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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 win_metrics_trader_level(weekly_data):
winning_trades = (
weekly_data.groupby(
["month_year_week", "market_creator", "trader_address"], sort=False
)["winning_trade"].sum()
/ weekly_data.groupby(
["month_year_week", "market_creator", "trader_address"], sort=False
)["winning_trade"].count()
* 100
)
# winning_trades is a series, give it a dataframe
winning_trades = winning_trades.reset_index()
winning_trades.columns = winning_trades.columns.astype(str)
winning_trades.rename(columns={"winning_trade": "winning_perc"}, inplace=True)
return winning_trades
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)
def compute_winning_metrics_by_trader(
trader_agents_data: pd.DataFrame,
) -> pd.DataFrame:
"""Function to compute the winning metrics at the trader level per week and with different market creators"""
market_all = trader_agents_data.copy(deep=True)
market_all["market_creator"] = "all"
# merging both dataframes
final_traders = pd.concat([market_all, trader_agents_data], ignore_index=True)
final_traders = final_traders.sort_values(by="creation_timestamp", ascending=True)
winning_df = win_metrics_trader_level(final_traders)
winning_df.head()
return winning_df
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