Skip to content

Instantly share code, notes, and snippets.

@sin32775
Created July 14, 2021 06:08
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save sin32775/d633449daeb43a03c9e918cd6424775c to your computer and use it in GitHub Desktop.
Save sin32775/d633449daeb43a03c9e918cd6424775c to your computer and use it in GitHub Desktop.
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# トレンドをモデル化しよう\n",
"\n",
"トレンドのモデル化を考えてみよう。\n",
"次の一次式の形  \n",
"\n",
"$$Y_{t}=α+β t+u_{t} (1) $$ \n",
"\n",
"で表されるトレンドを確定的トレンド、または時間トレンドとよぶ。\n",
"\n",
"時間の経過にともない、その経過時間に比例して価格が変化するモデルである。\n",
"\n",
"αとβは回帰係数である。 ここで t は時間を表し、$u_{t}$は定常なかく乱項または残差項です\n",
"\n",
"## 線形回帰モデルに息を吹き込む \n",
"### 線形回帰モデル  \n",
"2変数からなるモデルを考えよう。 \n",
"\n",
"それを  \n",
"\n",
"$$E(Y|X_{i}) =β_{1}+β_{2} X_{i} (2) $$ \n",
"\n",
"と表現する。\n",
"\n",
"Yを被説明変数、Xを説明変数という。\n",
"\n",
"$β_{1}$、$β_{2}$は定数です。 \n",
"これらの定数は説明変数と被説明変数の関係をとらえていて、回帰係数とよばれる。\n",
"\n",
"(2)式は係数と変数に関して線形であるという。それは係数と変数のそれぞれが1次の関数であるからです。 \n",
"\n",
"$E()$は条件付き期待値を表している。$E(Y|X_{i})$ は $X_{i}$ の時のY の期待値を表している。 \n",
"\n",
"(2)式は $X_{i}$ の時の Y の期待値が必ず $β_{1}+β_{2} X_{i}$ である確定的な関係を表現している。 これを母集団回帰関数とよぶ。\n",
"実際には平均からの乖離があるのでそれを表現すると(2)式は  \n",
"\n",
"$$Y_{i} = β_{1}+β_{2} X_{i}+u_{i} = E(Y|X_{i})+u_{i} (3) $$ \n",
"\n",
"となる。$E(Y|X_{i})$ は説明変数における被説明変数の平均的な値であり確定的な成分です。 一方、$u_{i}$ は予測不可能であるので確率的な成分です。(3)式を母集団回帰式とよび の数だけ存在する。  \n",
"\n",
"### 標本と母数  \n",
"すべてのデータを母集団というが、 (1)、(2)、(3)式での説明はこの母集団が手に入るという前提に立ち、実際に直面する問題には触れずに来た。\n",
"\n",
"しかし、金融の専門家が母集団を手に入れることは不可能であり、 手にできるのは母集団の部分です。\n",
"\n",
"それを標本という。 母集団の特性を表現する定数(平均、分散など)を母数(parameter)といい、(1)、(2)、(3)式では $β_{1}$ と $β_{2}$ が母数です。 \n",
"\n",
"母数は不変だが、未知です。  \n",
"\n",
"標本の特性を表現する定数を統計量(statistics)という。 我々が観測できるのは母集団から抽出された標本であるので、 母数を手に入れることはできない。\n",
"\n",
"しかし、標本から統計量を算出できるので、標本から母数を推定できる\n",
"\n",
"### 標本回帰式\n",
"我々が手にできるのはある固定値 X に対する限られた Y の値です。 \n",
"\n",
"従って、観察された実現値、または得られた標本から母集団回帰関数と同等な標本回帰関数を推定しなければならない。\n",
"\n",
"それを\n",
"\n",
" $$\\hat{Y}_{i} = \\hat{ \\beta } _{1} + \\hat{\\beta } _{2}X_{i}$$\n",
" \n",
" で表す。ここで $\\hat{Y}_{i}$、$\\hat{ \\beta } _{1}$、$\\hat{\\beta } _{2}X_{i}$ は統計量である。また上式は\n",
" \n",
" $$\\hat{Y}_{i} = \\hat{ \\beta } _{1} + \\hat{\\beta } _{2}X_{i}+\\hat{u}_{i} (3) $$ \n",
" \n",
" と書くこともできる。\n",
" \n",
" $\\hat{u}_{i}$ は統計量であり、残差項とよぶ。\n",
" \n",
" この式を標本回帰式とよぶ。\n",
" \n",
" $\\hat{ \\beta } _{1}$、$ \\hat{\\beta } _{2}$、$\\hat{u}_{i}$ は統計量であり、得られた標本からある規則や計算方法により求められる。\n",
"\n",
"この規則や計算方法を推定量(estimator)とよび、そこから得られる数値を推定量(estimate)という。\n",
"\n",
"$\\hat{ \\beta } _{1}$、$ \\hat{\\beta } _{2}$、$\\hat{u}_{i}$ は推定量です。\n",
"\n",
"### 最小二乗法\n",
"線形回帰関数の統計量は最小二乗法によって求められる。\n",
"\n",
"(3)式を書き直し\n",
"\n",
"$$ \\hat{u}_{i} = \\hat{Y}_{i} - (\\hat{ \\beta } _{1} + \\hat{\\beta } _{2}X_{i})$$\n",
"\n",
"を得る。この両辺を2乗して、残差の2乗和を最小にするような $\\hat{ \\beta } _{1}$ と$\\hat{\\beta } _{2}$ を求める。\n",
"\n",
"そうすると\n",
"\n",
"$$\\hat{ \\beta } _{1} = \\frac{ \\Sigma X^{2}_{i} \\Sigma Y_{i}- \\Sigma X_{i} \\Sigma X_{i} Y_{i}}{N \\Sigma X_{i}^{2}-( \\Sigma X_{i})^{2}} $$\n",
"\n",
"\n",
"$$\\hat{ \\beta } _{2} = \\frac{ \\Sigma X_{i} Y_{i} - \\Sigma X_{i} \\Sigma X_{i} Y_{i}}{N \\Sigma X^{2}-( \\Sigma X_{i})^{2}}$$\n",
"\n",
"が得られる。\n",
"\n",
"ここで N は標本数です。\n",
"\n",
"### 最小二乗法の仮定\n",
"\n",
"統計モデルは幾つかの仮定のもとに成り立つ。 \n",
"\n",
"古典的線形回帰モデルの仮定をここで列挙しておきます。  \n",
"\n",
"*回帰関数は線形でなければならない。  \n",
"\n",
"* $X_{i}$ は確率変数であってはならない。  \n",
"\n",
"* $u_{i}$ の平均はゼロである。  \n",
"\n",
"* $u_{i}$ の分散は一定である。  \n",
"\n",
"* $u_{i}$ と $u_{i+j}$ の相関はゼロである。j≠1\n",
"\n",
"* $u_{i}$​ と $X_{i}$​ の共分散はゼロである。 \n",
"\n",
"ここで注目してほしいのは残差項に関する仮定が多いことです。  \n",
"\n",
"### 推定の信頼性  \n",
"\n",
"単純な時間トレンドの標本線形回帰式には $\\hat{ a }$ , $\\hat{ \\beta }$ , $\\hat{ u }$ の3つの推定値があります。  \n",
"\n",
"### 標準誤差\n",
"\n",
"標準誤差(Standard Error,Std Err)は母数の推定値(統計量)と未知の母数との差です。 \n",
"\n",
"これは統計量の正確さの測度であり\n",
"\n",
"$$se(\\hat{ \\beta } _{1}) = \\sqrt{\\frac{ \\Sigma X_{i}^{2} }{N \\Sigma( X_{i}- \\overline{X})^{2}} \\sigma} $$\n",
"\n",
"$$se(\\hat{ \\beta } _{2}) = \\frac{\\sigma}{\\sqrt{\\Sigma( X_{i}- \\overline{X})^{2}}} $$\n",
"\n",
"から求めることができる。\n",
"\n",
"$\\overline{X}$ の標本平均です。 \n",
"\n",
"ここで σ は次式で与えられる $u_{i}$ の分散で推定の標準誤差とよばれる。 \n",
"\n",
"\n",
"$$\\sigma^{2} = E(\\hat{{\\sigma}}^{2}) = \\frac{E(\\Sigma u_{i}^{2})}{N-2} $$\n",
"\n",
"この母数は未知であるので、その推定量は、\n",
"\n",
"$$\\hat{{\\sigma}}^{2} = \\Sigma\\frac{\\hat{u}_{i}^{2}}{N-2} = \\frac{\\Sigma[Y_{i}-E(Y|X_{i})]^{2}}{{N-2}} $$\n",
"\n",
"である。標準誤差は、統計量のバラツキ具合、つまり精度の測度であり、\n",
"\n",
"推定の標準誤差は標本線形回帰線とデータとの適合度(goodness of fit)の目安です。\n",
"\n",
"### ※決定係数 $R^{2}$ (R-squared)\n",
"\n",
"$R^{2} $ (R-squared) は標本線形回帰線とデータとの間の適合度(goodness of fit)を表す測度として知られ、 決定係数(coefficient of determination)という。\n",
"\n",
"次式で与えられる。\n",
"\n",
"$$r^{2} = \\frac{\\Sigma(\\hat{Y}_{i}-\\overline{Y})^{2}}{\\Sigma({Y}_{i}-\\overline{Y})^{2}} $$\n",
"\n",
"ここで$\\overline{Y}$はYの標本平均です。\n",
"\n",
"決定係数が1に近いほど、相対的なバラツキは少ない。 \n",
"\n",
"自動調整済み決定係数(Adj R-squared)は説明変数の数の効果を考慮した係数です。\n",
"\n",
"説明変数の数が多くなると決定係数は良くなる傾向があるので、その分を調整している。  \n",
"\n",
"### 2乗平均平方根誤差\n",
"実測値と予測値の間の残差の目安が2乗平均平方根誤差 (RMSE:root-mean-square error)です。\n",
"\n",
"$$RMSE^{2} = MSE = E(\\hat{Y}-Y)^{2} = var(\\hat{Y})+E[E(\\hat{Y})-Y]^{2} $$\n",
"\n",
"で与えられる。ここで$E(\\hat{Y}-Y)^{2}$はバイアスです。\n",
"\n",
"$var(\\hat{Y})$は$\\hat{Y}$のの分散であって、実測値と予測値の間の残差の2乗の平均値を平均2乗誤差といい、2乗平均平方根誤差はその平方根です。\n",
"\n",
"小さいほうが良い。$var(\\hat{Y})$がゼロであれば、\n",
"\n",
"$RMSE^{2} = E(\\hat{Y}-Y)^{2}$です。\n",
"\n",
"\n",
"\n",
"### ※信頼区間\n",
"\n",
"母平均μが確率1-xで区間 $μ^{*}$と$μ^{**}$の間にいるとき、1-x を信頼係数(the confidence coefficient)、または信頼水準(the significance level)といい、$μ^{**}$<μ< $μ^{*}$を信頼区間という。\n",
"\n",
"$μ^{*}$、$μ^{**}$をそれぞれ信頼上限、信頼下限という。xを有意水準(the level of significance)という。  \n",
" \n",
"### ※p-値 \n",
" 信頼区間を標準化変換を用いて、\n",
" \n",
" $$ \\theta _{0}- \\delta < \\hat{ \\theta } <\\theta _{0}+\\delta$$\n",
" \n",
" \n",
" と書き換えることができる。\n",
" \n",
" $\\theta _{0}$は帰無仮説の母数の値です。\n",
" \n",
" $\\delta$は平均がゼロ、分散が1の標準正規分布に従う\n",
" \n",
" $$Z = ( \\widehat{ \\theta } - \\theta _{0})/se(\\widehat{ \\theta })$$\n",
" \n",
" から得ることができる。\n",
" \n",
" 母標準誤差が未知であるとき、 母標準誤差を$se(\\widehat{ \\theta })$に置き換え、ステューデントのt分布推定量(回帰係数)の信頼区間が得られる。\n",
" \n",
" この関係を利用すると推定量$\\widehat{ \\theta}$(回帰係数)の信頼区間が得られる。\n",
" \n",
" $\\theta _{0}$をゼロとし、標本平均を$\\theta _{0}$とすると確率1-p の信頼区間は\n",
" \n",
" $$0 - t_{p,n}se( \\widehat{ \\theta }) < \\widehat{ \\theta } < 0 + t_{p,n}se( \\widehat{ \\theta })$$\n",
" \n",
" で与えられる。\n",
" \n",
" ここで$t_{p,n}$は確率1-p の信頼区間を与える自由度n-1のt分布の値で臨界値とよぶ。\n",
" \n",
" ここで与えられる区間を採択域(the region of acceptance)といい、この外側の領域を棄却域(the region(s)of rejection)という。 \n",
" \n",
" これを危険域(the critical region(s))と呼ぶことがある。 \n",
" \n",
" そうすると大きな|t|値は帰無仮説の棄却域にいることになる。 \n",
" \n",
" $$t_{p,n} = \\frac{ \\widehat{ \\theta } }{se( \\widehat{ \\theta })} $$\n",
" \n",
" であるから、$t_{p,n}$からp-値(p-value)を計算できる。\n",
" \n",
" p-値臨界値が与える棄却域の確率であると定義される。\n",
" \n",
" 一般に次の表が棄却、棄却が難しいの目安になります。 \n",
" \n",
" |関係(pはp-値を表す) | 解釈 |\n",
"|---------------------|---------------------------- |\n",
"|p<0.01 |帰無仮説を棄却する |\n",
"|0.01<p<0.1 |帰無仮説を棄却するに足る |\n",
"|0.1<p |帰無仮説を棄却するのは難しい |\n",
" \n",
" 「帰無仮説(否定)を棄却する」とは0.01以下の確率でしか起こらないことが起こった、ということです。 \n",
" \n",
" 「帰無仮説(否定)の棄却は難しい」は棄却するに十分な証拠がないということです。 \n",
" \n",
" 統計学の目的は極力誤った判断を減らすことにあります。\n",
