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Professional dashboard: strategy detail panel with chart, trades, and signal reasons

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ramseshk
2026-08-04 06:30:36 +00:00
parent 9768bf80cc
commit cc2b7df740
2 changed files with 894 additions and 210 deletions
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<html lang="en"> <html lang="en">
<head> <head>
<meta charset="UTF-8"> <meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no"> <meta name="viewport" content="width=device-width,initial-scale=1.0,maximum-scale=1.0,user-scalable=no">
<title>FTDT Quant Lab — Live Strategy Dashboard</title> <title>FTDT Quant Lab — Professional Dashboard</title>
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500;600;700&display=swap" rel="stylesheet"> <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;900&family=JetBrains+Mono:wght@400;500;600;700&display=swap" rel="stylesheet">
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<style> <style>
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.strat .hdr{display:flex;justify-content:space-between;align-items:flex-start;margin-bottom:10px} /* Strategy grid */
.strat .name{font-size:12px;font-weight:600;color:var(--bright);line-height:1.3} .sgrid{display:grid;grid-template-columns:repeat(auto-fill,minmax(240px,1fr));gap:10px;margin-bottom:20px}
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.strat .pct{font-size:10px;color:var(--text);margin-bottom:10px;font-family:var(--mono)} .scard .stag{font-size:8px;padding:2px 7px;border-radius:3px;font-weight:500;white-space:nowrap}
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.strat-detail .mini-stats{display:grid;grid-template-columns:repeat(4,1fr);gap:8px;margin-bottom:12px} .scard .spnl.up{color:var(--gr)}.scard .spnl.dn{color:var(--rd)}
.strat-detail .signal-log{font-size:9px;color:var(--text);max-height:80px;overflow-y:auto} .scard .smeta{display:flex;gap:12px;font-size:9px;color:var(--tx);flex-wrap:wrap}
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.strat-detail .signal-log .sig.buy{color:var(--green)}.strat-detail .signal-log .sig.sell{color:var(--red)} /* Detail panel */
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th{font-size:9px;font-weight:600;color:var(--text);text-transform:uppercase;letter-spacing:0.5px;text-align:left;padding:8px 12px;border-bottom:1px solid var(--border)} .detail-panel{background:var(--bg);border:1px solid var(--ln);border-radius:12px;width:95%;max-width:1100px;max-height:85vh;overflow-y:auto;box-shadow:0 20px 60px rgba(0,0,0,.5)}
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.green{color:var(--green)}.red{color:var(--red)} .detail-header h2{font-size:16px;font-weight:700}
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.data-badge{display:inline-block;font-size:9px;padding:3px 8px;border-radius:4px;font-weight:500;margin-left:8px} .detail-section{margin-bottom:20px}
.data-badge.mainnet{background:rgba(168,85,247,0.15);color:var(--purple)} .detail-section h4{font-size:11px;font-weight:600;color:var(--tx);text-transform:uppercase;letter-spacing:.5px;margin-bottom:10px;padding-bottom:6px;border-bottom:1px solid var(--ln)}
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footer{text-align:center;padding:20px;font-size:10px;color:#3f3f46}footer a{color:#52525b;text-decoration:none}footer a:hover{color:var(--text)} .green{color:var(--gr)}.red{color:var(--rd)}
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.totals .pnl{font-size:24px}.stats{grid-template-columns:repeat(3,1fr);gap:6px}.stat-box{padding:10px}.stat-box .val{font-size:14px} /* Footer */
.grid{grid-template-columns:1fr 1fr;gap:8px}.strat .big{font-size:16px}.chart-wrap{height:160px} footer{text-align:center;padding:30px;font-size:10px;color:#2a2a35}
.bt-row{flex-direction:column;align-items:flex-start}.bt-row .k{width:100%;justify-content:space-between;gap:8px} footer a{color:#3f3f4a;text-decoration:none}footer a:hover{color:var(--tx)}
.strat-detail .mini-stats{grid-template-columns:repeat(2,1fr)}
@media(max-width:768px){
.topbar{padding:10px 14px;flex-direction:column;align-items:flex-start}
.tabs{padding:0 14px;top:88px;overflow-x:auto;white-space:nowrap}
.main-wrap{padding:12px 14px}
.stats-row{grid-template-columns:repeat(3,1fr)}.sgrid{grid-template-columns:1fr 1fr}
.detail-stats{grid-template-columns:repeat(3,1fr)}
.portfolio .pnl{font-size:22px}
} }
@media(max-width:380px){.stats{grid-template-columns:repeat(2,1fr)}.grid{grid-template-columns:1fr}} @media(max-width:380px){.stats-row{grid-template-columns:repeat(2,1fr)}.sgrid{grid-template-columns:1fr}}
</style> </style>
</head> </head>
<body> <body>
<div class="wrap"> <!-- Top bar -->
<div class="top"> <div class="topbar">
<div class="logo"><h1>FTDT Quant Lab</h1><span><span class="dot live" id="sdot"></span> <span id="scnx">connecting…</span> &middot; <span id="swlt">—</span></span></div> <div style="display:flex;align-items:center;gap:10px">
<div class="totals"><div class="label" style="font-size:10px;color:var(--text);text-transform:uppercase;letter-spacing:0.5px">Portfolio PnL</div><div class="pnl" id="stpnl">$0.00</div><div class="sub" id="stpct">0.00%</div></div> <span class="status-dot" id="sdot"></span><div><h1>FTDT Quant Lab<span>Professional Quant Dashboard</span></h1></div>
</div> </div>
<div class="portfolio">
<div style="font-size:9px;color:var(--tx);text-transform:uppercase;letter-spacing:.5px">Portfolio Equity</div>
<div class="pnl" id="stpnl">$0.00</div>
<div class="sub" id="stpct">—</div>
</div>
</div>
<!-- Tabs -->
<div class="tabs"> <div class="tabs">
<button class="tab on" id="tab-live" onclick="sw('live')">Live<span class="data-badge testnet">Testnet</span></button> <button class="tab on" id="tl-live" onclick="switchTab('live')">Live<span class="badge test">Testnet</span></button>
<button class="tab" id="tab-paper" onclick="sw('paper')">Paper<span class="data-badge mainnet">Mainnet</span></button> <button class="tab" id="tl-paper" onclick="switchTab('paper')">Paper<span class="badge main">$100K Mainnet</span></button>
<button class="tab" id="tab-backtest" onclick="sw('backtest')">Backtest</button> <button class="tab" id="tl-backtest" onclick="switchTab('backtest')">Backtest</button>
</div> </div>
<!-- Main -->
<!-- LIVE --> <div class="main-wrap">
<div class="panel show" id="pnl-live"> <div class="panel show" id="pnl-live">
<div class="stats" id="live-stats"></div> <div class="stats-row" id="live-stats"></div>
<div class="card"><h3>Equity Curve <span class="desc">real-time · Hyperliquid Testnet</span></h3><div class="chart-wrap" id="eq-chart"></div></div> <div class="sgrid" id="live-sgrid"></div>
<div class="grid" id="live-grid"></div>
<div class="card"><h3>Trade Log</h3><div class="tbl-scroll"><table><thead><tr><th>Time</th><th>Strategy</th><th>Side</th><th>Size</th><th>Price</th><th>Fee</th><th>PnL</th></tr></thead><tbody id="trade-tb"></tbody></table></div></div>
</div> </div>
<!-- PAPER -->
<div class="panel" id="pnl-paper"> <div class="panel" id="pnl-paper">
<div class="stats" id="paper-stats"></div> <div class="stats-row" id="paper-stats"></div>
<div class="card"><h3>Equity Curves <span class="desc">per-strategy · <span id="paper-regime">—</span></span></h3><div class="chart-wrap" id="paper-chart" style="height:300px"></div></div> <div class="sgrid" id="paper-sgrid"></div>
<div class="grid" id="paper-grid"></div>
<div class="card"><h3>Trade Log</h3><div class="tbl-scroll"><table><thead><tr><th>Time</th><th>Strategy</th><th>Side</th><th>Size</th><th>Price</th><th>Fee</th><th>PnL</th></tr></thead><tbody id="paper-tb"></tbody></table></div></div>
</div> </div>
<div class="panel" id="pnl-backtest">
<div class="sgrid" id="bt-sgrid"></div>
</div>
</div>
<footer><a href="https://git.ftdt.io/rams/ftdt-quant-lab" target="_blank">rams/ftdt-quant-lab</a> &middot; 12 strategies &middot; $100K paper &middot; Hyperliquid</footer>
<!-- BACKTEST --> <!-- Detail Overlay -->
<div class="panel" id="pnl-bt"> <div class="detail-overlay" id="detail-overlay" onclick="event.target===this&&closeDetail()">
<div class="card" id="bt-detail" style="display:none"><h3 id="bt-title">—</h3><div class="stats" id="bt-stats"></div><div class="chart-wrap" id="bt-chart"></div></div> <div class="detail-panel" id="detail-panel">
<div class="card"><h3>Saved Backtests <span class="desc">click to view</span></h3><div id="bt-list"></div></div> <div class="detail-header">
<h2 id="det-name">Strategy Detail</h2>
<button class="close-btn" onclick="closeDetail()">✕ Close</button>
</div>
<div class="detail-body">
<div class="desc-text" id="det-desc"></div>
<div class="detail-stats" id="det-stats"></div>
<div class="chart-wrap" id="det-chart-wrap"><div id="det-chart"></div></div>
<div class="detail-section"><h4>Trade History</h4>
<div style="overflow-x:auto"><table class="trade-table"><thead><tr><th>Time</th><th>Side</th><th>Size</th><th>Price</th><th>PnL</th><th>Fee</th><th>Reason / Signal</th></tr></thead><tbody id="det-trades"></tbody></table></div></div>
</div>
</div> </div>
<footer><a href="https://git.ftdt.io/rams/ftdt-quant-lab" target="_blank">rams/ftdt-quant-lab</a> &middot; 7 strategies &middot; $5,000 paper / $898 live &middot; Hyperliquid</footer>
</div> </div>
<script> <script>
var tab='live',lastData=null,lastPaper=null; // ═══════════ State ═══════════
function mkChart(el,w,h){var c=LightweightCharts.createChart(el,{layout:{background:{color:'transparent'},textColor:'#8b8b96'},grid:{vertLines:{color:'rgba(255,255,255,0.03)'},horzLines:{color:'rgba(255,255,255,0.03)'}},rightPriceScale:{borderColor:'rgba(255,255,255,0.06)'},timeScale:{borderColor:'rgba(255,255,255,0.06)',timeVisible:true},crosshair:{mode:0},width:w,height:h});return c} var currentTab='live', lastData=null, lastPaper=null, lastBT=null;
var eqChart=mkChart(document.getElementById('eq-chart'),0,0);var eqSer=eqChart.addAreaSeries({lineColor:'#3b82f6',topColor:'rgba(59,130,246,0.15)',bottomColor:'rgba(59,130,246,0.02)',lineWidth:2}); var STRAT_COLORS=['#22c55e','#3b82f6','#a855f7','#f59e0b','#ef4444','#06b6d4','#ec4899','#84cc16','#6366f1','#14b8a6','#f97316','#8b5cf6'];
var paperChart=mkChart(document.getElementById('paper-chart'),0,0);
// Per-strategy equity series (7 strategies, 7 colors)
var STRAT_COLORS = ['#22c55e','#3b82f6','#a855f7','#f59e0b','#ef4444','#06b6d4','#ec4899'];
var paperStrataSeries = {};
function getStratSeries(name){
if(!paperStrataSeries[name]){
var idx = Object.keys(paperStrataSeries).length;
var color = STRAT_COLORS[idx % STRAT_COLORS.length];
paperStrataSeries[name] = paperChart.addAreaSeries({
lineColor: color, topColor: color+'26', bottomColor: color+'05', lineWidth: 1.5
});
}
return paperStrataSeries[name];
}
var btChart=mkChart(document.getElementById('bt-chart'),0,0);var btSer=btChart.addAreaSeries({lineColor:'#a855f7',topColor:'rgba(168,85,247,0.12)',bottomColor:'rgba(168,85,247,0.02)',lineWidth:2});
function fitCharts(){ // ═══════════ Chart for detail view ═══════════
eqChart.applyOptions({width:document.getElementById('eq-chart').offsetWidth,height:document.getElementById('eq-chart').offsetHeight}); var detChart=null, detSer=null;
paperChart.applyOptions({width:document.getElementById('paper-chart').offsetWidth,height:document.getElementById('paper-chart').offsetHeight}); function initDetChart(){
btChart.applyOptions({width:document.getElementById('bt-chart').offsetWidth,height:document.getElementById('bt-chart').offsetHeight}); var el=document.getElementById('det-chart');
if(!el)return;
el.style.width='100%'; el.style.height='280px';
detChart=LightweightCharts.createChart(el,{
layout:{background:{color:'transparent'},textColor:'#d4d4e0'},
grid:{vertLines:{color:'rgba(255,255,255,.03)'},horzLines:{color:'rgba(255,255,255,.03)'}},
rightPriceScale:{borderColor:'rgba(255,255,255,.08)'},
timeScale:{borderColor:'rgba(255,255,255,.08)',timeVisible:true},
crosshair:{mode:0},width:el.offsetWidth,height:280
});
detSer=detChart.addAreaSeries({lineColor:'#3b82f6',topColor:'rgba(59,130,246,.15)',bottomColor:'rgba(59,130,246,.02)',lineWidth:2});
} }
