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import asyncio |
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import json |
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import logging |
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import time |
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import bisect |
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from aiohttp import web |
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import websockets |
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SYMBOL_KRAKEN = "BTC/USD" |
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PORT = 7860 |
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HISTORY_LENGTH = 1000 |
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s') |
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market_state = { |
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"bids": {}, |
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"asks": {}, |
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"history": [], |
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"current_mid": 0.0, |
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"prev_mid": 0.0, |
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"ready": False |
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} |
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def analyze_structure(diff_x, diff_y, current_mid): |
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""" |
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Analyzes the Net Liquidity Curve to find Support, Resistance, and Projected Trend. |
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""" |
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if not diff_y or len(diff_y) < 5: |
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return None |
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net_total = diff_y[-1] |
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momentum_shift = net_total * 0.2 |
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projected_price = current_mid + momentum_shift |
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support_level = None |
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resistance_level = None |
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scan_limit = len(diff_y) // 2 |
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for i in range(1, scan_limit): |
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prev_val = diff_y[i-1] |
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curr_val = diff_y[i] |
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dist = diff_x[i] |
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if prev_val > 0 and curr_val < 0 and resistance_level is None: |
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resistance_level = current_mid + dist |
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if prev_val < 0 and curr_val > 0 and support_level is None: |
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support_level = current_mid - dist |
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return { |
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"projected": projected_price, |
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"support": support_level, |
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"resistance": resistance_level, |
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"net_score": net_total |
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} |
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HTML_PAGE = f""" |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<title>AI Liquidity Dashboard | {SYMBOL_KRAKEN}</title> |
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<!-- TradingView Lightweight Charts --> |
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<script src="https://unpkg.com/lightweight-charts/dist/lightweight-charts.standalone.production.js"></script> |
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<!-- Plotly for Depth Chart --> |
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<script src="https://cdn.plot.ly/plotly-2.24.1.min.js"></script> |
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<style> |
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:root {{ |
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--bg-color: #0b0c10; |
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--panel-bg: #1f2833; |
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--text-main: #c5c6c7; |
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--accent-green: #66fcf1; |
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--accent-green-dim: #45a29e; |
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--accent-red: #ff3b3b; |
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--accent-red-dim: #a82828; |
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--border: #2d3842; |
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}} |
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body {{ |
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margin: 0; padding: 0; |
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background-color: var(--bg-color); |
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color: var(--text-main); |
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font-family: 'JetBrains Mono', 'Courier New', monospace; |
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overflow: hidden; |
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height: 100vh; |
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width: 100vw; |
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}} |
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/* --- GRID LAYOUT --- */ |
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.grid-container {{ |
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display: grid; |
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grid-template-columns: 3fr 1fr; /* Main chart 75%, Side panel 25% */ |
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grid-template-rows: 2fr 1fr; /* Top 66%, Bottom 33% */ |
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gap: 4px; |
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height: 100vh; |
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padding: 4px; |
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box-sizing: border-box; |
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}} |
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.panel {{ |
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background: #12141a; |
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border: 1px solid var(--border); |
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border-radius: 4px; |
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position: relative; |
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display: flex; |
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flex-direction: column; |
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overflow: hidden; |
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}} |
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/* Grid Areas */ |
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#p-price {{ grid-column: 1 / 2; grid-row: 1 / 2; }} |
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#p-depth {{ grid-column: 1 / 2; grid-row: 2 / 3; }} |
