Files
drift/exploration.ipynb
T
2022-01-26 23:22:43 +01:00

179 lines
44 KiB
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[2m\u001b[36m(__load_df pid=52067)\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=52067)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n",
"\u001b[2m\u001b[36m(__load_df pid=52074)\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=52074)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n",
"\u001b[2m\u001b[36m(__load_df pid=52071)\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=52071)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n",
"\u001b[2m\u001b[36m(__load_df pid=52072)\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=52072)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n",
"\u001b[2m\u001b[36m(__load_df pid=52069)\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=52069)\u001b[0m result = getattr(ufunc, method)(*inputs, **kwargs)\n"
]
}
],
"source": [
"import pandas as pd\n",
"import pandas_ta as ta\n",
"from config import get_default_ensemble_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_ensemble_config()\n",
"model_config, training_config, data_config = preprocess_config(model_config, training_config, data_config)\n",
"\n",
"config.target_asset'] = config.assets'][0]\n",
"X, returns, forward_returns = load_data(**data_config)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"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",
" 'msol_standard_scaling_0', 'dormancy_standard_scaling_0',\n",
" 'liveliness_standard_scaling_0',\n",
" 'relative_unrealized_profit_standard_scaling_0',\n",
" 'relative_unrealized_loss_standard_scaling_0',\n",
" 'nupl_standard_scaling_0', 'sth_nupl_standard_scaling_0',\n",
" 'lth_nupl_standard_scaling_0', 'ssr_standard_scaling_0',\n",
" 'bvin_standard_scaling_0'],\n",
" dtype='object', length=542)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 6,
"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['liveliness_standard_scaling_0'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 7,
"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": 7,
"metadata": {},
"outputs": [],
"source": [
"# pd.plotting.scatter_matrix(X, figsize=(12, 12));"
]
},
{
"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": "3.9.9"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}