>\n",
" <td>0.090068</td>\n",
" <td>0.662268</td>\n",
" <td>0.474932</td>\n",
" <td>0.399994</td>\n",
" <td>0.320725</td>\n",
" <td>0.017768</td>\n",
" <td>0.265142</td>\n",
" <td>0.201380</td>\n",
" <td>0.020603</td>\n",
" <td>0.194306</td>\n",
" <td>0.149951</td>\n",
" <td>0.001137</td>\n",
" <td>0.116842</td>\n",
" <td>0.104449</td>\n",
" <td>0.006648</td>\n",
" <td>0.102113</td>\n",
" <td>0.074360</td>\n",
" <td>0.009466</td>\n",
" <td>0.119070</td>\n",
" <td>0.064790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step5</th>\n",
" <td>-0.065444</td>\n",
" <td>0.026596</td>\n",
" <td>-0.018944</td>\n",
" <td>0.004966</td>\n",
" <td>-0.057185</td>\n",
" <td>0.209196</td>\n",
" <td>-0.053177</td>\n",
" <td>-0.047054</td>\n",
" <td>0.312608</td>\n",
" <td>0.719044</td>\n",
" <td>0.480877</td>\n",
" <td>-0.042713</td>\n",
" <td>0.700861</td>\n",
" <td>0.717524</td>\n",
" <td>-0.528572</td>\n",
" <td>0.593738</td>\n",
" <td>1.000000</td>\n",
" <td>-0.298328</td>\n",
" <td>0.244786</td>\n",
" <td>0.504278</td>\n",
" <td>-0.074589</td>\n",
" <td>-0.069186</td>\n",
" <td>0.020367</td>\n",
" <td>-0.138888</td>\n",
" <td>-0.061063</td>\n",
" <td>0.027815</td>\n",
" <td>-0.090398</td>\n",
" <td>-0.114066</td>\n",
" <td>0.014487</td>\n",
" <td>-0.062948</td>\n",
" <td>-0.079328</td>\n",
" <td>0.037898</td>\n",
" <td>-0.022679</td>\n",
" <td>-0.071654</td>\n",
" <td>0.049521</td>\n",
" <td>-0.008777</td>\n",
" <td>-0.072378</td>\n",
" <td>0.051313</td>\n",
" <td>-0.006321</td>\n",
" <td>-0.060049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_10</th>\n",
" <td>-0.041579</td>\n",
" <td>0.023814</td>\n",
" <td>-0.079864</td>\n",
" <td>-0.042187</td>\n",
" <td>-0.110637</td>\n",
" <td>-0.293206</td>\n",
" <td>-0.007743</td>\n",
" <td>0.180516</td>\n",
" <td>0.116856</td>\n",
" <td>0.092802</td>\n",
" <td>-0.001782</td>\n",
" <td>0.235220</td>\n",
" <td>0.109456</td>\n",
" <td>-0.084061</td>\n",
" <td>0.520399</td>\n",
" <td>0.166683</td>\n",
" <td>-0.298328</td>\n",
" <td>1.000000</td>\n",
" <td>0.420344</td>\n",
" <td>-0.643122</td>\n",
" <td>0.447415</td>\n",
" <td>0.752490</td>\n",
" <td>0.169130</td>\n",
" <td>0.738283</td>\n",
" <td>0.409633</td>\n",
" <td>0.048179</td>\n",
" <td>0.554164</td>\n",
" <td>0.406094</td>\n",
" <td>0.073476</td>\n",
" <td>0.439312</td>\n",
" <td>0.299948</td>\n",
" <td>-0.019067</td>\n",
" <td>0.278811</td>\n",
" <td>0.278595</td>\n",
" <td>-0.033317</td>\n",
" <td>0.230196</td>\n",
" <td>0.231264</td>\n",
" <td>-0.020847</td>\n",
" <td>0.244359</td>\n",
" <td>0.176962</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_10</th>\n",
" <td>-0.060965</td>\n",
" <td>0.025707</td>\n",
" <td>-0.084028</td>\n",
" <td>-0.014716</td>\n",
" <td>-0.114513</td>\n",
" <td>-0.030928</td>\n",
" <td>-0.012240</td>\n",
" <td>0.053522</td>\n",
" <td>0.718559</td>\n",
" <td>0.761850</td>\n",
" <td>0.186277</td>\n",
" <td>0.748908</td>\n",
" <td>0.817069</td>\n",
" <td>0.188926</td>\n",
" <td>0.678629</td>\n",
" <td>0.911254</td>\n",
" <td>0.244786</td>\n",
" <td>0.420344</td>\n",
" <td>1.000000</td>\n",
" <td>0.424494</td>\n",
" <td>0.174448</td>\n",
" <td>0.873496</td>\n",
" <td>0.566657</td>\n",
" <td>0.594824</td>\n",
" <td>0.442332</td>\n",
" <td>0.020665</td>\n",
" <td>0.404154</td>\n",
" <td>0.315566</td>\n",
" <td>0.033773</td>\n",
" <td>0.300505</td>\n",
" <td>0.229132</td>\n",
" <td>-0.008549</td>\n",
" <td>0.179856</td>\n",
" <td>0.174662</td>\n",
" <td>-0.009261</td>\n",
" <td>0.152257</td>\n",
" <td>0.136134</td>\n",
" <td>-0.006681</td>\n",
" <td>0.170120</td>\n",
" <td>0.114642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step10</th>\n",
" <td>-0.009961</td>\n",
" <td>-0.002068</td>\n",
" <td>0.008779</td>\n",
" <td>0.029678</td>\n",
" <td>0.013759</td>\n",
" <td>0.208870</td>\n",
" <td>-0.002616</td>\n",
" <td>-0.134962</td>\n",
" <td>0.489816</td>\n",
" <td>0.550353</td>\n",
" <td>0.158985</td>\n",
" <td>0.397316</td>\n",
" <td>0.580337</td>\n",
" <td>0.243324</td>\n",
" <td>0.053434</td>\n",
" <td>0.602718</td>\n",
" <td>0.504278</td>\n",
" <td>-0.643122</td>\n",
" <td>0.424494</td>\n",
" <td>1.000000</td>\n",
" <td>-0.299237</td>\n",
" <td>-0.013706</td>\n",
" <td>0.309457</td>\n",
" <td>-0.234712</td>\n",
" <td>-0.035457</td>\n",
" <td>-0.030637</td>\n",
" <td>-0.211900</td>\n",
" <td>-0.138908</td>\n",
" <td>-0.044817</td>\n",
" <td>-0.184767</td>\n",
" <td>-0.105935</td>\n",
" <td>0.011811</td>\n",
" <td>-0.126428</td>\n",
" <td>-0.130597</td>\n",
" <td>0.025430</td>\n",
" <td>-0.101210</td>\n",
" <td>-0.115882</td>\n",
" <td>0.015164</td>\n",
" <td>-0.100267</td>\n",
" <td>-0.079834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_20</th>\n",
" <td>-0.064844</td>\n",
" <td>0.193440</td>\n",
" <td>-0.045029</td>\n",
" <td>0.068271</td>\n",
" <td>-0.196904</td>\n",
" <td>0.048541</td>\n",
" <td>-0.036317</td>\n",
" <td>0.495968</td>\n",
" <td>0.062246</td>\n",
" <td>0.061562</td>\n",
" <td>0.011563</td>\n",
" <td>0.109206</td>\n",
" <td>0.068879</td>\n",
" <td>-0.021428</td>\n",
" <td>0.181165</td>\n",
" <td>0.090068</td>\n",
" <td>-0.074589</td>\n",
" <td>0.447415</td>\n",
" <td>0.174448</td>\n",
" <td>-0.299237</td>\n",
" <td>1.000000</td>\n",
" <td>0.484398</td>\n",
" <td>-0.655579</td>\n",
" <td>0.792579</td>\n",
" <td>0.070585</td>\n",
" <td>0.196739</td>\n",
" <td>0.722794</td>\n",
" <td>0.350174</td>\n",
" <td>0.141216</td>\n",
" <td>0.626304</td>\n",
" <td>0.374634</td>\n",
" <td>0.000366</td>\n",
" <td>0.445147</td>\n",
" <td>0.403276</td>\n",
" <td>0.020733</td>\n",
" <td>0.395378</td>\n",
" <td>0.294518</td>\n",
" <td>0.081155</td>\n",
" <td>0.421935</td>\n",
" <td>0.177568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_20</th>\n",
" <td>-0.053142</td>\n",
" <td>0.053981</td>\n",
" <td>-0.092966</td>\n",
" <td>-0.011837</td>\n",
" <td>-0.144189</td>\n",
" <td>-0.162178</td>\n",
" <td>-0.004077</td>\n",
" <td>0.195253</td>\n",
" <td>0.545786</td>\n",
" <td>0.500361</td>\n",
" <td>0.060710</td>\n",
" <td>0.670883</td>\n",
" <td>0.552241</td>\n",
" <td>-0.005853</td>\n",
" <td>0.778577</td>\n",
" <td>0.662268</td>\n",
" <td>-0.069186</td>\n",
" <td>0.752490</td>\n",
" <td>0.873496</td>\n",
" <td>-0.013706</td>\n",
" <td>0.484398</td>\n",
" <td>1.000000</td>\n",
" <td>0.343060</td>\n",
" <td>0.853246</td>\n",
" <td>0.489757</td>\n",
" <td>0.052487</td>\n",
" <td>0.621518</td>\n",
" <td>0.457526</td>\n",
" <td>0.064464</td>\n",
" <td>0.484415</td>\n",
" <td>0.354799</td>\n",
" <td>-0.026651</td>\n",
" <td>0.301223</td>\n",
" <td>0.309148</td>\n",
" <td>-0.029847</td>\n",
" <td>0.254321</td>\n",
" <td>0.246330</td>\n",
" <td>-0.011477</td>\n",
" <td>0.285359</td>\n",
" <td>0.192596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step20</th>\n",
" <td>0.023753</td>\n",
" <td>-0.161100</td>\n",
" <td>-0.031896</td>\n",
" <td>-0.083518</td>\n",
" <td>0.086957</td>\n",
" <td>-0.139372</td>\n",
" <td>0.035419</td>\n",
" <td>-0.363982</td>\n",
" <td>0.404263</td>\n",
" <td>0.365789</td>\n",
" <td>0.039987</td>\n",
" <td>0.461821</td>\n",
" <td>0.402713</td>\n",
" <td>0.017955</td>\n",
" <td>0.477515</td>\n",
" <td>0.474932</td>\n",
" <td>0.020367</td>\n",
" <td>0.169130</td>\n",
" <td>0.566657</td>\n",
" <td>0.309457</td>\n",
" <td>-0.655579</td>\n",
" <td>0.343060</td>\n",
" <td>1.000000</td>\n",
" <td>-0.114502</td>\n",
" <td>0.346948</td>\n",
" <td>-0.165931</td>\n",
" <td>-0.239591</td>\n",
" <td>0.018936</td>\n",
" <td>-0.095979</td>\n",
" <td>-0.254331</td>\n",
" <td>-0.095994</td>\n",
" <td>-0.023397</td>\n",
" <td>-0.217947</td>\n",
" <td>-0.166151</td>\n",
" <td>-0.048023</td>\n",
" <td>-0.204995</td>\n",
" <td>-0.103600</td>\n",
" <td>-0.097042</td>\n",
" <td>-0.206717</td>\n",
" <td>-0.024414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_40</th>\n",
" <td>-0.097234</td>\n",
" <td>0.167541</td>\n",
" <td>-0.109861</td>\n",
" <td>0.020098</td>\n",
" <td>-0.228803</td>\n",
" <td>-0.174560</td>\n",
" <td>-0.052167</td>\n",
" <td>0.585515</td>\n",
" <td>0.329986</td>\n",
" <td>0.286184</td>\n",
" <td>0.019851</td>\n",
" <td>0.441560</td>\n",
" <td>0.320373</td>\n",
" <td>-0.045846</td>\n",
" <td>0.582389</td>\n",
" <td>0.399994</td>\n",
" <td>-0.138888</td>\n",
" <td>0.738283</td>\n",
" <td>0.594824</td>\n",
" <td>-0.234712</td>\n",
" <td>0.792579</td>\n",
" <td>0.853246</td>\n",
" <td>-0.114502</td>\n",
" <td>1.000000</td>\n",
" <td>0.138231</td>\n",
" <td>0.291836</td>\n",
" <td>0.884895</td>\n",
" <td>0.360375</td>\n",
" <td>0.217046</td>\n",
" <td>0.764009</td>\n",
" <td>0.391932</td>\n",
" <td>0.004981</td>\n",
" <td>0.545377</td>\n",
" <td>0.487971</td>\n",
" <td>-0.011948</td>\n",
" <td>0.473341</td>\n",
" <td>0.401030</td>\n",
" <td>0.026161</td>\n",
" <td>0.488449</td>\n",
" <td>0.279621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step40</th>\n",
" <td>0.089757</td>\n",
" <td>-0.146911</td>\n",
" <td>0.075088</td>\n",
" <td>0.003373</td>\n",
" <td>0.106623</td>\n",
" <td>0.085000</td>\n",
" <td>0.126652</td>\n",
" <td>-0.721943</td>\n",
" <td>0.257869</td>\n",
" <td>0.238558</td>\n",
" <td>0.030902</td>\n",
" <td>0.338874</td>\n",
" <td>0.264060</td>\n",
" <td>-0.017461</td>\n",
" <td>0.408879</td>\n",
" <td>0.320725</td>\n",
" <td>-0.061063</td>\n",
" <td>0.409633</td>\n",
" <td>0.442332</td>\n",
" <td>-0.035457</td>\n",
" <td>0.070585</td>\n",
" <td>0.489757</td>\n",
" <td>0.346948</td>\n",
" <td>0.138231</td>\n",
" <td>1.000000</td>\n",
" <td>-0.562879</td>\n",
" <td>-0.172858</td>\n",
" <td>0.607728</td>\n",
" <td>-0.346270</td>\n",
" <td>-0.294168</td>\n",
" <td>0.230816</td>\n",
" <td>-0.078034</td>\n",
" <td>-0.331714</td>\n",
" <td>-0.195699</td>\n",
" <td>0.002510</td>\n",
" <td>-0.299684</td>\n",
" <td>-0.247244</td>\n",
" <td>0.016778</td>\n",
" <td>-0.250673</td>\n",
" <td>-0.176501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_60</th>\n",
" <td>-0.350755</td>\n",
" <td>0.503233</td>\n",
" <td>-0.176562</td>\n",
" <td>0.092282</td>\n",
" <td>-0.452798</td>\n",
" <td>0.014738</td>\n",
" <td>-0.317493</td>\n",
" <td>0.664622</td>\n",
" <td>0.016281</td>\n",
" <td>0.019176</td>\n",
" <td>0.006195</td>\n",
" <td>0.000491</td>\n",
" <td>0.019470</td>\n",
" <td>0.018573</td>\n",
" <td>-0.013380</td>\n",
" <td>0.017768</td>\n",
" <td>0.027815</td>\n",
" <td>0.048179</td>\n",
" <td>0.020665</td>\n",
" <td>-0.030637</td>\n",
" <td>0.196739</td>\n",
" <td>0.052487</td>\n",
" <td>-0.165931</td>\n",
" <td>0.291836</td>\n",
" <td>-0.562879</td>\n",
" <td>1.000000</td>\n",
" <td>0.666789</td>\n",
" <td>-0.774173</td>\n",
" <td>0.884327</td>\n",
" <td>0.815443</td>\n",
" <td>-0.530059</td>\n",
" <td>0.505247</td>\n",
" <td>0.870720</td>\n",
" <td>0.108761</td>\n",
" <td>0.348621</td>\n",
" <td>0.827250</td>\n",
" <td>0.214124</td>\n",
" <td>0.282330</td>\n",
" <td>0.648181</td>\n",
" <td>0.100537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_60</th>\n",
" <td>-0.246233</td>\n",
" <td>0.365989</td>\n",
" <td>-0.143334</td>\n",
" <td>0.066808</td>\n",
" <td>-0.386232</td>\n",
" <td>-0.039319</td>\n",
" <td>-0.195803</td>\n",
" <td>0.759728</td>\n",
" <td>0.217205</td>\n",
" <td>0.192245</td>\n",
" <td>0.017061</td>\n",
" <td>0.282819</td>\n",
" <td>0.214115</td>\n",
" <td>-0.020677</td>\n",
" <td>0.384120</td>\n",
" <td>0.265142</td>\n",
" <td>-0.090398</td>\n",
" <td>0.554164</td>\n",
" <td>0.404154</td>\n",
" <td>-0.211900</td>\n",
" <td>0.722794</td>\n",
" <td>0.621518</td>\n",
" <td>-0.239591</td>\n",
" <td>0.884895</td>\n",
" <td>-0.172858</td>\n",
" <td>0.666789</td>\n",
" <td>1.000000</td>\n",
" <td>-0.044489</td>\n",
" <td>0.542292</td>\n",
" <td>0.965239</td>\n",
" <td>0.105624</td>\n",
" <td>0.229367</td>\n",
" <td>0.817847</td>\n",
" <td>0.432662</td>\n",
" <td>0.164580</td>\n",
" <td>0.739078</td>\n",
" <td>0.384792</td>\n",
" <td>0.172784</td>\n",
" <td>0.648589</td>\n",
" <td>0.220476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step60</th>\n",
" <td>0.261053</td>\n",
" <td>-0.363736</td>\n",
" <td>0.114942</td>\n",
" <td>-0.066962</td>\n",
" <td>0.278934</td>\n",
" <td>-0.061609</td>\n",
" <td>0.259093</td>\n",
" <td>-0.245657</td>\n",
" <td>0.162657</td>\n",
" <td>0.137577</td>\n",
" <td>0.006186</td>\n",
" <td>0.239554</td>\n",
" <td>0.155759</td>\n",
" <td>-0.042458</td>\n",
" <td>0.344189</td>\n",
" <td>0.201380</td>\n",
" <td>-0.114066</td>\n",
" <td>0.406094</td>\n",
" <td>0.315566</td>\n",
" <td>-0.138908</td>\n",
" <td>0.350174</td>\n",
" <td>0.457526</td>\n",
" <td>0.018936</td>\n",
" <td>0.360375</td>\n",
" <td>0.607728</td>\n",
" <td>-0.774173</td>\n",
" <td>-0.044489</td>\n",
" <td>1.000000</td>\n",
" <td>-0.724853</td>\n",
" <td>-0.273284</td>\n",
" <td>0.800261</td>\n",
" <td>-0.482476</td>\n",
" <td>-0.472572</td>\n",
" <td>0.221686</td>\n",
" <td>-0.327544</td>\n",
" <td>-0.481202</td>\n",
" <td>0.039787</td>\n",
" <td>-0.231712</td>\n",
" <td>-0.318015</td>\n",
" <td>0.052490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_80</th>\n",
" <td>-0.334209</td>\n",
" <td>0.619542</td>\n",
" <td>-0.140039</td>\n",
" <td>0.178726</td>\n",
" <td>-0.474177</td>\n",
" <td>-0.035928</td>\n",
" <td>-0.311229</td>\n",
" <td>0.435070</td>\n",
" <td>0.019825</td>\n",
" <td>0.017496</td>\n",
" <td>0.001505</td>\n",
" <td>0.016462</td>\n",
" <td>0.019018</td>\n",
" <td>0.005183</td>\n",
" <td>0.005003</td>\n",
" <td>0.020603</td>\n",
" <td>0.014487</td>\n",
" <td>0.073476</td>\n",
" <td>0.033773</td>\n",
" <td>-0.044817</td>\n",
" <td>0.141216</td>\n",
" <td>0.064464</td>\n",
" <td>-0.095979</td>\n",
" <td>0.217046</td>\n",
" <td>-0.346270</td>\n",
" <td>0.884327</td>\n",
" <td>0.542292</td>\n",
" <td>-0.724853</td>\n",
" <td>1.000000</td>\n",
" <td>0.731658</td>\n",
" <td>-0.775709</td>\n",
" <td>0.758256</td>\n",
" <td>0.894891</td>\n",
" <td>-0.210347</td>\n",
" <td>0.550649</td>\n",
" <td>0.889318</td>\n",
" <td>-0.001488</td>\n",
" <td>0.410744</td>\n",
" <td>0.674832</td>\n",
" <td>-0.022942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_80</th>\n",
" <td>-0.290207</td>\n",
" <td>0.468312</td>\n",
" <td>-0.160778</td>\n",
" <td>0.099995</td>\n",
" <td>-0.442606</td>\n",
" <td>-0.046444</td>\n",
" <td>-0.244178</td>\n",
" <td>0.774564</td>\n",
" <td>0.159568</td>\n",
" <td>0.141574</td>\n",
" <td>0.012887</td>\n",
" <td>0.205261</td>\n",
" <td>0.157492</td>\n",
" <td>-0.012966</td>\n",
" <td>0.277689</td>\n",
" <td>0.194306</td>\n",
" <td>-0.062948</td>\n",
" <td>0.439312</td>\n",
" <td>0.300505</td>\n",
" <td>-0.184767</td>\n",
" <td>0.626304</td>\n",
" <td>0.484415</td>\n",
" <td>-0.254331</td>\n",
" <td>0.764009</td>\n",
" <td>-0.294168</td>\n",
" <td>0.815443</td>\n",
" <td>0.965239</td>\n",
" <td>-0.273284</td>\n",
" <td>0.731658</td>\n",
" <td>1.000000</td>\n",
" <td>-0.137357</td>\n",
" <td>0.387513</td>\n",
" <td>0.924109</td>\n",
" <td>0.315882</td>\n",
" <td>0.271615</td>\n",
" <td>0.856583</td>\n",
" <td>0.339440</td>\n",
" <td>0.240999</td>\n",
" <td>0.730133</td>\n",
" <td>0.197403</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step80</th>\n",
" <td>0.216959</td>\n",
" <td>-0.466680</td>\n",
" <td>0.054639</td>\n",
" <td>-0.167128</td>\n",
" <td>0.279251</td>\n",
" <td>0.004799</td>\n",
" <td>0.225678</td>\n",
" <td>0.084900</td>\n",
" <td>0.118920</td>\n",
" <td>0.105645</td>\n",
" <td>0.009744</td>\n",
" <td>0.166109</td>\n",
" <td>0.118172</td>\n",
" <td>-0.019534</td>\n",
" <td>0.249813</td>\n",
" <td>0.149951</td>\n",
" <td>-0.079328</td>\n",
" <td>0.299948</td>\n",
" <td>0.229132</td>\n",
" <td>-0.105935</td>\n",
" <td>0.374634</td>\n",
" <td>0.354799</td>\n",
" <td>-0.095994</td>\n",
" <td>0.391932</td>\n",
" <td>0.230816</td>\n",
" <td>-0.530059</td>\n",
" <td>0.105624</td>\n",
" <td>0.800261</td>\n",
" <td>-0.775709</td>\n",
" <td>-0.137357</td>\n",
" <td>1.000000</td>\n",
" <td>-0.743046</td>\n",
" <td>-0.444807</td>\n",
" <td>0.598094</td>\n",
" <td>-0.548675</td>\n",
" <td>-0.499224</td>\n",
" <td>0.316415</td>\n",
" <td>-0.373726</td>\n",
" <td>-0.304626</td>\n",
" <td>0.216092</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_120</th>\n",
" <td>-0.271450</td>\n",
" <td>0.745436</td>\n",
" <td>0.040982</td>\n",
" <td>0.371613</td>\n",
" <td>-0.472386</td>\n",
" <td>0.124796</td>\n",
" <td>-0.276848</td>\n",
" <td>0.067352</td>\n",
" <td>0.000581</td>\n",
" <td>0.008441</td>\n",
" <td>0.008223</td>\n",
" <td>-0.018795</td>\n",
" <td>0.007022</td>\n",
" <td>0.022080</td>\n",
" <td>-0.042570</td>\n",
" <td>0.001137</td>\n",
" <td>0.037898</td>\n",
" <td>-0.019067</td>\n",
" <td>-0.008549</td>\n",
" <td>0.011811</td>\n",
" <td>0.000366</td>\n",
" <td>-0.026651</td>\n",
" <td>-0.023397</td>\n",
" <td>0.004981</td>\n",
" <td>-0.078034</td>\n",
" <td>0.505247</td>\n",
" <td>0.229367</td>\n",
" <td>-0.482476</td>\n",
" <td>0.758256</td>\n",
" <td>0.387513</td>\n",
" <td>-0.743046</td>\n",
" <td>1.000000</td>\n",
" <td>0.670525</td>\n",
" <td>-0.739708</td>\n",
" <td>0.920013</td>\n",
" <td>0.765258</td>\n",
" <td>-0.589035</td>\n",
" <td>0.718785</td>\n",
" <td>0.529450</td>\n",
" <td>-0.451219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_120</th>\n",
" <td>-0.320824</td>\n",
" <td>0.644580</td>\n",
" <td>-0.123307</td>\n",
" <td>0.211026</td>\n",
" <td>-0.509751</td>\n",
" <td>0.009955</td>\n",
" <td>-0.285256</td>\n",
" <td>0.652547</td>\n",
" <td>0.094481</td>\n",
" <td>0.088304</td>\n",
" <td>0.012249</td>\n",
" <td>0.116565</td>\n",
" <td>0.097205</td>\n",
" <td>0.000205</td>\n",
" <td>0.149458</td>\n",
" <td>0.116842</td>\n",
" <td>-0.022679</td>\n",
" <td>0.278811</td>\n",
" <td>0.179856</td>\n",
" <td>-0.126428</td>\n",
" <td>0.445147</td>\n",
" <td>0.301223</td>\n",
" <td>-0.217947</td>\n",
" <td>0.545377</td>\n",
" <td>-0.331714</td>\n",
" <td>0.870720</td>\n",
" <td>0.817847</td>\n",
" <td>-0.472572</td>\n",
" <td>0.894891</td>\n",
" <td>0.924109</td>\n",
" <td>-0.444807</td>\n",
" <td>0.670525</td>\n",
" <td>1.000000</td>\n",
" <td>0.003245</td>\n",
" <td>0.522245</td>\n",
" <td>0.979178</td>\n",
" <td>0.109074</td>\n",
" <td>0.416676</td>\n",
" <td>0.809457</td>\n",
" <td>0.055523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step120</th>\n",
" <td>0.074887</td>\n",
" <td>-0.420112</td>\n",
" <td>-0.167086</td>\n",
" <td>-0.309489</td>\n",
" <td>0.174362</td>\n",
" <td>-0.143363</td>\n",
" <td>0.113570</td>\n",
" <td>0.501109</td>\n",
" <td>0.084916</td>\n",
" <td>0.068719</td>\n",
" <td>0.000027</td>\n",
" <td>0.131064</td>\n",
" <td>0.078705</td>\n",
" <td>-0.029576</td>\n",
