Update app.py

app.py

@@ -2,329 +2,29 @@ import asyncio
 import json
 import logging
 import time
-import bisect
-import math
-import statistics
 import aiohttp
 from aiohttp import web
 import websockets
 
 SYMBOL_KRAKEN = "BTC/USD"
 PORT = 7860
-HISTORY_LENGTH = 300
-BROADCAST_RATE = 0.1
-
-DECAY_LAMBDA = 50.0
-IMPACT_SENSITIVITY = 2.0
-Z_SCORE_THRESHOLD = 3.0
-WALL_LOOKBACK = 200
-
-# ML Hyperparameters
-LEARNING_RATE = 0.01
-MOMENTUM = 0.9
+BROADCAST_RATE = 0.5 # Slower broadcast is fine for just candles
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
 
-class OnlineScaler:
-    def __init__(self):
-        self.n = 0
-        self.mean = 0.0
-        self.M2 = 0.0
-
-    def update(self, x):
-        self.n += 1
-        delta = x - self.mean
-        self.mean += delta / self.n
-        delta2 = x - self.mean
-        self.M2 += delta * delta2
-        return self.transform(x)
-
-    def transform(self, x):
-        if self.n < 2: return 0.0
-        var = self.M2 / (self.n - 1)
-        if var == 0: return 0.0
-        std = math.sqrt(var)
-        return (x - self.mean) / std
-
-class QuantModel:
-    def __init__(self, num_features):
-        self.weights = [0.0] * num_features
-        self.bias = 0.0
-        self.velocity = [0.0] * num_features
-        self.bias_velocity = 0.0
-        self.scalers = [OnlineScaler() for _ in range(num_features)]
-        self.prev_features = None
-        self.prev_price = None
-
-    def predict(self, features):
-        scaled = [s.transform(f) for s, f in zip(self.scalers, features)]
-        dot = sum(w * x for w, x in zip(self.weights, scaled))
-        return dot + self.bias
-
-    def train(self, current_price, current_features):
-        if self.prev_features is None or self.prev_price is None:
-            self.prev_features = [s.update(f) for s, f in zip(self.scalers, current_features)]
-            self.prev_price = current_price
-            return
-
-        # Target: Price Change (Delta)
-        actual_delta = current_price - self.prev_price
-        
-        # Predict using PAST features
-        pred_delta = sum(w * x for w, x in zip(self.weights, self.prev_features)) + self.bias
-        
-        # Error
-        error = pred_delta - actual_delta
-        
-        # SGD with Momentum Update
-        for i in range(len(self.weights)):
-            grad = error * self.prev_features[i]
-            self.velocity[i] = MOMENTUM * self.velocity[i] - LEARNING_RATE * grad
-            self.weights[i] += self.velocity[i]
-        
-        self.bias_velocity = MOMENTUM * self.bias_velocity - LEARNING_RATE * error
-        self.bias += self.bias_velocity
-
-        # Store for next tick
-        self.prev_features = [s.update(f) for s, f in zip(self.scalers, current_features)]
-        self.prev_price = current_price
-
-    def get_forecast(self, current_price, current_features):
-        # Predict NEXT delta based on CURRENT features
-        pred_delta = self.predict(current_features)
-        return current_price + pred_delta
-
-# 4 Features: OFI, Depth Area, Best Imbalance, Velocity
-ml_model = QuantModel(4)
-
 market_state = {
-    "bids": {},
-    "asks": {},
-    "history": [],
-    "pred_history": [],
-    "ml_history": [],
-    "trade_vol_history": [],
     "ohlc_history": [],
-    "current_vol_window": {"buy": 0.0, "sell": 0.0, "start": time.time()},
-    "current_mid": 0.0,
-    "prev_mid": 0.0,
     "ready": False
 }
 
 connected_clients = set()
 
