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weekly-analysis
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import gradio as gr
import pandas as pd


def prepare_trades(trades_df):
    trades_df['creation_timestamp'] = pd.to_datetime(trades_df['creation_timestamp'])
    trades_df['month_year'] = trades_df['creation_timestamp'].dt.to_period('M').astype(str)
    trades_df['month_year_week'] = trades_df['creation_timestamp'].dt.to_period('W').astype(str)
    trades_df['winning_trade'] = trades_df['winning_trade'].astype(int)
    return trades_df


def get_overall_trades(trades_df):
    trades_count = trades_df.groupby('month_year_week').size().reset_index()
    trades_count.columns = trades_count.columns.astype(str)
    trades_count.rename(columns={'0': 'trades'}, inplace=True)
    return trades_count

def get_overall_winning_trades(trades_df):
    winning_trades = trades_df.groupby(['month_year_week'])['winning_trade'].sum() / trades_df.groupby(['month_year_week'])['winning_trade'].count() * 100
    # winning_trades is a series, give it a dataframe
    winning_trades = winning_trades.reset_index()
    winning_trades.columns = winning_trades.columns.astype(str)
    winning_trades.columns = ['month_year_week', 'winning_trade']
    return winning_trades

def plot_trade_details(trade_detail, trades_df):
    if trade_detail == "mech calls":
        # this is to filter out the data before 2023-09-01
        trades_filtered = trades_df[trades_df["creation_timestamp"] >"2023-09-01"]
        trades_filtered = trades_filtered.groupby("month_year_week")["num_mech_calls"].quantile([0.25, 0.5, 0.75]).unstack()
        trades_filtered.columns = trades_filtered.columns.astype(str)
        trades_filtered.reset_index(inplace=True)
        trades_filtered.columns = [
            "month_year_week",
            "25th_percentile",
            "50th_percentile",
            "75th_percentile"
        ]
        # reformat the data as percentile, date, value
        trades_filtered = trades_filtered.melt(id_vars=["month_year_week"], var_name="percentile", value_name="mech_calls")

        return gr.LinePlot(
            value=trades_filtered,
            x="month_year_week",
            y="mech_calls",
            color="percentile",
            show_label=True,
            interactive=True,
            show_actions_button=True,
            tooltip=["month_year_week", "percentile", "mech_calls"]
        )
    
    if trade_detail == "collateral amount":
        trades_filtered = trades_df[trades_df["creation_timestamp"] >"2023-09-01"]
        trades_filtered = trades_filtered.groupby("month_year_week")["collateral_amount"].quantile([0.25, 0.5, 0.75]).unstack()
        trades_filtered.columns = trades_filtered.columns.astype(str)
        trades_filtered.reset_index(inplace=True)
        trades_filtered.columns = [
            "month_year_week",
            "25th_percentile",
            "50th_percentile",
            "75th_percentile"
        ]
        # reformat the data as percentile, date, value
        trades_filtered = trades_filtered.melt(id_vars=["month_year_week"], var_name="percentile", value_name="collateral_amount")

        return gr.LinePlot(
            value=trades_filtered,
            x="month_year_week",
            y="collateral_amount",
            color="percentile",
            show_label=True,
            interactive=True,
            show_actions_button=True,
            tooltip=["month_year_week", "percentile", "collateral_amount"]
        )

    if trade_detail == "earnings":
        trades_filtered = trades_df[trades_df["creation_timestamp"] >"2023-09-01"]
        trades_filtered = trades_filtered.groupby("month_year_week")["earnings"].quantile([0.25, 0.5, 0.75]).unstack()
        trades_filtered.columns = trades_filtered.columns.astype(str)
        trades_filtered.reset_index(inplace=True)
        trades_filtered.columns = [
            "month_year_week",
            "25th_percentile",
            "50th_percentile",
            "75th_percentile"
        ]
        # reformat the data as percentile, date, value
        trades_filtered = trades_filtered.melt(id_vars=["month_year_week"], var_name="percentile", value_name="earnings")

        return gr.LinePlot(
            value=trades_filtered,
            x="month_year_week",
            y="earnings",
            color="percentile",
            show_label=True,
            interactive=True,
            show_actions_button=True,
            tooltip=["month_year_week", "percentile", "earnings"]
        )
    
    if trade_detail == "net earnings":
        trades_filtered = trades_df[trades_df["creation_timestamp"] >"2023-09-01"]
        trades_filtered = trades_filtered.groupby("month_year_week")["net_earnings"].quantile([0.25, 0.5, 0.75]).unstack()
        trades_filtered.columns = trades_filtered.columns.astype(str)
        trades_filtered.reset_index(inplace=True)
        trades_filtered.columns = [
            "month_year_week",
            "25th_percentile",
            "50th_percentile",
            "75th_percentile"
        ]
        # reformat the data as percentile, date, value
        trades_filtered = trades_filtered.melt(id_vars=["month_year_week"], var_name="percentile", value_name="net_earnings")

        return gr.LinePlot(
            value=trades_filtered,
            x="month_year_week",
            y="net_earnings",
            color="percentile",
            show_label=True,
            interactive=True,
            show_actions_button=True,
            tooltip=["month_year_week", "percentile", "net_earnings"]
        )        
    
    if trade_detail == "ROI":
        trades_filtered = trades_df[trades_df["creation_timestamp"] >"2023-09-01"]
        trades_filtered = trades_filtered.groupby("month_year_week")["roi"].quantile([0.25, 0.5, 0.75]).unstack()
        trades_filtered.columns = trades_filtered.columns.astype(str)
        trades_filtered.reset_index(inplace=True)
        trades_filtered.columns = [
            "month_year_week",
            "25th_percentile",
            "50th_percentile",
            "75th_percentile"
        ]
        # reformat the data as percentile, date, value
        trades_filtered = trades_filtered.melt(id_vars=["month_year_week"], var_name="percentile", value_name="ROI")

        return gr.LinePlot(
            value=trades_filtered,
            x="month_year_week",
            y="ROI",
            color="percentile",
            show_label=True,
            interactive=True,
            show_actions_button=True,
            tooltip=["month_year_week", "percentile", "ROI"]
        )    
    
def plot_trades_by_week(trades_df):
    return gr.BarPlot(
        value=trades_df,
        x="month_year_week",
        y="trades",
        show_label=True,
        interactive=True,
        show_actions_button=True,
        tooltip=["month_year_week", "trades"]
    )

def plot_winning_trades_by_week(trades_df):
    return gr.BarPlot(
        value=trades_df,
        x="month_year_week",
        y="winning_trade",
        show_label=True,
        interactive=True,
        show_actions_button=True,
        tooltip=["month_year_week", "winning_trade"]
    )