Update app.py

app.py

@@ -53,6 +53,140 @@ def get_strategy_presets():
 
 strategy_presets = get_strategy_presets()
 
+# === CORE SIMULATION ===
+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, peak, drawdown = starting_balance, starting_balance, 0
+    tp1_hits = tp2_hits = sl_hits = 0
+    max_win_streak = max_loss_streak = cur_win_streak = cur_loss_streak = 0
+    fatigue_multiplier = 1.0 - fatigue * 0.4
+    trump_vol_factor = np.random.normal(1.0, 0.2 * trump_vol)
+    log = []
+
+    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_amount = balance * base_risk_pct * np.random.uniform(0.9, 1.1)
+            risk_amount *= trump_vol_factor
+            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)
+
+        log.append({
+            "Week": week, "Start Balance": round(week_start, 2),
+            "End Balance": round(balance, 2),
+            "Weekly Return (%)": round((balance - week_start) / week_start * 100, 2)
+        })
+
+    df = pd.DataFrame(log)
+    returns = df["End Balance"].pct_change().dropna()
+    sharpe = 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, 2),
+        "EdgeCast Score": round(score, 2)
+    }
+    return df, summary
+
+# === VISUALIZATION ===
+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
+
+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 ===
+def get_strategy_presets():
+    return {
+        "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."
+        }
+    }
+
+strategy_presets = get_strategy_presets()
+
 # === CORE SIMULATION ===
 def simulate_tp_strategy_full(starting_balance, trades_min, trades_max, weeks,
                               tp1_prob, tp2_prob, tp1_r, tp2_r, base_risk_pct,