import os

import gradio as gr

import pandas as pd

import numpy as np

import plotly.graph_objs as go

import plotly.express as px




os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"

try:

    gr.analytics_enabled = False

except:

    pass




# 🔁 Strategy Presets

strategy_presets = {

    "Aggressive Prop Trader": {

        "starting_balance": 2500, "trades_min": 5, "trades_max": 10, "weeks": 12,

        "tp1_prob": 0.25, "tp2_prob": 0.4, "tp1_r": 1.2, "tp2_r": 2.4,

        "base_risk_pct": 0.015, "profit_target": None,

        "fatigue": 0.0, "trump_vol": 0.0,

        "description": "High-frequency, high-risk with strong upside potential."

    },

    "Conservative Swing Trader": {

        "starting_balance": 2500, "trades_min": 2, "trades_max": 5, "weeks": 12,

        "tp1_prob": 0.35, "tp2_prob": 0.25, "tp1_r": 0.9, "tp2_r": 1.8,

        "base_risk_pct": 0.01, "profit_target": None,

        "fatigue": 0.0, "trump_vol": 0.0,

        "description": "Lower frequency, prioritizes preservation and steady returns."

    },

    "Momentum Scalper": {

        "starting_balance": 2500, "trades_min": 4, "trades_max": 8, "weeks": 12,

        "tp1_prob": 0.3, "tp2_prob": 0.35, "tp1_r": 1.0, "tp2_r": 2.2,

        "base_risk_pct": 0.012, "profit_target": None,

        "fatigue": 0.0, "trump_vol": 0.0,

        "description": "Intraday momentum strategy for fast-paced trading windows."

    },

    "Swing Sniper": {

        "starting_balance": 2500, "trades_min": 2, "trades_max": 4, "weeks": 12,

        "tp1_prob": 0.2, "tp2_prob": 0.5, "tp1_r": 1.1, "tp2_r": 3.0,

        "base_risk_pct": 0.008, "profit_target": None,

        "fatigue": 0.0, "trump_vol": 0.0,

        "description": "Selective entries with high RR setups. Less frequent."

    },

    "Intraday Prop Mode": {

        "starting_balance": 2500, "trades_min": 3, "trades_max": 7, "weeks": 12,

        "tp1_prob": 0.3, "tp2_prob": 0.3, "tp1_r": 1.0, "tp2_r": 2.0,

        "base_risk_pct": 0.02, "profit_target": None,

        "fatigue": 0.0, "trump_vol": 0.0,

        "description": "Intraday consistency with a balanced reward profile."

    }

}

def get_scaled_risk_pct(balance, base_risk_pct):

    if balance < 5000:

        return base_risk_pct

    elif balance < 10000:

        return base_risk_pct * 0.75

    elif balance < 20000:

        return base_risk_pct * 0.5

    else:

        return base_risk_pct * 0.25




def simulate_tp_strategy_full(starting_balance, trades_min, trades_max, weeks,

                              tp1_prob, tp2_prob, tp1_r, tp2_r, base_risk_pct,

                              profit_target=None, fatigue=0.0, trump_vol=0.0):

    if tp1_prob + tp2_prob >= 1.0:

        return pd.DataFrame(), {"Error": "Invalid probability config. TP1 + TP2 must be < 1.0"}




    sl_prob = 1.0 - tp1_prob - tp2_prob

    balance = starting_balance

    peak = balance

    drawdown = 0

    tp1_hits = tp2_hits = sl_hits = 0

    max_win_streak = max_loss_streak = 0

    cur_win_streak = cur_loss_streak = 0

    log = []




    fatigue_multiplier = 1.0 - fatigue * 0.4  # Reduce reward at high fatigue

    trump_vol_factor = np.random.normal(1.0, 0.2 * trump_vol)  # Adds chaos




    for week in range(1, weeks + 1):

        if profit_target and balance >= profit_target: break

        week_start = balance

        num_trades = np.random.randint(trades_min, trades_max + 1)

        for _ in range(num_trades):

            risk_pct = get_scaled_risk_pct(balance, base_risk_pct)

            risk_amount = balance * risk_pct * np.random.uniform(0.9, 1.1)  # Risk % w/ some variability

