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from datetime import datetime, timedelta |
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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.trades import ( |
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prepare_trades, |
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get_overall_trades, |
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get_overall_winning_trades, |
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plot_trades_by_week, |
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plot_winning_trades_by_week, |
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plot_trade_details |
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) |
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from tabs.tool_win import ( |
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get_tool_winning_rate, |
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get_overall_winning_rate, |
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plot_tool_winnings_overall, |
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plot_tool_winnings_by_tool |
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) |
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from tabs.error import ( |
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get_error_data, |
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get_error_data_overall, |
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plot_error_data, |
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plot_tool_error_data, |
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plot_week_error_data |
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) |
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from tabs.about import about_olas_predict |
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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('%(asctime)s - %(name)s - %(levelname)s - %(message)s') |
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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 get_last_one_month_data(): |
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""" |
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Get the last one month data from the tools.parquet file |
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""" |
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logger.info("Getting last one month data") |
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con = duckdb.connect(':memory:') |
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one_months_ago = (datetime.now() - timedelta(days=60)).strftime('%Y-%m-%d') |
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query2 = f""" |
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SELECT * |
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FROM read_parquet('./data/all_trades_profitability.parquet') |
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WHERE creation_timestamp >= '{one_months_ago}' |
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""" |
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df2 = con.execute(query2).fetchdf() |
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logger.info("Got last one month data from all_trades_profitability.parquet") |
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query1 = f""" |
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SELECT * |
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FROM read_parquet('./data/tools.parquet') |
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WHERE request_time >= '{one_months_ago}' |
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""" |
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df1 = con.execute(query1).fetchdf() |
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logger.info("Got last one month data from tools.parquet") |
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con.close() |
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return df1, df2 |
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def prepare_data(): |
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""" |
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Prepare the data for the dashboard |
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""" |
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tools_df, trades_df = get_last_one_month_data() |
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tools_df['request_time'] = pd.to_datetime(tools_df['request_time']) |
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trades_df['creation_timestamp'] = pd.to_datetime(trades_df['creation_timestamp']) |
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trades_df = prepare_trades(trades_df) |
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return tools_df, trades_df |
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tools_df, trades_df = prepare_data() |
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demo = gr.Blocks() |
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INC_TOOLS = [ |
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'prediction-online', |
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'prediction-offline', |
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'claude-prediction-online', |
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'claude-prediction-offline', |
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'prediction-offline-sme', |
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'prediction-online-sme', |
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'prediction-request-rag', |
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'prediction-request-reasoning', |
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'prediction-url-cot-claude', |
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'prediction-request-rag-claude', |
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'prediction-request-reasoning-claude' |
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] |
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error_df = get_error_data( |
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tools_df=tools_df, |
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inc_tools=INC_TOOLS |
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) |
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error_overall_df = get_error_data_overall( |
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error_df=error_df |
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) |
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winning_rate_df = get_tool_winning_rate( |
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tools_df=tools_df, |
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inc_tools=INC_TOOLS |
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) |
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winning_rate_overall_df = get_overall_winning_rate( |
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wins_df=winning_rate_df |
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) |
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trades_count_df = get_overall_trades( |
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trades_df=trades_df |
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) |
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trades_winning_rate_df = get_overall_winning_trades( |
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trades_df=trades_df |
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) |
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with demo: |
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gr.HTML("<h1>Olas Predict Actual Performance</h1>") |
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gr.Markdown("This app shows the actual performance of Olas Predict tools on the live market.") |
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with gr.Tabs(): |
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with gr.TabItem("🔥Trades Dashboard"): |
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with gr.Row(): |
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gr.Markdown("# Plot of number of trades by week") |
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with gr.Row(): |
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trades_by_week_plot = plot_trades_by_week( |
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trades_df=trades_count_df |
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) |
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with gr.Row(): |
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gr.Markdown("# Plot of winning trades by week") |
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with gr.Row(): |
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winning_trades_by_week_plot = plot_winning_trades_by_week( |
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trades_df=trades_winning_rate_df |
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) |
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with gr.Row(): |
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gr.Markdown("# Plot of trade details") |
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with gr.Row(): |
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trade_details_selector = gr.Dropdown( |
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label="Select a trade", |
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choices=[ |
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"mech calls", |
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"collateral amount", |
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"earnings", |
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"net earnings", |
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"ROI" |
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], |
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value="mech calls" |
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) |
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with gr.Row(): |
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trade_details_plot = plot_trade_details( |
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trade_detail="mech calls", |
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trades_df=trades_df |
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) |
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def update_trade_details(trade_detail): |
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return plot_trade_details( |