" <td>0.192946</td>\n",
" <td>0.104449</td>\n",
" <td>-0.071654</td>\n",
" <td>0.278595</td>\n",
" <td>0.174662</td>\n",
" <td>-0.130597</td>\n",
" <td>0.403276</td>\n",
" <td>0.309148</td>\n",
" <td>-0.166151</td>\n",
" <td>0.487971</td>\n",
" <td>-0.195699</td>\n",
" <td>0.108761</td>\n",
" <td>0.432662</td>\n",
" <td>0.221686</td>\n",
" <td>-0.210347</td>\n",
" <td>0.315882</td>\n",
" <td>0.598094</td>\n",
" <td>-0.739708</td>\n",
" <td>0.003245</td>\n",
" <td>1.000000</td>\n",
" <td>-0.766389</td>\n",
" <td>-0.143333</td>\n",
" <td>0.892899</td>\n",
" <td>-0.590910</td>\n",
" <td>0.020568</td>\n",
" <td>0.658564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_150</th>\n",
" <td>-0.302172</td>\n",
" <td>0.719586</td>\n",
" <td>0.044275</td>\n",
" <td>0.413040</td>\n",
" <td>-0.516936</td>\n",
" <td>0.117644</td>\n",
" <td>-0.278183</td>\n",
" <td>-0.010402</td>\n",
" <td>0.001302</td>\n",
" <td>0.016434</td>\n",
" <td>0.015866</td>\n",
" <td>-0.022937</td>\n",
" <td>0.014384</td>\n",
" <td>0.032612</td>\n",
" <td>-0.050180</td>\n",
" <td>0.006648</td>\n",
" <td>0.049521</td>\n",
" <td>-0.033317</td>\n",
" <td>-0.009261</td>\n",
" <td>0.025430</td>\n",
" <td>0.020733</td>\n",
" <td>-0.029847</td>\n",
" <td>-0.048023</td>\n",
" <td>-0.011948</td>\n",
" <td>0.002510</td>\n",
" <td>0.348621</td>\n",
" <td>0.164580</td>\n",
" <td>-0.327544</td>\n",
" <td>0.550649</td>\n",
" <td>0.271615</td>\n",
" <td>-0.548675</td>\n",
" <td>0.920013</td>\n",
" <td>0.522245</td>\n",
" <td>-0.766389</td>\n",
" <td>1.000000</td>\n",
" <td>0.651829</td>\n",
" <td>-0.786732</td>\n",
" <td>0.890199</td>\n",
" <td>0.459665</td>\n",
" <td>-0.682527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_150</th>\n",
" <td>-0.313927</td>\n",
" <td>0.711037</td>\n",
" <td>-0.098405</td>\n",
" <td>0.288933</td>\n",
" <td>-0.537918</td>\n",
" <td>0.029261</td>\n",
" <td>-0.270781</td>\n",
" <td>0.576002</td>\n",
" <td>0.083766</td>\n",
" <td>0.078391</td>\n",
" <td>0.010965</td>\n",
" <td>0.099332</td>\n",
" <td>0.086072</td>\n",
" <td>0.003329</td>\n",
" <td>0.117863</td>\n",
" <td>0.102113</td>\n",
" <td>-0.008777</td>\n",
" <td>0.230196</td>\n",
" <td>0.152257</td>\n",
" <td>-0.101210</td>\n",
" <td>0.395378</td>\n",
" <td>0.254321</td>\n",
" <td>-0.204995</td>\n",
" <td>0.473341</td>\n",
" <td>-0.299684</td>\n",
" <td>0.827250</td>\n",
" <td>0.739078</td>\n",
" <td>-0.481202</td>\n",
" <td>0.889318</td>\n",
" <td>0.856583</td>\n",
" <td>-0.499224</td>\n",
" <td>0.765258</td>\n",
" <td>0.979178</td>\n",
" <td>-0.143333</td>\n",
" <td>0.651829</td>\n",
" <td>1.000000</td>\n",
" <td>-0.044679</td>\n",
" <td>0.535718</td>\n",
" <td>0.860410</td>\n",
" <td>-0.042384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step150</th>\n",
" <td>0.142523</td>\n",
" <td>-0.369146</td>\n",
" <td>-0.138423</td>\n",
" <td>-0.308915</td>\n",
" <td>0.243109</td>\n",
" <td>-0.120829</td>\n",
" <td>0.145253</td>\n",
" <td>0.482557</td>\n",
" <td>0.066469</td>\n",
" <td>0.042160</td>\n",
" <td>-0.011976</td>\n",
" <td>0.111069</td>\n",
" <td>0.051112</td>\n",
" <td>-0.040251</td>\n",
" <td>0.162041</td>\n",
" <td>0.074360</td>\n",
" <td>-0.072378</td>\n",
" <td>0.231264</td>\n",
" <td>0.136134</td>\n",
" <td>-0.115882</td>\n",
" <td>0.294518</td>\n",
" <td>0.246330</td>\n",
" <td>-0.103600</td>\n",
" <td>0.401030</td>\n",
" <td>-0.247244</td>\n",
" <td>0.214124</td>\n",
" <td>0.384792</td>\n",
" <td>0.039787</td>\n",
" <td>-0.001488</td>\n",
" <td>0.339440</td>\n",
" <td>0.316415</td>\n",
" <td>-0.589035</td>\n",
" <td>0.109074</td>\n",
" <td>0.892899</td>\n",
" <td>-0.786732</td>\n",
" <td>-0.044679</td>\n",
" <td>1.000000</td>\n",
" <td>-0.736602</td>\n",
" <td>0.094836</td>\n",
" <td>0.864597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cc_weighted_200</th>\n",
" <td>-0.422151</td>\n",
" <td>0.705825</td>\n",
" <td>-0.068797</td>\n",
" <td>0.352685</td>\n",
" <td>-0.641033</td>\n",
" <td>0.060222</td>\n",
" <td>-0.379649</td>\n",
" <td>0.004544</td>\n",
" <td>-0.001095</td>\n",
" <td>0.020298</td>\n",
" <td>0.021852</td>\n",
" <td>-0.022974</td>\n",
" <td>0.017804</td>\n",
" <td>0.035974</td>\n",
" <td>-0.049278</td>\n",
" <td>0.009466</td>\n",
" <td>0.051313</td>\n",
" <td>-0.020847</td>\n",
" <td>-0.006681</td>\n",
" <td>0.015164</td>\n",
" <td>0.081155</td>\n",
" <td>-0.011477</td>\n",
" <td>-0.097042</td>\n",
" <td>0.026161</td>\n",
" <td>0.016778</td>\n",
" <td>0.282330</td>\n",
" <td>0.172784</td>\n",
" <td>-0.231712</td>\n",
" <td>0.410744</td>\n",
" <td>0.240999</td>\n",
" <td>-0.373726</td>\n",
" <td>0.718785</td>\n",
" <td>0.416676</td>\n",
" <td>-0.590910</td>\n",
" <td>0.890199</td>\n",
" <td>0.535718</td>\n",
" <td>-0.736602</td>\n",
" <td>1.000000</td>\n",
" <td>0.429202</td>\n",
" <td>-0.821709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bb_weighted_200</th>\n",
" <td>-0.141308</td>\n",
" <td>0.602888</td>\n",
" <td>-0.063644</td>\n",
" <td>0.356759</td>\n",
" <td>-0.404657</td>\n",
" <td>0.000712</td>\n",
" <td>-0.066175</td>\n",
" <td>0.546441</td>\n",
" <td>0.100864</td>\n",
" <td>0.088914</td>\n",
" <td>0.007552</td>\n",
" <td>0.127136</td>\n",
" <td>0.098878</td>\n",
" <td>-0.006736</td>\n",
" <td>0.132915</td>\n",
" <td>0.119070</td>\n",
" <td>-0.006321</td>\n",
" <td>0.244359</td>\n",
" <td>0.170120</td>\n",
" <td>-0.100267</td>\n",
" <td>0.421935</td>\n",
" <td>0.285359</td>\n",
" <td>-0.206717</td>\n",
" <td>0.488449</td>\n",
" <td>-0.250673</td>\n",
" <td>0.648181</td>\n",
" <td>0.648589</td>\n",
" <td>-0.318015</td>\n",
" <td>0.674832</td>\n",
" <td>0.730133</td>\n",
" <td>-0.304626</td>\n",
" <td>0.529450</td>\n",
" <td>0.809457</td>\n",
" <td>0.020568</td>\n",
" <td>0.459665</td>\n",
" <td>0.860410</td>\n",
" <td>0.094836</td>\n",
" <td>0.429202</td>\n",
" <td>1.000000</td>\n",
" <td>0.162067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>causal_size_step200</th>\n",
" <td>0.372049</td>\n",
" <td>-0.390721</td>\n",
" <td>0.035004</td>\n",
" <td>-0.160210</td>\n",
" <td>0.445014</td>\n",
" <td>-0.063900</td>\n",
" <td>0.372963</td>\n",
" <td>0.339829</td>\n",
" <td>0.064839</td>\n",
" <td>0.033928</td>\n",
" <td>-0.019108</td>\n",
" <td>0.105320</td>\n",
" <td>0.042939</td>\n",
" <td>-0.043553</td>\n",
" <td>0.137705</td>\n",
" <td>0.064790</td>\n",
" <td>-0.060049</td>\n",
" <td>0.176962</td>\n",
" <td>0.114642</td>\n",
" <td>-0.079834</td>\n",
" <td>0.177568</td>\n",
" <td>0.192596</td>\n",
" <td>-0.024414</td>\n",
" <td>0.279621</td>\n",
" <td>-0.176501</td>\n",
" <td>0.100537</td>\n",
" <td>0.220476</td>\n",
" <td>0.052490</td>\n",
" <td>-0.022942</td>\n",
" <td>0.197403</td>\n",
" <td>0.216092</td>\n",
" <td>-0.451219</td>\n",
" <td>0.055523</td>\n",
" <td>0.658564</td>\n",
" <td>-0.682527</td>\n",
" <td>-0.042384</td>\n",
" <td>0.864597</td>\n",
" <td>-0.821709</td>\n",
" <td>0.162067</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" nok usd ... bb_weighted_200 causal_size_step200\n",
"nok 1.000000 -0.259508 ... -0.141308 0.372049\n",
"usd -0.259508 1.000000 ... 0.602888 -0.390721\n",
"eur 0.803596 0.122163 ... -0.063644 0.035004\n",
"gbp 0.582291 0.502560 ... 0.356759 -0.160210\n",
"brent 0.896812 -0.550984 ... -0.404657 0.445014\n",
"gdp yoy 0.258060 0.330808 ... 0.000712 -0.063900\n",
"interest rate 0.948522 -0.284277 ... -0.066175 0.372963\n",
"cc_weighted_40 -0.141402 0.237304 ... 0.546441 0.339829\n",
"cc_weighted_2 -0.056319 0.026735 ... 0.100864 0.064839\n",
"bb_weighted_2 -0.064972 0.021255 ... 0.088914 0.033928\n",
"causal_size_step2 -0.020026 -0.000384 ... 0.007552 -0.019108\n",
"cc_weighted_3 -0.021618 0.014072 ... 0.127136 0.105320\n",
"bb_weighted_3 -0.067087 0.023031 ... 0.098878 0.042939\n",
"causal_size_step3 -0.047840 0.011031 ... -0.006736 -0.043553\n",
"cc_weighted_5 0.005980 -0.005367 ... 0.132915 0.137705\n",
"bb_weighted_5 -0.065978 0.024029 ... 0.119070 0.064790\n",
"causal_size_step5 -0.065444 0.026596 ... -0.006321 -0.060049\n",
"cc_weighted_10 -0.041579 0.023814 ... 0.244359 0.176962\n",
"bb_weighted_10 -0.060965 0.025707 ... 0.170120 0.114642\n",
"causal_size_step10 -0.009961 -0.002068 ... -0.100267 -0.079834\n",
"cc_weighted_20 -0.064844 0.193440 ... 0.421935 0.177568\n",
"bb_weighted_20 -0.053142 0.053981 ... 0.285359 0.192596\n",
"causal_size_step20 0.023753 -0.161100 ... -0.206717 -0.024414\n",
"bb_weighted_40 -0.097234 0.167541 ... 0.488449 0.279621\n",
"causal_size_step40 0.089757 -0.146911 ... -0.250673 -0.176501\n",
"cc_weighted_60 -0.350755 0.503233 ... 0.648181 0.100537\n",
"bb_weighted_60 -0.246233 0.365989 ... 0.648589 0.220476\n",
"causal_size_step60 0.261053 -0.363736 ... -0.318015 0.052490\n",
"cc_weighted_80 -0.334209 0.619542 ... 0.674832 -0.022942\n",
"bb_weighted_80 -0.290207 0.468312 ... 0.730133 0.197403\n",
"causal_size_step80 0.216959 -0.466680 ... -0.304626 0.216092\n",
"cc_weighted_120 -0.271450 0.745436 ... 0.529450 -0.451219\n",
"bb_weighted_120 -0.320824 0.644580 ... 0.809457 0.055523\n",
"causal_size_step120 0.074887 -0.420112 ... 0.020568 0.658564\n",
"cc_weighted_150 -0.302172 0.719586 ... 0.459665 -0.682527\n",
"bb_weighted_150 -0.313927 0.711037 ... 0.860410 -0.042384\n",
"causal_size_step150 0.142523 -0.369146 ... 0.094836 0.864597\n",
"cc_weighted_200 -0.422151 0.705825 ... 0.429202 -0.821709\n",
"bb_weighted_200 -0.141308 0.602888 ... 1.000000 0.162067\n",
"causal_size_step200 0.372049 -0.390721 ... 0.162067 1.000000\n",
"\n",
"[40 rows x 40 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZfnPH5Jysx2z",
"outputId": "6480454b-5513-4042-e341-b5efc1009de5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 350
}
},
"source": [
"#identification\n",
"#first of first, using scatter plot to visualize the correlation\n",
"#lets denote data from 2013-4-25 to 2017-4-25 as estimation horizon/training set\n",
"#lets denote data from 2017-4-25 to 2018-4-25 as validation horizon/testing set\n",
"ax=plt.figure(figsize=(10,5)).add_subplot(111)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"ax.scatter(df['brent'][df.index<'2017-04-25'],df['nok'][df.index<'2017-04-25'],s=1,c='#5f0f4e')\n",
"\n",
"plt.title('NOK Brent Correlation')\n",
"plt.xlabel('Brent in JPY')\n",
"plt.ylabel('NOKJPY')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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EmUCr3QSIPg4nlHiVgohH10+ydS854XR8mF2XpN01R/pcW+ub9S5DY10v4zG9/QP67EcA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\nB+ztZzG0pdNOAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RNwMlDYcjGuo",
"outputId": "0250d2a4-0b47-40de-e432-536e2153253c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"df.tail()#dual axis plot\n",
"def dual_axis_plot(xaxis,data1,data2,fst_color='r',\n",
" sec_color='b',fig_size=(10,5),\n",
" x_label='',y_label1='',y_label2='',\n",
" legend1='',legend2='',grid=False,title=''):\n",
" \n",
" fig=plt.figure(figsize=fig_size)\n",
" ax=fig.add_subplot(111)\n",
" \n",
"\n",
" ax.set_xlabel(x_label)\n",
" ax.set_ylabel(y_label1, color=fst_color)\n",
" ax.plot(xaxis, data1, color=fst_color,label=legend1)\n",
" ax.tick_params(axis='y',labelcolor=fst_color)\n",
" ax.yaxis.labelpad=15\n",
"\n",
" plt.legend(loc=3)\n",
" ax2 = ax.twinx()\n",
"\n",
" ax2.set_ylabel(y_label2, color=sec_color,rotation=270)\n",
" ax2.plot(xaxis, data2, color=sec_color,label=legend2)\n",
" ax2.tick_params(axis='y',labelcolor=sec_color)\n",
" ax2.yaxis.labelpad=15\n",
"\n",
" fig.tight_layout()\n",
" plt.legend(loc=4)\n",
" plt.grid(grid)\n",
" plt.title(title)\n",
" plt.show()\n",
" \n",
"#nok vs ir\n",
"dual_axis_plot(df.index,df['nok'],df['interest rate'],\n",
" fst_color='#34262b',sec_color='#cb2800',\n",
" fig_size=(10,5),x_label='Date',\n",
" y_label1='NOKJPY',y_label2='Norges Bank Interest Rate %',\n",
" legend1='NOKJPY',legend2='Interest Rate',\n",
" grid=False,title='NOK vs Interest Rate')\n",
"\n",
"#nok vs brent\n",
"dual_axis_plot(df.index,df['nok'],df['brent'],\n",
" fst_color='#4f2d20',sec_color='#3feee6',\n",
" fig_size=(10,5),x_label='Date',\n",
" y_label1='NOKJPY',y_label2='Brent in JPY',\n",
" legend1='NOKJPY',legend2='Brent',\n",
" grid=False,title='NOK vs Brent')\n",
" \n",
"#nok vs gdp\n",
"#cuz gdp is released quarterly\n",
"#we need to convert nok into quarterly data as well\n",
"ind=df['gdp yoy'].dropna().index\n",
"dual_axis_plot(df.loc[ind].index,\n",
" df['nok'].loc[ind],\n",
" df['gdp yoy'].dropna(),\n",
" fst_color='#116466',sec_color='#ff652f',\n",
" fig_size=(10,5),x_label='Date',\n",
" y_label1='NOKJPY',y_label2='Norway GDP YoY %',\n",
" legend1='NOKJPY',legend2='GDP',\n",
" grid=False,title='NOK vs GDP')\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
"\n",
"To register the converters:\n",
"\t>>> from pandas.plotting import register_matplotlib_converters\n",
"\t>>> register_matplotlib_converters()\n",
" warnings.warn(msg, FutureWarning)\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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pyb7uCxt7BvmWu/7C0Scez9jx4/yOn37u2YybMIF9Dtw/Zp+VlJzc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9KH6ZMncvG4sYwfczbhCQmUHdyHI++Yz/GW7t3Frz//TF9zL7bs2MktN95Aa+cbytzcXK644go2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\nC2RJkiSpxAJZkiRJKrFAliRJkkr+ByUb97jcqG+dAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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zYvYMcjwWJWQLnj60babz2DLIHl/qV95p18rI5hYqB06lOMaXz6pk3AqIAVYm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\nTCZxQ9rGPr0bRD4e49fqoQ6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ECy4lr6DrjhBrli8F4PI//R877rFXp+NKKVwutx4gh4L4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6LQYF5edJs1Ne+XmbmMNB8nGWSR6iRAFkKIburtDPIoR2yWPt8MJqzSi8vrq7o8RijY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\nIYTN/wOdiFpEldSSnAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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85Lz0BATB9ctwdLfRkeQqkiC7qqsX4WLikIeI742NRbi8hpUq0adpE35ZvISz\nV66qO9fMAp/C0KBd2g8sWREGvAp71sOKvxwSqxBCOMzqCChW9v4mZWdUr/X9qXrCZiRBdlWHd6jb\nJl3USt+e9cbGI1zeu717Exsfz5fz5qvVCOtyaN4NPPOl/8Cg/uoXdPi3ajOLEELkBueOqQ4Rrfvc\n36TsjHz8oJpM1bM1J/4XF+mKsqq3uJ96H4qWgfDv5S1ukSOVSxRnaNsgJq9Zw4Ul0yA+Tj0xZETT\n1ACR/AXg13cgLtbuseYZVy9KKz0hjLJmllqZdcbNeSkFtJWpejYmCbKrioqECrXV6N/ez8KJfbDl\nH6OjEi7ute7d8Pb0JGbFDKhUD8pWy9wDCxWDp96D43tVZwuRc1cuwIcDYPJHRkciRN4TF6u6+AQE\nQeHiRkeTsQaJm/UjZRXZViRBdkWxd9XGvKom9XnTR6FcDZj9k6zeiRwpVrAgnzarQ/mYaA7XaJW1\nBzfsoDb0/T1BvcMhsi8hASa+Czcuw94Nane6EMJxrMtVqVkbJ5qcl56SFdXHDqlDthVJkF3R8b0q\nEa7aQH3u5qYamF88BatmGhubcHkD489wCzdeP3g51f7b6T/4ddUS7td3ZbJTTiz+Xe0tqNtKlVic\n2G90RELkLasjwL801G1hdCSZF9AWDmyR3702IgmyK4qKVLdVAu7fV7cl1Gyq2r7Jfw6RXXdu47lt\nKaerNGH54WMsityRtcd7+cCwT1Qd3PSv7BNjbnd0F8z+GRp1UrXdAPs2GRqSEHnKhZPqnZvWvV2r\nr3BAG7V4tls27duCJMiu6PAO1XYmeV2UpkG/F9VbQosnGRebcG1bF0PMTSr1NlO9VEk+jJhFXHwW\n396v1hC6DoW1s1UPUZF5MTch7P/U/+2n3le3pavAvs1GRyZE3rFmFmhuafeAd1ZJU/V2Sh2yLUiC\n7Gp0HQ5Z75dXJFepnhr5u2QSXLvk+NiE61szG0pWxKNmY97r04cDZ87w57psrEb0GKk2kU7+WHVi\nEJkz9TNVKjX8U9W6CdQ7Q4e2yf4CIRwhLhbWzoH6raFISaOjyRoPT6iXbKqeyBFJkF3NpdNw7WLq\nCTJA7+fUJr75FsfGJVzf2aNwaLtaNdE0ujU0EVi1Kl/MncetO3ezdi4PTxj+X7hzC34fo17YifRt\nXKBG2nY3Q/WG9++vFQh3bqvSCyGEfe1crZ5jXWVzXkoBbdU7yUfk90VOSYLsapLqj9NKkEtWVCMx\nV4XD+eOOi0u4vrWzVb1dix7A/RHUZ65cYdzSpVk/X+kq0O8l2LVGNo9m5MJJ+ONTVZ7y2IgHv1aj\nsSqhkjILIexvdbgqbaqXxS4QoadPAAAgAElEQVQ+zqJuK/V7XLpZ5JgkyK4mKlINZCiTTn/a7iHg\n4aE2+giRGXGxsH4e1G+jehonal6tGo+ZGvD9P/9w6fqNrJ+33UCo3RxmfKOmUomHxcVC2FuqG83w\nT8Hd48Gv+xaGcjVlo54Q9nbpNOxeB636PPz/0FX4+KkX2tIPOcecIkH2Dw6Z4B8cct4/OGRXivuf\n9w8O2ecfHLLbPzjkS6PicypRkVC5fvr/eQsVg85D1OCQo7sdF5twXbvWqLr1VDalvNe3Dzdj7vD1\nggVZP6+bGzzzIXjkkyl7aZk3VpVPPPW+apGXmlqBcDgS7sY4NjYh8pK1s9VtaxfbnJdSg7Zw+pDa\nzyCyzSkSZOA3oGvyO/yDQ9oDvYAG0WGhdYGvDYjLucTcgpMH0i6vSK7LEPAtAhHfS/2nyNia2VCo\nuNqYkkLN0qV5snUrJqxYybEL2dhwV6QkPPmuSgIXjrdBsLnIvk2waKIa6d24c9rH1WqqXlwklVgJ\nIWwrPk79HqzTEoqWMTqanAkIUrc7ZBU5J5wiQY4OC10FRKe4exTweXRY6J3EY847PDBnc3QX6AmZ\nS5C9faFbsHoC3iM9EUU6rlxQK8gteqT5zsSbPXvg4e7G53PnZe8aTbpAs24qQT68MwfB5iLXL8OE\nd6BkJRj4RvrHVm+k6gqlzEII+9i9Dq6chyAX3ZyX3L2pepIg54RTJMhpqAG08Q8O2egfHLLSPzik\naVoHappm1jRti6ZpW+Li4hwYooPdGxBSP3PHB/VXr4Qjvleja4VIzfp5qiVQq15pHlK6cGFGtG/P\njI0bOXj2bPauM/gtKFxCJYV3bmcz2FxC12HSh3DjCgR/Bvm90z/eywcq1YX9slFPCLtYFQ5+RdU+\njNxApurlmDMnyB6AP9AceB34yz84REvtQF3XLbquN9F1vYmHh4sW1mdGlBXKVIUCfpk73jMf9H5W\njand8o99YxOuSddV3V31xmrFIR3PdemMl6cn3yxYmL1rFSgIQz+GCyfUpr28bOVfELlCjYgvXytz\nj6ndTO0puJ2NzZJCiLRdPqfau7XqpVpU5gYBQTJVL4ecOUE+CUREh4Xq0WGhm4AEoFgGj8m9EhLU\nBL3MlFck1/RRKFcDZv8kG6TEww5uU+0AM7EppbifH8Pbt2Pmxk3ZX0Wu2QQ6P6XavuXVt/9OHYIZ\n/1NtpDo+kfnH1QxUK/0Ht9kvNiHyorVzVPliqz5GR2I71UxqMU3avWWbMyfIs4H2AP7BITWAfEDe\nHcl19gjcug5VArL2ODc31Yv24inpRSsetnYWePlCo06ZOvz5R7rg5enJ1/Oz0dEiSa/n1Iu238fA\n9ZRbD3K5uzEQ9qbaI/DMx6q/cWZVDVDdQKTMQgjbiY9To6VrN4MS5Y2OxnbcPRKn6q2RqXrZ5BQJ\nsn9wyFRgPVDTPzjkpH9wyHBgAlAlsfXbNODp6LDQvNuO4d6AEFPWH1unhWoTNd8ib8+K+25dhy1L\nIbBrxjWwiYoVLMiI9u0I37SZA2eyuYrsmQ+G/RduX4dJH+WtLisz/weno1SpiZ9/1h7rmV+tCslG\nPSFsZ+NCiD4D7QcbHYntBbSFG5fhiGyMzg6nKNiNDgtN6yfzSYcG4syiIsGnUIZ1oqnSNOj7Anz6\nJCyZBD1H2z4+4Xo2L4LYmCz3/HzukS78umIlX89fgCV4ePauXa469HlB1SKvna3anOV21uWw4i/V\no7xuy+ydo2ZTmPOz2tznW9i28QmR18THwYIwtQ+gQVujo7G9ei1V95vIVdlbXMvjnGIFWWRCVKSq\nP87KW7LJVaqnWm0tmQxX826likhmzSwoWx0q1s3Sw+6tIm/OwSoyqPrbmk1h+pdw/kT2z+MKLp9T\nJSUVakOf57N/nlqB6nb/FpuEJUSetnGh2jTcIyT7z63OrEDiVD2pQ84WSZBdwfXLcO5o1jfopdTr\nWYiNhQUWm4QlXNjJA3Bsj1o9zsYTw7NdOlMgX76c1SK7ualSAzd31fotPpe2aEyIT5wieBeCP8/Z\nLvmKddSo+f1SZiFEjsTHwcIwKF8TGrQzOhr7adBWlXXJVL0skwTZFSTVD1XJYYJcsiIE9YVVEXDu\nWM7jEq5rzSyVqDXrlq2HJ19F3n/mTPbj8C8F/3lbdWhZNDH753FmiyaqfqSD38peiVRyHp5qaMg+\n2agnRI5s+lu9c9U9l64eJ5GpetkmCbIriLKqVbZKWXsrPFXdzODpqeoYRd4Ue1e9tWhqn6M61ue6\ndMn5KjJA4KPQ5BGYF6r6/OYmh3fA3LHQtCu06Gmbc9YKVF1tLp+zzfmEyGvi49Q7qeVrqt+DuVnJ\nimpaZ6SUWWSVJMiuICpS/UfOZKeBdBUqBp2egi2L1ehqkfdYl8PNq9Aqa5vzUipa0JfgDu2J2Lwl\nZ6vImgZPvKO6Okx4N/dM2bt1Hcb/HxQpqb4/W61SSR2yEDmzaVHeWD1O0iBIvYslXayyRBJkZxcX\nC0d257z+OLkuQ8C3CET8kLdabAllzSzwLw21m+f4VM927oxPvnx8NS+Hq8g+fvDMR2plNOL7HMdl\nOF2HPz+F6LMw4jM1RdBWytVQm2+k3ZsQWZe0elyuRu5fPU4S0FZ933tkql5WSILs7E4dVK24bNmi\nxdsXugWrJ1j5D5O3XDoN+zZCy55qk1wOJa0iz9qyhX2nT+fsZHWaQ8f/wPJpsHtdjmMz1Ib5qsax\nx0jbvrgF9e9Ws6n6/ysvcIXImk2L1PTQHiPzxuoxqN9BMlUvyyRBdnb3BoTY+Ek2qD8UK6tW6xIS\nbHtu4bzWzlG3rXrZ7JTPdklcRc5pLTKo3silq8BvH6hev67o/HH48zOo0RgeHWafa9RqqoYbyM50\nITIvqXNFuRq5u3NFSvem6q2VqXpZIAmys4uKVDWM/qVse17PfKrt24n9amCEyP0S4mHdXKjVDIqW\nsdlp/X19MXfswOwtW9l7KoeryPm8YPinavrTlE9cb4U0LhbC3lJPSMP+qzbX2kNSHbKUWQiReZv/\nUR2cuofY5B00l5I0Ve/wDqMjcRl57CfEBSUNCLGHpl3V5r/ZP6nOBiJ327tRrTraYWrd6M6d8MmX\nj68X2GAVuUItNe1x21JVquBK5