add weekly and bi-weekly frequently

README.md

@@ -0,0 +1,91 @@
+There are 2 projects combined in this program. 
+First peogram : localhosr:5000/
+Data Analysis & Signal Generation layer that happens before the simulation even begins.
+
+Here is a summary of those specific capabilities:
+
+1. Automated Pricing Retrieval
+The program acts as a data bridge. Instead of you manually searching for prices, it can:
+
+Fetch real-time or historical data for specific tickers (like SPY).
+
+Maintain a local CSV database, ensuring you have a persistent record of price action to analyze without re-downloading everything every time.
+
+2. EMA (Exponential Moving Average) Calculation
+The program adds a layer of technical analysis to the raw prices:
+
+It calculates the EMA (typically a 200-day or 50-day trend line).
+
+Unlike a simple average, the EMA gives more weight to recent prices, making it more responsive to new market information.
+
+3. Price/EMA Ratio Analysis
+This is one of the program's most powerful analytical features. It generates a "Value Ratio" to help you understand market positioning:
+
+The Math: It divides the current price by the EMA.
+
+The Signal: * Ratio > 1.0: The asset is trading above its average (potentially overextended or in a strong uptrend).
+
+Ratio < 1.0: The asset is trading below its average (potentially undervalued or in a downtrend).
+
+Strategic Use: This ratio can be used to "tune" your DVA strategy—for example, investing more aggressively when the ratio is low and being more cautious when the ratio is high.
+
+4. Tabular Data Export
+All of this technical data—the raw price, the EMA, and the resulting Ratio—is organized into a clean, structured list. This allows you to:
+
+See a "Heat Map" of when the asset was historically cheap or expensive relative to its trend.
+
+Use these technical signals to justify the "Buy" or "Sell" recommendations the dashboard provides.
+Summary of the "Technical Stack"CapabilityBenefitPrice FetchingSaves time on manual data entry.EMA CalculationIdentifies the long-term "fair value" trend.Price/EMA RatioQuantifies exactly how "expensive" or "cheap" the market is today.
+Second Project ; localhost:5000/backtest 
+a Dynamic Value Averaging (DVA) Backtester. Its primary purpose is to help investors simulate a sophisticated "Value Averaging" strategy to determine exactly how much they should buy or sell of an asset (like SPY) on a specific day of the month to stay on track with a financial goal.
+
+Here is a summary of what the programs actually do:
+
+1. Automated Strategy Simulation
+The program takes a historical CSV file and "plays back" time.
+
+It identifies a Monthly Anchor Day (e.g., the 27th of every month) and simulates making a trade on that exact day.
+
+It calculates your Target Value Goal, which grows every month by a set amount (e.g., $500/month).
+
+2. Intelligent "Buy/Sell" Recommendations
+Instead of just buying a fixed amount (DCA), the program calculates the "Value Gap":
+
+If the market is down: It tells you to Buy More to reach your target.
+
+If the market is up: It tells you to Sell (or buy less) to lock in gains.
+
+It calculates the exact number of units/shares you need to trade based on the most recent price in your data.
+
+3. Real-Time Data Synchronization
+The program is designed to handle "incomplete" months:
+
+If today is the 28th but your anchor day is the 15th, the program doesn't just stop at the 15th.
+
+It forces the latest available data point from your CSV to be processed, ensuring your "Next Recommended Move" is based on the most recent price (e.g., yesterday’s close).
+
+4. Interactive Dashboard Reporting
+Once the simulation runs, the program visualizes the health of your investment:
+
+Target Value Goal: Where you should be according to your plan.
+
+Total Invested: The actual cash you've put in out of pocket.
+
+Current Portfolio Value: What your holdings are worth today.
+
+Data Freshness: A badge that turns red if your CSV data is more than 5 days old.
+
+Summary of the "User Flow"
+Input: You provide a CSV of stock prices and a monthly investment goal.
+
+Execution: The Python engine calculates the historical performance and the current gap.
+
+Output: The Web Dashboard gives you a clear instruction: "Buy/Sell X units for $Y.YY."
+1. Core Architecture OverviewThe project is split into three distinct layers that communicate in a linear pipeline.
+A. The Data Engine (data_engine.py / CSV)This is the "Source of Truth."Role: Manages the ingestion of historical market data (e.g., SPY).Logic: It ensures the CSV is sorted chronologically and provides a path for the strategy engine to read price points.
+B. The Strategy Engine (engine.py)This is the "Brain" of the program where the financial math happens.Data Slicing: Uses the Anchor Day logic to filter the CSV for specific monthly dates (e.g., the 27th of every month).Safety Net: Forces the inclusion of the absolute latest CSV row (e.g., 2026-01-27) to ensure the dashboard is never out of date.Simulation Loop: Iterates through dates, calculating:Target Value: (Monthly Increment $\times$ Month Number).Market Value: (Shares Owned $\times$ Current Price).The Move (va_diff): The gap between Target and Market value.JSON Serialization: Rounds all values and packages them into a history list of dictionaries to be sent to the frontend.
+C. The Web Dashboard (backtest.html / JS)This is the "Interface" for the user.Controller (runSimulation): Triggers the Python logic via an API/Socket call and receives the history array.KPI Processor (updateKPIs): * Maps Python keys (like va_invested) to UI elements.Determines the Action Verb (Buy/Sell) based on the sign of va_diff.Updates the DOM with formatted currency strings.Integrity Checker (updateSyncBadge): Compares the data date to the current system date to warn the user if data is stale.
+
+2. Key Data MappingsTo prevent the "Value = 0" issues we encountered, the architecture relies on strict key consistency:Python Key (Source)JavaScript VariableUI Element IDva_diffnextMoveAmountnextInvAmtva_investedtotalInvestedValuetotalSavedva_valuecurrentPortfolioValtotalValpricelatestPricelatestPriceDisplay
+
+3. Data Flow SequenceTrigger: User clicks "Run Simulation" in the browser.Process: Python reads CSV $\rightarrow$ Filters by Anchor Day $\rightarrow$ Appends Latest Row $\rightarrow$ Calculates DVA Steps.Transmit: Python returns a JSON array of the simulation history.Render: JavaScript grabs the last element of that array to fill the KPI cards and uses the full array to draw the charts.

