adding new column and new dist gap graph

app.py

@@ -11,6 +11,7 @@ from tabs.dist_gap import (
     get_distribution_plot,
     get_correlation_map,
     get_kde_with_trades,
+    get_kde_with_total_bet_amount,
     get_dist_gap_time_evolution,
 )
 
@@ -123,18 +124,14 @@ with demo:
                 kde_plot = get_distribution_plot(markets_data)
 
             with gr.Row():
-                gr.Markdown("# Relationship with number of trades")
-
-            with gr.Row():
-                kde_trades_plot = get_kde_with_trades(markets_data)
-
-            # with gr.Row():
-            #     gr.Markdown(
-            #         "# Relationship with mean trade size of the market (bet amount in USD)"
-            #     )
-
-            # with gr.Row():
-            #     reg_plot = get_regplot_with_mean_trade_size(markets_data)
+                with gr.Column(min_width=350):
+                    gr.Markdown("# Relationship with number of trades")
+                    kde_trades_plot = get_kde_with_trades(markets_data)
+                with gr.Column(min_width=350):
+                    gr.Markdown("# Relationship with total bet amount")
+                    kde_total_bet_amount_plot = get_kde_with_total_bet_amount(
+                        markets_data
+                    )
 
             with gr.Row():
                 gr.Markdown(

live_data/markets_live_data.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f3283dcea94638dee3b949a1cbdb90b7acdf8c50aa80c398888ed57525c07158
-size 27358
+oid sha256:e254bb09afa15f82a81192c5063d26e420b32d93f9ffb56a65afd4d9d28a27eb
+size 30562

scripts/live_markets_data.py

@@ -233,8 +233,6 @@ def transform_fpmms(fpmms: pd.DataFrame, filename: str, current_timestamp: int)
     fpmms["open"] = True
     fpmms["total_trades"] = 0
     fpmms["dist_gap_perc"] = 0.0
-    fpmms["votes_first_outcome_perc"] = 0.0
-    fpmms["votes_second_outcome_perc"] = 0.0
     fpmms["first_outcome"] = fpmms.question.apply(lambda x: x["outcomes"][0])
     fpmms["second_outcome"] = fpmms.question.apply(lambda x: x["outcomes"][1])
     # computation of token distributions

scripts/live_traders_data.py

@@ -2,7 +2,7 @@ import requests
 import logging
 import pandas as pd
 from collections import defaultdict
-from typing import Any, Optional
+from typing import Any, Optional, Tuple
 from tqdm import tqdm
 from utils import (
     OMEN_SUBGRAPH_URL,
@@ -11,7 +11,7 @@ from utils import (
     DATA_DIR,
 )
 from utils import SUBGRAPH_API_KEY, _to_content
-from queries import omen_trader_votes_query
+from queries import omen_market_trades_query
 
 
 
@@ -31,7 +31,7 @@ def _query_omen_xdai_subgraph(
     id_gt = ""
 
     while True:
-        query = omen_trader_votes_query.substitute(
+        query = omen_market_trades_query.substitute(
             fpmm_creator=CREATOR.lower(),
             first=BATCH_SIZE,
             id_gt=id_gt,
@@ -102,16 +102,17 @@ def compute_votes_distribution(market_trades: pd.DataFrame, logger):
     percentage_index_1 = round((sum_outcome_index_1 / total_trades) * 100, 2)
     return (100 - percentage_index_1), percentage_index_1
 
+def compute_bought_tokens_distribution(trades_outcome_0: pd.DataFrame, trades_outcome_1: pd.DataFrame) -> Tuple:
+    """Function to compute the distribution of bought tokens from the trades on each outcome """
+    total_tokens_outcome_0 = sum(trades_outcome_0.outcomeTokensTraded)
+    total_tokens_outcome_1 = sum(trades_outcome_1.outcomeTokensTraded)
+    total_bought_tokens = total_tokens_outcome_0 + total_tokens_outcome_1
+    percentage_bought_outcome_0 = round((total_tokens_outcome_0/total_bought_tokens)*100, 2)
+    return percentage_bought_outcome_0, 100 - percentage_bought_outcome_0 
 
-def compute_price_weighted_perc(market_trades: pd.DataFrame, logger):
+def compute_price_weighted_perc(trades_outcome_0: pd.DataFrame, trades_outcome_1: pd.DataFrame, logger) -> Tuple:
     """It computes the price weighted distribution with the percentages of each outcome"""
     logger.info("Computing the price weighted distribution")
-    # trades for outcome 0
-    trades_outcome_0 = market_trades.loc[market_trades["outcomeIndex"]==0]
-    logger.debug(f"Total trades for outcome 0 = {len(trades_outcome_0)}")
-    # trades for outcome 1
-    trades_outcome_1 = market_trades.loc[market_trades["outcomeIndex"]==1]
-    logger.debug(f"Total trades for outcome 1 = {len(trades_outcome_1)}")
     total_usd_outcome_0 = sum(trades_outcome_0.collateralAmountUSD)
     total_usd_outcome_1 = sum(trades_outcome_1.collateralAmountUSD)
     total_usd = total_usd_outcome_0 + total_usd_outcome_1
@@ -123,7 +124,7 @@ def compute_price_weighted_perc(market_trades: pd.DataFrame, logger):
 def add_trading_info(fpmms: pd.DataFrame, current_timestamp: int, logger) -> None:
     """Function to update only the information related with the current timestamp"""
     