vys+ks/Pcb2L2qTK1UZ/IpJP2QhMishPu/VHidXryW4ecCO1UZH\n4jIkQXZml8+phMZeCbKbG/R9UdWlrpppn2sI57F2thpXbocnB5uuIgM88rTq9zv1c9cpI9izHv75\nTZUvNexg32tpmiqz2LfZ9VbZhTDC5kVq9bibOe+tHoOqQa4uU/WyIg/+lLgQe9UfJ1enhXrLfUGY\ntIDJzW5cUe3dmndT5TV2MLpzJ3zy5+er+TZY9XVzV1P2ACa+5/x1c9ejYcJ7qn56wKuOuWatQLh2\nCc4cdsz1hHBVCfEwPwzKVrf/i1dnFhAkU/WyQBJkZxYVCZ5eUK6m/a6hadD3BVWbtHiS/a4jjLVx\ngaqPbWX78ook/r6+hHRoz5yt29hzyga/gIuVhUFvwMFtzv2zqetqU+Gta2qUtC36lWfGvTpkmaon\nRLo2/wPnjubN2uPkGrRVt3lxaIjZdA2z6Xri7bVkn9/GbEq1U0Ee/klxAVGRanqeh6d9r1Oprppk\ntmQSXL1o32sJx9N1WD1L/TuXq27XS43u0lmtIue0L3KSFj2hYUdV2xvxvXOOSF82FXauhv6vqPpG\nRylWVm22lDpkIdKWEA/zLVCmWt5ePQYoUUHtX8iLZRYWqx8Wa8HEWz+gDPAJcBr4JrWHSILsrO7G\nwPF99i2vSK73sxAXB/NDHXM94ThHd8HpQ3ZdPU5SxMeHkI4dmLN1q21WkTUNnnpPvTW4eBK81wu+\nGqa6cTho4t7lmzfT/uKJ/RD+rdoh3n6gQ+J5QK1ANVHP2UtQhDBK0upxjzy+epwkIAgObM27JZVm\nUxHMpjHATqAg0BSL9fXUDpWfFmd1bA8kxDkuQS5RAYL6wuoI51ylE9m3ZrYq1Wn6iEMuN7pzJwp6\ne/HlPBt1oPAtDKP+B58vUptKr16E396H1zvB5I/h8E67bVT7c+06qr38Kv9EptIa6c5t1dLNp7Dq\nWmHE0IFaTVVpx4kDjr+2EM4uIV7trylTTb0TJVSCHB/n+sOYsspsKoHZ9AWwDYgDArBY38VijU7r\nIZIgO6ukDXpVAhx3zW5mtYFrzs+Ou6awrzu31e7tJp1sO+44HWoVuSNzt25jz0kbbgYpXBy6DoWP\n58DrE9TbpRsXwOdPwYf9YclkuH7ZZpezHjvGq1P+QNd1fl+dSmukv75WK1PDPoGCRWx23Syp2VTd\nSpmFEA/bsliNr++eRztXpCZpqt7OVUZH4miHgUHAROA2YMZsevXeRyrkJ8ZZRUVCyYqOfeItVAw6\nP6V+qRzd5bjrOpuEeIi5pToTXDoNZ46ocpdD22HPBnWfq9i6BGJuOqS8IrlRnTqqVWRbdLRISdNU\nC7ihH8NXS+HJdyGfN8z4Bt7oDONeg105mxh18fp1hvwyjuJ+fjzRqhVLd+3iwrVr9w/Y9i+sDodH\nnoHazXL+PWVX4RKqc4YMDBHiQfdqj6tCo05GR+M83D2gfuu8OFXvC+DXxD/7pPLxEA/HxCWyRNdV\ngly/teOv3XkIrJwB4d/DKxbnnFUfewcunFS3d2Puf8TegbuJ98Umvy/ln5MfcyfZ126r27jY9K+f\nzwteGgvVGjrm+82JtbNV+Uz1Rg69bNIq8tfzF7D75Enqlitnnwt5+6q+w0H94dQhWDMLNixQQ0aK\nlIKWPdVY7WJlM33KuPh4gsPGc+HaNf5+6w3ye3jyx9q1hG/azMhOHSH6LEz6UG167DnaPt9XVtRs\nCuvnqp9be2/oFcJVbFmiVo/NX8jqcUoBbWHjQjVVz9mex8ym8sAkoCSgAxYs1u9zfF6L9eOsPkQS\nZGd0/rhqu+ao+uPkvH1VqcW0L9Tgg7otHR9DWuLjYN0cmDcOrlzI3GM8PFVC6+kFnvnVn/Ml3voU\nhiKJn9/7WuKHZ371mOTHe+ZX/Xn//BR+eA5eCYVK9ez7PefEuWOqRVqfFwx5oTO6U0dC//2XL+ct\n4PdRIfa/YNlqMPB1VaccuUK9OFgYpqZn1QqEVr2hUUf175iOj2fNZuXeffz0zNOYKlYEwFSxAtPW\nr2dkh3bw6zvqZ3HE586RkNYKhBXT4dhuqGoyOhohjJc0Na90FWjU2ehonE/dpKl6q5wvQVb1wa9i\nsW7DbCoIbMVsWoLFuidHZzWbKgA/AC0BDVgPPI/FmuamK0mQndG9ASEGPdkF9YelUyD8O6jd3PhX\n37quhlxE/KBqPqs2gD4vqppaz/yJCaz3gwmwZ+Ktm7vt43nFojopfDcaXg1T47qd0drZ6vtv0cOQ\nyxf28WFkx458NX8Bu06coF758o65sGc+aNJFfVw6A+vnqb+LX9+GqQUh8DE1brtCrYceOnvLVn78\nZzHD2rXlP63uvzgc2KIF/zdtOuenfUuJg1th6CdQwkHfT0ZqNFYvgPZtlgRZCFCrx2cOy+pxWgoU\nhBqNVD/kvi8aHc2DLNYzwJnEP1/HbNoLlAVyliCr2uOJQL/EzwcDE4A0d2/KT44ziooEL1/16tcI\nHp7Q+zk4eQA2/21MDEkOboMvnoaxr6gkYPS38MZv0KK7anpep7l6BVyhFpSuDEVLQ0F/8Cpgn+QY\noEhJlSTn94bvRqrJRM4mPg7WzYN6rdXmNoOM6tQRP29vvpxvo77IWVW0tNqg89/58HIo1GulyjA+\nGQQfD4Ll0+Cmqi3ec+oUz//2O02rVuHTgY8/cJp+gU1p6XaDoiv/gGaPqZ8/Z+FbWA0TkjpkIWT1\nOLMCgtSLiAsnjY4kbWZTJaAhsNEGZyuKxToFizU+8WMKUDS9B0iC7IyiIqFqgLGvfJs8AuVrweyf\nIfau469/+hD89KJaqY0+A0+9Dx/MAFN756iLLlY2sUbbHb4dqcpinMnONXDtIrTubWgYhX18GNmp\nI/O3bWfXiRPGBeLmpjbTjfgcvloCg94CdJj6ObzRmbuhb/Djd59QMH8+fhsZQj6PB99cK+au85vH\nMU7hRdzAN435HtJTK1D93rgbY3QkQhhr61KV+OX1qXkZCQhSt44fGuKhadqWZB/mVI8ym3yBcOAl\nLNZrqR6TNRcwm57BbPJI/BgKpFurKT89zubWNTgTZUz9cXJubtDvRdWxYdUMx103+qwa2/vh43Bw\nq6qf/XgutOmrdt86k5IVVR1yfBx8Y3au+fZrZ4NfUbWCbLB7q8i26oucUz6FoMMgeG86vDMVvWVP\nYrevYOzV9Wxz20HptTPg8rn7x+s6TPkE//jbDE+owoqjBib6aanVFOLuqk03QuRVCQlq2FXpKtBY\nOlekq0QFtcHXTj3k0xGn63qTZB+Wh44wmzxRyfEfWKwRNrruUKAHanLeaaBn4n1pcrKMQ3Bkl/qB\nrWJwggxQpwXUagbzw9TIX3v20b15Df7+VY3tRYdOT8Kjw9Tbx86sTFV4eSx8Ewz/C4HXf1UlGEa6\nckGNPu78lFNsIitUoACjOnXki3nz2Xn8BPUrOEntLkDF2ny1owHfxR9lSouadLi8X/UBnzsW6rZQ\nG/tuXIUti0noOZqoJXuZvn4Dneo52ebM6o1USdG+TWo1WYi8aFti7fGIz+1XYpebvBpmdAQPM5s0\nVDu2vVis/7PZeS3Wk9yvP84USZCdTZQVNDeo7CRPwH1fgE+fgCWToNeztj//3RhVB/r3r2r0ZfNu\nqnVW0TK2v5a9lK8FL/6iSi2SkmS/dEub7GvDfFWH18rY8orkRnbqyNil//LlvPlMfnaU0eHcs3jH\nTr6YN5+BLVrSfugzqnznwknVLWXtHAhNnEBasykejw2n39Xp/LF2Hddu3cavgLehsT/Ay0e1nZM6\nZJFXJSTAvMTV4yZSe+zCWgFPATsxm6yJ972NxbowR2c1myagulc8yGJNcxVZEmRnE7VDtavy9jU6\nEqVSXVWPvGQytBuohonYQkK86i4wd6x6O7tea5WMl6thm/M7WuX68PxP8P0olSS/Nt6Y1W9dV+UV\n1RpCqUqOv34akq8i7zh+nIAKFYwOicPnz2Me/yv1y5fjmyefQEuqbS9eTr0Y7DESdq+HPevgkaHg\n5s7AFi34dcVKZm/dypA2xpevPKBWICyaqAbDeKXa916I3EtWj3MHi3UNqSWyOZe8xs8HGACkWxcp\nNcjOJCEejuw0vv44pd7PQlycqu3KKV1XrWU+ehx+HwOFiqu3eV74yXWT4yTVG8Kz36sNe9+NUvXk\njnZou+p/bPDmvNSM7NSRQgUK8NU8gzpaJHMjJoanfhmLu5sbk0aNxDtfvocPcnNXw3oGvnGvE0jj\nypWoXqok09evd2zAmVErUP0OObjN6EiEcKyEBDU1r1RlWT0WqbNYI5J9TMZi7Qmk2xdTEmRncjpK\nrf44Wy/TEhUgqB+sjlDJV3ZFWVVXip9fVBvbQr6G/5usNgrkFrWbwaj/wamDaphIzE3HXn/NLLV6\n2LiLY6+bCUmryAusVnYcN67rh67rvDhpMvtPn2G8eQQVimX+XRFN0xjUogXrDx7i6IVMDqtxlCoB\n4JFPyixE3rNtqXr+7G6W1WORFeswm9L8gZEE2ZncGxDiZCvIoH7xeOaD2T9l/bFnDsMvL8MXz8D5\nE/DEu/DBTLXL2Blattla/dZg/hKO7oYfX4A7tx1z3VvXYesSaNpV9Wh2QiM7qlVkIzta/LxkKbM2\nb+G9Pr1pX6dOlh8/oHkzNE1j+voNdoguB/J5qd8dkiCL7Dp/AmLvGB1F1jyweux8CwPCyZhN5TCb\n/oPZ9AvQGYs1Pq1DJUF2JlGRanNXsbJGR/Iwv6LQeYhKwI7szNxjLp+DSR/CmP7qSbvXs/DfedC2\nv1N0V7Crhh1g2CdwaJt6ceCIJ53N/6hNj05YXpHEr4A3ozt3YqE10pBV5FV79zFmZjg9GjXiha6P\nZOsc5fz9CapVk+kbNqA7vkVS+moFwon9cOOK0ZHkzNHdMPljiIs1OpK849Y1+LA/fDX83vAcl7D9\nX9U3v1uwrB6LtJlNv2M27QemAwHAPCDdjSSSIDuTqEi1AuSsq6pdhkDBIhD+ffq9E29dg1k/wLu9\n1Ea8DoNUYtwt2GlXNu0i8FF4egzs3aC6Idj7yX7tLChTDSo5SQeUNIR06EChAgX4Yq5jV5FPXopm\nuCWMaqVK8dPQp+9vysuGgc2bc/TCRTYecrIpirUSy5X2bzE2jpya/ROsDlcj74VjnDigXsgf3QXf\njIBr0UZHlLGkzhUlK0HT7L3gFXmGG3AVOAocA45nNIBEEmRnce0SXDjhnOUVSbx8oFsIHNgCu9c9\n/PXYO6rbxTs94O8J0KgDfDxHbXIq6O/4eJ1By17wn7dhxyoY/3+q9toeTh5Uq26tezvvC6xEfgW8\nebZzJ/6OjCTymGNWkWNiY3l67DjuxscxefRICnp55eh83Rs1xCd/fqY622a9inXVi9D9LlxmcfYo\n7FkP+QuokcHJh7YI+zm5X90O/QTOHYevh8OV88bGlJHty9TqcXdZPRYZsFifwmINBN4BYoAJmE0X\n03uIJMjOwpnrj5ML6gfFykHE9+rVO9xv2fZeb5jxjWoN9+40GP6pc5aLOFq7x2HAq2ojyW8fqL8v\nW1s7S00abNbN9ue2A3OHDhQuUIAv5s2z+7V0Xef1P6ay/dgxxg4bSvVSpXJ8Tl8vL3o0asjsLVu4\nfdeAUexp8fBUQ0P2bTY6kuxbMV39LL88DuLjYYbtZgWIdJzYD37FoEV3ePFn9cLky2HONSE0uaSp\neSUrqn0XQqTHbGqB2fQG8AMQDFiBl9J7iCTIziIqUj25VahtdCTp8/BUbd9OHoCNC2HnGvhkMEx8\nT5VfvByqhmZUqGV0pM6l81PQ+znYuACmfHL/xYUtxN6FDQvA1F79G7iApFrkRZE7sB7LQWeUTPh9\n1Wr+WLuWV7s9xmMm23WIGdSiBddvx7DQGmmzc9pErUA4e8T5V/9SE3MT1s1Vm62qBEDXobDlH9dO\n+F3Fif1Qvqb6c43G8Eoo3LqqapJz0r3IXrYvU92CpHOFyJzVwHPAUqA/FmsIFmu6NVySIDuLqEiV\nHHvmNzqSjDV5RE2PmzQGfnxOdWkI/gL+b4pqcyZS99gI9bFmFkz/Mv067qyIXA43r0LrPrY5n4OY\nO6pVZHt2tNgUFcWbU6fRqV493urZw6bnbl2zBmX9izhfN4uaiaOm97tgUrl+nkqS2w9Wn3cdqt6F\nmvqZbNizp7hY1SYtKUEGNfzo1fEQd0e15zx1yLj4UkpIUOU3snosMq848CxQHojAbDqA2fR7eg+Q\nBNkZxN6FY3ucr/9xWtzcYNCbqq3O4Lfgwwi1QcJNfpwy1OtZtZq8fBqEf2ebJHnNbChSyuVenPh5\ne/Nsl84sitzB9qNHbX7+c1evMnSchbL+RbCMGIa7jX8+3dzcGNi8Oct27+bslas2PXeOlK8BBfxc\nb9VV12H5dFWiVaW+ui+fFzz+umoVuXyasfHlZmcOq/0RyRNkUJ+/9qv63f71CPU85Qysy9W7mN1k\n9VhkksV6GYt1Hhbr61iszYEGwKT0HiIZjTM4sQ/i7jp//XFy1RvCBzOg/aDc37LNljQN+r+i6pIX\n/w7zxuXsfJdOqy4ZrXq65BNFcIf2FPHxsfkqcmxcPMNCLVy5dZPJo0dR2Mc+o5cHtmhOgq4zc5MT\nbYpzc4eaTVyvH/LeDao0JGn1OEmDtmoU/bxxcMXJhrPkFicSN+ilTJABSleB1yaAlzd8Y1YDn4z0\nQO2xdK4QmWQ2VcNs+h9m0weYTX6ADqT7togkyM4gaYNelQBj4xCOoWkw6C1o1Vv9ol80MfvnWjdX\n3bbsZZvYHMzPW3W0+GfHTrYdOWqz8743YwbrDx7ihyFDqFuunM3Om1L1UqVoXLky09atd66eyDUD\n1YsnZ91glZrl01UNfcphD5oGg95Qiwjh3xoTW253Yr9arS9RPvWvlygPr08EP3/4bpSxL74iV6jV\n48eC1WZOITInHDgB+AA/AQnA5PQeIAmyM4iKhKJloHBxoyMRjuLmBk+9B4FdVUeQf//I+jkSEmDd\nHLUpy4W7hYyw8Sry9PUbsCxbzqhOHenXLNAm50zPoBbN2XPqFLtOnLT7tTKtduL37SqryBdPwY6V\n0KafmtiZUokK8MgzamPwga0ODy/XO7EfytVI/10o/1Lw+gQoWhZ+eA52rnZcfEkSEtQ7CSUqqN+d\nQmReAhbrt1isbwAmLNa7QIH0HiAJstF0HQ5ZXau8QtiGmzs887Gaujf9K1g1M2uP37cRLp2BVq61\nOS+lpFXkxTtzvoq84/hxXp48hVY1ajCmXz/bBJiBvoFNyefh4Vw9kUtVVi27XCVBXjEdNDdoOyDt\nY7oOA//SMPVz+/UTz4t0XfVATq28IqVCxeC18VC2mpoQunWp/eNLLmn1uJtZVo9FVi3CbBqK2eQO\nxGM2VcvoAekmyP7BIQNtFppI3aXTcO2iJMh5lYen6gBSrzX88V+1iz+z1sxWm7EatrdffA4S3LED\nRXx8ctQXOfrGDYb8Mo4iPj5MCAnG08MxNdlFfHx4JCCA8I2biI2zQ4/r7NA0NVVv32bbdUuxlzu3\n1c+yqT0UKZn2cfm94fHXVGuvFdMdF19uF30Gbl2HcplIkAF8C6t2npXqguUNWO+giZi6rkrSSpSX\n1WORHc8C44HbQHVgKqrtW5oyWkF+yj84ZJF/cEgV28QnHnJvQIiLdLAQtufhCaO+UXWjv32g+r5m\n5MYVsC6D5t1cozVgBgp6efFcl84s2bmLrUeOZPnx8QkJBIf9ytmrV5k0eiTF/fzsEGXaBrVozoXr\n11m2e7dDr5uuWoHqxffZrP99OtSmv9V4+g6DMz62YQeo0wLmjIWr6Q7BEpmV3ga9tBQoCC+NU5tB\nf3sv6+9+ZYd1uYpVVlZaoykAACAASURBVI9Fdlisflis7lis+bBYfbFYm2KxptujM90EOTostDsw\nDljgHxzynn9wSDH/4BD/pA9bxp5nRUWqlZGyGa72i9zMMz88+x1UM8H4d9STQXo2LlS9S1v1dkx8\nDjCiQ3v8fbNXi/zp7Dks37OHL/8zmMaVK9shuvR1qlePor6+zlVmUcsF6pB1XbVvK1dDTQDMiKap\n1pKxMRDxnf3jywtO7FflLVl9DsrvDc/9oN79mvIJLEl3v1POPLB6/Kj9riNyH7OpYxr3B+W4D3J0\nWOhsYBDwGrAF2Jr4sSXLgYqHRUWqhuzyilgkPeFUqKXeuty9LvXjdF0NG6lYJ2urPk5OrSJ3YcnO\nXWw5nPlVz3nbtvHt34sY0qY1Q9q0tmOEafP0cKdfs0AWRe7gys2bhsTwkGJl1eZfZ+6HfHCbqilt\nP0glv5lRsiJ0HqLe2j+03b7x5QUn9qu/0/zeWX9sPi8Y9T9o1AlmfAMLwmwfH6ja4xP7pXOFyI5f\nMZvUZjyzqQRm0+uYTTuBl1GdLdKUUQ1yfv/gkI+BacAT0WGhlaLDQisnfkjZRU7F3FJPDlJ/LJJ4\n+6pR3aWrqE0wqSU3x3arOkwXm5yXGcPbt8vSKvL+M2d4dsJvNKpciS8GD7JvcBkY3KIFd+PimLXZ\nidYOagWqiXq2HG1uS8unqTr6rK4KdgtW9cp/fgYJTlL37apOZHKDXlo8PCH4c2jeHeb8DLN+sG3d\nu66rzhXFy0Ozx2x3XpFXfANEYjb9DWwCYoF2WKx9sFjnpvfAjFaQdwDuQKPosFC7VeL7B4dM8A8O\nOe8fHLIr2X1j/INDTvkHh1gTP3Lf/4yju0BPgCqSIItkfPzgpbFqBfDnFx5eJVszGzy9cuWI1YJe\nXjzfpQtLd+1ic9ThdI+9dvs2Q34Zi3e+fPw+ciT5PY0dWBNQoTy1ypRh2gYnGj1dq6mq7z15wOhI\nHnb5HGxfBq17Z331Mr83DHhVfV8rZ9gnvrzg1jW1UTyn70S5e8AzH0FQf/h7Akz/0nYvypJWj7uN\nkNVjkXUW649AI2AWcAZVEdE/cVhIujJKkPsA3wJ1/YNDCuc0znT8BqT2bP9tdFioKfFjoR2vbwwZ\nECLSUtAfXrFAoeLww/NwNHHz153bsGkRNO6kNsrkQvdWkeen/Zo8ISGB0RMmcvj8BSaEmCnrX8SB\nEaZO0zQGtWzO5qjDHDp7zuhwlJpN1a0z1iGvnKEWCNpls1lS485Qq5latbwebdvY8ooTiS+cbFGq\n5eYGT7wDnZ6EZVNhysc5X93XdZgXmrh63C3nMYq8yWK9jsVqwWJtATwDVAW2YzZNSe9hGSXILYHd\nwI/APv/gkJ62iDWl6LDQVUDe+w0XFaneSvdx7I574SIKFVNJso+fml518gBsWwoxN3LV5ryUfBNX\nkf/dtTvNVeRv/17EQmskHw/oT6uaNRwcYdoGNGuGm6Yx3VlWkQuXUD2RnS1Bjr0Lq8OhflD2h9xo\nGgx+E2JuQ8QPto0vrziZjQ4W6dE0tbLfzaz2SUx4V20mzq7IlXBin6weC9uxWPclDgupDqTbLzKj\nBPkloG50WGgLVLL8f7aJMNOe8w8O2ZFYgmH8EpEtJSTA4R1SfyzS518KXg1Tm2G+DYHFk9RO7hqN\njY7Mroa3b0dRX99U+yIv3bWLT+fMpX9gICEdOxgQXdpKFy5Muzq1mb5+AwnOUvdbq6naDJeTRMXW\ntiyG65ehQw7rxktXgU5PwNrZ6vepyJoT+9VAGb+itjunpkGv0dD3RdXCz/KGekGUVUmdK4qVk9Vj\nYXsWawIWa7qN9zNKkO9Gh4VeAIgOCz0MOLLh6ljUMrgJVTfyTVoHappm1jRti6ZpW+LiXGTC0rmj\nqv5LEmSRkWJlVZKsuanNea16Z37Hv4vy9fLi+Ue6sGz3HjZFRd27/+iFC5jDfqVu2bJ8N+QpNCf8\nexjUogUno6NZd/Cg0aEoNQPhzi04tsfoSO5bPlWtbNdunvNzdTdD4eKyYS87crpBLz1dh8KgN1XL\nyl9eVuVhWbFjFRzfK6vHwjAZJcjl/INDfkj6SOVzu4kOCz0XHRYaHx0WmgCEAYFpHavrukXX9Sa6\nrjfx8HCR/0gyIERkRcmK8EqoSo7bOGaEstGGtWtLUV/fex0tbt25y1O/jANg0uiRFMifz8jw0vSY\nyYSvlxdT1zlJT+SaTdSts5RZHN6paurbD7TNCz0vH+j/ikqmVkfk/Hx5RVwsnI6yb6vIDoNhyBjY\nsw5+fA5iMtkCMalzhaweCwNllCC/zv2+x1tT+dxu/INDSif7tA+wK61jXVJUJPgUUomPEJlRpho8\nPUaNes0DfL28eL6rWkXeeCiKlyZNZs+pU1iCh1OpeHGjw0tTgfz56NW4MfO2buPmnTtGh6N+XsrX\nVO3enMHyaSqpbdHDduds2lWVHc36UZVuiIydOQzxcWpIiz217g3DP+X/2bvv8Car9oHj39NBC6UF\nypa9CwUNyGplbwUBEWSoLG3KVFHE9VPxVV4VB4ogtHWAgGwEXhzIVPZOhQIFCmiL7Ipl0/H8/ngC\nFEhpaZM8SXN/ritXkmed+/CU9s7JGRyywIQhcDEl+3N2r9M/8DzyrD6NnBD2YDb5YzYNxmz6xPoY\njNmUZc+Iuza3JsdE3XWVEXsJjoicDbQCSgRHRCYBbwOtgiMiTYAGHAUinRGL0yTE6t0rXPArYiFc\nxTOtWjFp+Qr6fzmF0+fP80b3brSrW9fosLLVN7wpszZs4Mddu3iiqR26EeRVSGNYMxeuXdH7sxsl\n5ay+lHrLXnqSbC9KQd/X4N3esPgLePot+107v8rNEtO51fhhfbXQmFfg0wh9GsvALBbjvdF6XA6a\nSuuxsBOzqSawDNjIzQbe5sArmE2dibYcuv2UuybIwRGR/0NPUG25CiQAk5NjohJzHTSQHBPV18bm\nr/NyTZd24RycOAJhXYyORAiXFuDnx8iOHXh7wUI6m0yMetg95n5uWr06lUqUYM6mza6RINdqpC8F\nfPiPm0tQG+H3hXqrZW6ndrubctX1r/RXzYLmPaCy63+QMlRivD6feumKzimvfhsY/rneH/njZ2HU\nVH2WldvtXqf3l+//trQeC3uaALxItOXW+UPNps7WfXd8pZVdF4uP0QfH2XpMBf4G5uU1ao9zfbS1\nLBAiRLbMbVozcUB/vnxmEF5e2f3Kcg1eXl480bQJv+3bz7FkF/jKv0YD8PI2th9yWir8Ph/qhEGZ\nyo4p49EhEFgcvv/AdVcPdBWJ8VC+hv5z4Syh4fDcZEg+AR89oy9Sktn11uPi9+kr8wlhP7XuSI4B\noi0/AiG2Tsjur02B5Jio32w9gA7JMVET0edJFvciIVb/pVQ51OhIhHB5fr6+PNXsIQL9DewakAt9\nwsLQNI0FW7YYHYq+hHnlUGMTZMtqOHdab+V1lIKFoecofZXSDYsdV4670zR9DmRndK+4Xa2Geuvx\nxXMwfjCc/PPmvuutx50jpPVY2NvN1ZvMpttH856zdUJ2CfLk4IjIWzoBBUdEegVHRE4DHgBIjol6\n9p7D9HQJsfovpntdXlUI4TaqlCpJk+rVmL1pM5qWVU81J6rVSJ89IqczCdjb6jn6rAR1H3JsOU0e\n0VvMF02Ei/86tix3lXwcLp03JkEGffXYl2Ig9arekvz3IWk9Fo52HrOpBWbTg+iLhOjMpjqAzZGj\n2SXIHYFPgiMiHwMIjogsCCwFCmCjv4bIgbRUvXVD5j8WIt/rExbGgePHsfz5Z/YHO1pIY32e4IM7\nnV924n44tEuf2s3RX+krBX1fhcvnYfEkx5blrpw5QC8rFULg5a/1+/XRs/DLN3rrscxcIRxjPDAW\n+AiIz7S9IfrEEHfIbhaLI8ERke2A5cERkaWBp4BtyTFRo+wSric6dlAfSS7zHwuR73Vv+CCvzp7D\nnI2bqF+5srHBVHtATzz2b4N6zZ1b9pq5+uwZ4d2cU175mtDqCX1KuWY9oFJt55TrLhLj9cS0XI3s\nj3WkslXh5W9gglmfoq/4ffad/k+I66Itq4HVNrZ/l9Up2c1i0cD68hVgOrACmHF9e3JMlAFNEW7u\n+gIhVe83Ng4hhMMVKVSIR+qbWLhtG+8+0YsCRi5kVMBfHxjs7H7IF87Blp/0r80DgpxXbtehsG05\nzH4fxkwDNxng6RSJ8VCqkmt08ytVAUZ/A9Pf1vunS+uxcASzacBd90db7pjWOLvfGNdnrHgJ+AMo\nnWnbx7kK0tMlxOpT2wSXMToSIYQT9AlrSvKFi6zY7QJrHYU01gdnXbA5JsUx1v+g9zVt3cd5ZQIU\nCoLHX9BnDdq01LlluzpHLjGdG8XLwovRYGptdCQi/xqP3p3iQRuPj2ydkF0XC/lptTdZIEQIj9K6\nTh1KBQUxZ+MmOtc3uGtV7caw9Es4sAMatHV8eRnp8Ns8fZW78gZ8nd+0C/y+ABZ9DqY2zm3BdlWX\nUvTp1Vr0NDoSIZzpGNGWkTb3mE02Rw5n+31fcERkKWA4cH1Osjj0xUFO5TJIz/XPSX30cLsnjY5E\nCOEkPt7e9GzSmJjVazh7/gLFAwsbF0ylUP1r9f1bnZMg//E7nD0OPV9yfFm2eHlBv9dhXD/43xTo\n84oxcbiSxAP6syu1IAvheHdbujPQ1sa7drEIjoh8CNhmffud9QGw1bpP3Ivr/Y9lBgshPErf8DBS\n09NZtG1b9gc7ko+vPgVavJPiWD0HipUBUyvnlGdLxRBo2VMfKJgYn/3x+V2SC8xgIYTzHcZsannH\nVrOpFfqq0HfIrgX5E6B7ckzUrkzblgZHRP4ARAFNchenh0qI1dejr2Bz0RYhRD4VWr48dcuXZ+6m\nzUS0MbjnWkhjWDBBX7SjaEnHlXP8MOzfAo+NBG8DBycCdBsB21fA7A/0WRM8uYtbYjwEFYciJYyO\nRAhnegdYhNkUA+wCNKABYAZ62Dohu0F6QbclxwAkx0RZyKJJWtxFQqy+mpWM0hXC4/QJD2Pn0aPE\nHz9ubCC1GuvPjm5FXjMHfApAs8ccW05OBARBj+f0uZg3/2h0NMZytQF6QjhDtGUz0Aq9q0UEemJc\nGGhFtGWTrVOyS5BVcERksds3BkdEBufgXJHZtSv6ZPnSvUIIj9SzSWO8vbyYu2mzsYFUqKnP8ODI\nBPnSedj0P2jUEQKDHVfOvQjvBlXqwcIJenyeKC0V/k6QBFl4pmjLAaIto4m2dLY+XiLakmW/q+y+\n95oA/BocETkauD7n8YPAh9Z9Iqf+3Afpafo8pEIIj1MqKIi2oaHM3bSZN7p3w9uoeXm9vKFWQ8fO\nh7xpKVy9rM9r6yq8vPQV9t5/Sl/SuPfLRkfkfMcP63+HykuCLDyM2fQNkHXfqmjLoNs33fU3dHJM\nVDR6v413gaPWx3+A95JjoqJyH6kHSrDoz9VkgRAhPFWf8DCOnzvHuv0GDxar1RjOHNMf9paRoQ+I\nq3o/VKpj/+vnReVQaP643v0j6aDR0TifKywxLYQxlgH/sz5aZnp9/f0dsh05kRwTtcx6YZEXCbFQ\nqqLrfN0ohHC6Tg/cT5FChZizaROt6hi4/HFII/15/zZoVs6+1967CU79BY8Ose917aX7CNhhHbA3\n+ivPGrCXGA++/lC6otGRCOFc0ZZFN16bTf93x3sbsltq+q277NaSY6LevccQPZOmweFYqNvM6EiE\nEAby9/XlsYYNmbd5M+efvEKgv78xgZStqs9kEL8VmnW377VXz4agEvBge/te114KF9Vn1pj5Hmz9\nGZo8YnREzpMYry/Y4uVtdCRCGOk0ZlMvYCnQAsiwdVB2neAu2ngAPAPIjOs5dToRzv8jA/SEEPQO\na8qla9f4346d2R/sKErprcj7t+kf4O3l1F8QtwFaPO7as/U0e0zv/rFgAly5mP3x+YGm6XMgS/cK\nIUYBLwJngY/RF8O7Q3ZLTX9y/XVwRGQg8DwwCJiDPkeyyIkbC4QYvMysEMJwjatVpWqpUszdtJl+\nD4UbF0itxrD1FzhxFMpWsc81184D5a0nyK7Myxv6vgYfPA3LoqDni0ZH5HjJx/XZOyRBFp4u2rIX\nCMvusJwsNR2Mnmk/CUwHGiTHRP2T5wA9SUIs+BfWv9YUQng0pRS9w5ry/pKlJJ49S4XixY0JJOT6\nfMhb7ZMgX7kEGxbDg+2gaKm8X8/RqtbTW5JXfq9PAXdfNaMjciwZoCfEPcluqemP0JeaPg/US46J\nGivJcS4kxOqzVxg1rZMQwqX0bqovQjpv8xbjgihRDoqX1btZ2MOWH+HyBWjTxz7Xc4bHRoJ/QZjz\noX27mriixHi9a025GkZHIkTWzKZvMJtOYTbtMTqU7DK2l4D7gP8D/g6OiEyxPs4HR0SmOD68fODS\nefj7kPQ/FkLcULFECR6qWZM5GzehGZWYKaV3s4jfpk/Nlheapk+dViHEveZ6DwzWZ7XYvxV2/Gp0\nNI6VGA+lKoFfQaMjEeJupgGdnFqi2dTO1ubs+iBLk2deHdmt//Fwpz8aQgiH6xPelJHTvmPb4cM0\nrmbQ1/shjWHjEkg6ABVDcn+d+O36Cm0DxrrftGktesK6H2DeJ1C3OfgXMjoix0iM11cSFMKVRVt+\nx2yq7JSyzKYywKdAZWDl7bslAXa0hFhQXlClrtGRCCFcyKMNGlCwgK+xS0/Xaqg/53VVvTWzIaAo\nNHJuw49deHlDv9fg3Cn4KcboaBzjUgqc/Vv6HwsBYDYpzKYRwDpgFdEWm6OlJUF2tIRYKFcdChY2\nOhIhhAsJKliQLvXrs2jbdq6kphoTRLHSULpy3hLks8fBshaa94ACBs3rnFfVHoDwrrBihj6rR36T\neEB/lgRZGM9HKbU908PslFLNphTMpvOYTSlACjAO6Ea05eusTpEE2ZEy0vUuFtL/WAhhQ5/wMP69\ndInlf/xhXBAhjeDgTkjLZZL+23z9uWVP+8VkhB4v6An+7A/y34C9JJnBQriMNE3TGmZ6RDul1GhL\nENGWwBvPMAJYjNn0AmaTzVxYEmRH+jtBn4Re+h8LIWxoERJC2aJFmbNxk3FBhDSGq5fgz733fu61\nK7BuEZhaQfH77B6aUwUFQ7fhsG8z7FxldDT2lXhAXzmxSAmjIxHCNURbZgCNgdrAdluHSILsSDcW\nCJEEWQhxJ28vL55o2oSVe+I4nWLQxEA189APeftyuHgOWve1b0xGadkLyteEeR/nvkXdFSXKCnrC\nTZhNs4FNQC3MpiTMpmccVla05RzRlkhgmK3d2S4UIvIgIVafRqhkeaMjEUK4qN5hTfn8l+Us2LqV\noe1szjbkWIHF9KQwfht0jsj5eZoGq+foC2xcH+zn7rx9oEskTH0JEixQq5HREeVdWiocT4DQbBcO\nE8J40RbHfNrWZ8aYBDQFFLAFGEa05SjRFpsjpaUF2ZESYvXWY3eb9kgI4TQh991H/UqVjJ3NonYT\nOGSB1Ks5P+dwLPy1D1r3yV+/42o3AS8fiDOw24s9nTiiJ8nlpQVZeLRvgBlASaAE8J11W5YkQXaU\nlLNwOlG6VwghstU7rCl//JXI3qRjxgRQqxGkXYOEexgsuHqOPjtPk86Oi8sIBQvrv7fjNhodiX3I\nEtNCAAQTbZlLtEWzPuYAwXc7QRJkR5H+x0KIHHq8cWN8vL2Ys8mgVssaDfT5gONz2A/53GnYsRLC\nu+XPhTVCwyBxv97Q4e4S48HXH0pXNDoSIYx0ErPpWcwmP+sjAjh5txMkQXaUw3/o/dkq1TE6EiGE\niyseWJgO9eoxf8sW0tLTnR9AwcJQOTTnA/V+XwBaOrTu7di4jBJqXTdgr4HdXuwlMR7K19A/AAnh\nuQYB7YGjwJ9AB+u2LMkgPUdJiNWTY18/oyMRQriBPmFh/GSJZe2+fbSra8DKm7UawfLp+tSU/gFZ\nH5eWCr8vhNCHoFQ+bZWsEKIPXozbCE3duAuJpukJcsMORkcihLGiLX8Dtj/Rm02diLb8cvtmaUF2\nhLRUOBon3SuEEDnWvl5digUEMGejQa2WIY0hIw0O7rr7cTtXQsoZaJNPpnazxcsLajeFvZsgI8Po\naHIv+YS+zLT0PxbiTmbTfZhN84A3bO2WFmRH+Gu/PuBFFggRQuSQn68vjzduxMz1G0i5dJmgQgWd\nG0C1B8DHV++HXK9Z1setnq23HNfJ59OGhYbD1p8h6QBUDDE6mtyRAXpC6MymzH2MvIDh1sd7RFum\n2zpFWpAdIcGiP1e739g4hBBupXdYU66kprJ4xw7nF17AX/9Qv39b1sf8uVcfX9G6t97Kmp9d/wDg\nzrNZJMXrU/CVq2F0JEIY7TRwxvp8GngH6JZVcgySIDtGQqy+7GrRUkZHIoRwIw0qV6ZGmTLMNWo2\ni5DG+uwNF/+1vX/NHPArCGFdnRuXEYqU0BdQidtgdCS5lxgPpSrp90wITxZtCSbaUsz6XBQYAizG\nbBp9W+vyDZIg25um6RPoS/9jIcQ9UkrRNzyMTQcPcfT0aecHENJI/x12wEYL9vlk2PoLNO0ChQKd\nH5sRQsPhUKw+cNEdyRLTQtgWbZkNNASqADttHSIJsr0lH9fnCJUEWQiRC72aNEEpxRwjVtarXFdv\nbbQ13dv6H/SxFa37OD8uo4SG6wMX79btxFVdSoEzxyRBFiIr0ZYUoi3DgWdt7ZYE2d5uLBBiMjYO\nIYRbKhdcjBYhIczdtBlN05xbuI8vVG9wZ4KcngZr50NIE7ivmnNjMlI1k943e68b9kNOOqg/S4Is\nxN1FW2x+ApYE2d4SYvUWmHLVjY5ECOGm+oQ15c8zZ9h88JDzCw9pBMcPw79nbm6L/Q3+OQFtPKj1\nGMC3gD4/dJxBfcLzQmawcAtJyclcunrN6DCEDZIg21tCLFSpp6+iJ4QQudClQX0C/PyYs9mAbhYh\njfXn+EyNKqtnQ/GycH8L58djtNBwOJ0IpxKNjuTeJMZDUHF9sKFwSVsOJdDojTdp/s5/2HHkiNHh\niNtIgmxPVy7pc2ZK/2MhRB4E+PnRtUEDFm/fzuVrTm5dqlBLH4R3vZtF0kE4sB1aPuGZyxXfWHba\nzbpZyAA9l/bn6TM8/eUUyhYryrX0NB7+cDwTfvqZdHdemMadmE0dMZtWYzb9htnUy9YhkiDb09E4\nyEiXBUKEEHnWJzyM85ev8JMl1rkFe3lDzYY3B6atnQu+ftDsMefG4SpKVYQS5dyrm0VaKhxPkATZ\nRaVcukyfLyaRmp7O3OdGsu6tN+lSvz7v/rCY7p9MICk52egQ8x+zqfxtW8YB3YF2wNu2TpEE2Z6u\nLxBSVRYIEULkzUM1a1CpRAnenDff+V+/hjSGM0n6nMibl0Hjh6FwUefG4CqU0luR92/VE093cOKI\nHmv5mkZHIm6Tlp7O4OhoEk6dZPqQSGqUKUPRgAC+NkcwaeAALH/+SYt33mXpDpszj4ncm4TZ9B/M\npkLW90nAk0A/9AVE7uASCXJwROQ3wRGRp4IjIvfY2PdScESkFhwR6fodqRJioWxVCAgyOhIhhJvz\n8vJi1ohh+Pn60mX8x8xzZn/kkEb687SxcO2K5w3Ou12dMLh66eYsRa5OBui5rDfmzmN13F4+fvJJ\nWtS+uYS5Uop+D4Wz9s03qFKyJAOnRvHc9O+4ePWqgdHmI9GW7sAmYClm0yDgaeAq4A90s3WKSyTI\nwDSg0+0bgyMiKwAdgL+cHdA9y8jQl2CV/sdCCDupU64cK19/jUbVqjLk628Zu2Chc/oolq0GgcF6\nC3L1+lAhJPtz8rOQxuDl4z79kBPjwdcfSlcyOhKRSczqNcSsWcvwDu3p37yZzWOqlS7NL6++wqiH\nOzFrw0ZavzsOy59/OjnSfCra8jN6TlkQWAgcJtoSRbTF5tKhLpEgJ8dE/Q7Y6nQzARgDOHky0Fw4\neVSfmF36Hwsh7Kh4YGEWvvACg1u1ZOLyX3ly0pekXL7s2EKVujmbhae3HgMULAzV7neffsiJ8fpU\no544qNJFrdyzh9fmzOXhBx5g7OM97nqsr483b/Z4jCUvjeLi1at0fP9DJv6ynAwZwJd7ZlNvzKbf\ngZ8BC9ATeBizaS5mk815eV0iQbYlOCKyG3AsOSbKPb7TurFAiCTIQgj78vXx5uMn+/Hxk/1YvTeO\nDu9/yOFTpxxbaPMeUL8tmNo4thx3UScM/toHKS