            risk_amount *= trump_vol_factor  # 🟠 Vol boost




            # Fatigue loss streak logic

            if fatigue > 0.6 and cur_loss_streak >= 3 and np.random.rand() < fatigue * 0.25:

                outcome = "SL"

            else:

                outcome = np.random.choice(["TP1", "TP2", "SL"], p=[tp1_prob, tp2_prob, sl_prob])




            if outcome == "TP1":

                balance += risk_amount * tp1_r * fatigue_multiplier

                tp1_hits += 1

                cur_win_streak += 1

                cur_loss_streak = 0

            elif outcome == "TP2":

                balance += risk_amount * tp2_r * fatigue_multiplier

                tp2_hits += 1

                cur_win_streak += 1

                cur_loss_streak = 0

            else:

                balance -= risk_amount

                sl_hits += 1

                cur_loss_streak += 1

                cur_win_streak = 0




            max_win_streak = max(max_win_streak, cur_win_streak)

            max_loss_streak = max(max_loss_streak, cur_loss_streak)

            peak = max(peak, balance)

            drawdown = max(drawdown, (peak - balance) / peak * 100)




        weekly_return = (balance - week_start) / week_start * 100

        log.append({

            "Week": week, "Start Balance": round(week_start, 2),

            "End Balance": round(balance, 2),

            "Weekly Return (%)": round(weekly_return, 2)

        })




    df = pd.DataFrame(log)

    returns = df["End Balance"].pct_change().dropna()

    volatility = returns.std() * np.sqrt(52)

    sharpe_ratio = returns.mean() / returns.std() * np.sqrt(52) if returns.std() > 0 else 0

    score = balance / (1 + drawdown)




    summary = {

        "Final Balance": round(balance, 2),

        "TP1 Hits": tp1_hits,

        "TP2 Hits": tp2_hits,

        "SL Hits": sl_hits,

        "Max Drawdown %": round(drawdown, 2),

        "Max Win Streak": max_win_streak,

        "Max Loss Streak": max_loss_streak,

        "Sharpe Ratio": round(sharpe_ratio, 2),

        "EdgeCast Score": round(score, 2)

    }

    return df, summary

# 📈 Plot

def equity_curve_plot(df, label="Equity Curve"):

    fig = go.Figure()

    fig.add_trace(go.Scatter(x=df["Week"], y=df["End Balance"], mode='lines+markers', name=label))

    fig.update_layout(title=f'📈 {label}', xaxis_title='Week', yaxis_title='Balance ($)', height=400)

    return fig




# 🎯 Preset Tab

def run_preset_strategy_with_toggle(style, enable_fatigue, fatigue, enable_trump, trump_vol):
    config = strategy_presets[style]
    applied_fatigue = fatigue if enable_fatigue else 0.0
    applied_trump = trump_vol if enable_trump else 0.0

    df, summary = simulate_tp_strategy_full(
        config["starting_balance"], config["trades_min"], config["trades_max"], config["weeks"],
        config["tp1_prob"], config["tp2_prob"], config["tp1_r"], config["tp2_r"],
        config["base_risk_pct"], config["profit_target"], applied_fatigue, applied_trump
    )

    return df, summary, equity_curve_plot(df, style), config["description"]


# 🛠️ Manual Tab

def run_manual_sim(starting_balance, trades_min, trades_max, weeks,

                   tp1_prob, tp2_prob, tp1_r, tp2_r, base_risk_pct, profit_target,

                   fatigue, trump_vol):

    df, summary = simulate_tp_strategy_full(starting_balance, trades_min, trades_max, weeks,

                                            tp1_prob, tp2_prob, tp1_r, tp2_r, base_risk_pct,

                                            profit_target, fatigue, trump_vol)

    chart = equity_curve_plot(df, "Manual Config")

    return df, summary, chart


# ⚔️ Manual Battle Mode (Dual Sim)

def dual_manual_battle(

    sb1, tmin1, tmax1, w1, tp1a, tp2a, r1a, r2a, risk1, pt1, fat1, trump1,

    sb2, tmin2, tmax2, w2, tp1b, tp2b, r1b, r2b, risk2, pt2, fat2, trump2

):

    df1, s1 = simulate_tp_strategy_full(sb1, tmin1, tmax1, w1, tp1a, tp2a, r1a, r2a, risk1, pt1, fat1, trump1)

    df2, s2 = simulate_tp_strategy_full(sb2, tmin2, tmax2, w2, tp1b, tp2b, r1b, r2b, risk2, pt2, fat2, trump2)




    s1["Strategy"] = "Manual A"

    s2["Strategy"] = "Manual B"




    comparison_df = pd.DataFrame([s1, s2])

    comparison_df = comparison_df[["Strategy", "Final Balance", "Sharpe Ratio", "EdgeCast Score", "Max Drawdown %"]]




