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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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import random |
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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 = 300 |
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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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"last_comment_time": 0 |
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} |
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def generate_ai_commentary(diff_y, mid, prev_mid): |
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""" |
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Analyzes the Net Liquidity (diff_y) and Price Action to generate commentary. |
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diff_y is a list of (BidVol - AskVol) at increasing distances. |
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""" |
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if not diff_y: |
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return {"text": "Initializing analysis...", "sentiment": "neutral"} |
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net_total = diff_y[-1] |
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avg_liquidity = sum(diff_y) / len(diff_y) |
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price_delta = mid - prev_mid |
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msg = "" |
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sentiment = "neutral" |
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if net_total > 50: |
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sentiment = "bullish" |
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msg = f"🚀 <b>STRONG BUY SUPPORT:</b> Net surplus of {int(net_total)} BTC. Orderbook is heavily tilted towards Bids." |
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elif net_total < -50: |
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sentiment = "bearish" |
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msg = f"📉 <b>HEAVY SELL PRESSURE:</b> Net deficit of {int(net_total)} BTC. Sellers are dominating the book." |
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elif price_delta < 0 and net_total > 20: |
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sentiment = "warning" |
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msg = f"🛡️ <b>ABSORPTION DETECTED:</b> Price is falling, but Bid depth is increasing (+{int(net_total)} BTC). Passive buyers are catching the dump." |
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elif price_delta > 0 and net_total < -20: |
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sentiment = "warning" |
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msg = f"🧱 <b>HIDDEN WALL:</b> Price is rising into heavy Sell liquidity ({int(net_total)} BTC diff). Breakout might fail." |
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elif abs(net_total) < 10: |
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sentiment = "neutral" |
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msg = f"⚖️ <b>EQUILIBRIUM:</b> Bids and Asks are perfectly balanced. Expect low volatility or a sudden breakout." |
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else: |
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if net_total > 0: |
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msg = f"📈 <b>Bullish Bias:</b> Moderate buy support (+{int(net_total)} BTC). Path of least resistance is UP." |
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sentiment = "bullish" |
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else: |
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msg = f"📉 <b>Bearish Bias:</b> Moderate sell overhang ({int(net_total)} BTC). Path of least resistance is DOWN." |
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return {"text": msg, "sentiment": sentiment, "net": net_total} |
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HTML_PAGE = f""" |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<title>BTC-USD AI Analyst</title> |
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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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body {{ margin: 0; padding: 0; background-color: #0e0e0e; color: #ccc; font-family: 'Courier New', monospace; overflow: hidden; }} |
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/* Layout Grid */ |
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#container {{ display: flex; flex-direction: column; height: 100vh; width: 100vw; }} |
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/* Row 1: Charts (60% Height) */ |
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#row-charts {{ flex: 6; display: flex; width: 100%; border-bottom: 2px solid #333; }} |
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.col-chart {{ width: 50%; height: 100%; border-right: 1px solid #333; }} |
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/* Row 2: AI Terminal (40% Height) */ |
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#row-terminal {{ flex: 4; display: flex; flex-direction: column; background-color: #050505; padding: 10px; overflow-y: auto; }} |
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/* Terminal Styling */ |
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.terminal-header {{ color: #00bcd4; font-weight: bold; border-bottom: 1px dashed #333; padding-bottom: 5px; margin-bottom: 10px; }} |
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.log-entry {{ margin-bottom: 6px; font-size: 14px; line-height: 1.4; border-left: 3px solid transparent; padding-left: 8px; }} |
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.log-time {{ color: #666; font-size: 12px; margin-right: 10px; }} |
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/* Sentiment Colors */ |
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.bullish {{ border-left-color: #00e676; color: #e8f5e9; }} |
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.bearish {{ border-left-color: #ff1744; color: #ffebee; }} |
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.neutral {{ border-left-color: #999; color: #ccc; }} |
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.warning {{ border-left-color: #ff9800; color: #fff3e0; }} |
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/* Highlight classes for inner HTML */ |
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b {{ font-weight: bold; }} |
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/* Charts fill their containers */ |
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.chart {{ width: 100%; height: 100%; }} |
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#status {{ position: absolute; top: 10px; left: 60px; z-index: 100; font-size: 14px; background: rgba(0,0,0,0.8); padding: 5px 10px; border-radius: 4px; border: 1px solid #333; }} |
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.green {{ color: #00e676; }} |
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.red {{ color: #ff1744; }} |
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</style> |
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</head> |
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<body> |
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<div id="status">Connecting to Neural Net...</div> |
