Update app.py

app.py

@@ -1,48 +1,62 @@
 import os
 import gradio as gr
 import pandas as pd
-import matplotlib.pyplot as plt
 import numpy as np
+import plotly.graph_objs as go
 
 # Disable Gradio analytics to avoid external API calls
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+try:
+    gr.analytics_enabled = False
+except AttributeError:
+    pass
 
-# Strategy Presets (risk_reward_ratio included per preset)
+# Strategy Presets with Descriptions
 strategy_presets = {
     "Aggressive Prop Trader": {
         "starting_balance": 2500, "trades_min": 5, "trades_max": 10, "weeks": 12,
-        "tp1_prob": 0.25, "tp2_prob": 0.4, "risk_reward_ratio": 1.2, "base_risk_pct": 0.015, "profit_target": None
+        "tp1_prob": 0.25, "tp2_prob": 0.4, "tp1_r": 1.2, "tp2_r": 2.4,
+        "base_risk_pct": 0.015, "profit_target": None,
+        "description": "Aggressive Prop Trader – High-frequency strategy with elevated risk. Targets larger returns with higher trade counts."
     },
     "Conservative Swing Trader": {
         "starting_balance": 2500, "trades_min": 2, "trades_max": 5, "weeks": 12,
-        "tp1_prob": 0.35, "tp2_prob": 0.25, "risk_reward_ratio": 0.9, "base_risk_pct": 0.01, "profit_target": None
+        "tp1_prob": 0.35, "tp2_prob": 0.25, "tp1_r": 0.9, "tp2_r": 1.8,
+        "base_risk_pct": 0.01, "profit_target": None,
+        "description": "Conservative Swing Trader – Lower trade frequency with modest risk. Prioritizes capital preservation."
     },
     "Momentum Scalper": {
         "starting_balance": 2500, "trades_min": 4, "trades_max": 8, "weeks": 12,
-        "tp1_prob": 0.3, "tp2_prob": 0.35, "risk_reward_ratio": 1.0, "base_risk_pct": 0.012, "profit_target": None
+        "tp1_prob": 0.3, "tp2_prob": 0.35, "tp1_r": 1.0, "tp2_r": 2.2,
+        "base_risk_pct": 0.012, "profit_target": None,
+        "description": "Momentum Scalper – Fast-paced intraday strategy capturing micro trends. Moderate volume, moderate risk."
     },
     "Swing Sniper": {
         "starting_balance": 2500, "trades_min": 2, "trades_max": 4, "weeks": 12,
-        "tp1_prob": 0.2, "tp2_prob": 0.5, "risk_reward_ratio": 1.1, "base_risk_pct": 0.008, "profit_target": None
+        "tp1_prob": 0.2, "tp2_prob": 0.5, "tp1_r": 1.1, "tp2_r": 3.0,
+        "base_risk_pct": 0.008, "profit_target": None,
+        "description": "Swing Sniper – Patient setup-driven trades targeting high reward-to-risk setups. Lower frequency."
     },
     "Intraday Prop Mode": {
         "starting_balance": 2500, "trades_min": 3, "trades_max": 7, "weeks": 12,
-        "tp1_prob": 0.3, "tp2_prob": 0.3, "risk_reward_ratio": 1.0, "base_risk_pct": 0.02, "profit_target": None
+        "tp1_prob": 0.3, "tp2_prob": 0.3, "tp1_r": 1.0, "tp2_r": 2.0,
+        "base_risk_pct": 0.02, "profit_target": None,
+        "description": "Intraday Prop Mode – Balanced approach to active trading. Good for consistent volume without extreme drawdowns."
     }
 }
 
+# Risk scaling
 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
 
+# Simulation core
 def simulate_tp_strategy_full(starting_balance, trades_min, trades_max, weeks,
-                              tp1_prob, tp2_prob, risk_reward_ratio, base_risk_pct,
+                              tp1_prob, tp2_prob, tp1_r, tp2_r, base_risk_pct,
                               profit_target=None):
     sl_prob = 1.0 - tp1_prob - tp2_prob
-    tp1_r = risk_reward_ratio
-    tp2_r = risk_reward_ratio * 2
     balance = starting_balance
     peak = balance
     drawdown = 0
@@ -80,16 +94,44 @@ def simulate_tp_strategy_full(starting_balance, trades_min, trades_max, weeks,
             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)})
-
-    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}
+        log.append({
+            "Week": week, "Start Balance": round(week_start, 2),
+            "End Balance": round(balance, 2), "Weekly Return (%)": round(weekly_return, 2)
+        })
+
+    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
+    }
     return pd.DataFrame(log), summary
 
+# For Preset tab
+def run_preset_strategy(style):
+    config = strategy_presets[style]
+    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"]
+    )
+    return df, plot_equity(df), config["description"]
+
+# Plot equity
+def plot_equity(df):
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df["Week"], y=df["End Balance"],
+                             mode='lines+markers', name='Equity Curve'))
+    fig.update_layout(title='📈 Equity Curve', xaxis_title='Week', yaxis_title='Balance')
+    return fig
+
+# Analytics leaderboard
 def analytics_dashboard():
     leaderboard = []
     for name, config in strategy_presets.items():
-        df, summary = simulate_tp_strategy_full(**config)
+        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"]
+        )
         returns = df['End Balance'].pct_change().dropna()
         volatility = returns.std() * np.sqrt(52)
         sharpe = returns.mean() / returns.std() * np.sqrt(52) if returns.std() > 0 else 0
@@ -98,93 +140,48 @@ def analytics_dashboard():
         max_dd = dd.max() * 100 if not dd.empty else 0
         final = df['End Balance'].iloc[-1] if not df.empty else config['starting_balance']
         score = final / (1 + max_dd)
-        leaderboard.append({"Strategy": name, "Final Balance": round(final, 2), "Max DD %": round(max_dd, 2), "Sharpe": round(sharpe, 2), "EdgeCast Score": round(score, 2)})
+        leaderboard.append({
+            "Strategy": name, "Final Balance": round(final, 2), "Max DD %": round(max_dd, 2),
+            "Sharpe": round(sharpe, 2), "EdgeCast Score": round(score, 2)
+        })
     return pd.DataFrame(leaderboard).sort_values("EdgeCast Score", ascending=False).reset_index(drop=True)
 