4+gErTZAYLF7P32DEGR8VQp1w5op4djLdXzlKv\nZrVqsX7sW3R64AHGLlzE4599zt///OPgaPOtN9Fbj7sBo6yr6L0CvAKMtXWCSybIwRGRhYDXgbdy\ncrxSyqyU2q6U2p6WlubY4LKSEAsBReQrLSGEwwxu1ZKFL7zA6ZQU2o17n9/27XNcYSGNYegn+up6\n4uZ0b/tcvBU5+YT+baYkyC7hdEoK/b6YTGE/P74fOZzC/v73dH6xgACmDTHzef+n2ZZwmObvvMuP\nuywOijZfOwv0QR+Ud/bG1mjLUaItT9k6wSUTZKAaUAWIDY6IPAqUB3YGR0SWsXWwpmnRmqY11DSt\noY+PQQt0JMTqs1fk8JOhEELkRvOQWqx64zXKFitKz88mErN6jfOXpPZEFWtD4WKu381CBui5jCup\nqTz95VROn09h1ohhlA8OztV1lFI83bwZa958g4rFi/P0l1N4ccYsWYHv3nRH73ucDjyfkxNccrm3\n5Jio3UCp6++tSXLD5Jgom1NxGO7iv/q0Ok07Gx2JEMIDVC5Zkl9efYUhX33DK7PnEJeUxPh+fSlg\nVAOBJ/DygjpNYO8mfVC2qzaGJMXrfcjL1TA6Eo+maRrPTfuOrQkJfDvETP3KlfN8zRplyrD8tVf4\n7+IlTFz+KxsOHCAm4hnur1gx7wHnd9GWf4Ap93KKS/wPD46InI0+/Uat4IjIpOCIyGeMjumeHP5D\nf5b+x0IIJwn092fGsCG8+MjDfLduPY99OoEz588bHVb+ViccUs7CsQNGR5K1xHh9cRP/QtkfKxzm\n4x9/YsHWrfxf15PIxQAAIABJREFU9250e/BBu123gI8PY3s+zqJRL3D+8mXa//cDJv+6QgbwOYDK\nb1/NBQQEaBcvXnRuoYsnwS/fwufrwa+gc8sWQni8hVu2MnL6d5QMCuL7EcMILX/7olHCLs6dhjHt\nocfz0GmQ0dHY9npnqBwK5vFGR+KxFm3bxrPRX9EnrCmTBw1EKeWQcs6ev8Dz333HT5ZYWtepw+RB\nAylTtIhDyrIXpdQlTdMCjI4jJ1yiBdntJcTq/b0kORZCGODxJo35ccxo0tLT6fTBeJbt3GV0SPlT\n0ZL66nRxLjof8qUUOHNM+h8baPvhI4z4djpNq1dnwtNPOSw5Bn0KyBnDhvLpU0+y+dBBmr/zH5bH\n/uGw8jyNJMh5lZ4GR3ZL9wohhKHqV67MqjdeJ+S+++g/ZSofLftRBu85Qp0wOLQLrlwyOpI7JR3U\nn8tLgmyExLNneXLSZMoULcKMYUPx83X8DDBKKQa2bMHq/3uDskWL0nfSZMZ8P5vL12QAX15JgpxX\nSQf1pVhlgRAhhMHKFC3C/15+id5Nm/L+kqUMjoqRpWrtLTRcbxg5sN3oSO4kM1gY5vyVK/T9YjJX\n09KYPXIExQMLO7X8WmXLsuL1VxnWvh1frVlL23HvE5eU5NQY8htJkPNKFggRQrgQf19fvhw8kHd6\nPs7SnTvpPP4jks66+OIW7qR6fSjg75rdLBLj9SXCi5QwOhKPkp6RQUT0V8QfP863Q8zUKlvWkDj8\nfH1574leLHjhef65eIF2494natVq+SYplyRBzqvDFihaCoJtTtEshBBOp5RiZMcOzBk5nCOnT9N2\n3H/ZcijB6LDyB98CULOh6ybIFWrp07wJp3lz/gJ+3b2b8X370LpOHaPDoU1oHda9/Rat6tTmtTlz\n6T1xEqdSUowOy+1IgpxXCbF667H8QhJCuJj29erx62uvEliwIN0++ZRZGzYYHVL+EBoOp/6C0y70\nFXZaKhxPkO4VTvbt2t+YunIVQ9q1ZVCrlkaHc0OJwEC+HzGc8f36sD4+nuZj/8OK3buNDsutSIKc\nF/+chLPHpXuFEMJlXe+bGFajBiOnfccbc+eRlp5udFju7fqy067UinziiJ4kS4LsNGv37mPM7Dl0\nqFePd3v1NDqcOyileLZ1a1a98Tolg4LoPXESr86Zy5XUVKNDcwuSIOeFLBAihHADxQICmP/8SMxt\n2zBl5Sr6fDGJfy+54CwM7qJ0JShe1rUSZBmg51Txx48zcGoUtcqWJcb8LN6uurIiULvcfax84zXM\nbdsQvWo17ca9z95jx4wOy+W57h11Bwmx4OsHFUKMjkQIIe7Kx9ubD/r05rP+T7Nufzzt//sBB0+c\nMDos96SUvqpe/Da91dYVJMaDr7+evAuHOnv+An2/mIS/ry+zRw4n0N/f6JCy5e/rywd9ejP3uZGc\nTkmh3bj3+WrNGhnAdxeSIOdFQqy+YpGP4+c6FEIIe+jfvBmLXxzFuUuXaP/fD1i1J87okNxTaDhc\nuXjzm0SjJcZDuerg5W10JPna1dRU+k+ZwvF/zjFz+DAqFC9udEj3pH29uqwb+xbNatVizPdzeG3O\nXKNDclmSIOfF4Peg10tGRyGEEPckrGYNVr3xGhWKF6f3xC+Y/OsKaUm6VyGN9WTUFbpZaNrNGSyE\nw2iaxqgZM9l08BCTBw+kYdUqRoeUK6WCgpj73Aje79Ob3mFNjQ7HZUmCnBelK+ktyEII4WYqFC/O\nz6+8zCP1Tbw5fwEjpk3nqgzeyblCgVClHuzdZHQkkHxCX2ZaEmSH+uznX5izaTOvdn2UHo0aGR1O\nniiliGzbhvqVKxsdisuSBFkIITxUYX9/pkWaGfNoF2Zv3ETXTz7l5L//Gh2W+wgNh7/2wXmDF2KR\nAXoOt3THTt79YTE9Gzfm5S6djQ5HOIEkyEII4cG8vLx4teujfBNpZk9iIm3HvU/sn38ZHZZ7CA3T\nuzfs3WxsHEnx+sDBcjWMjSOf2nX0KEO/+YZG1aoycWB/lKx74BEkQRZCCEH3hg/y8ytjUAoeGT+e\nH7ZtNzok11epDgQUgb0G90NOjIdSFcG/kLFx5ENJycn0mzSZkoFBzBw2FH9fGZTvKXyMDkAIIYRr\nuL9iRVa98Tr9v5zKM9Ex/LZvH2WLFiUtI4P0jAxS09NJS0/P9DqD9Az9OTU9XX99t303XmeQlpGu\nv07PIC0jgzTrtdOs+0oXKcKGt98mqFBBo/9ZsublDXXCIG6z3pJsVMtiYryMh3GAC1eu8OSkL7l4\n9RqLRo2iZFCQ0SEJJ5IEWQghxA2lgoJY8tIoxsyew3fr1t/Y7uvtja+3N97eXvh4WV97eeHj7Y3P\n9efM+7y98PHyooCPNwULFMDH28t6jnem1/p5vtZreHvp26+mpRG1ajUz1q9neIf2Bv5r5EBoGGz7\nBZIOGNMH+FIKnDkGzXs4v+x8LD0jg8ivviEuKYk5z42gdrn7jA5JOJkkyEIIIW7h5+vL5/2f5uN+\n/fD2UngZsErY7sREolevIbJtG3y8XXhu3zph+nPcRmMS5KSD+nN5GaBnT+8sXMTPsbF82LcP7erW\nNTocYQDpgyyEEMImXx9vQ5JjgKHt2pF49iw/WiyGlJ9jRUvpg+OMmu5NZrCwu+/WrWfSryuIaN2K\niDatjQ5HGEQSZCGEEC6n0wP3U6VkSaasWGV0KNkLDYNDu+DqZeeXnRgPgcFQpESeL3X83DnS0tPt\nEJT7Wrc/ntGzZtEmtA7jej9hdDjCQJIgCyGEcDneXl5Etm3D1oQEdhw5YnQ4d1cnHNJSIX6b88u+\nvoJeHgcIxv75F6ZXX6fvpMkeu2DMoRMnGTBlKtVKleYbs9m1u/YIh5MEWQghhEvq91A4QQULun4r\nco364Ovv/G4WaalwPCHP3SuupqYy7NtvKVigAKv2xDFgSpTHJcnJFy7Q54tJ+Hh7M2fkCNeePUU4\nhSTIQgghXFJhf3/6N2/Gkh07SDpr8Gp1d+PrB7Ue1AfqOdOJo3qSnMcE+aNlP7Lv2N9EP/sMnz79\nJL/u3s0z0TFcS0uzT5wu7lpaGgOnRpGUnMyMYUOpVDLv3VVELplNnTCb4jGbDmE2vWpkKJIgCyGE\ncFnmNm0AiFmzxuBIshEaDif/1KdccxY7DNDbeeQon/+ynH4PhdPh/noMbNGCj/r15SdLLM/GfEVq\nWv7uk5yRkcGLM2exPv4AXwzoT5Pq1YwOyXOZTd7AZOBhoA7QF7OpjlHhSIIshBDCZZUvHsyjDeoz\n/fd1XLhyxehwslYnXH+Oc2I3i6R4vWtH6Uq5Ov1KairDv51GqaAgxj3R68b2Z1q34v0+vVm2cxfm\nr77OtwP3rqamEvn1N3y/YSNjHu1Cr6ZNjA7J0zUGDhFtOUy05RowB+hmVDCSIAshhHBpQ9u1I+Xy\nZWZvNGgqtZwoUxmCyzp32enEeChXXV/RLxfG/28Z8ceP8/mApylS6NZlqiPbtuG9J3qxZMcOhnz9\nbb5LklMuX6b3xC9YuHUbYx/vwSuPdjE6JAHlgMRM75Os2wwhCbIQQgiX1qhaVRpWrULUqtVkZGQY\nHY5tSunTve3bqvcLdjRNuzmDRS5sP3yEib8s56lmD2W5EMaw9u0Y27MHi7ZtY8S06aS76r/9PTpx\n7l8e/egTNh48yJTBg3iuU0eUUcuEex4fpdT2TA+z0QFlRRJkIYQQLm9ou3YcPnWK5X/sNjqUrNUJ\nhysX4Mgex5f1z0m4+G+uEuTrXSvKFivKe7163fXY5zp25M3HujNv8xaem/ad635AyaGDJ07Q6YMP\nOXzqFLNHjKB3WFOjQ/I0aZqmNcz0iM607xhQIdP78tZthpClpoUQQri8RxvUp3xwMFNWruRh0wNG\nh2Nb7cZ6d4e9G/Wp3xwpDwP03l+ylIMnTrBw1PM5ms5s1CMPk5qezgdL/4e3txefPf2UYSss5sX2\nw0fo88UXeCkvlo5+kfqVKxsdkrjVNqAGZlMV9MS4D9DPqGDc7ydcCCGEx/Hx9iaiTWvWxx9g91+J\n2Z9ghEJBUKUu7Nng+LIS4/VuHeVq3NNpWxMSmPTrCga0aE7rOjmfIGDMo10Y3aUzM9dv4KVZ37td\nS/Kvf+ym+yefUqRgIX55dYwkx64o2pIGjACWA/uAeURb4owKR1qQhRBCuIX+zZsx/n/LmLJyJV8O\nHmR0OLaFhsP/psL5fyCwmOPKSYyHUhXBv1D2x1pdvnaNEd9Op3xwMP/p1fOei3yt66Okp6cz4edf\n8PHyYny/vm7Rd3fm+g2MmjGTehXKM+e5kZQKCjI6JJGVaMtPwE9GhwHSgiyEEMJNFClUiCcfCmfh\n1m2cOPev0eHYVidcH0C3b7Njy8nFAL1xi5dw6ORJJg54mkB//3suUinF/z3WnZEdO/D12t94fe48\nNE275+s4i6ZpfLzsR56b/h0tQkJYMvolSY5FjkmCLIQQwm1Etm1LWkYGX7vqwiGV60BAEceuqnfp\nPJxJuqcEefPBQ0xZuYrBrVrSsnbtXBetlGLs4z0Y2q4tUatW8+b8BS6ZJKdnZPDy97P575KlPNG0\nCbNHDs/VhwLhuaSLhRBCCLdRpVRJHnngAb79/Xde7PwIBQsUMDqkW3l5Q+0msHez3pLsiC4Ixw7q\nz+VzliBfunqNEdOmUyE4mLGP98hz8Uop3nuiF+kZGXy5YiU+Xl68/XgPl+lucSU1FfNXX7Ns5y6e\n69iBt3o85paDCoWx5CdGCCGEWxnavi3JFy4yd7ODuzHkVp1w+Pf0zUTW3u5xBot3f/iBw6dOMWnQ\nAArbqRVVKcX7fXozuFVLJi7/lXGLl7hES/K5ixd5fMJn/LjLwn97P8HYno9LcixyRVqQhRBCuJWw\nGjV4oGJFpq5czYDmzV2m5fKG0DD9OW4jlK9p/+snxkNgMBQpke2hGw8cIHr1GiJat6JZrdwtKpIV\npRTj+/YhPSODT3/6GR9vb17t+qhdy7gXScnJPPH5Fxw+dYqYiGfo0aiRYbEI9ycfq4QQQrgVpRRD\n27flwPHjrIozbBaorBUrDfdVgzgHLY19fYBeNh8MLl69yohp06lUojhv2aFrhS1eXl588mQ/nnzo\nIcb/bxkfLfvRIeVkZ9+xv+n0wXiO/ZPM/Oefk+RY5JkkyEIIIdxO94YNKVOkCFNWrDI6FNtCw+HQ\nTrh62b7XTUuFvw/lqHvFfxYu4ujpM0waOIAAPz/7xpGJl5cXn/d/ij5hTXl/yVIm/PSzw8qyZfPB\nQzwy/iMyMjL48eXRNA+xb0u58EySIAshhHA7BXx8eLZ1K9bs3cu+Y38bHc6d6oTpyeyB7fa97omj\n+nWz6bqxPj6emDVriWzbhvCaDujmcRsvLy++GDiAno0b8+4Pi5m4fLnDywRYtnMXj306gVJBgfzy\n6hjqVqiQ/UlC5IAkyEIIIdzSwJYtKFjAl6mrXLAVuUYD8PWz/3RvORigd+HKFUZ8O52qpUrx5mOP\n2bf8u/D28uLLwQN5rFFDxi5YxJcrVjq0vG/W/sbAqVHUq1iBn8aMoWKJ7PtkC5FTkiALIYRwS8GF\nC9O7aRjzNm3mzPnzRodzqwL+UPNB+/dDTorXE+/SlbI85J2Fi0hMTmbSwAEU8nPuNHg+3t5EPTOY\nrg824P/mzSd61Wq7l6FpGuMWL2H0rO9pX68ui198keKBhe1ejvBskiALIYRwW0PateVqWhrf/va7\n0aHcKTQcTh6Fs3bsApIYD+Wqg7ftSah+27ePr9f+xpC2bWhao7r9yr0HPt7exDz7LF3qm3h1zly+\nWfub3a6dlp7Oc9Nn8MmPP/FUs4eYMWyo0z8ECM8gCbIQQgi3VbNsGdrVrcvXa9ZyNTXV6HBuFRqu\nP9urm4Wm3XWJ6fNXrvDc9BlUL12aN7p3t0+ZueTr481X5gg6PXA/o2d9z/Tf1+X5mhevXuWpyVOY\ntWEDL3fpzOf9n8bH29sO0QpxJ0mQhRBCuLWh7dtyKiWFhVu3GR3KrcpUgWJl7NfN4p+TcPHfLBPk\nt+cv5FhyMpMGOb9rhS0FfHz4NtJM+3p1GTVjJjPXb8j1tc6ev0D3Tyawcs8ePnmyH6916+p681+L\nfEUSZCGEEG6tVe3a1C53H1NWrnKJ1dxuUEpfNGT/FkhPy/v17jJAb83evUz7/XeGtW9H42rV8l6W\nnfj5+jJ96BDahNbh+e9mMGfjvX9Y+OvMGR7+cDxxSUlMHzqEQa1aOiBSIW4lCbIQQgi3ppRiaLt2\nxCUlsW5/vNHh3Co0HC5fgCO7836txHg96S5X45bNKZcv89z076hRpgyvdeua93LszN/XlxnDhtIi\nJITh06Yzf/OWHJ+7+69EOn7wIafPn2fRqBfoXN/kwEiFuMkllpoOjoj8BugCnEqOiapr3fYu0A3I\nAE4BA5NjolxwskshhBBG69mkMe8u+oEpK1fSonaI0eHcFNIElJfezaJ6/bxdKzEeSlYA/4BbNr85\nfwHH/znHL6++QsECxnetsKVggQLMGj6Mvl9MYug33+Lt7ZXtane/79vPU19OoUihgvz84ihC7rvP\nSdEK4TotyNOATrdt+yg5Jur+5JgoE7AMeMvpUQkhhHAL/r6+DGrVkuV/7ObQiZNGh3NTQBBUqWuf\ngXo2Buit3LOHGevWM7JjBxpWrZL3MhyokF8Bvh85nCbVqxH51Tcs2bEjy2MXbt1Gr88nUqF4ML+8\n8ookx8LpXCJBTo6J+h1Ivm1bSqa3AYALdSwTQgjhaga3akkBHx+iXG3hkNBw+DMOLpzL/TUunYcz\nSbckyP9eusTz02dQq2xZXun6qB0CdbwAPz/mPDeShlWrEBHzFct27rrjmCkrVxIR8xUNq1bhx5dH\nUy64mAGRCk/nEglyVoIjIscFR0QmAk8iLchCCCHuolRQED2bNGb2xk38c/Gi0eHcFBquT9G2L+d9\nb+9w7KD+nClBfmPefE6lpDB50ED8fX3zGKTzBPr7M/e5kZgqVWJwdDQ/W2IByMjI4K35C3hj7nwe\nbdCAhaNeoGhAQDZXE8IxXDpBTo6JeiM5JqoCMAsYkdVxSimzUmq7Ump7WpodRgoLIYRwS8PatePS\ntWt8Z4d5d+2mcigUCspbN4vbZrBYsXs332/YyPOdOtKgSuU8h+hsQQULsuD557m/QkUGTo3ix10W\nhn7zLZN+XcGzrVvxTWSEWyX9Iv9x6QQ5k1nA41nt1DQtWtO0hpqmNfTxcYlxh0IIIQxQp3w5WtYO\nIXr1GlLT0o0OR+flDbWbwN6NektybiTGQ2AxKFKScxcv8sJ3M6ld7j5e7tLZvrE6UVChgiwc9Tyh\n5cvx9JdTmL9lK//XvRsf9u2Dt5e7pCciv3LZn8DgiMjM89h0A/YbFYsQQgj3MbRdO46fO3fXQWBO\nFxoO507D3wm5Oz8xHsrXAqV4fe68G10r/Ny8lbVIoUIsHPUC3R58kCmDB/Fi50dkARDhElyiuTU4\nInI20AooERwRmQS8DTwSHBFZC32atz+BIcZFKIQQwl20qxtKjTKlmbJiJY83buQaCdeNZac3QLnq\n93ZuWir8fQja9OOX2FjmbNrM6C6dMVWqZP84DVAsIIBvh5iNDkOIW7hEgpwcE9XXxuavnR6IEEII\nt+fl5UVk27aMnvU9Ww4l0LTGPSakjlCsNJStqvdD7jDg3s49cRTSUrlQqjKjZswktHx5Rnd+xCFh\nCiF0LtvFQgghhMit3mFNKVqoEF+uXGl0KDeFhsPBXXD18r2dZx2g90nsIc5euMDkQQMoIONthHAo\nSZCFEELkOwF+fgxs2YKfdln48/QZo8PRhYZD2jU4cI99o5PiSff2ZdIfB3nxkUe4v2JFx8QnhLhB\nEmQhhBD50rOtW+HlpYhavdroUHQ1GoCvnz6bxT1IPbqXOK0gdSpU5MVHHnZQcEKIzCRBFkIIkS/d\nV6wYjzVsyMz1G0i5fI/dGhyhgL+eJMdtyvk5msbVw3uIzSgoXSuEcCJJkIUQQuRbQ9u348KVK8xc\nv8HoUHSh4XDiCJw9nqPDV6xbReH0q5Sp15i6FSo4ODghxHWSIAshhMi3TJUqEVajOtGrVpOW7gIL\nh4SG6c856GZx5vx5FiyYAUDrDo85MiohxG0kQRZCCJGvDW3Xjr/OnuVHi8XoUKBsNShaKkfdLMZ8\nP5sqV5PRUPhUquWE4IQQ10mCLIQQIl972PQAlUuWYMqKVUaHAkrp3Sz2bYb0tCwPW7x9B4u37+Dx\nMkGoUhXAP8CJQQohJEEWQgiRr3l7eWFu04atCQnsOHLE6HD0bhaXL8DRPTZ3n05J4eVZ31O/UiWq\np56DCtJ6LISzSYIshBAi33uy2UMEFvR3jVbk2k1Beemr6t1G0zRGz5rN+StXmNKvF+rMMUmQhTCA\nJMhCCCHyvUB/f55u1owlO3aQlJxsbDABRaByqM1+yD9s387/du7kta6PUjP9vL5REmQhnE4SZCGE\nEB7B3KY1mqbx1eo1Roeid7M4GgcX/wX0luOFW7by0szvaVClMsM7tL+xxLQkyEI4nyTIQgghPELF\nEiXo0qA+09et58KVK8YGExoOWgbs28zBEyfoMeEzIr76mqolS/JVxLP4eHvrCXJgMShS0thYhfBA\nsiSPEEIIjzG0XTuW7tjJnE2beLZ1a+MCqVwXrWAgsT/NoePfSyhYoAAfP9mPAS2a4+1lbbtKjIfy\ntfSZL4QQTiUtyEIIITxG42pVebBKFaJWriYjI8OwOH6N28fK9ABKJe6hR8OGbH3vPwxu1fJmcpyW\nCn8fku4VQhhEEmQhhBAeQynF0PZtSTh1il9373Z6+Ulnk+n/5RT6fDGJbf5luE+lMuWRFpQKCrr1\nwBNH9SRZEmQhDCEJshBCCI/StUEDygUX48sVK51WZmpaOhOXL6fpW2+zKi6Ot3o8xugx7+k7ba2q\nl3RAf5YEWQhDSB9kIYQQHsXH25uINq0Zu2ARu/9KpF7FCg4tb9OBg7w063v2//03Dz/wAO/3eYKK\nJUroO8tW1edDbv/0rSclxoOvH5Su5NDYhBC2SQuyEEIIjzOgeXMC/PyYstJxrchnzp9n+DfT6PzR\nx1y8epVZw4cxa8Swm8kxQJ0wOLgTrt02q0ZiPJSrDt7SjiWEESRBFkII4XGKFCpEv/AwFm7dxolz\n/9r12hkZGUz77Xca/99bzN+6hRce7sTGd97mYdMDdx4cGg6pV/Uk+TpN0xNk6V4hhGEkQRZCCOGR\nItu1JS0jg6/XrrXbNf/46y86fjCeF2fOom6F8vz+1pu81eMxAvz8bJ9QowH4FLh12elzp+DiOUmQ\nhTCQfHcjhBDCI1UtVYpO99/PtN9+58VHHqZggQK5vlbK5cv8d8lSvlq9huKFCzP1mUH0atIEld0c\nxn4F9SQ5c4IsK+gJYTiPSJBTU1NJSkriitErJ7kof39/ypcvj6+vr9GhCCGEUw1t35afY2OZt3kL\nA1o0v+fzNU1j0bbtvDlvPidTUhjcsgVvdO9G0YCAnF8kNBwWfArJJyC4jJ4gKwXlatxzPML1eUJO\nkh/yCo9IkJOSkggMDKRy5crZf5r3MJqmcfbsWZKSkqhSpYrR4QghhFM9VLMm9SpUYOrKVfRv3uye\n/kYcOnGSl7+fzW/79mGqVJGZw4fRoErlew/ieoIctxGa99AT5JIVwP8ekmzhNvJ7TpJf8gqP6IN8\n5coVihcvni9/EPNKKUXx4sXz9SdZIYTIilKKoe3aEn/8OKvj9ubonMvXrvHfJUtp9s5/2HX0KOP7\n9WHF66/lLjkGuK8aFC11s5uFDNDL1/J7TuKwvMJs6oXZFIfZlIHZ1NC+F7+TRyTIQL79QbQH+bcR\nQniyHo0bUbpIUI6mfFuxew8PjX2Hj5f9SLcHG7Dl3Xd4tnXrm0tE54ZS+nRv+7fAxRQ4nSgJcj6X\n3//uOqh+e4AewO+OuPjtPCZBNppSipdeeunG+48//pixY8feeB8dHU1ISAghISE0btyY9evX39jX\nqlUrtm/fDsCRI0eoUaMGy5cvZ+3atXTp0gWAadOmUbJkSUwmE3Xq1CEmJoa4uDhq1qzJ5cuXb1yr\nc+fOzJ4928G1FUII91HAx4dnWrViddxe9h372+YxScnJDJgSRe+JX+Dr7cOSl14k6tlnKF2kiH2C\nCA2HS+dh/SL9vSTIwsFOnjxJv379qFq1Kg8++CBhYWH88MMPrF27liJFilC/fn1q1apFixYtWLZs\n2Y3zxo4dS7ly5TCZTNStW5elS5c6J+Boyz6iLfHOKUwSZKfx8/Nj0aJFnDlz5o59y5YtIyoqivXr\n17N//36mTp1Kv379OHHixC3HJSUl0alTJz755BM6dux4x3V69+6NxWJh7dq1vP7665QoUYIePXow\nbtw4ABYvXkxqaip9+/Z1TCWFEMJNDWrZEn9fX6auWnXL9tS0dL5Y/ithb41l5Z7dvPlYd9a9/SbN\nQ+ycwNZuorckr5ylv5cEWTiQpml0796dFi1acPjwYXbs2MGcOXNISkoCoHnz5uzatYv4+HgmTpzI\niBEjWJXp/8aoUaOwWCzMnz+fwYMHk5GRYVRVHEYSZCfx8fHBbDYzYcKEO/Z9+OGHfPTRR5Swrq7U\noEEDBgwYwOTJk28cc/z4cTp06MC4cePo2rXrXcsqVaoU1apV488//+Stt95i/vz5WCwWXn311Vuu\nKYQQQlc8sDC9w5oyb9Nmzpw/D8DmQ4do9d57vL1gIc1q1WTj2LGMeuRhCvg4YHx74aJQKRT+PQ2B\nxaBISfuXIYTV6tWrKVCgAEOGDLmxrVKlSowcOfKOY00mE2+99RaTJk26Y1/t2rXx8fGx2fiXBR+l\n1PZMD/Mte82mlZhNe2w8ut1L/ezBI2axyOy1OXPZk5hk12vWrVCe9/v0zva44cOHc//99zNmzJhb\ntsfFxfHggw/esq1hw4ZMnz79xvsBAwbw3nvv0bNnz2zLOXz4MIcPH6Z69eoUKlSIjz/+mBYtWvDi\niy9So4YvD+C5AAAMaklEQVRMGySEELYMadeW6b+vY8JPP/Pv5ct8v2Ej5YODmTl8KA8/8IDj+42G\nhsPRPVC+lt6aLPK/ueNvznttLxVqQe8xdz0kLi6OBg0a5PiSDRo04KOPPrpj+5YtW/Dy8qJkyRx/\noEvTNC3rAXbRlnY5DsrBPC5BNlJQUBD9+/dn4sSJFCxY8J7ObdeuHTNnzmTgwIEUKlTI5jFz585l\n/fr1+Pn5ERUVRXBwMACPPvooRYsWZdiwYXmugxBC5Fe1ypalbd1QpqxchY+3F8936sjoLp2zXgXP\n3kLD4Mdo6V4hnG748OGsX7+eAgUK2EyENU275f2ECROYOXMmgYGBzJ07N18OOvS4BDknLb2O9MIL\nL9CgQQMGDRp0Y1udOnXYsWMHbdq0ubFtx44dhIaG3ng/ZswYZsyYQa9evViyZAk+Nr7i6927t82v\nQAC8vLzwyssoayGE8ABv93iMkoGBjOzYkdrl7nNu4VXqQasnoGln55YrjJNNS6+jhIaGsnDhwhvv\nJ0+ezJkzZ2jY0Hbj7q5du6hdu/aN96NGjWL06NEOj/MWZtNjwBdASeBHzCYL0ZY7B2TZiWRMThYc\nHMwTTzzB119/fWPbmDFjeOWVVzh79iwAFouFadOm3dHi+9lnnxEUFMQzzzxzx6c5IYQQeVe3QgW+\nHDzI+ckxgLcP9Hsdytd0ftnCo7Rp04YrV64wZcqUG9suXbpk89g//viDd999l+HDhzsrPNuiLT8Q\nbSlPtMWPaEtpRybH4IEtyK7gpZdeuqWlt2vXrhw7dozw8HCUUgQGBjJz5kzKli17y3lKKaZPn06X\nLl0YM2YMHTt2xM9ZX/0JIYQQIl9QSrF48WJGjRrF+PHjKVmyJAEBAXz44YcArFu3jvr163Pp0iVK\nlSrFxIkTadu2rcFRO5fKby2RAQEB2sWLF2/Ztm/fvlu+GsgvPv/8c44dO8b48ePzfK38+m8khBBC\nuBJP+Xtrq55KqUuaprnFGurSguymnnnmGfbs2cO8efOMDkUIIYQQIl+RBNlNZe7DLIQQQggh7EcG\n6QkhhBBCCJGJxyTI+a2vtT3Jv40QQgjhPPn9725+qJ9HJMj+/v6cPXs2X9wwe9M0jbNnz+Lv7290\nKEIIIUS+l99zkvySV3jELBapqakkJSVx5coVg6Jybf7+/pQvXx5fX1+jQxFCCCHyNU/ISbLKK9xp\nFguPSJCFEEIIIYSx3ClB9oguFkIIIYQQQuSUJMhCCCGEEEJkIgmyEEIIIYQQmeS7PshKqQzgspOL\n9QHSnFymq/DEuntina/z1Lp7ar3Bc+su9fYsnlpvcG7dC2qa5haNs/kuQTaCUmq7pmkNjY7DCJ5Y\nd0+s83WeWndPrTd4bt2l3p7FU+sNnl33u3GLLF4IIYQQQghnkQRZCCGEEEKITCRBto9oowMwkCfW\n3RPrfJ2n1t1T6w2eW3ept2fx1HqDZ9c9S9IHWQghhBBCiEykBVkIIYQQQohMPDJBVkpVUEqtUUrt\nVUrFKaWet24PVkqtUEodtD4Xs24PUUptUkpdVUqNznQdf6XUVqVUrPU679ylzAHW6x5USg2wbgtU\nSlkyPc4opT5zh7pnup63UmqXUmrZXcq8o+7W7eOUUolKqQuOqGumclyizu5+v5VSR5VSu62xb79L\nmZ2UUvFKqUNKqVczbR9h3aYppUo4qs7Wslyp3usy3fO/lVKLHVVva3n2rHtRpdQCpdR+pdQ+pVTY\nPdbdXe95XuvttHtur3orpWrd9vspRSn1wj3W2+3ut53q7c7/x0dZr7FHKTVbKeWfRZmG/h03hKZp\nHvcAygINrK8DgQNAHWA88Kp1+6vAh9bXpYBGwDhgdKbrKKCw9bUvsAVoaqO8YOCw9bmY9XUxG8ft\nAFq4Q90zXe9F4HtgWRblZVl3oKk1ngueUmd3vt/AUaBENuV5AwlAVaAAEAvUse6rD1TOyXXyU71v\nO24h0N+N6j4deNb6ugBQ1EPueZ7q7cx7bs9631a3E0AlT7jfea23M++3PesOlAOOoM9NDDAPGGij\nPMP/jhvx8MgWZE3TjmuattP6+jywD/0HpRv6L0asz92tx5zSNG0bkHrbdTRN065/avK1Pmx16u4I\nrNA0LVnTtH+AFUCnzAcopWqi/xCvy3sNs2avultjLg90Br66S5FZ1l3TtM2aph23S8XuwpXqnOk6\nbne/c6gxcEjTtMOapl0D5ljLQtO0XZqmHc1tXe6FK9X7OqVUENAGcGjrkr3qrpQqArQAvrYed03T\ntHM2isxX99we9c50LYffcwf9rLcFEjRN+9PGvnx1v2+Tq3pf527/x618gIJKKR+gEPC3jWMM/ztu\nBI9MkDNTSlVG/9S7BSid6UafAErn4HxvpZQFOIX+A7TFxmHlgMRM75Os2zLrA8zVNM1poybzWnfg\nM2AMkHGXY3JSd6dxoTq74/3WgF+VUjuUUuYsjnGp+w0uVe/uwCpN01JyGHqe5bHuVYDTwLdK71L0\nlVIqwMZx+e2e27PeTr3ndvhZv64PMDuLffntfmeW13q71f9xTdOOAR8DfwHHgX81TfvVxqEud8+d\nwaMTZKVUYfSvQ164/Qfamrhkm7xompauaZoJKA80VkrVzWU4d/uPaXd5rbtSqgtwStO0HY6L0r5c\nrM5udb+tmmma1gB4GBiulGph/0jty8Xq3Rf3uuc+QANgiqZp9YGL6F/bujQXq7fT7rmdftZRShUA\nugLz7R6kA7hYvd3q/7i1j3I39A+F9wEBSqmnHBSu2/HYBFkp5Yv+gzVL07RF1s0nlVJlrfvLorcK\n54j1K7g1QCelVJNMHfa7AseACpkOL2/ddj2WBwAfZyWbdqr7Q0BXpdRR9K+a2iilZt5r3Z3Flers\npvf7emsDmqadAn5A/0BYIVPdh+Ai9xtcq95KH7DUGPgx7zXLnp3qngQkZfpWbAHQwAPuuV3q7cx7\nbue/Zw8DOzVNO2k9N7/f7+vyVG83/T/eDjiiadppTdNSgUVAuKv+HXc2j0yQlVIKvX/ZPk3TPs20\naylwfXTmAGBJNtcpqZQqan1dEGgP7Nc0bYumaSbrYymwHOiglCpm/cTWwbrtOme2Mtil7pqmvaZp\nWnlN0yqjt4au1jTtqVzU3eFcsM5ud7+VUgFKqcDrr9HrtEfTtMRMdZ8KbANqKKWqWFtk+ljLcioX\nrHdP9EGdV+xRv2xittfP+wkgUSlVy7qpLbA3v99zO9bbKffcXvXO5JbfT/n9fmeS13q73f9x9K4V\nTZVShazXbGu9psv9HTeE5gIjBZ39AJqhf/XwB2CxPh4BigOrgIPASiDYenwZ9FaFFOCc9XUQcD+w\ny3qdPcBbdylzMHDI+hh0277DQIg71f22a7Yiixkd7lZ39BG3Sej9eZOAsfm9zu56v9FHbsdaH3HA\nG3cp8xH0UdUJmY8DnrNeLw19IMhXnlBv6761QCd3uufWfSZgu/Vai7ExG0t+u+f2qLcz77md6x0A\nnAWKZFNmfrvfeaq3M++3A+r+DrAfPYeZAfhlUaahf8eNeMhKekIIIYQQQmTikV0shBBCCCGEyIok\nyEIIIYQQQmQiCbIQQgghhBCZSIIshBBCCCFEJpIgCyGEEEIIkYkkyEII4URKqXTrBPxxSqlYpdRL\nSqm7/i5WSlVWSvVzVoxCCOHpJEEWQgjnuqzpE/CHoi8u9DDwdjbnVAYkQRZCCCeReZCFEMKJlFIX\nNE0rnOl9VfRVukoAldAn6w+w7h6hadpGpdRmoDZwBJgOTAQ+QF+wxg+YrGlalNMqIYQQ+ZwkyEII\n4US3J8jWbeeAWsB5IEPTtCtKqRrAbE3TGiqlWgGjNU3rYj3eDJTSNO09pZQfsAHopWnaEadWRggh\n8ikfowMQQghxgy8wSSllAtKBmlkc1wG4XynV0/q+CFADvYVZCCFEHkmCLIQQBrJ2sUgHTqH3RT4J\nPIA+RuRKVqcBIzVNW+6UIIUQwsPIID0hhDCIUqokMBWYpOn93YoAxzVNywCeBryth54HAjOduhwY\nqpTytV6nplIqACGEEHYhLchCCOFcBZVSFvTuFGnog/I+te77EliolOoP/AJctG7/A0hXSsUC04DP\n0We22KmUUsBpoLuzKiCEEPmdDNITQgghhBAiE+liIYQQQgghRCaSIAshhBBCCJGJJMhCCCGEEEJk\nIgmyEEIIIYQQmUiCLIQQQgghRCaSIAshhBBCCJGJJMhCCCGEEEJkIgmyEEIIIf5/o2AUjAIkAABl\nKCbsx9c4BAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jKCh1BYgye0T",
"outputId": "c9b5bbcc-264f-47e3-ed9d-0fa24642f1fd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 357
}
},
"source": [
"df.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>nok</th>\n",
" <th>usd</th>\n",
" <th>eur</th>\n",
" <th>gbp</th>\n",
" <th>brent</th>\n",
" <th>gdp yoy</th>\n",
" <th>interest rate</th>\n",
" <th>cc_weighted_40</th>\n",
" <th>cc_weighted_2</th>\n",
" <th>bb_weighted_2</th>\n",
" <th>causal_size_step2</th>\n",
" <th>cc_weighted_3</th>\n",
" <th>bb_weighted_3</th>\n",
" <th>causal_size_step3</th>\n",
" <th>cc_weighted_5</th>\n",
" <th>bb_weighted_5</th>\n",
" <th>causal_size_step5</th>\n",
" <th>cc_weighted_10</th>\n",
" <th>bb_weighted_10</th>\n",
" <th>causal_size_step10</th>\n",
" <th>cc_weighted_20</th>\n",
" <th>bb_weighted_20</th>\n",
" <th>causal_size_step20</th>\n",
" <th>bb_weighted_40</th>\n",
" <th>causal_size_step40</th>\n",
" <th>cc_weighted_60</th>\n",
" <th>bb_weighted_60</th>\n",
" <th>causal_size_step60</th>\n",
" <th>cc_weighted_80</th>\n",
" <th>bb_weighted_80</th>\n",
" <th>causal_size_step80</th>\n",
" <th>cc_weighted_120</th>\n",
" <th>bb_weighted_120</th>\n",
" <th>causal_size_step120</th>\n",
" <th>cc_weighted_150</th>\n",
" <th>bb_weighted_150</th>\n",
" <th>causal_size_step150</th>\n",
" <th>cc_weighted_200</th>\n",
" <th>bb_weighted_200</th>\n",
" <th>causal_size_step200</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-04-25</th>\n",
" <td>16.901885</td>\n",
" <td>99.255002</td>\n",
" <td>129.165590</td>\n",
" <td>153.186275</td>\n",
" <td>10263.95955</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.901483</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-5.292433e-13</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.143452e-11</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>5.093951e-10</td>\n",
" <td>0.967715</td>\n",
" <td>0.346125</td>\n",
" <td>-0.62159</td>\n",
" <td>0.975575</td>\n",
" <td>0.843366</td>\n",
