Update app.py

app.py

@@ -16,9 +16,9 @@ HISTORY_LENGTH = 300
 BROADCAST_RATE = 0.1
 
 # Mathematical Constants
-DECAY_LAMBDA = 50.0      # Decay factor for liquidity weighting (closer orders matter more)
-IMPACT_SENSITIVITY = 2.0 # Multiplier for Micro-Price deviation
-WALL_DAMPENING = 0.8     # How much a Z-Score > 3 wall reduces momentum (0.0-1.0)
+DECAY_LAMBDA = 50.0      
+IMPACT_SENSITIVITY = 2.0 
+WALL_DAMPENING = 0.8     
 Z_SCORE_THRESHOLD = 3.0  
 WALL_LOOKBACK = 200      
 
@@ -30,8 +30,9 @@ market_state = {
     "asks": {},
     "history": [], 
     "pred_history": [], 
+    "trade_vol_history": [], # New: Store trade volume history
+    "current_vol_window": {"buy": 0.0, "sell": 0.0, "start": time.time()},
     "current_mid": 0.0,
-    "prev_mid": 0.0,
     "ready": False
 }
 
@@ -39,31 +40,7 @@ connected_clients = set()
 
 # --- QUANTITATIVE METHODS ---
 
-def calculate_ols_slope(x_values, y_values):
-    """
-    Calculates the slope (m) of the liquidity density using Ordinary Least Squares (OLS).
-    y = mx + c
-    Steep slope = High Liquidity Density (Hard to move price).
-    Flat slope = Low Liquidity Density (Price slips easily).
-    """
-    n = len(x_values)
-    if n < 2: return 0.0
-    
-    sum_x = sum(x_values)
-    sum_y = sum(y_values)
-    sum_xy = sum(x*y for x, y in zip(x_values, y_values))
-    sum_xx = sum(x*x for x in x_values)
-    
-    denominator = (n * sum_xx - sum_x * sum_x)
-    if denominator == 0: return 0.0
-    
-    slope = (n * sum_xy - sum_x * sum_y) / denominator
-    return slope
-
 def detect_anomalies(orders, scan_depth):
-    """
-    Standard Z-Score Outlier Detection.
-    """
     if len(orders) < 10: return []
     relevant_orders = orders[:scan_depth]
     volumes = [q for p, q in relevant_orders]
@@ -83,78 +60,46 @@ def detect_anomalies(orders, scan_depth):
         if z_score > Z_SCORE_THRESHOLD:
             walls.append({"price": price, "vol": qty, "z_score": z_score})
 
-    # Sort by Z-Score (Strongest first)
     walls.sort(key=lambda x: x['z_score'], reverse=True)
     return walls[:3]
 
-def calculate_micro_price_structure(diff_x, diff_y_raw, current_mid, best_bid, best_ask, walls):
-    """
-    Advanced Prediction Engine using:
-    1. Weighted Imbalance (Exponential Decay)
-    2. Liquidity Slope Elasticity
-    3. Wall Friction Dampening
-    """
+def calculate_micro_price_structure(diff_x, diff_y_net, current_mid, best_bid, best_ask, walls):
     if not diff_x or len(diff_x) < 5: return None
 
-    # 1. Calculate Weighted Volume Imbalance (VOI)
-    # Using exponential decay to value liquidity near the spread higher than deep liquidity
-    sum_weighted_bid = 0.0
-    sum_weighted_ask = 0.0
-    
-    # Reconstruct raw bid/ask curves from the net diff arrays for calculation
-    # (This is an approximation based on the diffs passed in, ideally we use raw state)
-    # For efficiency, we calculate 'imbalance' directly from the net curve
+    # Weighted Imbalance Calculation
     weighted_imbalance = 0.0
+    total_weight = 0.0
     
     for i in range(len(diff_x)):
         dist = diff_x[i]
-        net_vol = diff_y_raw[i] # This is BidVol - AskVol at this depth
-        
-        # Decay function: e^(-x / lambda)
+        net_vol = diff_y_net[i]
         weight = math.exp(-dist / DECAY_LAMBDA)
         weighted_imbalance += net_vol * weight
+        total_weight += weight
 
-    # Normalize Imbalance (-1 to 1 range roughly)
-    # We divide by the total weighted volume estimate to get a ratio (rho)
-    # Estimate total volume based on abs(net_vol) as a proxy
-    total_weighted_vol = sum(abs(v) * math.exp(-d/DECAY_LAMBDA) for d, v in zip(diff_x, diff_y_raw))
-    if total_weighted_vol == 0: rho = 0
-    else: rho = weighted_imbalance / total_weighted_vol
+    rho = weighted_imbalance / total_weight if total_weight > 0 else 0
 