" \n",
" ※:分析の判断目安"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 日経平均株価の確定的トレンド\n",
"\n",
"時間の経過とともに価格が線形に上昇、または下落する傾向をもつ時、その時系列は次のようにモデル化される。  \n",
"\n",
"$$Y_{t}=α+β・t+u_{t} $$\n",
"\n",
"ここでYtは日経平均株価、t は経過時間である。$Y_{t}$はトレンドの傾きである。$u_{t}$は残差項である。 \n",
"\n",
"このようなトレンドを確定的トレンドという。$Y_{t}$が一時的にトレンドから大きく乖離したとしても、その長期的なトレンドに変化はなく、\n",
"\n",
"日経平均株価には確定的トレンドに復帰する力があると考えます。 \n",
"\n",
"このような確定的トレンドが成り立つ理由として、 株式市場の動向が景気循環と関係があり、景気の悪化により株価が下落しても、\n",
"\n",
"その景気の悪化が景気循環の一次的な現象であり、 時間の経過と共に回復し、それにともない株式市場も回復すると考えるからです。  \n",
"\n",
"### 静的分析  \n",
"日経平均株価はバブル崩壊までは強い上昇トレンドを経験し、\n",
"\n",
"その後は幾つかの上昇トレンドと下落トレンドを繰り返しながら、いまだに最高値を更新できないでいます。\n",
"\n",
"今回の分析は対数価格を用いる。\n",
"\n",
"興味のある場合は価格で分析を行ってほしいと思います。\n",
"\n",
"その違いが判るはずです。"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Close R-squared: 0.756\n",
"Model: OLS Adj. R-squared: 0.756\n",
"Method: Least Squares F-statistic: 5.196e+04\n",
"Date: Wed, 02 Aug 2017 Prob (F-statistic): 0.00\n",
"Time: 12:47:55 Log-Likelihood: -18601.\n",
"No. Observations: 16769 AIC: 3.721e+04\n",
"Df Residuals: 16767 BIC: 3.722e+04\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 6.1967 0.011 546.859 0.000 6.174 6.219\n",
"x1 0.0003 1.17e-06 227.943 0.000 0.000 0.000\n",
"==============================================================================\n",
"Omnibus: 560.110 Durbin-Watson: 0.000\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 284.479\n",
"Skew: -0.119 Prob(JB): 1.68e-62\n",
"Kurtosis: 2.408 Cond. No. 1.94e+04\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+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"import pandas as pd\n",
"import pandas_datareader.data as web\n",
"import statsmodels.api as sm\n",
"import numpy as np\n",
"end='2016/9/30'\n",
"n225 = web.DataReader(\"NIKKEI225\", 'fred',\"1949/5/16\",end).dropna()\n",
"lnn225=np.log(n225.dropna())\n",
"lnn225.columns=['Close']\n",
"y=lnn225\n",
"x=range(len(lnn225))\n",
"x=sm.add_constant(x)\n",
"model=sm.OLS(y,x)\n",
"results=model.fit()\n",
"\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 結果の解釈  \n",
"\n",
"標本数(Number of Ovservations)は16769である。  \n",
"\n",
"決定係数(R-sqaured)は0.756である。\n",
"\n",
"これはモデルがどれ程うまく実現値を説明しているかの尺度であり、最大値は1である\n",
"\n",
"回帰係数αとβの帰無仮説はそれぞれα=0とβ=0である。 \n",
"\n",
"それぞれの標準誤差(Standard Error,Std Err)は0.011と0.0000である。αの帰無仮説が棄却されないと$Y_{t}=β・t+u_{t} $である。\n",
"\n",
"αのp-値がゼロであることで帰無仮説は棄却されα= 6.1967である。\n",
"\n",
"βの帰無仮説が棄却されないとYt=α+utでである。βのp-値がゼロであることで帰無仮説は棄却されβ= 0.0003である。 \n",
"\n",
"従って1949年以降の日経平均株価は上昇トレンドをもっている。  \n",
"\n",
"$$lnY_{t}=6.1967+0.0003・t$$ \n",
"\n",
"とモデル化された。\n",
"\n",
"[95.0% Conf.int.]は信頼区間である\n",
"以上のレポートだけでは結果の判断はできない。\n",
"\n",
"最小二乗法の仮定を思い出してほしい。まず、モデルの期待値と実際の日経平均株価をプロットして、その差を目視で確かめよう\n",
"\n",
"### Statsmobelの解説 : statmodelの線形回帰分析メゾット\n",
" \n",
" sm.OLS(y,x)\n",
"\n",
"xは説明変数、yは被説明変数である。ここではyを日経平均株価、xは経過時間とする。 \n",
"\n",
"### プログラムコードの解説 \n",
"\n",
"n225 = web.DataReader(\"NIKKEI225\", 'fred',\"1949/5/16\",end).dropna()\n",
"\n",
"fredからのデータの取得\n",
"\n",
"lnn225=np.log(n225.dropna())\n",
"\n",
"対数価格の計算、n/aとなるデータdropna()で削除\n",
"\n",
"lnn225.columns=['Close']\n",
"\n",
"数値の列にCloseと名前をつけている。\n",
"\n",
"列の名前をCloseに設定\n",
"\n",
"y=lnn225\n",
"\n",
"変数yの作成\n",
"\n",
"x=range(len(lnn225))\n",
"\n",
"x=sm.add_constant(x)\n",
"\n",
"変数xの作成\n",
"\n",
"lnn225と同じ長さに設定。Rangeによって1,2,...lnn225のデータ数までの整数を設定\n",
"\n",
"model=sm.OLS(y,x)\n",
"\n",
"results=model.fit()\n",
"\n",
"線形回帰の切片のために列(要素)を作成\n",
"\n",
"線形回帰分析を設定をmodelとして保存\n",
"\n",
"modelをfitを用いて最適化"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f935c7dbac8>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMI1X4ttJAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f93621a2da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.plot(y,label='Close',color=\"darkgray\")\n",
"results.fittedvalues.plot(label='prediction',style='--')\n",
"plt.ylabel('log(n225 index)')\n",
"plt.legend(loc='upper left')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"fittedvaluesはt時の予測値、または期待値を与えてくれる。 次に残差項だけを取り出し、時系列としてチャートを描いてみよう。residは残差項を与えてくれる。 "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935bef25c0>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEACAYAAACkvpHUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935bf140f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results.resid.plot(color=\"seagreen\")\n",
"plt.ylabel('residual')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"明らかに残差の時系列の中にトレンドが在りそうだ。残差のヒストグラムを描いてみるとどうだろうか?"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935bf335f8>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\neUi2SAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935b72b6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results.resid.hist(bins=100,color=\"lightgray\")\n",
"plt.xlabel('residual')\n",
"plt.ylabel('frequency')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"残差はいくつかの分布で構成されていて、残差の平均と分散はいくつか存在するように見える。残差が古典的線形回帰モデルの条件を満たしているようには見えない。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 結果の解釈\n",
"時間トレンドが算出する日経平均株価の期待値は、実際の終値の推移を直線でうまく説明しているように思える。\n",
"\n",
"しかし残差項のチャートではさらに期間の短いトレンドが存在するのではないかと疑わざるを得ない。\n",
"\n",
"最初の乖離がマイナス方向に大きく、時間の経過と共にそれが小さくなり、次に残差はプラスに転換し、その後は時間の経過と共にプラス方法に乖離幅が大きくなりバブルのピークの時に最大となっている。\n",
"\n",
"そして、その後再びゼロに近づき、マイナス方向に拡大していくことが分かる。このようなトレンドの存在は残差のヒストグラムに幾つかの分布が混合しているのではないかと思われることからも確認できる。残差の平均と分散が一定しているとは思えない。\n",
"\n",
"これらの結果は目による確認なので残差に関してはさらなる統計的な分析が必要である.\n",
"\n",
"## 景気循環と日経平均株価の確定的トレンド\n",
"1949年以降直近までの日経平均株価の時間トレンドの分析では明確な答えは得られていない。\n",
"\n",
"分析結果から直感的にはそれぞれの景気循環期に対して時間トレンドを求めることでより明確な判断ができるのではないかと考えた。\n",
"\n",
"そこで内閣府の定めた景気循環期に順じて分析してみよう。また、それに加えてバブル経済期の始まりから日経平均株価のピークまでとピークから暴落終焉までの期間を設定した\n",
"\n",
"### 戦後復興期(recover)\n",
"戦後復興期は2つの景気循環期で構成した。通称、特需景気と投資景気からなる。朝鮮戦争の特需から始まり、その休戦協定により終結した。\n",
"\n",
"この休戦協定は1951年7月10日から始まり1953年7月27日に締結されている。 R-squared=0.76と1949年以降のデータをすべて用いた場合よりも改善している。\n",
"\n",
"p-値から回帰係数と切片の帰無仮説を棄却することはできないので、この2つの推定値には意味がある。\n",
"\n",
"それぞれの景気循環期と時間トレンドの間には何か関係を見出せるかもしれないという期待を抱かせる。\n",
"\n",
"モデルが算出する日経平均株価の期待値と終値を比べて時間トレンドで指数の動きが説明できるかどうかを見てみよう。"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Close R-squared: 0.762\n",
"Model: OLS Adj. R-squared: 0.761\n",
"Method: Least Squares F-statistic: 4438.\n",
"Date: Wed, 02 Aug 2017 Prob (F-statistic): 0.00\n",
"Time: 12:15:38 Log-Likelihood: -65.389\n",
"No. Observations: 1391 AIC: 134.8\n",
"Df Residuals: 1389 BIC: 145.3\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 4.5236 0.014 332.550 0.000 4.497 4.550\n",
"x1 0.0011 1.69e-05 66.618 0.000 0.001 0.001\n",
"==============================================================================\n",
"Omnibus: 160.558 Durbin-Watson: 0.003\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 121.287\n",
"Skew: 0.624 Prob(JB): 4.60e-27\n",
"Kurtosis: 2.268 Cond. No. 1.60e+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, 1.6e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"y=lnn225.ix[:'1954/11/30'].dropna()\n",
"x=range(len(y))\n",
"x=sm.add_constant(x)\n",
"model=sm.OLS(y,x)\n",
