updating weekly data for the traders

app.py

@@ -118,13 +118,13 @@ def prepare_data():
         by="creation_timestamp", ascending=True
     )
     traders_data["month_year_week"] = (
-        traders_data["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d")
+        traders_data["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d-%Y")
     )
     unknown_traders["month_year_week"] = (
-        unknown_traders["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d")
+        unknown_traders["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d-%Y")
     )
     closed_markets["month_year_week"] = (
-        closed_markets["opening_datetime"].dt.to_period("W").dt.strftime("%b-%d")
+        closed_markets["opening_datetime"].dt.to_period("W").dt.strftime("%b-%d-%Y")
     )
     return traders_data, closed_markets, daily_info, unknown_traders
 

data/closed_markets_div.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7292472caaaba2984a91b8797d5b770d669bec7743693eeb4f1a90f716d653b7
-size 56326
+oid sha256:f6b6ff11a79e22ddcfcc3e24305ed6a0892e06888254b6360e89e2ad956529a1
+size 52009

data/daily_info.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d9f224f954dd108e164b12763dd628e05a5f17a94fd2422d9853f60f470a690d
-size 697569
+oid sha256:23cc8b305b1f2f5b6cc7b4a6017c1aeb2a003ec5cc921be1ad66f380d29a6102
+size 658465

data/unknown_daily_traders.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5228e7344adf08237bff1159c387f6435c0116e07378adf6981d5326750f9a1e
-size 42386
+oid sha256:1480859c96b05ac94e54f2a69c0ad46d3b840c37111051bb2a0174aa60d90a73
+size 25003

data/weekly_mech_calls.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0ebba634e4a93389e65a39a240e5fa9598b2ef90197e91250069b8abe7450b69
-size 55154
+oid sha256:04536d62dc403476e4b21032b86e781f38aab5c08d69ebc9ccc917e005b9af95
+size 50976

notebooks/trader_agent_metrics.ipynb

@@ -111,32 +111,33 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 8210 entries, 0 to 8209\n",
-      "Data columns (total 20 columns):\n",
+      "RangeIndex: 88355 entries, 0 to 88354\n",
+      "Data columns (total 21 columns):\n",
       " #   Column                  Non-Null Count  Dtype              \n",
       "---  ------                  --------------  -----              \n",
-      " 0   trader_address          8210 non-null   object             \n",
-      " 1   market_creator          8210 non-null   object             \n",
-      " 2   trade_id                8210 non-null   object             \n",
-      " 3   creation_timestamp      8210 non-null   datetime64[ns, UTC]\n",
-      " 4   title                   8210 non-null   object             \n",
-      " 5   market_status           8210 non-null   object             \n",
-      " 6   collateral_amount       8210 non-null   float64            \n",
-      " 7   outcome_index           8210 non-null   object             \n",
-      " 8   trade_fee_amount        8210 non-null   float64            \n",
-      " 9   outcomes_tokens_traded  8210 non-null   float64            \n",
-      " 10  current_answer          8210 non-null   int64              \n",
-      " 11  is_invalid              8210 non-null   bool               \n",
-      " 12  winning_trade           8210 non-null   bool               \n",
-      " 13  earnings                8210 non-null   float64            \n",
-      " 14  redeemed                8210 non-null   bool               \n",
-      " 15  redeemed_amount         8210 non-null   float64            \n",
-      " 16  num_mech_calls          8210 non-null   int64              \n",
-      " 17  mech_fee_amount         8210 non-null   float64            \n",
-      " 18  net_earnings            8210 non-null   float64            \n",
-      " 19  roi                     8210 non-null   float64            \n",
-      "dtypes: bool(3), datetime64[ns, UTC](1), float64(8), int64(2), object(6)\n",
-      "memory usage: 1.1+ MB\n"
+      " 0   trader_address          88355 non-null  object             \n",
+      " 1   market_creator          88355 non-null  object             \n",
+      " 2   trade_id                88355 non-null  object             \n",
+      " 3   creation_timestamp      88355 non-null  datetime64[ns, UTC]\n",
+      " 4   title                   88355 non-null  object             \n",
+      " 5   market_status           88355 non-null  object             \n",
+      " 6   collateral_amount       88355 non-null  float64            \n",
+      " 7   outcome_index           88355 non-null  object             \n",
+      " 8   trade_fee_amount        88355 non-null  float64            \n",
+      " 9   outcomes_tokens_traded  88355 non-null  float64            \n",
+      " 10  current_answer          88355 non-null  int64              \n",
+      " 11  is_invalid              88355 non-null  bool               \n",
+      " 12  winning_trade           88355 non-null  bool               \n",
+      " 13  earnings                88355 non-null  float64            \n",
+      " 14  redeemed                88355 non-null  bool               \n",
+      " 15  redeemed_amount         88355 non-null  float64            \n",
+      " 16  num_mech_calls          88355 non-null  int64              \n",
+      " 17  mech_fee_amount         88355 non-null  float64            \n",
+      " 18  net_earnings            88355 non-null  float64            \n",
+      " 19  roi                     88355 non-null  float64            \n",
+      " 20  staking                 88355 non-null  object             \n",
+      "dtypes: bool(3), datetime64[ns, UTC](1), float64(8), int64(2), object(7)\n",
+      "memory usage: 12.4+ MB\n"
      ]
     }
    ],
@@ -1359,6 +1360,78 @@
    "source": [
     "compute_weekly_metrics_by_market_creator(trader_agent_metrics)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools_df = pd.read_parquet(\"../tmp/tools.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "error\n",
+       "0    780323\n",
+       "1    214538\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tools_df.error.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "994861"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(tools_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21.564620585187278"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(214538/994861)*100"
+   ]
   }
  ],
  "metadata": {

