new dataset for 2-weeks rolling avg

scripts/predict_kpis.py

@@ -302,11 +302,12 @@ def get_trades_on_closed_markets_by_pearl_agents() -> pd.DataFrame:
     raise ValueError("No trades found for the pearl agents")
 
 
-def compute_market_agent_roi(
+def compute_markets_agent_roi(
     agent_trades: pd.DataFrame,
     mech_calls: pd.DataFrame,
     agent: str,
-    week: datetime,
+    period: str,
+    period_value: datetime,
 ) -> dict:
     # ROI formula net_earnings/total_costs
     earnings = agent_trades.earnings.sum()
@@ -316,24 +317,38 @@ def compute_market_agent_roi(
     total_costs = total_bet_amount + total_market_fees + total_mech_fees
     net_earnings = earnings - total_costs
     if total_costs == 0:
-        raise ValueError(f"Total costs for agent {agent} in week {week} are zero")
+        raise ValueError(f"Total costs for agent {agent} are zero")
     roi = net_earnings / total_costs
-    return {
-        "trader_address": agent,
-        "week_start": week,
-        "roi": roi,
-        "net_earnings": net_earnings,
-        "total_bet_amount": total_bet_amount,
-        "total_mech_calls": len(mech_calls),
-    }
+    if period == "week":
+        return {
+            "trader_address": agent,
+            "week_start": period_value,
+            "roi": roi,
+            "net_earnings": net_earnings,
+            "total_bet_amount": total_bet_amount,
+            "total_mech_calls": len(mech_calls),
+        }
+    if period == "day":
+        return {
+            "trader_address": agent,
+            "creation_date": period_value,
+            "roi": roi,
+            "net_earnings": net_earnings,
+            "total_bet_amount": total_bet_amount,
+            "total_mech_calls": len(mech_calls),
+        }
+    raise ValueError(
+        f"Invalid period {period} for agent {agent}. Expected 'week' or 'day'."
+    )
 
 
-def compute_weekly_avg_roi_pearl_agents() -> pd.DataFrame:
+def prepare_agents_data() -> Tuple[pd.DataFrame, pd.DataFrame]:
+    """Function to prepare the agents data for the predict ROI KPIs computation"""
+    # Get the trades done by pearl agents on closed markets
     agent_trades = get_trades_on_closed_markets_by_pearl_agents()
-    agent_mech_requests = get_mech_requests_on_closed_markets_by_pearl_agents(
-        agent_trades
+    print(
+        f"Number of trades done by pearl agents on closed markets: {len(agent_trades)}"
     )
-
     agent_trades["creation_timestamp"] = pd.to_datetime(
         agent_trades["creation_timestamp"]
     )
@@ -342,21 +357,40 @@ def compute_weekly_avg_roi_pearl_agents() -> pd.DataFrame:
     ].dt.tz_convert("UTC")
     agent_trades["creation_date"] = agent_trades["creation_timestamp"].dt.date
     agent_trades = agent_trades.sort_values(by="creation_timestamp", ascending=True)
+    # Get the mech requests done by pearl agents on closed markets
+    agent_mech_requests = get_mech_requests_on_closed_markets_by_pearl_agents(
+        agent_trades
+    )
+    agent_mech_requests["request_time"] = pd.to_datetime(
+        agent_mech_requests["request_time"], utc=True
+    )
+    agent_mech_requests = agent_mech_requests.sort_values(
+        by="request_time", ascending=True
+    )
+    agent_mech_requests["request_date"] = agent_mech_requests["request_time"].dt.date
+    print(
+        f"Number of mech requests done by pearl agents on closed markets: {len(agent_mech_requests)}"
+    )
+
+    return agent_trades, agent_mech_requests
 
+
+def compute_weekly_avg_roi_pearl_agents(
+    agent_trades, agent_mech_requests
+) -> pd.DataFrame:
     agent_trades["week_start"] = (
         agent_trades["creation_timestamp"].dt.to_period("W").dt.start_time
     )
 
     grouped_trades = agent_trades.groupby("week_start")
     contents = []
-
+    agents = agent_trades.trader_address.unique()
     # Iterate through the groups (each group represents a week)
     for week, week_data in grouped_trades:
         print(f"Week: {week}")  # Print the week identifier
 
         # for all closed markets
         closed_markets = week_data.title.unique()
-        agents = week_data.trader_address.unique()
         for agent in agents:
             # compute all trades done by the agent on those markets, no matter from which week
             agent_markets_data = agent_trades.loc[
@@ -371,11 +405,12 @@ def compute_weekly_avg_roi_pearl_agents() -> pd.DataFrame:
                 (agent_mech_requests["trader_address"] == agent)
                 & (agent_mech_requests["title"].isin(closed_markets))
             ]
+
             # compute the ROI for that market, that trader and that week
             try:
                 # Convert the dictionary to DataFrame before appending
-                roi_dict = compute_market_agent_roi(
-                    agent_markets_data, agent_mech_calls, agent, week
+                roi_dict = compute_markets_agent_roi(
+                    agent_markets_data, agent_mech_calls, agent, "week", week
                 )
                 contents.append(pd.DataFrame([roi_dict]))
             except ValueError as e:
@@ -392,9 +427,59 @@ def compute_weekly_avg_roi_pearl_agents() -> pd.DataFrame:
     return weekly_avg_roi_data
 
 
-def compute_daily_avg_roi_pearl_agents() -> pd.DataFrame:
-    # TODO Implementation
-    print("WIP")
+def compute_two_weeks_rolling_avg_roi_pearl_agents(
+    agents_trades: pd.DataFrame, agents_mech_requests: pd.DataFrame
+) -> pd.DataFrame:
+    grouped_trades = agents_trades.groupby("creation_date")
+    contents = []
+    agents = agents_trades.trader_address.unique()
+    # Iterate through the groups (each group represents a day)
+    for day, day_data in grouped_trades:
+        # take all closed markets in two weeks before that day
+        print(f"Day: {day}")  # Print the day identifier
+        two_weeks_ago = day - timedelta(days=14)
+        two_weeks_data = agents_trades.loc[
+            (agents_trades["creation_date"] >= two_weeks_ago)
+            & (agents_trades["creation_date"] <= day)
+        ]
+        if len(two_weeks_data) == 0:
+            # not betting activity
+            continue
+        # for all closed markets
+        closed_markets = two_weeks_data.title.unique()
+        for agent in agents:
+            # take trades done by the agent two weeks before that day using creation_date and delta
+            agent_markets_data = agents_trades.loc[
+                (agents_trades["trader_address"] == agent)
+                & (agents_trades["title"].isin(closed_markets))
+            ]
+            if len(agent_markets_data) == 0:
+                # not betting activity
+                continue
+
+            # filter mech requests done by the agent on that market
+            agent_mech_calls = agents_mech_requests.loc[
+                (agents_mech_requests["trader_address"] == agent)
+                & (agents_mech_requests["title"].isin(closed_markets))
+            ]
+            # compute the ROI for these markets, that trader and for this period
+            try:
+                # Convert the dictionary to DataFrame before appending
+                roi_dict = compute_markets_agent_roi(
+                    agent_markets_data, agent_mech_calls, agent, "day", day
+                )
+                contents.append(pd.DataFrame([roi_dict]))
+            except ValueError as e:
+                print(f"Skipping ROI calculation: {e}")
+                continue
+    two_weeks_avg_data = pd.concat(contents, ignore_index=True)
+
+    two_weeks_rolling_avg_roi = (
+        two_weeks_avg_data.groupby("creation_date")["roi"]
+        .mean()
+        .reset_index(name="two_weeks_avg_roi")
+    )
+    return two_weeks_rolling_avg_roi
 
 
 if __name__ == "__main__":
@@ -402,7 +487,18 @@ if __name__ == "__main__":
     prepare_predict_services_dataset()
     # dune = setup_dune_python_client()
     # load_predict_services_file(dune_client=dune)
-    # final_dataset = compute_weekly_avg_roi_pearl_agents()
-    # print(final_dataset.head())
-    # # save in a file
-    # final_dataset.to_parquet(ROOT_DIR / "weekly_avg_roi_pearl_agents.parquet")
+    agents_trades, agents_mech_requests = prepare_agents_data()
+
+    weekly_avg = compute_weekly_avg_roi_pearl_agents(
+        agents_trades, agents_mech_requests
+    )
+    print(weekly_avg.head())
+    # save in a file
+    weekly_avg.to_parquet(ROOT_DIR / "weekly_avg_roi_pearl_agents.parquet")
+
+    two_weeks_avg = compute_two_weeks_rolling_avg_roi_pearl_agents(
+        agents_trades, agents_mech_requests
+    )
+    print(two_weeks_avg.head())
+    # save in a file
+    two_weeks_avg.to_parquet(ROOT_DIR / "two_weeks_avg_roi_pearl_agents.parquet")

two_weeks_avg_roi_pearl_agents.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f7dcb86cafacfcf4f1fd0c8ff9dbe2f5fba45efe6725b7900dcefd7d497019f0
+size 3045