-    # 2. Base Micro-Price Projection
-    # P_micro = P_mid + (Spread * ImbalanceRatio * Sensitivity)
+    # Base Projection
     spread = best_ask - best_bid
     theoretical_delta = (spread / 2) * rho * IMPACT_SENSITIVITY
-    
     projected_price = current_mid + theoretical_delta
 
-    # 3. Wall Friction Logic
-    # If the projection tries to cross a wall, the Z-Score of that wall reduces the delta.
-    
+    # Wall Friction
     final_delta = theoretical_delta
-
-    # Check Ask Walls (Resistance)
     if final_delta > 0 and walls['asks']:
-        nearest_wall = walls['asks'][0] # Strongest wall
+        nearest_wall = walls['asks'][0]
         if projected_price >= nearest_wall['price']:
-            # Dampen based on Z-Score. Higher Z = More damping.
-            # Factor: 1 / (1 + (Z * 0.1))
             damp_factor = 1.0 / (1.0 + (nearest_wall['z_score'] * 0.2))
             final_delta *= damp_factor
-
-    # Check Bid Walls (Support)
     elif final_delta < 0 and walls['bids']:
-        nearest_wall = walls['bids'][0] # Strongest wall
+        nearest_wall = walls['bids'][0]
         if projected_price <= nearest_wall['price']:
             damp_factor = 1.0 / (1.0 + (nearest_wall['z_score'] * 0.2))
             final_delta *= damp_factor
 
-    final_projected = current_mid + final_delta
-
     return {
-        "projected": final_projected,
-        "net_score": weighted_imbalance, # Raw score for the UI meter
-        "rho": rho # Imbalance ratio
+        "projected": current_mid + final_delta,
+        "rho": rho
     }
 
 def process_market_data():
@@ -162,7 +107,21 @@ def process_market_data():
 
     mid = market_state['current_mid']
     
-    # Get raw sorted orders
+    # Process Trade Volume Window (Reset every 1 second)
+    now = time.time()
+    if now - market_state['current_vol_window']['start'] >= 1.0:
+        market_state['trade_vol_history'].append({
+            't': now,
+            'buy': market_state['current_vol_window']['buy'],
+            'sell': market_state['current_vol_window']['sell']
+        })
+        if len(market_state['trade_vol_history']) > 60: # Keep last 60 seconds
+            market_state['trade_vol_history'].pop(0)
+        
+        # Reset window
+        market_state['current_vol_window'] = {"buy": 0.0, "sell": 0.0, "start": now}
+
+    # Order Book Processing
     sorted_bids = sorted(market_state['bids'].items(), key=lambda x: -x[0])
     sorted_asks = sorted(market_state['asks'].items(), key=lambda x: x[0])
     
@@ -171,11 +130,9 @@ def process_market_data():
     best_bid = sorted_bids[0][0]
     best_ask = sorted_asks[0][0]
 
-    # --- WALL DETECTION ---
     bid_walls = detect_anomalies(sorted_bids, WALL_LOOKBACK)
     ask_walls = detect_anomalies(sorted_asks, WALL_LOOKBACK)
 
-    # --- DEPTH AGGREGATION ---
     d_b_x, d_b_y, cum = [], [], 0
     for p, q in sorted_bids[:300]:
         d = mid - p
@@ -190,44 +147,31 @@ def process_market_data():
             cum += q
             d_a_x.append(d); d_a_y.append(cum)
 
-    # --- UNIFIED GRID FOR ANALYSIS ---
     diff_x, diff_y_net = [], []
     chart_bids, chart_asks = [], []
     
     if d_b_x and d_a_x:
         max_dist = min(d_b_x[-1], d_a_x[-1])
-        # Create 100 buckets up to the max common depth
         step_size = max_dist / 100
         steps = [i * step_size for i in range(1, 101)]
         
         for s in steps:
-            # Interpolate Bid Volume at step s
             idx_b = bisect.bisect_right(d_b_x, s)
             vol_b = d_b_y[idx_b-1] if idx_b > 0 else 0
-            
-            # Interpolate Ask Volume at step s
             idx_a = bisect.bisect_right(d_a_x, s)
             vol_a = d_a_y[idx_a-1] if idx_a > 0 else 0
             
             diff_x.append(s)
-            diff_y_net.append(vol_b - vol_a) # Net Imbalance at this depth
-            
+            diff_y_net.append(vol_b - vol_a)
             chart_bids.append(vol_b)
             chart_asks.append(vol_a)
 