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#p-stats {{ grid-column: 2 / 3; grid-row: 1 / 3; border-left: 2px solid var(--accent-green-dim); }} |
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/* Headers */ |
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.panel-header {{ |
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padding: 8px 12px; |
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background: #0f1116; |
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border-bottom: 1px solid var(--border); |
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font-size: 12px; |
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text-transform: uppercase; |
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font-weight: bold; |
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display: flex; |
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justify-content: space-between; |
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align-items: center; |
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color: var(--accent-green); |
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}} |
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/* --- CHART CONTAINERS --- */ |
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#tv-chart {{ flex: 1; width: 100%; }} |
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#depth-chart {{ flex: 1; width: 100%; }} |
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/* --- STATS PANEL --- */ |
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.stats-content {{ |
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padding: 15px; |
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overflow-y: auto; |
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flex: 1; |
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}} |
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.stat-box {{ |
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margin-bottom: 20px; |
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padding: 10px; |
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background: rgba(255,255,255,0.02); |
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border-radius: 4px; |
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}} |
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.stat-label {{ font-size: 11px; color: #666; display: block; margin-bottom: 4px; }} |
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.stat-value {{ font-size: 24px; font-weight: bold; }} |
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.stat-sub {{ font-size: 12px; margin-left: 5px; }} |
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.green {{ color: var(--accent-green); }} |
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.red {{ color: var(--accent-red); }} |
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/* --- TERMINAL --- */ |
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.terminal-box {{ |
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margin-top: auto; |
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font-size: 11px; |
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height: 300px; |
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overflow-y: hidden; |
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display: flex; |
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flex-direction: column; |
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}} |
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.term-header {{ border-bottom: 1px dashed #444; margin-bottom: 5px; padding-bottom: 5px; opacity: 0.7; }} |
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#term-logs {{ flex: 1; overflow-y: hidden; display: flex; flex-direction: column-reverse; }} |
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.log-line {{ margin-top: 4px; padding-left: 8px; border-left: 2px solid #333; }} |
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.log-bull {{ border-left-color: var(--accent-green); color: #e0f2f1; }} |
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.log-bear {{ border-left-color: var(--accent-red); color: #ffebee; }} |
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/* --- METER --- */ |
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.meter-container {{ width: 100%; height: 6px; background: #333; border-radius: 3px; margin-top: 10px; position: relative; overflow: hidden; }} |
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.meter-bar {{ height: 100%; width: 50%; background: #555; transition: all 0.5s ease; position: absolute; left: 0; }} |
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.mid-mark {{ position: absolute; left: 50%; height: 100%; width: 2px; background: #fff; z-index: 10; }} |
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/* Load Overlay */ |
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#loader {{ |
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position: absolute; top:0; left:0; width:100%; height:100%; |
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background: rgba(0,0,0,0.9); z-index: 999; |
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display: flex; justify-content: center; align-items: center; |
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color: var(--accent-green); font-size: 20px; |
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}} |
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</style> |
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</head> |
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<body> |
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<div id="loader">INITIALIZING AI MODELS...</div> |
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<div class="grid-container"> |
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<!-- PRICE CHART PANEL --> |
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<div id="p-price" class="panel"> |
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<div class="panel-header"> |
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<span>BTC/USD Price Action</span> |
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<span id="live-price" style="color:white;">---</span> |
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</div> |
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<div id="tv-chart"></div> |
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</div> |
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<!-- DEPTH / LIQUIDITY PANEL --> |
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<div id="p-depth" class="panel"> |
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<div class="panel-header"> |
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<span>Net Liquidity Structure (Bid - Ask)</span> |
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<span style="font-size:10px; color:#666;">DEPTH 300</span> |
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</div> |
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<div id="depth-chart"></div> |
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</div> |
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<!-- SIDEBAR / AI STATS --> |
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<div id="p-stats" class="panel"> |
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<div class="panel-header">AI ANALYTICS ENGINE</div> |