app.py

@@ -65,36 +65,40 @@ def api_backtest():
     
     # 1. Extract and Sanitize Symbol
     symbol = data.get('symbol', '').strip().upper()
-    print(f"DEBUG: Processing {symbol}")
+    print(f"DEBUG: Processing {symbol} with payload: {data}")
 
     if not symbol:
         return jsonify({"error": "Symbol is required"}), 400
 
     try:
-        # 2. Extract Numerical and Logic Inputs (Matching updated JS keys)
+        # 2. Extract and Cast Inputs (Ensuring types match engine requirements)
+        # Note: JS uses 'startDate' (camelCase), Python often uses 'start_date'
         initial = float(data.get('initial_inv', 0))
         monthly = float(data.get('monthly_target', 0))
-        start_date = data.get('start_date', '2024-01-01')
+        start_date = data.get('startDate') or data.get('start_date', '2024-01-01')
+        frequency = data.get('frequency', 'Monthly')
         
-        # Robust Boolean Check for toggles
+        # Robust Boolean Check
         allow_sell = data.get('allow_sell') is True
         allow_frac = data.get('allow_fractional') is True
 
         # 3. Initialize Engines
-        # DataEngine __init__ now calls ensure_data() to download if missing
         data_eng = DataEngine(symbol=symbol)
         
+        # Verify file exists after DataEngine logic
         if not os.path.exists(data_eng.file_path):
             return jsonify({"error": f"Data for {symbol} could not be retrieved."}), 404
             
         strat_eng = StrategyEngine(data_eng)
         
-        # 4. Calculation
-        history = strat_eng.calculate_va_vs_dca(
-            initial_inv=initial, 
-            monthly_target=monthly, 
-            start_date=start_date, 
-            allow_sell=allow_sell, 
+        # 4. Calculation - Calling the correctly named method 'run_simulation'
+        # Ensure arguments match the signature in your engine.py
+        history = strat_eng.run_simulation(
+            start_date=start_date,
+            monthly_goal=monthly,
+            initial_inv=initial,
+            frequency=frequency,
+            allow_sell=allow_sell,
             allow_fractional=allow_frac
         )
 
@@ -104,7 +108,8 @@ def api_backtest():
         import traceback
         print("CRITICAL ERROR in /api/backtest:")
         print(traceback.format_exc()) 
-        return jsonify({"error": "Internal Server Error. Check terminal logs."}), 500
+        # Returning the actual error message helps debugging the '500' faster
+        return jsonify({"error": str(e)}), 500
 
 @app.route('/backtest') # This is the URL you will actually visit
 def backtest_ui():

engine.py

@@ -478,32 +478,52 @@ class StrategyEngine:
         anchor_day = start_dt_obj.day 
         latest_csv_date = df['date'].max() # This captures 2026-01-27
 
-        # 3. Filter data starting from your start_date
-        df_filtered = df[df['date'] >= start_dt_obj].copy()
+        # --- 3. Filter data starting from your start_date ---
+        df_from_start = df[df['date'] >= start_dt_obj].copy()
+        if df_from_start.empty:
+            return []
 
-        # 4. Select recurring monthly days (The first trading day on/after the anchor day)
-        monthly_df = df_filtered[df_filtered['date'].dt.day >= anchor_day].groupby([
-            df_filtered['date'].dt.year, 
-            df_filtered['date'].dt.month
-        ], as_index=False).first()
+        # --- 4. Choose rows based on Frequency ---
+        if frequency == "Weekly":
+            # Take every 5th trading day
+            final_df = df_from_start.iloc[::5].copy()
+            step_increment = float((monthly_goal * 12) / 52)
 