" <td>-0.132209</td>\n",
" <td>0.777633</td>\n",
" <td>-0.12385</td>\n",
" <td>-0.540355</td>\n",
" <td>0.193216</td>\n",
" <td>0.733571</td>\n",
" <td>-0.671686</td>\n",
" <td>0.056723</td>\n",
" <td>0.728408</td>\n",
" <td>-0.698249</td>\n",
" <td>-0.146385</td>\n",
" <td>0.551863</td>\n",
" <td>-0.665819</td>\n",
" <td>-0.088546</td>\n",
" <td>0.577273</td>\n",
" <td>-0.305538</td>\n",
" <td>0.396544</td>\n",
" <td>0.702082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-04-26</th>\n",
" <td>16.760666</td>\n",
" <td>98.050001</td>\n",
" <td>127.746551</td>\n",
" <td>151.768098</td>\n",
" <td>10114.83800</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.901483</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-5.292433e-13</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.143452e-11</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>5.093951e-10</td>\n",
" <td>0.967715</td>\n",
" <td>0.346125</td>\n",
" <td>-0.62159</td>\n",
" <td>0.975575</td>\n",
" <td>0.843366</td>\n",
" <td>-0.132209</td>\n",
" <td>0.777633</td>\n",
" <td>-0.12385</td>\n",
" <td>-0.540355</td>\n",
" <td>0.193216</td>\n",
" <td>0.733571</td>\n",
" <td>-0.671686</td>\n",
" <td>0.056723</td>\n",
" <td>0.728408</td>\n",
" <td>-0.698249</td>\n",
" <td>-0.146385</td>\n",
" <td>0.551863</td>\n",
" <td>-0.665819</td>\n",
" <td>-0.088546</td>\n",
" <td>0.577273</td>\n",
" <td>-0.305538</td>\n",
" <td>0.396544</td>\n",
" <td>0.702082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-04-29</th>\n",
" <td>16.818312</td>\n",
" <td>97.765004</td>\n",
" <td>128.073770</td>\n",
" <td>151.538112</td>\n",
" <td>10148.98465</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.901483</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-5.292433e-13</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.143452e-11</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>5.093951e-10</td>\n",
" <td>0.967715</td>\n",
" <td>0.346125</td>\n",
" <td>-0.62159</td>\n",
" <td>0.975575</td>\n",
" <td>0.843366</td>\n",
" <td>-0.132209</td>\n",
" <td>0.777633</td>\n",
" <td>-0.12385</td>\n",
" <td>-0.540355</td>\n",
" <td>0.193216</td>\n",
" <td>0.733571</td>\n",
" <td>-0.671686</td>\n",
" <td>0.056723</td>\n",
" <td>0.728408</td>\n",
" <td>-0.698249</td>\n",
" <td>-0.146385</td>\n",
" <td>0.551863</td>\n",
" <td>-0.665819</td>\n",
" <td>-0.088546</td>\n",
" <td>0.577273</td>\n",
" <td>-0.305538</td>\n",
" <td>0.396544</td>\n",
" <td>0.702082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-04-30</th>\n",
" <td>16.882766</td>\n",
" <td>97.424999</td>\n",
" <td>128.287364</td>\n",
" <td>151.331719</td>\n",
" <td>9973.39725</td>\n",
" <td>NaN</td>\n",
" <td>1.5</td>\n",
" <td>0.901483</td>\n",
" <td>0.782927</td>\n",
" <td>-0.782927</td>\n",
" <td>-1.565853e+00</td>\n",
" <td>0.906140</td>\n",
" <td>-0.527497</td>\n",
" <td>-1.433637e+00</td>\n",
" <td>0.955405</td>\n",
" <td>-0.128090</td>\n",
" <td>-1.083496e+00</td>\n",
" <td>0.967715</td>\n",
" <td>0.346125</td>\n",
" <td>-0.62159</td>\n",
" <td>0.975575</td>\n",
" <td>0.843366</td>\n",
" <td>-0.132209</td>\n",
" <td>0.777633</td>\n",
" <td>-0.12385</td>\n",
" <td>-0.540355</td>\n",
" <td>0.193216</td>\n",
" <td>0.733571</td>\n",
" <td>-0.671686</td>\n",
" <td>0.056723</td>\n",
" <td>0.728408</td>\n",
" <td>-0.698249</td>\n",
" <td>-0.146385</td>\n",
" <td>0.551863</td>\n",
" <td>-0.665819</td>\n",
" <td>-0.088546</td>\n",
" <td>0.577273</td>\n",
" <td>-0.305538</td>\n",
" <td>0.396544</td>\n",
" <td>0.702082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-05-01</th>\n",
" <td>16.883051</td>\n",
" <td>97.389997</td>\n",
" <td>128.353228</td>\n",
" <td>151.492198</td>\n",
" <td>9734.13050</td>\n",
" <td>NaN</td>\n",
" <td>1.5</td>\n",
" <td>0.901483</td>\n",
" <td>-0.848331</td>\n",
" <td>-0.981534</td>\n",
" <td>-1.332030e-01</td>\n",
" <td>0.559707</td>\n",
" <td>-0.873930</td>\n",
" <td>-1.433637e+00</td>\n",
" <td>0.863902</td>\n",
" <td>-0.588313</td>\n",
" <td>-1.452215e+00</td>\n",
" <td>0.967715</td>\n",
" <td>0.346125</td>\n",
" <td>-0.62159</td>\n",
" <td>0.975575</td>\n",
" <td>0.843366</td>\n",
" <td>-0.132209</td>\n",
" <td>0.777633</td>\n",
" <td>-0.12385</td>\n",
" <td>-0.540355</td>\n",
" <td>0.193216</td>\n",
" <td>0.733571</td>\n",
" <td>-0.671686</td>\n",
" <td>0.056723</td>\n",
" <td>0.728408</td>\n",
" <td>-0.698249</td>\n",
" <td>-0.146385</td>\n",
" <td>0.551863</td>\n",
" <td>-0.665819</td>\n",
" <td>-0.088546</td>\n",
" <td>0.577273</td>\n",
" <td>-0.305538</td>\n",
" <td>0.396544</td>\n",
" <td>0.702082</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" nok usd ... bb_weighted_200 causal_size_step200\n",
"date ... \n",
"2013-04-25 16.901885 99.255002 ... 0.396544 0.702082\n",
"2013-04-26 16.760666 98.050001 ... 0.396544 0.702082\n",
"2013-04-29 16.818312 97.765004 ... 0.396544 0.702082\n",
"2013-04-30 16.882766 97.424999 ... 0.396544 0.702082\n",
"2013-05-01 16.883051 97.389997 ... 0.396544 0.702082\n",
"\n",
"[5 rows x 40 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EOH5eMgo3G8J"
},
"source": [
"# You don't have to shift, you are not predicting next day return, just establishing fundamental relationship\n",
"# df['nok'][df.index<'2017-04-25'].shift(1).head()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mrTEkT0naNxI",
"outputId": "f6e92b45-e0a2-4c51-ebbe-7c4bd2f1176d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 678
}
},
"source": [
"#Now we do our linear regression\n",
"x0=pd.concat([df['usd'],df['gbp'],df['eur'],df['brent'],df['causal_size_step200'],df['causal_size_step60'],df['causal_size_step20'],df['causal_size_step10']],axis=1)\n",
"x1=sm.add_constant(x0)\n",
"x=x1[x1.index<'2017-04-25']\n",
"y=df['nok'][df.index<'2017-04-25'].shift(1)\n",
"\n",
"model=sm.OLS(y,x).fit()\n",
"print(model.summary(),'\\n')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: nok R-squared: 0.961\n",
"Model: OLS Adj. R-squared: 0.961\n",
"Method: Least Squares F-statistic: 3161.\n",
"Date: Fri, 28 Jun 2019 Prob (F-statistic): 0.00\n",
"Time: 11:17:29 Log-Likelihood: -315.38\n",
"No. Observations: 1031 AIC: 648.8\n",
"Df Residuals: 1022 BIC: 693.2\n",
"Df Model: 8 \n",
"Covariance Type: nonrobust \n",
"=======================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------------\n",
"const 1.0764 0.336 3.199 0.001 0.416 1.737\n",
"usd 0.0088 0.003 2.937 0.003 0.003 0.015\n",
"gbp 0.0114 0.002 5.684 0.000 0.007 0.015\n",
"eur 0.0604 0.004 15.637 0.000 0.053 0.068\n",
"brent 0.0004 1.03e-05 40.387 0.000 0.000 0.000\n",
"causal_size_step200 0.2513 0.035 7.215 0.000 0.183 0.320\n",
"causal_size_step60 0.3378 0.028 12.003 0.000 0.283 0.393\n",
"causal_size_step20 -0.0245 0.023 -1.074 0.283 -0.069 0.020\n",
"causal_size_step10 -0.0103 0.018 -0.556 0.578 -0.047 0.026\n",
"==============================================================================\n",
"Omnibus: 41.445 Durbin-Watson: 0.071\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 46.511\n",
"Skew: 0.470 Prob(JB): 7.95e-11\n",
"Kurtosis: 3.446 Cond. No. 2.69e+05\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 2.69e+05. This might indicate that there are\n",
"strong multicollinearity or other numerical problems. \n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
" return ptp(axis=axis, out=out, **kwargs)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z5_EinFK0T8n"
},
"source": [
"Sometimes condition numbers are used (see the appendix). An informal rule of thumb is that if the condition number is 15, multicollinearity is a concern; if it is greater than 30 multicollinearity is a very serious concern. (But again, these are just informal rules of thumb.) "
]
},
{
"cell_type": "code",
"metadata": {
"id": "bzWvyShUyYRg",
"outputId": "0b429d54-69b2-48a9-c912-a64a407e57da",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 106
}
},
"source": [
"#from the summary (above) you can tell there is multicollinearity\n",
"#the condition number is skyrocketing\n",
"#alternatively, i can use elastic net regression to achieve the convergence\n",
"m=en(alphas=[0.0001, 0.0005, 0.001, 0.01, 0.1, 1, 10],\n",
" l1_ratio=[.01, .1, .5, .9, .99], max_iter=5000).fit(x0[x0.index<'2017-04-25'], y) \n",
"print(m.intercept_,m.coef_)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"3.7977623083935512 [ 0.00388958 0.01992038 0.02823187 0.00050092 0. 0.\n",
" -0. -0. ]\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
" warnings.warn(CV_WARNING, FutureWarning)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GfdQVfHy2F7t",
"outputId": "ce0cf1cf-a898-4fd3-a853-c9d4e4c8f68e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 342
}
},
"source": [
"#calculate the fitted value of nok\n",
"df['sk_fit']=(df['usd']*m.coef_[0]+df['gbp']*m.coef_[1]+\n",
" df['eur']*m.coef_[2]+df['brent']*m.coef_[3]+m.intercept_)\n",
"\n",
"\n",
"# In[6]:\n",
"\n",
"\n",
"#getting the residual\n",
"df['sk_residual']=df['nok']-df['sk_fit']\n",
"\n",
"\n",
"#one can always argue what if we eliminate some regressors\n",
"#in econometrics, if adding extra variables do not decrease adjusted r squared\n",
"#or worsen AIC, BIC\n",
"#we should include more information as long as it makes sense\n",
"\n",
"#lets generate signals based on the elastic net\n",
"#we set one sigma of the residual as thresholds\n",
"#two sigmas of the residual as stop orders\n",
"#which is common practise in statistics\n",
"upper=np.std(df['sk_residual'][df.index<'2017-04-25'])\n",
"lower=-upper\n",
"\n",
"signals=pd.concat([df[i] for i in ['nok', 'usd', 'eur', 'gbp', 'brent', 'sk_fit','sk_residual']], \\\n",
" axis=1)[df.index>='2017-04-25']\n",
"signals['fitted']=signals['sk_fit']\n",
"del signals['sk_fit']\n",
"\n",
"signals['upper']=signals['fitted']+upper\n",
"signals['lower']=signals['fitted']+lower\n",
"signals['stop profit']=signals['fitted']+2*upper\n",
"signals['stop loss']=signals['fitted']+2*lower\n",
"signals['signals']=0\n",
"\n",
"#while doing a traversal\n",
"#we apply the rules mentioned before\n",
"#if actual price goes beyond upper threshold\n",
"#we take a short and bet on its reversion process\n",
"#vice versa\n",
"#we use cumsum to make sure our signals only get generated\n",
"#for the first time condions are met\n",
"#when actual price hits the stop order boundary\n",
"#we revert our positions\n",
"#u may wonder whats next for breaking the boundary\n",
"#well, we stop the signal generation algorithm\n",
"#we need to recalibrate our model or use other trend following strategies\n",
"\n",
"index=list(signals.columns).index('signals')\n",
"\n",
"for j in range(len(signals)):\n",
" \n",
" if signals['nok'].iloc[j]>signals['upper'].iloc[j]:\n",
" signals.iloc[j,index]=-1 \n",
" \n",
" if signals['nok'].iloc[j]<signals['lower'].iloc[j]:\n",
" signals.iloc[j,index]=1 \n",
" \n",
" signals['cumsum']=signals['signals'].cumsum()\n",
"\n",
" if signals['cumsum'].iloc[j]>1 or signals['cumsum'].iloc[j]<-1:\n",
" signals.iloc[j,index]=0\n",
" \n",
" if signals['nok'].iloc[j]>signals['stop profit'].iloc[j]: \n",
" signals['cumsum']=signals['signals'].cumsum()\n",
" signals.iloc[j,index]=-signals['cumsum'].iloc[j]+1\n",
" signals['cumsum']=signals['signals'].cumsum()\n",
" break\n",
"\n",
" if signals['nok'].iloc[j]<signals['stop loss'].iloc[j]:\n",
" signals['cumsum']=signals['signals'].cumsum()\n",
" signals.iloc[j,index]=-signals['cumsum'].iloc[j]-1\n",
" signals['cumsum']=signals['signals'].cumsum()\n",
" break\n",
"\n",
"\n",
"# In[9]:\n",
"\n",
"\n",
"#next, we plot the usual positions as the first figure\n",
"ax=plt.figure(figsize=(10,5)).add_subplot(111)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"\n",
"signals['nok'].plot(label='NOKJPY',c='#594f4f',alpha=0.5)\n",
"ax.plot(signals.loc[signals['signals']>0].index,\n",
" signals['nok'][signals['signals']>0],\n",
" lw=0,marker='^',c='#83af9b',label='LONG', markersize=10)\n",
"ax.plot(signals.loc[signals['signals']<0].index,\n",
" signals['nok'][signals['signals']<0],\n",
" lw=0,marker='v',c='#fe4365',label='SHORT', markersize=10)\n",
"ax.plot(pd.to_datetime('2017-12-20'),\n",
" signals['nok'].loc['2017-12-20'],\n",
" lw=0,marker='*',c='#f9d423', markersize=15, alpha=0.8,\n",
" label='Potential Exit Point of Momentum Trading')\n",
"\n",
"plt.axvline('2017/11/15',linestyle=':',c='k',label='Exit')\n",
"plt.legend()\n",
"plt.title('NOKJPY Positions')\n",
"plt.ylabel('NOKJPY')\n",
"plt.xlabel('Date')\n",
"plt.show()\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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Rj08mk0ykW5ote7AWCi3o71StDbobVK054XCYurq6zILhUCg0ZykMYwwmmVxy\nq6alikajwNIK0lZWVRGPxwlNTlLu9TIZDHL65Elu2rFjxs9nfbJvaGxkeHCQkXQz+LWaWbMvIrMW\niUTwT0zQkId+rABf+MIXAHjf+96Xl/OtFQG/n3Xr1+PxeDhz6hRtGzcu6P9JIpHg3JkznDx+nHAo\nlOrhucC6hvngdrtTH86WkElXq0vBMmsi8m0RGRSRrhz3fVJEjIg0zPLY3xaR0+l/v12oMaq1KRaN\n4iwru1LPaNpUqDGGtw4f5sBLLwFw+uRJnnjssaJ/ys1Hq6eqqiogtckC4FhXF6dPnsw0RJ9yvfSa\nmfZ04c7zZ88Che1eUMocC1yzlkgk+Pnzz/PCM8/kbdr0kUce4ZFHHsnLudaKZCLBZDBIRUUF22+6\nCU95Oa8fPDjn/8NkMsn5s2d54qc/5c3XX6eqqoo9t93Gve95T0F7z07nyepiMBkMcvDAgaKtl1Sl\noZDToA8D902/UUTagHcB3bkeJCJ1wGeAvcAe4DMiUlu4Yaq1xBhDLBbD6XROaeuSrevIEU4eP05f\nby/GGMbHxggGAkVbr2SJpktuLDWzBqm1POFwmJ6LFwGYDAZnHJvJrK1bR0Nj45XM2hqdBrUvcBr0\nzddfxzcyQjKZXPD6wPk0NDTQ0JDzs62aRTAYJJlMUlFZicPpZFdnJxPj45w8dizn8b6REZ587DFe\ne/VVPB4Pd7zjHbz97rtp7+hY9ql/V1YXg/6+Pi6eP5+355JamQoWrBljXgB8Oe76R+A/ALOlKd4N\nPGmM8RljRoEnyRH0KXU14vE4yWSSMpcrs7srO7N24uhRTh47RrnXSyKRIBqNZnaDFXvnaCQ9DbqU\nNw6X202Zy4V/YoJzp09nPq3n+tnCWWvUNmcVAXWv1czaAqZBY7EY586cyfSFzNcb7A9/+EN++MMf\n5uVca0XA7wegMt0sfUNzM+0dHZw4dixTZzHbiWPHiEaj3HbHHdx1zz2sL+KGjkxmLRzO/G2ulLW1\nqjCWdYOBiLwfuGyMmWulZwuQXQjqUvo2pZYslg54nE4nDoeDsqzik2dOnaLryBHaOzq4accOIPXJ\n1sq8FXsrfT4yayJCZWUlw0NDnDl9mqbmZux2+6yZNZvNhrOsjJa2tlSgV1ZW9LV7xbKQ0h1W4L+x\nowObzZa3YO1LX/oSX/rSl/JyrrUiEAgA4E0HawA379qFw+nk+NGjM46PhMNU19bS0tq6rFOeuVgf\nyEKhUOY5Vawaf6o0LNsGAxEpBz5Nago0X+d8EHgQoL1IDZ7VymIt0rcWC3vcbsZGRzlz6hSHX3uN\n5tZWOvfswZeuxRYqoWAts2a6bv+cAAAgAElEQVRtiQudq6qrOX/2LA6Hgx27dvHzF17InVkLhXB7\nPIgIdrud67dvz5mRWCsWklmzfo/lXi9erzezNnCp/u3f/i0v51lLAn4/ZWVlU9Z4ut1u6urrCaYD\nuWzRSIQar3c5hzgrR/rDZCQczrzuaGZtbVvOzNoWYBPwpohcAFqB10Vkeq75MpDdCK81fdsMxpiv\nG2M6jTGd6/K060qtblZmzWrAXNfQwMjwMIdfe43Gpib27tuHzW7PrGcL+P2ZAK/YdY8ikQhOp3PJ\nma2q6moAdnV2UllVRXl5ee7MWig0Zcp16zXX8LY9e5Z07ZXM7nCkSjkE/XznxZ8QCM+s0Zddk2sx\nNe3mU11dTXX6/5taGL/fT0Vl5YwsmcfjydlmLhKNztqYvRg8Hs/UzFqR18yq4lq2zJox5i2g0fo+\nHbB1GmOGpx36BPC5rE0F7wL+47IMUq160fSnU6tUxy27d7P1mmsY9flobWvDng6ErDUjVrcDmH3N\n2mQwiMPpLPgLfTQSycvi/k2bN1NdU0Pj+vVAqjL70ODgjOPC4TDeEsk0lAKHw0GcJN96/t8IRsO8\neOIN3rPz9inHZLcmqqqqor+3l2QikQmww+EwY6OjrG9qWtRU2w9+8AMAPvjBD+bpp1n9gn4/9Tk2\nZXg8HiLh8JT/L/moYZhvrnRh3FD6Q6Jm1ta2Qpbu+B6wH7hWRC6JyEfnOLZTRL4JYIzxAX8LHEz/\n+8/p25Rasti0aVARobqmho7Nm3Fk1Vqzp5spj2UFa7mmQcOhEE89/jhvHT5c4JHnr4m6w+nMBGoA\n5eXlhEOhTL9US3gN9wHNxW63M+iMEoym3jzf7D41I7sWCoUoc7lwOBxUVlWRTCYJBALEYzGOd3Xx\n+KOP8tJzzzE4MLCoa3/1q1/lq1/9at5+ltUumUwSCoXwVlTMuM+dtXjfko/1oPnm8XgIBgKZsema\ntbWtYJk1Y8xvzHN/R9bXh4CPZX3/beDbhRqbWrui06ZB5+L2eDLVy8vSVdCnO/LGG0Sj0ZzTiPkW\niUQKUkXdU15OMpkkHA5nCgVbmYblrNpe6qImwYT9ygaDpDEzsmvhcBhPOsC1pptPHDvG4MAA4VCI\n1rY2hoaGOHvq1KJ2Gz722GN5+inWhkgkQjKZzNl8PXsXeHk6cxxJB0Sl1J3D7XZP2amu06Brm7ab\nUmtKLBZDRObsWGDxeDyZQri1tbUzgrX+vj66L17EZrMty3q2SDic881nqTJvXlkBZyQSwRhTkOut\nVG/0nZ3yfdIkZ2TXQpOTmd+ZVTKi+8IFKiorece993LrL/wCm7dupa+3N1NawhjD0MAAb735ZiaL\nMl15eXkmkFbzy6wdzPE7yxTDLvHM2vS/PZ0GXdu03ZRaU6KRCM6ysgWtF7JeLO12O1XV1ZmisJD6\nlPvGoUNUVlZS19DAQH9/wcYMqUxXJBIpyCd/KwiYnJyknlTl9wvpbgU6DZriD09y1tcL054207Nr\noVAoU3jY4XTSuXcvLpeLpubmzHNu89atnDx2jDdff52bdu3i9VdfZXhoCIBgIMDefftmPD+/+93v\nAvDAAw8U8sdcNaz1pbkyw7mKYVs1DEtpzVr237rNZtNp0DVOgzW1pljdCxbCeqF3ezy4PR7i8Xjm\n8SeOHiUYCHDn3XczODiYWrCcTGKzFSZZHY1GSSaTBQmePFk9UocGBjj82muMj4/T1Nxc1MKgpeTZ\nrldz3m5l1+64bhflZW4iWVPJAB2bN894jMfj4cYdO3jr8GH6entxOBzcsns34VCIY11dNLe00N7R\nMeUx3/zmNwEN1hYqe1fudC6XC5vNNmWKsdQzaxWVlZpZW+M0WFNrSmwR2/OtF0tPefmVqZN0r75T\nJ07QsXkz69avZ2JiAmMM0UikYNOGVo21QpzfKhB86sQJwqEQXq+X2+64g+aWlqIXBy0F/vAkb106\nM+v9VnbtHdfeMus6qemuue46auvqOH/mDNdt305VdTXJZJKB/n4Ov/YaDY2NU4K+J598Mi8/y1oR\nCoWw2Ww5P9yISKYshiVfNQzzycqsORwOPOkPi2rt0jVrak2Jppu4L4SVcfKkM2uQmjp5/eBBnE4n\nN+3cCWT18SvgurVMSYgCZNZEhMqqKqKRCNdv3869999fElXcS8WzXa/O2hsPrmTXhsdHgYUH1Osa\nG9mzb19mI4LNZmP3rbeSTCZ57ZVXMuslIRVQLzQjrKYWdM7F7fFMyaxFIpGS687hyfqw6HQ6NbO2\nxmlmTa0psVhswTscp0yDpoOktw4fZtTnY/ett2bWt7iXI1jL6tNZCLfefjsiktkdp1Lmy6pZksZw\n4GwXkHvqbaEqKiu5edcuXj94kHNnzrBl2zYAHn74YQA+8pGPXPW515LQ5OSc/x885eWMj45mvo9G\nIiU1BQqp8kJ2ux2Px4NDg7U1TzNrak2JLSazZn2yzcqsjfp8bNm2bcqaIiuAiqzgYM1bUaGBWg4v\nnXh9zqyaJWmSDPhHgNw7EBdj05YtNG3YwFuHD2c6IDz88MOZgE3NLxQKzR2spadBrexlvmoY5pOI\nUFFZSWVVFU6nUzcYrHEarKlVJxqJZMoizLhvkWvWbvuFX6Bj0yacTifX3XADe267jV2dnVOmVxab\nWUskEgwNDtJ7+fKC16GEw2EcDseUwr2q8C75ZnZ2yGV9VR13NW9HRJb8pi8ivG3vXmx2O28cOgTA\nc889x3PPPbek864VxphUZm2OoNlTXp7ZMASLe11YTm+/+25u2rEDh9NJPB6fMjWu1hadBlWrhjGG\nNw4d4sK5c4gI73/qBHLu0pRjfhmAp6ZmS7a0IV/7q5znbGm70qb2xh07ch5jNV1eaLDW9eabnD55\nEki1u9q8deu8j4mEQlpGowh+7+5fnfP+/S+9xNDgIPe//Zd48dlnqaqqysuOYI/Hw5Zt2zhx9Oii\ndjCr1FKHeDw+Z7CW+YAVClFWVkY4HM6sHSwlVuDvdDoxxpCIx/UD2xqlmTW1aoQmJzl35gxer5dE\nIkF0ays45lkw7LDD9pnlFRbL5XYTmaV36HTBQICKdMHUXF0RcgmHwyVVXV2lbL3mGqKRCG8dPszI\n8DAbc5TquFoN69ZhjGFkeJhvfOMbfOMb38jbuVezhWzGyd7dbe3kLrVp0GwORyqvEtMdoWuWBmtq\n1fCl+3huTi/KHn/vPpgvy2GzwQO/uORru91uwrNUn58uEong9XoXtcMrXKDuBWppGtato7aujrOn\nT2O32+nYtClv566vr8dmszE8OMgPfvCDTDN3Nbe5uhdYsltOJeJxEolESWeuM8GarltbszRYU6vG\nqM+HzWajuaUFAL/TBu/eN3t2zWGH+/YhdUuf/nB7PAvOrFkN0p1lZZlepQt5jGbWSo+IsPWaawBo\nbW/P645Ch9NJTW0tw8PDPPXUUzz11FN5O/dqNlf3Akt2KR6rL2gpZ9asaXDdZLB2abCmVo1Rn4/q\nmhrKvV7sdnuqufoD7509u5anrBqkXugXumYtYgVrC8ysJRKJVMFdDdZKUlt7O9uuvZbrbrgh7+du\nWLeO0ZEREonE/AcrACaDwVTh2zkyaw6HgzKXi1AolPm7LbXSHdmsdWqaWVu7NFhTq4IxhjGfj5ra\n2ky9sMlgEKmvyZ1dy2NWDVLToJFIhOQ8b6rx9OJnl8tF2QIza5nq6hqslSSb3c6OW27J9ATNp/p1\n60gkEjz00EP80z/9U97PvxoF/H7Ky8uxz1Pg1uN2ZzqSAHhLuHSNZtaUBmtqVQj4/USjUerq64HU\nC28w/SLMA+/FTK9knsesGmRNq8yzbs2acnGnp0FjCwjWwgVsNaVKW21dHQCPPfYYjz76aJFHszL4\n/f4FBc5uj4dwKEQwEABKO1jTzJrSYE2tCqPpzQXWm5u3oiITrCVrKum5rpWELR2w5TmrBlCZ3t3p\nGx6e8zgr8HK5XAuaBrX6RULhCuKq0mXV/vraV7/Kv//7vxd5NKXPGIN/YmJBwZqnvJzQ5CTBYBB3\nuktAqcpk1nQ36JqlddbUqjA6Oso7f3SQ6m89iwF2pv+Zf3ocG9CefXAimdesGqSmqzzl5Vw4f57W\n9vZZj8ssZna7KXO5iM6Tidv/4ov09fZSV19fknWgVGHZ7XZsNptOfy1QaHKSeDy+sGDN4yEcDuP3\n+0s6qwZXdoPq82Dt0syaWhVGR0YItjXOX1cNYOOGvGbVINWEu72jg8H+/jlrp0WyMmtl6arkyWQy\n57HxeJz+vj62bNvGO+69N/OCrdYOEcHpdPI/vvMdvvjFLxZ7OCXPas9lZbrn4vZ4MmtdvRUVhR7a\nklhBu9ZZW7s0WFMrXjKZZGx0lLGF1FUTgU9/tCDj2LhpE8lkku6LF2c9JnuzgNWjdLap0InxcYwx\nNK5