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from datetime import datetime |
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import gradio as gr |
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import pandas as pd |
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import duckdb |
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import logging |
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from tabs.tokens_dist import ( |
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get_extreme_cases, |
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) |
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from tabs.dist_gap import ( |
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get_distribution_plot, |
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get_avg_gap_time_evolution_grouped_markets, |
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get_correlation_map, |
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get_kde_with_trades, |
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get_kde_with_total_bet_amount, |
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get_dist_gap_time_evolution, |
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get_dist_gap_timeline_plotly, |
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) |
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def get_logger(): |
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logger = logging.getLogger(__name__) |
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logger.setLevel(logging.DEBUG) |
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stream_handler = logging.StreamHandler() |
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stream_handler.setLevel(logging.DEBUG) |
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formatter = logging.Formatter( |
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"%(asctime)s - %(name)s - %(levelname)s - %(message)s" |
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) |
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stream_handler.setFormatter(formatter) |
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logger.addHandler(stream_handler) |
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return logger |
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logger = get_logger() |
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def prepare_data(): |
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""" |
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Get all data from the parquet files |
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""" |
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logger.info("Getting all data") |
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con = duckdb.connect(":memory:") |
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query = f""" |
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SELECT * |
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FROM read_parquet('./live_data/markets_live_data.parquet') |
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""" |
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df = con.execute(query).fetchdf() |
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df["sample_datetime"] = df["sample_timestamp"].apply( |
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lambda x: datetime.fromtimestamp(x) |
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) |
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df["opening_datetime"] = df["openingTimestamp"].apply( |
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lambda x: datetime.fromtimestamp(int(x)) |
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) |
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df["days_to_resolution"] = (df["opening_datetime"] - df["sample_datetime"]).dt.days |
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return df |
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demo = gr.Blocks() |
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markets_data = prepare_data() |
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markets_data["sample_date"] = pd.to_datetime(markets_data["sample_datetime"]).dt.date |
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live_markets_data = markets_data.loc[markets_data["open"] == True] |
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markets_data = markets_data.loc[markets_data["total_trades"] > 0] |
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with demo: |
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gr.HTML("<h1>Olas Predict Live Markets </h1>") |
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gr.Markdown("This app shows the distributions of predictions on the live markets.") |
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best_market_id, best_gap, worst_market_id, worst_gap = get_extreme_cases( |
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live_markets_data |
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) |
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with gr.Tabs(): |
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with gr.TabItem("💹 Probability distributions of some markets"): |
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with gr.Row(): |
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gr.Markdown("Best case: a market with a low gap between distributions") |
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with gr.Row(): |
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gr.Markdown( |
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f"Market id = {best_market_id} Dist gap = {round(best_gap,2)}" |
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) |
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with gr.Row(): |
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best_case = get_dist_gap_timeline_plotly( |
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best_market_id, live_markets_data |
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) |
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with gr.Row(): |
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gr.Markdown("Worst case: a market with a high distribution gap metric") |
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with gr.Row(): |
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gr.Markdown( |
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f"Market id = {worst_market_id} Dist gap = {round(worst_gap,2)}" |
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) |
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with gr.Row(): |
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worst_case = get_dist_gap_timeline_plotly( |
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worst_market_id, live_markets_data |
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) |
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with gr.Row(): |
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gr.Markdown( |
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"Time evolution of the average distribution gap percentage of markets created the same day" |
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) |
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with gr.Row(): |
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mean_plot = get_avg_gap_time_evolution_grouped_markets(markets_data) |
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with gr.TabItem("📏 Distribution gap metric for all markets"): |
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with gr.Row(): |
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gr.Markdown( |
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"This metric measures the difference between the probability distribution based on the tokens distribution and the one based on the price weighted distribution" |
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) |
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with gr.Row(): |
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gr.Markdown("# Density distribution") |
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with gr.Row(): |
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kde_plot = get_distribution_plot(markets_data) |
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with gr.Row(): |
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with gr.Column(min_width=350): |
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gr.Markdown("# Relationship with number of trades") |
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kde_trades_plot = get_kde_with_trades(markets_data) |
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with gr.Column(min_width=350): |
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gr.Markdown("# Relationship with total bet amount") |
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kde_total_bet_amount_plot = get_kde_with_total_bet_amount( |
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markets_data |
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) |
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with gr.Row(): |
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gr.Markdown( |
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"# Correlation analysis between the metric and market variables" |
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) |
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with gr.Row(): |
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correlation_plot = get_correlation_map(markets_data) |
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demo.queue(default_concurrency_limit=40).launch() |
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