    # 🏆 Winners

    for col in ["Final Balance", "Sharpe Ratio", "EdgeCast Score"]:

        best_val = comparison_df[col].astype(float).max()

        comparison_df[col] = [

            f"{val} 🏆" if float(val) == best_val else f"{val}" for val in comparison_df[col]

        ]




    # Chart

    fig = go.Figure()

    fig.add_trace(go.Scatter(x=df1["Week"], y=df1["End Balance"], name="Manual A"))

    fig.add_trace(go.Scatter(x=df2["Week"], y=df2["End Balance"], name="Manual B"))

    fig.update_layout(title="⚔️ Manual Strategy Battle", xaxis_title="Week", yaxis_title="Balance")




    return comparison_df, fig

# 📊 Leaderboard Tab

def analytics_dashboard(rank_by="EdgeCast Score"):

    results = []

    for name, config in strategy_presets.items():

        _, summary = simulate_tp_strategy_full(**{k: v for k, v in config.items() if k != "description"})

        summary["Strategy"] = name

        results.append(summary)




    df = pd.DataFrame(results)




    # Get numeric winners before formatting

    winner_vals = {

        "Final Balance": df["Final Balance"].max(),

        "Sharpe Ratio": df["Sharpe Ratio"].max(),

        "EdgeCast Score": df["EdgeCast Score"].max(),

        "Max Drawdown %": df["Max Drawdown %"].min()

    }




    # Sort leaderboard by selected metric

    ascending = rank_by == "Max Drawdown %"

    df = df.sort_values(rank_by, ascending=ascending).reset_index(drop=True)




    # Rank column

    df.insert(0, "🏅 Rank", [f"#{i+1}" for i in df.index])




    # 🏆 Emoji highlights

    for col in winner_vals:

        df[col] = df[col].apply(lambda x: f"{round(x, 2)} 🏆" if x == winner_vals[col] else f"{round(x, 2)}")




    return df[["🏅 Rank", "Strategy", "Final Balance", "Sharpe Ratio", "EdgeCast Score", "Max Drawdown %"]]







# 📘 Description Tab

def show_descriptions():

    return pd.DataFrame([

        {"Strategy": name, "Description": config["description"]}

        for name, config in strategy_presets.items()

    ])







# 🔬 Risk Matrix Heatmap

def generate_risk_matrix():

    names = list(strategy_presets.keys())

    scores = {

        name: simulate_tp_strategy_full(**{k: v for k, v in cfg.items() if k != "description"})[1]["EdgeCast Score"]

        for name, cfg in strategy_presets.items()

    }

    matrix = np.zeros((len(names), len(names)))

    for i, a in enumerate(names):

        for j, b in enumerate(names):

            matrix[i, j] = abs(scores[a] - scores[b])




    fig = px.imshow(

        matrix,

        x=names,

        y=names,

        text_auto=".2f",

        color_continuous_scale="RdYlGn_r",

        labels={"color": "Score Δ"},

        title="🧠 Risk Matrix (Δ Score Heatmap)"

    )

    fig.update_traces(

        hovertemplate="<b>%{y}</b> vs <b>%{x}</b><br>Δ Score: %{z:.2f}<extra></extra>"