window.addEventListener('resize',fitCharts);setTimeout(fitCharts,300);
function sw(t){ // ═══════════ Tab switching ═══════════
tab=t; function switchTab(t){
['live','paper','backtest'].forEach(function(x){document.getElementById('tab-'+x).className=t===x?'tab on':'tab'}); currentTab=t;
['live','paper','backtest'].forEach(function(x){document.getElementById('tl-'+x).className=t===x?'tab on':'tab'});
document.getElementById('pnl-live').className=t==='live'?'panel show':'panel'; document.getElementById('pnl-live').className=t==='live'?'panel show':'panel';
document.getElementById('pnl-paper').className=t==='paper'?'panel show':'panel'; document.getElementById('pnl-paper').className=t==='paper'?'panel show':'panel';
document.getElementById('pnl-bt').className=t==='backtest'?'panel show':'panel'; document.getElementById('pnl-backtest').className=t==='backtest'?'panel show':'panel';
if(t==='live'&&lastData)renLive(lastData); if(t==='live'&&lastData)renLive(lastData);
if(t==='paper'){if(lastPaper)renPaper(lastPaper);setTimeout(fitCharts,200)} if(t==='paper'&&lastPaper)renPaper(lastPaper);
if(t==='backtest'){setTimeout(fitCharts,200);loadBt()} if(t==='backtest')loadBT();
} }
// WebSocket // ═══════════ Render strategy cards ═══════════
var ws,wsPaper; function renCards(sgridId,ss,baseEq,tab,statsRowId){
function conn(){ var keys=Object.keys(ss),totalPnl=0,trades=0,fees=0,active=0;
if(ws)try{ws.close()}catch(e){} for(var i=0;i<keys.length;i++){var s=ss[keys[i]];totalPnl+=s.pnl||0;trades+=s.trades_today||0;fees+=s.fee_paid||0;if(s.status==='running')active++}
ws=new WebSocket((location.protocol==='https:'?'wss:':'ws:')+'//'+location.host+'/cv/ws'); if(statsRowId){
ws.onopen=function(){document.getElementById('scnx').innerHTML='<span style=\"color:#22c55e\">live</span>';document.getElementById('sdot').className='dot live'}; document.getElementById(statsRowId).innerHTML='<div class="stat"><div class="lbl">Equity</div><div class="val">$'+((baseEq||0)+totalPnl).toFixed(0)+'</div></div>'+
ws.onclose=function(){document.getElementById('sdot').className='dot dead';setTimeout(conn,3000)}; '<div class="stat"><div class="lbl">PnL</div><div class="val '+(totalPnl>=0?'up':'dn')+'">'+(totalPnl>=0?'+':'')+'$'+Math.abs(totalPnl).toFixed(2)+'</div></div>'+
ws.onmessage=function(e){try{lastData=JSON.parse(e.data)}catch(ex){return};if(tab==='live')renLive(lastData)}; '<div class="stat"><div class="lbl">Trades</div><div class="val">'+trades+'</div></div>'+
// Paper WS '<div class="stat"><div class="lbl">Fees</div><div class="val dn">$'+fees.toFixed(2)+'</div></div>'+
if(wsPaper)try{wsPaper.close()}catch(e){} '<div class="stat"><div class="lbl">Active</div><div class="val">'+active+'/'+keys.length+'</div></div>'+
wsPaper=new WebSocket((location.protocol==='https:'?'wss:':'ws:')+'//'+location.host+'/cv/ws/paper'); '<div class="stat"><div class="lbl">Alloc</div><div class="val">$'+(keys[0]?ss[keys[0]].allocation||0:0)+'k/strat</div></div>';
wsPaper.onmessage=function(e){try{lastPaper=JSON.parse(e.data)}catch(ex){return};if(tab==='paper')renPaper(lastPaper)}; }
} var h='';
// ═══════════ Render helpers ═══════════
function renGrid(containerId, ss, baseEq, reserve, headerStatsId, tradesTbId, chartId, chartSer, hist, trades){
var keys=Object.keys(ss),tr=0,fe=0,fu=0;
for(var i=0;i<keys.length;i++){var s=ss[keys[i]];tr+=s.trades_today||0;fe+=s.fee_paid||0;if(s.status==='running')fu++};
var pnl=0;for(var j=0;j<keys.length;j++)pnl+=ss[keys[j]].pnl||0;
document.getElementById(headerStatsId).innerHTML=
'<div class="stat-box"><div class="lbl">Equity</div><div class="val">$'+((baseEq||0)+pnl).toFixed(0)+'</div></div>'+
'<div class="stat-box"><div class="lbl">Reserve</div><div class="val">$'+(reserve||0)+'</div></div>'+
'<div class="stat-box"><div class="lbl">Trades</div><div class="val">'+tr+'</div></div>'+
'<div class="stat-box"><div class="lbl">Fees</div><div class="val dn">$'+fe.toFixed(2)+'</div></div>'+
'<div class="stat-box"><div class="lbl">Active</div><div class="val">'+fu+' / '+keys.length+'</div></div>';
var g='';
for(var k=0;k<keys.length;k++){ for(var k=0;k<keys.length;k++){
var name=keys[k],s=ss[name],sp=s.pnl||0,cls=sp>=0?'up':'dn',pStr=(sp>=0?'+':'')+'$'+Math.abs(sp).toFixed(2); var name=keys[k],s=ss[name],sp=s.pnl||0,cls=sp>=0?'up':'dn',pStr=(sp>=0?'+':'')+'$'+Math.abs(sp).toFixed(2);
var signals=s.signals||[],sigHtml=''; var fm=s.fee_model||'taker';
if(signals.length>0){for(var si=Math.max(0,signals.length-5);si<signals.length;si++){var sg=signals[si];var sc=sg.signal&&sg.signal.indexOf('BUY')>=0?'buy':'sell';sigHtml+='<div class="sig '+sc+'"><span>'+new Date(sg.time*1000).toLocaleTimeString('en-US',{hour12:false})+'</span><span>'+sg.signal+'</span></div>'}} h+='<div class="scard" onclick="openDetail(\''+name+'\',\''+tab+'\')" id="scard-'+tab+'-'+name.replace(/\s/g,'_')+'">'+
g+='<div class="strat" id="strat-'+name.replace(/\s/g,'')+'" onclick="toggleStrat(\''+name.replace(/\s/g,'')+'\')">'+ '<div class="sh"><div><div class="sname">'+name+'</div><div class="salloc">$'+s.allocation+' &middot; '+s.type+'</div></div>'+
'<div class="hdr"><div><div class="name">'+name+'</div><div class="alloc">'+(s.allocation||'?')+' USDC &middot; '+(s.type||'')+'</div></div><span class="status '+(s.status==='running'?'run':'idle')+'">'+(s.status==='running'?'RUNNING':'IDLE')+'</span></div>'+ '<div><span class="stag '+(s.status==='running'?'run':'idle')+'">'+(s.status==='running'?'RUNNING':'IDLE')+'</span>'+
'<div class="big '+cls+'">'+pStr+'</div><div class="pct">'+(s.pnl_pct>=0?'+':'')+(s.pnl_pct||0).toFixed(2)+'% | Fee: $'+(s.fee_paid||0).toFixed(2)+'</div>'+ '<span class="stag '+fm+'">'+fm.toUpperCase()+'</span></div></div>'+
'<div class="r"><span>Trades: <b>'+(s.trades_today||0)+'</b></span><span>Win: <b>'+Math.round((s.win_rate||0)*100)+'%</b></span><span>Pos: <b>'+(s.position||0).toFixed(4)+'</b></span></div>'+ '<div class="spnl '+cls+'">'+pStr+'</div>'+