-def detect_anomalies(orders, scan_depth):
-    if len(orders) < 10: return []
-    relevant_orders = orders[:scan_depth]
-    volumes = [q for p, q in relevant_orders]
-    if not volumes: return []
-
-    try:
-        avg_vol = statistics.mean(volumes)
-        stdev_vol = statistics.stdev(volumes)
-    except statistics.StatisticsError:
-        return []
-
-    if stdev_vol == 0: return []
-
-    walls = []
-    for price, qty in relevant_orders:
-        z_score = (qty - avg_vol) / stdev_vol
-        if z_score > Z_SCORE_THRESHOLD:
-            walls.append({"price": price, "vol": qty, "z_score": z_score})
-
-    walls.sort(key=lambda x: x['z_score'], reverse=True)
-    return walls[:3]
-
-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, 0
-
-    weighted_imbalance = 0.0
-    total_weight = 0.0
-
-    for i in range(len(diff_x)):
-        dist = diff_x[i]
-        net_vol = diff_y_net[i]
-        weight = math.exp(-dist / DECAY_LAMBDA)
-        weighted_imbalance += net_vol * weight
-        total_weight += weight
-
-    rho = weighted_imbalance / total_weight if total_weight > 0 else 0
-
-    spread = best_ask - best_bid
-    theoretical_delta = (spread / 2) * rho * IMPACT_SENSITIVITY
-    projected_price = current_mid + theoretical_delta
-
-    final_delta = theoretical_delta
-    if final_delta > 0 and walls['asks']:
-        nearest_wall = walls['asks'][0]
-        if projected_price >= nearest_wall['price']:
-            damp_factor = 1.0 / (1.0 + (nearest_wall['z_score'] * 0.2))
-            final_delta *= damp_factor
-    elif final_delta < 0 and walls['bids']:
-        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
-
-    return {
-        "projected": current_mid + final_delta,
-        "rho": rho
-    }, sum(diff_y_net)
-
-def calculate_polr(bids, asks, mid):
-    if not bids or not asks: return []
-
-    sorted_bids = sorted(bids.items(), key=lambda x: -x[0])
-    sorted_asks = sorted(asks.items(), key=lambda x: x[0])
-
-    path_points = []
-    volume_steps = [i * 0.5 for i in range(1, 61)] 
-    
-    for i, target_vol in enumerate(volume_steps):
-        ask_cost_dist = 0
-        cum_vol = 0
-        target_ask_price = mid
-        for p, q in sorted_asks:
-            cum_vol += q
-            if cum_vol >= target_vol:
-                target_ask_price = p
-                break
-        ask_cost_dist = target_ask_price - mid
-
-        bid_cost_dist = 0
-        cum_vol = 0
-        target_bid_price = mid
-        for p, q in sorted_bids:
-            cum_vol += q
-            if cum_vol >= target_vol:
-                target_bid_price = p
-                break
-        bid_cost_dist = mid - target_bid_price
-
-        if bid_cost_dist <= 0: bid_cost_dist = 0.01
-        if ask_cost_dist <= 0: ask_cost_dist = 0.01
-
-        projected_p = mid
-        if ask_cost_dist > bid_cost_dist:
-             projected_p = target_ask_price 
-        else:
-             projected_p = target_bid_price
-        
-        path_points.append({'index': i, 'p': projected_p})
-
-    return path_points
-
 def process_market_data():
-    if not market_state['ready']: return {"error": "Initializing..."}
-
-    mid = market_state['current_mid']
-    
-    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: 
-            market_state['trade_vol_history'].pop(0)
-        market_state['current_vol_window'] = {"buy": 0.0, "sell": 0.0, "start": now}
-
-    sorted_bids = sorted(market_state['bids'].items(), key=lambda x: -x[0])
-    sorted_asks = sorted(market_state['asks'].items(), key=lambda x: x[0])
-
-    if not sorted_bids or not sorted_asks: return {"error": "Empty Book"}
-
-    best_bid_p, best_bid_q = sorted_bids[0]
-    best_ask_p, best_ask_q = sorted_asks[0]
-
-    bid_walls = detect_anomalies(sorted_bids, WALL_LOOKBACK)
-    ask_walls = detect_anomalies(sorted_asks, WALL_LOOKBACK)
-
-    d_b_x, d_b_y, cum = [], [], 0
-    for p, q in sorted_bids[:300]:
-        d = mid - p
-        if d >= 0:
-            cum += q
-            d_b_x.append(d); d_b_y.append(cum)
-
-    d_a_x, d_a_y, cum = [], [], 0
-    for p, q in sorted_asks[:300]:
-        d = p - mid
-        if d >= 0:
-            cum += q
-            d_a_x.append(d); d_a_y.append(cum)
-
-    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])
-        step_size = max_dist / 100
-        steps = [i * step_size for i in range(1, 101)]
-        
-        for s in steps:
-            idx_b = bisect.bisect_right(d_b_x, s)
-            vol_b = d_b_y[idx_b-1] if idx_b > 0 else 0
-            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)
-            chart_bids.append(vol_b)
-            chart_asks.append(vol_a)
-
-    analysis, depth_integral = calculate_micro_price_structure(
-        diff_x, diff_y_net, mid, best_bid_p, best_ask_p, 
-        {"bids": bid_walls, "asks": ask_walls}
-    )
-    
-    # --- MACHINE LEARNING FEATURE EXTRACTION ---
-    # 1. OFI: Net Buy-Sell Vol in current window
-    feat_ofi = market_state['current_vol_window']['buy'] - market_state['current_vol_window']['sell']
-    # 2. Depth Difference: Area under the Net Liquidity Curve (Bids - Asks)
-    feat_depth = depth_integral 
-    # 3. Orderbook Imbalance at L1
-    feat_l1_imb = (best_bid_q - best_ask_q) / (best_bid_q + best_ask_q)
-    # 4. Price Momentum (Current - Prev)
-    feat_mom = mid - market_state['prev_mid']
-
-    features = [feat_ofi, feat_depth, feat_l1_imb, feat_mom]
-
-    # Train (Learn from last tick's prediction vs this tick's reality)
-    ml_model.train(mid, features)
-    # Predict (Forecast next tick)
-    ml_prediction = ml_model.get_forecast(mid, features)
-    
-    if len(market_state['ml_history']) == 0 or (now - market_state['ml_history'][-1]['t'] > 0.5):
-        market_state['ml_history'].append({'t': now, 'p': ml_prediction})
-        if len(market_state['ml_history']) > HISTORY_LENGTH:
-            market_state['ml_history'].pop(0)
-    # -------------------------------------------
-
-    polr_path = calculate_polr(market_state['bids'], market_state['asks'], mid)
-
-    if analysis:
-        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:
-                market_state['pred_history'].pop(0)
+    if not market_state['ready']: 
+        return {"error": "Initializing..."}
 
