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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"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": 4,
"metadata": {},
"outputs": [],
"source": [
"all_trades = pd.read_parquet('../data/all_trades_profitability.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_trades.columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def compute_winning_metric_per_trader_per_market_creator(\n",
" trader_address: str, week_traders_data: pd.DataFrame, market_creator: str = \"all\"\n",
") -> float:\n",
" assert \"market_creator\" in week_traders_data.columns\n",
" filtered_traders_data = week_traders_data.loc[\n",
" week_traders_data[\"trader_address\"] == trader_address\n",
" ]\n",
" if market_creator != \"all\": # compute only for the specific market creator\n",
" filtered_traders_data = filtered_traders_data.loc[\n",
" filtered_traders_data[\"market_creator\"] == market_creator\n",
" ]\n",
" if len(filtered_traders_data) == 0:\n",
" return None # No Data\n",
" winning_perc = (\n",
" filtered_traders_data[\"winning_trade\"].sum()\n",
" / filtered_traders_data[\"winning_trade\"].count()\n",
" * 100.0\n",
" )\n",
" return winning_perc"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def merge_winning_metrics_by_trader(\n",
" trader: str, weekly_data: pd.DataFrame, week: str\n",
") -> pd.DataFrame:\n",
" trader_metrics = []\n",
" # computation as specification 1 for all market creators\n",
" winning_metrics_all = {}\n",
" winning_metric_all = compute_winning_metric_per_trader_per_market_creator(\n",
" trader, weekly_data, market_creator=\"all\"\n",
" )\n",
" winning_metrics_all[\"winning_perc\"] = winning_metric_all\n",
" winning_metrics_all[\"month_year_week\"] = week\n",
" winning_metrics_all[\"market_creator\"] = \"all\"\n",
" trader_metrics.append(winning_metrics_all)\n",
" if week == \"Jul-21\":\n",
" print(f\"trader = {trader}, win_perc for all ={winning_metric_all}\")\n",
"\n",
" # computation as specification 1 for quickstart markets\n",
" winning_metrics_qs = {}\n",
" winning_metric = compute_winning_metric_per_trader_per_market_creator(\n",
" trader, weekly_data, market_creator=\"quickstart\"\n",
" )\n",
" if winning_metric:\n",
" winning_metrics_qs[\"winning_perc\"] = winning_metric\n",
" winning_metrics_qs[\"month_year_week\"] = week\n",
" winning_metrics_qs[\"market_creator\"] = \"quickstart\"\n",
" trader_metrics.append(winning_metrics_qs)\n",
"\n",
" # computation as specification 1 for pearl markets\n",
" winning_metrics_pearl = {}\n",
" winning_metric = compute_winning_metric_per_trader_per_market_creator(\n",
" trader, weekly_data, market_creator=\"pearl\"\n",
" )\n",
" if winning_metric:\n",
" winning_metrics_pearl[\"winning_perc\"] = winning_metric\n",
" winning_metrics_pearl[\"month_year_week\"] = week\n",
" winning_metrics_pearl[\"market_creator\"] = \"pearl\"\n",
" trader_metrics.append(winning_metrics_pearl)\n",
"\n",
" result = pd.DataFrame.from_dict(trader_metrics, orient=\"columns\")\n",
" # tqdm.write(f\"Total length of all winning metrics for this week = {len(result)}\")\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"def win_metrics_trader_level(weekly_data):\n",
" winning_trades = (\n",
" weekly_data.groupby([\"month_year_week\", \"market_creator\",\"trader_address\"], sort=False)[\n",
" \"winning_trade\"\n",
" ].sum()\n",
" / weekly_data.groupby([\"month_year_week\", \"market_creator\",\"trader_address\"], sort=False)[\n",
" \"winning_trade\"\n",
" ].count()\n",
" * 100\n",
" )\n",
" # winning_trades is a series, give it a dataframe\n",
" winning_trades = winning_trades.reset_index()\n",
" winning_trades.columns = winning_trades.columns.astype(str)\n",
" winning_trades.columns = [\"month_year_week\", \"market_creator\", \"trader_address\", \"winning_trade\"]\n",
" winning_trades.rename(columns={\"winning_trade\": \"winning_perc\"})\n",
" return winning_trades"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tqdm import tqdm\n",
"\n",
"market_all = all_trades.copy(deep=True)\n",
"market_all[\"market_creator\"] = \"all\"\n",
"\n",
"# merging both dataframes\n",
"final_traders = pd.concat([market_all, all_trades], ignore_index=True)\n",
"final_traders = final_traders.sort_values(\n",
" by=\"creation_timestamp\", ascending=True)\n",
"\n",
"\n",
"winning_df = win_metrics_trader_level(final_traders)\n",
"winning_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"winning_df = compute_winning_metrics_by_trader(all_trades)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"winning_pearl = winning_df.loc[winning_df[\"market_creator\"]==\"pearl\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"winning_pearl.head()"
]
}
],
"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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"nbformat": 4,
"nbformat_minor": 2
}
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