-        # 5. FORCE LAST ROW: If the latest date from CSV isn't in our list, append it
-        if monthly_df.empty or monthly_df.iloc[-1]['date'] != latest_csv_date:
-            last_row = df_filtered[df_filtered['date'] == latest_csv_date]
-            monthly_df = pd.concat([monthly_df, last_row]).drop_duplicates(subset=['date'])
+        elif frequency == "Bi-Weekly":
+            # Take every 10th trading day
+            final_df = df_from_start.iloc[::10].copy()
+            step_increment = float((monthly_goal * 12) / 26)
 
-        # 6. Finalize index for the strategy loop
-        monthly_df.index = pd.to_datetime(monthly_df['date'])
+        else: # Default to Monthly
+            # Group by year/month and take the first available day >= anchor_day
+            final_df = df_from_start[df_from_start['date'].dt.day >= anchor_day].groupby([
+                df_from_start['date'].dt.year, 
+                df_from_start['date'].dt.month
+            ], as_index=False).first()
+            step_increment = float(monthly_goal)
 
-        if monthly_df.empty:
+        # --- 5. FORCE LAST ROW SAFETY ---
+        # Reset index to make concatenation clean
+        final_df = final_df.reset_index(drop=True)
+        
+        if not df_from_start.empty:
+            latest_row = df_from_start.iloc[[-1]] # Gets the absolute last available day
+            if final_df.empty or latest_row.iloc[0]['date'] != final_df.iloc[-1]['date']:
+                final_df = pd.concat([final_df, latest_row]).drop_duplicates(subset=['date'])
+        
+        # Sort and final index reset before the loop
+        final_df = final_df.sort_values('date').reset_index(drop=True)
+
+        if final_df.empty:
             return []
         
-        # Helper for share calculation based on user toggle
+        # --- 6. Helper for share calculation ---
         def get_shares(cash, prc):
             if prc <= 0: return 0
+            # Note: allow_fractional should be defined in your class/scope
             return cash / prc if allow_fractional else math.floor(cash / prc)
 
-        # 2. Initial Setup
+        # --- 7. Initial Setup ---
         va_shares = 0
         dca_shares = 0
         va_invested = 0
@@ -511,41 +531,43 @@ class StrategyEngine:
         va_target_value = 0
         history = []
 
-        # 3. Strategy Loop
-        for i, row in monthly_df.iterrows():
-            actual_date_str = i.strftime('%Y-%m-%d')
+        # --- 8. Strategy Loop ---
+        for step, (idx, row) in enumerate(final_df.iterrows()):
+            actual_date_str = row['date'].strftime('%Y-%m-%d')
             price = float(row['close'])
             
-            if i == monthly_df.index[0]:
-                # --- MONTH 0: INITIAL DEPOSIT ---
-                actual_inv = initial_inv      # This is the 'va_diff'
+            if step == 0:
+                # --- STEP 0: INITIAL DEPOSIT ---
+                actual_inv = initial_inv 
                 dca_actual_inv = initial_inv
                 
                 va_target_value = initial_inv
-                diff = 0
-
                 va_new_shares = get_shares(actual_inv, price)
                 dca_new_shares = va_new_shares
                 
             else:
-                # --- MONTH 1+: DVA vs DCA ---
-                
+                # --- STEP 1+: DVA vs DCA ---
+                # Use step_increment (calculated in your frequency logic) 
+                # to ensure growth matches frequency (Weekly/Monthly)
+                current_increment = step_increment 
+
                 # DCA Logic
-                dca_actual_inv = monthly_target
+                dca_actual_inv = current_increment
                 dca_new_shares = get_shares(dca_actual_inv, price)
 
                 # DVA Logic (Fixed Value Path)
-                va_target_value += monthly_target
+                va_target_value += current_increment
                 
-                # Gap calculation: Target vs. current value BEFORE this month's investment
+                # Gap calculation: Target vs. current value BEFORE this step's investment
                 current_va_val_pre = va_shares * price
                 diff = va_target_value - current_va_val_pre
                 
                 # Apply Buy/Sell constraints
+                # note: allow_sell should be defined in your class/scope
                 actual_inv = diff if (diff >= 0 or allow_sell) else 0
                 va_new_shares = get_shares(actual_inv, price)
 
-            # --- STATE UPDATES (Must happen for both Month 0 and Month 1+) ---
+            # --- STATE UPDATES ---
             va_shares += va_new_shares
             dca_shares += dca_new_shares
             
@@ -553,7 +575,6 @@ class StrategyEngine:
             dca_invested += dca_actual_inv
 
