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{
"cells": [
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{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"\u001b[2m\u001b[36m(__load_df pid=33339)\u001b[0m /usr/local/anaconda3/envs/quant/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
"\u001b[2m\u001b[36m(__load_df pid=33339)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n",
"\u001b[2m\u001b[36m(__load_df pid=33345)\u001b[0m /usr/local/anaconda3/envs/quant/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
"\u001b[2m\u001b[36m(__load_df pid=33345)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n",
"\u001b[2m\u001b[36m(__load_df pid=33342)\u001b[0m /usr/local/anaconda3/envs/quant/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
"\u001b[2m\u001b[36m(__load_df pid=33342)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n",
"\u001b[2m\u001b[36m(__load_df pid=33344)\u001b[0m /usr/local/anaconda3/envs/quant/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
"\u001b[2m\u001b[36m(__load_df pid=33344)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n",
"\u001b[2m\u001b[36m(__load_df pid=33349)\u001b[0m /usr/local/anaconda3/envs/quant/lib/python3.9/site-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
"\u001b[2m\u001b[36m(__load_df pid=33349)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n",
"\u001b[2m\u001b[36m(__load_df pid=33341)\u001b[0m \n"
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]
}
],
"source": [
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"import pandas as pd\n",
"import pandas_ta as ta\n",
"from config.config import get_default_level_2_daily_config\n",
"from config.preprocess import preprocess_config\n",
"from data_loader.load_data import load_data\n",
"import seaborn as sns\n",
"import numpy as np\n",
"\n",
"model_config, training_config, data_config = get_default_level_2_daily_config()\n",
"model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config)\n",
"\n",
"data_config['target_asset'] = data_config['assets'][0]\n",
"X, y, target_returns = load_data(**data_config)"
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]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 11,
"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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"X['sth_nupl_fracdiff_30'].plot()"
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]
},
{
"cell_type": "code",
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"execution_count": 12,
"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": 12,
"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['sth_nupl_fracdiff_30']).plot()"
]
},
{
"cell_type": "code",
"execution_count": 6,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
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"execution_count": 6,
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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": [
"X['BTC_USD_returns'].plot()"
]
},
{
"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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"# pd.plotting.scatter_matrix(X, figsize=(12, 12));"
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]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"Index(['ADA_USD_returns', 'ADA_USD_mom_10', 'ADA_USD_mom_20', 'ADA_USD_mom_30',\n",
" 'ADA_USD_mom_60', 'ADA_USD_mom_90', 'ADA_USD_vol_10', 'ADA_USD_vol_20',\n",
" 'ADA_USD_vol_30', 'ADA_USD_vol_60',\n",
" ...\n",
" 'sth_nupl_fracdiff_30', 'lth_nupl_returns', 'lth_nupl_fracdiff_10',\n",
" 'lth_nupl_fracdiff_30', 'ssr_returns', 'ssr_fracdiff_10',\n",
" 'ssr_fracdiff_30', 'bvin_returns', 'bvin_fracdiff_10',\n",
" 'bvin_fracdiff_30'],\n",
" dtype='object', length=588)"
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]
},
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"execution_count": 8,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"X.columns"
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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
}