"results=model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.2537083247963737"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results.resid.std()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935bdce320>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnIvl/wFkZv+Vsw3VGQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935bbd9f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(y,label='Close',color='darkgray')\n",
"results.fittedvalues.plot(label='prediction',style='--')\n",
"plt.legend(loc='upper left')\n",
"plt.ylabel('log(n225 index)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"この時期の日経平均株価は必ずしも景気の山と谷に一致していない。また、景気循環期をさらに別の基準で分割する必要がありそうです。\n",
"\n",
"### 高度経済成長期 (growth)\n",
"神武景気から始まりニクソン不況で終わる4つの景気循環期を高度経済成長期は含んでいる。\n",
"\n",
"神武景気から岩戸景気の山までは日経平均株価は強いが、その後の証券不況で長い低迷期に入る。\n",
"\n",
"オリンピックの終焉と共に不況に入った日本経済は低迷し、山一證券の取り付け騒ぎに至った。\n",
"\n",
"大蔵大臣田中角栄による無制限、無担保の日銀特融により、事態は沈静化したが、日本経済が危機的状況に陥れば政府がラストリゾートになるという考え方が生まれた。\n",
"\n",
"いざなぎ景気、ニクソンショックの期間において日経平均株価は堅調に推移した"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Close R-squared: 0.824\n",
"Model: OLS Adj. R-squared: 0.824\n",
"Method: Least Squares F-statistic: 1.995e+04\n",
"Date: Wed, 02 Aug 2017 Prob (F-statistic): 0.00\n",
"Time: 12:15:53 Log-Likelihood: 257.20\n",
"No. Observations: 4272 AIC: -510.4\n",
"Df Residuals: 4270 BIC: -497.7\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 6.1675 0.007 884.618 0.000 6.154 6.181\n",
"x1 0.0004 2.83e-06 141.244 0.000 0.000 0.000\n",
"==============================================================================\n",
"Omnibus: 396.700 Durbin-Watson: 0.002\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 516.452\n",
"Skew: 0.851 Prob(JB): 7.14e-113\n",
"Kurtosis: 3.082 Cond. No. 4.93e+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, 4.93e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"y=lnn225.ix['1954/12/1':'1971/12/31'].dropna()\n",
"x=range(len(y))\n",
"x=sm.add_constant(x)\n",
"model=sm.OLS(y,x)\n",
"results=model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935ba5c0f0>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEACAYAAABcXmojAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935baa6748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(y,label='Close',color='seagreen')\n",
"results.fittedvalues.plot(label='prediction',style='--')\n",
"plt.legend(loc='upper left')\n",
"plt.ylabel('log(n225 index)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"証券不況の間しばらく日経平均株価が低迷したことが分かる。証券不況まで、証券不況の間、そして証券不況からの回復とさらに期間を分けることで時間トレンドで日経平均株価の動きを説明できそうです。\n",
"\n",
"### 安定期 (stable)\n",
"列島改造景気から2度目の円高不況までの4つの経済循環期が日経平均株価が安定上昇した時期です。\n",
"\n",
"この時期には第一次石油危機、第2次石油危機、アメリカとの貿易摩擦、プラザ合意などが含まれている。\n",
"\n",
"町工場の倒産も続出した。鉄鋼、造船、石油産業は構造不況業種とよばれていた。\n",
"\n",
"それでも第3次産業の情報処理産業が日本経済を牽引し始めた時期ですね。 "
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Close R-squared: 0.910\n",
"Model: OLS Adj. R-squared: 0.910\n",
"Method: Least Squares F-statistic: 3.791e+04\n",
"Date: Wed, 02 Aug 2017 Prob (F-statistic): 0.00\n",
"Time: 12:15:57 Log-Likelihood: 2451.9\n",
"No. Observations: 3768 AIC: -4900.\n",
"Df Residuals: 3766 BIC: -4887.\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 8.1001 0.004 1969.386 0.000 8.092 8.108\n",
"x1 0.0004 1.89e-06 194.702 0.000 0.000 0.000\n",
"==============================================================================\n",
"Omnibus: 548.042 Durbin-Watson: 0.004\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 813.397\n",
"Skew: 1.077 Prob(JB): 2.36e-177\n",
"Kurtosis: 3.736 Cond. No. 4.35e+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, 4.35e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"y=lnn225.ix['1972/1/1':'1986/11/30'].dropna()\n",
"x=range(len(y))\n",
"x=sm.add_constant(x)\n",
"model=sm.OLS(y,x)\n",
"results=model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"日本経済にとって大変に難しい時期であるにも関わらず日経平均株価は順調に推移した。R-squared=0.91と前の2つの景気循環期よりも結果は良好である。チャートを用いて確認してみよう"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935b972320>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935bb8ab38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(y,label='Close',color='hotpink')\n",
"results.fittedvalues.plot(label='prediction',style='--')\n",
"plt.legend(loc='upper left')\n",
"plt.ylabel('log(n225 index)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"非常に安定した上昇トレンドが見て取れる。日本経済の高度成長時代が終わり安定期に入ると思われた。\n",
"\n",
"人々の心は熱狂的なブームから一気にパニックに陥り、不安に駆られた時期です。\n",
"\n",
"設備投資と個人消費が見込めず、急激な円高で貿易の拡大が見込めない中、財政出動により構造変化を成し遂げた。証券市場は経済の構造的変化に対応してその姿を変え、マネーフローに大きな変化があらわれました。\n",
"\n",
"貸付市場から証券市場への相対的な移行でもある時期でした。\n",
"\n",
"このような金融の構造変化の中で不況下の株高がもたらされた\n",
"\n",
"### バブル成長期 (bubble)\n",
"プラザ合意をきっかけにした円急騰が交易条件の改善をもたらし、それが実質所得の増加と結びつき内需主導の景気拡大が自律的に発生した。\n",
"\n",
"上昇を続けていた株式市場はブラックマンデーにより大幅下落するが、そのダメージからいち早く回復したため、バブルであるという認識が強くなった。\n",
"\n",
"日本発の世界株式の暴落を避けたい大蔵省は決算弾力化方針を打ち出しました。\n",
"\n",
"また、銀行の貸し出し過多、証券不祥事に代表される損失補てんなどのモラルを逸脱した金融機関の業務拡大もバブルの要因でした。\n",
"\n",
"また、大企業のエクイティーファイナンスと外債発行による新しい資金調達手段もその要因の1つに挙げられる\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Close R-squared: 0.215\n",
"Model: OLS Adj. R-squared: 0.215\n",
"Method: Least Squares F-statistic: 467.5\n",
"Date: Wed, 02 Aug 2017 Prob (F-statistic): 7.99e-92\n",
"Time: 12:16:02 Log-Likelihood: 305.16\n",
"No. Observations: 1707 AIC: -606.3\n",
"Df Residuals: 1705 BIC: -595.4\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 10.2897 0.010 1050.279 0.000 10.271 10.309\n",
"x1 -0.0002 9.95e-06 -21.622 0.000 -0.000 -0.000\n",
"==============================================================================\n",
"Omnibus: 40.208 Durbin-Watson: 0.005\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 22.069\n",
"Skew: 0.077 Prob(JB): 1.61e-05\n",
"Kurtosis: 2.465 Cond. No. 1.97e+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, 1.97e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"y=lnn225.ix['1986/12/1':'1993/10/31'].dropna()\n",
"x=range(len(y))\n",
"x=sm.add_constant(x)\n",
"model=sm.OLS(y,x)\n",
"results=model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"バブルによる上昇相場とバブル終焉による暴落相場を含んでいるためにR-squared=0.22と好ましい数値ではない。チャートにより確認してみよう"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935b804b38>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935b9109e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(y,label='Close',color='seagreen')\n",
"results.fittedvalues.plot(label='prediction',style='--')\n",
"plt.legend()\n",
"plt.ylabel('log(n225 index)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"バブルのピークの前と後では明らかにトレンドの形成の仕方が異なる。バブルの前では明確な強い上昇トレンド、ピークの後は明確な下落トレンドを形成している\n",
"\n",
"### バブル成長期(bubble)(日経平均株価ピークまで)  \n",
"特金・ファントラが貯蓄超過経済を象徴した。\n",
"\n",
"また、画一的な機関投資家の運用姿勢も株価の上下動を大きくしたと考えられた。\n",
"\n",
"NTT株売り出しによる株フィーバーは株式ブームの象徴であった。\n",
"\n",
"ブラックマンデー以降も上昇を続ける株価はPER70倍前後を維持した。 "
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Close R-squared: 0.913\n",
"Model: OLS Adj. R-squared: 0.913\n",
"Method: Least Squares F-statistic: 8011.\n",
"Date: Wed, 02 Aug 2017 Prob (F-statistic): 0.00\n",
"Time: 12:16:06 Log-Likelihood: 1142.0\n",
"No. Observations: 765 AIC: -2280.\n",
"Df Residuals: 763 BIC: -2271.\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 9.9122 0.004 2520.009 0.000 9.904 9.920\n",
"x1 0.0008 8.91e-06 89.506 0.000 0.001 0.001\n",
"==============================================================================\n",
"Omnibus: 1.148 Durbin-Watson: 0.043\n",
"Prob(Omnibus): 0.563 Jarque-Bera (JB): 1.193\n",