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<div class="stats-content"> |
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<!-- CURRENT SCORE --> |
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<div class="stat-box"> |
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<span class="stat-label">NET LIQUIDITY SCORE</span> |
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<div style="display:flex; align-items:baseline;"> |
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<span id="score-val" class="stat-value">0</span> |
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</div> |
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<div class="meter-container"> |
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<div class="mid-mark"></div> |
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<div id="score-bar" class="meter-bar" style="width: 0%; left: 50%;"></div> |
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</div> |
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</div> |
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<!-- KEY LEVELS --> |
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<div class="stat-box"> |
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<span class="stat-label">DETECTED STRUCTURE</span> |
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<div style="margin-top:8px;"> |
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<div style="display:flex; justify-content:space-between;"> |
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<span style="color:#aaa;">RESIST:</span> |
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<span id="res-val" class="red">---</span> |
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</div> |
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<div style="display:flex; justify-content:space-between; margin-top:4px;"> |
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<span style="color:#aaa;">SUPPORT:</span> |
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<span id="sup-val" class="green">---</span> |
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</div> |
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</div> |
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</div> |
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<!-- PREDICTED TARGET --> |
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<div class="stat-box" style="border: 1px solid #444;"> |
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<span class="stat-label" style="color:var(--accent-green);">AI PRICE PROJECTION</span> |
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<span id="proj-val" class="stat-value" style="font-size:20px;">---</span> |
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</div> |
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<!-- TERMINAL --> |
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<div class="terminal-box"> |
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<div class="term-header">> SYSTEM LOGS</div> |
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<div id="term-logs"></div> |
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</div> |
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</div> |
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</div> |
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</div> |
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<script> |
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// --- 1. SETUP TRADINGVIEW LIGHTWEIGHT CHART --- |
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const chartContainer = document.getElementById('tv-chart'); |
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const chart = LightweightCharts.createChart(chartContainer, {{ |
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layout: {{ background: {{ type: 'solid', color: '#12141a' }}, textColor: '#888' }}, |
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grid: {{ vertLines: {{ color: '#1f2833' }}, horzLines: {{ color: '#1f2833' }} }}, |
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crosshair: {{ mode: LightweightCharts.CrosshairMode.Normal }}, |
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rightPriceScale: {{ borderColor: '#2d3842' }}, |
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timeScale: {{ borderColor: '#2d3842', timeVisible: true, secondsVisible: true }}, |
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}}); |
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const lineSeries = chart.addLineSeries({{ |
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color: '#2962FF', |
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lineWidth: 2, |
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crosshairMarkerVisible: true, |
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lastValueVisible: false, |
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priceLineVisible: false, |
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}}); |
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// Forecast Line (Dashed) |
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const predSeries = chart.addLineSeries({{ |
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color: '#ff9800', |
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lineWidth: 2, |
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lineStyle: LightweightCharts.LineStyle.Dotted, |
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title: 'Forecast' |
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}}); |
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// S/R Lines (Using PriceLines) |
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let supportLine = null; |
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let resistanceLine = null; |
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// Auto Resize |
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new ResizeObserver(entries => {{ |
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if (entries.length === 0 || entries[0].target !== chartContainer) {{ return; }} |
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const newRect = entries[0].contentRect; |
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chart.applyOptions({{ width: newRect.width, height: newRect.height }}); |
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}}).observe(chartContainer); |
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// --- 2. SETUP PLOTLY (DEPTH) --- |
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// We use Plotly here because LightweightCharts is bad at non-time-series X/Y plots |
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const depthDiv = document.getElementById('depth-chart'); |
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const plotlyLayout = {{ |
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paper_bgcolor: '#12141a', |
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plot_bgcolor: '#12141a', |
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font: {{ color: '#888', family: 'JetBrains Mono' }}, |
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margin: {{ t: 10, b: 30, l: 40, r: 20 }}, |
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showlegend: false, |
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xaxis: {{ gridcolor: '#1f2833', title: 'Distance ($)' }}, |
