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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import sys\n",
"sys.path.append('..')\n",
"from scripts.metrics import compute_weekly_metrics_by_market_creator"
]
},
{
"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": [
"num_mech_calls\n",
"1 5231\n",
"2 4590\n",
"0 4555\n",
"4 4457\n",
"3 4387\n",
" ... \n",
"63 1\n",
"59 1\n",
"37 1\n",
"65 1\n",
"53 1\n",
"Name: count, Length: 67, dtype: int64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_trades.num_mech_calls.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"all_trades[\"creation_date\"] = all_trades[\"creation_timestamp\"].dt.date\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/gp/02mb1d514ng739czlxw1lhh00000gn/T/ipykernel_15029/1825242321.py:6: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
" all_trades[\"creation_timestamp\"].dt.to_period(\"W\").dt.strftime(\"%b-%d\")\n"
]
}
],
"source": [
"all_trades = all_trades.sort_values(\n",
" by=\"creation_timestamp\", ascending=True\n",
")\n",
"\n",
"all_trades[\"month_year_week\"] = (\n",
" all_trades[\"creation_timestamp\"].dt.to_period(\"W\").dt.strftime(\"%b-%d\")\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Sep-15 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 38/38 [00:00<00:00, 858.56metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Sep-22 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 95/95 [00:00<00:00, 726.25metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Sep-29 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 119/119 [00:00<00:00, 724.34metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Oct-06 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 95/95 [00:00<00:00, 662.54metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Oct-13 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 117/117 [00:00<00:00, 665.98metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Oct-20 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 129/129 [00:00<00:00, 819.97metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Oct-27 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 205/205 [00:00<00:00, 679.75metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Nov-03 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 361/361 [00:00<00:00, 754.52metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Nov-10 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 357/357 [00:00<00:00, 723.25metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing weekly metrics for week =Nov-17 by market creator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Trader' metrics: 100%|██████████| 411/411 [00:00<00:00, 714.79metrics/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"End computing all weekly metrics by market creator\n"
]
}
],
"source": [
"weekly_metrics_by_market_creator = compute_weekly_metrics_by_market_creator(\n",
" all_trades\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"weekly_metrics_by_market_creator_pearl = weekly_metrics_by_market_creator.loc[weekly_metrics_by_market_creator[\"market_creator\"]==\"pearl\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"nr_mech_calls\n",
"0 191\n",
"1 152\n",
"2 105\n",
"3 63\n",
"4 41\n",
" ... \n",
"62 1\n",
"13429 1\n",
"1099 1\n",
"154 1\n",
"254 1\n",
"Name: count, Length: 88, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weekly_metrics_by_market_creator_pearl.nr_mech_calls.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>trader_address</th>\n",
" <th>net_earnings</th>\n",
" <th>earnings</th>\n",
" <th>bet_amount</th>\n",
" <th>nr_mech_calls</th>\n",
" <th>nr_trades</th>\n",
" <th>roi</th>\n",
" <th>month_year_week</th>\n",
" <th>market_creator</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1998</th>\n",
" <td>0x87f0fcfe810502555f8d1439793155cbfa2eb583</td>\n",
" <td>-135.245314</td>\n",
" <td>1.014186</td>\n",
" <td>1.95</td>\n",
" <td>13429</td>\n",
" <td>78</td>\n",
" <td>-0.499927</td>\n",
" <td>Nov-03</td>\n",
" <td>pearl</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" trader_address net_earnings earnings \\\n",
"1998 0x87f0fcfe810502555f8d1439793155cbfa2eb583 -135.245314 1.014186 \n",
"\n",
" bet_amount nr_mech_calls nr_trades roi month_year_week \\\n",
"1998 1.95 13429 78 -0.499927 Nov-03 \n",
"\n",
" market_creator \n",
