diff --git a/site/data/manifest.json b/site/data/manifest.json
index 384d653..b171c71 100644
--- a/site/data/manifest.json
+++ b/site/data/manifest.json
@@ -1,14 +1,14 @@
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diff --git a/site/horizons.html b/site/horizons.html
index afff2dd..27c24fa 100644
--- a/site/horizons.html
+++ b/site/horizons.html
@@ -35,13 +35,13 @@
 <div class="toolbar panel"><div class="toolbar-group"><label>Decision date<select id="decisionSelect"></select></label><label>Family<select id="familyFilter"><option value="all">All families</option><option value="ridge">Ridge</option><option value="xgb_ranker">XGBoost</option></select></label><label>Horizon<select id="horizonFilter"><option value="all">All horizons</option><option value="5">5 sessions</option><option value="10">10 sessions</option><option value="20">20 sessions</option></select></label></div><div class="toolbar-group"><button id="refreshData">Refresh data</button><div class="status" id="dataStatus">Frozen evidence loaded</div></div></div>
 <section class="notice panel"><div class="notice-main"><div class="notice-icon">→</div><div><strong id="noticeTitle"></strong><small id="noticeCopy"></small></div></div><div class="policy" id="policyBadge"></div></section>
 <div class="kpi-grid" id="kpis"></div>
-<section class="section panel"><div class="section-head"><div><h2>Prospective strategy evidence</h2><p>Partial mark-to-market outcomes appear as soon as an entered portfolio has a new observable open. They remain interim until the complete 5, 10, or 20-session window matures.</p></div><div class="legend" id="strategyLegend"></div></div><div class="progress-grid" id="strategyProgress"></div><div class="chart" id="strategyChart"></div><div class="interim-note">The decision session is not a holding session. A July 29 close signal enters at the July 30 open, so July 29 must remain 0/5 rather than borrowing a pre-entry return. Interim marks answer “what has happened so far?” and are never mixed with validation estimates.</div></section>
+<section class="section panel"><div class="section-head"><div><h2>Prospective strategy evidence</h2><p>An official close mark appears on the entry day. Complete session counts and the cumulative chart advance only when a new open-to-open interval is observable.</p></div><div class="legend" id="strategyLegend"></div></div><div class="progress-grid" id="strategyProgress"></div><div class="chart" id="strategyChart"></div><div class="interim-note">The decision session is not a holding session. A July 29 close signal enters at the July 30 open. Its July 30 close can provide an entry-day mark, but the first complete open-to-open session is available at the July 31 open. Entry-day marks are never mixed with validation estimates or final horizon outcomes.</div></section>
 <section class="section panel"><div class="section-head"><div><h2>Horizon comparison</h2><p>Paired Ridge and XGBoost development evidence with each arm’s own purge, feature subset, refit interval, and maturity date.</p></div></div><div class="horizon-grid" id="horizonGrid"></div></section>
 <section class="section panel"><div class="section-head"><div><h2>Selected model specifications</h2><p>The winning development configuration for each visible arm. Open a model’s explainability panel to inspect genuine SHAP importance when that frozen vintage captured it.</p></div></div><div class="model-grid" id="modelGrid"></div></section>
 <section class="section panel"><div class="section-head"><div><h2>Pipeline and risk controls</h2><p>Freshness, universe, NLP, tactical routing, survival guardrails, and artifact integrity for the selected decision date.</p></div></div><div class="health-grid" id="healthGrid"></div><div class="warning-list" id="warnings"></div></section>
 <section class="section panel"><div class="section-head"><div><h2>Matured forecast history</h2><p>Prospective outcomes appear only after their full label interval matures. Pending forecasts are not scored as zero.</p></div></div><div id="maturedTable"></div></section>
