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{
"cells": [
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from utils.load_data import load_data\n",
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"import pandas as pd\n",
"from pandas.plotting import lag_plot\n",
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"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
"from feature_extractors.feature_extractor_presets import presets\n",
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"\n",
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"X, y, target_returns = load_data(path = './data', target_asset = 'BTC_USD', load_other_assets = True, log_returns=False, forecasting_horizon= 1, own_features= presets[\"level_2\"],\n",
" other_features= presets[\"level_2\"],\n",
" index_column= 'int',\n",
" method= 'classification',\n",
" no_of_classes= 'three-balanced',\n",
" narrow_format=False)"
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]
},
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{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [
{
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" <th></th>\n",
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" <th>BTC_USD_returns</th>\n",
" <th>BTC_USD_fracdiff_10</th>\n",
" <th>BTC_USD_fracdiff_30</th>\n",
" <th>BTC_USD_mom_10</th>\n",
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" <th>...</th>\n",
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" <th>DOT_USD_rsi_30</th>\n",
" <th>DOT_USD_rsi_100</th>\n",
" <th>DOT_USD_stod_10</th>\n",
" <th>DOT_USD_stod_30</th>\n",
" <th>DOT_USD_stod_200</th>\n",
" <th>DOT_USD_stok_10</th>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>-90.588311</td>\n",
" <td>559.909509</td>\n",
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" <th>1495</th>\n",
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" <td>-0.008444</td>\n",
" <td>0.668922</td>\n",
" <td>0.631444</td>\n",
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" <td>713.812241</td>\n",
" <td>-3.209040</td>\n",
" <td>713.812241</td>\n",
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" <td>38.924924</td>\n",
" <td>12.026726</td>\n",
" <td>4.707934</td>\n",
" <td>34.846790</td>\n",
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" <th>1497</th>\n",
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"<p>1500 rows ×
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" BTC_USD_returns BTC_USD_fracdiff_10 BTC_USD_fracdiff_30 \\\n",
"0 0.000000 7444.360000 7444.360000 \n",
"1 -0.042283 1139.206563 2535.024063 \n",
"2 -0.079077 243.709664 1286.077535 \n",
