daily data of current week

data/closed_markets_div.parquet

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data/unknown_daily_traders.parquet

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data/unknown_traders.parquet

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data/weekly_mech_calls.parquet

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scripts/wow_retentions.py

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+import pandas as pd
+from datetime import datetime, timedelta
+from utils import DATA_DIR
+
+
+# Basic Week over Week Retention
+def calculate_wow_retention(
+    traders_df: pd.DataFrame, trader_filter: str
+) -> pd.DataFrame:
+    """Function to compute the wow retention at the week level"""
+    if trader_filter == "Olas":
+        df = traders_df.loc[traders_df["staking"] != "non_Olas"]
+    elif trader_filter == "non_Olas":
+        df = traders_df.loc[traders_df["staking"] == "non_Olas"]
+    else:
+        # unknown traders
+        print("Not implemented yet")
+    # Get unique traders per week
+    weekly_traders = (
+        df.groupby("month_year_week")["trader_address"].nunique().reset_index()
+    )
+
+    # Calculate retention
+    retention = []
+    for i in range(1, len(weekly_traders)):
+        current_week = weekly_traders.iloc[i]["month_year_week"]
+        previous_week = weekly_traders.iloc[i - 1]["month_year_week"]
+
+        # Get traders in both weeks
+        current_traders = set(
+            df[df["month_year_week"] == current_week]["trader_address"]
+        )
+        previous_traders = set(
+            df[df["month_year_week"] == previous_week]["trader_address"]
+        )
+
+        retained = len(current_traders.intersection(previous_traders))
+        retention_rate = (retained / len(previous_traders)) * 100
+
+        retention.append(
+            {
+                "month_year_week": current_week,
+                "retained_traders": retained,
+                "previous_traders": len(previous_traders),
+                "retention_rate": retention_rate,
+            }
+        )
+
+    return pd.DataFrame(retention)
+
+
+# N-Week Rolling Retention
+def calculate_nweek_retention(df: pd.DataFrame, n_weeks=4):
+    # Get first and last trade for each trader
+    trader_activity = (
+        df.groupby("trader_address")
+        .agg({"creation_timestamp": ["min", "max"]})
+        .reset_index()
+    )
+
+    trader_activity.columns = ["trader_address", "first_trade", "last_trade"]
+    trader_activity["weeks_active"] = (
+        pd.to_datetime(trader_activity["last_trade"])
+        - pd.to_datetime(trader_activity["first_trade"])
+    ).dt.days / 7
+
+    return {
+        "total_traders": len(trader_activity),
+        f"{n_weeks}_week_retained": len(
+            trader_activity[trader_activity["weeks_active"] >= n_weeks]
+        ),
+        "retention_rate": (
+            len(trader_activity[trader_activity["weeks_active"] >= n_weeks])
+            / len(trader_activity)
+        )
+        * 100,
+    }
+
+
+# Cohort Retention
+def calculate_cohort_retention(df, max_weeks=12):
+    # Get first week for each trader
+    # TODO check if first will retrieve the first week of the data or not
+    first_trades = (
+        df.groupby("trader_address")
+        .agg({"creation_timestamp": "min", "month_year_week": "first"})
+        .reset_index()
+    )
+    first_trades.columns = ["trader_address", "first_trade", "cohort_week"]
+
+    # Get ordered list of unique weeks - converting to datetime for proper sorting
+    all_weeks = df["month_year_week"].unique()
+    weeks_datetime = pd.to_datetime(all_weeks)
+    sorted_weeks_idx = weeks_datetime.argsort()
+    all_weeks = all_weeks[sorted_weeks_idx]
+
+    # Create mapping from week string to numeric index
+    week_to_number = {week: idx for idx, week in enumerate(all_weeks)}
+
+    # Merge back to get all activities
+    cohort_data = pd.merge(
+        df, first_trades[["trader_address", "cohort_week"]], on="trader_address"
+    )
+
+    # Calculate week number since first activity
+    cohort_data["cohort_number"] = cohort_data["cohort_week"].map(week_to_number)
+    cohort_data["activity_number"] = cohort_data["month_year_week"].map(week_to_number)
+    cohort_data["week_number"] = (
+        cohort_data["activity_number"] - cohort_data["cohort_number"]
+    )
+
+    # Calculate retention by cohort
+    cohort_sizes = cohort_data.groupby("cohort_week")["trader_address"].nunique()
+    retention_matrix = cohort_data.groupby(["cohort_week", "week_number"])[
+        "trader_address"
+    ].nunique()
+    retention_matrix = retention_matrix.unstack(fill_value=0)
+
+    # Convert to percentages
+    retention_matrix = retention_matrix.div(cohort_sizes, axis=0) * 100
+
+    # Limit to max_weeks if specified
+    if max_weeks is not None and max_weeks < retention_matrix.shape[1]:
+        retention_matrix = retention_matrix.iloc[:, :max_weeks]
+
+    return retention_matrix.round(2)
+
+
+if __name__ == "__main__":
+    # read trades dataset
+    trades_df = pd.read_parquet(DATA_DIR / "all_trades_profitability.parquet")
+    # First, create week numbers from timestamps
+    trades_df["creation_timestamp"] = pd.to_datetime(trades_df["creation_timestamp"])
+    trades_df = trades_df.sort_values(by="creation_timestamp", ascending=True)
+    trades_df["month_year_week"] = (
+        trades_df["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d-%Y")
+    )
+    # Usage example:
+    wow_retention = calculate_wow_retention(trades_df, trader_filter="Olas")
+    rolling_retention = calculate_nweek_retention(trades_df, n_weeks=4)
+    cohort_retention = calculate_cohort_retention(trades_df)