-    # --- MATHEMATICAL ANALYSIS ---
     analysis = calculate_micro_price_structure(
-        diff_x, 
-        diff_y_net, 
-        mid, 
-        best_bid, 
-        best_ask, 
+        diff_x, diff_y_net, mid, best_bid, best_ask, 
         {"bids": bid_walls, "asks": ask_walls}
     )
     
-    now = time.time()
     if analysis:
-        # Rate limit prediction history update
         if not market_state['pred_history'] or (now - market_state['pred_history'][-1]['t'] > 0.5):
             market_state['pred_history'].append({'t': now, 'p': analysis['projected']})
             if len(market_state['pred_history']) > HISTORY_LENGTH:
@@ -237,6 +181,7 @@ def process_market_data():
         "mid": mid,
         "history": market_state['history'],
         "pred_history": market_state['pred_history'],
+        "trade_history": market_state['trade_vol_history'],
         "depth_x": diff_x,
         "depth_net": diff_y_net,
         "depth_bids": chart_bids,
@@ -264,6 +209,7 @@ HTML_PAGE = f"""
             --green: #00ff9d;
             --red: #ff3b3b;
             --blue: #2979ff;
+            --yellow: #ffeb3b; 
         }}
         body {{ 
             margin: 0; padding: 0; 
@@ -322,8 +268,9 @@ HTML_PAGE = f"""
             padding: 20px;
             display: flex;
             flex-direction: column;
-            gap: 25px;
+            gap: 15px; /* Tighter gap to fit the new chart */
             border-left: 1px solid var(--border);
+            overflow-y: hidden;
         }}
 
         .chart-header {{
@@ -351,7 +298,7 @@ HTML_PAGE = f"""
         .c-red {{ color: var(--red); }}
         .c-dim {{ color: var(--text-dim); }}
 
-        .list-container {{ display: flex; flex-direction: column; gap: 8px; overflow-y: hidden; }}
+        .list-container {{ display: flex; flex-direction: column; gap: 8px; overflow-y: auto; max-height: 120px; }}
         .list-item {{ 
             display: flex; justify-content: space-between; 
             font-family: 'JetBrains Mono', monospace; 
@@ -361,6 +308,14 @@ HTML_PAGE = f"""
         }}
         .list-item span:first-child {{ color: #e0e0e0; }}
         .list-item:last-child {{ border: none; }}
+        
+        #sidebar-chart {{
+            flex: 1;
+            background: rgba(255,255,255,0.02);
+            border: 1px solid var(--border);
+            border-radius: 4px;
+            min-height: 100px;
+        }}
     </style>
 </head>
 <body>
@@ -376,17 +331,17 @@ HTML_PAGE = f"""
         </div>
 
         <div id="p-chart" class="panel">
-            <div class="chart-header">PRICE ACTION // MICRO-STRUCTURE FORECAST</div>
+            <div class="chart-header">PRICE ACTION (BLUE) // PREDICTION (YELLOW)</div>
             <div id="tv-price" style="flex: 1; width: 100%;"></div>
         </div>
 
         <div id="p-depth">
             <div class="depth-sub">
-                <div class="chart-header">LIQUIDITY DENSITY PROFILE</div>
+                <div class="chart-header">LIQUIDITY DENSITY</div>
                 <div id="tv-raw" style="flex: 1; width: 100%;"></div>
             </div>
             <div class="depth-sub">
-                <div class="chart-header">ORDER FLOW IMBALANCE (OFI)</div>
+                <div class="chart-header">ORDER FLOW IMBALANCE</div>
                 <div id="tv-net" style="flex: 1; width: 100%;"></div>
             </div>
         </div>
@@ -394,7 +349,7 @@ HTML_PAGE = f"""
         <div id="p-sidebar" class="panel">
             
             <div class="data-group">
-                <span class="label">Micro-Price Delta (5s)</span>
+                <span class="label">Micro-Price Delta</span>
                 <div style="display:flex; align-items: baseline; gap: 10px;">
                     <span id="proj-pct" class="value value-lg">--%</span>
                     <span id="proj-val" class="value-sub">---</span>
@@ -404,23 +359,23 @@ HTML_PAGE = f"""
             <div class="divider"></div>
 
             <div class="data-group">
-                <span class="label">OFI Imbalance Ratio (ρ)</span>
+                <span class="label">OFI Imbalance Ratio</span>
                 <span id="score-val" class="value">0.00</span>
-                <span class="value-sub">Range: -1.0 (Bear) to 1.0 (Bull)</span>
             </div>
 