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yaxis: {{ gridcolor: '#1f2833' }} |
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}}; |
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const plotlyConfig = {{ responsive: true, displayModeBar: false }}; |
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Plotly.newPlot(depthDiv, [], plotlyLayout, plotlyConfig); |
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// --- 3. LOGIC & UPDATES --- |
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const dom = {{ |
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price: document.getElementById('live-price'), |
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loader: document.getElementById('loader'), |
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scoreVal: document.getElementById('score-val'), |
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scoreBar: document.getElementById('score-bar'), |
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resVal: document.getElementById('res-val'), |
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supVal: document.getElementById('sup-val'), |
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projVal: document.getElementById('proj-val'), |
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logs: document.getElementById('term-logs') |
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}}; |
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let lastTime = 0; |
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function log(msg, type='neutral') {{ |
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const div = document.createElement('div'); |
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div.className = `log-line ${{type === 'bull' ? 'log-bull' : type === 'bear' ? 'log-bear' : ''}}`; |
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const timeStr = new Date().toLocaleTimeString('en-US', {{hour12:false}}); |
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div.innerHTML = `<span style="opacity:0.5; font-size:10px;">${{timeStr}}</span> ${{msg}}`; |
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dom.logs.prepend(div); |
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if (dom.logs.children.length > 20) dom.logs.removeChild(dom.logs.lastChild); |
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}} |
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async function fetchData() {{ |
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try {{ |
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const res = await fetch('/data'); |
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const data = await res.json(); |
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if (data.error) return; // Still initializing |
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dom.loader.style.display = 'none'; |
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// -- A. UPDATE PRICE CHART -- |
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const history = data.history.map(d => ({{ time: Math.floor(d.t), value: d.p }})); |
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// De-duplicate times for Lightweight Charts |
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const uniqueHistory = []; |
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const seenTimes = new Set(); |
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history.forEach(h => {{ |
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if(!seenTimes.has(h.time)) {{ seenTimes.add(h.time); uniqueHistory.push(h); }} |
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}}); |
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lineSeries.setData(uniqueHistory); |
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dom.price.innerHTML = `$${{data.mid.toLocaleString(undefined, {{minimumFractionDigits: 2}})}}`; |
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// -- B. UPDATE AI ANALYSIS -- |
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if (data.analysis) {{ |
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const {{ projected, support, resistance, net_score }} = data.analysis; |
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// 1. Prediction Line (Current Time -> Future) |
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const lastT = uniqueHistory[uniqueHistory.length-1].time; |
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predSeries.setData([ |
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{{ time: lastT, value: data.mid }}, |
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{{ time: lastT + 60, value: projected }} // 1 min projection |
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]); |
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dom.projVal.innerHTML = `$${{projected.toLocaleString(undefined, {{maximumFractionDigits:0}})}}`; |
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dom.projVal.style.color = projected > data.mid ? '#66fcf1' : '#ff3b3b'; |
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|
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// 2. S/R Lines |
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if (support) {{ |
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dom.supVal.innerText = `$${{support.toFixed(0)}}`; |
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if (!supportLine) {{ |
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supportLine = lineSeries.createPriceLine({{ |
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price: support, color: '#00e676', lineWidth: 1, lineStyle: LightweightCharts.LineStyle.Solid, axisLabelVisible: true, title: 'SUP' |
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}}); |
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}} else supportLine.applyOptions({{ price: support }}); |
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}} else {{ |
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dom.supVal.innerText = '---'; |
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if(supportLine) {{ lineSeries.removePriceLine(supportLine); supportLine=null; }} |
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}} |
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if (resistance) {{ |
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dom.resVal.innerText = `$${{resistance.toFixed(0)}}`; |
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if (!resistanceLine) {{ |
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resistanceLine = lineSeries.createPriceLine({{ |
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price: resistance, color: '#ff1744', lineWidth: 1, lineStyle: LightweightCharts.LineStyle.Solid, axisLabelVisible: true, title: 'RES' |
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}}); |
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}} else resistanceLine.applyOptions({{ price: resistance }}); |
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}} else {{ |
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dom.resVal.innerText = '---'; |
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if(resistanceLine) {{ lineSeries.removePriceLine(resistanceLine); resistanceLine=null; }} |
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}} |
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|
|
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// 3. Score Meter |