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"2026-07-30", "completed_open_to_open_sessions": 0, "gross_mark_to_close": 0.0130896232416, "spy_mark_to_close": 0.0076625429549275, "active_mark_vs_spy": 0.0054270802866725, "initial_holdings": 40, "status": "interim_official_close_mark"}, {"experiment_version": "live_forward_horizon_matched_v2", "family": "ridge", "horizon_sessions": 10, "rebalance_decision_date": "2026-07-29", "entry_date": "2026-07-30", "mark_date": "2026-07-30", "completed_open_to_open_sessions": 0, "gross_mark_to_close": 0.0030249030694353, "spy_mark_to_close": 0.0076625429549275, "active_mark_vs_spy": -0.0046376398854921, "initial_holdings": 40, "status": "interim_official_close_mark"}, {"experiment_version": "live_forward_horizon_matched_v2", "family": "ridge", "horizon_sessions": 20, "rebalance_decision_date": "2026-07-29", "entry_date": "2026-07-30", "mark_date": "2026-07-30", "completed_open_to_open_sessions": 0, "gross_mark_to_close": 0.0013029446777482, "spy_mark_to_close": 0.0076625429549275, "active_mark_vs_spy": -0.0063595982771793, "initial_holdings": 40, "status": "interim_official_close_mark"}, {"experiment_version": "live_forward_horizon_matched_v2", "family": "xgb_ranker", "horizon_sessions": 5, "rebalance_decision_date": "2026-07-29", "entry_date": "2026-07-30", "mark_date": "2026-07-30", "completed_open_to_open_sessions": 0, "gross_mark_to_close": 0.0078168273023959, "spy_mark_to_close": 0.0076625429549275, "active_mark_vs_spy": 0.0001542843474684, "initial_holdings": 40, "status": "interim_official_close_mark"}, {"experiment_version": "live_forward_horizon_matched_v2", "family": "xgb_ranker", "horizon_sessions": 10, "rebalance_decision_date": "2026-07-29", "entry_date": "2026-07-30", "mark_date": "2026-07-30", "completed_open_to_open_sessions": 0, "gross_mark_to_close": 0.0141644538319386, "spy_mark_to_close": 0.0076625429549275, "active_mark_vs_spy": 0.0065019108770111, "initial_holdings": 40, "status": "interim_official_close_mark"}, {"experiment_version": "live_forward_horizon_matched_v2", "family": "xgb_ranker", "horizon_sessions": 20, "rebalance_decision_date": "2026-07-29", "entry_date": "2026-07-30", "mark_date": "2026-07-30", "completed_open_to_open_sessions": 0, "gross_mark_to_close": 0.0089900669664831, "spy_mark_to_close": 0.0076625429549275, "active_mark_vs_spy": 0.0013275240115555, "initial_holdings": 40, "status": "interim_official_close_mark"}], "runs": [{"decision_date": "2026-07-29", "status": "live_forward_20d_cycle_complete", "matured_forecasts": 0, "strategy_daily_return_rows": 0, "tactical_orders": 0, "model_failures": 0}, {"decision_date": "2026-07-30", "status": "live_forward_20d_cycle_complete", "matured_forecasts": 0, "strategy_daily_return_rows": 0, "tactical_orders": 0, "model_failures": 0}], "tactical_orders": []}</script>
 <script>
 const data=JSON.parse(document.getElementById('dashboard-data').textContent),decisionSelect=document.getElementById('decisionSelect'),familyFilter=document.getElementById('familyFilter'),horizonFilter=document.getElementById('horizonFilter'),STATE_KEY='live-forward-horizon-dashboard-v1';
 const COLORS={ridge:'#3156d3',xgb_ranker:'#0f9f8f',ridge_h5:'#7290eb',ridge_h10:'#4f70dc',ridge_h20:'#3156d3',xgb_ranker_h5:'#70c8bd',xgb_ranker_h10:'#3eb2a4',xgb_ranker_h20:'#0f9f8f',spy:'#d89614'},names={ridge:'Ridge',xgb_ranker:'XGBoost'},fmtPct=v=>v==null||!Number.isFinite(Number(v))?'n/a':(Number(v)*100).toFixed(2)+'%',fmtNum=(v,d=3)=>v==null||!Number.isFinite(Number(v))?'n/a':Number(v).toFixed(d),human=v=>String(v??'').replaceAll('_',' ').replace(/\b\w/g,c=>c.toUpperCase()),tone=v=>Number(v)>=0?'pos':'neg',armColor=a=>COLORS[`${a.family}_h${a.horizon_sessions}`]||COLORS[a.family],armLabel=a=>`${names[a.family]||human(a.family)} · ${a.horizon_sessions} sessions`;
@@ -55,7 +55,7 @@
 function renderNotice(){const arms=decisionArms(),pending=arms.filter(a=>a.status==='pending').length,next=[...new Set(arms.map(a=>a.expected_entry_date).filter(Boolean))].sort()[0],policy=data.rebalance_policy||{};document.getElementById('noticeTitle').textContent=`${pending} frozen forecasts ready for the next session`;document.getElementById('noticeCopy').textContent=`Decision ${decisionSelect.value} · expected entry ${next||'pending'} · partial marks update session by session; final labels wait for full maturity.`;document.getElementById('policyBadge').textContent=`${human(policy.status||'under audit')} · buffer ${policy.buffer_enabled?'on':'off'}`;document.getElementById('heroDate').textContent=`Latest decision · ${dates[0]||'n/a'}`}
