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{
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|