             # --- Unified History Append ---
-            # We calculate these here so they are ALWAYS defined for every row
             history.append({
                 "date": actual_date_str,
                 "price": round(price, 2),
@@ -561,14 +582,124 @@ class StrategyEngine:
                 "dca_invested": round(dca_invested, 2),
                 "dca_shares_trans": round(dca_new_shares, 4),
                 "dca_shares_total": round(dca_shares, 4),
-                "va_value": round(va_shares * price, 2),        # Becomes 'Current Portfolio Value'
-                "va_invested": round(va_invested, 2),       # Becomes 'Total Invested'
+                "va_value": round(va_shares * price, 2),
+                "va_invested": round(va_invested, 2),
                 "va_diff": round(actual_inv, 2),
                 "va_shares_trans": round(va_new_shares, 4),
                 "va_shares_total": round(va_shares, 4),
-                "va_target_value": round(va_target_value, 2)    # Used for next goal
+                "va_target_value": round(va_target_value, 2)
             })
-            # Debugging print
-            print(f"Date: {i.strftime('%Y-%m')}, Target: {va_target_value:.2f}, Portfolio: {va_invested:.2f}, Diff: {diff:.2f}")
             
+        return history   
+    
+    def run_simulation(self, start_date, monthly_goal, initial_inv, frequency="Monthly", allow_sell=True, allow_fractional=True):
+        # 1. DATA LOADING
+        df = pd.read_csv(self.data_engine.file_path)
+        df['date'] = pd.to_datetime(df['date'])
+        df = df.sort_values('date')
+
+        # 2. DATE PREP
+        start_dt_obj = pd.to_datetime(start_date)
+        latest_csv_date = df['date'].max()
+        
+        # Filter data starting from user's choice
+        df_from_start = df[df['date'] >= start_dt_obj].copy()
+        
+        if df_from_start.empty:
+            return []
+
+        # 3. FREQUENCY LOGIC (Hardened)
+        # Standardize string for comparison
+        freq_check = str(frequency).strip().title() 
+
+        if freq_check == "Weekly":
+            final_df = df_from_start.iloc[::5].copy()
+            step_increment = float((monthly_goal * 12) / 52)
+        elif freq_check == "Bi-Weekly":
+            final_df = df_from_start.iloc[::10].copy()
+            step_increment = float((monthly_goal * 12) / 26)
+        else:
+            # For Monthly, we need the anchor day
+            anchor_day = start_dt_obj.day
+            final_df = df_from_start[df_from_start['date'].dt.day >= anchor_day].groupby([
+                df_from_start['date'].dt.year, 
+                df_from_start['date'].dt.month
+            ], as_index=False).first()
+            step_increment = float(monthly_goal)
+
+        # 4. SAFETY: Ensure most recent price is included
+        final_df = final_df.reset_index(drop=True)
+        last_actual_row = df_from_start.iloc[[-1]]
+        
+        if final_df.empty or last_actual_row.iloc[0]['date'] != final_df.iloc[-1]['date']:
+            final_df = pd.concat([final_df, last_actual_row]).drop_duplicates(subset=['date'])
+
+        final_df = final_df.sort_values('date').reset_index(drop=True)
+
+        # 5. STRATEGY INITIALIZATION
+        def get_shares(cash, prc):
+            if prc <= 0: return 0
+            if allow_fractional:
+                return float(cash / prc)
+            else:
+                # Handles both buying (+) and selling (-) for whole shares
+                return float(math.floor(cash / prc)) if cash >= 0 else float(math.ceil(cash / prc))
+
+        # Ensure these are initialized as floats
+        va_shares, dca_shares = 0.0, 0.0
+        va_invested, dca_invested = 0.0, 0.0
+        
+        history = []
+
+        for step, (idx, row) in enumerate(final_df.iterrows()):
+            actual_date_str = row['date'].strftime('%Y-%m-%d')
+            price = float(row['close'])
+            
+            if step == 0:
+                # First row: Both strategies start with the Initial Investment
+                va_actual_inv = float(initial_inv)
+                dca_actual_inv = float(initial_inv)
+                va_target_value = float(initial_inv)
+            else:
+                # Subsequent rows: Use the frequency-adjusted step_increment
+                va_target_value += step_increment
+                
+                # DCA logic: Always invests the same amount every period
+                dca_actual_inv = float(step_increment)
+                
+                # VA logic: Invests enough to hit the target value
+                current_va_market_val = va_shares * price
+                diff = va_target_value - current_va_market_val
+                
+                # Apply "Allow Sell" constraint
+                va_actual_inv = diff if (diff >= 0 or allow_sell) else 0.0
+
+            # Update Shares based on fractional setting
+            va_new_shares = get_shares(va_actual_inv, price)
+            dca_new_shares = get_shares(dca_actual_inv, price)
+
+            # Running totals for shares
+            va_shares += va_new_shares
+            dca_shares += dca_new_shares
+            
+            # Running totals for principal invested
+            va_invested += va_actual_inv
+            dca_invested += dca_actual_inv
+
+            history.append({
+                "date": actual_date_str,
+                "price": round(price, 2),
+                "va_diff": round(va_actual_inv, 2),        # Invested this step (VA)
+                "va_shares_trans": round(va_new_shares, 4),
+                "va_value": round(va_shares * price, 2),   # Current Portfolio Value (VA)
+                "va_invested": round(va_invested, 2),      # Total Out-of-Pocket (VA)
+                "va_shares_total": round(va_shares, 4),
+                "va_target_value": round(float(va_target_value), 2),
+                "dca_diff": round(dca_actual_inv, 2),      # Invested this step (DCA)
+                "dca_shares_trans": round(dca_new_shares, 4),
+                "dca_value": round(dca_shares * price, 2), # Current Portfolio Value (DCA)
+                "dca_invested": round(dca_invested, 2),    # Total Out-of-Pocket (DCA)
+                "dca_shares_total": round(dca_shares, 4)
+            })
+
         return history

