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| import pandas as pd | |
| from tqdm import tqdm | |
| DEFAULT_MECH_FEE = 0.01 # xDAI | |
| def compute_metrics(trader_address: str, trader_data: pd.DataFrame) -> dict: | |
| if len(trader_data) == 0: | |
| print("No data to compute metrics") | |
| return {} | |
| weekly_metrics = {} | |
| weekly_metrics["trader_address"] = trader_address | |
| total_net_earnings = trader_data.net_earnings.sum() | |
| total_bet_amounts = trader_data.collateral_amount.sum() | |
| total_num_mech_calls = trader_data.num_mech_calls.sum() | |
| weekly_metrics["net_earnings"] = total_net_earnings | |
| weekly_metrics["earnings"] = trader_data.earnings.sum() | |
| weekly_metrics["bet_amount"] = total_bet_amounts | |
| weekly_metrics["nr_mech_calls"] = total_num_mech_calls | |
| total_fee_amounts = trader_data.mech_fee_amount.sum() | |
| total_costs = ( | |
| total_bet_amounts | |
| + total_fee_amounts | |
| + (total_num_mech_calls * DEFAULT_MECH_FEE) | |
| ) | |
| weekly_metrics["roi"] = total_net_earnings / total_costs | |
| return weekly_metrics | |
| def compute_trader_metrics_by_trader_type( | |
| trader_address: str, week_traders_data: pd.DataFrame, trader_type: str = "all" | |
| ) -> pd.DataFrame: | |
| """This function computes for a specific week the different metrics: roi, net_earnings, earnings, bet_amount, nr_mech_calls. | |
| The global roi of the trader agent by computing the individual net profit and the indivicual costs values | |
| achieved per market and dividing both. | |
| It is possible to filter by trader type: multibet, singlebet, all""" | |
| assert "trader_type" in week_traders_data.columns | |
| filtered_traders_data = week_traders_data.loc[ | |
| week_traders_data["trader_address"] == trader_address | |
| ] | |
| if trader_type != "all": # compute only for the specific type | |
| filtered_traders_data = filtered_traders_data.loc[ | |
| filtered_traders_data["trader_type"] == trader_type | |
| ] | |
| if len(filtered_traders_data) == 0: | |
| return pd.DataFrame() # No Data | |
| return compute_metrics(trader_address, filtered_traders_data) | |
| def compute_trader_metrics_by_market_creator( | |
| trader_address: str, week_traders_data: pd.DataFrame, market_creator: str = "all" | |
| ) -> dict: | |
| """This function computes for a specific week the different metrics: roi, net_earnings, earnings, bet_amount, nr_mech_calls. | |
| The global roi of the trader agent by computing the individual net profit and the indivicual costs values | |
| achieved per market and dividing both. | |
| It is possible to filter by market creator: quickstart, pearl, all""" | |
| assert "market_creator" in week_traders_data.columns | |
| filtered_traders_data = week_traders_data.loc[ | |
| week_traders_data["trader_address"] == trader_address | |
| ] | |
| if market_creator != "all": # compute only for the specific market creator | |
| filtered_traders_data = filtered_traders_data.loc[ | |
| filtered_traders_data["market_creator"] == market_creator | |
| ] | |
| if len(filtered_traders_data) == 0: | |
| tqdm.write(f"No data. Skipping market creator {market_creator}") | |
| return {} # No Data | |
| tqdm.write( | |
| f"Volume of data for trader {trader_address} and market creator {market_creator} = {len(filtered_traders_data)}" | |
| ) | |
| metrics = compute_metrics(trader_address, filtered_traders_data) | |
| return metrics | |
| def merge_trader_metrics( | |
| trader: str, weekly_data: pd.DataFrame, week: str | |
| ) -> pd.DataFrame: | |
| trader_metrics = [] | |
| # computation as specification 1 for all types of markets | |
| weekly_metrics_all = compute_trader_metrics_by_market_creator( | |
| trader, weekly_data, market_creator="all" | |
| ) | |
| weekly_metrics_all["month_year_week"] = week | |
| weekly_metrics_all["market_creator"] = "all" | |
| trader_metrics.append(weekly_metrics_all) | |
| # computation as specification 1 for quickstart markets | |
| weekly_metrics_qs = compute_trader_metrics_by_market_creator( | |