 function renderKpis(){const arms=visibleArms(),matured=data.matured.filter(x=>!decisionSelect.value||x.decision_date<=decisionSelect.value),audit=data.input_audit||{},universe=data.universe||{},items=[['Active arms',arms.length,'#3156d3',`${decisionArms().length} total for this decision`],['Matured forecasts',matured.length,'#7559c7',matured.length?'Prospective outcomes available':'First outcomes still pending'],['Eligible universe',universe.final_names,'#0f9f8f',`${universe.current_liquid_names??0} liquid names`],['Sentiment coverage',arms[0]?.sentiment_coverage,'#d89614',`${audit.processed_news_rows??0} recent articles processed`]];document.getElementById('kpis').innerHTML=items.map(([label,value,color,detail],i)=>`<article class="kpi panel" style="--signal:${color}"><span>${label}</span><strong>${i===3?fmtPct(value):value??'n/a'}</strong><small>${detail}</small></article>`).join('');document.getElementById('dataStatus').textContent=`${dates.length} decision date${dates.length===1?'':'s'} · ${data.arms.length} frozen forecasts`}
 function strategyGroups(){const by=new Map,spyByDate=new Map;data.strategy_returns.forEach(row=>{const id=`${row.family}_h${row.horizon_sessions}`;if(!by.has(id))by.set(id,[]);by.get(id).push(row);if(Number.isFinite(Number(row.spy_return))&&!spyByDate.has(row.return_date))spyByDate.set(row.return_date,{return_date:row.return_date,gross_return:Number(row.spy_return)})});if(spyByDate.size)by.set('spy',[...spyByDate.values()]);return [...by.entries()].map(([id,rows])=>({id,rows:rows.sort((a,b)=>String(a.return_date).localeCompare(String(b.return_date)))}))}
-function renderStrategy(){const arms=visibleArms(),visibleIds=new Set([...arms.map(a=>`${a.family}_h${a.horizon_sessions}`),'spy']),groups=strategyGroups().filter(g=>visibleIds.has(g.id)),legend=document.getElementById('strategyLegend');document.getElementById('strategyProgress').innerHTML=arms.map(a=>{const rows=data.strategy_returns.filter(r=>r.family===a.family&&Number(r.horizon_sessions)===a.horizon_sessions&&r.rebalance_decision_date===a.decision_date).sort((x,y)=>String(x.return_date).localeCompare(String(y.return_date))),observed=rows.length,cumulative=rows.reduce((value,row)=>value*(1+Number(row.gross_return)),1)-1,spyCumulative=rows.reduce((value,row)=>Number.isFinite(Number(row.spy_return))?value*(1+Number(row.spy_return)):value,1)-1,asOf=data.experiment.decision_date,status=observed?`${fmtPct(cumulative)} interim · ${fmtPct(cumulative-spyCumulative)} vs SPY`:(asOf<a.expected_entry_date?`Awaiting ${a.expected_entry_date} open`:'Entered · first mark pending');return`<article class="progress-card"><div class="progress-head"><strong>${armLabel(a)}</strong><span>${observed}/${a.horizon_sessions} sessions held</span></div><div class="progress-track"><i style="width:${Math.min(100,observed/a.horizon_sessions*100)}%;--series:${armColor(a)}"></i></div><small>${status} · complete label ${a.expected_maturity_date}</small></article>`}).join('');legend.innerHTML=groups.map(g=>`<button class="${hiddenSeries.has(g.id)?'off':'active'}" data-series="${g.id}" style="--series:${COLORS[g.id]||'#7559c7'}"><i></i>${g.id==='spy'?'SPY':human(g.id.replace('_h',' · h'))}</button>`).join('');legend.querySelectorAll('[data-series]').forEach(b=>b.addEventListener('click',()=>{hiddenSeries.has(b.dataset.series)?hiddenSeries.delete(b.dataset.series):hiddenSeries.add(b.dataset.series);renderStrategy();save()}));const visible=groups.filter(g=>!hiddenSeries.has(g.id));if(!visible.length){const entries=arms.map(a=>a.expected_entry_date).filter(Boolean).sort();document.getElementById('strategyChart').innerHTML=`<div class="empty"><div><strong>No observable post-entry mark yet</strong><span>The ${decisionSelect.value} close decision first becomes invested at the ${entries[0]||'next configured'} open. The chart begins with the first post-entry open-to-open return and does not wait for full maturity.