     return {
-        "mid": mid,
-        "history": market_state['history'],
-        "pred_history": market_state['pred_history'],
-        "ml_history": market_state['ml_history'],
-        "polr": polr_path,
-        "trade_history": market_state['trade_vol_history'],
-        "ohlc": market_state['ohlc_history'],
-        "depth_x": diff_x,
-        "depth_net": diff_y_net,
-        "depth_bids": chart_bids,
-        "depth_asks": chart_asks,
-        "analysis": analysis,
-        "walls": {"bids": bid_walls, "asks": ask_walls}
+        "ohlc": market_state['ohlc_history']
     }
 
 HTML_PAGE = f"""
@@ -332,7 +32,7 @@ HTML_PAGE = f"""
 <html lang="en">
 <head>
     <meta charset="UTF-8">
-    <title>{SYMBOL_KRAKEN}</title>
+    <title>{SYMBOL_KRAKEN} Klines</title>
     <script src="https://unpkg.com/lightweight-charts@4.1.1/dist/lightweight-charts.standalone.production.js"></script>
     <link href="https://fonts.googleapis.com/css2?family=Inter:wght@500;600&family=JetBrains+Mono:wght@400;700&display=swap" rel="stylesheet">
     <style>
@@ -341,13 +41,8 @@ HTML_PAGE = f"""
             --bg-panel: #0a0a0a;
             --border: #252525;
             --text-main: #FFFFFF;
-            --text-dim: #999999;
             --green: #00ff9d;
             --red: #ff3b3b;
-            --blue: #2979ff;
-            --yellow: #ffeb3b; 
-            --purple: #d500f9;
-            --cyan: #00bcd4;
         }}
         body {{ 
             margin: 0; padding: 0; 
@@ -356,181 +51,41 @@ HTML_PAGE = f"""
             font-family: 'Inter', sans-serif; 
             overflow: hidden; 
             height: 100vh; width: 100vw;
+            display: flex;
+            flex-direction: column;
         }}
-        .layout {{ 
-            display: grid; 
-            grid-template-rows: 34px 1fr 1fr; 
-            grid-template-columns: 3fr 1fr; 
-            gap: 1px;
-            background-color: var(--border); 
-            height: 100vh; 
-            box-sizing: border-box;
-        }}
-        .panel {{ background: var(--bg-panel); display: flex; flex-direction: column; overflow: hidden; }}
-        
-        .status-bar {{
-            grid-column: 1 / 3;
-            grid-row: 1 / 2;
+        .header {{
+            height: 40px;
             background: var(--bg-panel);
+            border-bottom: 1px solid var(--border);
             display: flex;
             align-items: center;
             justify-content: space-between;
-            padding: 0 12px;
+            padding: 0 15px;
             font-family: 'JetBrains Mono', monospace;
-            font-size: 12px;
-            text-transform: uppercase;
-            border-bottom: 1px solid var(--border);
-            z-index: 50;
+            font-size: 13px;
         }}
-        .status-left {{ display: flex; gap: 20px; align-items: center; }}
-        .live-dot {{ width: 8px; height: 8px; background-color: var(--green); border-radius: 50%; display: inline-block; box-shadow: 0 0 8px var(--green); }}
-        .ticker-val {{ font-weight: 700; color: #fff; font-size: 13px; }}
-
-        #p-chart {{ grid-column: 1 / 2; grid-row: 2 / 3; }}
+        .status-left {{ display: flex; gap: 15px; align-items: center; }}
+        .live-dot {{ width: 8px; height: 8px; background-color: var(--green); border-radius: 50%; box-shadow: 0 0 8px var(--green); }}
         