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dom.scoreVal.innerText = net_score.toFixed(1); |
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dom.scoreVal.className = net_score > 0 ? "stat-value green" : "stat-value red"; |
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|
|
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// Meter logic: Range approx -50 to 50 |
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let barWidth = Math.min(Math.abs(net_score)*2, 50); |
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dom.scoreBar.style.width = `${{barWidth}}%`; |
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dom.scoreBar.style.left = net_score > 0 ? "50%" : `${{50 - barWidth}}%`; |
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dom.scoreBar.style.backgroundColor = net_score > 0 ? "var(--accent-green)" : "var(--accent-red)"; |
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|
|
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// 4. Logs |
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if (net_score > 25 && Math.random() > 0.9) log(`High Buying Pressure detected`, 'bull'); |
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if (net_score < -25 && Math.random() > 0.9) log(`High Selling Pressure detected`, 'bear'); |
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if (support && Math.random() > 0.95) log(`Price approaching support floor`, 'bull'); |
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}} |
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|
|
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// -- C. UPDATE DEPTH CHART -- |
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|
const traceDiff = {{ |
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x: data.diff.x, |
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y: data.diff.y, |
|
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type: 'scatter', |
|
|
mode: 'lines', |
|
|
fill: 'tozeroy', |
|
|
line: {{color: '#e040fb', width: 2}}, |
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fillcolor: 'rgba(224, 64, 251, 0.1)' |
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}}; |
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|
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// Add a zero line |
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const zeroLine = {{ type: 'line', x0: 0, x1: 1, xref: 'paper', y0: 0, y1: 0, line: {{color: '#444', width: 1}} }}; |
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const layoutUpdate = {{ ...plotlyLayout, shapes: [zeroLine] }}; |
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|
Plotly.react(depthDiv, [traceDiff], layoutUpdate, plotlyConfig); |
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|
|
|
}} catch (e) {{ |
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|
console.error(e); |
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|
}} |
|
|
}} |
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|
|
|
setInterval(fetchData, 1000); |
|
|
</script> |
|
|
</body> |
|
|
</html> |
|
|
""" |
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|
|
|
|
async def kraken_worker(): |
|
|
global market_state |
|
|
while True: |
|
|
try: |
|
|
async with websockets.connect("wss://ws.kraken.com/v2") as ws: |
|
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logging.info(f"🔌 Connected to Kraken ({SYMBOL_KRAKEN})") |
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await ws.send(json.dumps({ |
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"method": "subscribe", |
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"params": {"channel": "book", "symbol": [SYMBOL_KRAKEN], "depth": 500} |
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})) |
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|
|
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async for message in ws: |
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payload = json.loads(message) |
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channel = payload.get("channel") |
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data_entries = payload.get("data", []) |
|
|
|
|
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if channel == "book": |
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for item in data_entries: |
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|
|
|
|
for bid in item.get('bids', []): |
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q, p = float(bid['qty']), float(bid['price']) |
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if q == 0: market_state['bids'].pop(p, None) |
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else: market_state['bids'][p] = q |
|
|
|
|
|
for ask in item.get('asks', []): |
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q, p = float(ask['qty']), float(ask['price']) |
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if q == 0: market_state['asks'].pop(p, None) |
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else: market_state['asks'][p] = q |
|
|
|
|
|
if market_state['bids'] and market_state['asks']: |
|
|
best_bid = max(market_state['bids'].keys()) |
|
|
best_ask = min(market_state['asks'].keys()) |
|
|
mid = (best_bid + best_ask) / 2 |
|
|
|
|
|
market_state['prev_mid'] = market_state['current_mid'] |
|
|
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) |
|
|
|
|
|
except Exception as e: |
|
|
logging.warning(f"⚠️ Reconnecting: {e}") |
|
|
await asyncio.sleep(3) |
|
|
|
|
|
async def handle_index(request): |
|
|
return web.Response(text=HTML_PAGE, content_type='text/html') |
|
|
|
|
|
async def handle_data(request): |
|
|
if not market_state['ready']: |
|
|
return web.json_response({"error": "Initializing..."}) |
|
|
|
|
|
mid = market_state['current_mid'] |
|
|
|
|
|
|
|
|
raw_bids = sorted(market_state['bids'].items(), key=lambda x: -x[0])[:300] |
|
|
raw_asks = sorted(market_state['asks'].items(), key=lambda x: x[0])[:300] |
|
|
|
|
|
|
|
|
d_b_x, d_b_y, cum = [], [], 0 |
|
|
for p, q in raw_bids: |
|
|
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 raw_asks: |
|
|
d = p - mid |
|
|
if d >= 0: |
|
|
cum += q |
|
|
d_a_x.append(d); d_a_y.append(cum) |
|
|
|
|
|
|
|
|
diff_x, diff_y = [], [] |
|
|
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.append(vol_b - vol_a) |
|
|
|
|
|
|
|
|
analysis = analyze_structure(diff_x, diff_y, mid) |
|
|
|
|
|
return web.json_response({ |
|
|
"mid": mid, |
|
|
"history": market_state['history'], |
|
|
"diff": { "x": diff_x, "y": diff_y }, |
|
|
"analysis": analysis |
|
|
}) |
|
|
|
|
|
async def start_background(app): |
|
|
app['kraken_task'] = asyncio.create_task(kraken_worker()) |
|
|
|
|
|
async def cleanup_background(app): |
|
|
app['kraken_task'].cancel() |
|
|
try: await app['kraken_task'] |
|
|
except asyncio.CancelledError: pass |
|
|
|
|
|
async def main(): |
|
|
app = web.Application() |
|
|
app.router.add_get('/', handle_index) |
|
|
app.router.add_get('/data', handle_data) |
|
|
app.on_startup.append(start_background) |
|
|
app.on_cleanup.append(cleanup_background) |
|
|
|
|
|
runner = web.AppRunner(app) |
|
|
await runner.setup() |
|
|
site = web.TCPSite(runner, '0.0.0.0', PORT) |
|
|
await site.start() |
|
|
|
|
|
print(f"🚀 AI Dashboard: http://localhost:{PORT}") |
|
|
await asyncio.Event().wait() |
|
|
|
|
|
if __name__ == "__main__": |
|
|
try: asyncio.run(main()) |
|
|
except KeyboardInterrupt: pass |