"Skew: 0.092 Prob(JB): 0.551\n",
"Kurtosis: 2.943 Cond. No. 882.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"y=lnn225.ix['1986/12/1':'1989/12/31'].dropna()\n",
"x=range(len(y))\n",
"x=sm.add_constant(x)\n",
"model=sm.OLS(y,x)\n",
"results=model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"また回帰係数は今までで最も大きな値である。これだけでは強いトレンドが在るとは判断できない。そこで目視でモデルの期待値と実際の日経平均株価の推移を比べてみよう"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"return 0.2624257002375478\n",
"volatility 0.177502142933\n",
"std of residual 0.054413688241352745\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935bba9438>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEICAYAAABMGMOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4nNWZ8P/vmVEd9W7JqpYlS7Ysyw3buIBxwRiDqQYC\ngRCCA6SwJbyQZHfz2/0lb8hml0CyZMH0JEAA04zpNu5FxkXGsmXZlq3e+6hMP+8fU1DXSNZItnQ+\n18U189Q5ozjPPafdR0gpURRFUZTBaMa6AIqiKMrlQQUMRVEUxS0qYCiKoihuUQFDURRFcYsKGIqi\nKIpbVMBQFEVR3KIChqIoiuIWjwUMIcTLQohaIUR+l33hQogvhRBnHa9h/VwbKoTYLIQ4LYQoEEIs\n8lQ5FUVRFPd4sobxKrCmx74ngO1SyjRgu2O7L88An0kpM4BZQIGnCqkoiqK4R3hyprcQIhnYKqXM\ncmwXAldLKauEELHATinltB7XhAB5wBQ5xMJFRkbK5OTkkSi6oijKhHDkyJF6KWWUO+d6ebowPcRI\nKasc76uBmD7OSQHqgFeEELOAI8CjUsr2wW6enJzM4cOHR6ywiqIo450QosTdc8es09tRe+irBuEF\nzAH+V0o5G2in/6YrhBAbhRCHhRCH6+rqPFNYRVEUZdQDRo2jKQrHa20f55QD5VLKXMf2ZuwBpE9S\nyk1SynlSynlRUW7VqhRFUZRhGO2AsQW4z/H+PuDDnidIKauBMiGEs29jBXBqdIqnKIqi9MdjfRhC\niDeBq4FIIUQ58CvgSeBtIcQDQAmwwXFuHPCilHKt4/KfAK8LIXyA88D9wy2H2WymvLwcg8Ew7O+i\ndOfn50d8fDze3t5jXRRFUUaRR0dJjbZ58+bJnp3eFy5cICgoiIiICIQQY1Sy8UNKSUNDA3q9npSU\nlLEujqIoF0kIcURKOc+dc8f9TG+DwaCCxQgSQhAREaFqbIoyAY37gAGoYDHC1N9TUcaHzs7OIZ0/\nIQKGoiiK0p2Ukr179w7pGhUwRkF1dTV33nknqampzJ07l7Vr13LmzBmysrLGumiKokwwzc3N1NTU\n0NzcTFNT05CuHe2Z3hOOlJKbb76Z++67j7///e8AHD9+nJqamjEumaIoE42Uki+++AKAwMDAITcv\nqxqGh+3YsQNvb28eeugh175Zs2aRkJDg2jYYDNx///3MnDmT2bNns2PHDgBOnjzJFVdcQU5ODtnZ\n2Zw9exaAv/3tb679P/zhD7FaraP7pRRFuSxVVla63re1tREZGTmk6ydUDePYsWM0NzeP6D1DQ0OZ\nPXt2v8fz8/OZO3fugPd49tlnEUJw4sQJTp8+zerVqzlz5gzPPfccjz76KHfffTcmkwmr1UpBQQFv\nvfUW+/btw9vbm0ceeYTXX3+de++9d0S/l6Io44uUktOnTxMQEIDZbMZkMhEfHz+ke0yogHGp2rt3\nLz/5yU8AyMjIICkpiTNnzrBo0SJ+85vfUF5ezi233EJaWhrbt2/nyJEjzJ8/H7CPcoiOjh7L4iuK\nchnQ6/U0NDSQk5NDY2MjpaWlTJ48eUj3mFABY6CagKfMmDGDzZs3D+va73znOyxYsICPP/6YtWvX\n8vzzzyOl5L777uO3v/3tCJdUUZTxSErJp59+SltbGwDR0dFMmTKF9PR0dDrdkO6l+jA87JprrsFo\nNLJp0ybXvm+++YaysjLX9tKlS3n99dcBOHPmDKWlpUybNo3z588zZcoUfvrTn7J+/Xq++eYbVqxY\nwebNm6mttedtbGxspKTE7ezEiqJMMHq93hUsQkJCCA4OxsvLi/Dw8CHfSwUMDxNC8P7777Nt2zZS\nU1OZMWMGP//5z5k0aZLrnEceeQSbzcbMmTO54447ePXVV/H19eXtt98mKyuLnJwc8vPzuffee5k+\nfTq//vWvWb16NdnZ2axatYqqqqoBSqAoykTm/HG5YMECVq5ciUYz/Mf+uM8lVVBQQGZm5hiVaPxS\nf1dFufQ1NDSQn5+PXq/n+uuv73MY7VBySU2oPgxFUZSJwmw2s337dgDS09P7DBYtHeYh3VMFDEVR\nlHHI2VQthGDmzJndju0srOXNQ6UcutA4pHuqgKEoijIOnTlzBp1Ox5o1a9Bqta79n5+s5od/PUJs\niB9XTo0kbwj39FintxDiZSFErRAiv8u+cCHEl0KIs47XsH6uLRZCnBBC5AkhDvd1jqIoitK31tZW\nGhsbSUtLwyIF//1FIW/klgKQOSmYn1+Xwc7HrubZ7/S7+nWfPDlK6lVgTY99TwDbpZRpwHbHdn+W\nSylz3O2MURRFUeyKi4upNmj436PtzPv1Nv701Tnq9EYAEiN0/PCqVHy9tIPcpTePNUlJKXcLIZJ7\n7F6PfdlWgNeAncDjniqDoijKRGOxWPj9VyXsqfbHR1vDzbMnc/OcySycEnHR9x7teRgxUkrnpIFq\nIKaf8ySwTQhxRAixcaAbCiE2CiEOCyEO19XVjWRZL0mBgYGAPYnYbbfdNuC5Tz/9NB0dHa7ttWvX\njnguLUVRxl5xfTv/u7OI2lYD5eXlZAQaeeyaRA78/Bp+d1v2iAQLGMNObymlFEL0NwlkiZSyQggR\nDXwphDgtpdzdz302AZvAPg/DQ8X1KKvV2q1Tyh1xcXGDphx5+umnueeee1zT/z/55JNhl1FRlEuL\n0WJl95l6dp+p4/XcEmyOp99cXQMzIr1YvyprxFfHHO0aRo0QIhbA8Vrb10lSygrHay3wPnDFqJVw\nhBUXF5ORkcHdd99NZmYmt912Gx0dHSQnJ/P4448zZ84c3nnnHYqKilizZg1z585l6dKlnD59GoAL\nFy6waNEiZs6cyb/8y790u69zASar1crPfvYzsrKyyM7O5k9/+hN//OMfqaysZPny5SxfvhyA5ORk\n6uvrAXjqqafIysoiKyuLp59+2nXPzMxMHnzwQWbMmMHq1auHvISjoiijY+NfjvDgXw7zem4Jd12R\nyJf/sJhUUxHFxcVERkZ6ZCnl0a5hbAHuA550vH7Y8wQhRACgkVLqHe9XA/8xUgW44/kDvfaty47l\nu4uS6TRZ+d4rh3odv21uPLfPS6Cx3cTDfzvS7dhbP1w06GcWFhby0ksvsXjxYr7//e/z5z//GYCI\niAiOHj0KwIoVK3juuedIS0sjNzeXRx55hK+++opHH32Uhx9+mHvvvZdnn322z/tv2rSJ4uJi8vLy\n8PLyorGxkfDwcJ566il27NjRK+f9kSNHeOWVV8jNzUVKyYIFC7jqqqsICwvj7NmzvPnmm7zwwgts\n2LCBd999l3vuuWfQ76goysgwGo0IIfDx8em2v7i+ned2FfFv66bjrZEsmBLO3QsSuXJqJF7SwqFD\nh1xNzklJSR4pm8cChhDiTewd3JFCiHLgV9gDxdtCiAeAEmCD49w44EUp5Vrs/RrvO6KjF/CGlPIz\nT5VzNCQkJLB48WIA7rnnHv74xz8CcMcddwD2hUz279/P7bff7rrGaLSPaNi3bx/vvvsuAN/97nd5\n/PHeYwS2bdvGQw89hJeX/X/OwZKK7d27l5tvvpmAgAAAbrnlFvbs2cONN95ISkoKOTk5AMydO5fi\n4uLhfm1FUYZh9+7dNDU1MWXKFKbPnMWh4ib+drCEPWfr0QhItZYSQge3XnUV3t7etDc3sHfvXmw2\nG9nZ2WRkZHisbJ4cJXVXP4dW9HFuJbDW8f48MMtT5RqoRuDvox3weHiAj1s1ip56Vg2d284Hts1m\nIzQ0lLy8vqfQeKJq2R9fX1/Xe61Wq5qkFGUUdXZ2utbZzj11gbveq8ZsE0QH+bI+O4bpsgQd9nQe\nu3bt6nbttddeS0hIiEfLp7LVjoLS0lIOHLA3hb3xxhssWbKk2/Hg4GBSUlJ45513AHv++uPHjwOw\nePFi11rgzhToPa1atYrnn38ei8UC2FOeAwQFBaHX63udv3TpUj744AM6Ojpob2/n/fffZ+nSpSPw\nTRVFGS4pJXvyCjnS4IVOpyPKT7ImzsT308xsfXg+9073IUBj6dbcFBwc7Hrv6WABKmCMimnTpvHs\ns8+SmZlJU1MTDz/8cK9zXn/9dV566SVmzZrFjBkz+PBDe/fOM888w7PPPsvMmTOpqKjo8/4/+MEP\nSExMJDs7m1mzZvHGG28AsHHjRtasWePq9HaaM2cO3/ve97jiiitYsGABP/jBD8ZkcSlFGe8sFguV\nlZWcOnWq3yHtLZ1m9p2r547nD7LxwwreK/Vh9qJlhIeHszTaQkaQidx9u2lubiYoKIgFCxawYcMG\nbr/9dtasWYNOp2Pq1Kmj8n1UenMPKy4uZt26deTn5w9+8mVkrP+uinKpsdlsNDU1ER4e7mpGPnbs\nGGfPngUgKiqq14+3c7Vt3PCnvXSarYTqvLkpzZdETTPfv2M9JpOJmpoaV+sEQGJiIgsXLhzRcqv0\n5oqiKKPIarVy5MgRiouLSUxMZPLkya7+CI1GQ3R0NPX19VitVvaca6DVYOHGWXFEB/uyJC2SexYm\nMSNGx4HdXxEQEASAj48PCQkJ3QJG14XXxoIKGB6WnJw87moXiqJ0d+zYMdeIwrKyMkpLS13HUlNT\nmRQbx5b8el74r88oaIL5yWHcOCuOYD9vXrjX/uN+z549GAwG14hKp8jISNf8KX9//9H5Qv2YEH0Y\n46nZ7VKg/p6K8i0pJefPnwdg9erVxMR8m/FIo9FQ0B7A9986w+ZSX6rbbayNM/HqfXOw2Wyu8ywW\nC1VVVSQnJ/caFr9s2TLX+66jGMfCuK9h+Pn50dDQQERExKgOTx2vpJQ0NDTg5+c31kVRlEtCa2sr\nYK8JhIaGsmTJEs6W1RIUEkxsiD+FNXo2n2jgj3fm4Fd7kubmTj7+aAuTJk1iyZIl5OfnU1hYCNj7\nOXry8vIiLi6OysrKMQ8Y477T22w2U15ejsFgGKNSjT9+fn7Ex8fj7e091kVRlItiMBhoaWmhvr6e\nGTNmDOse1dXV7N69m+XLl1Nn9uUP286w/1w9i6dGsune7n3JJpOJzz//3DW/KTExkdLSUmJiYkhI\nSCAxMdE1Abcri8VCfX29R/owVKd3F97e3qSkpIx1MRRFGSNSSkpLS7lw4QKpqakkJCRgtVqpra3l\nwIEDrvlL6enpw/oRZDAYKGzV8vrfC8ivaiPE35sbZsXx0FWpvc718fFh9erVtLW1sX37dldfx6JF\ni3qlAunKy8trzDu8YQIEDEVRJi6bzUZubi5lZWUA1NbWUldXx7lz53qd29bWRlhYn4uA9slitSGA\n8+fPU2P0osVg5ZGrU3lgSQqhuv4f/r6+vvj4+JCWlkZ1dTVpaWkDBotLiQoYiqKMW9XV1ZSVlZGZ\nmUlLSwuVlZW9gsXy5cvZsWOH2wFDbzDz5x3n+OvBUh5aNIno1noevXYeiclJbq9iJ4S4LCfLqoCh\nKMq41NTUxIEDB/D19SUzMxMhBFu3bsVoNBIeHk5wcDABAQGuIFFXV8epU6eYPXs20dHRrvt0dnai\n0Wiobbfymy3H+fx0AxLBirRQArBnlp2SkjTkNW0uRypgKIoyLlVUVGC1WlmzZo2rI/naa6+lqKiI\njIyMbg/40NBQV83j4MGD3HDDDQghKC2rYP/+fUyKieb96hC2FTayJNrCrDALqaEmLHoLoaGhEyJY\nwASZh6Eoyvhks9koKyvDarUC9g7uiooKTCYTZWVlhIWFubJCg32E34wZM3o94GfN+jZBtsFg4M3N\nH7D1eAW3vHiUig4NtbW1PLgkmV8v9uX+nGAeun0NUkqklJdEZ/Ro8eR6GC8D64BaKWWWY1848BaQ\nDBQDG6SUTf1crwUOAxVSynWeKqeiKJev/Px8Tp8+TUZGBp2dnbS0tNDc3ExcXBx6vd7tfoKYmBiW\nLVtGoxGe/2g/X1Z503EkjwhfCPDzBoyIlko0HY1EJmYQGBjIokWL2LNnT59zJ8YrT9YwXgXW9Nj3\nBLBdSpkGbHds9+dRoMAzRVMUZTxwZoA9ffo0JSUlru3KykqAIY16ioiK5oE3TvJhuS9RfjYenNrJ\nP2d2cuvKKwE4efIkYE/3AxAbG8sNN9wwoWoYHgsYUsrdQGOP3euB1xzvXwNu6utaIUQ8cD3woqfK\npyjK5c1qtbpmWTsFBQUxffp01/ZAuZeklHx1uobffHwKo8WKt1bDH+7I4d0fzOGRdANpwTbi4yZ1\nS9WRk5PTbQ2Ksc7tNNpGu9M7RkpZ5XhfjX051r48DfwfIGhUSqUoymVFSsnhw4fp6OggISEBnU5H\nSEgICQkJGI1GTp06BdBnChuTxcb7x8r54FglB8434Oul4ar0aJakRbJwSgRms5mio/Zzs7Oz0Wq1\nxMXFYTQaSUtLG82veckZs1FSUkophOiVl0QI4ez3OCKEuHqw+wghNgIbwT7NXlGU8a+xsZGSkhKm\nTJnCvHnds1rodDoyMjKoqanp1bktpWT9s/soqGolOsiX/1g/gzvnJ+Lj9W1ji7e3N9dffz3+/v5o\nNPb9PVfJnKhGO2DUCCFipZRVQohYoLaPcxYDNwoh1gJ+QLAQ4m9Synv6uqGUchOwCey5pDxVcEVR\nLh3OpYenTZvW5/Hs7GxsNsmRkka2FdTS1G7iyVuzEUKQOSmIf16VzorM6H4TknYdWaV8a7QDxhbg\nPuBJx+uHPU+QUv4c+DmAo4bxs/6ChaIo40tNTQ15eXksWLCA0NDQfs9zJhPtq8nJYLays7COl/ae\n5+viJjQCUqMCsdkkGo3gqTtyPFb+8c6Tw2rfBK4GIoUQ5cCvsAeKt4UQDwAlwAbHuXHAi1LKtZ4q\nj6Iol76ioiJaWlrYuXMnq1evRqfT9XmewWDAy8urz2SBn5+s5tG/5xGq8+Y3N2exZsYkIgLHNi34\neOGxgCGlvKufQyv6OLcS6BUspJQ7gZ0jWjBFUS4Z7e3tFBUV0dbWRnl5uWu/yWSipKTEtW680Wik\npqaGyZMno9VqMZlMroR9F+rb+eBYBaE6b+5fnGJf+jTIj9mJofh5T4wZ2KNFpQZRFGXM7Nmzp9fQ\nWI1Gg81m48SJE/j6+pKQkMD58+c5ceIEmZmZzJw5E7PZTHGbll++f4J3Dpdjstq4eloU37syGSEE\ni1IjxugbjW8qYCiKMiZaW1tpbW0lKCgIi8VCdnY2J06cYObMmRQVFVFfX8/hw4cpKSlxzX0oKSkh\nKyuLg+UG/pRnw9+7guuzY/n5dRlEB6tVID1NBQxFUUaVlBKLxcKBAwfw9vZm2bJlrlFJSUlJgH2I\nvMFg4KOPPqKuro6KxnbeK/ZlRoiRBfX1hPnYWDfFm99/bwX+PqrZabSogKEoyqiwWCzs2LGDpqZv\n08d1DRZdCSHw9/cnNnMe/74lnwttoBFepAXb2LFjB4HAndPjVLAYZSpgKIoyKkpLS7sFC2DAPEwP\nvPo120/XEuLnzdJoAw9cnUlrZRHt7WbAvnKdMrpUwFAUxWNaW1upqamhpKSEzs5OAK655hq++uqr\nbsuSSinJK2vmo+NVPHx1KlFBvmxcNoVFqRHcOieeIF8NWq2W+vgIjh49yvTp04mIUB3bo23QgCGE\niMY++zoO6ATygcNSSpuHy6YoymVMSsmePXtob2937UtKSiIyMpKFCxcSFhaGxWrjq9O1vHmolB2F\ndWgEZMeHcNPsySyYEsGCKd2DQlRUFNdee+1ofxXFod+AIYRYjj39eDhwDHsaDz/sGWZThRCbgf+W\nUrb2dw9FUSaumpoa2tvbiYmJwWQyAbjWp0hMTMRitXHr/+7neHkLAT5a/uX6TG6aPZlINcnukjVQ\nDWMt8KCUsrTnASGEF/bFkVYB73qobIqiXMZqa+2p4q688krXjOzK5k7e23uWjctS8fHScH12LBuX\npbIiM1pNsrsM9BswpJSPDXDMAnzgkRIpijIutLe3ExgYiLe3N/kVLbxzuIx3jpTTYbKyavokpk0K\nYuOy1LEupjIE7vRh/BX4sZSyxbGdDLwkpeyV4kNRFMWpo6ODFpsf1z2zh8LqVny9tFyVHsUvr88k\nIbzvHFHKpc2dUVJ7gVwhxD8Bk4HHgH/2aKkURbksnaxsoaiuHSElpuZmpqWkcCV+rMqM5oGlUwjx\n750sULl8DBowpJTPCyFOAjuAemC2lLLa4yVTFOWy0NJh5o9fnWV/UQMFVfYxML5eGh7PtBIdHc2/\nzokf4xIqI8WdJqnvAv8K3AtkA58IIe6XUh73dOEURbn0fZBXwSv7LnBlaiSPr8nginh/ik8dw2qw\nD4NVxg93mqRuBZZIKWuBN4UQ7wOvAWoVEkWZgI6VNvHbT06zMDWCf1qVzt0LElkwJZxoHwuHDh2i\n6Ki9lqHT6dRs7HHGnSapm3psHxJCXDHYdUKIl7EPva2VUmY59oUDbwHJQDGwQUrZ1OM6P2A34Oso\n32Yp5a/c+TKKoniG1Sb5/GQ1//nZaYobOgjzFdw2197U5KXVkDEpmH379tHa2kpOjv235EBpP5TL\nk2awE4QQ6UKI7UKIfMd2NvB/3Lj3q8CaHvueALZLKdOA7Y7tnozANVLKWdhrMWuEEAvd+DxFUTzk\nsc3HeeT1o+h8vLgx3sg/ZrRx+7xv+yY6OzuprKxk2rRppKenk56e7kpJrowfgwYM4AXsa2ybAaSU\n3wB3DnaRlHI30Nhj93rszVk4Xm/qcRxp1+bY9Hb8J90op6IoI6SssYN//+gkJ8pbAHjs2mn88a7Z\nbPnxYpZEW/DTwu7duykoKMBoNPLRRx8hpWTKlCljXHLFk9zpw9A5mqG67rMM8/NipJRVjvfVQExf\nJwkhtMARYCrwrJQyt78bCiE2AhvBnm5AUZThsdokx8ubeW5nEV+cqkEImBzqz8z4EGJD/Lk2Q0Ob\n/ttMQDU1NdTU1NDQ0ODaFxQUNBZFV0aJOwGjXgiRiuNXvhDiNqBq4EsGJ6WUQog+aw5SSiuQI4QI\nBd4XQmRJKfP7OXcTsAlg3rx5qiaiKMP04F8O89XpWvy8NfzjynSuz45lanQgFosFrVbL/v37qaur\nQ6PRsGzZMnbt2oWUksrKSgDWrVs3xt9A8TR3AsaPsD+QM4QQFcAF4J5hfl6NECJWSlklhIjFntCw\nX1LKZiHEDux9IX0GDEVRhqdOb2TH6VpuzInDz1vLhnnxXDsjhhWZMa4EgAaDgS1btnS7buXKlYSG\nhnLrrbdSWFhIfn4+8+bNQ6dTs7fHO3dGSZ0HVgohAgCNlFJ/EZ+3BbgPeNLx+mHPE4QQUYDZESz8\nsSc4/N1FfKaiKF2crdGz9ZsqXtp7gTajBb3RwgNLUliTFdvr3PPnz3fbzszMJDQ0FACNRkNmZibp\n6elotSpx4EQwUHrzf+pnPwBSyqcGurEQ4k3gaiBSCFEO/Ap7oHhbCPEAUAJscJwbB7wopVwLxAKv\nOfoxNMDbUsqtQ/taiqL05Z/ezuPDvEqsNsnStEh+fl0mGZP67neoqqoiPz+fuLg4qqqqkFL2WYtQ\nwWLiGKiG4fxXNA2Yj712AHADcGiwG0sp7+rnUK+khVLKSuzp1J2jsGYPdn9FUQbX0mnm5b0X+IeV\naQgh8NZouGdBIj++Jo2ooIEn1Z07dw4/Pz8WLVrEV199RVNTE/7+/qNUcuVSNFB6838HEELsBuY4\nm6KEEP8f8PGolE5RlCHrMFn4/GQ1m4+U8015C+1GC99dlERkoC+/uy3brXtYrVZqamqYOnUqWq2W\n7Oxsdu/eTVhYmIdLr1zK3On0jgFMXbZN9DMcVlGUsXXoQiPfe+UQHSYrieE61mbFcs/CpCGvYtfS\n0oLNZiMyMhKAmJgYbr/9dk8UWbmMuBMw/gIccuSQAvtku9cGOF9RlFHiXBNbCMGq6THMTQrjB0tS\nWJIWxbykMDQaMfhN+uBchzswMHAki6tc5twZJfUbIcRnwBLHrvullMc8WyxFUQbSbrTwYV4lbx4q\n5URFC1FBvixNi8TPW8s/rZ52Ufc2Go2UlJQAqKGySjfu1DAA8rBP1vMCEEIk9rXWt6IofWttbSUo\nKIgeGRMG1NHRwYEDB1wT5ZyjkfIrWtj4l8NUthiIC/HjmTtzWJM1CV+vkRmtVFpaSmVlJQkJCa61\nuBUF3FsP4yfYh8TWAFZAYJ/17V7vmaJMcM3NzXzxxRdkZmYyc+ZMt687dOiQK+3Gpwe+IWxSAoun\nRiJtVqKC/XjmrtnMSwobUhByR1NTE0IIFi5cOOL3Vi5v7tQwHgWmSSkbBj1TUZRenA/9goICMjIy\nev1qNxqNWK1W/P39XQ9os9lMXV0dZ9u8OVTvxYmjFaydaaPi6FcAvHD71URHh494WW02G8XFxQAq\nWCi9uBMwyoAWTxdEUcYTs9nM3r17qaurw8/Pz7W/oaGh1zoRH35oT3iwePFiJk+eDMAb+87wymlf\nitu1ROi8WBjVyT8ui+PIgSIADhw4wA033IBG407C6cF1dHRQWlrqmsXtHB2lKF25EzDOAzuFEB9j\nX6sCGHymt6JMVAaDgZ07d9La2uraXr58OTt27KCpqalbwDhy5Ijr/fmSciKiJ+HnrUXf2oLBJvjZ\nqjTWpflz6OABKi6cBSA5OZni4mJKS0tJTk7u9fm1tbXU1taSnp6Oj48P33zzDREREa5g1Jfc3Fzq\n6upc23PmzLnYP4MyDrnz86QU+BLwwT772/mfokx4NTU1bNu2DYPB4Nq3Z88eV7AQQrBmzRqioqII\nCwvjxIkTmM1miouL0ev1FBUVERwdz0c1ITz0SR1PbztjX1fCq5nfLgvkxyvS0fnZ51BUV1fj6+tL\neno6YO/jcLLZbLS1tfH111+zc+dOTp06RVVVFUajkdOnT7Nv375+v0NDQwN1dXUkJSXh6+uLv78/\nISEhnvhzKZc5d4bV/vtoFERRLgfnzp0jICCA2Fh7or6jR4+i1+upqKggNTUVsHdyA9x6663d8iwl\nJSXR1NTE++/bpzRVdwq2VvhyIc9+/sxQK36N5zl/3oe2tjYyMjIAuvV59GyGstlsaDQaTp48SUFB\nAQApKSlcuHCB3Nxc4uO/XRXPZDLh4+PT7ftIKTl8+DDe3t7Mnj0bLy8vLBaL6r9Q+tRvDUMI8bTj\n9SMhxJYZfvYZAAAgAElEQVSe/41eERXl0lBXV8fRo0fZu3cvJSUl6PV69Hp78mZnDcNisSClJCsr\nq1dSvqlTp2KTYHWs2vJ1gzfl7VruuzKZT366hDuTjcR5t7uaqZz9CM77BAQEuILF3LlzAXuCQJvN\n5mpOysnJYd68ea7PNJvNrvfV1dW9vpNer6elpYXp06fj4+ODRqPpFVQUxWmgGsZfHa//NRoFUZRL\nmZSSo0ePotPp0Ol05ObmEhISghACKSV1dXXk5eXh62tvPgoICOh2fbvRwtZvKvltQTA3T25neoiV\nBZFm7rsilpVLpgPQczZs13vFxcUxffp017GoqCgA9u3bR1ZWFlarlZiYGFdzlUajwWaz0dTURExM\nDG1tbZw+fRq9Xk9TUxNXXnkl7e3trhFRMTEq248yuIGSDx5xvO4aveIoyqWpra2NlpYW5syZQ1BQ\nELt27aKlpYXAwEBsNpuro9nJGTAqmzt5+3AZf95ZhMliY05CCMHe9iXro/0kceF9dwcKIVxNUVqt\nliVLlnQ7HhwcTExMDDU1NZw5cwaTyURaWprr+LXXXssXX3yByWQiPDyc2NhY8vLyXM1lBw4coKKi\nwnW+WlpVcYe7M72HTAjxMrAOqJVSZjn2hQNvAclAMbBBStnU47oE7PmrYrBPENwkpXzGU+VUFHd8\n8cUXAISEhHQbciqEYNGiRWzfvr3b+SEhIVhtknteyuV8XTs3zIrjtrnxLJ0aicViRqvVUlpaOuDI\npcGGzC5ZsoT33nsPk8meGzQhIcF1LCgoiNWrV1NYWEhycjIdHR3dru0aLCZNmqTWtFDc4rGAAbwK\n/A/2h7/TE8B2KeWTQognHNuP97jOAvyzlPKoECIIOCKE+FJKecqDZVWUbjo6OsjNzWXBggXYbDas\nVisAoaGhCCFYunQpe/bsITk5mYiICNauXYu+vYO9Z+vYkl/PKpMkLEDw/D1zMVsl0+OCXfd29hGk\npKR0+0wfHx9MJhNhYWHExcUNWkatVktgYCB6vZ4ZM2b0mjsRFBTk6s9w1lZmz55NfHw8e/bsobm5\nmRtvvBEvL08+BpTxxGP/UqSUu4UQyT12r8e+Ch/YM97upEfAkFJWYc9bhZRSL4QoACYDKmAoo+bY\nsWPU1dVRUlLiethed911rvexsbGsW7cOnU5HS6eZLwubeX5XEWdr2wjy9aKgupUrUyNJi3G/qee6\n667DYrH06v8YiLPTPTExccDz/Pz8uo3auuqqq+jo6Og2qVBRBjPQEq3ZjtXvEEJ4Y3+wXwHkA7+W\nUnb0d+0AYhwBAaCaQdbVcASc2UDuMD5LUYalpqbG1WRTXV1NXV0dOp2uV6pvnU5Hh8nC6j/soqbV\nSHyYP/979xyWpUcR4Dv032K+vr6ujm53OYfQupOGvGuz03A+S1EG+lf9KuCc7vkkEAH8N/b1MJ4D\n7r2YD5ZSSiGE7O+4ECIQeBf4Byll6wDnbQQ2wuC/shRlMK2trezatQt/f38CAwNdw1WTk5PpNFs5\nW9NGYY0evcHCA0tS0Pl48ePlU5keF0JOQijaYa4/MVxz584lJydHzZtQRsVAAaPrv8AVwHwppdmx\nZOvxYX5ejRAiVkpZJYSIBWr7OslRo3kXeF1K+d5AN5RSbgI2AcybN6/fAKR4xpkzZwBcwzkvd865\nCjk5Ofj7+9vXsrb68kWlNy+8tY1Os70vw9dLw7ykMGYlhPLdRcljVl6NRjNi+aQUZTADBYwQIcTN\n2Cf3+UspzTB4zWAQW4D7sNdY7gM+7HmCsP9UegkoUPmqLn15eXnA+AkYer0eb29vJk+ejMUG8+fP\n51S9hR+9e4aVmTHcPi+e9JggEsN1o16bUJSxNlDA2AXc6Hi/XwgRI6WsEUJMAuoHu7EQ4k3sHdyR\nQohy7GtqPAm8LYR4ACgBNjjOjQNelFKuBRYD3wVOCCHyHLf7hZTykyF/O8WjLBaL673ZbB4Xi+00\nt7RyQq/jg1cPExPsy3/eNouUFFg8I4lQnZoBrUxsA03cu7+f/dXYm6gGJKW8q59Dva6VUlYCax3v\n99K9OUy5BFVXV7N7927XdkVFRZ+ZUy8XXx/7htcPlZNbbaGqU0tkYAuLp05BSokQQgULRWGQYbVC\niGAgSkpZ1GO/awSVMn7YbDZsNptb4/Kdo4jCw8NpbGx0ZWe9XD2/q4ht1T5M8hP833Wp3LV4mupI\nVpQeBko+uAE4DbwrhDgphJjf5fCrni6YMvoOHTrEe++9h5T9d1GZTCY6OjooKrL/hli5ciXBwcGX\nVcCQUvLBsQqu/cNuXtl3AYCrYsz8aFonv1sezHeWZKhgoSh9GOin5C+AuY4RTVcAfxVC/FxK+T6q\nyWhMWCwWNBoNVVVVxMbGjtjoGJvNxsGDBykvLwfss5z7mjzW1tbGJ5/07koKDg525Si6lHWarLy8\n7wIv7jlPU4eZlBAtwV72WpW/t4ZpAT4sWrRorIupKJesgQKG1jnJTkp5SAixHNjqyPWkhq+OMpvN\nxieffOJKoz1z5kwyMzNH5N61tbWUl5cTGBhIW1sbra2tfQaMxsbGbtvOFNtBQUFUVFRgtVovyZxE\nzn6IX75/gveOVbAyM5qccBuRHSVYS46wq6MUm83GtGnTVGpvRRnAQD9R9UKIVOeGI3hcjT29xwwP\nl0vpoaWlpduqbiPVBCSldHVeO39dHz/e9zQbZxoKgPXr17sWDAoODkZKSVtb24iUaSRYbZIT5S18\n75VDHC+3/+3uXpDImw8u5MX75jMr1IS3lz24OSfn+fv7j2WRFeWSN1AN42F6BBRHbqc1OIbDKqPH\n+TBOTU2lqKhowH6GwUgpXc1OzmYosD/4of9g5CxDdHR0t7QSznxEzqypY6mlw8wn+VW8tr+Y09V6\nAny0dBhMbNlizzYbHR1NqfcUWltbiYuLIzY21rXUqcqrpCgDG2hYbZ8/Mx0T+F73WImUPh09ehSA\nGTNm0NjYeFEP53PnznHs2DEWL15MYWEhYG9e0mq19vxIHR1YLJZeo6Xa2tqIjo7m6quv7rbf2UF8\nMUFsJHSarFzz3ztpaDeRGhXAk7fMZEVmDFpzO86w2HXdiuTkZGJjY4mIiEBKqdaxVpRBDJR8MAH4\nPfZMsZ8Cv3fO9hZCfCClvGl0iqhUVVVhNBoBewpsZxrs4XL2RXz99deYTCYyMzNdzUvZ2dkcPHiQ\n9vb2Xg9QvV7fbY3onkY7YEgp+fhEFUdLmvnF2gz8fbT84Y4cwnQ+ZE0OdgWy0tIaADIzM13rXgOE\nhYXh6+vLihWDTitSFIWBm6Rexp7P6SDwALBLCHGDlLIBSBqNwin2Zp49e/a4tp1rLre3tw/7np2d\nnXh5ebmCTlhYmOuYs7O7Z8AwmUyYTKY+s6KO9hBUo8XKXw+U8PGJKo6VNhMR4MP12ZOYmxTOsvSo\nXuc7+16mT59OQkICQghqamqIjY0d1XIryuVuoIARJaV8zvH+J0KIe4DdQogbUaOkRk3XldJuv/12\nwN7WbjAYXKN/hkqv1xMXF4fJZKK6urrPgLF3715uv/121/2dD92+lvIczSYps9XGyqd2UdbYSXpM\nIL+9ZSZ3zEtA009ep/b2dkpKSggMDESr1RIaGgqgmp8UZRgGChjeQgg/KaUBQEr5NyFENfA54P4K\nL8pF6ezsBGD58uWuB3NgYCAWi4Xm5mZ72grHQ9Dd+3V2dhIWFkZKSgp1dXXdhtB27cw2GAyukUPO\nDu+B1l3wRMAwmK3sOVvPtlM1/O62bLy1GuYkhvHrm2ZyVR+1iZ6OHj2KwWDotSa2oihDN1DAeBFY\ngD0JIQBSym1CiNuB//R0wRT7A9jZHNX1oe78lf/ll18CdFtJbTDO9N3R0dH4+Pj0WlO6a42lsrKS\njo4OUlNTaWpqQqPRjEqT1L5z9Tyz7SwF1a3oDfYEh7Piv60RPHPnbLfuYzQaqaqqIj09nejo6BEt\no6JMRAONkvpDP/uPAas8ViLFpaamxvW+6xyBns1Cra2t3ZqVBlJZWYm/v/+AtZKcnBzy8vI4cuQI\ngKujOCoqqs/ANBJNUjabxGKT+HhpaOowUaM3cPPsycQE+xEf5s91WUPvb3COAEtISBh2uRRF+dag\nWeaEEFHAg0By1/OllN/3XLEmpqamJoKCglzDWc+ePQvAkiVLuv2K7znBrKWlxa2AYbPZqKmpITEx\nccBawdSpU13rXERERNDQ0ICfn59rZndPFxMw2o0W/rzzHH87WMp3FiTy+JoMrp8Zy+rpk/DxGn7q\nk+LiYgoLC13DZhVFuXjuLDz8IbAH2AZY3b2xEOJlYB1QK6XMcuwLB97CHnyKgQ1SyiZ3rh3PrFYr\nDQ0N7Ny5k5SUFObPt+d5bGxsJCUlhbi4uG7n98whVVZWRkhIyKBBo6WlBYvFMmjzjEajYenSpfj6\n+rruOdLNTlUtnfznZ4VsOV6J1SZZO3MSs+JDXZ/l43Vxn1dYWEhISAgLFy4cieIqioJ7AUMnpXx8\nGPd+Ffgf4C9d9j0BbJdSPimEeMKx3de9+7p2XDKbzWzduhWz2QzYm6GsVitffvklRqOx33QVKSkp\ntLe3YzKZqKqqoqqqig0b+p6AL6VEr9e7ZnC7M0JoKENO3a1h1OoN+HtrCfLz5rX9JWz9ppLvLkzi\nhlmxzE0Kd/vzBmOz2WhtbSUjI2NcLOqkKJcKdwLGViHE2qGueCel3C2ESO6xez32fFQArwE76SNg\n9HPtuNTW1uYKFmAfmdTU1OR6uPc3Qc9ZCzl48KArU2x/yf/Onj3ramKCkU+B4QwY/c0NOVOj5+Nv\nqnhhz3n+5zuzuSYjhjvnJ3Dn/ASSI0d+wF1HRwdSyj4TKCqKMnzuBIxHgV8IIYyAGXtqcymlDB7G\n58U4M+AC1UDMMO4xrnRNKAj2X8fFxcWu7aSkgedIdq2BtLW19ao9SCm7zW4GPJaR9fjx40yePNk1\nkmrfuXq2flPJW1+XYZNwTUY0GZPs/2w8ESicGhoaADXXQlFG2qABQ0rZe6bWCJBSSiHERQ/cF0Js\nBDYCJCYmXnS5Rpsz5cf8+fNpbW2lsLCQ8+fPo9PpWLdu3aDXd503odfrez0kjUYjRqORmTNncuLE\nCWDk+yO63q+j00BgYCBWm+TXHxdQVNvGdxYk8tMVaUQHjU5yv+rqavz8/AgPH7lmLkVRBs4llSyl\nLB7guAAmSynL+zunDzVCiFjHokyxQO0Qru2TlHITsAlg3rx5l/wM9Pb2dry9vV2/8p0T8+Lj49Fo\nNJw5cwYppdsje7o2QbW0tGCz2YBvg6fz/n3N0B4pQggajILPK3146uwx3vvREuJC/XnpvnmEB/jg\n5z16a2Q4R4JFRkaqVfMUZYQNVMP4vRBCg32U1BGgDvADpgLLgRXAr4ChBIwtwH3Ak47XD4dR5sva\nxx9/jJeXFzfffDNCCIxGI15eXq7OWX9/fzo6OgacUd3VlClT8PX1JS8vj4KCAlfA0Gg0xMfHu+Zy\nBAYGsmzZshFv1z9c3MjvPj3F0VJ/vASsyAjGYLYPposLHf31JSorKzEYDCQnJ4/6ZyvKeNfvQHcp\n5e3AvwLTgGexD63dgn1ORiFwjZTyy/6uF0K8CRwApgkhyoUQD2APFKuEEGeBlY5thBBxQohPBrn2\nsqbX6zl48CBgX2rVmTHWYDD02QndtalpIFqtlsTERIKDg13BAmD//v3U1dVRUFBAbGwsoaGhTJo0\naURrGjab5L6XD1HWZGBxlJmfTe/kP9amMiXKvWDnCRcuXECn0zFp0qQxK4OijFcD9mFIKU8BvxzO\njaWUd/VzqFcuaSllJbDWjWsvW4WFhZSWlrq2Ozo60Ol0VFVVERX1bU4kq9X+63yoI5mCg4Nd6zw4\nkxOWl5djNptJSUkZgW8ArQYzrx8sZcvxSj59dCkajeDJW7NZkBjAzi8/B+gWtEab2WympqaG1NTU\nEVvvXFGUb7kz0/uWPna3ACeklBfdBzFRtLW1ER4eTlZWFrt378ZgMHDs2DGsViuzZs1ynecMGO7W\nMJzi4uI4d+4cANdffz3vvvuua2huz4WQhsJgtrLvXD3vHa3gy4IaTBYby6dFYbLY8PHScMOsuG7D\naZ3lHy02m438/Hw6OzspKSkBhjaHRFEU97nzJHkAWATscGxfjb1PI0UI8R9Syr96qGzjitFoJCAg\ngJiYGIQQ1NbWUlFRwYwZM7o1E1ks9mR7Qw0YXfs8nCvnOQOGu4kJu3KmTt9ZWMtDfztKsJ8Xd81P\n4OY58eQkdM9D1bVzeTRrGFJKjh07RlFRUbf9kZGRo1YGRZlI3AkYXkCmlLIGQAgRg30G9gJgN6AC\nxiAsFgstLS2Eh4cjhMDPz8/Vh9Ffmo6hBoye5wcEBFBXVwcMLWBsO1XDe8fKSYoI4PE1GVw7YxJ/\n37iQ7PgQdD59/3MZq4BRWVlJUVERAQEBtLe3k5GRQXR09EXVqBRF6Z87/89KcAYLh1rHvkYhhLm/\ni5RvOR/czqG0fn5+NDXZU2j1FxiGOrnO+ZB01laGEjCsNskbh0rZklfB18VNRAX5MjnU31XLWDjF\n/eR9oxkw9u3bB8CiRYsIDAz02IRERVHs3AkYO4UQW4F3HNu3OfYFAM0eK9k44mxmcg717Nqh3fMh\nd8UVV1BUVDTkZiQhBKtWrUKn0wHd188YrAP4zUOl/OsH+UyLCeIXazO4f3EK3lr3O4271jCsVitm\ns5mPPvqIBQsW9FpvA75dItY5lNiZGmUoeZ+69pXodDoVLBRlFLgTMH4E3AI4lyx7DXhX2jPNLfdU\nwcaLjo4Ozpw5A3xbC+iazqPngy45OXnYcwi6Zqvt2afRVU2rgd9/XsiSqZHcNHsyV6SE86e7ZrMu\nO3ZYk916Nkk5s+Lm5eX1Chgmk4lPP/2UkJAQVqywD5h7//338fLy4pZb+hpf0Tdn/4yfn9+Qm+8U\nRRked1KDSCHEXsCEfS3vQ3I0Fm8eJ44ePerKbeQMGM4ahr+/v8eGf3atYTgDRkFVK//3kwJyLzQi\ngNmJ9s7r9Jgg0mNGZn6G1Wp1jZrqmlTRqbGxEYvFQkNDAzabzfX9nbUwdzkTLl599dVqRreijBJ3\nhtVuAH6PPbOsAP4khHhMSrnZw2Ubd3oGDE+m3u4ZMH70+lE+PlFFRIAP9y5M4p6FSSOWALDrA9to\nNLoChcVicfWDdD3u1N7e3m2EWH/ZdvvS3NyMVqt1e0a8oigXz50mqV8C851zLhwr8G0DVMBwg16v\nd713Pgw9mdfJySa8yK33YkaIBa1WywNLU8iaHMJdVyQQqhvZ9v6uAcFgMLj6F2w2GxaLpVtg7Bow\nvvnmG6ZNm+ba3rdvHyEhITQ2NvaqOTgDj5SSsrIyqqurCQkJURP0FGUUuRMwND0m6DUwQEoR5Vud\nnZ2ugNF1NrczYPRcSW8k1LQa+MuBYl7YfQGT1ZeOOMH3hWBOYhhzEt1b9/tidE3NDvY+nK4ZdNva\n2lzvKyoqqKiocG1XV1dTXV0N2AOPs6+nubmZL774wrVUrTPFypQpUzz1NRRF6YM7AeMzIcTnwJuO\n7TuAIS2mNFG1tLQA9nb2rvMtdDoda9eudY1oGglSSh7b/A2bj9hzQd6UE0dE23nSgzw/87qvPgSd\nTkdHRweff/45GzZswGAwsG/fPhoaGkhMTOyWJgXguuuu49NPP3Vt6/V6V8A4fvw4AKdPn+42wmwk\n/36KogzOnU7vx4QQtwKLHbs2SSnf92yxxgdnx2xfC/mMRNu70WIlr7SZK1LsEwJjQ/x4+OpUbsiO\nY3pcMJs3F+Ht7fkRRH0FjJ79M107/+fOndstYHh5eREUFMSGDRswGo1s3bqV0tJSV5B1zu2or68H\nIDQ0lObmZpVgUFFGmVtTYqWU7wLvergs4059fT3+/v4jPuyzucPEtoJant1xjgv17Tx3zxzWZMXy\nz6undTvvpptuGtHPHYpJkya5algNDQ2Ul9trPqtWrcLb25vrr7+eEydOUFpa2q2m4OvrS2RkpGti\nI/SeDDhv3jxCQ0NV/4WijLKBFlDSYx9G2+sQw1+idcJoaWmhsrKSzMzMEb3vC7vP8+ed52jqMJMU\noeO5e+awJC2qz3NHK0VGXzWMgIAArrnmGr766iv27t3r2u+sbQUEBDB//nySkpJ6NS0FBgZSVlbm\n2rbZbEyaNMnVvxEUFKSChaKMgX6fKJ5amnWiOHnyJF5eXqSnp1/0vc7U6F3zJDYfKWdqdCA/X5vJ\nrPhQtJqxn4PQV8DQaDSuiYRdR0Z1fdBrtdo+M8v6+PhgNptdI6NsNlu34baeHI6sKEr/PPYzTQjx\nshCiVgiR32VfuBDiSyHEWcdrn8N2hBBrhBCFQohzQognPFVGT2psbCQuLm7YzVFSSj7Lr+JHbxxl\n7TN76DDZJ7a998iVvPPQlcxJDLskgsVAtFpttwd9fHy8W9f5+PggpXTN53BO8IuJifFIORVFcY8n\n2yxeBf4He2ZbpyeA7VLKJx2B4Ang8a4XCSG02Ff4W4V9+devhRBbHIs5XTZMJtOQF0ECMFlsvJFb\nwscnqvi6uIlQnTcPLE1B4/gVH+B76WVi7auG4ex30Gq1WK1WUlJSmD9/vlv3c6ZLMZlM+Pj4uALG\nsmXLUEkGFGXseOzpI6XcLYRI7rF7Pfb1NMCek2onPQIGcAVwTkp5HkAI8XfHdZdFwLBYLJhMJiwW\ny7AS4tW1GfndZ4XEhvrx/6+fwXcWJF3yNYmuAgMDaWtrcz3YnTWMoSRTdP7djEYjgYGBroAhhFBp\nQBRlDI32z9UYKWWV43010Fcbw2SgrMt2Ofa1Ny4Lu3btcg0fHSxg1LcZOVfbxuYj5TS2m3jh3nlM\nDvXni39cRkL45TnHIDs7m/3797v6L5wd70PppHYOOW5rayMiIqJbzilFUcbOmLVvOJIaXnT7ghBi\nI7ARIDEx8aLLdbGcwQL6DxgnK1t48tPT7Dlrn1eg89GyfFo0je0mooJ8L9tg4evrS3x8POvXr3f1\n3QynhuEMGM5Z8ipgKMqlYbQDRo0QIlZKWSWEiMW+GFNPFUBCl+14x74+SSk3AZsA5s2bN+YN3BqN\nxtV+3zVgNLQZaWg3kR4TRH2biYKqVv5xZTrZCSHMnBxCZODlnaJ76dKlriGzXTv6nQ/6oQQMrVZL\nQECAK42IChiKcmkY7YCxBbgPeNLx+mEf53wNpAkhUrAHijuB74xaCYehvb2dAwcOMGfOHAICAly/\njH18fCiqa+Pvh0p550g562fF8e/rs1gyNZJDv1iJ5jLqmxhMX8Nj4dsO8aE+8IOCglQNQ1EuMR4L\nGEKIN7F3cEcKIcqBX2EPFG8LIR4ASoANjnPjgBellGullBYhxI+BzwEt8LKU8qSnyjkSysrKaGxs\nZNu2ba595/Ua/u2TIr4oqAcBC6dEcPfCJIDLqhP7YjkDxlBXEAwKCqK+vh6bzYaUUgUMRbkEeHKU\n1F39HFrRx7mVwNou259wmSQ4LC8vd+VF8vLyxmw2IwQUtWk5XNHETbPjeOzaDKKCLu8mp4s11IAR\nHByMxWJxe11yRVE879Ib1H8Z6ezsZP/+/VhskNus40i9lsURkp/euIA1IWF4+/ji76MedDD0NCWJ\niYnk5+dz9OhRYPARZ4qieJ4KGBehrKaBD8p8yG/xotUkmB0fSGJiBHFxcWq+gINzAMBQ03l4e3sz\nY8YMV8BQ63YrythTAeMi/O6Lcxys82L1jBjumJ/E8ozowS+aYJwT+IaTCHHKlCmcPXsWvV6vahiK\ncglQAcNNUkoOlzTx648L+OW1qRQf3c1UrYalC0P53k3upbyYiHrO+B4KjUZDTk4Ohw4dGpVlbRVF\nGZgKGIMwmK18cqKK//q8kMoWA7EhfjQ61mpIDbKx6qq5Y1zCS5szYAx3lFNsbCw33nijauJTlEuA\nChgDkFJy7dO7KWnoIGtyMD9ZkcYNs+LIP3aYVuDGG28cVoLBiSQjI4Pc3FwCAgKGfQ8VLBTl0qAC\nRhcGs5XP8qs5VNzIf9w4Ay+thn9YmUZUoB+LUiPQagQGg4HS0lLS0tJUsHBDUlISSUlJY10MRVFG\ngAoYQGO7ifeOlvN6bikX6tsJ03lz76IkMiYFc/Ps7ms4tLa2Av3PbFYURRmvJnzAqG4xsPKpXbQZ\nLaRFB/LK/fNZlhbV72xsZ7oK1QmrKMpEM+EChtUmeT23hOL6Dv7thunEBPty35VJrMuOIzN28GXK\n9Xo9Wq221zrUiqIo492ECBiN7Sa2F9Tw96/LKKpro7nDzPJpUYC9Q/WxazPcvldraytBQUGqI1ZR\nlAlnQgSMNw+V8vvPC0mO0HFdVixL0yK5LmvSkO/T3t5OdXU1kZGRHiiloijKpW1cBoyvixvZeryS\n7PhQbp0bz4NLp7B4aiRZccF4aYef9dS5OJJzNTlFUZSJZFwFjJZOMw/99QifnaxGqxHc7wgOPl4a\nchJCh31fm81Ga2srRqMRgMzMzBEpr6IoyuVkTAKGEOJR4EFAAC9IKZ/ucTwMeBlIBQzA96WU+YPd\nt7Sxg/1F9Ty6Io2Ny6YQ4DsyX+/s2bMcP36c6OhohBAqEZ6iKBPSqAcMIUQW9mBxBWACPhNCbJVS\nnuty2i+APCnlzUKIDOBZ+lhHo6f4UH/y/m31Ra1kV1tbS2dnZ7fJZs65F7W1tarDW1GUCWssljHL\nBHKllB1SSguwC7ilxznTga8ApJSngWQhRMxgNw4L8LmoYFFTU8POnTvJzc2ltvbb5ca71ijCw8OH\nfX9FUZTL2VgEjHxgqRAiQgihw77SXkKPc47jCCJCiCuAJCAeD+saJI4dO4aUEqPRiMVice0PDAz0\ndDEURVEuSaPeJCWlLBBC/A74AmgH8gBrj9OeBJ4RQuQBJ4BjfZwDgBBiI7AR7Ku0XYzGxkZCQ0Mx\nm810dnayd+9eqqqqiI2NRafTkZ6eTnJy8kV9hqIoyuVqLGoYSClfklLOlVIuA5qAMz2Ot0op75dS\n5sUYuJwAAA5MSURBVAD3AlHA+X7utUlKOU9KOS8qKupiykRTUxNhYWGkpKRgMpmoqqoC7IEkICCA\n9PR0tZCPoigT1liNkoqWUtYKIRKxNz0t7HE8FOiQUpqAHwC7pZStnixTe3s7JpOJ8PDwXqOgjEaj\nykyrKMqEN1bzMN4VQkQAZuBHUspmIcRDAFLK57B3jL8mhJDASeABTxeoybEoUlhYGIGBgcTFxWGz\n2aiurgZQuaMURZnwxiRgSCmX9rHvuS7vDwDpo1Uem83G2bNn8fb2JiQkBK1Wy5IlSwDIzc2lpKSE\niIiI0SqOoijKJWlczfQeDiklmzdvBmDhwoW91p6eP38+8fHxav0LRVEmvAkfMMxms+t9wv9r79yD\n7SrPOvz8coMkpGlKLiQhpTDEcDEQc6lpp3E63AXUsdbWKB2JUkBbSK3tYMeh1WGcAa3TtKUKdDq2\nSm/aEBRomcrY0hCVmZgGA0TSmBTKAYdQg5AEkp7k9Y/v2yeb3ZNzds7Ze132+T0za/ba61t77+ec\nd5/zru9b32VBa+/etBb1/Pnzi1QyxphKUkovqSpx4MABAJYuXeoR3MYYMwRjOmG89tprbN26FcD3\nKIwxZhjGdMLYuXMne/bsYcmSJZ6y3BhjhmFMJ4x9+/YNjOA2xhgzND2bMHbu3MmGDRvYsWMHzz77\nLBEBpPmiNm7cyOHDh9m/fz9Tp04t2dQYY+pBT/aS6uvrY8uWLQAD9yjmz5/PnDlzBo4//fTT7N+/\n391ljTGmTXouYfT19bFp06ZBj/f19Q0837x5M4BrGMYY0yY9lTD6+/sHksWMGTPYu3cvixYtor+/\nn1NPPZXp06cDaQW97du3A57ywxhj2qWnEsYrr7wysH/RRRfx8ssvM23aNMaNe/2tmsWLF7Nr1y4O\nHjzI5MmTi9Y0xpha0lMJA2DevHnMnTsXSQM1isGYOXMmfX19TJw4sUA7Y4ypLz2XMFauXMmECcP/\nWCtWrGD27Nkef2GMMW3SUwlDUlvJAmDSpEksXLiwy0bGGNM79NQ4jMZYC2OMMZ2nlIQhaa2kxyU9\nIelDg5RPl3SfpMfyOWvK8DTGGHOUwhOGpJ8F3g+8FTgfuFLSmS2nfQB4MiLOB94J/KWkYRfTbl1a\n1RhjTOcoo4ZxNvBoRByIiH7gYdK63s0EME1pvvGTgP8F+od7Y4+pMMaY7lFGwngcWCXpZElTgMuB\n1pWLbicllueAbcDaiDgy2JtJulbSZkmb9+zZ001vY4wZ0xSeMCJiO3Ab8G3gQWArcLjltEvz8XnA\nEuB2SW84xvvdFRHLI2L5rFmzuidujDFjnFJuekfEFyJiWUT8ArAX2NFyyhrgnkjsBHYDZxXtaYwx\n5ihl9ZKanR/fTLp/8ZWWU54BLsznzAEWAbuKdDTGGPN6yhq4t17SycBPgA9ExEuSrgeIiDuAW4Av\nStoGCLgpIl4sydUYYwwlJYyIWDXIsTua9p8DLilUyhhjzJD01EhvY4wx3UO9NJ2GpD3A0wV81Eyg\nak1kVXOyz/BUzck+Q1M1H+iM02kR0VYX055KGEUhaXNELC/bo5mqOdlneKrmZJ+hqZoPFO/kJilj\njDFt4YRhjDGmLZwwRsZdZQsMQtWc7DM8VXOyz9BUzQcKdvI9DGOMMW3hGoYxxpi2cMKoEXm6d1Mj\nHLN64XgNjRNGvZgO/lLXDMesXjheQ+CE0YKkiyXdIGlJ2S4NJF0g6SngywBR8o0nSeflSSErgWPW\nlo9jNgRVixdUL2bghDGApFMkrQc+QbrK+Jyky0p2WiDpa8CfAPcBuyXNLNHnjZLuBbYAV0g6sSyX\n7OOYDe/jmA3tU6l4ZadKxayZsmarrSJvB74bEZ8FkDSVn17YqWh+E/jXiPhMXgt9HWn9kMKQpKar\nrfnAd4CNwLmkVRG/X6RPC47ZIDhmx0Xp8YLKx+woETFmN9KaG+fk/XFNx68DXgJuBC5sLS/A6dxj\nlP0AuCjvqyCfk5v2pwAn5O3zwA3ADMfMMatTzKoWryrG7FjbmKxhSFoA/BPpSuJIrpJ+A3gpt6me\nAVxFqoHdLem8iOjqguHHcFofEXslTSKtHbIeOA2638YqaTnwNaCfvNphRBxoKr8HeA/wuKTvRkS0\nXCV12scxG97HMTt+n9LilZ0qFbPhGKv3MM4CHoqIC4BbSSv6fTiXbYuImyLi/oi4l1Qt/N2SnD4E\nEBGH8hfkZOBNAJLGd0tE0gnArwF/Drwq6cZ8fOACIyK+BfwYeFv+Ep/Y+DJ3ScsxGwLHbMQ+pcQr\nv38VYzYkYzVhnAecmfc3kq4qlkpaHhGHJY2DgcC9CvxLSU7L8hVIg28BqwEioivtvvnq5SDw+Yi4\nC1gLfFzShIjolzSu8fsBbgPOlvQA8F+STunilY9jdgwcs1H5FB4vqHTMhqTnE4akaflRTVn5b4F5\nkpbmoG0n3WT69Vw+U9L7gEdI1dQnK+AE8BywU9Lp3fJpfBEjYld+/B6wCfhcPn1cRBzJ+ytINw33\nAqsi4n866dVwyrulxmyETtClmDVTlZg1fi9VidkIfKDL8Wq4VCVmx0vPJgxJSyV9g1zNjUwufgnY\nAPxefv4y8DzQKH8LsAr4aERcExH7ynTKVVeAZ4GPRcTubvm0lDeqxtcDqyXNzVc/0/Lx2cAlEXFV\nRPyoAz5LJL1f0imNYxWI2Yicuhizn/JpKS86ZudIWgWv+ydYWsxG6tOteB3LqaW80JiNiqjAnfdO\nbqQ2yM8C/w7sAD6Sj08AJjSddwbwTeDa/PxK4IsVdPqbgn3Gt5zbmKDyJuBR4FPAhzvsMxG4E/gP\n4G7gC8DPN5xKitlonLoRs7Z8SojZY8DfAx8FluWyE4qO2Sh9Oh6v43EqKmad2HqxhvFJUiJfCVxD\n6oVBRPRHRD+ApN8m9YT4M+A6SXeSqoIbc3mnbyiNxumRDrsM53M4+1wt6fLI32JSbXQFKal8psM+\ni4HpEbEsIq7Kn/Viwyn7FB2z0Th1I2bt+BQZs3Ozz/mkK/afAH8gaUqk5p6iYzYan27Eq12nImM2\nesrOWB3K5EuBRa2ZmzTg5atNZW8iZfsvA3PzsdOAdwMLe9lpBD53A3PysctI8+6f2WGfn8n75wNH\nSCN/30VqW74WWAYI2FZgzCrjNAKfImO2EniKXCMlNadsAdbm50X/fkr3GaFTV2PW6a10gVEG53Tg\nAeDfSNW4C/LxCflxEfAwsKDpNeeNJadO+NDBAUyD+Fycj9+W/6BfAN4H3ALcD8wB3lJwzEp16oRP\nl2N2ITCJdBP5TlIzz5eAm/PjRPJAvYJ+P6X6dMqpkzHr1la7JqmWauxHgK0R8TbgXlLzCpGr6BHx\nFGnagV9pvDYi/jPvd+xnr5pTp30if5u76QP8Eemq+d0R8XekKRp2A1dExA+bfTpB1Zw67dPFmP0j\nsCYiDgF/DBwCPk26cr4PGA8ciYgnm31GS9V8uuE02pgVQe0SBnAiDARrP6ldEFJVfbukRS3n/wMw\nW9L45oDE0S5rvehUJ5/HJZ2TP/cg8N782T8G5gFPdMGnik518XkD8FT2+VFE3EBKYJ8mdaCYAUwe\nAz5VdeoqtUkYStMh/zPwF5Lek/94HgEWSvo+qf1vPGmKgUuasv98UnNLxwfhVM2pxj5fknQJ8CBw\nqaRPStpI+gPc1ctOdffJ36FDkn4Z+B6wGThwrPevu09VnQqj7DaxdjbS6MxHSc0mPwd8haNdQRcB\n9zSdezOpqt7opnY68Iu97lRzn48Dn8r7S0iT0v1qyTHrulPNfW4G1jWVrQfe1cs+VXUqcitdYIjA\njCPPXAn8FvBXTWW/QxqEMweYRWofPDuXvYM0wVnHbyBVzakHfTo+U2nVnOxTL5+qOpW1VbJJStIa\n0ojLW/KhbcBv6Ohw/YmkavgtwCukrqA3SlpL6pHwUK879ahPRydVq5qTferlU1WnUik7Yw2SzU8i\n9QxZS+pVcFY+vo40XmATqe/yYtJEYVNJYwluIHVXW9nrTvapn5N96uVTVaeyt9IFjhGoN+fHW4Gv\n5/3xpOz9jvx8QQ7KpLHoZJ/6OdmnXj5VdSpzq2STVEQ8k3fXAadLujRSD57/i4jGMP7rSV3ZClne\nsWpO9qmfk33q5VNVp1IpO2O1keGvAx5uev5W0sCYbwKn2Mk+dXSyT718qupU9NboVllJJI2LiCNK\nU3A/Txq09BDwg4j4bzvZp45O9qmXT1WdyqCSTVINcoCmkOaDXw08ExEPlhmgqjnZp35O9qmXT1Wd\nymDC8KeUzu+TeihcHHlK4ApQNSf7DE/VnOwzNFXzgWo6FUqlm6TgaFWwbI9mquZkn+GpmpN9hqZq\nPlBNp6KpfMIwxhhTDSp9D8MYY0x1cMIwxhjTFk4Yxhhj2sIJwxhjTFs4YRgzAiQdlrRV0hOSHpP0\nh2pZ/lPSOkl9jeOS1uTXbJV0SNK2vH+rpKsl7Wkq3yrpnHJ+OmMGx72kjBkBkvZFxEl5fzZpIZ1N\nEfGJfGwcab3t54GPRcR3Wl7/Q2B5RLyYn1+dn3+wsB/CmOPENQxjRklEvABcC3ywad2Dd5LW2v5r\n0shgY2qPE4YxHSAidpGmvZ6dD60mrZmwAbhC0sQ23ua9LU1Sk7uka8yIcMIwpsNImgRcDtwbES+T\n1oC+tI2Xfj0iljRtr3ZV1JjjpA5zSRlTeSSdQVoP4QXgSuCNwLbcQjUFeBW4vzRBYzqAE4Yxo0TS\nLOAO4PaICEmrgWsi4qu5fCqwW9KUiDhQpqsxo8FNUsaMjMmNbrWkdRG+DfxpngL7MuCBxokRsR94\nBPilYd6z9R7G27slb8xIcLdaY4wxbeEahjHGmLZwwjDGGNMWThjGGGPawgnDGGNMWzhhGGOMaQsn\nDGOMMW3hhGGMMaYtnDCMMca0xf8DLw1lKgsYxuEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935ba59860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"return \",np.exp(y.Close).pct_change().mean()*250)\n",
"print(\"volatility \",y.Close.diff().std()*np.sqrt(250))\n",
"print(\"std of residual\",results.resid.std())\n",
"plt.plot(y,label='Close',color='darkgray')\n",
"results.fittedvalues.plot(label='prediction',style='--')\n",
"plt.legend(loc='upper left')\n",
