{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_trades = pd.read_parquet('../data/all_trades_profitability.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2024-11-23 01:38:25+0000', tz='UTC')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(all_trades.creation_timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2024-09-22 00:02:05+0000', tz='UTC')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(all_trades.creation_timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_trades = pd.read_parquet('../data/new_fpmmTrades.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3798 entries, 0 to 3797\n",
      "Data columns (total 24 columns):\n",
      " #   Column                         Non-Null Count  Dtype \n",
      "---  ------                         --------------  ----- \n",
      " 0   collateralAmount               3798 non-null   object\n",
      " 1   collateralAmountUSD            3798 non-null   object\n",
      " 2   collateralToken                3798 non-null   object\n",
      " 3   creationTimestamp              3798 non-null   object\n",
      " 4   trader_address                 3798 non-null   object\n",
      " 5   feeAmount                      3798 non-null   object\n",
      " 6   id                             3798 non-null   object\n",
      " 7   oldOutcomeTokenMarginalPrice   3798 non-null   object\n",
      " 8   outcomeIndex                   3798 non-null   object\n",
      " 9   outcomeTokenMarginalPrice      3798 non-null   object\n",
      " 10  outcomeTokensTraded            3798 non-null   object\n",
      " 11  title                          3798 non-null   object\n",
      " 12  transactionHash                3798 non-null   object\n",
      " 13  type                           3798 non-null   object\n",
      " 14  market_creator                 3798 non-null   object\n",
      " 15  fpmm.answerFinalizedTimestamp  0 non-null      object\n",
      " 16  fpmm.arbitrationOccurred       3798 non-null   bool  \n",
      " 17  fpmm.currentAnswer             0 non-null      object\n",
      " 18  fpmm.id                        3798 non-null   object\n",
      " 19  fpmm.isPendingArbitration      3798 non-null   bool  \n",
      " 20  fpmm.openingTimestamp          3798 non-null   object\n",
      " 21  fpmm.outcomes                  3798 non-null   object\n",
      " 22  fpmm.title                     3798 non-null   object\n",
      " 23  fpmm.condition.id              3798 non-null   object\n",
      "dtypes: bool(2), object(22)\n",
      "memory usage: 660.3+ KB\n"
     ]
    }
   ],
   "source": [
    "new_trades.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3798"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_trades.id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['collateralAmount', 'collateralAmountUSD', 'collateralToken',\n",
       "       'creationTimestamp', 'trader_address', 'feeAmount', 'id',\n",
       "       'oldOutcomeTokenMarginalPrice', 'outcomeIndex',\n",
       "       'outcomeTokenMarginalPrice', 'outcomeTokensTraded', 'title',\n",
       "       'transactionHash', 'type', 'market_creator',\n",
       "       'fpmm.answerFinalizedTimestamp', 'fpmm.arbitrationOccurred',\n",
       "       'fpmm.currentAnswer', 'fpmm.id', 'fpmm.isPendingArbitration',\n",
       "       'fpmm.openingTimestamp', 'fpmm.outcomes', 'fpmm.title',\n",
       "       'fpmm.condition.id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_trades.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1732609530'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(new_trades.creationTimestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "old_trades = pd.read_parquet('../data/fpmmTrades.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1732609530'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(old_trades.creationTimestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_trades_before = pd.read_parquet('../data/daily_info.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3882 entries, 0 to 3881\n",
      "Data columns (total 21 columns):\n",
      " #   Column                  Non-Null Count  Dtype              \n",
      "---  ------                  --------------  -----              \n",
      " 0   trader_address          3882 non-null   object             \n",
      " 1   market_creator          3882 non-null   object             \n",
      " 2   trade_id                3882 non-null   object             \n",
      " 3   creation_timestamp      3882 non-null   datetime64[ns, UTC]\n",
      " 4   title                   3882 non-null   object             \n",
      " 5   market_status           3882 non-null   object             \n",
      " 6   collateral_amount       3882 non-null   float64            \n",
      " 7   outcome_index           3882 non-null   object             \n",
      " 8   trade_fee_amount        3882 non-null   float64            \n",
      " 9   outcomes_tokens_traded  3882 non-null   float64            \n",
      " 10  current_answer          0 non-null      object             \n",
      " 11  is_invalid              3882 non-null   bool               \n",
      " 12  winning_trade           0 non-null      object             \n",
      " 13  earnings                3882 non-null   float64            \n",
      " 14  redeemed                3882 non-null   bool               \n",
      " 15  redeemed_amount         3882 non-null   int64              \n",
      " 16  num_mech_calls          3882 non-null   int64              \n",
      " 17  mech_fee_amount         3882 non-null   float64            \n",
      " 18  net_earnings            3882 non-null   float64            \n",
      " 19  roi                     3882 non-null   float64            \n",
      " 20  staking                 3882 non-null   object             \n",
      "dtypes: bool(2), datetime64[ns, UTC](1), float64(7), int64(2), object(9)\n",
      "memory usage: 583.9+ KB\n"
     ]
    }
   ],
   "source": [
    "all_trades_before.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['trader_address', 'market_creator', 'trade_id', 'creation_timestamp',\n",
       "       'title', 'market_status', 'collateral_amount', 'outcome_index',\n",
       "       'trade_fee_amount', 'outcomes_tokens_traded', 'current_answer',\n",
       "       'is_invalid', 'winning_trade', 'earnings', 'redeemed',\n",
       "       'redeemed_amount', 'num_mech_calls', 'mech_fee_amount', 'net_earnings',\n",
       "       'roi', 'staking'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_trades_before.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2024-11-26 10:19:30+0000', tz='UTC')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(all_trades_before.creation_timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "staking\n",
       "non_agent      2376\n",
       "quickstart      672\n",
       "pearl           502\n",
       "non_staking     332\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_trades_before.staking.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_trades_df = pd.read_parquet('../json_data/all_trades_df.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['trader_address', 'market_creator', 'trade_id', 'creation_timestamp',\n",
       "       'title', 'market_status', 'collateral_amount', 'outcome_index',\n",
       "       'trade_fee_amount', 'outcomes_tokens_traded', 'current_answer',\n",
       "       'is_invalid', 'winning_trade', 'earnings', 'redeemed',\n",
       "       'redeemed_amount', 'num_mech_calls', 'mech_fee_amount', 'net_earnings',\n",
       "       'roi', 'staking', 'nr_mech_calls'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_trades_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2024-11-23 01:38:25+0000', tz='UTC')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(all_trades_df.creation_timestamp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hf_dashboards",
   "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": "3.12.2"
  }
 },
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}