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<div id="container"> |
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<!-- ROW 1: CONTEXT CHARTS --> |
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<div id="row-charts"> |
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<div class="col-chart"> |
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<div id="price-chart" class="chart"></div> |
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</div> |
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<div class="col-chart"> |
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<div id="vol-chart" class="chart"></div> |
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</div> |
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</div> |
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<!-- ROW 2: AI COMMENTATOR --> |
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<div id="row-terminal"> |
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<div class="terminal-header">> AI MARKET ANALYST (Based on Net Liquidity)</div> |
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<div id="terminal-logs"></div> |
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</div> |
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</div> |
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<script> |
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const priceDiv = document.getElementById('price-chart'); |
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const volDiv = document.getElementById('vol-chart'); |
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const termLogs = document.getElementById('terminal-logs'); |
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const statusDiv = document.getElementById('status'); |
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let initPrice = false, initVol = false; |
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let lastLogText = ""; |
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const commonConfig = {{ responsive: true, displayModeBar: false }}; |
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const commonLayout = {{ |
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paper_bgcolor: '#0e0e0e', |
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plot_bgcolor: '#0e0e0e', |
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font: {{ color: '#aaa', family: 'Courier New' }}, |
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margin: {{ t: 30, b: 25, l: 40, r: 20 }}, |
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showlegend: false, |
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xaxis: {{ gridcolor: '#222' }}, |
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yaxis: {{ gridcolor: '#222' }} |
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}}; |
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function addLog(data) {{ |
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// Prevent spamming the exact same message |
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if (data.comment.text === lastLogText) return; |
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lastLogText = data.comment.text; |
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const div = document.createElement('div'); |
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div.className = `log-entry ${{data.comment.sentiment}}`; |
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const timeStr = new Date().toLocaleTimeString(); |
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div.innerHTML = `<span class="log-time">[${{timeStr}}]</span> ${{data.comment.text}}`; |
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// Insert at top |
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termLogs.prepend(div); |
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// Keep max 20 logs |
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if (termLogs.children.length > 20) {{ |
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termLogs.removeChild(termLogs.lastChild); |
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}} |
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}} |
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async function updateCharts() {{ |
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try {{ |
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const res = await fetch('/data'); |
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const data = await res.json(); |
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if (data.error) {{ |
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statusDiv.innerHTML = "Waiting for data..."; |
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return; |
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}} |
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// Update Status |
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statusDiv.innerHTML = `Mid: <span class="${{data.mid >= data.prev_mid ? 'green' : 'red'}}">$${{data.mid.toLocaleString(undefined, {{minimumFractionDigits: 2}})}}</span> | Net Liq: ${{data.comment.net.toFixed(2)}} BTC`; |
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// 1. PRICE CHART |
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const tracePrice = {{ x: data.history.map(d=>new Date(d.t*1000)), y: data.history.map(d=>d.p), type: 'scatter', mode:'lines', line: {{color: '#29b6f6', width: 2}} }}; |
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if (!initPrice) {{ Plotly.newPlot(priceDiv, [tracePrice], {{ ...commonLayout, title: '<b>Midprice</b>', xaxis: {{type:'date', gridcolor:'#222'}} }}, commonConfig); initPrice = true; }} |
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else {{ Plotly.react(priceDiv, [tracePrice], {{ ...commonLayout, title: '<b>Midprice</b>', xaxis: {{type:'date', gridcolor:'#222'}} }}, commonConfig); }} |
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// 2. VOLUME CHART |
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const tracesVol = [ |
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{{ x: data.vol.dist_bids, y: data.vol.vol_bids, type: 'scatter', name: 'Bids', line: {{color: '#00e676'}} }}, |
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{{ x: data.vol.dist_asks, y: data.vol.vol_asks, type: 'scatter', name: 'Asks', line: {{color: '#ff1744'}} }} |
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]; |
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if (!initVol) {{ Plotly.newPlot(volDiv, tracesVol, {{ ...commonLayout, title: '<b>Cumulative Volume by Distance</b>', xaxis: {{title:'Distance ($)'}} }}, commonConfig); initVol = true; }} |
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else {{ Plotly.react(volDiv, tracesVol, {{ ...commonLayout, title: '<b>Cumulative Volume by Distance</b>', xaxis: {{title:'Distance ($)'}} }}, commonConfig); }} |
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// 3. AI COMMENTARY LOG |
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addLog(data); |
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}} catch (e) {{ console.error("Fetch error:", e); }} |
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}} |
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setInterval(updateCharts, 750); // Slower update for readability |
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</script> |
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</body> |