-        #p-bottom {{ 
-            grid-column: 1 / 2; grid-row: 3 / 4; 
-            display: grid; 
-            grid-template-columns: 1fr 1fr; 
-            gap: 1px; 
-            background: var(--border); 
-        }}
-        .bottom-sub {{ background: var(--bg-panel); display: flex; flex-direction: column; position: relative; }}
-
-        #p-sidebar {{ 
-            grid-column: 2 / 3; 
-            grid-row: 2 / 4; 
-            padding: 15px;
-            display: flex;
-            flex-direction: column;
-            gap: 15px;
-            border-left: 1px solid var(--border);
-            overflow: hidden;
-        }}
-
-        .chart-header {{
-            height: 24px;
-            min-height: 24px;
-            display: flex;
-            align-items: center;
-            padding-left: 12px;
-            font-size: 10px;
-            font-weight: 700;
-            color: var(--text-dim);
-            background: #050505; 
-            border-bottom: 1px solid #151515;
-            letter-spacing: 0.5px;
-        }}
-
-        .data-group {{ display: flex; flex-direction: column; gap: 4px; }}
-        .label {{ font-size: 10px; color: var(--text-dim); font-weight: 600; text-transform: uppercase; letter-spacing: 0.5px; }}
-        .value {{ font-family: 'JetBrains Mono', monospace; font-size: 20px; font-weight: 700; color: #fff; }}
-        .value-lg {{ font-size: 26px; }}
-        .value-sub {{ font-family: 'JetBrains Mono', monospace; font-size: 11px; margin-top: 2px; color: #666; }}
-
-        .divider {{ height: 1px; background: var(--border); width: 100%; }}
-        .c-green {{ color: var(--green); }}
-        .c-red {{ color: var(--red); }}
-        .c-dim {{ color: var(--text-dim); }}
-        .c-purp {{ color: var(--purple); }}
-        .c-cyan {{ color: var(--cyan); }}
-
-        .list-container {{ display: flex; flex-direction: column; gap: 8px; overflow-y: auto; height: 100px; }}
-        .list-item {{ 
-            display: flex; justify-content: space-between; 
-            font-family: 'JetBrains Mono', monospace; 
-            font-size: 11px; 
-            border-bottom: 1px solid #151515;
-            padding-bottom: 4px;
-        }}
-        .list-item span:first-child {{ color: #e0e0e0; }}
-        .list-item:last-child {{ border: none; }}
-        
-        .sidebar-chart-box {{
+        #chart-container {{
             flex: 1;
-            display: flex;
-            flex-direction: column;
-            min-height: 0;
-        }}
-        .mini-chart {{
-            flex: 1;
-            background: rgba(255,255,255,0.02);
-            border: 1px solid var(--border);
-            border-radius: 4px;
+            width: 100%;
+            position: relative;
         }}
     </style>
 </head>
 <body>
-<div class="layout">
-    <div class="status-bar">
+    <div class="header">
         <div class="status-left">
             <span class="live-dot"></span>
-            <span style="font-weight:700; color:#fff;">{SYMBOL_KRAKEN}</span>
-            <span id="price-ticker" class="ticker-val">---</span>
+            <span style="font-weight:700;">{SYMBOL_KRAKEN}</span>
+            <span>1 Minute Candles</span>
         </div>
-        <div class="status-right" id="clock">00:00:00 UTC</div>
+        <div id="clock">00:00:00 UTC</div>
     </div>
 