-def battle_strategies(style1, style2):
-    p1 = strategy_presets[style1]
-    p2 = strategy_presets[style2]
-    df1, s1 = simulate_tp_strategy_full(**p1)
-    df2, s2 = simulate_tp_strategy_full(**p2)
-
-    def enrich_summary(df, summary):
-        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
-        peak_balance = df['End Balance'].cummax()
-        drawdowns = (peak_balance - df['End Balance']) / peak_balance
-        max_drawdown = drawdowns.max() * 100 if not drawdowns.empty else 0
-        final_balance = df['End Balance'].iloc[-1] if not df.empty else df['End Balance'].iloc[0]
-        edgecast_score = final_balance / (1 + max_drawdown)
-        summary["Volatility (Annualized)"] = round(volatility * 100, 2)
-        summary["Sharpe Ratio"] = round(sharpe_ratio, 2)
-        summary["Max Drawdown %"] = round(max_drawdown, 2)
-        summary["EdgeCast Score"] = round(edgecast_score, 2)
-        return summary
-
-    s1 = enrich_summary(df1, s1)
-    s2 = enrich_summary(df2, s2)
-
-    def badge(score):
-        if score >= 30000: return "💎 Elite"
-        elif score >= 20000: return "🥇 Pro"
-        elif score >= 10000: return "📈 Growing"
-        else: return "🟡 Developing"
-
-    badge1 = badge(s1["EdgeCast Score"])
-    badge2 = badge(s2["EdgeCast Score"])
-
-    plt.figure(figsize=(9, 5))
-    plt.plot(df1['Week'], df1['End Balance'], label=f"{style1} ({badge1})", marker='o')
-    plt.plot(df2['Week'], df2['End Balance'], label=f"{style2} ({badge2})", marker='o')
-    plt.title(f"🎨 Strategy Battle – {style1} vs {style2}")
-    plt.xlabel("Week")
-    plt.ylabel("Account Balance")
-    plt.grid(True)
-    plt.legend()
-    plt.tight_layout()
-    plot_path = "battle_chart.png"
-    plt.savefig(plot_path)
-    plt.close()
-
-    df_compare = pd.DataFrame({"Metric": list(s1.keys()) + ["Badge"], style1: list(s1.values()) + [badge1], style2: list(s2.values()) + [badge2]})
-    return df_compare, plot_path
-
-# Interface Setup
-gr.TabbedInterface(
-    interface_list=[
-        gr.Interface(fn=lambda style: simulate_tp_strategy_full(**strategy_presets[style]),
-                     inputs=gr.Dropdown(choices=list(strategy_presets.keys()), label="Choose Preset Strategy"),
-                     outputs=["dataframe", "json"], title="🌟 Preset Mode"),
-
-        gr.Interface(fn=simulate_tp_strategy_full,
-                     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, 20, 2.0, step=0.1, label="Risk Reward Ratio"),
-                         gr.Slider(0.001, 0.05, 0.01, step=0.001, label="Risk %"),
-                         gr.Slider(0, 100000, 0, step=500, label="Profit Target 💰")
-                     ],
-                     outputs=["dataframe", "json"],
-                     title="🛠️ Manual Config"),
-
-        gr.Interface(fn=battle_strategies,
-                     inputs=[
-                         gr.Dropdown(choices=list(strategy_presets.keys()), label="Strategy 1"),
-                         gr.Dropdown(choices=list(strategy_presets.keys()), label="Strategy 2")
-                     ],
-                     outputs=["dataframe", gr.Image(type="filepath")],
-                     title="🎨 Battle Mode"),
-
-        gr.Interface(fn=analytics_dashboard,
-                     inputs=[],
-                     outputs="dataframe",
-                     title="📊 Analytics")
+# Gradio UI
+preset_tab = gr.Interface(
+    fn=run_preset_strategy,
+    inputs=gr.Dropdown(choices=list(strategy_presets.keys()), value="Aggressive Prop Trader", label="Choose Preset Strategy"),
+    outputs=["dataframe", gr.Plot(), gr.Textbox(label="📘 Strategy Description")],
+    title="🌟 Preset Mode"
+)
+
+manual_tab = gr.Interface(
+    fn=simulate_tp_strategy_full,
+    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, 20, 2.0, step=0.1, label="TP1 Reward"),
+        gr.Slider(0.1, 20, 4.0, step=0.1, label="TP2 Reward"),
+        gr.Slider(0.001, 0.05, 0.01, step=0.001, label="Risk %"),
+        gr.Slider(0, 100000, 0, step=500, label="Profit Target 💰")
     ],
-    tab_names=["Preset", "Manual", "Battle", "Analytics"],
+    outputs=["dataframe", "json"],
+    title="🛠️ Manual Config"
+)
+
+analytics_tab = gr.Interface(
+    fn=analytics_dashboard,
+    inputs=[],
+    outputs="dataframe",
+    title="📊 Analytics"
+)
+
+# Main Launch
+gr.TabbedInterface(
+    interface_list=[preset_tab, manual_tab, analytics_tab],
+    tab_names=["Preset", "Manual", "Analytics"],
     title="EdgeCast – Strategy Simulation Suite"
 ).launch()