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</html> |
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""" |
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async def kraken_worker(): |
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global market_state |
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while True: |
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try: |
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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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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 |
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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 |
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if market_state['bids'] and market_state['asks']: |
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best_bid = max(market_state['bids'].keys()) |
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best_ask = min(market_state['asks'].keys()) |
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market_state['prev_mid'] = market_state['current_mid'] |
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mid = (best_bid + best_ask) / 2 |
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market_state['current_mid'] = mid |
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market_state['ready'] = True |
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now = time.time() |
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if not market_state['history'] or (now - market_state['history'][-1]['t'] > 0.5): |
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market_state['history'].append({'t': now, 'p': mid}) |
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if len(market_state['history']) > HISTORY_LENGTH: |
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market_state['history'].pop(0) |
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except Exception as e: |
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logging.warning(f"⚠️ Reconnecting: {e}") |
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await asyncio.sleep(3) |
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async def handle_index(request): |
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return web.Response(text=HTML_PAGE, content_type='text/html') |
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async def handle_data(request): |
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if not market_state['ready']: |
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return web.json_response({"error": "Initializing..."}) |
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mid = market_state['current_mid'] |
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raw_bids = sorted(market_state['bids'].items(), key=lambda x: -x[0])[:300] |
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raw_asks = sorted(market_state['asks'].items(), key=lambda x: x[0])[:300] |
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d_b_x, d_b_y, cum = [], [], 0 |
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for p, q in raw_bids: |
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d = mid - p |
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if d >= 0: |
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cum += q |
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d_b_x.append(d); d_b_y.append(cum) |
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d_a_x, d_a_y, cum = [], [], 0 |
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for p, q in raw_asks: |
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d = p - mid |
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if d >= 0: |
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cum += q |
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d_a_x.append(d); d_a_y.append(cum) |
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diff_values = [] |
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if d_b_x and d_a_x: |
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max_dist = min(d_b_x[-1], d_a_x[-1]) |
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step_size = max_dist / 50 |
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steps = [i * step_size for i in range(1, 51)] |
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for s in steps: |
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idx_b = bisect.bisect_right(d_b_x, s) |
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vol_b = d_b_y[idx_b-1] if idx_b > 0 else 0 |
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idx_a = bisect.bisect_right(d_a_x, s) |
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vol_a = d_a_y[idx_a-1] if idx_a > 0 else 0 |
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diff_values.append(vol_b - vol_a) |
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ai_output = generate_ai_commentary(diff_values, mid, market_state['prev_mid']) |
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return web.json_response({ |
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"mid": mid, |
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"prev_mid": market_state['prev_mid'], |
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"vol": { "dist_bids": d_b_x, "vol_bids": d_b_y, "dist_asks": d_a_x, "vol_asks": d_a_y }, |
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"comment": ai_output, |
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"history": market_state['history'] |
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}) |
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async def start_background(app): |
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app['kraken_task'] = asyncio.create_task(kraken_worker()) |
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async def cleanup_background(app): |
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app['kraken_task'].cancel() |
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try: await app['kraken_task'] |
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except asyncio.CancelledError: pass |
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async def main(): |
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app = web.Application() |
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app.router.add_get('/', handle_index) |
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app.router.add_get('/data', handle_data) |
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app.on_startup.append(start_background) |
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app.on_cleanup.append(cleanup_background) |
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runner = web.AppRunner(app) |
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await runner.setup() |
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site = web.TCPSite(runner, '0.0.0.0', PORT) |
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await site.start() |
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print(f"🚀 BTC-USD AI Dashboard: http://localhost:{PORT}") |
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await asyncio.Event().wait() |
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if __name__ == "__main__": |
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try: asyncio.run(main()) |
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except KeyboardInterrupt: pass |