"3 -0.034412 263.094633 1039.924935 \n",
"4 -0.072830 -90.588311 559.909509 \n",
"... ... ... ... \n",
"1495 0.014405 2660.331638 3144.986598 \n",
"1496 -0.067431 -1392.796403 -763.198378 \n",
"1497 0.035451 2891.701218 2900.158370 \n",
"1498 0.010279 2284.616961 2760.190939 \n",
"1499 -0.025552 594.386779 1195.664451 \n",
"\n",
" BTC_USD_mom_10 BTC_USD_mom_20 BTC_USD_mom_30 BTC_USD_mom_60 \\\n",
"0 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 0.000000 \n",
"3 0.000000 0.000000 0.000000 0.000000 \n",
"4 0.000000 0.000000 0.000000 0.000000 \n",
"... ... ... ... ... \n",
"1495 -0.113455 -0.109995 -0.218939 -0.126532 \n",
"1496 -0.129180 -0.188227 -0.274471 -0.185292 \n",
"1497 -0.017377 -0.153693 -0.261359 -0.215567 \n",
"1498 -0.011734 -0.170976 -0.231537 -0.196997 \n",
"1499 -0.057700 -0.114380 -0.207506 -0.225691 \n",
"\n",
" BTC_USD_mom_90 BTC_USD_vol_10 BTC_USD_vol_20 ... DOT_USD_roc_30 \\\n",
"0 0.000000 0.000000 0.000000 ... 0.000000 \n",
"1 0.000000 0.000000 0.000000 ... 0.000000 \n",
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"... ... ... ... ... ... \n",
"1495 0.114582 0.634974 0.602183 ... 152.766095 \n",
"1496 -0.008444 0.668922 0.631444 ... 713.812241 \n",
"1497 0.004969 0.582283 0.651423 ... -127.240548 \n",
"1498 0.023461 0.584564 0.638944 ... -141.804006 \n",
"1499 0.007145 0.581942 0.571090 ... -189.814479 \n",
"\n",
" DOT_USD_rsi_10 DOT_USD_rsi_30 DOT_USD_rsi_100 DOT_USD_stod_10 \\\n",
"0 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 0.000000 \n",
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"4 0.000000 0.000000 0.000000 0.000000 \n",
"... ... ... ... ... \n",
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"\n",
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" DOT_USD_stod_30 DOT_USD_stod_200 DOT_USD_stok_10 DOT_USD_stok_30 \\\n",
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"1497 10.375772 37.739506 20.121029 5.887561 \n",
"1498 8.219332 35.927831 39.944521 14.062500 \n",
"1499 9.647436 35.972564 24.133148 8.992248 \n",
"\n",
" DOT_USD_stok_200 \n",
"0 0.000000 \n",
"1 0.000000 \n",
"2 0.000000 \n",
"3 0.000000 \n",
"4 0.000000 \n",
"... ... \n",
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"1496 34.846790 \n",
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"1499 34.980989 \n",
"\n",
"[1500 rows x 483 columns]"
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]
},
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"execution_count": 3,
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],
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]
},