templates/val_avg_cal.html

@@ -29,47 +29,57 @@
     </style>
 </head>
 <body>
-    <div class="card shadow-sm p-4 mb-4">
-        <h4 class="fw-bold mb-4">🛠️ Value Averging Strategy Analysis</h4>
-        <form id="calcForm">
-            <div class="row g-3 mb-4">
-                <div class="col-md-3">
-                    <label class="form-label small fw-bold text-uppercase">Ticker</label>
-                    <input type="text" id="symbol" class="form-control" value="SPY">
-                </div>
-                <div class="col-md-3">
-                    <label class="form-label small fw-bold text-uppercase">Initial ($)</label>
-                    <input type="number" id="initial_inv" class="form-control" value="20000">
-                </div>
-                <div class="col-md-3">
-                    <label class="form-label small fw-bold text-uppercase">Monthly Target ($)</label>
-                    <input type="number" id="monthly_target" class="form-control" value="500">
-                </div>
-                <div class="col-md-3">
-                    <label class="form-label small fw-bold text-uppercase">Start Date</label>
-                    <input type="date" id="startDate" class="form-control" value="2024-01-01">
-                </div>
-            </div>
+    <div class="container-fluid py-3" style="max-width: 1200px; margin-left: 0;">
+        <div class="d-flex justify-content-between align-items-center mb-3">
+            <h2 class="mb-0 fw-bold" style="font-size: 1.5rem;">🛠️ Value Averaging Strategy Analysis</h2>
+            <button onclick="resetAll()" class="btn btn-warning btn-sm px-3">Reset</button>
+        </div>
 
-            <div class="row g-3 align-items-center">
-                <div class="col-md-3">
-                    <div class="form-check form-switch">
-                        <input class="form-check-input" type="checkbox" id="allow_sell" checked>
-                        <label class="form-check-label fw-bold small text-uppercase" for="allow_sell">Allow Share Sales</label>
-                    </div>
-                </div>
-                <div class="col-md-3">
-                    <div class="form-check form-switch">
-                        <input class="form-check-input" type="checkbox" id="allow_fractional" checked>
-                        <label class="form-check-label fw-bold small text-uppercase" for="allow_fractional">Fractional Shares</label>
-                    </div>
-                </div>
-                <div class="col-md-6 d-flex justify-content-end gap-2">
-                    <button type="button" onclick="runSimulation()" class="btn btn-primary px-5 fw-bold">Run Analysis</button>
-                    <button type="button" onclick="resetAll()" class="btn btn-outline-secondary px-4">Reset</button>
+        <div class="row g-2 mb-3">
+            <div class="col-md-3">
+                <label class="form-label small fw-bold text-uppercase mb-1">Ticker</label>
+                <input type="text" id="symbol" class="form-control form-control-sm" value="SPY">
+            </div>
+            <div class="col-md-3">
+                <label class="form-label small fw-bold text-uppercase mb-1">Initial ($)</label>
+                <input type="number" id="initial_inv" class="form-control form-control-sm" value="20000">
+            </div>
+            <div class="col-md-3">
+                <label class="form-label small fw-bold text-uppercase mb-1">Monthly Target ($)</label>
+                <input type="number" id="monthly_target" class="form-control form-control-sm" value="500">
+            </div>
+            <div class="col-md-3">
+                <label class="form-label small fw-bold text-uppercase mb-1">Start Date</label>
+                <input type="date" id="startDate" class="form-control form-control-sm" value="2024-01-01">
+            </div>
+        </div>
+
+        <div class="row align-items-center g-3">
+            <div class="col-auto">
+                <div class="d-flex align-items-center">
+                    <label class="small fw-bold text-uppercase me-2 mb-0">Frequency</label>
+                    <select id="frequency" class="form-select form-select-sm" style="width: auto;">
+                        <option value="Weekly">Weekly</option>
+                        <option value="Bi-Weekly">Bi-Weekly</option>
+                        <option value="Monthly" selected>Monthly</option>
+                    </select>
                 </div>
             </div>
-        </form>
+        <div class="col-auto">
+            <div class="form-check form-switch">
+                <input class="form-check-input" type="checkbox" id="allow_sell" checked>
+                <label class="form-check-label small fw-bold text-uppercase" for="allow_sell">Allow Share Sales</label>
+            </div>
+        </div>
+        <div class="col-auto">
+            <div class="form-check form-switch">
+                <input class="form-check-input" type="checkbox" id="allow_fractional" checked>
+                <label class="form-check-label small fw-bold text-uppercase" for="allow_fractional">Fractional Shares</label>
+            </div>
+        </div>
+        <div class="col-auto">
+            <button onclick="runSimulation()" class="btn btn-primary fw-bold px-4">Run Analysis</button>
+        </div>
     </div>
     <div id="kpiArea" class="d-none mb-4">
         <div class="d-flex justify-content-end mb-2">
@@ -154,73 +164,77 @@
 <script>
     let myChart = null;
 
-    async function runSimulation() {
-    // 1. Get references to all elements first
-    const el = {
-        symbol: document.getElementById('symbol'),
-        initial: document.getElementById('initial_inv'),
-        monthly: document.getElementById('monthly_target'),
-        date: document.getElementById('startDate'),
-        sell: document.getElementById('allow_sell'),
-        frac: document.getElementById('allow_fractional'),
-        btn: document.querySelector("button[onclick='runSimulation()']")
-    };
-
-    // 2. Safety Check: Ensure all elements exist before proceeding
-    for (const [key, element] of Object.entries(el)) {
-        if (!element) {
-            console.error(`Error: Element with ID or selector for '${key}' not found.`);
-            alert(`UI Error: Component '${key}' is missing. Please refresh.`);
-            return;
-        }
-    }
-
-    const originalText = el.btn.innerHTML;
-
-    try {
-        const payload = {
-            symbol: el.symbol.value.trim().toUpperCase(),
-            initial_inv: parseFloat(el.initial.value) || 0,
-            monthly_target: parseFloat(el.monthly.value) || 0,
-            start_date: el.date.value,
-            allow_sell: el.sell.checked,
-            allow_fractional: el.frac.checked
+   async function runSimulation() {
+    // 1. Get references (Added frequency)
+        const el = {
+            symbol: document.getElementById('symbol'),
+            initial: document.getElementById('initial_inv'),
+            monthly: document.getElementById('monthly_target'),
+            date: document.getElementById('startDate'),
+            freq: document.getElementById('frequency'), // <--- ADDED
+            sell: document.getElementById('allow_sell'),
+            frac: document.getElementById('allow_fractional'),
+            btn: document.querySelector("button[onclick='runSimulation()']")
         };
 
-        if (!payload.symbol) {
-            alert("Please enter a ticker symbol.");
-            return;
+        // 2. Safety Check
+        for (const [key, element] of Object.entries(el)) {
+            if (!element) {
+                console.error(`Error: Component '${key}' not found.`);
+                return;
+            }
         }
 
-        // Loading State
-        el.btn.disabled = true;
-        el.btn.innerHTML = '<span class="spinner-border spinner-border-sm"></span> Loading...';
+        const originalText = el.btn.innerHTML;
 
-        const res = await fetch('/api/backtest', {
-            method: 'POST',
-            headers: { 'Content-Type': 'application/json' },
-            body: JSON.stringify(payload)
-        });
+        try {
+            const payload = {
+                symbol: el.symbol.value.trim().toUpperCase(),
+                initial_inv: parseFloat(el.initial.value) || 0,
+                monthly_target: parseFloat(el.monthly.value) || 0,
+                startDate: el.date.value,        // Match backend's preferred key
+                frequency: el.freq.value,        // <--- CRITICAL: MUST BE SENT
+                allow_sell: el.sell.checked,
+                allow_fractional: el.frac.checked
+            };
 
-        if (!res.ok) throw new Error(`Server returned ${res.status}`);
-        
-        const data = await res.json();
+            if (!payload.symbol) {
+                alert("Please enter a ticker symbol.");
+                return;
+            }
 
-        // Update UI
-        document.getElementById('kpiArea')?.classList.remove('d-none');
-        document.getElementById('resultsArea')?.classList.remove('d-none');
+            // Loading State
+            el.btn.disabled = true;
+            el.btn.innerHTML = '<span class="spinner-border spinner-border-sm"></span> Loading...';
 
-        updateKPIs(data, payload.monthly_target);
-        renderDetailedChart(data);
-        renderTable(data);
+            const res = await fetch('/api/backtest', {
+                method: 'POST',
+                headers: { 'Content-Type': 'application/json' },
+                body: JSON.stringify(payload)
+            });
 