</span></div></div>`;return}const lines=visible.map(g=>{let cumulative=1;return{id:g.id,points:g.rows.map(r=>({date:r.return_date,value:(cumulative*=1+Number(r.gross_return))-1}))}}),stamps=[...new Set(lines.flatMap(l=>l.points.map(p=>p.date)))].sort(),vals=lines.flatMap(l=>l.points.map(p=>p.value)),min=Math.min(0,...vals),max=Math.max(0,...vals),pad=Math.max((max-min)*.14,.001),w=1180,h=360,left=66,right=22,top=18,bottom=48,pw=w-left-right,ph=h-top-bottom,x=s=>left+stamps.indexOf(s)/Math.max(1,stamps.length-1)*pw,y=v=>top+(1-(v-(min-pad))/((max+pad)-(min-pad)||1))*ph,ticks=[0,.25,.5,.75,1].map(f=>max+pad-f*((max+pad)-(min-pad))),grid=ticks.map(v=>`<line x1="${left}" x2="${left+pw}" y1="${y(v)}" y2="${y(v)}" stroke="#e6ebf2"/><text x="${left-9}" y="${y(v)+4}" text-anchor="end" fill="#69768a" font-size="10">${fmtPct(v)}</text>`).join(''),paths=lines.map(line=>`<path d="${line.points.map((p,i)=>`${i?'L':'M'}${x(p.date)},${y(p.value)}`).join(' ')}" fill="none" stroke="${COLORS[line.id]}" stroke-width="${line.id==='spy'?2:3}" stroke-dasharray="${line.id==='spy'?'6 5':''}" stroke-linecap="round"/>`).join('');document.getElementById('strategyChart').innerHTML=`<svg viewBox="0 0 ${w} ${h}">${grid}<line x1="${left}" x2="${left+pw}" y1="${y(0)}" y2="${y(0)}" stroke="#9aa7b8" stroke-dasharray="5 5"/>${paths}</svg>`}
+function renderStrategy(){const arms=visibleArms(),visibleIds=new Set([...arms.map(a=>`${a.family}_h${a.horizon_sessions}`),'spy']),groups=strategyGroups().filter(g=>visibleIds.has(g.id)),legend=document.getElementById('strategyLegend');document.getElementById('strategyProgress').innerHTML=arms.map(a=>{const rows=data.strategy_returns.filter(r=>r.family===a.family&&Number(r.horizon_sessions)===a.horizon_sessions&&r.rebalance_decision_date===a.decision_date).sort((x,y)=>String(x.return_date).localeCompare(String(y.return_date))),mark=(data.strategy_interim_marks||[]).find(r=>r.family===a.family&&Number(r.horizon_sessions)===a.horizon_sessions&&r.rebalance_decision_date===a.decision_date),observed=rows.length,cumulative=rows.reduce((value,row)=>value*(1+Number(row.gross_return)),1)-1,spyCumulative=rows.reduce((value,row)=>Number.isFinite(Number(row.spy_return))?value*(1+Number(row.spy_return)):value,1)-1,asOf=data.experiment.decision_date,status=observed?`${fmtPct(cumulative)} interim · ${fmtPct(cumulative-spyCumulative)} vs SPY`:mark?`Entry-day close mark ${fmtPct(mark.gross_mark_to_close)} · ${fmtPct(mark.active_mark_vs_spy)} vs SPY`:(asOf<a.expected_entry_date?`Awaiting ${a.expected_entry_date} open`:'Entered · first mark pending');return`<article class="progress-card"><div class="progress-head"><strong>${armLabel(a)}</strong><span>${observed}/${a.horizon_sessions} complete sessions</span></div><div class="progress-track"><i style="width:${Math.min(100,observed/a.horizon_sessions*100)}%;--series:${armColor(a)}"></i></div><small>${status} · complete label ${a.expected_maturity_date}</small></article>`}).join('');legend.innerHTML=groups.map(g=>`<button class="${hiddenSeries.has(g.id)?'off':'active'}" data-series="${g.id}" style="--series:${COLORS[g.id]||'#7559c7'}"><i></i>${g.id==='spy'?'SPY':human(g.id.replace('_h',' · h'))}</button>`).join('');legend.querySelectorAll('[data-series]').forEach(b=>b.addEventListener('click',()=>{hiddenSeries.has(b.dataset.series)?hiddenSeries.delete(b.dataset.series):hiddenSeries.add(b.dataset.series);renderStrategy();save()}));const visible=groups.filter(g=>!hiddenSeries.has(g.id));if(!visible.length){const entries=arms.map(a=>a.expected_entry_date).filter(Boolean).sort(),marks=arms.map(a=>(data.strategy_interim_marks||[]).find(r=>r.family===a.family&&Number(r.horizon_sessions)===a.horizon_sessions&&r.rebalance_decision_date===a.decision_date)).filter(Boolean),message=marks.length?'Official entry-day close marks are shown above. The cumulative chart begins after the first complete open-to-open session.':`The ${decisionSelect.value} close decision first becomes invested at the ${entries[0]||'next configured'} open.