"plt.ylabel('log(n225 index)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"米国での1987年11月のブラックマンデーの影響による暴落で上昇トレンドは一旦途切れたように見えるが順調に回復している。さらに残差を調べてみよう"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935b622e10>"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935b90ce10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results.resid.hist(bins=10,color=\"mistyrose\")\n",
"plt.xlabel('residual')\n",
"plt.ylabel('frequency')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ブラックマンデー後の大きな上昇トレンドからの乖離を含んでいるにも関わらす、目視では残差は1つの分布にしたがい平均、分散は一定しているように見える\n",
"\n",
"### バブル暴落時(日経平均株価ピークから) (bubble)\n",
"1990年の年初から1992年の夏まで若干の上下動はあるものの急激な下落である。この相場をバブルの上昇期のように時間トレンドでとらえることができるだろうか?"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Close R-squared: 0.816\n",
"Model: OLS Adj. R-squared: 0.815\n",
"Method: Least Squares F-statistic: 2891.\n",
"Date: Wed, 02 Aug 2017 Prob (F-statistic): 3.24e-242\n",
"Time: 12:16:16 Log-Likelihood: 622.33\n",
"No. Observations: 656 AIC: -1241.\n",
"Df Residuals: 654 BIC: -1232.\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 10.4342 0.007 1425.476 0.000 10.420 10.449\n",
"x1 -0.0010 1.93e-05 -53.767 0.000 -0.001 -0.001\n",
"==============================================================================\n",
"Omnibus: 49.072 Durbin-Watson: 0.038\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 39.778\n",
"Skew: -0.516 Prob(JB): 2.30e-09\n",
"Kurtosis: 2.375 Cond. No. 756.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
}
],
"source": [
"y=lnn225.ix['1990/1/1':'1992/8/31'].dropna()\n",
"x=range(len(y))\n",
"x=sm.add_constant(x)\n",
"model=sm.OLS(y,x)\n",
"results=model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"結果はR-squaredも0.816と決して悪くはない。期待値と実測値をチャートに描いてみよう"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"return -0.2494591088293264\n",
"volatility 0.287514843973\n",
"std of residual 0.0937749947107439\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935b4fb780>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEICAYAAABMGMOEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvO9y3fuFPwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935b5acb00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"return \",np.exp(y.Close).pct_change().mean()*250)\n",
"print(\"volatility \",y.Close.diff().std()*np.sqrt(250))\n",
"print(\"std of residual\",results.resid.std())\n",
"plt.plot(y,label='Close',color='seagreen')\n",
"results.fittedvalues.plot(label='prediction',style='--')\n",
"plt.legend()\n",
"plt.ylabel('log(n225 index)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"こちらも見た目うまく回帰できているように見える。次に残差のヒストグラムを見てみよう"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935b445be0>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935b8d0da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results.resid.hist(bins=10,color=\"mistyrose\")\n",
"plt.xlabel('residual')\n",
"plt.ylabel('frequency')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"この結果からは、直感的に残差が1つの分布にしたがうのではなく、よって平均がゼロで分散が一定しているというふうには感じられない\n",
"\n",
"### 経済変革期(reform) \n",
" \n",
" カンフル景気から欧州危機までとそれ以降を経済変革期としている。\n",
" \n",
" バブル崩壊後も大ブームの酔いから醒めることはできずに、政府が景気後退を認めるのは1992年1月であった。\n",
" \n",
" ヨーロッパのEMS(欧州通貨システム)の混乱を狙った通貨投機の後、1993年から1995年まで急激な円高が進み、日本経済は再び混乱した。\n",
" \n",
" 経済変革期には4つの気循環期が含まれている。また、この時期は、拡張期よりも後退期に強い印象がある。\n",
" \n",
" 平成第2、3次不況、世界同時不況、そして欧州危機と続く。 "
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Close R-squared: 0.138\n",
"Model: OLS Adj. R-squared: 0.138\n",
"Method: Least Squares F-statistic: 902.6\n",
"Date: Wed, 02 Aug 2017 Prob (F-statistic): 4.76e-184\n",
"Time: 12:16:26 Log-Likelihood: -528.67\n",
"No. Observations: 5631 AIC: 1061.\n",
"Df Residuals: 5629 BIC: 1075.\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 9.7139 0.007 1371.196 0.000 9.700 9.728\n",
"x1 -6.547e-05 2.18e-06 -30.043 0.000 -6.97e-05 -6.12e-05\n",
"==============================================================================\n",
"Omnibus: 865.725 Durbin-Watson: 0.003\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 213.450\n",
"Skew: -0.124 Prob(JB): 4.47e-47\n",
"Kurtosis: 2.079 Cond. No. 6.50e+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, 6.5e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n"
]
}
],
"source": [
"y=lnn225.ix['1993/11/1':].dropna()\n",
"x=range(len(y))\n",
"x=sm.add_constant(x)\n",
"model=sm.OLS(y,x)\n",
"results=model.fit()\n",
"print(results.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"結果はいままでで最悪である。R-squared=0.138と共に最悪の結果である。チャートを用いて確認してみよう"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"return 0.021601615793050276\n",
"volatility 0.241061913359\n",
"std of residual 0.26581255278187066\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935b3968d0>"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEACAYAAACgS0HpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJKdJhJEmSJKVIh5EkSZKU4n+70P0QR3QJhgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935b4450f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"return \",np.exp(y.Close).pct_change().mean()*250)\n",
"print(\"volatility \",y.Close.diff().std()*np.sqrt(250))\n",
"print(\"std of residual\",results.resid.std())\n",
"plt.plot(y,label='Close',color='hotpink')\n",
"results.fittedvalues.plot(label='prediction',style='--')\n",
"plt.legend()\n",
"plt.ylabel('log(n225 index)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"チャートからも回帰の期間の取り方が適切でない様子が見て取れる。残差のヒストグラムでも確認してみよう"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f935b2368d0>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEKCAYAAADq59mMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f935b44f7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"results.resid.hist(color='lightgray')\n",
"plt.xlabel('residual')\n",
"plt.ylabel('frequency')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"結果は予想通りに、残差の分布は幾つかの分布から構成されていると考えられる。この期間をさらに短く分割することで、時間トレンドで日経平均株価の動きを説明できる可能性をこのヒストグラムは示唆している"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"|景気 |期間始点  |終点 |ror |Vol |係数 |切片   |残差STD |\n",
"|---------------------------- |-------------|-------------|-------|-------|-------|--------|--------|\n",
"|戦後復興期(recover) |1949/5/16 |1954/11/30 |14% |23%  |0.011 |4.52 |0.25 |\n",
"|高度経済成長期(growth) |1954/12/1 |1971/12/31 |13% |14% |0.004 |6.15 |0.23 |\n",
"|安定期(stable) |1972/1/1 |1986/11/30 |13% |13% |0.004 |8.10 |0.13 |\n",
"|バブル経済期(bubble) |1986/12/0 |1993/10/31 |3% |23% |-0.0003 |10.28 |0.20 |\n",
"|バブルのピークまで  |1986/12/1  |1989/12/31   |26% |18% |0.0008 |9.9 |0.05 |\n",
"|バブルのピークから谷 |1990/1/1 |1992/8/31   |-25% |29% |-0.0010 |10.43 |0.09 |\n",
"|経済変革期(reform) |1993/11/1 |現在   |2% |24% |-0.0001 |9.7 |0.26 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 結果の解釈 \n",
"時間の経過に比例して価格が上昇、または下落するという一見単純すぎる時間トレンドモデルのもつ意味を理解いただけましたか?  \n",
"\n",
"資料の例から時間トレンド、または確定的トレンドの有無の判断が統計的に如何に難しいかが理解できます。\n",
"\n",
"決定係数(R-squared)などが良好であっても、残差のチェックが必要である。\n",
"\n",
"また、バブルが崩壊するまでは日経平均株価の上昇率は一定水準を確保し、下落の後に常に回復してきていた。\n",
"\n",
"しかしバブル経済の終焉と共にその傾向は無くなった。\n",
"\n",
"背後にある経済の状態の理解こそがこのモデルの採用の要因であることには間違いがない。\n",
"\n",
"確定的トレンドは人々の経済活動の結果であり、継続がもたらす成果でもあります。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"navigate_num": "#000000",
"navigate_text": "#333333",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "493px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment