updating description of trader types

app.py

@@ -177,7 +177,7 @@ with demo:
                         traders_df=weekly_metrics_by_market_creator,
                     )
                 with gr.Column(scale=1):
-                    trade_details_text = get_metrics_text()
+                    trade_details_text = get_metrics_text(trader_type=None)
 
             def update_trader_details(trader_detail):
                 return plot_trader_metrics_by_market_creator(
@@ -207,7 +207,7 @@ with demo:
                         traders_df=weekly_o_metrics_by_market_creator,
                     )
                 with gr.Column(scale=1):
-                    trade_details_text = get_metrics_text()
+                    trade_details_text = get_metrics_text(trader_type="Olas")
 
             def update_a_trader_details(trader_detail):
                 return plot_trader_metrics_by_market_creator(
@@ -238,7 +238,7 @@ with demo:
                         traders_df=weekly_non_olas_metrics_by_market_creator,
                     )
                 with gr.Column(scale=1):
-                    trade_details_text = get_metrics_text()
+                    trade_details_text = get_metrics_text(trader_type="non_Olas")
 
             def update_no_trader_details(trader_detail):
                 return plot_trader_metrics_by_market_creator(
@@ -254,7 +254,7 @@ with demo:
             # Unknown traders graph
             if weekly_unknown_trader_metrics_by_market_creator is not None:
                 with gr.Row():
-                    gr.Markdown("# Weekly metrics of Unknown traders")
+                    gr.Markdown("# Weekly metrics of Unclassified traders")
                 with gr.Row():
                     trader_u_details_selector = gr.Dropdown(
                         label="Select a weekly trader metric",
@@ -269,7 +269,9 @@ with demo:
                             traders_df=weekly_unknown_trader_metrics_by_market_creator,
                         )
                     with gr.Column(scale=1):
-                        trade_details_text = get_metrics_text()
+                        trade_details_text = get_metrics_text(
+                            trader_type="unclassified"
+                        )
 
                 def update_u_trader_details(trader_detail):
                     return plot_trader_metrics_by_market_creator(

scripts/metrics.py

@@ -1,7 +1,6 @@
 import pandas as pd
 from tqdm import tqdm
 from scripts.num_mech_calls import (
-    get_daily_total_mech_calls,
     get_weekly_total_mech_calls,
 )
 
@@ -23,8 +22,8 @@ def compute_metrics(
     agg_metrics["trader_address"] = trader_address
     total_bet_amounts = trader_data.collateral_amount.sum()
     if live_metrics:
-        # the total can be computed from daily_info.parquet
-        total_nr_mech_calls_all_markets = get_daily_total_mech_calls(trader_data)
+        # the total is already computed in daily_info per trader address and trading day
+        total_nr_mech_calls_all_markets = trader_data["num_mech_calls"].iloc[0]
     elif unknown_trader:
         # num of mech calls is always zero
         total_nr_mech_calls_all_markets = 0

scripts/num_mech_calls.py

@@ -8,23 +8,6 @@ def transform_to_datetime(x):
     return datetime.fromtimestamp(int(x), tz=timezone.utc)
 
 
-def get_daily_total_mech_calls(trader_data: pd.DataFrame) -> int:
-    """Function to compute the total daily number of mech calls for all markets
-    that the trader bet upon"""
-    daily_markets = trader_data.title.unique()
-    trading_day = trader_data.creation_date.unique()
-    if len(trading_day) > 1:
-        raise ValueError("The trader data should contain only one day information")
-    total_mech_calls = 0
-    for market in daily_markets:
-        # in num_mech_calls we have the total mech calls done for that market that day
-        total_mech_calls_on_market = trader_data.loc[
-            trader_data["title"] == market, "num_mech_calls"
-        ].iloc[0]
-        total_mech_calls += total_mech_calls_on_market
-    return total_mech_calls
-
-
 def get_weekly_total_mech_calls(trader_data: pd.DataFrame) -> int:
     """Function to compute the total weekly number of mech calls for all markets
     that the trader bet upon"""

tabs/trader_plots.py

@@ -15,14 +15,7 @@ trader_metric_choices = [
 default_trader_metric = "ROI"
 
 
-def get_metrics_text(daily: bool = False) -> gr.Markdown:
-    metric_text = """ 
-        ## Metrics at the graph
-        These metrics are computed weekly. The statistical measures are:
-        * min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
-        * the upper and lower fences to delimit possible outliers
-        * the average values as the dotted lines
-        """
+def get_metrics_text(trader_type: str = None, daily: bool = False) -> gr.Markdown:
     if daily:
         metric_text = """ 
             ## Metrics at the graph
@@ -31,6 +24,44 @@ def get_metrics_text(daily: bool = False) -> gr.Markdown:
             * the upper and lower fences to delimit possible outliers
             * the average values as the dotted lines
             """
+    elif trader_type is None:
+        metric_text = """ 
+            ## Description of the graph
+            These metrics are computed weekly. The statistical measures are:
+            * min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
+            * the upper and lower fences to delimit possible outliers
+            * the average values as the dotted lines
+            """
+    elif trader_type == "Olas":
+        metric_text = """ 
+            ## Definition of Olas trader
+            Agents using Mech, with a service ID and the corresponding safe in the registry
+            ## Description of the graph
+            These metrics are computed weekly. The statistical measures are:
+            * min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
+            * the upper and lower fences to delimit possible outliers
+            * the average values as the dotted lines
+            """
+    elif trader_type == "non_Olas":
+        metric_text = """ 
+            ## Definition of non-Olas trader
+            Agents using Mech, with no service ID
+            ## Description of the graph
+            These metrics are computed weekly. The statistical measures are:
+            * min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
+            * the upper and lower fences to delimit possible outliers
+            * the average values as the dotted lines
+            """
+    else:  # Unclassified
+        metric_text = """ 
+            ## Definition of unclassified trader
+            Agents (safe/EOAs) not using Mechs
+            ## Description of the graph
+            These metrics are computed weekly. The statistical measures are:
+            * min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
+            * the upper and lower fences to delimit possible outliers
+            * the average values as the dotted lines
+            """
     return gr.Markdown(metric_text)