             <div class="divider"></div>
 
-            <div class="data-group" style="flex: 1;">
-                <span class="label" style="margin-bottom: 10px;">High Sigma Walls (Z > 3.0)</span>
+            <div class="data-group">
+                <span class="label">Detected Walls (Z > 3.0)</span>
                 <div id="wall-list" class="list-container">
-                    <span class="c-dim" style="font-size: 11px;">Initializing Quantitative Scan...</span>
+                    <span class="c-dim" style="font-size: 11px;">Scanning...</span>
                 </div>
             </div>
-            
-            <div style="margin-top: auto;">
-                <span class="label">Model Latency</span>
-                <div class="value-sub c-green">Optimized</div>
+
+            <!-- NEW CHART UNDER WALLS -->
+            <div class="data-group" style="flex: 1; display:flex; flex-direction:column;">
+                <span class="label">Real-time Volume (Ticks)</span>
+                <div id="sidebar-chart"></div>
             </div>
         </div>
     </div>
@@ -448,39 +403,55 @@ HTML_PAGE = f"""
                 crosshair: {{ mode: 1, vertLine: {{ color: '#444', labelBackgroundColor: '#444' }}, horzLine: {{ color: '#444', labelBackgroundColor: '#444' }} }}
             }};
 
+            // 1. MAIN PRICE CHART
             const priceChart = LightweightCharts.createChart(document.getElementById('tv-price'), chartOpts);
-            const priceSeries = priceChart.addLineSeries({{ color: '#FFFFFF', lineWidth: 1, title: 'Price' }});
-            const predSeries = priceChart.addLineSeries({{ color: '#2979ff', lineWidth: 1, lineStyle: 2, title: 'Forecast' }});
+            const priceSeries = priceChart.addLineSeries({{ color: '#2979ff', lineWidth: 2, title: 'Price' }}); // BLUE
+            const predSeries = priceChart.addLineSeries({{ color: '#ffeb3b', lineWidth: 2, lineStyle: 2, title: 'Forecast' }}); // YELLOW
 
+            // 2. DEPTH CHARTS
             const rawChart = LightweightCharts.createChart(document.getElementById('tv-raw'), {{
-                ...chartOpts,
-                localization: {{ timeFormatter: t => '$' + t.toFixed(2) }}
+                ...chartOpts, localization: {{ timeFormatter: t => '$' + t.toFixed(2) }}
             }});
             const bidSeries = rawChart.addAreaSeries({{ lineColor: '#00ff9d', topColor: 'rgba(0, 255, 157, 0.15)', bottomColor: 'rgba(0,0,0,0)', lineWidth: 1 }});
             const askSeries = rawChart.addAreaSeries({{ lineColor: '#ff3b3b', topColor: 'rgba(255, 59, 59, 0.15)', bottomColor: 'rgba(0,0,0,0)', lineWidth: 1 }});
 
             const netChart = LightweightCharts.createChart(document.getElementById('tv-net'), {{
-                ...chartOpts,
-                localization: {{ timeFormatter: t => '$' + t.toFixed(2) }}
+                ...chartOpts, localization: {{ timeFormatter: t => '$' + t.toFixed(2) }}
             }});
             const netSeries = netChart.addHistogramSeries({{ color: '#2979ff' }});
 
+            // 3. SIDEBAR VOLUME CHART
+            const volChart = LightweightCharts.createChart(document.getElementById('sidebar-chart'), {{
+                ...chartOpts,
+                grid: {{ vertLines: {{ visible: false }}, horzLines: {{ visible: false }} }},
+                rightPriceScale: {{ visible: false }},
+                timeScale: {{ visible: false }},
+                handleScroll: false,
+                handleScale: false
+            }});
+            const volBuySeries = volChart.addHistogramSeries({{ color: '#00ff9d' }});
+            const volSellSeries = volChart.addHistogramSeries({{ color: '#ff3b3b' }});
+
             let activeLines = [];
 
+            // RESIZE OBSERVER
             new ResizeObserver(entries => {{
                 for(let entry of entries) {{
                     const {{width, height}} = entry.contentRect;
                     if(entry.target.id === 'tv-price') priceChart.applyOptions({{width, height}});
                     if(entry.target.id === 'tv-raw') rawChart.applyOptions({{width, height}});
                     if(entry.target.id === 'tv-net') netChart.applyOptions({{width, height}});
+                    if(entry.target.id === 'sidebar-chart') volChart.applyOptions({{width, height}});
                 }}
             }}).observe(document.body);
 