    )

    return fig







# 🥊 Battle Strategies (Preset vs Preset)

def battle_strategies(
    style1, enable_fatigue1, fatigue1, enable_trump1, trump1,
    style2, enable_fatigue2, fatigue2, enable_trump2, trump2
):
    if style1 == "None" or style2 == "None":
        return pd.DataFrame([{"⚠️ Error": "Please select two valid strategies."}]), go.Figure()

    if style1 == style2:
        return pd.DataFrame([{"⚠️ Error": "Please select two different strategies."}]), go.Figure()

    fatigue_a = fatigue1 if enable_fatigue1 else 0.0
    trump_a = trump1 if enable_trump1 else 0.0
    fatigue_b = fatigue2 if enable_fatigue2 else 0.0
    trump_b = trump2 if enable_trump2 else 0.0

    try:
        config1 = strategy_presets[style1]
        config2 = strategy_presets[style2]

        df1, s1 = simulate_tp_strategy_full(
            config1["starting_balance"], config1["trades_min"], config1["trades_max"], config1["weeks"],
            config1["tp1_prob"], config1["tp2_prob"], config1["tp1_r"], config1["tp2_r"],
            config1["base_risk_pct"], config1["profit_target"], fatigue_a, trump_a
        )

        df2, s2 = simulate_tp_strategy_full(
            config2["starting_balance"], config2["trades_min"], config2["trades_max"], config2["weeks"],
            config2["tp1_prob"], config2["tp2_prob"], config2["tp1_r"], config2["tp2_r"],
            config2["base_risk_pct"], config2["profit_target"], fatigue_b, trump_b
        )

        s1["Strategy"], s2["Strategy"] = style1, style2
        df_compare = pd.DataFrame([s1, s2])[["Strategy", "Final Balance", "Sharpe Ratio", "EdgeCast Score", "Max Drawdown %"]]

        for col in ["Final Balance", "Sharpe Ratio", "EdgeCast Score"]:
            best_val = df_compare[col].astype(float).max()
            df_compare[col] = [f"{val} 🏆" if float(val) == best_val else val for val in df_compare[col]]

        fig = go.Figure()
        fig.add_trace(go.Scatter(x=df1["Week"], y=df1["End Balance"], name=style1))
        fig.add_trace(go.Scatter(x=df2["Week"], y=df2["End Balance"], name=style2))
        fig.update_layout(title=f"🥊 {style1} vs {style2}", xaxis_title="Week", yaxis_title="Balance")

        return df_compare, fig

    except Exception as e:
        return pd.DataFrame([{"Error": str(e)}]), go.Figure()



# 🚀 App UI Launch

app = gr.TabbedInterface(

    interface_list=[

        # 🎯 Preset Mode

    gr.Interface(
        fn=run_preset_strategy_with_toggle,
    inputs=[
        gr.Dropdown(choices=list(strategy_presets.keys()), label="Select Strategy"),
        gr.Checkbox(label="Enable Fatigue"), 
        gr.Slider(0, 1, 0.0, step=0.1, label="Fatigue Level"),
        gr.Checkbox(label="Enable Trump Volatility"),
        gr.Slider(0, 1, 0.0, step=0.1, label="Trump Volatility Index")
    ],
    outputs=["dataframe", "json", gr.Plot(), "text"],
    title="🎯 Preset Mode"
),

        # 🛠️ Manual Config

        gr.Interface(

            fn=run_manual_sim,

            inputs=[

                gr.Slider(100, 20000, 2500, label="Start Balance"),

                gr.Slider(1, 10, 3, label="Trades Min"),

                gr.Slider(1, 15, 7, label="Trades Max"),

                gr.Slider(1, 52, 12, label="Weeks"),

                gr.Slider(0, 1, 0.3, step=0.05, label="TP1 %"),

                gr.Slider(0, 1, 0.3, step=0.05, label="TP2 %"),

                gr.Slider(0.1, 5.0, 1.0, step=0.1, label="TP1 R"),

                gr.Slider(0.1, 20.0, 2.0, step=0.1, label="TP2 R"),

                gr.Slider(0.001, 0.05, 0.01, step=0.001, label="Risk %"),

                gr.Slider(0, 100000, 0, step=500, label="Profit Target 💰"),

                gr.Slider(0, 1, 0.0, step=0.1, label="Fatigue Level"),

                gr.Slider(0, 1, 0.0, step=0.1, label="Trump Volatility Index")

            ],

            outputs=["dataframe", "json", gr.Plot()],

            title="🛠️ Manual Config"

        ),




        # 🥊 Battle Mode – Preset

       gr.Interface(
    fn=battle_strategies,
    inputs=[
        gr.Dropdown(choices=["None"] + list(strategy_presets.keys()), value="None", label="Strategy A"),
        gr.Checkbox(label="Enable Fatigue for A"),
        gr.Slider(0, 1, 0.0, step=0.1, label="Fatigue Level A"),
        gr.Checkbox(label="Enable Trump Volatility for A"),
        gr.Slider(0, 1, 0.0, step=0.1, label="Trump Volatility A"),

        gr.Dropdown(choices=["None"] + list(strategy_presets.keys()), value="None", label="Strategy B"),
        gr.Checkbox(label="Enable Fatigue for B"),
        gr.Slider(0, 1, 0.0, step=0.1, label="Fatigue Level B"),
        gr.Checkbox(label="Enable Trump Volatility for B"),
        gr.Slider(0, 1, 0.0, step=0.1, label="Trump Volatility B"),
    ],
    outputs=["dataframe", gr.Plot()],
    title="🥊 Battle Mode"
),


        # 🧪 Manual Battle Mode

        # 🧪 Manual Battle Mode (Slider Version)
gr.Interface(
    fn=dual_manual_battle,
    inputs=[
        # Config A
        gr.Slider(100, 20000, 2500, label="A: Start Balance"),
        gr.Slider(1, 10, 3, label="A: Trades Min"),
        gr.Slider(1, 15, 7, label="A: Trades Max"),
        gr.Slider(1, 52, 12, label="A: Weeks"),
        gr.Slider(0, 1, 0.3, step=0.05, label="A: TP1 %"),
        gr.Slider(0, 1, 0.3, step=0.05, label="A: TP2 %"),
        gr.Slider(0.1, 5.0, 1.0, step=0.1, label="A: TP1 R"),
        gr.Slider(0.1, 20.0, 2.0, step=0.1, label="A: TP2 R"),
        gr.Slider(0.001, 0.05, 0.01, step=0.001, label="A: Risk %"),
        gr.Slider(0, 100000, 0, step=500, label="A: Profit Target"),
        gr.Slider(0, 1, 0.0, step=0.1, label="A: Fatigue"),
        gr.Slider(0, 1, 0.0, step=0.1, label="A: Trump Volatility"),
        # Config B
        gr.Slider(100, 20000, 2500, label="B: Start Balance"),
        gr.Slider(1, 10, 3, label="B: Trades Min"),
        gr.Slider(1, 15, 7, label="B: Trades Max"),
        gr.Slider(1, 52, 12, label="B: Weeks"),
        gr.Slider(0, 1, 0.3, step=0.05, label="B: TP1 %"),
        gr.Slider(0, 1, 0.3, step=0.05, label="B: TP2 %"),
        gr.Slider(0.1, 5.0, 1.0, step=0.1, label="B: TP1 R"),
        gr.Slider(0.1, 20.0, 2.0, step=0.1, label="B: TP2 R"),
        gr.Slider(0.001, 0.05, 0.01, step=0.001, label="B: Risk %"),
        gr.Slider(0, 100000, 0, step=500, label="B: Profit Target"),
        gr.Slider(0, 1, 0.0, step=0.1, label="B: Fatigue"),
        gr.Slider(0, 1, 0.0, step=0.1, label="B: Trump Volatility")
    ],
    outputs=["dataframe", gr.Plot()],
    title="🧪 Manual Battle Mode"
),

        # 📊 Analytics Leaderboard

        gr.Interface(

            fn=analytics_dashboard,

            inputs=gr.Dropdown(

                choices=["EdgeCast Score", "Final Balance", "Sharpe Ratio", "Max Drawdown %"],

                value="EdgeCast Score",

                label="Sort leaderboard by:"

            ),

            outputs="dataframe",

            title="📊 Analytics Leaderboard"

        ),




        # 📘 Strategy Descriptions

        gr.Interface(

            fn=show_descriptions,

            inputs=[], outputs="dataframe",

            title="📘 Strategy Descriptions"

        ),




        # 🔬 Risk Matrix Heatmap

        gr.Interface(

            fn=generate_risk_matrix,

            inputs=[], outputs=gr.Plot(),

            title="🔬 Risk Matrix"

        )

    ],

    tab_names=["Preset", "Manual", "Battle", "Manual Battle", "Analytics", "Descriptions", "Risk Matrix"],

    title="EdgeCast – Strategy Simulation Suite"

)




app.launch()