`;document.getElementById('strategyChart').innerHTML=`<div class="empty"><div><strong>No complete open-to-open session yet</strong><span>${message}</span></div></div>`;return}const lines=visible.map(g=>{let cumulative=1;return{id:g.id,points:g.rows.map(r=>({date:r.return_date,value:(cumulative*=1+Number(r.gross_return))-1}))}}),stamps=[...new Set(lines.flatMap(l=>l.points.map(p=>p.date)))].sort(),vals=lines.flatMap(l=>l.points.map(p=>p.value)),min=Math.min(0,...vals),max=Math.max(0,...vals),pad=Math.max((max-min)*.14,.001),w=1180,h=360,left=66,right=22,top=18,bottom=48,pw=w-left-right,ph=h-top-bottom,x=s=>left+stamps.indexOf(s)/Math.max(1,stamps.length-1)*pw,y=v=>top+(1-(v-(min-pad))/((max+pad)-(min-pad)||1))*ph,ticks=[0,.25,.5,.75,1].map(f=>max+pad-f*((max+pad)-(min-pad))),grid=ticks.map(v=>`<line x1="${left}" x2="${left+pw}" y1="${y(v)}" y2="${y(v)}" stroke="#e6ebf2"/><text x="${left-9}" y="${y(v)+4}" text-anchor="end" fill="#69768a" font-size="10">${fmtPct(v)}</text>`).join(''),paths=lines.map(line=>`<path d="${line.points.map((p,i)=>`${i?'L':'M'}${x(p.date)},${y(p.value)}`).join(' ')}" fill="none" stroke="${COLORS[line.id]}" stroke-width="${line.id==='spy'?2:3}" stroke-dasharray="${line.id==='spy'?'6 5':''}" stroke-linecap="round"/>`).join('');document.getElementById('strategyChart').innerHTML=`<svg viewBox="0 0 ${w} ${h}">${grid}<line x1="${left}" x2="${left+pw}" y1="${y(0)}" y2="${y(0)}" stroke="#9aa7b8" stroke-dasharray="5 5"/>${paths}</svg>`}
 function renderHorizons(){const all=decisionArms(),cards=[5,10,20].map(h=>{const pair=all.filter(a=>a.horizon_sessions===h).sort((a,b)=>a.family.localeCompare(b.family)),slices=pair.map(a=>`<div class="model-slice" style="--series:${armColor(a)}"><span>${names[a.family]}</span><strong class="${tone(a.validation?.top40_raw_excess)}">${fmtPct(a.validation?.top40_raw_excess)}</strong><small>Validation top-40 excess<br>Rank IC ${fmtNum(a.validation?.rank_ic)} · ${a.feature_count} features</small></div>`).join(''),maturity=pair[0]?.expected_maturity_date||'pending';return`<article class="horizon-card"><div class="horizon-title"><h3>${h}-session horizon</h3><span class="clock">Refit every ${h}</span></div><div class="pair">${slices}</div><div class="maturity">Expected first maturity · <strong>${maturity}</strong></div></article>`});document.getElementById('horizonGrid').innerHTML=cards.join('')}
 function renderModels(){const node=document.getElementById('modelGrid');node.innerHTML=visibleArms().map(a=>{const v=a.validation||{},features=a.features.slice(0,12).map(f=>`<span class="feature">${human(f)}</span>`).join(''),extra=Math.max(0,a.features.length-12),shap=a.shap_summary||[],max=Math.max(...shap.map(x=>Number(x.mean_abs_shap)||0),0),chart=shap.length?`<div class="model-meta">${human(shap[0].method)} · ${shap[0].explanation_rows} current-cohort rows · mean absolute SHAP</div>${shap.slice(0,12).map(x=>`<div class="shap-row"><span title="${human(x.feature)}">${human(x.feature)}</span><div class="shap-bar"><i style="width:${max?Number(x.mean_abs_shap)/max*100:0}%"></i></div><strong>${fmtNum(x.mean_abs_shap,4)}</strong></div>`).join('')}`:`<div class="shap-empty"><strong>SHAP was not captured for this frozen vintage.</strong><br>The July 29 artifacts remain immutable. Genuine ${a.family==='ridge'?'linear SHAP':'TreeSHAP'} summaries will be saved with each new forecast; coefficients or XGBoost gain are not substituted or mislabeled as SHAP.</div>`;return`<article class="model-card"><div class="model-title"><h3>${armLabel(a)}</h3><span class="badge">${a.refitted_today?'Refit today':'Frozen model'}</span></div><div class="model-meta">Training ${a.training_start||'n/a'} to ${a.training_end||'n/a'} · ${Number(a.training_rows||0).toLocaleString()} rows · ${v.completed_trials||'n/a'} eligible trials</div><div class="metrics"><div class="metric"><span>Top-40 excess</span><strong class="${tone(v.top40_raw_excess)}">${fmtPct(v.top40_raw_excess)}</strong></div><div class="metric"><span>Rank IC</span><strong>${fmtNum(v.rank_ic)}</strong></div><div class="metric"><span>Positive IC</span><strong>${fmtPct(v.positive_ic_rate)}</strong></div></div><div class="model-meta">Selected ${a.feature_count} features · ${human(v.selection_metric||'n/a')}</div><div class="features">${features}${extra?`<span class="feature">+${extra} more</span>`:''}</div><button class="explain-toggle" type="button" data-explain="${a.id}" aria-expanded="false">Show SHAP feature importance</button><div class="explain-panel" data-explain-panel="${a.id}" hidden>${chart}</div></article>`}).join('')||'<div class="empty"><div><strong>No visible model specifications</strong><span>Change the family or horizon filter.</span></div></div>';node.querySelectorAll('[data-explain]').forEach(button=>button.addEventListener('click',()=>{const panel=node.querySelector(`[data-explain-panel="${button.dataset.explain}"]`),opening=panel.hidden;panel.hidden=!opening;button.setAttribute('aria-expanded',String(opening));button.textContent=opening?'Hide SHAP feature importance':'Show SHAP feature importance'}))}