-    } catch (err) {
-        console.error("Simulation Error:", err);
-        alert("Failed to run analysis. See console for details.");
-    } finally {
-        el.btn.disabled = false;
-        el.btn.innerHTML = originalText;
-    }
+            // Parse response to check for specific Python errors
+            const data = await res.json();
+            
+            if (!res.ok) {
+                throw new Error(data.error || `Server returned ${res.status}`);
+            }
+
+            // Update UI
+            document.getElementById('kpiArea')?.classList.remove('d-none');
+            document.getElementById('resultsArea')?.classList.remove('d-none');
+
+            updateKPIs(data, payload.monthly_target);
+            renderDetailedChart(data);
+            renderTable(data);
+
+        } catch (err) {
+            console.error("Simulation Error:", err);
+            alert(`Analysis Failed: ${err.message}`);
+        } finally {
+            el.btn.disabled = false;
+            el.btn.innerHTML = originalText;
+        }
 }
 /**
  * Updates the summary cards with the DVA "Next Move" recommendation.
@@ -235,55 +249,34 @@ function updateKPIs(data, monthlyTarget) {
     if (!data || data.length === 0) return;
 
     const last = data[data.length - 1];
-    
-    // 1. Extract Data with multiple fallback options for key names
-    const latestPrice = last.price || last.close || 0;
-    const latestDate = last.date || "N/A";
-    const nextMoveAmount = last.va_diff || 0;
-    const sharesToMove = last.va_shares_trans || (latestPrice > 0 ? Math.abs(nextMoveAmount / latestPrice) : 0);
-    
-    // Check these against your history.append keys
-    const totalInvestedValue = last.va_invested || 0; 
-    const currentPortfolioVal = last.va_value || 0; 
-    const currentTarget = last.va_target_value || 0;
 
-    // 2. Helper to set text safely
-    const updateEl = (id, val) => {
+    // Helper function to update text ONLY if the element exists
+    const safeUpdate = (id, value) => {
         const el = document.getElementById(id);
-        if (el) el.innerText = val;
+        if (el) {
+            el.innerText = value;
+        } else {
+            console.warn(`KPI Error: Element with ID '${id}' not found in HTML.`);
+        }
     };
 
-    // 3. Update the UI
-    updateEl('targetValueCard', `$${currentTarget.toLocaleString(undefined, {minimumFractionDigits: 2})}`);
-    updateEl('totalSaved', `$${totalInvestedValue.toLocaleString(undefined, {minimumFractionDigits: 2})}`);
-    updateEl('totalVal', `$${currentPortfolioVal.toLocaleString(undefined, {minimumFractionDigits: 2})}`);
-    updateEl('priceDateLabel', latestDate);
-    updateEl('latestPriceDisplay', `$${latestPrice.toFixed(2)}`);
+    // 1. Format the values from the Python response
+    const targetVal = (last.va_target_value || 0).toLocaleString(undefined, {minimumFractionDigits: 2});
+    const investedVal = (last.va_invested || 0).toLocaleString(undefined, {minimumFractionDigits: 2});
+    const marketVal = (last.va_value || 0).toLocaleString(undefined, {minimumFractionDigits: 2});
 
-    // 4. Handle the Recommendation Card
-    const amtEl = document.getElementById('nextInvAmt');
-    if (amtEl) {
-        // This ensures the minus sign shows up naturally from the number
-        const sign = nextMoveAmount < 0 ? "-" : (nextMoveAmount > 0 ? "+" : "");
-        amtEl.innerText = `${sign}$${Math.abs(nextMoveAmount).toLocaleString(undefined, {minimumFractionDigits: 2})}`;
-        amtEl.className = nextMoveAmount >= 0 ? "fw-bold text-primary" : "fw-bold text-danger";
+    // 2. Push to the UI using the IDs defined in the HTML above
+    safeUpdate('targetValueCard', `$${targetVal}`);
+    safeUpdate('totalSaved', `$${investedVal}`);
+    safeUpdate('totalVal', `$${marketVal}`);
+
+    // 3. Update the Recommendation Card (Next Move)
+    const nextMove = last.va_diff || 0;
+    const nextAmtEl = document.getElementById('nextInvAmt');
+    if (nextAmtEl) {
+        nextAmtEl.innerText = (nextMove >= 0 ? "+" : "") + "$" + Math.abs(nextMove).toLocaleString();
+        nextAmtEl.className = nextMove >= 0 ? "fw-bold text-success" : "fw-bold text-danger";
     }
-
-    // 5. Handle the Action Message
-    const msgEl = document.getElementById('nextInvMsg');
-    if (msgEl) {
-        const actionVerb = nextMoveAmount >= 0 ? "Buy" : "Sell";
-        const actionColor = nextMoveAmount >= 0 ? "text-primary" : "text-danger";
-        msgEl.innerHTML = `<span class="${actionColor} fw-bold">${actionVerb}</span> <strong>${Math.abs(sharesToMove).toFixed(4)}</strong> units @ $${latestPrice.toFixed(2)}`;
-    }
-
-    if (typeof updateSyncBadge === "function") updateSyncBadge(latestDate);
-}
-
-// Helper function to prevent errors if an ID is missing from HTML
-function safeSetText(id, text) {
-    const el = document.getElementById(id);
-    if (el) el.innerText = text;
 }
 /**
  * Updates a visual badge showing if data is fresh or stale
@@ -350,48 +343,52 @@ function updateSyncBadge(dateString) {
         const initialInput = parseFloat(document.getElementById('initial_inv').value) || 0;
         const monthlyInput = parseFloat(document.getElementById('monthly_target').value) || 0;
         