-    <div id="p-chart" class="panel">
-        <div class="chart-header">
-            PRICE (BLUE) // <span class="c-purp">POLR</span> // <span style="color:var(--yellow)">MICRO</span> // <span class="c-cyan">ML MODEL</span>
-        </div>
-        <div id="tv-price" style="flex: 1; width: 100%;"></div>
-    </div>
-
-    <div id="p-bottom">
-        <div class="bottom-sub">
-            <div class="chart-header">1M KLINE (KRAKEN OHLC)</div>
-            <div id="tv-candles" style="flex: 1; width: 100%;"></div>
-        </div>
-        <div class="bottom-sub">
-            <div class="chart-header">ORDER FLOW IMBALANCE</div>
-            <div id="tv-net" style="flex: 1; width: 100%;"></div>
-        </div>
-    </div>
-
-    <div id="p-sidebar" class="panel">
-        
-        <div class="data-group">
-            <span class="label">ML Prediction</span>
-            <span id="ml-val" class="value c-cyan">---</span>
-        </div>
-        
-        <div class="data-group">
-            <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>
-            </div>
-        </div>
-
-        <div class="divider"></div>
-
-        <div class="data-group">
-            <span class="label">OFI Imbalance Ratio</span>
-            <span id="score-val" class="value">0.00</span>
-        </div>
-
-        <div class="divider"></div>
-
-        <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;">Scanning...</span>
-            </div>
-        </div>
-
-        <div class="sidebar-chart-box">
-            <span class="label" style="margin-bottom:4px;">Real-time Volume Ticks</span>
-            <div id="sidebar-vol" class="mini-chart"></div>
-        </div>
-
-        <div class="sidebar-chart-box">
-            <span class="label" style="margin-bottom:4px;">Liquidity Density</span>
-            <div id="sidebar-density" class="mini-chart"></div>
-        </div>
-    </div>
-</div>
+    <div id="chart-container"></div>
 
 <script>
     setInterval(() => {{
@@ -539,94 +94,26 @@ HTML_PAGE = f"""
     }}, 1000);
 
     document.addEventListener('DOMContentLoaded', () => {{
-        const dom = {{
-            ticker: document.getElementById('price-ticker'),
-            score: document.getElementById('score-val'),
-            projVal: document.getElementById('proj-val'),
-            projPct: document.getElementById('proj-pct'),
-            mlVal: document.getElementById('ml-val'),
-            wallList: document.getElementById('wall-list')
-        }};
+        const chartContainer = document.getElementById('chart-container');
 
-        const chartOpts = {{
-            layout: {{ background: {{ type: 'solid', color: '#0a0a0a' }}, textColor: '#888', fontFamily: 'JetBrains Mono' }},
+        const chart = LightweightCharts.createChart(chartContainer, {{
+            layout: {{ background: {{ type: 'solid', color: '#000000' }}, textColor: '#888', fontFamily: 'JetBrains Mono' }},
             grid: {{ vertLines: {{ color: '#151515' }}, horzLines: {{ color: '#151515' }} }},
             rightPriceScale: {{ borderColor: '#222', scaleMargins: {{ top: 0.1, bottom: 0.1 }} }},
-            timeScale: {{ borderColor: '#222', timeVisible: true, secondsVisible: true }},
+            timeScale: {{ borderColor: '#222', timeVisible: true, secondsVisible: false }},
             crosshair: {{ mode: 1, vertLine: {{ color: '#444', labelBackgroundColor: '#444' }}, horzLine: {{ color: '#444', labelBackgroundColor: '#444' }} }}
-        }};
-
-        const priceChart = LightweightCharts.createChart(document.getElementById('tv-price'), chartOpts);
-        
-        const polrLines = [];
-        const polrCount = 60; 
-        
-        for(let i=0; i<polrCount; i++) {{
-            const opacity = 1.0 - (i / (polrCount + 5)); 
-            const color = `rgba(213, 0, 249, ${{opacity.toFixed(2)}})`; 
-            
-            polrLines.push(
-                priceChart.addLineSeries({{
-                    color: color,
-                    lineWidth: 1,
-                    crosshairMarkerVisible: false,
-                    lastValueVisible: false,
-                    priceLineVisible: false,
-                    title: '' 
-                }})
-            );
-        }}
-
-        const priceSeries = priceChart.addLineSeries({{ color: '#2979ff', lineWidth: 2, title: 'Price' }}); 
-        const predSeries = priceChart.addLineSeries({{ color: '#ffeb3b', lineWidth: 2, lineStyle: 2, title: 'Micro-Structure' }});
-        const mlSeries = priceChart.addLineSeries({{ color: '#00bcd4', lineWidth: 2, lineStyle: 0, title: 'ML Forecast' }});
-        
-        const candleChart = LightweightCharts.createChart(document.getElementById('tv-candles'), {{
-            ...chartOpts, 
-            timeScale: {{ timeVisible: true, secondsVisible: false }}
-        }});
-        const candleSeries = candleChart.addCandlestickSeries({{ 
-            upColor: '#00ff9d', downColor: '#ff3b3b', borderVisible: false, wickUpColor: '#00ff9d', wickDownColor: '#ff3b3b' 
         }});
 