fP6W9lVpbHE4nr7z6Kk8//XSxh1Ly/Ok2cxULnAaF1IeiUg/WAO0PusZpsKZWPP/ERGrqo6Nt\n/rpq73s7srkt9/1LVFVVRXV1NYPpNWa5RCKRVI9PhyOzDmW2TQZjo6MAVNfW5n+wasVwOp38/Wc/\ny49//ONiD6Xk+ScmcDqdC1rfmV2HrdSnQSEVtOsGg7VLgzW14k3ZXLBMddVm462omLPeWiSrE4G1\neHy28h3jY2M4nc4V8UaiCsfpdOr01wJZO0EXkomeEqytkMzaQnaPq9WpYMGaiHxbRAZFpCvrtr8V\nkSMiclhEfiYizbM89r+KyFEROS4iXxKdA1JzGPX5cDgcqRfpZaqrNhtXunbTbMJZPQgz06BzZNaq\n03Xj1NrldDr53ve+x0MPPVTsoZS8he4EhVQRXFv6g91KCNbcbveCC2+r4pqcnORyT09ez1nIzNrD\nwH3TbvsHY8zNxpidwE+Av57+IBHZB9wO3AzcCOwG7izgONUKN+rzUVtXdyWoyZVdW4asGqQWLUcj\nkVk3DVjdC+DKdvxoNMrxo0c5d+ZMZprDGMP42Bg1NTUFH7Mqbc6yMrqOHWP//v3FHkpJGxsdJTQ5\nSc0Clw3YbDZcbjd2u33O1lSlwqXBWkk4euQIzz75JEMDA3Mes/+ll+b84L5YBQvWjDEvAL5pt01k\nfesFTK6HAm6gDHABTmD234pa0+LxOGOjo5n6asDM7NoyZdUgNbVijJn1RTWSlVmz2tsE/H6OHjnC\n6wcP8ti//RtvHDpEX28v8Xh8wW88avVyOBz8+ac+xb/8y78Ueygl7dSJEzgcDjZu2rTgx5SXl+Ot\nqFgR2Wu3x0MkHMaYXG+barlc6u5mZHiY5595hhNHj864Px6PZ7JqA319ebvustcCEJG/A34LGAfe\nMf1+Y8x+EXkW6AME+LIx5vjyjlKtFGdOniSZTNLc2jr1jgfeC0+8DCSWLasGV1pCRcJhyqf1JjTG\nTOnxaWXWhoeGALhp504mxsc5f/YsZ0+fBqBaM2trnrUL0BizIoKKYpgMBrnU3c2Wbdsya0EXYtt1\n183bIq5UeDwekskkEe0TXDTxWIxAIMC1N9xAaHKSriNHSCaT3HDTTZlj+vv6iMfj2Gw2+vv66Ni8\nOee5Fvv3vOwbDIwx/8kY0wb8T+Dj0+8Xka3A9UAr0ALcLSJ35DqXiDwoIodE5NBQ+g1PrR3hcJiT\nx4/T3NJCw7p1U+7LZNdEli2rBlcWLeeqtRZJT49aAZ1VO2kk3fVgY0cHu2+9lfvf/35u3LGDjs2b\nqdZCuGues6yMf/nRj/j7z32u2EMpWadPnQJg6zXXLOpxrW1ttHd0FGBE+efO+iCoimM8XU6prr6e\nzr172bhpE8e6ujj61luZjGfPxYu4PR7aNm5ksL8/55IYYwxdb765qGsXczfo/wR+LcftvwIcMMYE\njDEB4N+B23KdwBjzdWNMpzGmc920N2u1+p04epR4PM6NO3bkPuCB98KNW5ctqwZXXlCnr1WIxWIc\neuUVgMwCaBHBWVaWaexuBXFut5vrbriBzr17sc3TjFqtfk6nkwsXLvDG4cPFHkpJikajnD9zhta2\nthWxUeBquef4ILjSnT55kldffnne44o9BTw+NgZATU0NNpuNzr172bRlC8e7ujh65AhDg4P0Xb5M\nW3s7G1paiEaj+EZGppwjmUxy7K23OHl8cROGyxqsici2rG/fD5zIcVg3cKeIOETESWpzgU6DqikC\nfj/nzpyhY/PmWdswSX0N8o9/tmxZNcj96XdycpLnn36awf5+btm9m/VNTZn7rCmbyupqneJSOTmd\nTv70E5/gW9/8JqM+nxZGneb82bPE43G2XXddsYdSULN9EFypJicn8U9MYIzh1IkT9HR3z/nc7rl4\nkZ/86EdF7Y86NjpKWVkZ5elySiLCLbt3s3nrVk4cO8aLzz6Lt6KCa2+4gcb167HZbPRkFUkPBAK8\n8OyzHD96dFFrK2GeNWsi8kFjzA+u4mdCRL4H3AU0iMgl4DPA/SJyLZAELgJ/kD62E/gDY8zHgEeA\nu4G3SG02eNwY8+jVjEGtXkffegubzTZlrUApsNntuFyuzAvq+NgYLz3/PLFolNvuuIMNzVOr1VjB\nmk53qtlYDcYnJyd5+YUXuGnnTrZde22RR1UakokEZ06epLGpacomo9XICtZWy47QQwcOMD4+zp7b\nbiM0OQmkejyva2zMefzZ06eJhMNEwmEcRcqgjo+NUV1TM+WDtYiwq7MTh8PB8NAQt91xR+b/1cZN\nmzh/9ixbtm1jeGiII2+8gYiw+9ZbFz39Pt8Ggw+LyO8Af2SMObeYExtjfiPHzd+a5dhDwMfSXyeA\n31/MtdTqNBkMcuDnP+e2O+6YsrXeNzJCz8WLXH/jjSW55d6qhzQ4MMD+F1/E4XRy1z335NzZaW0y\n0CbtajZOp5NHfvhDXnj5Zd6+b9+qebPOh+6LFwmFQrxt795iD6XgHE4nTqdzVUyDRqNRhoeGSCaT\nHDxwAJvNRjKZZNTnyxmsBQKBzEasYmXWkskk42NjbNqyZcZ9IsLNu3bNuH37zTdzqbubZ598klgs\nRmNTE5179mQyc4sx5zSoMeYXgf8O/FRE/kpEGkSkzvq36KsptQhDg4P4RkYyHQogtWbhrcOHcbnd\nXFOi0x7WFvs3Dh3C7XbzjnvvnbUEh1UYt2qBhTzV2uN0Ount68ssotdp0BRjDKdPnKC6pmbK0oLV\nbLUUxh3o6yOZTKY6voRCrG9qotzrnfJany17KrEYz/9kMsnF8+eJx+OL2qHvdru5cccOjDHs6uzk\njrvuuqpADRZQusMY8yMROQ+8AHyUK7XRDJB7T6pSeRAIBICpC2r7e3sZGhxkV2dnJitVatweD5e6\nu0kkEuzq7JxRwiObNQ1apSU61CycTief+OM/prq6mvHxcW05lNbf18f4+Di7b711zaz3dHs8qyJY\n6+vtxeVysee223j+6afZuGkTPd3dOYO1ZCLBxfPnKSsrIxqNFqX12qEDB+i+eJGq6mqamnM2XprV\nlm3b2Lx165Kfo/OtWXMBfwl8APhNY8xPlnQ1pRYh4PcDU4O10ydP4q2oYNMstWtKgdvtJpGu3bSh\npWXOYxubmohFo5lCuUpNZ2Vf/em/B23mnXL6xAk85eW0tbcXeyjLxu3xzJp9WimSySQDfX00NTdT\n39DA/b/0S7jcbgKBAJd7eohGIpmC4QDHjx4l4Pez/eabOXrkyLJn1uLxOJcvXaJj82betmfPVQVd\n+fgwMV9m7QjwL8AtxpiVP1GuVhQrWLMW60cjEYaHhrjmuutKuqSFtcW+vqFhzqwaQEtrKy3TC/oq\nlcXhcPCDRx7BGMOHfv3XNVgj1WJucGCAm3buLOnXgnxzu90rcs3a5OQkg/39DPT3M9jfTyQSoTn9\nQdZ6vbQ2iBw5fBiXy0U8Hicej9N94QIdmzfTvnFjKlhb5szayPAwiUSCltbWomZw5wvWfgUYAraL\nyBljzNgyjEkpjDEE09OgVhmM/vQ6h+k7KkuNtRNoRlcFpa6CiODz+TLFNXUaNNVayul05lzsvZq5\nPZ5UEBOLZXYJQ+r1sufiRZpbWqbcXgpee/VVzp89C6TG39TczIaWlhmvj3X19bjcbi6cO4fD4cBm\nt2O32Whcv54dt9yCsZ7/y/xhZXBgAJvNRsMsu1SXy3zB2j7gc8BZYJOIPGiM+XHhh6XWumg0SjT9\npmRl1vp7e3G53dTW1xdzaPOqb2igobGRto0biz0UtUp88k//lMlgEGBRa3aMMRhjsNmKWf88v8Kh\nEJd7eth6zTWLai21GmTXWqvMCsr6e3t5df9+btyxg2uvv56Tx47RtnFjSRQJHhocpL6hgV2dnTPK\nXmRzOp384i//MpB72tBqCzbbNOjQwACIzFr642oN9vdTV19f9DXS8wVrfwJsN8YMichmUl0HNFhT\nBReYmABSvTbD4TDJRIL+vj6aW1tL/o2n3Ovlrne+s9jDUCuY+f2/hbM9me/fM/3+L6WXD29pQ772\nV7OeZ/+LL2J3ONi7b18BRlkcw8PDJJNJWtraij2UZedJL6sIhUKZTihAppdw3+XL1NbW0nXkCIlk\nku0lUIcyFovRuH79rDvis801zWiz27Hb7bNOg76ZrmH2zne/+6rHOl00EmFsdJTrt2/P2zmv1nzv\nelFjzBBAus6aroJWy8LaCdqwbh2RcBifz0c0Gl30ThylVqQbNoPjylqs/3jmVf7jmVenHuOww/bZ\nN9okk0kGBwYyi7ZXC9/wMHa7ndoFvPmvNhWVlQD40x9mra/7+/rwlJczMjzM8WPHAJgYHy/KGKeL\nx2J5y0o5nM6c06DGGAJ+P8FA4KpaUg0ODPDMz342IxC81NODMaYkSsPMF6y1isiXrH85vleqIAKB\nACJCfUND6k2nvx+AulVepVwpINXXNiuDPBKLMBKbFnDZbHP2vQ0EAsTjcZLJJL2XLxdqpMsiEonw\n7JNPMj42xsjwMLV1dWtqY4HF4/HgcDimBGvnzpzBZrOx59ZbARgeHASu9LGcbnxsjLcOH87sWC+k\nRCJBIpHI2zo6p9OZcxo0Eg4Tj8enLJ9ZjP7eXnwjI1N+Z/F4nONdXdQ3NFDX0LCkcefDfNOgfzbt\n+9cKNRClsgX8fsq93kwBwYH+fspcrsw0gFKrmdTXYN69D/79JYgn+Pr1d0y53zjsyH375ux7O5Yu\n8eBwOLjU00NHCZe7mc+oz5fKGnV1Merzrdl2WyJCZVVVZqd8PBbj4vnztLa1sW79eqqqq5kYH6e1\nvZ3LPT0zNiJEIxFefuEFgsEgiUSCmtpaTp84wZ3vfOeUchn5YmWq8pZZczhyToNaMzEAwUBg0aWQ\nrOB3fGyM+nRgdubkSUKhEHtvv70k6vjNGawZY/7Hcg1EKYsxhrHRUSorKzMLan0jI6xrbCyJPxql\nlsUD74UnXgZyZEByZNVGhoZ4/eBB7rznHsrKyhgbHcVut7Np61bOnjo1o37VSmLtDL/Uk1rHVwqZ\njmKprKpiJN16qfviRaLRKFu2bQPg2uuvZ2R4mPUbNnCpu5uJiQnq0huyjDEcPHCAUChEc0sLZ9Id\nMSAVrNSvW5f3sVpTlg7HvPX3F2S2aVAreLW+rlvkJjSrhqGVWYtGIpw6cYLmlhYaCvB7uRpzToOK\nyKMi8uNZ/v1/IvJ5EVl7qzxVQflGRvBPTNDc2poJ1owxVK/BNSpq7ZL6Gnj3PnDY+dTpA3zq9AEA\nEjYh/PZdM7JqIyMjjI+P4xsZAWBsdJSq6mraN25c8VOhk8HglI1F9SW+I7yQKquqCAaDxGMxzp4+\nTU1tbSZ43bhpE7fs3k11utdw9rTeyePH6evt5eZdu9h7++00NjXRmF6LVaiuCFaZGWeedu06Z8ms\nBQOBzPMjmJVlW4hEIpF5zNjoKAAnjh0jFoux/eablzji/Jkv3H1onsduB/4XcFveRqTWvPNnz+Jw\nOGjbuHFKJq1GWzKptSadXQtlrS8yIky87w6mLwiw6hGO+Xysb2pibHSUlrY2ampr8Xq9K3oqNBgI\n4PV68VZUEAwGM4VU1yJrF+iF8+cZHxvLWVXfW1GBw+HIBGtDAwMcPXKEto0b2bJtGyLC29/xDsKh\nED/50Y8KFqxZ68vyusEgK4tmCQQClHu9JBKJKVOiCxHw+zHG4PZ4GB8bIxgIcPb0ado7OhbVB7TQ\n5gvWyowxT+a6Q0T+izHmz0WkdEJPteLFYjEudXfTtnFj5g/cmU59L2Trt1KribV27SvJJMQTGIed\ni1vXU+adOZ1pveGOjY4SDAaJRqPU1tUhIrS2t3P65Mm8TIVOjI9z8MABbn/725ctaAoGg3grKtiz\nb9+yLIwvZZXpHaHH3nqLMpcrZz1HEaG6poaJ8XFCoRCv7N9PRWUlt+zePSWwK3O5EJFZuyJMjI8T\nj8cXPa1oiS3XmjW/n4qKilSwliOYm4s1BdrS2srZ06c59MorACVR9iTbfLtBvyIi782+QURsIvIw\nsAPAGPOxAo1NrUE9Fy8Sj8enVCZ3ezzYbLbMi5RSa0r2zlCbjeO7OnLueLPecEdHRzNrmqwWPq3t\n7XmbCh0cGGDU52MgvUN7OQSDQcq9XsrKyvCs4awapMp3iAjRaJSOTZtmXQ9WVV3N8NAQzzzxBPFY\njFtvv31G0GSz2TK1LKeLxWK8+Nxz/Pz55zMFaRcrnuc1a7l2g1rdbrwVFVRUVhJMF49eKH+6xElr\nusfs0OAgm7dty2xuKxXzBWvvBr4gIr8CICIeUkVxy4D3FXhsag06f/Ys1TU1mTcZAK/Xu2a36isl\n9TX8SfAsf3JqP7zrNiLlrpyLrK033GAgwJlTp/BWVGSy0Zmp0O7uJY/HWt8znA4ICy0WixGNREqi\nGn8psNvtlHu9iAibt26d9bhrrruOTVu24K2ooPPWW2ed0put32jXm28SmpwkEonQ19t7VWON5Xsa\nNJ1Zs1qvwZVuNxWVlXi9XsKh0KJaUvknJvB6vdTV12Oz2XA6nVx3ww15GW8+zbcb9LyI3AM8ISLr\ngQeAg8aYP12W0ak1ZSi3My4AACAASURBVNTnY9TnY+fb3jYlVX/L7t1XVehQqVXj+k0wEUR+633Y\nn3oid62pSASv10swGGTU52P7TTdl/o7yORWaCdbS9bwKzbqet8QyHcXU3NpKPBbLFMnNpbKqil2d\nnfOey+3xzMisjQwPc+7MGbZs20bvpUtcPH/+qjpGZHaD5nHNGqRKglitxqxpT2saFFLPmYUum/H7\n/VRWVWG329m8dSs1tbWLLv2xHObbDXoL0Aj8OfB3wCXgn0XklvR9SuXNhXPnsNvttE9bg1GeXlis\n1Fr1xf/+Vb74xotIXTXOsrIZ06DJZJJIOJzp8CEitHd0TDkmX1Oh1gJuv9+f6dtbSNa0lr4GXLFj\n1y7etmdPXs7lnjYNmkwkeP3VV3F7PNy4YwftHR309/XNuq4tW9/ly/z0Rz/KBGnxWAybzYY9T7Mi\nVoYu+8NKJpivrMzZ4WEuyWQS/8REZtPGzre9rWQ34cw3DfqF9L9PAkeA9Vm3zbVTVKlFicdidF+4\nQGt7+4qtBaXUcnA6HDOmeaKRCMYYKquqqKyspHH9+hnBTU1tLd6KiiVNhVrrgxrSzbJHlmEq1Hoz\nLrU1RKuF2+0mEg5nZi9OnTjB+Pg4uzo7cTqdmSB/cGBg3nMNDgwQCoWYSO9CjaWL8uarPqa19i37\n+R/w+xERvF4vlVVV2Gy2BbfasjZQ1K6AzjjzTYO+Y7kGota2Sz09xGKxkv1Uo1Qx/fEf/zEAX/nK\nV3CWlc2YBo2ke3+6PR5uv+uunAu6RYTWtrYlTYWGJidJJBK0trUx5vMxNDiYWZhdKJPBIE6nMzPt\npfLL7fGkMrORCLFolONHj9La3k5zSwtwZfo5soD+slZGy+/3U79uHfF4HGeeNhfA1GlQSyAQoLy8\nPJO981ZUML7AYM2qSbgSiizPl1lDRBpF5G9E5JH0v78RkcblGJxaO86fPUtlVVXJVItWqpR4PJ7M\nLkhnjiru1hSVy+WioqIiU0x6uubWVpLJJEOLWG/2wjPPcOrECeBKlquyqor6hoaCbzIYHhqir7cX\nb0WFdi8pEKv8SjgU4vWDB7Hb7ey85coqJ2dZWWb36Xwm0sGaldmKTWt3tVSzTYN6s9buVdfUzNoX\ndbqR4WFcbveKWA8535q124GD6W+/k/4H8Gr6vrke+20RGRSRrqzb/lZEjojIYRH5mYg0z/LY9vT9\nx0XkmIh0LPQHUiuP1Zx505Yt+oKsVA4PPfQQDz2UWnmSM1hLrzmaLUizWFOjC11rZoxheGgos87N\nWq/mraigobGRifFxogvIuCzWZDDIKy+/zHNPPUUymeSmnTvzfg2VYj1nLpw7x9DgIDfu2DGlfp6I\npNZJzvP/ORaLMZleX2hl2OKxWN52gsKVYC2WnVlL11izVFdXEwwEcm7Cmc43PEx9Q8OKeN+ZLz/5\nBeCXjTFvZN32YxH5V+BrwN45Hvsw8GWuBHgA/2CM+SsAEfkE8NfAH+R47HeAvzPGPCkiFUAyxzFq\nlejv6wOYsbFAKTWTs6ws08bHkj0NOheXy4XNZlvQYnFItQtKJpOM+Xwkk0kC6bY+5eXlNKxblwrm\nhoczU2ZLFY/HOXX8OKdOnMAYww033sg111+ftzpdaqbsYM3ldudciuLKsallOitAczqdmQxbLBab\n9wPEYtjTzwMrEItGIkQikSm7YqvSrbay+6LmEolE8Pv9bFwhS2/mmwatmhaoAWCMOQzMWaHUGPMC\n4Jt2W/YWDS8wox6DiNwAOKzOCcaYgDFmcp5xqhVsfHSUcq93TbeQUWouDz74IA8++CCQejOMRqNT\nsmvhUAi73T5vUCMiuNzuBWfWwukgMB6P45+YIOj34/V6sdlsmbpUVgmPaDTKS889t+h2P5a+3l5+\n9thjHOvqYkNLC+9+73u54aabNFArMCuYisfjtLW3T+nBailzuebNrFlTnxuam5kMBonH48QKlFmz\n1qzl2imcCdbmWbfmGx4GVk6f2fmCNRGRGcVKRKRuAY+d7YR/JyI9wG+SyqxNdw0wJiL/P3vnHR9F\nnf//52d30ytpJHQIJRBKCBEVRKMoxYJdVPwKFjzL3XneKervPM/6PU+873mWO0U98ZRTzoanp2dH\nQEWKBqX3EghJSK9bP78/ZmbZ9E2yJRs+z8eDB5mZz8y8dnZ29z3vz7u8I4T4QQixWAihqqH2Yqoq\nK1XfT4WiHZKTk90/Kmnp6Ugp+eLjj92xOY2NjURGRno1nWNk/3mD57iK8nJqamrcP4xms5kkj7i1\nstJSjhYVUepF1mBzXC4X69euxWKxcMb06Zw8ZYrK/gwQlrAwt0HcWusqoNVyMc2pqanBZDKR3q8f\nUkpqa2p8HrPWPBvUs8aaQfO+qG1RqW8PhUxQ6Njg+jPwiRDiDCFEnP4vH/hI39ZppJS/lVIOBJYB\nP29liAWYBtwJnAQMAxa0diwhxE1CiA1CiA2lAaqmrfAtTqeTmpqaHtUwV6HoafzhD3/gD3/4AwDp\nGRlMO/NMHA4HX3zyCfv37sXa2Oi1ZzqqlSKobeHpTdm7ezdVlZWkpae716WkpFBZUYHDbndn4NXX\nd34ipLysDJvVyphx40hNU/lrgSYqKorYuLg2pw0jvPCs1VRVERcX5/4ur66q8nnMmtlsxmQyuadB\nPWMoDUwmE3Hx8R0aazarFYvF4lNj0p90VLpjiRDiCPAwkK2v3gI8IqV8v5vnXgZ8CPy+2fpCoEBK\nuRdACLECOAV4qTV9wBKAvLw8VeI+BKmuqsLlcpGgmrQrFF6TmpbG9JkzWfftt2z47jvNo5GR4dW+\nEZGRlOklCzrCiIWLi4+nvKyM8IgIhnn07U1JS2P71q2UlZW5p53qO9mbEaC4qAiTyURa376d3lfR\nfcbm5BDWTj208GaetZrqag4eOIDL6cTlcuFyuSg7dozUtDR379KqqiqcTqfPjaGwsDD3NGhtTQ1R\n0dEtpsrTMzLYvnUrFeXlbXrO7HY7YSFUDqbDYAAp5QfAB744mRBihJRyl754IbC9lWHrgUQhRKqU\nshQ4C9jgi/Mreh6VFRUAahpUoWiH6667DoCXX37ZvS4yKorT8vPZtnkz27Zs8brCf2RUFDarFZfL\n1Wp8kifGNGh6v37UVFczYuTIJj++RibdsdLSbhlrR4uKSEpJUbXUgkT/AQPa3R4eHo7dbsfldGIy\nm9m2eTMHDxxwe7qEyYTZZKLfgAGYzWbi4+MpOXoU8F1fUAPPxvN1tbVNpkANRmRlsXf3bn4qKGDa\nmWe2aoT6Op7O37RrrAkhWospM5BSyofb2fd1IB9IEUIUonnQzhVCjELL7jyAngkqhMgDbpZS3iil\ndAoh7gQ+F9oV3gi80InXpAghqiorsVgsqpWMQtEOA9voy2gymcgeP56Bgwd7nXUXFRWFlBKr1equ\n3dYWVquV8PBwBg4aRGV5OZkjRjTZHhYWRmKfPpQWFx831jo5DdrQ0EBFeTljJ0zo1H6KwGEUULbZ\nbERERlJcXMygwYOZPGVKq+P7ZmS4a/P5OkEkOibGXe+vtraWjH4tK4CFh4czeuxYCjZupLSkpFWP\nba8y1oDWHpFigBuAZLTp0VaRUl7VyuoWU5n62A3AjR7LnwLjO9Cm6AVUVlaSkJgYEnVuFIpg8dBD\nD7W73ciA84YI3ahrbGjwyliLiIggKTmZM6ZPb3VMSloau/Qf5qjoaBrq673y2hkYCQneTuMqAo/h\n8bTZbFitVqyNjU1iF5uT3q+f21jztUEUExNDWWkpdrudxoaGNh/0h2ZmsnnTJo4UFrZurNlsPbJh\ne1u0+2mSUv7J+IcWGxYFXAe8gRb4r1B0isaGBjZ89x1lpaVIKanWjTWFQhEYDA9cXW0tX3/1FRXl\n5W2OtTY2dtiWyrPrSHpGBi6Xy+sEBjje2zFeb6at6Hl4GmvF+vRme/GFKSkpbo+ar2PWYmJjsdvt\n7vvWs8aaJ2azmZS0NIr1Op7NcYRYzJo37aaShBCPoDVytwC5Usq7pZTe9ytRKNCmPD/Xs9f279tH\nY2MjNputU14BheJE5JprruGaa67xybEMb9qBffsoOnKE3Tt3tjnW2tjo9sS1hWGsCSHc3rHOxK3V\n19cTGRmJyawqNPVUDIPdarVSUlxMXFxcu6VVTGYzffV7wR+eNcBtNLYWs2aQnpFBTU2Nu8SHJza7\nPaRq+HXUbmoxWsB/DTBOSvmAlLIiIMoUvY6tmzfjcjqJi4+nuqrKHeMSp56oFYp2GTVqFKNGjfLJ\nsQzjy+gcUnT4MC6ns9WxVi+miiIiIkhISCAmNtb9Wa6rrWX7li1edUqor6tTNdV6OIZnzdrYyLGS\nknanQA0y+vVDCOHTDgZwvExHiRfGWl9dp2HYeeKw20MqoaUjs/I3gBW4D/itR1yRQEswUL+yCq+w\nWa0UHT5M5siRuJxODu7f725PojxrCkX7/O53v/PZscxms7sifWRUFI0NDRQfPUpYeDgJiYluT4iU\nUhvjxY/t+NxcXE4n0dHRAOzeuZOK8nIsYWEMHzmy3X3r6+tDpjDpiYrhWSs5ehSHw+FVLbzBQ4eS\nlJzsc0PcOF5lRQWRUVHtTrPGxsURExtLcVFRk+QYp9Ppl7Ii/qSjOmtd6lKgUDSn8NAhXC4XgwYP\nprysDLvdTsnRo4SFhfn8yUuhULRPVGQkNquVrDFj2PrTT6z/7jtsVit9kpKYduaZ7rpaLperw5g1\nOO7BAO2H3Ygn6qhTgpSS+rq6DktHKIKLxWLBZDK5vbGecYptIYTwy4N4eHi4+2GjPa+aoSE9I4MD\n+/a5y47A8Q4IoZQNqowxRUA4eOAAcfHxJPbp4/4AHy0qIj4hQWWCKhQdcOWVV3LllVf67HgRetxa\nvwED6D9wIA67nWHDh1NVWcmalSvdWX9ApzPmYjw8KW0lGhQdPsznH39MXc1RstNXEBPdxReiCAhC\nCMIjInA4HMTFxQW9j7Nxj3lT8qlvRgYOh4Njei9Q0DJBIbSMtdCJrlOELHW1tRwrKSF7/PgmT1su\nl0vFqykUXpCTk+PT4yWnpAAQHR3NhNxcxowbR1RUFOn9+rF2zRrWrFxJVrbWtKajBIPmRMfEUFNd\nTUQ7PUiPFhVRUV5OVeEK+iVuwWb5AfDta1T4lvDwcBobGkj2wqvmb2JiYqgoL28zE9ST1LQ0TCYT\nxUVF7gxWowNCKGWDKmNN4XcOHTwIHG8SHBER4Y6VUcaaQtEx99xzj0+Plz1unPtvi8Xizorr178/\np5x2GmvXrGHD2rVA5z1r2ePHM3zkSHZs3dqmZ01LLpKENbyHU4QT7fwIKRcoL3sPxgjG92YK1N8Y\nHrWOpkFB856lpKZytKiIcfpDj9E6K6y3ZIMqFN1FSsnB/ftJSU1t8sEyvGuqtpJC0bPo178/J0+d\n6vY+dNazFh8fT2paWpO2QM2prq4mIaoIiyjH5ozCLI8hG7d2W7vCfxixiz3KWPPCswbaVGhVZSUN\nenay0Qg+lDxrylhT+JWqykqqq6oYNGRIk/WGkaY8awpFx1x66aVceumlATtf/wEDOPW00xg0ZEiX\nE4AM77mUssl6owL+gD6bAIkQJkDiqlrRfeEKvxETE0NMbGyPaA04YOBAxk6Y4HVBdaP+n1Eg10gw\n6DXZoApFdzm4fz8mk4n+zXobDhw8GIfD0SM++ApFT+fUU08N+Dkz+vcno3//Lu8fGRmJy+XCbrNh\nKn8cV82nALicTs7OasRsNtNgjcBsMYEpFlfVf7FVf9zkGKa4GVgy7uvW61D4huzx48nKzu4RU9Xh\nERFkjRnj9fj4hASioqIoPnqUIcOGuY21cGWsKRRaAsGhAwdIz8hoEfeSnJLiDnJWKBTtc+eddwZb\nQqcxPHKNjY3EJc9HWncibYU4XVHYHGZio+LAWoPJZEIIM1iSAZDSBa4aRPhAzMnXBvMlKDzwjG0M\nNYQQpKWnU3TkiPsBAkLLs6amQRV+o7SkhIaGhhZToAqFovfjaayJ8EFYBr2AKX4WuGowmZyEhYVh\nMpkwm48bANJl1bbHz8YyaAkifFCw5Ct6GekZGdisVirKy7E7HO7acaFCaJrJipDg0IEDhIWFkd6v\nX7ClKBQhzZw5cwD497//HWQl3mPUcjNaTglTJJb0u9m318TgmDcBPdHIc1ZN1mPuew/mhNmBlqvo\n5aSlpyOEoLioCLvNFlI11kAZawo/4XQ6OXzoEP0HDgxZ17lC0VOYPn16sCV0Gk/PmoHL6eRYZRyD\nY7WwiBbxTyICU8TwgGlUnDhERESQlJxM8dGjREVFhVQmKChjTeEnamtqsNvt7iwchULRdW6//fZg\nS+g0YWFhmM1md2Fcp9PJd998QziHsFi06SfpqgXpAmFCmGIBB9K6GxE5op0jKxRdo296Otu2bCGx\nT5+Q86yFzoStIqSor6sD8HkTX4VCERoIIYjUa605HA6+WbWKI4WFjBzqxGIB6axChPXD0v9/EWEZ\nSGcVSCeuhs3Blq7opfTNyEBKSUV5uTLWFAqA+vp6QGtno1Aousfs2bOZPTv04rgio6KoralhzVdf\nUVJcTN7JJ5MYdQSkzZ1EYIo55XjygbQhG38KtmxFL6VPUpK7uG8oZYKCmgZV+In6+nrMZnOnq58r\nFIqWXHDBBcGW0CUiIiM5UliIyWRi8qmnavUVjwzDlHQt5viz3eOM5ANn9CRk7ZogKlb0ZkwmE33T\n093Jb6GEMtYUfqGhro6o6OgeUUBRoQh1br311mBL6BIxMTGYTCZOOe00+ukFdi39HmhzvDn+bPAw\n4hQKX6OMNYXCg/r6ejUFqlCc4IwZN47MkSO9aritUASCvhkZCCFCbtbHbzFrQoi/CyFKhBCbPdY9\nLIT4UQhRIIT4RAjRZgEuIUS8EKJQCPGMvzQq/Ed9fT1RylhTKHzC2Wefzdlnh57HKSwsTBlqih5F\nVFQU+dOnMzQzM9hSOoU/EwyWArOarVsspRwvpcwBPgDub2f/h4FVftKm8CMul4uG+nqVCapQ+Ii5\nc+cyd+7cYMtQKHoFyamphKs6axpSylVCiCHN1lV7LMYAsrV9hRCTgL7Af4E8P0lU+InGhgaklGoa\nVKHoBna7ncLCQhobGznttNMA2LZtW5BVKRSKzhIZGcmAAQO6FScX8Jg1IcSjwLVAFXBmK9tNwJ+A\na4DQ8/srVNkOhcIHFBYWEhcXx5AhQ1SijkIRokgpKSsro7CwkKFDh3b5OAGvsyal/K2UciCwDPh5\nK0NuBT6UUhZ2dCwhxE1CiA1CiA2lpaW+lqroIkZB3Cg1DapQdJnGxkaSk5MRQrBjxw527NgRbEkK\nhaKTCCFITk5u0natKwSzKO4y4NJW1p8K/FwIsR94ArhWCPFYaweQUi6RUuZJKfNSU1P9p9QPbPnx\nRw7s2xdsGX7B7VnTGzkrFIquYXjUkpOTSU5ODrIahULRFXzhGQ/oNKgQYoSUcpe+eCGwvfkYKeU8\nj/ELgDwp5T2BURgYbDYbO7ZtIzklhcHdcIv2VBrq64mIiAi5CtEKRU8lJSUl2BIUCkUQ8WfpjteB\nb4FRegmOG4DHhBCbhRA/AjOA2/WxeUKIF/2lpadRUlyMy+WipqYm2FL8QnVVFbFxccGWoVD0Glwu\nFy6XK+DnFULwm9/8xr38xBNP8MADD7iXlyxZQlZWFllZWUyePJk1a453H8jPz2fDhg0A7Nu3jxEj\nRvDxxx+zcuVKzj//fACWLl1KamoqOTk5jBkzhhdeeIEtW7YwcuRIGhoa3Mc677zzeP311/38ahWK\nnovfjDUp5VVSygwpZZiUcoCU8iUp5aVSyrF6+Y4LpJSH9bEbpJQ3tnKMpVLK1uLaehQ2q5X3332X\n4qIir8YXHT4MaFmTdrvdn9ICjsvloqK8nD5JScGWolD0Gnbt2sWuXbs6HuhjIiIieOeddzh27FiL\nbR988AHPP/88a9asYfv27Tz33HNcffXVHD16tMm4wsJCZs2axZ/+9CdmzpzZ4jhz586loKCAlStX\n8v/+3/8jJSWFSy65hEcffRSAFStWYLfbueqqq/zzIhWKEEA1cvcBlZWVWBsbqa6u7nCslJLio0fd\nzWRre5l3raa6GofDQZKKr1EofEZKSkpQpkItFgs33XQTf/7zn1ts++Mf/8jixYvdunJzc5k/fz7P\nPvuse0xRUREzZszg0UcfZc6cOe2eKy0tjczMTA4cOMD999/Pm2++SUFBAffcc0+TYyoUJyKq3ZQP\nqK6qAsBus7U77uD+/ZQdO0ZjQwNZY8awfetWaqqre5UXqkx/AlfGmkLhOwoPHKCyosKnx0zs04cJ\nubkdjrvtttsYP348ixYtarJ+y5YtTJo0qcm6vLw8XnnlFffy/PnzeeSRR7jssss6PM/evXvZu3cv\nw4cPJzo6mieeeILTTz+dX//614wYMcLLV6VQ9E6UseYDanSPmt3haHNMdXU169euRQhBVHQ0mSNH\nsmPbtl4Tt7Z961Ya6utxOp1EREQQo1rMKBQ+w4hZM5kCPxkSHx/Ptddey1NPPUVUJzO8zz77bF57\n7TUWLFjQZt3F5cuXs2bNGiIiInj++edJ0h9eL7jgAhITE0O2ib1C4UuUseYDDM+ao534s+1btmA2\nm5l1wQVE6g1ko2Ni3IZeqFN0+DBlx45hsVhISUtTRTwVCh8SHRdHdFwco0aNCsr5f/WrX5Gbm8t1\n113nXjdmzBg2btzIWWed5V63ceNGsrOz3cuLFi3i1Vdf5fLLL+e9997DYmn5kzN37lyeeab1FtAm\nkykoBqpC0dNQnwIfYMSqtTUNWl1dzaEDB8gcMcJtqAHExcf3mpg1I1HC4XC4n4wVCoVvSE1NJZi1\nJJOSkrjiiit46aWX3OsWLVrE3XffTVlZGQAFBQUsXbq0hSfsySefJD4+nhtuuAEpW+0wqFAoOkAZ\na93EarVi1SsTt5XZaXjVRmRlNVkfFxdHbU1Nr/gCs9ls9ElKIjw8nL4ZGcGWo1D0KpKSkoL+EPSb\n3/ymSVbonDlzuP7665kyZQpZWVksXLiQ1157jYxmn38hBK+88gpFRUUsWrQIh8NBhJ5gpVAovENN\ng3YTYwrUZDLhaCVmzfCqjczKauJVA82z5nA4aKivJzrEWzPZbTYGDxnC2Bkz1BSoQuFjjO+W1qYR\n/Ultba377759+7q7kxjccsst3HLLLa3uu3LlSvff4eHhfPLJJwD85S9/ITMzE4AFCxawYMGCNs+/\nf//+rglXKHoZyljrJkbMWZ+kJGytTIO25VWD4xmThwsLGRGkWBRf4HQ6cTqdhIWFKUNNofADe/bs\nAQhazJqvuOGGG9i8eTP/+te/gi1FoQgplLHWTaoqK7FYLMTFx7coitueVw201PnklBT27trF8JEj\nQ9bQMYzUsPDwICtRKHonaWlpwZbgEzxj3hQKhfeomLVuYLfbKTx4kNS0NMLCw1vErLXnVTPIHDGC\nmpoaiptV/Q4ljMSKcGWsKRR+oU+fPvTp0yfYMhQKRZBQxlo32LNzJ1arldFjxxJmseBwONz9+9rK\nAG1O/4EDiYiMZM/OnYGS7XOUZ02h8C92u73XtaZTKBTeo4y1LmKz2di5YwcZ/fqRlJzsNlSMWmuH\nDhwAaNerBmA2mxk+YgRFR45QVVnpX9F+wvCshYWFBVmJQtE7Mar7KxSKExNlrHWRPTt3YrNaGTNu\nHHDcUDGefq2NjYRHRLTrVTMYNmIEFouFndu3+0+wHzFes5oGVSj8Q9++fenbt2+H42oa6/nH6g+o\nbazvcKxCoQgdlLHWBWw2G7t27KDfgAHuvp4W3VgzUuxtVqvXxktERARDhg3j0IEDLVLjQwE1DapQ\n+JfExEQSExM7HLdm+/ccKjvK6u0/+OS8sW20jVuyZAlZWVlkZWUxefJk1qxZ496Wn59PXl6ee3nD\nhg3k5+e7l9etW0d+fj4jRowgNzeX8847j59++sknehWK3ooy1rrArh07sNlsjBk71r3O8KwZhovN\nZutU4UejdMfuHTt8qDQwuBMM1DSoQuEXvIlZq2msZ9PBXUhg08GdfvOuffDBBzz//POsWbOG7du3\n89xzz3H11Vdz1CNJqqSkhI8++qjFvsXFxVxxxRX87//+L7t27eL777/n3nvvdZcmUSgUraOMtQ5o\nbGzkhw0bsFqtgOYx271jBwMGDiTRIzvLMNaMmDWbzdapacGY2FgGDBzIvj17Wq3X1pOx2e1YLBZM\nZnOwpSgUvRJvYtbWbP/e3Q1FSukz71pz/vjHP7J48WJSUlIAyM3NZf78+Tz77LPuMXfddRePPvpo\ni32feeYZ5s+fz5QpU9zrTjvtNC666CK/aFUoegvKWOuA0uJi9uzaxcZ165BSsnP7dhwOB6M9vGpw\nfBrUHbNmtXZ6WnDk6NHY7Xb27t7tG/EBwtaF16pQKLwnPT2d9PT0NrcbXjWn1LLRndLlN+/ali1b\nmDRpUpN1eXl5bNmyxb186qmnEh4ezpdfftli39zcXJ9rUih6O8pY6wDDo3aksJBv16xh986dDBg0\niIRm8SPhzYw1eyenQUErkts3PZ3dO3fidDp9oD4w2O12lVygUPiRhIQEEhIS2tzu6VUz8Kd3zRvu\nu+8+HnnkkXbHnHzyyYwePZrbb789QKoUitBEGWsdYMRjDRoyhPKyMuLi48nWM0A98fSsORwOHA5H\nlwyYkaNH09jQwMEQ6olns9mUZ02h8CM2m63N8IjmXjUDf3nXxowZw8aNG5us27hxI9nZ2U3WnXXW\nWTQ0NLB27Vr3uuzsbL7//nv38nfffcfDDz9Mld5jWaFQtI4y1jrAZrMRFhbG5FNP5fyLLmL6zJnE\nxsW1GGc2m7Vm7na7+0s1vJOeNYC0vn3pk5TEzu3bWzwp91TsnYzPUygUnWPfvn3s27ev1W2tedUM\n/OFdW7RoEXfffTdlZWUAFBQUsHTpUm699dYWY++77z4ef/xx9/Jtt93G0qVL+eabb9zrQjEDXqEI\nNKo3aAdYvSzBQMhMawAAIABJREFUIYQgLCxMy9rqhrEmhGBkVhbfffMNJcXF9G0nTqWnYLfbVUFc\nhcKPZGRktLq+La+ageFdm5Y1kdjI6E6ft76+ngEDBriXf/3rX/PrX/+aw4cPM2XKFIQQxMXF8dpr\nr7Wq8dxzzyU1NdW9nJ6ezvLly7n77rs5fPgwaWlppKSkcP/993dam0JxIuE3Y00I8XfgfKBESjlW\nX/cwcCHgAkqABVLKI832ywH+BsQDTuBRKeVyf+nsCJvN5rXRZdGNNSPOraveplS9+GV1VVVIGGud\nqSmnUCg6T3x8fKvr2/OqGRjetdk5Uzt9XqN9XnNuueUWbrnllla3rVy5ssly8ynTU045ha+++qrT\nWhSKExl/ToMuBWY1W7dYSjleSpkDfAC09jhVD1wrpczW939SCNFxNUg/0RlDJCwsrOk0aBcNmIiI\nCCwWC3W1tV3aP5C4