| trader, weekly_data, market_creator="quickstart" | |
| ) | |
| if len(weekly_metrics_qs) > 0: | |
| weekly_metrics_qs["month_year_week"] = week | |
| weekly_metrics_qs["market_creator"] = "quickstart" | |
| trader_metrics.append(weekly_metrics_qs) | |
| # computation as specification 1 for pearl markets | |
| weekly_metrics_pearl = compute_trader_metrics_by_market_creator( | |
| trader, weekly_data, market_creator="pearl" | |
| ) | |
| if len(weekly_metrics_pearl) > 0: | |
| weekly_metrics_pearl["month_year_week"] = week | |
| weekly_metrics_pearl["market_creator"] = "pearl" | |
| trader_metrics.append(weekly_metrics_pearl) | |
| result = pd.DataFrame.from_dict(trader_metrics, orient="columns") | |
| # tqdm.write(f"Total length of all trader metrics for this week = {len(result)}") | |
| return result | |
| def merge_trader_metrics_by_type( | |
| trader: str, weekly_data: pd.DataFrame, week: str | |
| ) -> pd.DataFrame: | |
| trader_metrics = [] | |
| # computation as specification 1 for all types of traders | |
| weekly_metrics_all = compute_trader_metrics_by_trader_type( | |
| trader, weekly_data, trader_type="all" | |
| ) | |
| weekly_metrics_all["month_year_week"] = week | |
| weekly_metrics_all["trader_type"] = "all" | |
| trader_metrics.append(weekly_metrics_all) | |
| # computation as specification 1 for multibet traders | |
| weekly_metrics_mb = compute_trader_metrics_by_trader_type( | |
| trader, weekly_data, trader_type="multibet" | |
| ) | |
| if len(weekly_metrics_mb) > 0: | |
| weekly_metrics_mb["month_year_week"] = week | |
| weekly_metrics_mb["trader_type"] = "multibet" | |
| trader_metrics.append(weekly_metrics_mb) | |
| # computation as specification 1 for singlebet traders | |
| weekly_metrics_sb = compute_trader_metrics_by_trader_type( | |
| trader, weekly_data, trader_type="singlebet" | |
| ) | |
| if len(weekly_metrics_sb) > 0: | |
| weekly_metrics_sb["month_year_week"] = week | |
| weekly_metrics_sb["trader_type"] = "singlebet" | |
| trader_metrics.append(weekly_metrics_sb) | |
| result = pd.DataFrame.from_dict(trader_metrics, orient="columns") | |
| # tqdm.write(f"Total length of all trader metrics for this week = {len(result)}") | |
| return result | |
| def compute_weekly_metrics_by_market_creator( | |
| trader_agents_data: pd.DataFrame, | |
| ) -> pd.DataFrame: | |
| """Function to compute the metrics at the trader level per week and with different categories by market creator""" | |
| contents = [] | |
| all_weeks = list(trader_agents_data.month_year_week.unique()) | |
| for week in all_weeks: | |
| weekly_data = trader_agents_data.loc[ | |
| trader_agents_data["month_year_week"] == week | |
| ] | |
| print(f"Computing weekly metrics for week ={week} by market creator") | |
| # traverse each trader agent | |
| traders = list(weekly_data.trader_address.unique()) | |
| for trader in tqdm(traders, desc=f"Trader' metrics", unit="metrics"): | |
| contents.append(merge_trader_metrics(trader, weekly_data, week)) | |
| print("End computing all weekly metrics by market creator") | |
| return pd.concat(contents, ignore_index=True) | |
| def compute_weekly_metrics_by_trader_type( | |
| trader_agents_data: pd.DataFrame, | |
| ) -> pd.DataFrame: | |
| """Function to compute the metrics at the trader level per week and with different types of traders""" | |
| contents = [] | |
| all_weeks = list(trader_agents_data.month_year_week.unique()) | |
| for week in all_weeks: | |
| weekly_data = trader_agents_data.loc[ | |
| trader_agents_data["month_year_week"] == week | |
| ] | |
| print(f"Computing weekly metrics for week ={week} by trader type") | |
| # traverse each trader agent | |
| traders = list(weekly_data.trader_address.unique()) | |
| for trader in tqdm(traders, desc=f"Trader' metrics", unit="metrics"): | |
| contents.append(merge_trader_metrics_by_type(trader, weekly_data, week)) | |
| print("End computing all weekly metrics by trader types") | |
| return pd.concat(contents, ignore_index=True) | |