 function renderHealth(){const audit=data.input_audit||{},universe=data.universe||{},survival=data.survival_validation||{},tactical=data.tactical_orders||[],cards=[['Market data',audit.market_end===data.experiment.decision_date?'Current':'Review',`${audit.downloaded_stock_tickers??0}/${audit.requested_stock_tickers??0} histories · through ${audit.market_end||'n/a'}`,audit.market_end===data.experiment.decision_date],['News + NLP',audit.finbert_fallback_permitted===false?'Strict FinBERT':'Review',`${audit.processed_news_rows??0} articles · ${audit.nlp_feature_tickers??0} tickers`,audit.finbert_fallback_permitted===false],['Universe',`${universe.final_names??0} eligible`,`Hard exclusions ${universe.hard_exclusions??0} · ML exclusions ${universe.ml_exclusions??0}`,Number(universe.final_names)>=40],['Tactical routing',`${tactical.length} orders`,tactical.length?'Review routed orders':'No event passed every frozen gate',true],['Survival ML',survival.activated?'Active':'Shadow only',survival.activated?'Activation gates passed':human(survival.reason||'not activated'),!survival.activated],['Artifacts',`${decisionArms().length} forecasts`,`${decisionArms().filter(a=>a.status==='pending').length} pending · immutable manifests`,decisionArms().length===6],['Sentiment freshness',audit.latest_news_age_hours!=null?`${Number(audit.latest_news_age_hours).toFixed(2)} hours`:'n/a',`Latest publication ${audit.latest_news_publication||'n/a'}`,Number(audit.latest_news_age_hours)<=72],['Failures',`${(data.runs.at(-1)?.model_failures)||0} model failures`,`Latest cycle ${data.runs.at(-1)?.status||'n/a'}`,!(data.runs.at(-1)?.model_failures)]],node=document.getElementById('healthGrid');node.innerHTML=cards.map(([label,value,detail,ok])=>`<article class="health"><div class="health-head"><span>${label}</span><i class="dot ${ok?'':'warn'}"></i></div><strong>${value}</strong><small>${detail}</small></article>`).join('');const warnings=[];if(!survival.activated)warnings.push('Survival ML remains shadow-only; no stock is excluded by an unvalidated threshold.');if(data.rebalance_policy?.status==='under_audit')warnings.push('Full top-40 refresh remains under audit; the rank/score buffer has not been activated.');if(!data.matured.length)warnings.push('No v2 forecast has matured. Today’s successful run is operational evidence, not realized alpha.');document.getElementById('warnings').innerHTML=warnings.map(x=>`<div class="warning">${x}</div>`).join('')}
diff --git a/src/experiments/live_forward_20d.py b/src/experiments/live_forward_20d.py
index a535e88..6ef9f9a 100644
--- a/src/experiments/live_forward_20d.py
+++ b/src/experiments/live_forward_20d.py
@@ -4,6 +4,7 @@
 
 import hashlib
 import json
+from copy import deepcopy
 from datetime import UTC
 from pathlib import Path
 from typing import Any, Iterable
@@ -90,6 +91,95 @@ def _payload_sha256(payload: dict[str, Any]) -> str:
     return hashlib.sha256(encoded).hexdigest()
 
 
+def _canonical_payload_sha256(payload: dict[str, Any]) -> str:
+    def string_keys(value: Any) -> Any:
+        if isinstance(value, dict):
+            return {
+                str(key): string_keys(local)
+                for key, local in value.items()
+            }
+        if isinstance(value, (list, tuple)):
+            return [string_keys(local) for local in value]
+        return value
+
+    return _payload_sha256(string_keys(payload))
+
+
+def _training_data_revision(config: dict[str, Any]) -> int:
+    return int(config.get("live_forward_20d", {}).get("training_data_revision", 1))
+
+
+def _experiment_design_payload(config: dict[str, Any]) -> dict[str, Any]:
+    """Remove the operational data revision from the frozen experiment design."""