nM4ux+cpFArvsFqt7odATwrLS9r0qhk4pYvC8mJ/SVMoFAHAb541KeUqIcSQ\nZuuqPRZjgBaPhFLKnR5/HxFClACpQFAaZ9psNmLaqOLdnLCwMGx2OzbDs9aFaVDQpkJjYmOpr6vr\n0v6BxK53bFAJBgqF/9ivJxyN0otnGyw865IgqFEoFIEm4DFrQohHgWuBKuDMDsZOBsKBVstbCyFu\nAm4CGDRokG+F6nSmuG1YWBj19fXHjbVuGDAxMTEh4VkzXquKWVMo/Ee/fv2CLUGhUASRgGeDSil/\nK6UcCCwDft7WOCFEBvAqcJ2Urfv5pZRLpJR5Uso8zyBWX+FyubRMx07GrNlsNsxmMxZL123hmNhY\n6urqenxGaENDA6CauCsU/iQuLo64VrLQFQrFiUEwS3csAy5tbYMQIh74D/BbKeXa1sYEArvdjpTS\n6+K24eHhWBsbtQzSLk6BGsTExuJwOFqNU+kJ1NbW8v369axZuRKz2UxcOwU7FQpF92hsbKSxsTHY\nMhQKRZAI6DSoEGKElHKXvnghsL2VMeHAu8A/pJRvBVJfczqb1ZmUnMzunTspKS7utqcpOiYGgLra\nWiIjI7WimFZrqzXeAkltbS3bNm/m0IEDAAwZNoyRo0cT62Vcn0Kh6DwH9M9b85g1+bOHYc+hjg+Q\nORDx/O/8IU2hUAQAv3nWhBCvA98Co4QQhUKIG4DHhBCbhRA/AjOA2/WxeUKIF/VdrwBOBxYIIQr0\nfzn+0tkediMey0vDK00vudFQX9/pVlPNifEw1gB+Kijgy08/Dfq06LerVnH40CEyR45k1gUXkHvS\nScpQUyj8TP/+/enfv3/LDWOGgcXc/s4WM2QP6/K5H330UbKzsxk/fjw5OTl899135Ofns2HDBveY\n/fv3M9ajX/KaNWuYPHkyWVlZZGVlsWTJEve2Bx54gP79+5OTk8OYMWN4/fXXAa1grrEuKiqKnJwc\ncnJyeOutoD6zKxQ9An9mg17VyuqX2hi7AbhR//s14DV/6eoMRgkObw2vyKgoEhISqKqq6rZnzTDW\njIzQkqNHsVqt1NTUtFlzyd847HaqqqrIHjeuRSN7hULhP9p8ILrmPPj4G7SSlG1gMsE153fpvN9+\n+y0ffPAB33//PRERERw7dqzNtlcGR48e5eqrr2bFihXk5uZy7NgxZs6cSf/+/TnvvPMAuOOOO7jz\nzjvZtWsXkyZN4rLLLuPZZ58FNMPv/PPPp6CgoEuaFYreiGo31Q5dKW6bphex7W4pC0tYGBGRkdTV\n1VFXW0udbrRVlpd367jdobpaq7wSnxi0sncKxQlJQ0ODO5nHE5GcCDOntO1ds5hh1hREUtdiSouK\nikhJSXE/sKakpHSYmfrss8+yYMECcnNz3fs8/vjjPPbYYy3GjhgxgujoaCoqKrqkT6E4UVDGWjt0\npcen0X2gu9OgoD1N19XWUlpS4l5XrvfjCwbVerPlBJVMoFAElIMHD3Lw4MHWN15znuY9a41ueNUA\nZsyYwaFDhxg5ciS33nprk84D8+bNc09Vnnvuue71W7ZsYdKkSU2Ok5eXx5YtW1oc//vvv2fEiBGk\npaV1WaNCcSKgeoO2g91mc/f89JbUtDTCIyJ8kggQGxfH4UOHQAgi9GNWBNGzVlVVhdlsdic/KBSK\nwODZn7M5IjkROXMKfLQGHB7Tod30qoH2wLhx40ZWr17Nl19+ydy5c90esmXLlpGXlwccn7r0lj//\n+c+8/PLL7Ny5k/fff7/L+hSKEwXlWWsHo4m7EMLrfcLCwjh3zhwGDx3a7fOPHjsWIQQlR4+SmpZG\nUnIylRUVbfbr8zfVlZXEJyRgauspXqFQ+IWYmBh3HGurtOZd66ZXzcBsNpOfn8+DDz7IM888w9tv\nv93u+DFjxrToB7px40ays7Pdy3fccQdbtmzh7bff5oYbblBlSRSKDlC/uu3QmSbunlgslk4ZeG0R\nGxvLpMmTAW16tU9SEk6n0z0d6Uu8yTKtrqoiXk2BKhQBp76+nvr6+ja3t4hd84FXDWDHjh3s2rXL\nvVxQUMDgwYPb3ee2225j6dKl7gSBsrIy7r77bhYtWtRi7Jw5c8jLy+OVV17plk6ForejpkHboTNN\n3P3FgEGDmB4bS0JCgjvJ4FhpKYl9+vjsHNu3bmX7li0kpaSQmpamefGSkjCZjwct26xWGhoalLGm\nUASBQ4e0WmrN66w1wTMz1EdetdraWn7xi19QWVmJxWJh+PDhLFmyhMsuu6zNfTIyMnjttddYuHAh\nNTU1SCn51a9+xQUXXNDq+Pvvv5+rr76ahQsXKq+9QtEGylgD1qxcSURkJOMnTmySGGBtbPS6ibs/\n6ZOUBGgxbH2SktixbRtDhg7F4qN+nOVlZZjMZqyNjWz58UdA8w7mnXIKAwYOBDwyQZWxplAEnIH6\n57A93LFrH6zyiVcNYNKkSXzzzTct1q9cubLJ8pAhQ9i8ebN7+fTTT2f9+vWtHvOBBx5ocY4dO3a0\neSyFQqGmQXE5nRwtKuLAvn188p//cHD/fqSUlBYXU1VVRbIfeo52FSEEE3JzaaivZ8f2Fs0fukx9\nXR1JycmcM3s2F1xyCaeedhomk4nioiL3GJUJqlAEj+joaKKjozseeM15MHa4T7xqCoWi53DCe9Zs\ndjsAmSNGUFlRwbpvv+XggQNYGxuJjokhc8SIICtsSkpqKoMGD2an7l3zheevvr6e5JQUQCs50n/g\nQHZu305tTY17TFVlJWFhYUR584OhUCh8ihEC0W6SAXrs2p/vCoQkhUIRQE54z5pRSy05JYUzpk8n\nZ9IkjpWUUFFeTva4cVgsPc+eHTthAkIIftq0qdvHctjt2KzWFkZYbFwctXqrKzieXOCLxAmFQtE5\nCgsLKSwsDLYMhUIRJE54Y82uG2th4eGYTCaGjxzJObNnk3fyyQwaMiS44togOiaGUaNHU3jwIKXF\nxd06lpFh1nyKJTY2lob6ehwOB1JKlQmqUASRQYMGMWjQoA7HSWcNjiP3I501HY5VKBShgzLW9GnQ\ncI9g/ZjYWIYMG9ajvUgjsrKIjomh4Pvvu1V3zW2sNZteMaZX6+vqsDY2YrVaSVBtphSKoBAVFUVU\nVFSH41w1X+Cq/ghXzRcBUKVQKALFCW+s2fT+n93t5RloLBYL43NyqKqsZP/evV0+jtEovoVnTe/A\nUFtbS5WeXBAXpAbyCsWJTm1tbZOwhNaQUuKqeANENK6K5V7VTlQoFKHBCW+suT1rIWasAfQfOJDU\ntDS2/PgjLqez4x1aoaG+HpPJRGSzp3bDs1ZbU6MyQRWKIHP48GEOHz7c7hjZuAVpLwFzItJejGzc\n2u3zms1mcnJyGDt2LJdffnm7hXkrKyv561//2q3zLV26lCNHjriXb7zxRrZubf915Ofns2HDhlbX\njxo1yt2/tL3acKDVe/vss88AePLJJ9t8rcZxJ0yYwNSpU5uUHWkNb17DihUrOhzTnNLSUk4++WQm\nTpzI6tWrW2gcNGhQE4P9oosuIjZIpah8cW+0RllZmfv9TU9Pp3///u5lIx69s3z22WdcdNFFALz7\n7rssXrzYl5K7jDLWPGLWQg0hBAMHD8ZqtdKoewg7S11dHVFRUS2KUYaHhxMeHk5dbS3VVVVEREQQ\nERnpC9kKhaKTDB48uMPOAa6q9wCXHr7hwlW1otvnjYqKoqCggM2bNxMeHs5zzz3X5lh/GGsvvvgi\nY8aM6fLxli1bRkFBAQUFBbz11lvtjn3ooYc4++yzgfaNNeO4mzZtYv78+dx1V/vZt968hq4Ya59/\n/jnjxo3jhx9+YNq0aS22JyYm8vXXXwPae1PkUYop0PjLWEtOTna/vzfffDN33HGHe9nTASOl7FK4\n0MUXX9zh+xsolLFmt2M2mzF7VOsPJYwivlYve+vZbDZWf/mluyxHfX09Ua2UAxBCEBMb6zbWVCao\nQhE8IiMjifR4WHIUPYxt5+lN/rmq/gsm3XNiisVV9d8WYxxFj3RZw7Rp09i9ezcA//d//8fYsWMZ\nO3YsTz75JAD33HMPe/bsIScnx/0Dt3jxYk466STGjx/P73//e0Br+j569GgWLlxIdnY2M2bMoKGh\ngbfeeosNGzYwb948cnJyaGhoaOI1u+WWW8jLyyM7O9t9rK5w4YUX8o9//AOA559/nnnz5gGwYMEC\n3nrrLZ566imOHDnCmWeeyZlnntnusU4//XT3Nfn888+ZOHEi48aN4/rrr8eqP0B7vobY2Fh++9vf\nMmHCBE455RSKi4v55ptv+Pe//81dd91FTk4Oe/bsaXKO/fv3c9ZZZzF+/HimT5/OwYMHKSgoYNGi\nRbz33nvua9WcK6+8kjfeeAOAd955h0suucS9TUrJXXfdxdixYxk3bhzLly8HtGLHZ5xxBhdeeCHD\nhg3jnnvuYdmyZUyePJlx48a5tZWWlnLppZdy0kkncdJJJ7mNwgceeIDrr7+e/Px8hg0bxlNPPQW0\nvDdWrlzJ+ecfrwX485//nKVLlwJaUeR7772XnJwc8vLy+P7775k5cyaZmZntPiw0Z/fu3YwZM4Z5\n8+aRnZ1NUVERN910k/seeuihh9xj//Of/zBq1Chyc3N577333OtffPFFfvWrXwFwzTXXcPvttzNl\nyhSGDRvGu+++C4DT6eTmm28mKyuLGTNmMGvWLFas6P6DUnNOeGPNZrOF5BSogdG71OalZ62ivJzi\no0c5rJcBaKira7PYZmxcHMdKSykvKyMpOdk3ghUKRaepqamhxqPuoTl5PiJ8MGAGUyLCnISwJCOE\n9tAphFlbNieBKREwI8KHYE6+tkvndzgcfPTRR4wbN46NGzfy8ssv891337F27VpeeOEFfvjhBx57\n7DEyMzMpKChg8eLFfPLJJ+zatYt169ZRUFDAxo0bWbVqFQC7du3itttuY8uWLSQmJvL2229z2WWX\nkZeX5/aGNU+oePTRR9mwYQM//vgjX331FT/q3VbawzD8PA3IJUuW8NBDD7F69Wr+9Kc/8fTTTzfZ\n55e//CX9+vXjyy+/5Msvv2z3+O+//z7jxo2jsbGRBQsWsHz5cn766SccDgd/+9vfWoyvq6vjlFNO\nYdOmTZx++um88MILTJkyhTlz5rB48WIKCgrIzMxsss8vfvEL5s+fz48//si8efP45S9/SU5ODg89\n9BBz585t9VoBTJ8+nVWrVuF0OnnjjTeYO3eue9s777xDQUEBmzZt4rPPPuOuu+5ye942bdrEc889\nx7Zt23j11VfZuXMn69at48Ybb3Rfq9tvv5077riD9evX8/bbb3PjjTe6j719+3Y+/vhj1q1bx4MP\nPojdbm9xb3TEoEGDKCgoYNq0aW4jeu3atZ020rdv384dd9zB1q1b6d+/P4899hgbNmxg06ZNfPrp\np2zdupX6+np+9rOf8eGHH7Jx48Ymnt3mlJSU8PXXX7NixQruvfdeAN58800OHz7M1q1bWbp0Kd9+\n+22nNHpLzysiFmDsNltIToEauD1rXhprRkJBRVkZLpeLhoaGto212FgcDgdpffsyOjvbN4IVCkWn\nMX5AjN6gInwQlkEv4Cz5C67q/yJFFMLU8ntMuqwgGzHFz8ac9kuEqXOhDA0NDeTk5ACaZ+2GG27g\nb3/7GxdffLG7QO8ll1zC6tWrmTNnTpN9P/nkEz755BMmTpwIaEkSu3btYtCgQQwdOtR93EmTJrF/\n//4OtfzrX/9iyZIlOBwOioqK2Lp1K+PHj293n2XLlpGXl9dkXd++fXnooYc488wzeffdd0nS2/l1\nhnnz5hEVFcWQIUN4+umn2bFjB0OHDmXkyJEAzJ8/n2effdbtlTEIDw93e5QmTZrEp59+2uG5vv32\nW9555x0A/ud//odFixZ5pdFsNnPaaafxxhtv0NDQwBCPUlRr1qzhqquuwmw207dvX8444wzWr19P\nfHw8J510EhkZGQBkZmYyY8YMAMaNG+c2Xj/77LMm07bV1dXuBJjzzjtPC5uJiCAtLY3iLpSXMu6l\ncePGUVtbS1xcHHFxcURERFBZWUmil5UJMjMzm7z/r7/+Oi+99BIOh4MjR464jbWRI0e6jeR58+a5\nPa/NueiiixBCMH78eHcM6Zo1a7jiiiswmUz069ePM844o9Ov1xtOOGNt/9697N+7l+EjR9J/4EBs\ndjthPuqxGQyMODJvgykNY628vJz6ujpcLleLsh0GA/UP96gxY3pkcWCF4kRhSCs1H4UpEkv63Tij\nxuMsfgxo5aFT1mPuew/mhNldOq8Rs9YVpJTce++9/OxnP2uyfv/+/U16MJvN5lan8TzZt28fTzzx\nBOvXr6dPnz4sWLCARi9DP1rjp59+Ijk5uV0vSns0NwLLy8u92i8sLMwdTmI2m3E4HF06v7dceeWV\nXHzxxS36sbaH53tjMpncyyaTya3X5XKxdu3aJlPzre3f1mu0WCxNYsiav5ee52yupzPXzLPjx65d\nu/jLX/7CunXrSExM5Jprrun0PeSpJdDZ1ifUNKjL6WTLTz9RXlbG2q+/5sC+fdhDfBrU+PB7G7Nm\nBM3W19WxV4+1SOvbt9Wx8fHxZI8frww1hSLIGJ6K1hARmSDa+A4TEZgihvtUy7Rp01ixYgX19fXU\n1dXx7rvvMm3aNOLi4ppM1c6cOZO///3vbo/L4cOHKSkpaffYzY9hUF1dTUxMDAkJCRQXF/PRRx91\nWf+6dev46KOP+OGHH3jiiSfYt2+f1zraYtSoUezfv98dv/bqq692ysPS3vmmTJnijj1btmxZq8kE\nbTFt2jTuvfderrrqqhbrly9fjtPppLS0lFWrVjF58mSvjztjxowm08cdGfTNX9/gwYPZunUrVquV\nyspKPv/8c6/P3VWqq6uJi4sjPj6eoqIiPv74YwDGjBnDrl272LdvH1JKXn/99U4dd+rUqbz11ltI\nKSkqKnJP9fuaE+pX+MiRIzTU13PqtGl8v24dZceOYbPZQrp+mMlkIjw83Otp0Lq6OiwWCw6Hgz27\ndtEnKcldU02hUPRMqqurAe0BqgXW3YDmpZCuWpAuECaEKRZwIK27EZG+63Gcm5vLggUL3D/uN954\no3uqc+rUqYwdO5bZs2ezePFitm3bxqmnngpoYRWvvfZau8lcCxYs4OabbyYqKqpJ7M+ECROYOHEi\nWVlZDBw4kKlTp3ql1ZiuBEhJSeE///kPCxcu5OWXX6Zfv3786U9/4vrrr+eLL5oWEb7pppuYNWuW\nO3atIyIjI3n55Ze5/PLLcTgcnHTSSdx8881eaQTNA7Zw4UKeeuop3nrrrSZxa08//TTXXXcdixcv\nJjU1lZdfftnr4wohuPPOO1usv/jii/n222+ZoLcufPzxx0lPT2f79u1eHfepp57itttuY/z48Tgc\nDk4//fR2g/+Tk5Nb3BtXXHEFY8eOZejQoe77x5/k5uYyZswYsrKyGDx4sPseio6O5rnnnmP27NnE\nxMQwdepUDh486PVxr7jiCr744gtGjx7N4MGDmThxol/KXAl/ufKEEH8HzgdKpJRj9XUPAxeifbOU\nAAuklC380EKI+cB9+uIjUspXOjpfXl6ebK3Wjidfff459XV1zDz/fL76/HPMZjOVlZUMHDSIic3i\nGkKJTz78kPj4eE457bQOx37473/TJymJosOHcblcjMvJYdTo0QFQqVAoOsO2bdsYrX82jVpeRsya\nJ46jf8RV/SFgQoQPxJx6K87SZ5G2QsCFKf58LOk9o/yAQtEbqa2tJTY21l377rvvviM1NbXJGM/P\nswdel1jwp2dtKfAM4Bmpt1hK+TsAIcQvgfuBJo8fQogk4PdAHiCBjUKIf0spK7oqpKqykp8KCigt\nKWH8xImYTCZi4+IoKS7WpkHbmF4IFcIjIrzyrLlcLhobGoiPj6eutpbKigoGDBwYAIUKhaI7DB06\ntM1tsnELSBumhIvdSQQiKkdLPqhagWz8KYBKFYoTj9mzZ1NdXY3dbufBBx9sYaj5Ar8Za1LKVUKI\nIc3WVXssxqAZY82ZCXwqpSwHEEJ8CswCOjeRDDQ2NLB182b27dlDWFgY4ydOZLierRMbG8sBPVYh\nlBMMQItnqamu7nBcQ329O6Fg0JAhJCQmujsVKBSKnkt7cbUifBimpGsxx599fJ2RfBA9CVm7JhAS\nFYoTluYdJPxBwGPWhBCPAtcCVUBrFQf7A4c8lgv1da0d6ybgJoD+wwZT21hPbGQ0dbW17N+7l907\nd+J0OskcMYLRY8c2CdD1NFJCOcEAaDNmzeV0YvKID3H3AY2JoW96esD0KRSK7lHVTss3S78H2tzP\nHH82eBhxCoUiNAl4NqiU8rdSyoHAMuDn3TzWEillnpQyTwpYvf0Hqqur+eTDD9m+dStp6emcM3s2\nOZMmtcik8gyqD+U6a6AFt9qs1iapxKXFxbz75ptsXLfObcjVtdG0XaFQ9GyOHj3K0aNHgy1DoVAE\niWBmgy4DPkSLT/PkMJDvsTwAWOnNATcd3MmgiEScTifTZ86kTzvFDj0b2oaH+DRoeEQEUkpsNpvb\nKD1y5AhCCA7s28fhwkLGjh9Po17LqK26agqFomcybNiwYEtQKBRBJKCeNSGEZ/74hUBrecIfAzOE\nEH2EEH2AGfq6DpFSsvHQTsIjIkjs06fdseEedYtC3bPWWheDYyUlJKekMH3mTOITEvh+/Xq2b91K\nVFRUyPZBVShOVMLCwkI+tlahUHQdvxlrQojXgW+BUUKIQiHEDcBjQojNQogf0Yyw2/WxeUKIFwH0\nxIKHgfX6v4eMZIOOcEoXhfXlRMfHetV03JgKDfWYtYhm/UFtNhuVFRWkpqWRkJjIGWedxeRTT/XK\niFUoFD2PyspKKisrA35es9ns7q2Zk5PDY4891u74c8891631r3/9a4BUKhS9H39mg17VyuqX2hi7\nAbjRY/nvwN+7cl6XlBRR79XYmNhYyo4dC3nPWngzz1rZsWNIKUlJSwO0woiDhgxhwMCBrabfKhSK\nno3RX9Hbnoi+orPtpj788ENAayn117/+lVtvvdVf0hSKE4re125KQGFdObWNHRtsKampREVHh3w7\nJfc0qN5y6lhJCSaTiaTk5CbjTGazmgJVKEKQYcOG9Zi4taqqKkaNGuUu1HvVVVfxwgsvAFoP02PH\njnHPPfewZ88ecnJyuOsuVZBXoeguvc9YQyvetnr7Dx2OG5qZyblz5ng1ZdqTCW82DVpSXExScnLI\nG6EKhUIjLCyMc845h6VLlwJgt9vJz8/ntddeA7Sev/n5+SxfvhzQDKr8/HzeeecdAI4dO0Z+fj7v\nv/8+gNeZpQ0NDU2mQZcvX05CQgLPPPMMCxYs4I033qCiooKFCxc22e+xxx4jMzOTgoICFi9e7ItL\noFCc0PTKX3OXdLHp4E6mZU0kNrLtMhWhbqQZWCwWLBYL9fX1lJWWUlFezoTc3GDLUigUPqKiogKH\nwxHw87Y1DXrOOefw5ptvctttt7Fp06aA61IoTjR6pbEGWmbo6u0/MDvHu4a/oU6//v3Zv3cvFeXl\nhEdEMLSHTJkoFIruU1JSwksvveTuDRoWFsbKlSvd26Ojo5ssJyQkNFlOSUlpspzezaLYLpeLbdu2\nER0dTUVFBQMGDOjW8RQKRfv0ymlQ0DNDy4uDLSNgTJg0ifCICCrKyxk+YgQWleavUPQaMjMzyczM\nDLYMN3/+858ZPXo0//znP7nuuuuw2+1NtsfFxVFTUxMkdQpF76PXeNYyElO476IbOx7YS4mIiOCk\nU05hx7ZtZOr9TxUKRe8gWPGnRsyawaxZs7juuut48cUXWbduHXFxcZx++uk88sgjPPjgg+5xycnJ\nTJ06lbFjxzJ79mwVt6ZQdBPh2aIolMnLy5MbNmwItgyFQqHwCdu2bWP06NEAlJdrpSaT2unKolAo\nei6en2cPvA6c7zWeNYVCoeitlJaWAspYUyhOVJSxplAoFD2c4cOHB1uCQqEIIspYUygUih6KlBIh\nhCpmrVCEML4IN+u12aAKhUIRykRGRlJWVoaUkrKyMsrKyoItSaFQdBLj8xsZGdmt4yjPmkKhUPRA\nBgwYQGFhIaWlpe6OA92tj6ZQKAJPZGRkt2sRKmNNoVAoeiBhYWEMHToUOB6zFqbqJyoUJyTKWFMo\nFIoejjLSFIoTGxWzplAoFD2cpUuXupu4KxSKEw9lrCkUCkUPRxlrCsWJTa/pYCCEKAUOeDE0BTjm\nZzn+IlS1h6pug1DWH6raQ1W3QajqD1XdENraIbT0h5LW5vQk7ceklLO8GdhrjDVvEUJskFLmBVtH\nVwhV7aGq2yCU9Yeq9lDVbRCq+kNVN4S2dggt/aGktTmhql1NgyoUCoVCoVD0YJSxplAoFAqFQtGD\nORGNtSXBFtANQlV7qOo2CGX9oao9VHUbhKr+UNUNoa0dQkt/KGltTkhqP+Fi1hQKhUKhUChCiRPR\ns6ZQKBQKhUIRMihjTaFQKBQKhaIHo4w1hSLEEUKIYGvoCqGqO5RR1zy4hNL1DyWtzQll7W3R64w1\nIcQoIUQ+vorwAAAQmElEQVTIvi4hxFlCiPRg6+gsQoirhRAT9L9D6oMihEj0+DuktOuE6v0eafwR\notc9FAkPtoDuEsrf71IFiQeKWAAhhDnYQnxFyN70zRFCnCOE+A64kRB8XUKIKUKILcAC9BstFBBC\nnC2EWA08CUyE0PlCEkLMFkJ8BTwrhLgXQkc7gBDiPCHEB8DDQoipwdbjLUKIGUKIb4BnhBDzIHSu\nuxDiIiHE00KIpGBr6QxCiHOFEP8F/iKE+J9g6+ksQog5QohfB1tHV9E/q/8UQvxeCDE82HraQwgx\nSwjxHtr3SsgUjxUaaUKIlcCLAFJKZ3BV+Q5LsAV0B/1p3AL8DrgKuFtK+Y7n9lD4EdCt/4XAo1LK\nfwZbT0fo1z0SeAVIAx4BLgSi9e3mnv4hEUJMBh4AHgWqgJ8LIcZKKTcHVZiXCCEmAb9Hew3xwHwh\nxAgp5VIhhElK6QqqwDYQQqQCDwGPAdXAr4QQg6SUf+jhugVwMdr9EgesFEK821P1GgghLMAiNO2/\nA5KB84UQlVLK94Mqzgt0/b8BbgEGCSG+kFIWhMJ3DIAQIpLjD7KPAJcBNwshnpVS7guqOA/0+zsC\neA4YDjwOnAXcIITYL6XsKe2Z2kRKKYUQjUAjMF4IMVtK+VFP/l7pDCHngfJEatgBF/CWYagJIaYJ\nIcKCq65TxAMC+FAIES6E+B8hxHAhRDj0vCki/bo3AMuklPlSyo+Bb4D/0bf3+C9RYCqwSkr5b+AQ\n4AT2GFMsPe2at8LZwGop5YfAe8BR4JdCiAQppasn6tc19QU2SSlXSCm/AO4B7hJCpPRU3eD2/O0F\nTgNuB64BBgRVlBdIKR1ouq+UUv4X+DdwhBCZDtX17wCygF8Dz+vrQ+E7BillI7ANuEw3jv8A5KIZ\nFD0G/Tu9Ee275Az9e/EdtPJePd5QA/f0+ACgAO175X6A3mCoQYgaa0KIXwohXhBC3KSveg7IEEK8\nLIT4Ce1J8iXgen18j/oB8NB/g77KBAwDxgNvAhcA/4v+xYRmyAUdD90LAaSU7+nrzcA+YIsQYmAw\nNbZFc+3AZ8DVQoingVVAP+BvwIPB0tgerej/ErhACNFHN5ztaB7Cu6HnTCsKIeYLIc4Bt6ZaYIox\njSil3Ar8C3g6eCpbx1O7zmYpZZmU8m20632J8UDVk2hF9zvAPiFEmJSyBu0HLTo46jpGv9cfE0Jc\noa/6j5SyUUr5JJAmhLhaH9cjH8g99F+ur1oCFAohIqSU29EeDDOCp/A4za+1lPJdKaVTX34byBJC\nPCyEOC24Slviof1ScBtlR4CRwNdAkRDiZiHEiGDq9BlSypD6hxbTtRaYBXwF3Af0AS4ClqE9gQm0\nabn/AIOCrbkD/b8DotCmhfYAc/VxsUApkBdszW3o/n/AMI/t44D1QFywtXp5zRP1++b/gAv0caOB\nzUB2sDV3oP+3aNPPTwMfAKuBl4GZwF+BmB6guQ/wFlAE/AiYPbb9A3i12djvgKHB1t2edrSHKqOQ\n+FTgcyC32b6iJ+r2GBMJrABGBfs6t6JfAHeg/dBehuaRWgCkeYy5GDgcbK2d1J/qMWagvj2+h2rt\nq2/P17/TLWhT0C96vo4eqj0JyAN+r4+7E6gD3teXLcHW3p1/oehZmw78UWou/d+gzbP/TEq5ArhJ\nSrldau/Mj0Al2hNwT6I1/beiuWxj0JMLpJS1wBtoX8A9gea6w9GmggCQUv6E5tq/Mjjy2qW59jDg\nF1LKCrSnsAP6uO3At2jvSU+iuf5I4Fop5S/Q7p2HpJTXoV3/KCllXfCkaujX9hM0A3gj+pSEzs+B\nWUKIk/TlOmATYAuoyDZoT7v+3YKU8mu06ZbZQogsw8tvbA8GHVxzgz5ApJRyhxBioOGV6Ano1+5M\n4D4p5VtoP8jj0R5CjDHvAjuFEHeCluAUDK2t0Yb+CWgPWQbjgR1SymohRD8hRE4QpHaoVUq5Ukr5\nk9SmoX9C88Q2BENrc9rQngOcgxYOMk0I8SFwHZpBt1ffNSSmztsiZIw1cTxd+wfgfAAp5Qa0N2Oo\nEGJqsx+p+Wgeq4qACm2DdvSvAbLR3OKLgJlCiAuEEPehPb1vC4JcN+3oXgv0N9zj+lTzx0BkT5l2\nbkf7N8BgIcQY4AvgRSFENJqXdixQGAS5Lejgnh8phJgmpTwopfxUH3cemnc2qHi8//+QUlaiefsu\nEUIMBpBSVqNNN/9OCDEf7bpno02RBpX2tEstps7s8b48CdyL5u1Ma7Z/QPFCt5FMNgyIE0L8Ci1+\nLTUIcltcJ49rugGYBqA/nOwCsoUQozyG3wI8LoQ4CvQPgNwWdEL/TjT92fr2FKBRCPELtO9Lv4eN\ndFLraCHEyGaHmIFmqAXcWOuE9h1oxuZEtO/v9VLKbDTnQb4Qon8wH6R8QY811oQQU4UQmcayPB4k\n+DVgEkKcri9vRnP799P3u1QIsQntS+kWqQVNBpxO6j8ETJJS/gMt/u40YBBwvpQyoIZDJ3UfQY+9\n0D8IaUBdsD4UndReCGRJKf8P7YP+FjAGuERKWRJA2W66cO3T9f1OF1oJkhFo909AaUW34X1q1P9f\nD3yElklpjHkGzdiZBAwGLpdSVgVSN3Reu5TSqRs/fYFn0Iz9HCnlI57790DdDn1oLnAqWsbfeVLK\ngN8vOlGeCx73+m40Y3KcvvwVkICWgYvuiXoBLZ4qV0r5SmDktqCz+o3xFwE3o13/WTIwGbmd1Rov\njie6/QgMAe6VwUnq8Fb7KrR7pAS4WUr5e318OTBVSnk4QHr9Ro8z1oQQuUKIT9C+BBM81htadwFb\ngLlCS98uRMswG6pv34n2Zl0rpSwOoHRDZ1f0p6H90CK1DLl7pZQ3SSmP9HDd6WgfZIM7pZR/D5Bk\nN924Z4yn9RuAq6WUV0kpiwIo3dDZ3Xt+P3CrlPJiGcDMrXZ0C9GycOkzwHAhRLYQoq8QYrh+r98h\npZwfyHtd19hV7alCiKHAMbSp9DmBvGe6ec2T0RJTzpBS/jzQ11zXeYoQ4m202oYzhF601MPztw5w\nADOEEBapJaD0R4tFAihDu9cvDzH9k/XtrwLTpZS3+9uA6IbWSVJKG5oT4Rb9tzSgD7Bd0L4F7aFv\nopSyUWgecAHukKKQp8cYa0KIMCHE82iZM0+huYjz9W1mD4u6Bi2gOgJ4QmgZQX3QvjzR59m/DbB8\nX+gvNY4lA5hq7APdZR66Axpz5APtxYZufeoooPjwnj+of1n1FN1S9z5FCSGMGMyDwLto8S9foZWr\nIdBP6z7Qvhroo3vYDoaQ7lXAYCnlZinl6kDpbvYa8tGmZ99B82ZfA/QRWh0sh655N9oUVyZa+QUA\nK3pcqZTykNTiYwNON/Xv1be/I6X8sodrNa71SqnFZgaUbmrfr293BmuGx1/0GGMN7YdoFTBNSvkB\n2hs1WreanQBCiAeBf6KVKPgd2g/Wan05WO5wg1DVH6q6IbS1Q+jq90b379Gys4fpy1ehJUM8AYyT\nUn4fFOWhq727uscG8ZobjEeLJVoGvIaW6FNrPJQIIR4RQryElhzxFDBZCLERKEczToNNd/R/orR6\nTShr9x8yuCm4pwAj9b9Fs203AM8Z29DewH8CmR5jTASxVESo6g9V3aGuPZT1+0D3KQSpNEeoag9V\n3a3p15dz0H5Qf4/m1V4J/B2YC0zR9Q/3GB8LJCr9vUtrb9Ie0OsUpDcnEa0GWg1aJliMvl6g1wRC\nC8AsRptyaPJlhUfdIKW/9+sOde2hrN8Hus2B1NsbtIeq7nb0x3psm4z2w3upvnwDWsLABI8xPe2z\n2mP1h5LW3qQ9GP+CNQ0ag+bW/oX+9+ngbnnh0gNl9+tjzjC2gRZ0LYPfPiJU9Yeqbght7RC6+rur\nO5i1jUJVe6jqNmiuf5qxQUq5Dq1ciFHb8Au0H+0KCPq9bhBK+kNJa3NCWXvACZixJoS4VghxhhAi\nXmpZMEvQ2sw0AicLIYzSG0J/E4zCpI3Geghen69Q1R+qukNdu37+kNQfqrpDWXuo6jbohP4ItDqH\nt+q7TkerPG+UHFH6e5HW3qQ92PjVWBMaGUKIL9GK1M4D/ia0ps2NUsp6tB6NfYCzQHtCFFp2U52u\n7xRjvT+19ib9oao71LWHsv5Q1R3K2kNVdxf1T9d1WtGK8cYKIVbx/9u7exC5qjAAw+9nEoOJEgt/\nGsEQSEQUTSEWFhoLBVHQQguViCIIQkREK0EUDNEqoMSg2CgKAUErC4NtFEESNoKduHbiTyH4kwju\nfhbnTrIsRAmZvXu+mfeBC5k7M5t3NhAO98w9Bx4G9uU6rG1Yqb9S6yy1dyXXbj56si/dLuDDyTna\nfoafrHrtc8B+2rpBW1acv3it+ma1v2p39fbK/VW7K7dX7b7A/stp26FBW+x0x1i9lfsrtc5Se2/H\n1K+sRVuM7gBwICLuoC08ugRn1lR6FrhteG7iXdodHZ8Di5NLoTnyul1Qt79qd/V2qNtftbtye9Xu\niSn0/xBt659Tmfk9I6vUX6l1ltp7NdXB2vCLP067nPkd8CptI/U7I+JWODPX/MpwTNxLm5s+SVvH\naPSVqaFuf9VuqN0OdfurdkPd9qrdE1PoX6D1r8vWP5X6K7WuVrm9a9O8TEe7m2PviseHaZvuPg4c\nH85dRNum6CNg+3DufuD29b7MWLW/anf19sr9Vbsrt1fttn/8/kqts9Te8zHtf6QttLuUJvPUjwKv\nDX9eoO2lB22ftyPr/eFnpb9qd/X2yv1Vuyu3V+2239Z5ae/5mOo0aGb+lZl/59l1fu7i7J6XT9C2\nR/kUOAKcgLO3nPegan/VbqjdDnX7q3ZD3faq3RP2j6dS62qV23u28f9fcv4iYgOQwNW022+hrVL8\nInAjsJjDfHQOQ+yeVO2v2g2126Fuf9VuqNtetXvC/vFUal2tcnuP1mqdtWXa5qu/AjcNo+iXgOXM\nPJb9f3Gwan/VbqjdDnX7q3ZD3faq3RP2j6dS62qV2/sz7XnVyUFbrHEZOAY8uVZ/j/2z0V29vXJ/\n1e7K7VW77bd1Xtp7O2L4hU5dRFwD7AUOZluNuJSq/VW7oXY71O2v2g1126t2T9g/nkqtq1Vu782a\nDdYkSZJ04UbbyF2SJEnnz8GaJElSxxysSZIkdczBmiRJUsccrEmSJHXMwZqkuRQRSxGxEBHfRsTJ\niHg+Iv7z/8SI2B4Rj4zVKEngYE3S/DqVmbsz8wba/oX3AC//z3u2Aw7WJI3KddYkzaWI+CMzL13x\neAfwNXAFcC3wAbB1eHpfZn4ZEV8B1wOLwPvAm8DrwB5gM/BWZr4z2oeQNBccrEmaS6sHa8O534Dr\naBtOL2fm6YjYCRzJzFsiYg/wQmbeN7z+KeCqzNwfEZuBL4CHMnNx1A8jaaZtXO8ASerQJuBQROwG\nloBd53jd3bRNqh8cHm8DdtKuvEnSVDhYkyTOTIMuAT/Tvrv2E3Az7bu9p8/1NuCZzDw6SqSkueQN\nBpLmXkRcCbwNHMr23ZBtwI+ZuUzbiHrD8NLfgctWvPUo8HREbBp+zq6I2IokTZFX1iTNq0siYoE2\n5fkP7YaCg8Nzh4GPI+Ix4DPgz+H8N8BSRJwE3gPeoN0heiIiAvgFeGCsDyBpPniDgSRJUsecBpUk\nSeqYgzVJkqSOOViTJEnqmIM1SZKkjjlYkyRJ6piDNUmSpI45WJMkSerYv7zRNmRH+1lnAAAAAElF\nTkSuQmCC\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tnEkMrVO1sjM",
"outputId": "a74b1b44-882c-472f-cf0a-bb5347085003",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 342
}
},
"source": [
"#the second figure explores thresholds and boundaries for signal generation\n",
"#we can see after 2017/11/15, nokjpy price went skyrocketing\n",
"#as a data scientist, we must ask why?\n",
"#is it a problem of our model identification\n",
"#or the fundamental situation of nokjpy or oil changed\n",
"\n",
"ax=plt.figure(figsize=(10,5)).add_subplot(111)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"\n",
"signals['fitted'].plot(lw=2.5,label='Fitted',c='w',alpha=0.6)\n",
"signals['nok'].plot(lw=2,label='Actual',c='#04060f',alpha=0.8)\n",
"ax.fill_between(signals.index,signals['upper'],\n",
" signals['lower'],alpha=0.2,label='1 Sigma',color='#2a3457')\n",
"ax.fill_between(signals.index,signals['stop profit'],\n",
" signals['stop loss'],alpha=0.1,label='2 Sigma',color='#720017')\n",
"\n",
"plt.legend(loc='best')\n",
"plt.title('Fitted vs Actual')\n",
"plt.ylabel('NOKJPY')\n",
"plt.xlabel('Date')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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XEWqe65PPd3+ffM/H88NO/GI/uOLE2kAuTalc69tjoAcf9Bvf+EZuvfVWTp06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\ng38PJxhOj4tAcSGJWJyOlnYMw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WLNSizbItWWIjc0Mu8X6bBtMZwrMDJcaA6FnwzDMOgf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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YTU82or52slg",
"outputId": "43de123e-dd87-47f9-db05-e7a965e6c36f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 342
}
},
"source": [
"#if we decompose nokjpy into long term trend and short term random process\n",