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trade_detail=trade_detail, |
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trades_df=trades_df |
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) |
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trade_details_selector.change( |
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update_trade_details, |
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inputs=trade_details_selector, |
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outputs=trade_details_plot |
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) |
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with gr.Row(): |
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trade_details_selector |
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with gr.Row(): |
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trade_details_plot |
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with gr.TabItem("🚀 Tool Winning Dashboard"): |
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with gr.Row(): |
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gr.Markdown("# Plot showing overall winning rate") |
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with gr.Row(): |
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winning_selector = gr.Dropdown( |
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label="Select Metric", |
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choices=['losses', 'wins', 'total_request', 'win_perc'], |
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value='win_perc', |
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) |
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with gr.Row(): |
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winning_plot = plot_tool_winnings_overall( |
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wins_df=winning_rate_overall_df, |
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winning_selector="win_perc" |
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) |
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def update_tool_winnings_overall_plot(winning_selector): |
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return plot_tool_winnings_overall( |
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wins_df=winning_rate_overall_df, |
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winning_selector=winning_selector |
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) |
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winning_selector.change( |
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update_tool_winnings_overall_plot, |
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inputs=winning_selector, |
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outputs=winning_plot |
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) |
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with gr.Row(): |
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winning_selector |
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with gr.Row(): |
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winning_plot |
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with gr.Row(): |
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gr.Markdown("# Plot showing winning rate by tool") |
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with gr.Row(): |
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sel_tool = gr.Dropdown( |
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label="Select a tool", |
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choices=INC_TOOLS, |
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value=INC_TOOLS[0] |
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) |
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with gr.Row(): |
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tool_winnings_by_tool_plot = plot_tool_winnings_by_tool( |
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wins_df=winning_rate_df, |
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tool=INC_TOOLS[0] |
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) |
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def update_tool_winnings_by_tool_plot(tool): |
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return plot_tool_winnings_by_tool( |
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wins_df=winning_rate_df, |
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tool=tool |
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) |
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sel_tool.change( |
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update_tool_winnings_by_tool_plot, |
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inputs=sel_tool, |
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outputs=tool_winnings_by_tool_plot |
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) |
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with gr.Row(): |
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sel_tool |
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with gr.Row(): |
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tool_winnings_by_tool_plot |
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with gr.TabItem("🏥 Tool Error Dashboard"): |
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with gr.Row(): |
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gr.Markdown("# Plot showing overall error") |
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with gr.Row(): |
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error_overall_plot = plot_error_data( |
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error_all_df=error_overall_df |
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) |
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with gr.Row(): |
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gr.Markdown("# Plot showing error by tool") |
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with gr.Row(): |
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sel_tool = gr.Dropdown( |
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label="Select a tool", |
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choices=INC_TOOLS, |
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value=INC_TOOLS[0] |
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) |
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with gr.Row(): |
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tool_error_plot = plot_tool_error_data( |
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error_df=error_df, |
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tool=INC_TOOLS[0] |
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) |
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def update_tool_error_plot(tool): |
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return plot_tool_error_data( |
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error_df=error_df, |
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tool=tool |
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) |
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sel_tool.change( |
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update_tool_error_plot, |
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inputs=sel_tool, |
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outputs=tool_error_plot |
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) |
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with gr.Row(): |
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sel_tool |
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with gr.Row(): |
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tool_error_plot |
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with gr.Row(): |
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gr.Markdown("# Plot showing error by week") |
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with gr.Row(): |
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choices = error_overall_df['request_month_year_week'].unique().tolist() |
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choices = sorted(choices) |
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sel_week = gr.Dropdown( |
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label="Select a week", |
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choices=choices, |
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value=choices[-1] |
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) |
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with gr.Row(): |
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week_error_plot = plot_week_error_data( |
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error_df=error_df, |
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week=choices[-1] |
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) |
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def update_week_error_plot(selected_week): |
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return plot_week_error_data( |
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error_df=error_df, |
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week=selected_week |
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) |
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sel_tool.change(update_tool_error_plot, inputs=sel_tool, outputs=tool_error_plot) |
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sel_week.change(update_week_error_plot, inputs=sel_week, outputs=week_error_plot) |
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with gr.Row(): |
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sel_tool |
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with gr.Row(): |
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tool_error_plot |
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with gr.Row(): |
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sel_week |
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with gr.Row(): |
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week_error_plot |
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with gr.TabItem("ℹ️ About"): |
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with gr.Accordion("About Olas Predict"): |
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gr.Markdown(about_olas_predict) |
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demo.queue(default_concurrency_limit=40).launch() |
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