-        const netChart = LightweightCharts.createChart(document.getElementById('tv-net'), {{
-            ...chartOpts, localization: {{ timeFormatter: t => '$' + t.toFixed(2) }}
+        const candleSeries = chart.addCandlestickSeries({{ 
+            upColor: '#00ff9d', downColor: '#ff3b3b', 
+            borderVisible: false, 
+            wickUpColor: '#00ff9d', wickDownColor: '#ff3b3b' 
         }});
-        const netSeries = netChart.addHistogramSeries({{ color: '#2979ff' }});
-
-        const volChart = LightweightCharts.createChart(document.getElementById('sidebar-vol'), {{
-            ...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' }});
-
-        const denChart = LightweightCharts.createChart(document.getElementById('sidebar-density'), {{
-            ...chartOpts,
-            grid: {{ vertLines: {{ visible: false }}, horzLines: {{ visible: false }} }},
-            rightPriceScale: {{ visible: false }},
-            timeScale: {{ visible: false }},
-            handleScroll: false, handleScale: false
-        }});
-        const bidSeries = denChart.addAreaSeries({{ lineColor: '#00ff9d', topColor: 'rgba(0, 255, 157, 0.15)', bottomColor: 'rgba(0,0,0,0)', lineWidth: 1 }});
-        const askSeries = denChart.addAreaSeries({{ lineColor: '#ff3b3b', topColor: 'rgba(255, 59, 59, 0.15)', bottomColor: 'rgba(0,0,0,0)', lineWidth: 1 }});
-
-        let activeLines = [];
-        let activeCandleLines = [];
 
         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-candles') candleChart.applyOptions({{width, height}});
-                if(entry.target.id === 'tv-net') netChart.applyOptions({{width, height}});
-                if(entry.target.id === 'sidebar-vol') volChart.applyOptions({{width, height}});
-                if(entry.target.id === 'sidebar-density') denChart.applyOptions({{width, height}});
-            }}
-        }}).observe(document.body);
+            const {{width, height}} = entries[0].contentRect;
+            chart.applyOptions({{ width, height }});
+        }}).observe(chartContainer);
 
         function connect() {{
             const ws = new WebSocket((location.protocol === 'https:' ? 'wss' : 'ws') + '://' + location.host + '/ws');
@@ -635,53 +122,6 @@ HTML_PAGE = f"""
                 const data = JSON.parse(e.data);
                 if (data.error) return;
 