'<div class="strat-detail"><div class="desc-text">'+(s.description||'')+'</div>'+ '<div class="smeta"><span>PnL: <b class="'+(sp>=0?'green':'red')+'">'+(s.pnl_pct>=0?'+':'')+(s.pnl_pct||0).toFixed(2)+'%</b></span><span>Trades: <b>'+(s.trades_today||0)+'</b></span><span>Win: <b>'+Math.round((s.win_rate||0)*100)+'%</b></span><span>Pos: <b>'+(s.position||0).toFixed(4)+'</b></span></div>'+
'<div class="mini-stats"><div class="stat-box"><div class="lbl">PnL</div><div class="val '+(sp>=0?'up':'dn')+'">'+pStr+'</div></div><div class="stat-box"><div class="lbl">Fees</div><div class="val dn">$'+(s.fee_paid||0).toFixed(2)+'</div></div><div class="stat-box"><div class="lbl">Win Rate</div><div class="val">'+Math.round((s.win_rate||0)*100)+'%</div></div><div class="stat-box"><div class="lbl">Trades</div><div class="val">'+(s.trades_today||0)+'</div></div></div>'+ '</div>';
(signals.length>0?'<div class="signal-log" style="max-height:100px;overflow-y:auto"><div style="font-size:10px;color:var(--bright);margin-bottom:6px">Recent Signals</div>'+sigHtml+'</div>':'')+
'</div></div>';
} }
document.getElementById(containerId).innerHTML=g; document.getElementById(sgridId).innerHTML=h;
// Chart (skip if chartSer is null — caller handles separately) }
if(chartSer && hist && hist.length>0){var pts=[];for(var m=0;m<hist.length;m++)pts.push({time:hist[m].t,value:hist[m].v});chartSer.setData(pts);chartId.timeScale().fitContent()}
// ═══════════ Open strategy detail ═══════════
function openDetail(name,tab){
document.getElementById('detail-overlay').classList.add('on');
document.getElementById('det-name').textContent=name;
var ss=null, equity={}, trades=[];
if(tab==='paper'&&lastPaper){
ss=lastPaper.strategies||{}; equity=lastPaper.strategy_equity||{};
trades=(lastPaper.per_strategy_trades||{})[name]||[];
} else if(tab==='live'&&lastData){
ss=lastData.strategies||{};
} else if(tab==='backtest'&&lastBT){
ss={}; ss[name]=lastBT;
}
var s=ss?ss[name]:null;
if(!s){closeDetail();return}
// Description
document.getElementById('det-desc').textContent=s.description||'No description available.';
// Stats
var sp=s.pnl||0;
document.getElementById('det-stats').innerHTML='<div class="stat"><div class="lbl">PnL</div><div class="val '+(sp>=0?'up':'dn')+'">'+(sp>=0?'+':'')+'$'+Math.abs(sp).toFixed(4)+'</div></div>'+
'<div class="stat"><div class="lbl">PnL%</div><div class="val '+(sp>=0?'up':'dn')+'">'+(s.pnl_pct>=0?'+':'')+(s.pnl_pct||0).toFixed(2)+'%</div></div>'+
'<div class="stat"><div class="lbl">Trades</div><div class="val">'+(s.trades_today||0)+'</div></div>'+
'<div class="stat"><div class="lbl">Win Rate</div><div class="val">'+Math.round((s.win_rate||0)*100)+'%</div></div>'+
'<div class="stat"><div class="lbl">Fees Paid</div><div class="val dn">$'+(s.fee_paid||0).toFixed(4)+'</div></div>'+
'<div class="stat"><div class="lbl">Position</div><div class="val">'+(s.position||0).toFixed(4)+'</div></div>';
// Equity chart
if(!detChart)initDetChart();
var eqData=equity[name]||[];
if(eqData.length>0){
var pts=[];for(var i=0;i<eqData.length;i++){if(eqData[i]&&eqData[i].t)pts.push({time:eqData[i].t,value:eqData[i].v})}
detSer.setData(pts);detChart.timeScale().fitContent();
}
// Trades // Trades
var rows='',trs=(trades||[]).slice(-20).reverse(); var rows='';
for(var n=0;n<trs.length;n++){var t=trs[n];rows+='<tr><td>'+t.time+'</td><td>'+t.strategy+'</td><td class="'+(t.side.indexOf('BUY')>=0?'green':'red')+'">'+t.side+'</td><td>'+t.size+'</td><td>'+(t.price||'—')+'</td><td class="red">$'+(t.fee||0).toFixed(4)+'</td><td class="'+(t.pnl>=0?'green':'red')+'">'+(t.pnl>=0?'+':'')+'$'+Math.abs(t.pnl).toFixed(4)+'</td></tr>'} for(var j=Math.max(0,trades.length-50);j<trades.length;j++){
document.getElementById(tradesTbId).innerHTML=rows; var t=trades[j],tp=t.pnl||0;
} rows+='<tr><td>'+t.time+'</td><td class="'+(t.side==='BUY'?'green':'red')+'">'+t.side+'</td><td>'+t.size+'</td><td>$'+t.price+'</td><td class="'+(tp>=0?'green':'red')+'">'+(tp>=0?'+':'')+'$'+Math.abs(tp).toFixed(4)+'</td><td class="red">$'+(t.fee||0).toFixed(4)+'</td><td class="reason" title="'+t.reason+'">'+(t.reason||'—')+'</td></tr>';
function renLive(d){
if(!d)return;
var pnl=d.total_pnl||0;document.getElementById('stpnl').textContent=(pnl>=0?'+':'')+'$'+Math.abs(pnl).toFixed(2);document.getElementById('stpnl').className='pnl '+(pnl>=0?'up':'dn');
document.getElementById('stpct').textContent='Testnet · Equity: $'+((d.base_equity||898)+pnl).toFixed(2);document.getElementById('swlt').textContent=(d.wallet||'').slice(0,10)+'…';
renGrid('live-grid',d.strategies||{},d.base_equity||898,d.reserve||398,'live-stats','trade-tb',eqChart,eqSer,d.equity_history||[],d.trades||[]);
}
function renPaper(d){
if(!d)return;
var pnl=d.total_pnl||0;document.getElementById('stpnl').textContent=(pnl>=0?'+':'')+'$'+Math.abs(pnl).toFixed(2);document.getElementById('stpnl').className='pnl '+(pnl>=0?'up':'dn');
document.getElementById('stpct').textContent='Mainnet Paper · Equity: $'+(d.total_equity||100000).toFixed(0)+' · BTC: $'+(d.btc_price||0).toLocaleString('en-US',{maximumFractionDigits:0});
// Regime badge
var regime = d.regime || 'NORMAL';
var regimeLabel = regime.replace('_',' ').toLowerCase();
var regimeClass = regime.toLowerCase().replace('_','-');
document.getElementById('paper-regime').innerHTML = 'Regime: <span class="data-badge '+regimeClass+'">'+regimeLabel+'</span>';
renGrid('paper-grid',d.strategies||{},d.base_equity||100000,d.reserve||30000,'paper-stats','paper-tb',paperChart,null,d.equity_history||[],d.trades||[]);
// Per-strategy equity curves
var seq = d.strategy_equity || {};
var keyz = Object.keys(seq);
for(var i=0;i<keyz.length;i++){
var name=keyz[i],pts=seq[name],ser=getStratSeries(name);
if(pts&&pts.length>0){
var arr=[];for(var j=0;j<pts.length;j++) if(pts[j]&&pts[j].t) arr.push({time:pts[j].t,value:pts[j].v});
ser.setData(arr);
}
} }
paperChart.timeScale().fitContent(); document.getElementById('det-trades').innerHTML=rows||'<tr><td colspan="7" style="text-align:center;color:var(--tx);padding:20px">No trades yet</td></tr>';
// Resize chart
setTimeout(function(){if(detChart){detChart.applyOptions({width:document.getElementById('det-chart').offsetWidth,height:280});detChart.timeScale().fitContent()}},300);
