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from datetime import datetime, timedelta
import gradio as gr
import pandas as pd
import duckdb
import logging
from scripts.metrics import (
compute_weekly_metrics_by_market_creator,
compute_weekly_metrics_by_trader_type,
compute_winning_metrics_by_trader,
)
from tabs.trader_plots import (
plot_trader_metrics_by_market_creator,
plot_trader_metrics_by_trader_type,
default_trader_metric,
trader_metric_choices,
get_metrics_text,
plot_winning_metric_per_trader,
)
from tabs.market_plots import plot_kl_div_per_market
def get_logger():
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
# stream handler and formatter
stream_handler = logging.StreamHandler()
stream_handler.setLevel(logging.DEBUG)
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
stream_handler.setFormatter(formatter)
logger.addHandler(stream_handler)
return logger
logger = get_logger()
def get_all_data():
"""
Get parquet files from weekly stats and new generated
"""
logger.info("Getting traders data")
con = duckdb.connect(":memory:")
# Query to fetch data from all_trades_profitability.parquet
query1 = f"""
SELECT *
FROM read_parquet('./data/all_trades_profitability.parquet')
"""
df1 = con.execute(query1).fetchdf()
logger.info("Got all data from all_trades_profitability.parquet")
# Query to fetch data from closed_markets_div.parquet
query2 = f"""
SELECT *
FROM read_parquet('./data/closed_markets_div.parquet')
"""
df2 = con.execute(query2).fetchdf()
logger.info("Got all data from closed_markets_div.parquet")
con.close()
return df1, df2
def prepare_data():
all_trades, closed_markets = get_all_data()
all_trades["creation_date"] = all_trades["creation_timestamp"].dt.date
# adding multi-bet variables
volume_trades_per_trader_and_market = (
all_trades.groupby(["trader_address", "title"])["roi"].count().reset_index()
)
volume_trades_per_trader_and_market.rename(
columns={"roi": "nr_trades_per_market"}, inplace=True
)
trader_agents_data = pd.merge(
all_trades, volume_trades_per_trader_and_market, on=["trader_address", "title"]
)
# right now all traders are of the same type: singlebet
trader_agents_data["trader_type"] = "singlebet"
trader_agents_data = trader_agents_data.sort_values(
by="creation_timestamp", ascending=True
)
trader_agents_data["month_year_week"] = (
trader_agents_data["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d")
)
closed_markets["month_year_week"] = (
closed_markets["opening_datetime"].dt.to_period("W").dt.strftime("%b-%d")
)
return trader_agents_data, closed_markets
trader_agents_data, closed_markets = prepare_data()
print("trader agents data before computing metrics")
print(trader_agents_data.head())
demo = gr.Blocks()
# get weekly metrics by market creator: qs, pearl or all.
weekly_metrics_by_market_creator = compute_weekly_metrics_by_market_creator(
trader_agents_data
)
print("weekly metrics by market creator")
print(weekly_metrics_by_market_creator.head())
# get weekly metrics by trader type: multibet, singlebet or all.
weekly_metrics_by_trader_type = compute_weekly_metrics_by_trader_type(
trader_agents_data
)
weekly_winning_metrics = compute_winning_metrics_by_trader(
trader_agents_data=trader_agents_data
)
with demo:
gr.HTML("<h1>Trader agents monitoring dashboard </h1>")
gr.Markdown(
"This app shows the weekly performance of the trader agents in Olas Predict."
)
with gr.Tabs():
with gr.TabItem("🔥Trader Agents Dashboard"):
with gr.Row():
gr.Markdown("# Weekly metrics of trader agents by market creator")
with gr.Row():
trader_details_selector = gr.Dropdown(
label="Select a trader metric",
choices=trader_metric_choices,
value=default_trader_metric,
)
with gr.Row():
with gr.Column(scale=3):
trader_markets_plot = plot_trader_metrics_by_market_creator(
metric_name=default_trader_metric,
traders_df=weekly_metrics_by_market_creator,
)
with gr.Column(scale=1):
trade_details_text = get_metrics_text()
def update_trader_details(trader_detail):
return plot_trader_metrics_by_market_creator(
metric_name=trader_detail,
traders_df=weekly_metrics_by_market_creator,
)
trader_details_selector.change(
update_trader_details,
inputs=trader_details_selector,
outputs=trader_markets_plot,
)
with gr.Row():
gr.Markdown(
"# Weekly metrics for trader agents by trader type (multibet or singlebet)"
)
with gr.Row():
trader_metric_selector = gr.Dropdown(
label="Select a trader metric",
choices=trader_metric_choices,
value=default_trader_metric,
)
with gr.Row():
with gr.Column(scale=3):
trader_type_plot = plot_trader_metrics_by_trader_type(
metric_name=default_trader_metric,
traders_df=weekly_metrics_by_trader_type,
)
with gr.Column(scale=1):
trader_metrics_text = get_metrics_text()
def update_trader_metric(trader_metric):
return plot_trader_metrics_by_trader_type(
metric_name=trader_metric,
traders_df=weekly_metrics_by_trader_type,
)
trader_metric_selector.change(
update_trader_metric,
inputs=trader_metric_selector,
outputs=trader_type_plot,
)
with gr.TabItem("📉Closed Markets Kullback–Leibler divergence"):
with gr.Row():
gr.Markdown(
"# Weekly Kullback–Leibler divergence computed for the closed markets"
)
with gr.Row():
gr.Markdown(
"This divergence is a type of statistical distance between two probability distributions P and Q. In our case P is the probability defined by the final liquidity distribution of the market. While Q is the distribution of the final outcome."
)
with gr.Row():
with gr.Column(scale=3):
kl_div_plot = plot_kl_div_per_market(closed_markets=closed_markets)
with gr.Column(scale=1):
metrics_text = get_metrics_text()
with gr.TabItem("🎖️Weekly winning trades % per trader"):
with gr.Row():
gr.Markdown("# Winning trades percentage from weekly trades by trader")
with gr.Row():
winning_metric = plot_winning_metric_per_trader(weekly_winning_metrics)
demo.queue(default_concurrency_limit=40).launch()
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