-            ['tv-price', 'tv-raw', 'tv-net'].forEach(id => {{
+            ['tv-price', 'tv-raw', 'tv-net', 'sidebar-chart'].forEach(id => {{
                 new ResizeObserver(e => {{
-                     if(id === 'tv-price') priceChart.applyOptions({{ width: e[0].contentRect.width, height: e[0].contentRect.height }});
-                     if(id === 'tv-raw') rawChart.applyOptions({{ width: e[0].contentRect.width, height: e[0].contentRect.height }});
-                     if(id === 'tv-net') netChart.applyOptions({{ width: e[0].contentRect.width, height: e[0].contentRect.height }});
+                     const t = document.getElementById(id);
+                     if(id === 'tv-price') priceChart.applyOptions({{ width: t.clientWidth, height: t.clientHeight }});
+                     if(id === 'tv-raw') rawChart.applyOptions({{ width: t.clientWidth, height: t.clientHeight }});
+                     if(id === 'tv-net') netChart.applyOptions({{ width: t.clientWidth, height: t.clientHeight }});
+                     if(id === 'sidebar-chart') volChart.applyOptions({{ width: t.clientWidth, height: t.clientHeight }});
                 }}).observe(document.getElementById(id));
             }});
 
@@ -501,7 +472,6 @@ HTML_PAGE = f"""
 
                         if (data.analysis) {{
                             const proj = data.analysis.projected;
-                            // Using the Imbalance Ratio (rho) for the score display
                             const rho = data.analysis.rho; 
 
                             predSeries.setData([
@@ -522,6 +492,7 @@ HTML_PAGE = f"""
                         }}
                     }}
 
+                    // WALLS
                     if (data.walls) {{
                         activeLines.forEach(l => priceSeries.removePriceLine(l));
                         activeLines = [];
@@ -538,10 +509,27 @@ HTML_PAGE = f"""
 
                         data.walls.asks.forEach(w => addWall(w, 'ASK'));
                         data.walls.bids.forEach(w => addWall(w, 'BID'));
+                        dom.wallList.innerHTML = html || '<span class="c-dim" style="font-size:11px">Scanning...</span>';
+                    }}
+
+                    // VOLUME CHART IN SIDEBAR
+                    if (data.trade_history && data.trade_history.length) {{
+                        const buyData = [];
+                        const sellData = [];
+                        data.trade_history.forEach(t => {{
+                            const time = Math.floor(t.t);
+                            buyData.push({{ time: time, value: t.buy }});
+                            sellData.push({{ time: time, value: t.sell }});
+                        }});
+                        // Ensure unique time points for LW Charts
+                        const uniqueBuys = [...new Map(buyData.map(i => [i.time, i])).values()];
+                        const uniqueSells = [...new Map(sellData.map(i => [i.time, i])).values()];
                         
-                        dom.wallList.innerHTML = html || '<span class="c-dim" style="font-size:11px">Scanning liquidity surface...</span>';
+                        volBuySeries.setData(uniqueBuys);
+                        volSellSeries.setData(uniqueSells);
                     }}
 
+                    // DEPTH
                     if (data.depth_x.length) {{
                         const bids = [], asks = [], nets = [];
                         for(let i=0; i<data.depth_x.length; i++) {{
@@ -572,10 +560,16 @@ async def kraken_worker():
         try:
             async with websockets.connect("wss://ws.kraken.com/v2") as ws:
                 logging.info(f"🔌 Connected to Kraken ({SYMBOL_KRAKEN})")
+                
+                # SUBSCRIBE TO BOOK AND TRADES
                 await ws.send(json.dumps({
                     "method": "subscribe",
                     "params": {"channel": "book", "symbol": [SYMBOL_KRAKEN], "depth": 500}
                 }))
+                await ws.send(json.dumps({
+                    "method": "subscribe",
+                    "params": {"channel": "trade", "symbol": [SYMBOL_KRAKEN]}
+                }))
 
                 async for message in ws:
                     payload = json.loads(message)
@@ -606,6 +600,21 @@ async def kraken_worker():
                                 market_state['history'].append({'t': now, 'p': mid})
                                 if len(market_state['history']) > HISTORY_LENGTH:
                                     market_state['history'].pop(0)
+                    
+                    elif channel == "trade":
+                        # Process trades for volume history
+                        for trade in data:
+                            # Kraken Trade format: [price, qty, time, side, order_type, misc]
+                            # side: 'buy' or 'sell'
+                            try:
+                                qty = float(trade['qty'])
+                                side = trade['side'] # 'buy' or 'sell'
+                                if side == 'buy':
+                                    market_state['current_vol_window']['buy'] += qty
+                                else:
+                                    market_state['current_vol_window']['sell'] += qty
+                            except:
+                                pass
 
         except Exception as e:
             logging.warning(f"⚠️ Reconnecting: {e}")