} }
function toggleStrat(sid){var el=document.getElementById('strat-'+sid);if(!el)return;el.classList.toggle('open');setTimeout(fitCharts,200)} function closeDetail(){document.getElementById('detail-overlay').classList.remove('on')}
document.addEventListener('keydown',function(e){if(e.key==='Escape')closeDetail()});
// Backtests // ═══════════ WebSocket + render ═══════════
function loadBt(){ var ws,wsPaper;
function connect(){
if(ws)try{ws.close()}catch(e){}
ws=new WebSocket((location.protocol==='https:'?'wss:':'ws:')+'//'+location.host+'/cv/ws');
ws.onopen=function(){document.getElementById('sdot').className='status-dot'};
ws.onclose=function(){document.getElementById('sdot').className='status-dot off';setTimeout(connect,5000)};
ws.onmessage=function(e){try{lastData=JSON.parse(e.data)}catch(ex){return};if(currentTab==='live')renLive(lastData)};
if(wsPaper)try{wsPaper.close()}catch(e){}
wsPaper=new WebSocket((location.protocol==='https:'?'wss:':'ws:')+'//'+location.host+'/cv/ws/paper');
wsPaper.onmessage=function(e){try{lastPaper=JSON.parse(e.data)}catch(ex){return};if(currentTab==='paper')renPaper(lastPaper)};
}
function renLive(d){if(!d)return;var p=d.total_pnl||0;document.getElementById('stpnl').textContent=(p>=0?'+':'')+'$'+Math.abs(p).toFixed(2);document.getElementById('stpnl').className='pnl '+(p>=0?'up':'dn');document.getElementById('stpct').textContent='Testnet · Equity: $'+((d.base_equity||898)+p).toFixed(2);renCards('live-sgrid',d.strategies||{},d.base_equity||898,'live','live-stats')}
function renPaper(d){if(!d)return;var p=d.total_pnl||0;document.getElementById('stpnl').textContent=(p>=0?'+':'')+'$'+Math.abs(p).toFixed(2);document.getElementById('stpnl').className='pnl '+(p>=0?'up':'dn');document.getElementById('stpct').textContent='Paper · '+d.total_equity+' · Regime: '+(d.regime||'—');renCards('paper-sgrid',d.strategies||{},d.base_equity||100000,'paper','paper-stats')}
// ═══════════ Backtests ═══════════
function loadBT(){
fetch('/cv/api/backtests').then(function(r){return r.json()}).then(function(data){ fetch('/cv/api/backtests').then(function(r){return r.json()}).then(function(data){
var h='';for(var i=0;i<data.length;i++){var b=data[i];h+='<div class="bt-row" onclick="viewBt(\''+b.name+'\')" id="btr-'+b.name+'"><div><div class="n">'+b.strategy+'</div><div class="m">30-day · '+b.name+'</div></div><div class="k"><div class="kv"><div class="kl">PnL</div><div class="kd '+(b.pnl_pct>=0?'green':'red')+'">'+(b.pnl_pct>=0?'+':'')+b.pnl_pct.toFixed(2)+'%</div></div><div class="kv"><div class="kl">Sharpe</div><div class="kd">'+b.sharpe.toFixed(2)+'</div></div><div class="kv"><div class="kl">DD</div><div class="kd red">'+b.max_dd.toFixed(2)+'%</div></div><div class="kv"><div class="kl">Win</div><div class="kd">'+(b.win_rate*100).toFixed(0)+'%</div></div></div></div>'} var h='';for(var i=0;i<data.length;i++){var b=data[i];h+='<div class="scard" onclick="openDetail(\''+b.strategy+'\',\'backtest\')\"><div class="sh"><div><div class="sname">'+b.strategy+'</div><div class="salloc">30-day &middot; $'+b.allocation+'</div></div><span class="stag run">BACKTEST</span></div><div class="spnl '+(b.pnl>=0?'up':'dn')+'">'+(b.pnl>=0?'+':'')+b.pnl.toFixed(2)+'%</div><div class="smeta"><span>Sharpe: <b>'+b.sharpe.toFixed(2)+'</b></span><span>DD: <b class="red">'+b.max_dd_pct.toFixed(2)+'%</b></span><span>Win: <b>'+Math.round(b.win_rate*100)+'%</b></span></div></div>';lastBT=b}
document.getElementById('bt-list').innerHTML=h||'<div style="padding:12px;color:var(--text);font-size:12px">No backtests yet.</div>'; document.getElementById('bt-sgrid').innerHTML=h||'<div style="padding:20px;color:var(--tx)">No backtests.</div>';
})
}
function viewBt(name){
fetch('/cv/api/backtest/'+name).then(function(r){return r.json()}).then(function(b){
document.getElementById('bt-detail').style.display='block';document.getElementById('bt-title').innerHTML=b.strategy+' <span class="desc">'+b.description+'</span>';
document.querySelectorAll('.bt-row').forEach(function(e){e.classList.remove('sel')});document.getElementById('btr-'+name).classList.add('sel');
document.getElementById('bt-stats').innerHTML='<div class="stat-box"><div class="lbl">Return</div><div class="val '+(b.pnl>=0?'up':'dn')+'">'+(b.pnl>=0?'+':'')+b.pnl.toFixed(2)+'%</div></div><div class="stat-box"><div class="lbl">Ann.</div><div class="val '+(b.ann_return_pct>=0?'up':'dn')+'">'+(b.ann_return_pct>=0?'+':'')+b.ann_return_pct.toFixed(1)+'%</div></div><div class="stat-box"><div class="lbl">Sharpe</div><div class="val">'+b.sharpe.toFixed(2)+'</div></div><div class="stat-box"><div class="lbl">Sortino</div><div class="val">'+b.sortino.toFixed(2)+'</div></div><div class="stat-box"><div class="lbl">Max DD</div><div class="val dn">'+b.max_dd_pct.toFixed(2)+'%</div></div><div class="stat-box"><div class="lbl">Win</div><div class="val">'+(b.win_rate*100).toFixed(0)+'%</div></div><div class="stat-box"><div class="lbl">Trades</div><div class="val">'+b.total_trades+'</div></div><div class="stat-box"><div class="lbl">Alloc</div><div class="val">$'+b.allocation+'</div></div><div class="stat-box"><div class="lbl">End Eq</div><div class="val">$'+b.end_equity.toFixed(2)+'</div></div><div class="stat-box"><div class="lbl">Period</div><div class="val">30d</div></div>';
var pts=[],curve=b.equity_curve||[];for(var i=0;i<curve.length;i++)pts.push({time:(new Date(curve[i].t).getTime()/1000),value:curve[i].v});btSer.setData(pts);btChart.timeScale().fitContent();setTimeout(fitCharts,200);
}) })
} }
fitCharts();conn();loadBt(); // ═══════════ Init ═══════════
initDetChart();connect();loadBT();
</script> </script>
</body> </body>
</html> </html>
live/paper_trader.py
+660
View File
@@ -0,0 +1,660 @@
"""
Paper trading engine — runs strategies against HYPERLIQUID MAINNET data.
Pulls real mainnet prices, orderbooks, and funding rates every second.
Executes all 7 strategies in simulation mode — tracks virtual positions,
computes PnL with realistic fees and slippage. No real orders.
Writes to /tmp/ftdt-paper-metrics.json for the dashboard.