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{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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{
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{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"X['BTC_USD_fracdiff_10'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/anaconda3/envs/quant/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: invalid value encountered in log\n",
" result = getattr(ufunc, method)(*inputs, **kwargs)\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 10,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"np.log(X['BTC_USD_fracdiff_10']).plot()"
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]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"<AxesSubplot:>"
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]
},
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"execution_count": 12,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAARQ0lEQVR4nO3dW4yc9X3G8e+v3nDcgO2Sbi3b6jqSRQuxkoYVCUlVrUtaXECYSkVyRSK7JfJFCSWpo2qdXKS9sEqSUoWKotQKrdxCs3EcWiwsSqibVdsLTHBOjjEum9ghNtQmLZA6QrRLf72Y1/V4Pbs7rN/xzPz5fqTRvPN/T88c/MzsOwdHZiJJKstPdTuAJKl+lrskFchyl6QCWe6SVCDLXZIKNNDtAACXXXZZDg8PdzvGrH7yk59w8cUXdztGW/ola7/kBLN2Qr/khN7Nunfv3h9l5ttazeuJch8eHuapp57qdoxZTUxMMDo62u0YbemXrP2SE8zaCf2SE3o3a0T8YKZ5HpaRpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QC9cQ3VNU/hsd21batTaum2NDm9g7fdUNt+5XeDHzlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklSgtso9Ij4WEfsj4rsR8cWIuCAiFkfE4xHxbHW+qGn5zRExGREHI+K6zsWXJLUyZ7lHxFLg94CRzHwHsABYB4wBuzNzJbC7ukxEXFHNvxJYA9wXEQs6E1+S1Eq7h2UGgAsjYgC4CHgeWAtsq+ZvA26uptcC45n5WmYeAiaBq2tLLEmaU2Tm3AtF3AlsAV4FvpqZt0bEy5m5sGmZlzJzUUTcCzyRmQ9U4/cDj2bmjmnb3AhsBBgaGrpqfHy8ruvUESdOnGBwcLDbMdrSyaz7jr5S27aGLoRjr7a37Kqll9a23/nw/q9fv+SE3s26evXqvZk50mrewFwrV8fS1wIrgJeBL0fEB2dbpcXYGc8gmbkV2AowMjKSo6Ojc0XpqomJCXo940mdzLphbFdt29q0aoq79835EATg8K2jte13Prz/69cvOaG/sp7UzmGZDwCHMvPFzPwf4CHgfcCxiFgCUJ0fr5Y/AixvWn8ZjcM4kqRzpJ1yfw54b0RcFBEBXAscAHYC66tl1gMPV9M7gXURcX5ErABWAk/WG1uSNJs5/ybOzD0RsQP4BjAFfJPG4ZRBYHtE3EbjCeCWavn9EbEdeLpa/vbMfL1D+SVJLbR1wDMzPwV8atrwazRexbdafguNN2AlSV3gN1QlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVKC2yj0iFkbEjoh4JiIORMQ1EbE4Ih6PiGer80VNy2+OiMmIOBgR13UuviSplXZfud8D/ENm/jzwTuAAMAbszsyVwO7qMhFxBbAOuBJYA9wXEQvqDi5Jmtmc5R4RlwC/DNwPkJn/nZkvA2uBbdVi24Cbq+m1wHhmvpaZh4BJ4Op6Y0uSZhOZOfsCEe8CtgJP03jVvhe4EziamQublnspMxdFxL3AE5n5QDV+P/BoZu6Ytt2NwEaAoaGhq8bHx+u6Th1x4sQJBgcHux2jLZ3Muu/oK7Vta+hCOPZqe8uuWnppbfudD+//+vVLTujdrKtXr96bmSOt5g20sf4A8G7gjszcExH3UB2CmUG0GDvjGSQzt9J40mBkZCRHR0fbiNI9ExMT9HrGkzqZdcPYrtq2tWnVFHfva+chCIdvHa1tv/Ph/V+/fskJ/ZX1pHaOuR8BjmTmnuryDhplfywilgBU58ebll/etP4y4Pl64kqS2jFnuWfmvwM/jIjLq6FraRyi2Qmsr8bWAw9X0zuBdRFxfkSsAFYCT9aaWpI0q/b+JoY7gAcj4jzg+8Bv03hi2B4RtwHPAbcAZOb+iNhO4wlgCrg9M1+vPbkkaUZtlXtmfgtoddD+2hmW3wJsmX8sSdLZ8BuqklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBWq73CNiQUR8MyIeqS4vjojHI+LZ6nxR07KbI2IyIg5GxHWdCC5JmtkbeeV+J3Cg6fIYsDszVwK7q8tExBXAOuBKYA1wX0QsqCeuJKkdbZV7RCwDbgC+0DS8FthWTW8Dbm4aH8/M1zLzEDAJXF1LWklSWyIz514oYgfwx8BbgY9n5o0R8XJmLmxa5qXMXBQR9wJPZOYD1fj9wKOZuWPaNjcCGwGGhoauGh8fr+s6dcSJEycYHBzsdoy2dDLrvqOv1LatoQvh2KvtLbtq6aW17Xc+vP/r1y85oXezrl69em9mjrSaNzDXyhFxI3A8M/dGxGgb+4sWY2c8g2TmVmArwMjISI6OtrPp7pmYmKDXM57UyawbxnbVtq1Nq6a4e9+cD0EADt86Wtt+58P7v379khP6K+tJ7fzLej9wU0RcD1wAXBIRDwDHImJJZr4QEUuA49XyR4DlTesvA56vM7QkaXZzHnPPzM2ZuSwzh2m8UfpPmflBYCewvlpsPfBwNb0TWBcR50fECmAl8GTtySVJM2rvb+LW7gK2R8RtwHPALQCZuT8itgNPA1PA7Zn5+lknlSS17Q2Ve2ZOABPV9H8A186w3BZgy1lmk/7fcI3H+t+ow3fd0LV9S/PlN1QlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVCDLXZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgpkuUtSgSx3SSqQ5S5JBbLcJalAlrskFchyl6QCWe6SVKA5yz0ilkfE1yLiQETsj4g7q/HFEfF4RDxbnS9qWmdzRExGxMGIuK6TV0CSdKZ2XrlPAZsy8xeA9wK3R8QVwBiwOzNXArury1Tz1gFXAmuA+yJiQSfCS5Jam7PcM/OFzPxGNf1fwAFgKbAW2FYttg24uZpeC4xn5muZeQiYBK6uObckaRaRme0vHDEM/DPwDuC5zFzYNO+lzFwUEfcCT2TmA9X4/cCjmblj2rY2AhsBhoaGrhofHz/Lq9JZJ06cYHBwsNsx2tLJrPuOvlLbtoYuhGOv1ra5jlm19FLv/w7ol5zQu1lXr169NzNHWs0baHcjETEIfAX4aGb+OCJmXLTF2BnPIJm5FdgKMDIykqOjo+1G6YqJiQl6PeNJncy6YWxXbdvatGqKu/e1/RDsmsO3jnr/d0C/5IT+ynpSW5+WiYi30Cj2BzPzoWr4WEQsqeYvAY5X40eA5U2rLwOeryeuJKkd7XxaJoD7gQOZ+adNs3YC66vp9cDDTePrIuL8iFgBrASerC+yJGku7fxN/H7gQ8C+iPhWNfYJ4C5ge0TcBjwH3AKQmfsjYjvwNI1P2tyema/XHVySNLM5yz0z/5XWx9EBrp1hnS3AlrPIJUk6C35DVZIKZLlLUoEsd0kqkOUuSQWy3CWpQJa7JBXIcpekAlnuklQgy12SCmS5S1KBLHdJKpDlLkkFstwlqUCWuyQVyHKXpAJZ7pJUIMtdkgrU+//1vNRlw2O72LRqig1ju87
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"X['BTC_USD_fracdiff_10'].hist()"
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]
},
{
"cell_type": "code",
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"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/anaconda3/envs/quant/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: invalid value encountered in log\n",
" result = getattr(ufunc, method)(*inputs, **kwargs)\n"
]
},
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"np.log(X['BTC_USD_fracdiff_10']).hist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"X['BTC_USD_returns'].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/var/folders/tf/6j1p9w152qscfmn1hcd_06qh0000gn/T/ipykernel_43797/1283503941.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlag_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'BTC_returns'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
]
}
],
"source": [
"lag_plot(X['BTC_returns'])"
]
},
{
"cell_type": "code",
"execution_count": null,
2021-11-12 16:10:55 +01:00
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAALJCAYAAACQkGSIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9d5hc2XnfiX9uqpyrc0ajkTEABoMZDCYPg5iDSEukKIq0ZUm2tT+vvevw2Ja962z/dm3LtryWrZUt26IpkaLENGKcnBMwyLFz7sq5br77x6kuoJHjDEj193nwoLuqu+rW6XPPec/7ft/vV/I8j3WsYx3rWMc61rGOdaxjHbcG+b2+gHWsYx3rWMc61rGOdazjpwHrgfU61rGOdaxjHetYxzrWcRuwHlivYx3rWMc61rGOdaxjHbcB64H1OtaxjnWsYx3rWMc61nEbsB5Yr2Md61jHOtaxjnWsYx23AeuB9TrWsY51rGMd61jHOtZxG6C+1xdwu9DR0eGNjIy815dx12N6epq7dZyapkPTcgj5FAKa8l5fzl09Vp4HFd3C9TxiAQ1Flt6za7mbx+lGYNgudcPGr8qE/bd/afxpGac7gTXzOagxNzOzPlZXwOo6GfQprCzM/VSPk+V41HQLVZGIBrRbeq31++/KqOo2luMSDagszM3e8DhdOCeDd8He/W7g4MGDOc/zOi/33E9NYD0yMsLbb7/9Xl/GXY99+/bdlePkeR7//plxXM8joCn8lSc2vteXdNeOFcDJxQo/PLEMwN7hJI9vvuz9/a7gbh6nG8HvvzZNrmYC8GuPjd724PqnZZzuBE4slvnRiRUA7htO8r9/4SPrY3UF/PtnzuG4Hj5V5vf+1ud/qsfp24cXmMzWAfjc/YP0JYI3/Vrr99/lkasZ/P5rMwAMpkL8y7/06Rsep9U56ddkfv2JsTtxmXcdJEmaudJz61SQddwVkCSJwZRYNFf/X8eV0R3z41NlZEliILk+XrcDg6kQAJ1R/11RMfmzhJ5YYH0+XyeGWvN09f+fZqzek9GASjLke4+v5qcT0YBKIiSqAYM3ee+19+7kT/+cvB781GSsf9Iw8nf+9IrPTf/Lj72LV/LuYqncRJEluqKBS5779J5+KrpFPHhrJb8/C0hH/PzFRzZgux6e5zGdqzOYCr2nlJCfRLiux1yxQTLs44ktXewZTBDxq+vjeAWYtstCqUlvPHBbDx8XzufIHaDh3AwunBuxW6Qh3E58cncfFd0iFtD4R3foPcpNi1LDZCgVQpLeu3th71CSjZ0RgpqCT/2zmwfMVHQcz6M3fvsPnX5V4YsPDtMwHOKhm5vnn9rd356TdzMaps1KxWAwGURV7tx8ujtWsHX8mcCZ5SrfO7aEJMFn9w60sxGrkGWJxHpW4roR0BR0y+G/vzpDw3TY0RfjZ3b0vNeX9ROFF85lOTxbwq/J/PmHRtbn3zXw7cMLzBebpMI+vvzQyG197butSnDx3Aj57o7t8k6vkw3T5n++MYNhudw7lOCJLV137L2uB3/WEy2z+QZ/8s48ngcf29XL5u7obX8PTZGJh24+0PxJ2Lsd1+MP3pyj0rTY2BXhk7v77th73R0rxTp+KlEzbL5xcI664fD+bV1UmjYgGpXKTYvB9/j67hZkqwY/OrlMNKDxM9u7ObtSJRrQ2NARvubvmo5L03IAKDWtO32p7znemS1ydL7Mzv449w0nb+h3p3J1qrrFjr54OyNdbogxMyyXpuncNcHTe4WJbI1XxnMMpkI8eZmAqtyaY5Wmhet6yBdk9g3b4eRiha5YgP5b4MLeDTixWOapI4sADCRD6JbLrcYNZ1eq2I7Htt7oe5oFvhaapoNhuYD4ezdMm+8dW8ZxXT5yT+8tZyXLTYvxTI3RjjDJ8N0djF0PXp3IcW6lxv0jKbb3xW7765ebFp4nvi41rm+Nn87VqegW23tjdzQzu4pSw2QiW2esM9LOeq+OywMbUmzrvf3jcqOwXZeaLmKQcsO8o+/1Z3sXWccdxTcPzfP9Y8uoskRAlfnigWHqpo0qS3fFjXa34J3ZIpmKQaZi8F3LYb7YBODzDwxes/QXC2h8YFs388UG94+k3o3LfU/x6kQe03Z5ZTx3Q4H1UrnJt95ZAEQH/MNjHQA8vrmTgKbQGw+QjvjvyDX/JOGNyQL5mkm+ZrJ3MHlJafjDO3s4vlBmU3d0TVAN8NzpLKeWKiiyxJcfGvmJzjS+NpEnHfGzUGzy2OYOUrcYAJ5bqfKnR5cAscHvGkjchqu8M0hH/LxvaxdLZZ0HR1OcWa4yV2gAcGKhwoGN6Vt6/W8fXiBfMzk0U+RXHxu9HZf8nsGwHd6YLADw2mT+jgTW2/tilJomtuuxZzBxzZ9fqeh86/BCO4H16KY739j+jYPzVHWbY/Ml/vzDG9CtC8ZlIn9X7Pd+VeEj9/Qwma2xZ/DGkjI3ivXAeh13DLIkEQtoVHWLxXKTP3xzjo/s7KErdim/+s8yNnSEObVUJeRTiAZUxjM1qrrF/tHUdXHqdvbH2dkffxeu9L3HaEeY08tVRjvDlBsWTx1bRJYkPrG776rcXNe78Ovz3yTDPj6886efPrNc1vnhiWWiAZWP7+q7Il91tDPMSkWnK+Yn7L+UmjGQDDFwhQYlrzWunnf+67sR1zNvNnZGqOo279vaxX3Dt35gvXD+Oe7dNzZnlqu8dC7LcDrMB7Z1sXswwe5WSXEgKRRIPM9jKH3rzWlu6/O7nugPuZuy9xPZGs+fydIXD/ChHT2XHB4vhk+RGUyFmCs0GO28doXxZqDI0g0Fx2Jcxdd3eq5N5eo8ezrDicUyg8lQe5771Ts/LjeDzd3RO0KluRjXHVhLkrQRmPc8z5Ak6QlgF/A/PM8r3ZlLW8dPOj6+u48NnWF00+XIfIlC3eTYQpn3rwfWa7CpO8pfSoVQZYlczeDZ0xmSYa2duV7HeXx4Zw+Pb+kkqCm8MVUgUzEAUWbfO3TlLER/IsjHdvVS1a27Olt4p3C0df8V6iazhQZjXZHL/tyDo2l2DcQJqMo1g4qL8eTWLtIRP11R/13Ntzy1XLnmvHlyaxf7R1O3TZN3c3cEy+nGdj123YWH4LemC1R1m+MLZR7YkFpTbeiM+vnVR0fx8PCrtz4en9zTz9mVKhs7I3dVUA1waKZIpWlRaVrsG0nRGb16FUuSJD67t7/lv3B35Cl740E+sbuXUuPOr3UHW+PVEfGzrS/G/g2imnE3jsu7iRv5xH8M7JMkaQz4L8B3gK8CH70TF7aOn3zEgxoPbeygqltM5mo0TYeNnZff0N9NlBomL4/nSIV9PLSxo/2453lMZGtE/Bo98Xc3+F9t3OqI+NkzmCRT1d+Vk/XFcF2Pl8dz1AybRzd1rDFlmMzW8KnyFTOW7wYkSWov1Bs6whycKSJL0nVJj93O8SzWxRzqiPjXlMZvZozyNYNXJ/J0xwI8sOHy2dGJbI2Aptw0d3ljV4TTy1XCfpXea8ztm90IA5pyxeu/FZQbFi+NZ0mFfBzYmG4HY8tlnZph3XCAdr3z5nYGBJIkvStVJcN2ePFsDtfz6E8E6IoFLqvAdDG29ETJVg36E0Gil8ng305FjlTYx4Ojt0YnuZ2YLzb4wfFlRtJhNndHWSg16YoG2hJ018KFa9LdgrGuO7t3vDlVYK7QQLdsTMdpVTq60S7gc9+N4/Ju4UY+tet5ni1J0s8C/9bzvN+SJOmdO3Vh6/jJgOW4qLKEJEmUmxZ+VV7T3d8wbd6cKrCjL8a+4STaBRmPim7xzUMLWI7Lp/b0XzM7cLvw6kSecys1AIbT4Xaw8uZUgVcn8kgSfGH/0B29Bstx1yxCTdPBcl1iAY1feGAQyxHl14pu8cZkgVTYd8PNelfCYqnJU0cXCftVPnPvAEHf+b/JVL7OwZkiAAFN5n1buwE4Mlfi2dMZAP7cfZcqutxJXDxWq/ApMr/88Ag+VbmsPN5Etsa5lRq7BuK3ZCyxCs/z+N6xZabzdSSEU+N4psZIR4jeeJBj82WePiVMTj67d+C6y+Yvncsxlau3X+viQOid2SLPn8liOi6Blivkjd4
"text/plain": [
"<Figure size 864x864 with 81 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"pd.plotting.scatter_matrix(data, figsize=(12, 12));"
]
},
{
"cell_type": "code",
2021-12-28 22:50:09 +01:00
"execution_count": null,
2021-11-10 14:51:12 +01:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
2021-11-12 16:10:55 +01:00
"execution_count": 6,
2021-11-10 14:51:12 +01:00
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
2021-11-12 16:10:55 +01:00
"image/png": "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"text/plain": [
"<Figure size 1440x1440 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(20,20))\n",
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"sns.heatmap(data.corr(), annot=True, ax=ax);"
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "51432b8e5767c06330d9b51dfad63f9db0ea39868e37d921b9c2e277373f8d11"
},
"kernelspec": {
"display_name": "Python 3.9.2 64-bit ('deeplearning': conda)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