"1998 pearl "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weekly_metrics_by_market_creator_pearl.loc[weekly_metrics_by_market_creator_pearl[\"nr_mech_calls\"]==13429]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"trader = \"0x87f0fcfe810502555f8d1439793155cbfa2eb583\"\n",
"selected_week = \"Nov-03\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"trader_data = all_trades.loc[(all_trades[\"trader_address\"]==trader)&(all_trades[\"month_year_week\"]==selected_week)]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 78 entries, 26553 to 31970\n",
"Data columns (total 23 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 trader_address 78 non-null object \n",
" 1 market_creator 78 non-null object \n",
" 2 trade_id 78 non-null object \n",
" 3 creation_timestamp 78 non-null datetime64[ns, UTC]\n",
" 4 title 78 non-null object \n",
" 5 market_status 78 non-null object \n",
" 6 collateral_amount 78 non-null float64 \n",
" 7 outcome_index 78 non-null object \n",
" 8 trade_fee_amount 78 non-null float64 \n",
" 9 outcomes_tokens_traded 78 non-null float64 \n",
" 10 current_answer 78 non-null int64 \n",
" 11 is_invalid 78 non-null bool \n",
" 12 winning_trade 78 non-null bool \n",
" 13 earnings 78 non-null float64 \n",
" 14 redeemed 78 non-null bool \n",
" 15 redeemed_amount 78 non-null float64 \n",
" 16 num_mech_calls 78 non-null int64 \n",
" 17 mech_fee_amount 78 non-null float64 \n",
" 18 net_earnings 78 non-null float64 \n",
" 19 roi 78 non-null float64 \n",
" 20 staking 78 non-null object \n",
" 21 creation_date 78 non-null object \n",
" 22 month_year_week 78 non-null object \n",
"dtypes: bool(3), datetime64[ns, UTC](1), float64(8), int64(2), object(9)\n",
"memory usage: 13.0+ KB\n"
]
}
],
"source": [
"trader_data.info()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 78.000000\n",
"mean 172.166667\n",
"std 73.238698\n",
"min 1.000000\n",
"25% 206.000000\n",
"50% 206.000000\n",
"75% 206.000000\n",
"max 206.000000\n",
"Name: num_mech_calls, dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trader_data.num_mech_calls.describe()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"trader_data_selected = trader_data.loc[trader_data[\"num_mech_calls\"]>200]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 64 entries, 26553 to 26582\n",
"Data columns (total 23 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 trader_address 64 non-null object \n",
" 1 market_creator 64 non-null object \n",
" 2 trade_id 64 non-null object \n",
" 3 creation_timestamp 64 non-null datetime64[ns, UTC]\n",
" 4 title 64 non-null object \n",
" 5 market_status 64 non-null object \n",
" 6 collateral_amount 64 non-null float64 \n",
" 7 outcome_index 64 non-null object \n",
" 8 trade_fee_amount 64 non-null float64 \n",
" 9 outcomes_tokens_traded 64 non-null float64 \n",
" 10 current_answer 64 non-null int64 \n",
" 11 is_invalid 64 non-null bool \n",
" 12 winning_trade 64 non-null bool \n",
" 13 earnings 64 non-null float64 \n",
" 14 redeemed 64 non-null bool \n",
" 15 redeemed_amount 64 non-null float64 \n",
" 16 num_mech_calls 64 non-null int64 \n",
" 17 mech_fee_amount 64 non-null float64 \n",
" 18 net_earnings 64 non-null float64 \n",
" 19 roi 64 non-null float64 \n",
" 20 staking 64 non-null object \n",
" 21 creation_date 64 non-null object \n",
" 22 month_year_week 64 non-null object \n",
"dtypes: bool(3), datetime64[ns, UTC](1), float64(8), int64(2), object(9)\n",
"memory usage: 10.7+ KB\n"
]
}
],
"source": [
"trader_data_selected.info()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"title\n",
"Will the U.S. Congress hold a hearing to discuss the security threats faced by former U.S. Presidents before November 1, 2024? 64\n",
"Name: count, dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trader_data_selected.title.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"creation_date\n",
"2024-10-29 32\n",
"2024-10-30 29\n",
"2024-10-28 3\n",
"Name: count, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trader_data_selected.creation_date.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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",
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},
"language_info": {
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"file_extension": ".py",
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|