"#we could clearly see that brent crude price has dominated short term random process\n",
"#so what changed the long term trend?\n",
"#there are a few possible reasons\n",
"#saudi and iran endorsed an extension of production caps on that particular date\n",
"#donald trump got elected as potus so he would encourage a depreciated us dollar\n",
"#which ultimately pushed up the oil price\n",
"\n",
"# In[12]:\n",
"\n",
"#lets normalize all prices by 100\n",
"#its easy to see that nok follows euro\n",
"#and economics explanation would be norway is in eea\n",
"#its economy heavily relies on eu\n",
"ax=plt.figure(figsize=(10,5)).add_subplot(111)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"\n",
"(df['nok']/df['nok'][0]*100).plot(c='#ff8c94',label='Norwegian Krone',alpha=0.9)\n",
"(df['usd']/df['usd'][0]*100).plot(c='#9de0ad',label='US Dollar',alpha=0.9)\n",
"(df['eur']/df['eur'][0]*100).plot(c='#45ada8',label='Euro',alpha=0.9)\n",
"(df['gbp']/df['gbp'][0]*100).plot(c='#f8b195',label='UK Sterling',alpha=0.9)\n",
"(df['brent']/df['brent'][0]*100).plot(c='#6c5b7c',label='Brent Crude',alpha=0.5)\n",
"\n",
"plt.legend(loc='best')\n",
"plt.ylabel('Normalized Price by 100')\n",
"plt.xlabel('Date')\n",
"plt.title('Trend')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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3fbQQvWMF9+2QUpIqz6ZNDOXH70uMfDR1htHCJB7NRcEq8fHUGb7R+tI9j+ez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\n/wcviZmVL2/Y3QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ply4Ih694bCX",
"outputId": "d7f1fd18-36e8-4986-8b98-f5d0616d89a2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 557
}
},
"source": [
"#that still doesnt sound convincable\n",
"#lets try cointegration test\n",
"#academically we should use johansen test which works on multi dimensions\n",
"#unfortunately, there is no johansen test in statsmodels (at the time i wrote this script)\n",
"#well, here we go again\n",
"#we have to use Engle-Granger two step!\n",
"\n",
"\n",
"x2=df['eur'][df.index<'2017-04-25']\n",
"x3=sm.add_constant(x2)\n",
"\n",
"model=sm.OLS(y,x3).fit()\n",
"ero=model.resid\n",
"\n",
"print(adf(ero))\n",
"print(model.summary())"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"(-2.5593457642923028, 0.1016940976193893, 0, 1030, {'1%': -3.436714730058834, '5%': -2.8643501440982058, '10%': -2.5682662399849185}, -1904.8360920752439)\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: nok R-squared: 0.731\n",
"Model: OLS Adj. R-squared: 0.731\n",
"Method: Least Squares F-statistic: 2799.\n",
"Date: Fri, 28 Jun 2019 Prob (F-statistic): 8.04e-296\n",
"Time: 11:42:05 Log-Likelihood: -1312.6\n",
"No. Observations: 1031 AIC: 2629.\n",
"Df Residuals: 1029 BIC: 2639.\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const -5.9310 0.398 -14.885 0.000 -6.713 -5.149\n",
"eur 0.1606 0.003 52.907 0.000 0.155 0.167\n",
"==============================================================================\n",
"Omnibus: 30.547 Durbin-Watson: 0.012\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 32.512\n",
"Skew: 0.429 Prob(JB): 8.71e-08\n",
"Kurtosis: 3.142 Cond. No. 1.94e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.94e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
" return ptp(axis=axis, out=out, **kwargs)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "alPC2BAd5Hsv",
"outputId": "393e4ebe-c5de-4016-bd3a-bccd5ad72f1b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 350
}
},
"source": [
"#unfortunately, the residual hasnt even reached 90% confidence interval\n",
"#we cant conclude any cointegration from the test\n",
"#still, from the visualization\n",
"#we can tell nok and eur are somewhat correlated\n",
"#our rsquared suggested euro has the power of 73% explanation on nok\n",
"\n",
"#then lets do a pnl analysis\n",
"capital0=2000\n",
"positions=100\n",
"portfolio=pd.DataFrame(index=signals.index)\n",
"portfolio['holding']=signals['nok']*signals['cumsum']*positions\n",
"portfolio['cash']=capital0-(signals['nok']*signals['signals']*positions).cumsum()\n",
"portfolio['total asset']=portfolio['holding']+portfolio['cash']\n",
"portfolio['signals']=signals['signals']\n",
"\n",
"\n",
"# In[15]:\n",
"\n",
"\n",
"portfolio=portfolio[portfolio.index>'2017-10-01']\n",
"portfolio=portfolio[portfolio.index<'2018-01-01']\n",
"\n",
"\n",
"# In[16]:\n",
"\n",
"\n",
"#we plot how our asset value changes over time\n",
"ax=plt.figure(figsize=(10,5)).add_subplot(111)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"\n",
"portfolio['total asset'].plot(c='#594f4f',alpha=0.5,label='Total Asset')\n",
"ax.plot(portfolio.loc[portfolio['signals']>0].index,portfolio['total asset'][portfolio['signals']>0],\n",
" lw=0,marker='^',c='#2a3457',label='LONG',markersize=10,alpha=0.5)\n",
"ax.plot(portfolio.loc[portfolio['signals']<0].index,portfolio['total asset'][portfolio['signals']<0],\n",
" lw=0,marker='v',c='#720017',label='The Big Short',markersize=15,alpha=0.5)\n",
"ax.fill_between(portfolio['2017-11-20':'2017-12-20'].index,\n",
" (portfolio['total asset']+np.std(portfolio['total asset']))['2017-11-20':'2017-12-20'],\n",
" (portfolio['total asset']-np.std(portfolio['total asset']))['2017-11-20':'2017-12-20'],\n",
" alpha=0.2, color='#547980')\n",
"\n",
"plt.text(pd.to_datetime('2017-12-20'),\n",
" (portfolio['total asset']+np.std(portfolio['total asset'])).loc['2017-12-20'],\n",
" 'What if we use MACD here?')\n",
"plt.axvline('2017/11/15',linestyle=':',label='Exit',c='#ff847c')\n",
"plt.legend()\n",
"plt.title('Portfolio Performance')\n",
"plt.ylabel('Asset Value')\n",
"plt.xlabel('Date')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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crDlNi8vNxHFjcdjtgzBKTeudjMQyI4sWLVJbt27tsG/fvn3MmjVrkEak9Ub/+2hD3v5w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pG1k0mlWerBqlRrjI/llzzozMwOyxEs/ZPL5RZ93ySSSTZs3Mj2bdsWzIEtvRPnu6EG/J67nwqc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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "12v5yp5k5eK0",
"outputId": "b8e89f7f-d8c9-48ee-a64e-97929d83899b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 768
}
},
"source": [
"#surprising when our model is valid for prediction\n",
"#its difficult to make money from thresholds oscillating\n",
"#when actual price goes beyond stop order boundary\n",
"#that is basically the most profitable trade ever\n",
"#best to follow up with a momentum strategy\n",
"#maybe this is not a statistical arbitrage after all\n",
"#the model is a trend following entry indicator\n",
"import copy\n",
"\n",
"\n",
"def oil_money(dataset):\n",
" \n",
" df=copy.deepcopy(dataset)\n",
" \n",
" df['signals']=0\n",
" df['pos2 sigma']=0.0\n",
" df['neg2 sigma']=0.0\n",
" df['pos1 sigma']=0.0\n",
" df['neg1 sigma']=0.0\n",
" df['forecast']=0.0\n",
" \n",
" return df\n",
" \n",
"def signal_generation(dataset,x,y,method, \\\n",
" holding_threshold=10, \\\n",
" stop=0.5,rsquared_threshold=0.7, \\\n",
" train_len=50):\n",
" \n",
" df=method(dataset)\n",
" \n",
" #variable holding takes 3 values, -1,0,1\n",
" #0 implies no holding positions\n",
" #1 implies long, -1 implies short\n",
" #when we wanna clear our positions\n",
" #we just reverse the sign of holding\n",
" #which is quite convenient\n",
" holding=0\n",
" \n",
" #trained is a boolean value\n",
" #it indicates whether the current model is valid\n",
" #in another word,when trained==True, r squared is over 0.7 by default\n",
" #and the regressand is within two sigma range from the fitted value\n",
" trained=False\n",
" \n",
" #counter counts the days of position holding\n",
" counter=0\n",
" \n",
"\n",
" for i in range(train_len,len(df)):\n",
" \n",
" #when we have uncleared positions\n",
" if holding!=0:\n",
" \n",
" #when counter exceeds holding threshold\n",
" #we clear our positions and reset all the parameters\n",
" if counter>holding_threshold:\n",
" df.at[i,'signals']=-holding \n",
" holding=0\n",
" trained=False\n",
" counter=0\n",
" \n",
" #we use continue to skip this round of iteration\n",
" #only if the clearing condition gets triggered\n",
" continue\n",
" \n",
" #plz note i make stop loss and stop profit symmetric\n",
" #thats why we use absolute value of the spread between current price and entry price\n",
" #usually stop loss and stop profit are asymmetric \n",
" #as ppl cannot take as much loss as profit\n",
" if np.abs( \\\n",
" df[y].iloc[i]-df[y][df['signals']!=0].iloc[-1] \\\n",
" )>=stop:\n",
" df.at[i,'signals']=-holding \n",
" holding=0\n",
" trained=False\n",
" counter=0\n",
" \n",
" continue\n",
" \n",
" counter+=1\n",
" \n",
" else:\n",
" \n",
" #if we do not have a valid model yet\n",
" #we would keep trying the latest 50 data points\n",
" if not trained:\n",
" X=sm.add_constant(df[x].iloc[i-train_len:i])\n",
" Y=df[y].iloc[i-train_len:i]\n",
" m=sm.OLS(Y,X).fit()\n",
" \n",
" #if r squared meets the statistical request\n",
" #which is 0.7 by default\n",
" #we can start to build up confidence intervals\n",
" if m.rsquared>rsquared_threshold:\n",
" trained=True\n",
" sigma=np.std(Y-m.predict(X))\n",
" \n",
" #plz note that we set the forecast and confidence intervals\n",
" #for every data point after the current one\n",
" #this would fill in the blank once our model turns invalid\n",
" #when we have a new valid model\n",
" #the new forecast and confidence intervals would cover the former one\n",
" df.at[i:,'forecast']= \\\n",
" m.predict(sm.add_constant(df[x].iloc[i:]))\n",
" \n",
" df.at[i:,'pos2 sigma']= \\\n",
" df['forecast'].iloc[i:]+2*sigma\n",
" \n",
" df.at[i:,'neg2 sigma']= \\\n",
" df['forecast'].iloc[i:]-2*sigma\n",
" \n",
" df.at[i:,'pos1 sigma']= \\\n",
" df['forecast'].iloc[i:]+sigma\n",
" \n",
" df.at[i:,'neg1 sigma']= \\\n",
" df['forecast'].iloc[i:]-sigma\n",
" \n",
" #once we have a valid model\n",
" #we can feel free to generate trading signals\n",
" if trained:\n",
" if df[y].iloc[i]>df['pos2 sigma'].iloc[i]:\n",
" df.at[i,'signals']=1\n",
" holding=1\n",
" \n",
" #once the positions are entered\n",
" #we set confidence intervals back to the fitted value\n",
" #so we could avoid the confusion in our visualization\n",
" #for instance. if we dont do that, \n",
" #there would be confidence intervals even when the model is broken\n",
" #we could have been asking why no trade has been executed,\n",
" #even when actual price falls out of the confidence intervals?\n",
" df.at[i:,'pos2 sigma']=df['forecast']\n",
" df.at[i:,'neg2 sigma']=df['forecast']\n",
" df.at[i:,'pos1 sigma']=df['forecast']\n",
" df.at[i:,'neg1 sigma']=df['forecast']\n",
" \n",
" if df[y].iloc[i]<df['neg2 sigma'].iloc[i]:\n",
" df.at[i,'signals']=-1\n",
" holding=-1\n",
" \n",
" df.at[i:,'pos2 sigma']=df['forecast']\n",
" df.at[i:,'neg2 sigma']=df['forecast']\n",
" df.at[i:,'pos1 sigma']=df['forecast']\n",
" df.at[i:,'neg1 sigma']=df['forecast']\n",
"\n",
" \n",
" return df\n",
" \n",
"def portfolio(signals,close_price,capital0=5000,positions=250): \n",
"\n",
" portfolio=pd.DataFrame()\n",
" portfolio['close']=signals[close_price]\n",
" portfolio['signals']=signals['signals']\n",
" \n",
" portfolio['holding']=portfolio['signals'].cumsum()* \\\n",
" portfolio['close']*positions\n",
"\n",
" portfolio['cash']=capital0-(portfolio['signals']* \\\n",
" portfolio['close']*positions).cumsum()\n",
" \n",
" portfolio['asset']=portfolio['holding']+portfolio['cash']\n",
" \n",
"\n",
" return portfolio\n",
" \n",
"#plotting fitted vs actual price with confidence intervals and positions\n",
"def plot(signals,close_price):\n",
" \n",
" data=copy.deepcopy(signals[signals['forecast']!=0])\n",
" ax=plt.figure(figsize=(10,5)).add_subplot(111)\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
" \n",
" data['forecast'].plot(label='Fitted',color='#f4f4f8',alpha=0.7)\n",
" data[close_price].plot(label='Actual',color='#3c2f2f',alpha=0.7)\n",
" \n",
" ax.fill_between(data.index,data['pos1 sigma'], \\\n",
" data['neg1 sigma'],alpha=0.3, \\\n",
" color='#011f4b', label='1 Sigma')\n",
" ax.fill_between(data.index,data['pos2 sigma'], \\\n",
" data['neg2 sigma'],alpha=0.3, \\\n",
" color='#ffc425', label='2 Sigma')\n",
" \n",
" ax.plot(data.loc[data['signals']==1].index, \\\n",
" data[close_price][data['signals']==1],marker='^', \\\n",
" c='#00b159',linewidth=0,label='LONG',markersize=11, \\\n",
" alpha=1)\n",
" ax.plot(data.loc[data['signals']==-1].index, \\\n",
" data[close_price][data['signals']==-1],marker='v', \\\n",
" c='#ff6f69',linewidth=0,label='SHORT',markersize=11, \\\n",
" alpha=1)\n",
" \n",
" plt.title(f'Oil Money Project\\n{close_price.upper()} Positions')\n",
" plt.legend(loc='best')\n",
" plt.xlabel('Date')\n",
" plt.ylabel('Price')\n",
" plt.show()\n",
"\n",
"#plotting portfolio performance over time with positions\n",
"def profit(portfolio,close_price):\n",
" \n",
" data=copy.deepcopy(portfolio)\n",
" ax=plt.figure(figsize=(10,5)).add_subplot(111)\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
" \n",
" data['asset'].plot(label='Total Asset',color='#58668b')\n",
" \n",
" ax.plot(data.loc[data['signals']==1].index, \\\n",
" data['asset'][data['signals']==1],marker='^', \\\n",
" c='#00b159',linewidth=0,label='LONG',markersize=11, \\\n",
" alpha=1)\n",
" ax.plot(data.loc[data['signals']==-1].index, \\\n",
" data['asset'][data['signals']==-1],marker='v', \\\n",
" c='#ff6f69',linewidth=0,label='SHORT',markersize=11, \\\n",
" alpha=1)\n",
" \n",
" plt.title(f'Oil Money Project\\n{close_price.upper()} Total Asset')\n",
" plt.legend(loc='best')\n",
" plt.xlabel('Date')\n",
" plt.ylabel('Asset Value')\n",
" plt.show()\n",
"\n",
" \n",
"#generate signals,monitor portfolio performance\n",
"#plot positions and total asset\n",
"\n",
"data=pd.read_csv('input/brent crude nokjpy.csv')\n",
"signals=signal_generation(data,'brent','nok',oil_money)\n",
"p= portfolio(signals,'nok')\n",
"plot(signals,'nok')\n",
"profit(p,'nok')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
" return ptp(axis=axis, out=out, **kwargs)\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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UtFBKKaWUWi/rkQl7FfCkMebkxd6olpSm2Lt//7ypSJidCfsPv/EbvPjlLwdmSloopZRS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\ny08nUREE3gusBnYAX5dSzijjTEr5OyFE+ySrbgM+DNwzq5F6eOCG56R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Rd73QQA0zKUIp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\nlRVFWPOUK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c7m7HABm0omcbrGZkMxViprmqmum4Y9H9XKpKPYHX56u7ZjGBI9lyKXO0D3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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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UuIlIjug24I1pA14RkU5S4CYiOSJbnBA3zXETEekkBW4ikiOTccv7BrxmzJ63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\nocRNREREJCX+HyqLygsbDyiwAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3CuCzll557j-",
"outputId": "e9adba8f-92fc-44a9-afd3-7e5de94c1066",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 760
}
},
"source": [
"#but thats not enough, we are not happy with the return\n",
"#come on, 2 percent return?\n",
"#i may as well as deposit the money into the current account \n",
"#and get 0.75% risk free interest rate\n",
"#therefore, we gotta try different holding period and stop loss/profit point\n",
"#the double loop is very slow, i almost wanna do it in julia\n",
"#plz go get a coffee or even lunch and dont wait for it\n",
"dic={}\n",
"for holdingt in range(5,20):\n",
" for stopp in np.arange(0.3,1.1,0.05):\n",
" signals=signal_generation(data,'brent','nok',oil_money,holding_threshold=holdingt, stop=stopp)\n",
" \n",
" p=portfolio(signals,'nok')\n",
" dic[holdingt,stopp]=p['asset'].iloc[-1]/p['asset'].iloc[0]-1\n",
" \n",
" \n",
"profile=pd.DataFrame({'params':list(dic.keys()),'return':list(dic.values())})\n",
"\n",
"\n",
"# In[18]:\n",
"\n",
"#plotting the distribution of return\n",
"#in average the return is 2%\n",
"#but we can get -6% and 6% as extreme values\n",
"#we dont give a crap about average\n",
"#we want the largest positive return\n",
"\n",
"ax=plt.figure(figsize=(10,5)).add_subplot(111)\n",
"ax.spines['top'].set_visible(False)\n",
"ax.spines['right'].set_visible(False)\n",
"profile['return'].apply(lambda x:x*100).hist(histtype='bar', color='#f09e8c',width=0.45,bins=20)\n",
"plt.title('Distribution of Return on NOK Trading')\n",
"plt.grid(False)\n",
"plt.ylabel('Frequency')\n",
"plt.xlabel('Return (%)')\n",
"plt.show()\n",
"\n",
"\n",
"# In[19]:\n",
"\n",
"#plotting the heatmap of return under different parameters\n",
"#try to find the optimal parameters to maximize the return\n",
"\n",
"#turn the dataframe into a matrix format first\n",
"matrix=pd.DataFrame(columns= [round(i,2) for i in np.arange(0.3,1.1,0.05)])\n",
"\n",
"matrix['index']=np.arange(5,20)\n",
"matrix.set_index('index',inplace=True)\n",
"\n",
"for i,j in profile['params']:\n",
" matrix.at[i,round(j,2)]= profile['return'][profile['params']==(i,j)].item()*100\n",
"\n",
"for i in matrix.columns:\n",
" matrix[i]=matrix[i].apply(float)\n",
"\n",
"\n",
"#plotting\n",
"fig=plt.figure(figsize=(10,5))\n",
"ax=fig.add_subplot(111)\n",
"sns.heatmap(matrix,cmap='gist_heat_r',square=True, xticklabels=3,yticklabels=3)\n",
"ax.collections[0].colorbar.set_label('Return(%) \\n', rotation=270)\n",
"plt.xlabel('\\nStop Loss/Profit (points)')\n",
"plt.ylabel('Position Holding Period (days)\\n')\n",
"plt.title('Profit Heatmap\\n',fontsize=10)\n",
"plt.style.use('default')\n",
"\n",
"#it seems like the return doesnt depend on the stop profit/loss point\n",
"#it is correlated with the length of holding period\n",
"#the ideal one should be 9 trading days\n",
"#as for stop loss/profit point could range from 0.6 to 1.05"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
" return ptp(axis=axis, out=out, **kwargs)\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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hOFKSJGnRMYRJkiQ1YAiTJElqwBAmSZLUgCFMkiSpAUOYJElSA4YwSQtOktuT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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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fsn1quc2ngCUUV9VGspPt2yQ9FThb0jW2z+8lzm6S0w+AiyWdUn7ei+JZp4iI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\nySkiImrn/wNrqiCG9tpswwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x360 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mcKQkz4t8HsZ"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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