-    logger.info("Adding votes distribution per market")
+    logger.info("Adding price weighted distribution per market")
     fpmms["liquidityMeasure"] = fpmms["liquidityMeasure"].apply(lambda x: int(x))
     # Iterate over the markets
     for i, fpmm in tqdm(fpmms.iterrows(), total=len(fpmms), desc="Analysing trades"):
@@ -145,14 +146,25 @@ def add_trading_info(fpmms: pd.DataFrame, current_timestamp: int, logger) -> Non
         if len(market_trades) > 0:
             # adding average trade size
             market_trades["collateralAmountUSD"] = market_trades.collateralAmountUSD.apply(lambda x: round(float(x),3))
+            market_trades["outcomeTokensTraded"] = market_trades.outcomeTokensTraded.apply(lambda x: int(x))
             mean_trade_size = market_trades.collateralAmountUSD.mean()
             total_bet_amount = sum(market_trades.collateralAmountUSD)
-            first_outcome, second_outcome = compute_price_weighted_perc(market_trades, logger)
+            # trades for outcome 0
+            trades_outcome_0 = market_trades.loc[market_trades["outcomeIndex"]==0]
+            logger.debug(f"Total trades for outcome 0 = {len(trades_outcome_0)}")
+            # trades for outcome 1
+            trades_outcome_1 = market_trades.loc[market_trades["outcomeIndex"]==1]
+            logger.debug(f"Total trades for outcome 1 = {len(trades_outcome_1)}")
+            first_outcome, second_outcome = compute_price_weighted_perc(trades_outcome_0, trades_outcome_1, logger)
+            bought_tokens_first, bought_tokens_second = compute_bought_tokens_distribution(trades_outcome_0, trades_outcome_1)
+            metric = abs(fpmm["first_token_perc"] - first_outcome)
+            logger.info(f"metric for this market {metric}")
         else:
             logger.info("No trades for this market")
-            mean_trade_size = 0.0
-            total_bet_amount = 0.0
-            first_outcome, second_outcome = 50.0, 50.0
+            mean_trade_size = total_bet_amount = 0.0
+            bought_tokens_first = bought_tokens_second = 0.0
+            first_outcome = second_outcome = 0.0
+            metric = 0.0
         fpmms.at[i,"mean_trade_size"] = mean_trade_size
         fpmms.at[i,"total_bet_amount"] = total_bet_amount
         logger.info(
@@ -160,8 +172,8 @@ def add_trading_info(fpmms: pd.DataFrame, current_timestamp: int, logger) -> Non
         )
         fpmms.at[i,"price_weighted_first_outcome_perc"] = first_outcome
         fpmms.at[i,"price_weighted_second_outcome_perc"] = second_outcome
-        metric = abs(fpmm["first_token_perc"] - first_outcome)
-        logger.info(f"metric for this market {metric}")
+        fpmms.at[i,"bought_tokens_first_perc"] = bought_tokens_first
+        fpmms.at[i,"bought_tokens_second_perc"] = bought_tokens_second
         fpmms.at[i,"dist_gap_perc"] = metric
     logger.debug("Dataset after adding trading info")
     logger.debug(fpmms.head())

scripts/queries.py

@@ -72,7 +72,7 @@ FPMMS_WITH_TOKENS_QUERY = Template(
 )
 
 
-omen_trader_votes_query = Template(
+omen_market_trades_query = Template(
     """
     {
         fpmmTrades(

tabs/dist_gap.py

@@ -5,8 +5,8 @@ import seaborn as sns
 from seaborn import FacetGrid
 import plotly.express as px
 
-HEIGHT = 600
-WIDTH = 1000
+HEIGHT = 300
+WIDTH = 600
 
 
 def get_dist_gap_time_evolution(market_id: str, all_markets: pd.DataFrame):
@@ -64,6 +64,13 @@ def get_kde_with_trades(markets_data: pd.DataFrame):
     return gr.Plot(value=plot.get_figure())
 
 
+def get_kde_with_total_bet_amount(markets_data: pd.DataFrame):
+    """Function to paint the density plot of the metric in terms of the total bet amount"""
+    plot = sns.kdeplot(markets_data, x="dist_gap_perc", y="total_bet_amount", fill=True)
+    plt.ylabel("Total bet amount per market")
+    return gr.Plot(value=plot.get_figure())
+
+
 def get_regplot_with_mean_trade_size(markets_data: pd.DataFrame):
     """Function to Plot data and a linear regression model fit between the metric and the mean trade size"""
     regplot = sns.regplot(markets_data, x="dist_gap_perc", y="mean_trade_size")