Update app.py

app.py

@@ -7,7 +7,7 @@ import numpy as np
 # Disable Gradio analytics to avoid external API calls
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
 
-# 📌 Strategy Presets
+# Strategy Presets (risk_reward_ratio included per preset)
 strategy_presets = {
     "Aggressive Prop Trader": {
         "starting_balance": 2500, "trades_min": 5, "trades_max": 10, "weeks": 12,
@@ -31,8 +31,6 @@ strategy_presets = {
     }
 }
 
-
-# 🔁 Simulation Function
 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
@@ -44,8 +42,7 @@ def simulate_tp_strategy_full(starting_balance, trades_min, trades_max, weeks,
                               profit_target=None):
     sl_prob = 1.0 - tp1_prob - tp2_prob
     tp1_r = risk_reward_ratio
-    tp2_r = risk_reward_ratio * 2  # TP2 is set as 2x TP1 (modify as desired)
-
+    tp2_r = risk_reward_ratio * 2
     balance = starting_balance
     peak = balance
     drawdown = 0
@@ -83,23 +80,16 @@ 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
+
 def analytics_dashboard():
     leaderboard = []
     for name, config in strategy_presets.items():
-        df, summary = simulate_tp_strategy_full(**config, risk_reward_ratio=2)
+        df, summary = simulate_tp_strategy_full(**config)
         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
@@ -108,46 +98,14 @@ 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)
 
-
-# UI
-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="Risk Reward Ratio"),  # Updated
-        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",
-)
-
-
-preset_tab = gr.Interface(
-    fn=lambda style: simulate_tp_strategy_full(**strategy_presets[style], risk_reward_ratio=2),
-    inputs=gr.Dropdown(choices=list(strategy_presets.keys()), label="Choose Preset Strategy"),
-    outputs=["dataframe", "json"],
-    title="🎯 Preset Mode"
-)
 def battle_strategies(style1, style2):
     p1 = strategy_presets[style1]
     p2 = strategy_presets[style2]
-    df1, s1 = simulate_tp_strategy_full(**p1, risk_reward_ratio=2)
-    df2, s2 = simulate_tp_strategy_full(**p2, risk_reward_ratio=2)
+    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()
@@ -179,7 +137,7 @@ def battle_strategies(style1, style2):
     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.title(f"🎨 Strategy Battle – {style1} vs {style2}")
     plt.xlabel("Week")
     plt.ylabel("Account Balance")
     plt.grid(True)
@@ -189,27 +147,44 @@ def battle_strategies(style1, style2):
     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]
-    })
-
+    df_compare = pd.DataFrame({"Metric": list(s1.keys()) + ["Badge"], style1: list(s1.values()) + [badge1], style2: list(s2.values()) + [badge2]})
     return df_compare, plot_path
 
-
-battle_tab = 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"
-)
-
-analytics_tab = gr.Interface(fn=analytics_dashboard, inputs=[], outputs="dataframe", title="📊 Analytics")
-
-# Main App Interface
+# Interface Setup
 gr.TabbedInterface(
-    interface_list=[preset_tab, manual_tab, battle_tab, analytics_tab],
-    tab_names=["🎯 Preset", "🛠️ Manual", "🥊 Battle", "📊 Analytics"],
+    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")
+    ],
+    tab_names=["Preset", "Manual", "Battle", "Analytics"],
     title="EdgeCast – Strategy Simulation Suite"
 ).launch()