scripts/num_mech_calls.py

@@ -60,7 +60,7 @@ def compute_total_mech_calls():
         tools["request_date"] = tools["request_time"].dt.date
         tools = tools.sort_values(by="request_time", ascending=True)
         tools["month_year_week"] = (
-            tools["request_time"].dt.to_period("W").dt.strftime("%b-%d")
+            tools["request_time"].dt.to_period("W").dt.strftime("%b-%d-%Y")
         )
 
     except Exception as e:
@@ -79,7 +79,7 @@ def compute_total_mech_calls():
     fpmmTrades["creation_date"] = fpmmTrades["creation_timestamp"].dt.date
     fpmmTrades = fpmmTrades.sort_values(by="creation_timestamp", ascending=True)
     fpmmTrades["month_year_week"] = (
-        fpmmTrades["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d")
+        fpmmTrades["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d-%Y")
     )
 
     nr_traders = len(fpmmTrades["trader_address"].unique())

scripts/utils.py

@@ -22,10 +22,10 @@ def get_current_week():
 
     # Get the start and end dates of the current week (starting on Sunday)
     current_week_start = current_date - timedelta(days=current_date.weekday() + 1)
-    return current_week_start.strftime("%b-%d")
+    return current_week_start.strftime("%b-%d-%Y")
 
 
 def get_next_week():
     current_date = datetime.now()
     next_week_start = current_date + timedelta(days=7 - (current_date.weekday() + 1))
-    return next_week_start.strftime("%b-%d")
+    return next_week_start.strftime("%b-%d-%Y")

tabs/market_plots.py

@@ -134,12 +134,12 @@ def plot_total_bet_amount_per_trader_per_market(
     # Convert string dates to datetime and sort them
     all_dates_dt = sorted(
         [
-            datetime.strptime(date, "%b-%d")
+            datetime.strptime(date, "%b-%d-%Y")
             for date in total_bet_amount["month_year_week"].unique()
         ]
     )
     # Convert back to string format
-    all_dates = [date.strftime("%b-%d") for date in all_dates_dt]
+    all_dates = [date.strftime("%b-%d-%Y") for date in all_dates_dt]
     fig = px.box(
         total_bet_amount,
         x="month_year_week",

tabs/trader_plots.py

@@ -115,12 +115,12 @@ def plot_trader_metrics_by_market_creator(
     # Convert string dates to datetime and sort them
     all_dates_dt = sorted(
         [
-            datetime.strptime(date, "%b-%d")
+            datetime.strptime(date, "%b-%d-%Y")
             for date in traders_filtered["month_year_week"].unique()
         ]
     )
     # Convert back to string format
-    all_dates = [date.strftime("%b-%d") for date in all_dates_dt]
+    all_dates = [date.strftime("%b-%d-%Y") for date in all_dates_dt]
     fig = px.box(
         traders_filtered,
         x="month_year_week",
@@ -243,12 +243,12 @@ def plot_total_bet_amount(
     # Convert string dates to datetime and sort them
     all_dates_dt = sorted(
         [
-            datetime.strptime(date, "%b-%d")
+            datetime.strptime(date, "%b-%d-%Y")
             for date in total_bet_amount["month_year_week"].unique()
         ]
     )
     # Convert back to string format
-    all_dates = [date.strftime("%b-%d") for date in all_dates_dt]
+    all_dates = [date.strftime("%b-%d-%Y") for date in all_dates_dt]
     total_bet_amount["trader_market"] = total_bet_amount.apply(
         lambda x: (x["trader_type"], x["market_creator"]), axis=1
     )
@@ -330,12 +330,12 @@ def plot_active_traders(
     # Convert string dates to datetime and sort them
     all_dates_dt = sorted(
         [
-            datetime.strptime(date, "%b-%d")
+            datetime.strptime(date, "%b-%d-%Y")
             for date in active_traders["month_year_week"].unique()
         ]
     )
     # Convert back to string format
-    all_dates = [date.strftime("%b-%d") for date in all_dates_dt]
+    all_dates = [date.strftime("%b-%d-%Y") for date in all_dates_dt]
     color_mapping = [
         "royalblue",
         "goldenrod",