-                if (data.history.length) {{
-                    const hist = data.history.map(d => ({{ time: Math.floor(d.t), value: d.p }}));
-                    const cleanHist = [...new Map(hist.map(i => [i.time, i])).values()];
-                    priceSeries.setData(cleanHist);
-
-                    const lastP = cleanHist[cleanHist.length-1].value;
-                    const lastTime = cleanHist[cleanHist.length-1].time;
-                    dom.ticker.innerText = lastP.toLocaleString('en-US', {{ minimumFractionDigits: 2 }});
-
-                    if (data.analysis) {{
-                        const proj = data.analysis.projected;
-                        const rho = data.analysis.rho; 
-                        predSeries.setData([
-                            cleanHist[cleanHist.length-1],
-                            {{ time: lastTime + 60, value: proj }}
-                        ]);
-                        const pct = ((proj - lastP) / lastP) * 100;
-                        const sign = pct >= 0 ? "+" : "";
-                        dom.projPct.innerText = `${{sign}}${{pct.toFixed(4)}}%`;
-                        dom.projPct.style.color = pct >= 0 ? "var(--green)" : "var(--red)";
-                        dom.projVal.innerText = proj.toLocaleString('en-US', {{ minimumFractionDigits: 2 }});
-                        dom.score.innerText = rho.toFixed(3);
-                        dom.score.style.color = rho > 0 ? "var(--green)" : (rho < 0 ? "var(--red)" : "var(--text-main)");
-                    }}
-                    
-                    if (data.ml_history && data.ml_history.length) {{
-                         const mlLast = data.ml_history[data.ml_history.length-1];
-                         dom.mlVal.innerText = mlLast.p.toLocaleString('en-US', {{ minimumFractionDigits: 2 }});
-                         
-                         mlSeries.setData([
-                            cleanHist[cleanHist.length-1],
-                            {{ time: lastTime + 30, value: mlLast.p }}
-                         ]);
-                    }}
-
-                    if (data.polr && data.polr.length) {{
-                        data.polr.forEach((point, index) => {{
-                            if (index < polrLines.length) {{
-                                polrLines[index].update({{
-                                    time: lastTime, 
-                                    value: point.p
-                                }});
-                            }}
-                        }});
-                    }}
-                }}
-
                 if (data.ohlc && data.ohlc.length) {{
                     const candles = data.ohlc.map(c => ({{
                         time: c.time,
@@ -690,54 +130,11 @@ HTML_PAGE = f"""
                         low: c.low,
                         close: c.close
                     }}));
+                    
+                    // Deduplicate based on time to prevent flickering
                     const uniqueCandles = [...new Map(candles.map(i => [i.time, i])).values()];
                     candleSeries.setData(uniqueCandles);
                 }}
-
-                if (data.walls) {{
-                    activeLines.forEach(l => priceSeries.removePriceLine(l));
-                    activeLines = [];
-                    activeCandleLines.forEach(l => candleSeries.removePriceLine(l));
-                    activeCandleLines = [];
-                    
-                    let html = "";
-                    const addWall = (w, type) => {{
-                        const color = type === 'BID' ? '#00ff9d' : '#ff3b3b';
-                        const lineOpts = {{ price: w.price, color: color, lineWidth: 1, lineStyle: 2, axisLabelVisible: false }};
-                        
-                        activeLines.push(priceSeries.createPriceLine(lineOpts));
-                        activeCandleLines.push(candleSeries.createPriceLine(lineOpts));
-                        
-                        html += `<div class="list-item"><span style="color:${{color}}">${{type}} ${{w.price}}</span><span class="c-dim">Z:${{w.z_score.toFixed(1)}}</span></div>`;
-                    }};
-                    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>';
-                }}
-
-                if (data.trade_history && data.trade_history.length) {{
-                    const buyData = [], 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 }});
-                    }});
-                    volBuySeries.setData([...new Map(buyData.map(i => [i.time, i])).values()]);
-                    volSellSeries.setData([...new Map(sellData.map(i => [i.time, i])).values()]);
-                }}
-
-                if (data.depth_x.length) {{
-                    const bids = [], asks = [], nets = [];
-                    for(let i=0; i<data.depth_x.length; i++) {{
-                        const t = data.depth_x[i];
-                        bids.push({{ time: t, value: data.depth_bids[i] }});
-                        asks.push({{ time: t, value: data.depth_asks[i] }});
-                        nets.push({{ time: t, value: data.depth_net[i], color: data.depth_net[i] > 0 ? '#00ff9d' : '#ff3b3b' }});
-                    }}
-                    bidSeries.setData(bids);
-                    askSeries.setData(asks);
-                    netSeries.setData(nets);
-                }}
             }};
             ws.onclose = () => setTimeout(connect, 2000);
         }}
@@ -750,6 +147,8 @@ HTML_PAGE = f"""
 
 async def kraken_worker():
     global market_state
+    
+    # 1. Fetch initial History via REST
     try:
         async with aiohttp.ClientSession() as session:
             url = "https://api.kraken.com/0/public/OHLC?pair=XBTUSD&interval=1"
@@ -768,21 +167,20 @@ async def kraken_worker():
                                         'low': float(c[3]),
                                         'close': float(c[4])
                                     }
-                                    for c in raw_candles[-120:]
+                                    for c in raw_candles[-300:] # Keep last 300 candles
                                 ]
+                                market_state['ready'] = True
                                 break
     except Exception as e:
         logging.error(f"History fetch failed: {e}")
 
