drift/exploration.ipynb

{
 "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",
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