+    design = deepcopy(config)
+    design.get("live_forward_20d", {}).pop("training_data_revision", None)
+    return design
+
+
+def _ensure_experiment_manifest(path: Path, config: dict[str, Any]) -> None:
+    """Freeze design while allowing an audited monotonic training-data repair."""
+    config_sha256 = _payload_sha256(config)
+    if not path.exists():
+        _write_json(
+            path,
+            {
+                "status": "frozen_paper_live_experiment",
+                "experiment_version": config.get("experiment", {}).get(
+                    "id",
+                    "live_forward_20d_v1",
+                ),
+                "config_sha256": config_sha256,
+                "design_sha256": _canonical_payload_sha256(
+                    _experiment_design_payload(config)
+                ),
+                "training_data_revision": _training_data_revision(config),
+                "config": config,
+                "config_history": [],
+                "created_utc": pd.Timestamp.now(tz=UTC).isoformat(),
+            },
+        )
+        return
+
+    frozen = json.loads(path.read_text(encoding="utf-8"))
+    if frozen.get("config_sha256") == config_sha256:
+        return
+    old_config = frozen.get("config", {})
+    old_design_sha = _canonical_payload_sha256(
+        _experiment_design_payload(old_config)
+    )
+    new_design_sha = _canonical_payload_sha256(
+        _experiment_design_payload(config)
+    )
+    old_revision = _training_data_revision(old_config)
+    new_revision = _training_data_revision(config)
+    if old_design_sha != new_design_sha or new_revision <= old_revision:
+        raise RuntimeError(
+            "Output root already contains a different frozen experiment config"
+        )
+
+    history = list(frozen.get("config_history", []))
+    history.append(
+        {
+            "config_sha256": frozen.get("config_sha256"),
+            "training_data_revision": old_revision,
+            "config": old_config,
+            "superseded_utc": pd.Timestamp.now(tz=UTC).isoformat(),
+            "reason": "training_data_revision_advanced",
+        }
+    )
+    frozen.update(
+        {
+            "config_sha256": config_sha256,
+            "design_sha256": new_design_sha,
+            "training_data_revision": new_revision,
+            "config": config,
+            "config_history": history,
+            "updated_utc": pd.Timestamp.now(tz=UTC).isoformat(),
+        }
+    )
+    _write_json(path, frozen)
+
+
 def build_timestamp_safe_nlp_features(
     posts: list[Any],
     price_dates: pd.DatetimeIndex,
@@ -1751,6 +1841,99 @@ def evaluate_horizon_matched_strategies(
     return result
 
 
+def build_strategy_interim_marks(
+    output_root: Path,
+    prices: pd.DataFrame,
+    as_of: pd.Timestamp,
+) -> pd.DataFrame:
+    """Mark each currently entered strategy at the latest official close."""