+    # 2. Connect via WebSocket for Live Updates
     while True:
         try:
             async with websockets.connect("wss://ws.kraken.com/v2") as ws:
                 logging.info(f"🔌 Connected to Kraken ({SYMBOL_KRAKEN})")
                 
-                await ws.send(json.dumps({
-                    "method": "subscribe",
-                    "params": {"channel": "book", "symbol": [SYMBOL_KRAKEN], "depth": 500}
-                }))
+                # Subscribe to Trade (for real-time ticks) and OHLC (for official close)
                 await ws.send(json.dumps({
                     "method": "subscribe",
                     "params": {"channel": "trade", "symbol": [SYMBOL_KRAKEN]}
@@ -797,51 +195,23 @@ async def kraken_worker():
                     channel = payload.get("channel")
                     data = payload.get("data", [])
 
-                    if channel == "book":
-                        for item in data:
-                            for bid in item.get('bids', []):
-                                q, p = float(bid['qty']), float(bid['price'])
-                                if q == 0: market_state['bids'].pop(p, None)
-                                else: market_state['bids'][p] = q
-                            for ask in item.get('asks', []):
-                                q, p = float(ask['qty']), float(ask['price'])
-                                if q == 0: market_state['asks'].pop(p, None)
-                                else: market_state['asks'][p] = q
-                        
-                        if market_state['bids'] and market_state['asks']:
-                            market_state['prev_mid'] = market_state['current_mid']
-                            best_bid = max(market_state['bids'].keys())
-                            best_ask = min(market_state['asks'].keys())
-                            mid = (best_bid + best_ask) / 2
-                            market_state['current_mid'] = mid
-                            market_state['ready'] = True
-                            
-                            now = time.time()
-                            if not market_state['history'] or (now - market_state['history'][-1]['t'] > 0.5):
-                                market_state['history'].append({'t': now, 'p': mid})
-                                if len(market_state['history']) > HISTORY_LENGTH:
-                                    market_state['history'].pop(0)
-                    
-                    elif channel == "trade":
+                    # A. Real-time Tick Updates (Simulating live candle)
+                    if channel == "trade":
                         for trade in data:
                             try:
-                                qty = float(trade['qty'])
                                 price = float(trade['price'])
-                                side = trade['side']
-                                
-                                if side == 'buy': market_state['current_vol_window']['buy'] += qty
-                                else: market_state['current_vol_window']['sell'] += qty
-
                                 current_minute_start = int(time.time()) // 60 * 60
                                 
                                 if market_state['ohlc_history']:
                                     last_candle = market_state['ohlc_history'][-1]
                                     
+                                    # Update current candle
                                     if last_candle['time'] == current_minute_start:
                                         last_candle['close'] = price
                                         if price > last_candle['high']: last_candle['high'] = price
                                         if price < last_candle['low']: last_candle['low'] = price
                                     
+                                    # Create new candle
                                     elif current_minute_start > last_candle['time']:
                                         new_candle = {
                                             'time': current_minute_start,
@@ -851,13 +221,15 @@ async def kraken_worker():
                                             'close': price
                                         }
                                         market_state['ohlc_history'].append(new_candle)
-                                        if len(market_state['ohlc_history']) > 200:
+                                        if len(market_state['ohlc_history']) > 300:
                                             market_state['ohlc_history'].pop(0)
                             except: pass
 
+                    # B. Confirmed Candle Updates
                     elif channel == "ohlc":
                         for candle in data:
                             try:
+                                # Kraken sends 'endtime', convert to 'starttime' for charts
                                 start_time = int(float(candle['endtime'])) - 60
                                 c_data = {
                                     'time': start_time, 
@@ -872,7 +244,7 @@ async def kraken_worker():
                                         market_state['ohlc_history'][-1] = c_data
                                     elif market_state['ohlc_history'][-1]['time'] < start_time:
                                         market_state['ohlc_history'].append(c_data)
-                                        if len(market_state['ohlc_history']) > 200:
+                                        if len(market_state['ohlc_history']) > 300:
                                             market_state['ohlc_history'].pop(0)
                             except Exception as e:
                                 pass
@@ -925,7 +297,7 @@ async def main():
     await runner.setup()
     site = web.TCPSite(runner, '0.0.0.0', PORT)
     await site.start()
-    print(f"🚀 Quant Dashboard: http://localhost:{PORT}")
+    print(f"🚀 Kline Chart: http://localhost:{PORT}")
     await asyncio.Event().wait()
 
 if __name__ == "__main__":