"""
import os, sys, asyncio, json, time, logging, random, math
from pathlib import Path
from datetime import datetime
from collections import deque
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
import requests
from strategies.hawkes_ofi import HawkesOFI
from strategies.deep_lob import DeepLOB
from strategies.cartea_jaimungal import CarteaJaimungal
from strategies.queue_imbalance import QueueImbalance
from strategies.gueant import GueantMM
logging.basicConfig(level=logging.INFO, format="%(asctime)s [paper] %(message)s", datefmt="%H:%M:%S")
log = logging.getLogger("ftdt-paper")
# ═══════════════════════ Config ═══════════════════════
MAINNET_API = "https://api.hyperliquid.xyz/info"
METRICS_FILE = "/tmp/ftdt-paper-metrics.json"
STARTING_CAPITAL = 100000.0 # $100,000 paper trading capital
RESERVE = 30000.0
TAKER_FEE = 0.0005 # 5 bps taker
MAKER_FEE = 0.0002 # 2 bps maker
SLIPPAGE_BPS = 1.0 # 1 bps slippage
MIN_SIGNAL_STRENGTH = 0.25 # Minimum signal strength to overcome fees
# ═══════════════════════ Strategy state ═══════════════════════
STRATEGIES = {
"Order Book Imbalance": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "reversal", "size": 0.002, "fee_model": "taker",
"description": "L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate. Mean-reverting at volume extremes.",
},
"Iceberg Detection": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "momentum", "size": 0.001, "fee_model": "taker",
"description": "Detects whale accumulation (many small buys over time). Follows the smart money flow.",
},
"Funding Rate Arb": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "carry", "size": 0.005, "fee_model": "taker",
"description": "Delta-neutral carry trade — shorts perp when funding rate is high, collects hourly payments.",
},
"Pairs Trading": {
"allocation": 10000.0, "instrument": "ETH", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "stat_arb", "size": 0.05, "fee_model": "taker",
"description": "BTC/ETH spread mean reversion — trades when Z-score exceeds 1.5 sigma. Pairs converge back to equilibrium.",
},
"Avellaneda-Stoikov": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "market_making", "size": 0.001, "fee_model": "maker",
"description": "Dual-sided quoting at best bid/ask — captures spread via stochastic control. Simulated fill when spread is crossed.",
},
"Momentum Breakout": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "momentum", "size": 0.002, "fee_model": "taker",
"description": "Bollinger Band (2σ) breakout — enters when price breaks bands with volume confirmation.",
},
"Mean Reversion": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "reversal", "size": 0.002, "fee_model": "taker",
"description": "VWAP deviation — buys below VWAP, sells above. Oscillates around fair value.",
},
"Hawkes OFI (new)": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "hawkes", "size": 0.002, "fee_model": "taker",
"description": "Hawkes process OFI — self-exciting point process model capturing clustered order flow. Predicts direction from buy/sell intensity imbalance. Academically rigorous stochastic process.",
},
"Deep LOB (new)": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "deep_lob", "size": 0.002, "fee_model": "maker",
"description": "Full orderbook depth analysis — wall detection, depth imbalance, thin-side prediction. Uses 10 levels of LOB to find fair value and directional pressure.",
},
"Cartea-Jaimungal": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "cartea", "size": 0.002, "fee_model": "maker",
"description": "Stochastic control HFT model — solves HJB equation for optimal quotes with alpha + inventory. Reservation price dynamically shifts to manage risk. (Cartea-Jaimungal 2015)",
},
"Queue Imbalance": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "queue_imb", "size": 0.002, "fee_model": "taker",
"description": "Queue dynamics model — weighted imbalance across LOB levels with exponential decay weights. Detects adverse selection when price moves against queue dominance. (Stoikov-Sağlam framework)",
},
"Guéant Market Making": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "gueant", "size": 0.001, "fee_model": "maker",
"description": "Closed-form market making — Guéant-Lehalle asymptotic solution. Handles asymmetric information with adverse-selection-adjusted spreads. Computationally efficient closed form.",
},
}
trades_log: list[dict] = []
equity_history: list[dict] = []
strategy_equity: dict = {name: deque(maxlen=300) for name in STRATEGIES}
per_strategy_trades: dict = {name: deque(maxlen=200) for name in STRATEGIES}
btc_prices: deque = deque(maxlen=120)
eth_prices: deque = deque(maxlen=120)
funding_rates: deque = deque(maxlen=100)
# ═══════════════════════ Regime Detection ═══════════════════════
# Uses rolling volatility to classify market regime:
# LOW_VOL: quiet markets → tight spreads, aggressive size
# NORMAL: standard conditions → baseline parameters
# HIGH_VOL: turbulence → wide spreads, reduced size, cautious signals
current_regime = "NORMAL"
regime_confidence = 0.5
def detect_regime():
"""Classify market regime from rolling BTC price volatility."""
global current_regime, regime_confidence
if len(btc_prices) < 30:
return "NORMAL"
window = list(btc_prices)[-30:]
# Compute 30-tick log returns
returns = [math.log(window[i] / window[i-1]) for i in range(1, len(window))]
realized_vol = math.sqrt(sum(r**2 for r in returns) / len(returns))
# Annualize (30 ticks at ~1s each → 30s window, annualize to 1yr)
annual_vol = realized_vol * math.sqrt(365 * 24 * 60 * 60 / 30)
regime_confidence = min(0.95, max(0.2, annual_vol / 2.0))
if annual_vol < 0.15: # <15% annualized
return "LOW_VOL"
elif annual_vol > 0.60: # >60% annualized
return "HIGH_VOL"
return "NORMAL"
# ═══════════════════════ Mainnet Data ═══════════════════════
def get_mainnet_prices():
"""Get mark prices from mainnet."""
try:
r = requests.post(MAINNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
data = r.json()
prices = {}
for i, u in enumerate(data[0]["universe"]):
if u["name"] in ("BTC", "ETH"):
prices[u["name"]] = float(data[1][i]["markPx"])
return prices
except Exception as e:
log.warning(f"Mainnet price error: {e}")
return {}
def get_mainnet_funding():
"""Get funding rates from mainnet."""
try:
r = requests.post(MAINNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
data = r.json()
rates = {}
for i, u in enumerate(data[0]["universe"]):
if u["name"] in ("BTC", "ETH"):
rates[u["name"]] = float(data[1][i].get("funding", 0))
return rates
except:
return {}
def get_mainnet_orderbook(coin):
"""Get L2 orderbook from mainnet."""
try:
r = requests.post(MAINNET_API, json={"type":"l2Book","coin":coin}, timeout=10)
data = r.json()
best_bid = float(data["levels"][0][0]["px"]) if data["levels"][0] else 0
best_ask = float(data["levels"][1][0]["px"]) if data["levels"][1] else 0
return best_bid, best_ask
except: return 0,0
def get_deep_orderbook(coin, depth=10):
"""Get full LOB levels. Returns (bids, asks) where each is [(price,size),...]."""
try:
r = requests.post(MAINNET_API, json={"type":"l2Book","coin":coin}, timeout=10)
data = r.json()
bids = [(float(l["px"]), float(l["sz"])) for l in data["levels"][0][:depth]]
asks = [(float(l["px"]), float(l["sz"])) for l in data["levels"][1][:depth]]
return bids, asks
except: return [], []
# Initialize models
hawkes_btc = HawkesOFI(alpha=0.3, beta=0.5)
deep_lob = DeepLOB(depth_levels=10)
cartea = CarteaJaimungal(gamma=0.1, sigma=0.015, kappa=1.5, T=3600, max_inventory=0.01)
queue_imb = QueueImbalance(depth_levels=10)
gueant = GueantMM(gamma=0.1, sigma=0.015, k=1.5, T=3600, max_pos=0.005)
prev_bids = None
prev_asks = None
# ═══════════════════════ Signal Engine ═══════════════════════
def compute_signals():
if len(btc_prices) < 20: return
btc = btc_prices[-1]; eth = eth_prices[-1] if eth_prices else btc/34
# OFI
if len(btc_prices) >= 5:
ret = (btc - btc_prices[-5]) / btc_prices[-5]
if ret > 0.0005:
STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"SELL","strength":ret})
elif ret < -0.0005:
STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(ret)})
# Iceberg
if len(btc_prices) >= 10:
up = sum(1 for i in range(-9,0) if btc_prices[i+1] > btc_prices[i])
if up >= 7:
STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"BUY","strength":up/10})
elif up <= 3:
STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"SELL","strength":1-up/10})
# Funding Arb — use actual mainnet funding rate
if funding_rates:
btc_fr = funding_rates[-1].get("BTC", 0) if isinstance(funding_rates[-1], dict) else 0
# Annualized: funding every 8h → 3× daily → 1095× yearly
annual_fr = abs(btc_fr) * 365 * 3 if btc_fr else 0
if annual_fr > 0.05: # >5% APR
STRATEGIES["Funding Rate Arb"]["signals"].append(
{"time":time.time(),"signal":"SELL" if btc_fr > 0 else "BUY",
"strength":annual_fr/100}
)
# Pairs: BTC/ETH ratio Z-score
if len(btc_prices) >= 20 and len(eth_prices) >= 20:
ratios = [btc_prices[i] / max(eth_prices[i], 0.01) for i in range(-20, 0)]
mu = sum(ratios) / len(ratios)
std = math.sqrt(sum((r-mu)**2 for r in ratios) / len(ratios))
cur = btc / max(eth, 0.01)
if std > 0:
z = (cur - mu) / std
if z > 1.5:
STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"SELL_ETH","strength":z})
elif z < -1.5:
STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"BUY_ETH","strength":abs(z)})
# Momentum Breakout
if len(btc_prices) >= 20:
w = list(btc_prices)[-20:]; sma = sum(w)/len(w)
variance = sum((p-sma)**2 for p in w)/len(w); std = math.sqrt(variance)
if std > 0:
if btc > sma + 2*std:
STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"BUY","strength":(btc-sma-2*std)/std})
elif btc < sma - 2*std:
STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"SELL","strength":(sma-2*std-btc)/std})
# Mean Reversion
if len(btc_prices) >= 20:
w = list(btc_prices)[-20:]; vols = [1 + i/len(w) for i in range(len(w))]
vwap = sum(p*v for p,v in zip(w, vols)) / sum(vols)
vstd = math.sqrt(sum((p-vwap)**2 for p in w) / len(w))
dev = (btc - vwap) / vstd if vstd > 0 else 0
if dev > 1.5:
STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"SELL","strength":dev})
elif dev < -1.5:
STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(dev)})
for s in STRATEGIES.values():
s["signals"] = s["signals"][-20:]
# ═══════════════════════ Fill Simulation ═══════════════════════
def simulate_fill(name: str, side: str, coin: str, price: float, reason: str = ""):
"""Simulate a trade fill at market price with strategy-specific fees."""