-        if (!tableBody) return;
+        if (!tableBody || !data) return;
 
         tableBody.innerHTML = data.map((d, index) => {
-            // Use initial investment for row 0, monthly target for others
             const dcaInvestedThisMonth = index === 0 ? initialInput : monthlyInput;
             
-            // Calculate Returns for badges
-            const vaAnnRet = d.va_invested > 0 ? (((d.va_value - d.va_invested) / d.va_invested) * 100).toFixed(2) : 0;
-            const dcaAnnRet = d.dca_invested > 0 ? (((d.dca_value - d.dca_invested) / d.dca_invested) * 100).toFixed(2) : 0;
+            // Safety Fallbacks: Ensure numbers exist before calculation
+            const vaVal = d.va_value || 0;
+            const vaInv = d.va_invested || 0;
+            const dcaVal = d.dca_value || 0;
+            const dcaInv = d.dca_invested || 0;
+
+            const vaAnnRet = vaInv > 0 ? (((vaVal - vaInv) / vaInv) * 100).toFixed(2) : "0.00";
+            const dcaAnnRet = dcaInv > 0 ? (((dcaVal - dcaInv) / dcaInv) * 100).toFixed(2) : "0.00";
 
             return `
                 <tr>
                     <td class="small fw-bold">${d.date}</td>
-                    <td>$${d.price.toLocaleString(undefined, {minimumFractionDigits: 2})}</td>
+                    <td>$${(d.price || 0).toLocaleString(undefined, {minimumFractionDigits: 2})}</td>
                     
-                    <td class="text-success fw-bold">
-                        $${d.va_diff.toLocaleString()}<br>
-                        <small class="text-muted">${d.va_shares_trans >= 0 ? '+' : ''}${d.va_shares_trans.toFixed(2)}</small>
+                    <td class="${(d.va_diff || 0) >= 0 ? 'text-success' : 'text-danger'} fw-bold">
+                        $${(d.va_diff || 0).toLocaleString()}<br>
+                        <small class="text-muted">${(d.va_shares_trans || 0) >= 0 ? '+' : ''}${(d.va_shares_trans || 0).toFixed(2)}</small>
                     </td>
                     
                     <td>
                         $${dcaInvestedThisMonth.toLocaleString()}<br>
-                        <small class="text-muted">+${d.dca_shares_trans.toFixed(2)}</small>
+                        <small class="text-muted">+${(d.dca_shares_trans || 0).toFixed(2)}</small>
                     </td>
                     
                     <td class="fw-bold">
-                        $${d.va_value.toLocaleString()}<br>
-                        <small class="text-primary">${d.va_shares_total.toFixed(2)}</small>
+                        $${vaVal.toLocaleString()}<br>
+                        <small class="text-primary">${(d.va_shares_total || 0).toFixed(2)}</small>
                     </td>
                     
                     <td>
-                        $${d.dca_value.toLocaleString()}<br>
-                        <small class="text-primary">${d.dca_shares_total.toFixed(2)}</small>
+                        $${dcaVal.toLocaleString()}<br>
+                        <small class="text-primary">${(d.dca_shares_total || 0).toFixed(2)}</small>
                     </td>
                     
                     <td>
-                        <span class="badge ${vaAnnRet >= 0 ? 'bg-success' : 'bg-danger'}">
+                        <span class="badge ${parseFloat(vaAnnRet) >= 0 ? 'bg-success' : 'bg-danger'}">
                             ${vaAnnRet}%
                         </span>
                     </td>
                     <td>
-                        <span class="badge ${dcaAnnRet >= 0 ? 'bg-info' : 'bg-danger'}">
+                        <span class="badge ${parseFloat(dcaAnnRet) >= 0 ? 'bg-info' : 'bg-danger'}">
                             ${dcaAnnRet}%
                         </span>
                     </td>
@@ -406,12 +403,13 @@ function updateSyncBadge(dateString) {
     }
 
     function resetAll() {
-    // 1. Reset Inputs to Defaults
+    // 1. Reset Inputs to Defaults (Including Frequency)
     const defaults = {
         'symbol': 'SPY',
         'initial_inv': 20000,
         'monthly_target': 500,
-        'startDate': '2024-01-01'
+        'startDate': '2024-01-01',
+        'frequency': 'Monthly'  // <--- Added this
     };
 
     Object.keys(defaults).forEach(id => {
@@ -439,7 +437,7 @@ function updateSyncBadge(dateString) {
         window.detailedChart = null;
     }
 
-    console.log("Dashboard reset successfully.");
+    console.log("Dashboard reset to defaults.");
 }
 </script>
 </body>