cfg = STRATEGIES[name]
sz = cfg["size"]
notional = sz * price
# Use strategy's fee model
fee_rate = MAKER_FEE if cfg.get("fee_model") == "maker" else TAKER_FEE
fee = notional * fee_rate
slippage = notional * SLIPPAGE_BPS / 10000
cfg["fee_paid"] += fee
if side == "BUY":
# Opening or adding long
if cfg["position"] <= 0:
# Close short if any
if cfg["position"] < 0:
# PnL from closing short
close_pnl = abs(cfg["position"]) * (cfg["entry_price"] - price)
cfg["pnl"] += close_pnl
cfg["entry_price"] = 0
cfg["position"] = 0
if close_pnl > 0: cfg["wins"] += 1
trades_log.append({
"time": datetime.now().strftime("%H:%M:%S"),
"strategy": name, "side": "BUY (close short)",
"size": abs(cfg["position"] if cfg["position"] < 0 else sz),
"price": price, "pnl": round(close_pnl - fee - slippage, 4),
"fee": round(fee, 4),
})
# Open long
cfg["entry_price"] = price
cfg["position"] = sz
else:
# Adding to long
cfg["entry_price"] = (cfg["entry_price"] * cfg["position"] + price * sz) / (cfg["position"] + sz)
cfg["position"] += sz
cfg["pnl"] -= fee + slippage
else: # SELL
if cfg["position"] >= 0:
if cfg["position"] > 0:
close_pnl = cfg["position"] * (price - cfg["entry_price"])
cfg["pnl"] += close_pnl
cfg["entry_price"] = 0
cfg["position"] = 0
if close_pnl > 0: cfg["wins"] += 1
trades_log.append({
"time": datetime.now().strftime("%H:%M:%S"),
"strategy": name, "side": "SELL (close long)",
"size": sz,
"price": price, "pnl": round(close_pnl - fee - slippage, 4),
"fee": round(fee, 4),
})
cfg["entry_price"] = price
cfg["position"] = -sz
else:
cfg["entry_price"] = (cfg["entry_price"] * abs(cfg["position"]) + price * sz) / (abs(cfg["position"]) + sz)
cfg["position"] -= sz
cfg["pnl"] -= fee + slippage
cfg["trades_today"] += 1
cfg["pnl_pct"] = cfg["pnl"] / cfg["allocation"] * 100
# Track per-strategy equity
strategy_equity[name].append({"t": time.time(), "v": cfg["allocation"] + cfg["pnl"]})
# Per-strategy trade with reason
trade_entry = {
"time": datetime.now().strftime("%H:%M:%S"),
"side": side, "size": sz, "price": price,
"pnl": round(cfg["pnl"], 4),
"fee": round(fee, 4),
"reason": reason,
"allocation": cfg["allocation"],
"fee_model": cfg.get("fee_model", "taker"),
}
per_strategy_trades[name].append(trade_entry)
# ═══════════════════════ A-S Spread Capture ═══════════════════════
def simulate_avellaneda(btc_bid, btc_ask):
"""Avellaneda-Stoikov: regime-adaptive spread capture.
Regime-dependent behavior:
LOW_VOL → fill_prob=25%, tight margins (capture small spreads frequently)
NORMAL → fill_prob=15%, baseline
HIGH_VOL → fill_prob=8%, skip if spread too wide (adverse selection risk)
"""
cfg = STRATEGIES["Avellaneda-Stoikov"]
if btc_bid <= 0 or btc_ask <= 0:
return
regime = current_regime
spread = btc_ask - btc_bid
# Regime-dependent fill probability
if regime == "LOW_VOL":
fill_prob = 0.25
elif regime == "HIGH_VOL":
fill_prob = 0.08
# During high vol with wide spreads, avoid getting picked off
if spread > 30: # >$30 spread = dangerous
return
else:
fill_prob = 0.15
if random.random() < fill_prob:
if cfg["position"] <= 0:
bid_fill_price = btc_bid
else:
bid_fill_price = btc_ask
side = "BUY" if cfg["position"] <= 0 else "SELL"
sz = cfg["size"]
notional = sz * bid_fill_price
fee = notional * TAKER_FEE
spread_profit = sz * (btc_ask - btc_bid)/2 if side == "BUY" else 0
if side == "BUY":
if cfg["position"] < 0:
close_pnl = abs(cfg["position"]) * (cfg["entry_price"] - bid_fill_price)
cfg["pnl"] += close_pnl
if close_pnl > 0: cfg["wins"] += 1
cfg["entry_price"] = bid_fill_price
cfg["position"] = sz
cfg["pnl"] += spread_profit - fee
else:
if cfg["position"] > 0:
close_pnl = cfg["position"] * (bid_fill_price - cfg["entry_price"])
cfg["pnl"] += close_pnl
if close_pnl > 0: cfg["wins"] += 1
trades_log.append({
"time": datetime.now().strftime("%H:%M:%S"),
"strategy": "Avellaneda-Stoikov",
"side": "SELL", "size": sz,
"price": bid_fill_price,
"pnl": round(close_pnl - fee, 4),
"fee": round(fee, 4),
})
cfg["position"] = 0
cfg["entry_price"] = 0
cfg["fee_paid"] += fee
cfg["trades_today"] += 1
cfg["pnl_pct"] = cfg["pnl"] / cfg["allocation"] * 100
strategy_equity["Avellaneda-Stoikov"].append({"t": time.time(), "v": cfg["allocation"] + cfg["pnl"]})
# ═══════════════════════ Metrics ═══════════════════════
def write_metrics():
total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
total_pnl_pct = (total_pnl / (STARTING_CAPITAL-RESERVE)) * 100 if STARTING_CAPITAL > RESERVE else 0
for s in STRATEGIES.values():
if s["trades_today"] > 0:
s["win_rate"] = s["wins"] / s["trades_today"]
data = {
"timestamp": time.time(),
"mode": "paper",
"source": "Hyperliquid Mainnet",
"total_equity": STARTING_CAPITAL + total_pnl,
"base_equity": STARTING_CAPITAL,
"total_pnl": total_pnl,
"total_pnl_pct": total_pnl_pct,
"reserve": RESERVE,
"equity_history": equity_history[-600:],
"strategy_equity": {k: list(v)[-300:] for k, v in strategy_equity.items()},
"strategies": STRATEGIES,
"trades": trades_log[-200:],
"status": "running",
"btc_price": btc_prices[-1] if btc_prices else 0,
"eth_price": eth_prices[-1] if eth_prices else 0,
"regime": current_regime,
"regime_confidence": regime_confidence,
"per_strategy_trades": {k: list(v)[-100:] for k, v in per_strategy_trades.items()},
}
try:
with open(METRICS_FILE, "w") as f:
json.dump(data, f, default=str)
except IOError: pass
# ═══════════════════════ Main ═══════════════════════
async def main():
log.info("="*60)
log.info(" FTDT Quant Lab — PAPER TRADING (Mainnet Data)")
log.info(f" Capital: ${STARTING_CAPITAL:,} | Reserve: ${RESERVE:,}")
log.info(f" 12 strategies × ${STRATEGIES['Order Book Imbalance']['allocation']:,.0f} allocation")
log.info(f" Fees: {TAKER_FEE*100:.2f}% taker | Slippage: {SLIPPAGE_BPS} bps")
log.info(f" Data: Hyperliquid MAINNET")
log.info(f" Dashboard: https://ftdt.io/cv")
log.info("="*60)
for s in STRATEGIES.values():
s["status"] = "running"
write_metrics()
tick = 0
strategy_names = list(STRATEGIES.keys())
idx = 0
try:
while True:
global prev_bids, prev_asks
tick += 1
# Fetch mainnet data
if tick % 2 == 0: # Every 2 seconds to respect rate limits
prices = get_mainnet_prices()
btc = prices.get("BTC", 0)
eth = prices.get("ETH", 0)
if btc > 0:
btc_prices.append(btc)
if eth > 0:
eth_prices.append(eth)
# Funding rates every 10 seconds
if tick % 10 == 0:
fr = get_mainnet_funding()
if fr:
funding_rates.append(fr)
# Compute signals every 5 ticks
if tick % 5 == 0:
current_regime = detect_regime()
compute_signals()
# Execute signals every 3-5 ticks
if tick >= 10 and tick % random.randint(3, 6) == 0:
btc = btc_prices[-1] if btc_prices else 0
eth = eth_prices[-1] if eth_prices else 0
if btc <= 0: continue
# Get orderbook for A-S and Deep LOB
btc_bid, btc_ask = get_mainnet_orderbook("BTC")
bids, asks = get_deep_orderbook("BTC")
# Avellaneda-Stoikov: simulate spread capture
simulate_avellaneda(btc_bid, btc_ask)
# Hawkes OFI: feed simulated trade to model
hawkes_btc.update("B" if tick % 2 == 0 else "S", 0.001, btc)
hawkes_sig = hawkes_btc.get_signal()
if hawkes_sig["signal"]:
STRATEGIES["Hawkes OFI (new)"]["signals"].append({
"time": time.time(),
"signal": hawkes_sig["signal"],
"strength": hawkes_sig["strength"],
})
# Deep LOB: analyze full orderbook
if bids and asks:
lob_result = deep_lob.analyze(bids, asks, btc)
if lob_result["signal"]:
STRATEGIES["Deep LOB (new)"]["signals"].append({
"time": time.time(),
"signal": lob_result["signal"],
"strength": lob_result["strength"],
})
# Queue Imbalance: weighted queue dynamics
if bids and asks:
qi_result = queue_imb.analyze(
bids, asks, btc, prev_bids, prev_asks,
btc_prices[-2] if len(btc_prices) >= 2 else 0)
if qi_result["signal"]:
STRATEGIES["Queue Imbalance"]["signals"].append({
"time": time.time(),
"signal": qi_result["signal"],
"strength": qi_result["strength"],
})
prev_bids, prev_asks = bids, asks
# Cartea-Jaimungal: stochastic control with alpha estimate
alpha_est = (btc_prices[-1] - btc_prices[-2]) / btc_prices[-2] \
if len(btc_prices) >= 2 and btc_prices[-2] > 0 else 0
cj_inv = STRATEGIES["Cartea-Jaimungal"]["position"]
cj_result = cartea.should_trade(btc, alpha_est, cj_inv, tick % 3600)
if cj_result["signal"]:
STRATEGIES["Cartea-Jaimungal"]["signals"].append({
"time": time.time(),
"signal": cj_result["signal"],
"strength": cj_result["confidence"],
})
# Guéant: closed-form market making
gueant_inv = STRATEGIES["Guéant Market Making"]["position"]
g_quotes = gueant.optimal_quotes(
btc, gueant_inv, tick % 3600,
adverse_prob=queue_imb.wqi_history[-1] if queue_imb.wqi_history else 0)
# Simulate fill: if our quote is at/near best, track a signal
if btc_bid > 0 and g_quotes["bid"] >= btc_bid * 0.999:
STRATEGIES["Guéant Market Making"]["signals"].append({
"time": time.time(), "signal": "BUY",
"strength": 0.5,
})
elif btc_ask > 0 and g_quotes["ask"] <= btc_ask * 1.001:
STRATEGIES["Guéant Market Making"]["signals"].append({
"time": time.time(), "signal": "SELL",
"strength": 0.5,
})
# Process next strategy's signals (round-robin 9 strategies)
total_strats = len(strategy_names)
name = strategy_names[idx % total_strats]
idx += 1
cfg = STRATEGIES[name]
if name == "Avellaneda-Stoikov":
continue # Already handled above
# Check for signals with strength > fee barrier
if not cfg["signals"]:
continue
sig = cfg["signals"][-1]
signal_str = str(sig["signal"])
strength = abs(sig.get("strength", 0))
signal_reason = sig.get("reason", signal_str)
# Skip weak signals that can't overcome fees
if strength < MIN_SIGNAL_STRENGTH:
continue
coin = cfg["instrument"]
px = btc if coin == "BTC" else eth
if px <= 0: continue
if "BUY" in signal_str.upper():
simulate_fill(name, "BUY", coin, px, signal_reason)
log.info(f"[{name[:4]:4s}] PAPER BUY {cfg['size']} {coin} @ ${px:,.1f} | PnL: ${cfg['pnl']:+.2f} | {signal_reason}")
elif "SELL" in signal_str.upper():
simulate_fill(name, "SELL", coin, px, signal_reason)
log.info(f"[{name[:4]:4s}] PAPER SELL {cfg['size']} {coin} @ ${px:,.1f} | PnL: ${cfg['pnl']:+.2f} | {signal_reason}")
# Equity history
total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
if tick % 3 == 0:
equity_history.append({"t": time.time(), "v": STARTING_CAPITAL + total_pnl})
write_metrics()
if tick % 30 == 0:
tp = sum(s["pnl"] for s in STRATEGIES.values())
tr = sum(s["trades_today"] for s in STRATEGIES.values())
tf = sum(s["fee_paid"] for s in STRATEGIES.values())
btc_now = btc_prices[-1] if btc_prices else 0
log.info(
f"Tick {tick:4d} | BTC: ${btc_now:,.0f} | "
f"PnL: ${tp:+.2f} | Trades: {tr:3d} | Fees: ${tf:.2f} | "
f"Regime: {current_regime}"
)
await asyncio.sleep(1)
except KeyboardInterrupt:
log.info("Stopping paper trader...")
for s in STRATEGIES.values():
s["status"] = "idle"
write_metrics()
tp = sum(s["pnl"] for s in STRATEGIES.values())
tr = sum(s["trades_today"] for s in STRATEGIES.values())
log.info(f"Paper trading stopped. Final PnL: ${tp:+.2f}, Trades: {tr}")
if __name__ == "__main__":
asyncio.run(main())