+    price_dates = pd.DatetimeIndex(
+        prices.index.get_level_values("date").unique()
+    ).sort_values()
+    as_of_pos = price_dates.get_indexer([as_of])[0]
+    if as_of_pos < 0:
+        return pd.DataFrame()
+    opened = prices["open"].unstack("ticker")
+    close_column = "adj_close" if "adj_close" in prices else "close"
+    closed = prices[close_column].unstack("ticker")
+    candidates: dict[
+        tuple[str, int],
+        list[tuple[pd.Timestamp, Path, dict[str, Any]]],
+    ] = {}
+    for manifest_path in output_root.glob("forecasts/*/*/manifest.json"):
+        manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
+        if not manifest.get("strategy_rebalance", False):
+            continue
+        target_path = manifest_path.parent / "strategy_rebalance_target.csv"
+        if not target_path.exists():
+            continue
+        decision = pd.Timestamp(manifest["decision_date"])
+        decision_pos = price_dates.get_indexer([decision])[0]
+        if decision_pos < 0 or decision_pos + 1 > as_of_pos:
+            continue
+        key = (str(manifest["family"]), int(manifest["horizon_sessions"]))
+        candidates.setdefault(key, []).append((decision, target_path, manifest))
+
+    rows: list[dict[str, Any]] = []
+    for (family, horizon), items in candidates.items():
+        decision, target_path, manifest = max(items, key=lambda item: item[0])
+        decision_pos = price_dates.get_indexer([decision])[0]
+        entry_pos = decision_pos + 1
+        entry_date = price_dates[entry_pos]
+        target = pd.read_csv(target_path)
+        if not {"ticker", "weight"}.issubset(target.columns):
+            raise ValueError(f"Invalid strategy target schema: {target_path}")
+        weights = target.set_index(target["ticker"].astype(str))["weight"].astype(
+            float
+        )
+        entry = opened.loc[entry_date].reindex(weights.index)
+        mark = closed.loc[as_of].reindex(weights.index)
+        usable = (
+            weights.notna()
+            & weights.gt(0)
+            & entry.notna()
+            & entry.gt(0)
+            & mark.notna()
+            & mark.gt(0)
+        )
+        if not usable.all():
+            missing = weights.index[~usable].astype(str).tolist()
+            raise RuntimeError(
+                f"Invalid interim mark prices or weights for {family} h{horizon} "
+                f"vintage {decision.date()}: {', '.join(missing[:10])}"
+            )
+        weights = weights / weights.sum()
+        gross_mark = float(((mark / entry - 1.0) * weights).sum())
+        spy_mark = float("nan")
+        if "SPY" in opened.columns and "SPY" in closed.columns:
+            spy_mark = float(
+                closed.loc[as_of, "SPY"] / opened.loc[entry_date, "SPY"] - 1.0
+            )
+        rows.append(
+            {
+                "experiment_version": manifest["experiment_version"],
+                "family": family,
+                "horizon_sessions": horizon,
+                "rebalance_decision_date": str(decision.date()),
+                "entry_date": str(entry_date.date()),
+                "mark_date": str(as_of.date()),
+                "completed_open_to_open_sessions": int(as_of_pos - entry_pos),
+                "gross_mark_to_close": gross_mark,
+                "spy_mark_to_close": spy_mark,
+                "active_mark_vs_spy": float(gross_mark - spy_mark),
+                "initial_holdings": int(len(weights)),
+                "status": "interim_official_close_mark",
+            }
+        )
+    result = pd.DataFrame(rows)
+    if not result.empty:
+        result = result.sort_values(
+            ["family", "horizon_sessions"]
+        ).reset_index(drop=True)
+        result.to_csv(output_root / "strategy_interim_marks.csv", index=False)
+    return result
+
+
 def build_live_20d_dashboard(
     output_root: Path,
     run_summary: dict[str, Any],
@@ -1812,29 +1995,7 @@ def run_live_forward_20d(
             "holdings": int(settings["holdings"]),
         }
     experiment_manifest_path = output / "experiment_manifest.json"
-    config_sha256 = _payload_sha256(config)
-    if experiment_manifest_path.exists():
-        frozen_experiment = json.loads(
-            experiment_manifest_path.read_text(encoding="utf-8")
-        )
-        if frozen_experiment.get("config_sha256") != config_sha256:
-            raise RuntimeError(
-                "Output root already contains a different frozen experiment config"
-            )
-    else:
-        _write_json(
-            experiment_manifest_path,
-            {
-                "status": "frozen_paper_live_experiment",
-                "experiment_version": config.get("experiment", {}).get(
-                    "id",
-                    "live_forward_20d_v1",
-                ),
-                "config_sha256": config_sha256,
-                "config": config,
-                "created_utc": pd.Timestamp.now(tz=UTC).isoformat(),
-            },
-        )
+    _ensure_experiment_manifest(experiment_manifest_path, config)
     universe_cfg = settings["universe"]
     eligible_features, eligibility = prepare_eligible_panel(
         data.loc[data.index.get_level_values("date") <= decision_date],
@@ -2056,6 +2217,11 @@ def run_live_forward_20d(
         price_data,
         decision_date,
     )
+    strategy_interim_marks = build_strategy_interim_marks(
+        output,
+        price_data,
+        decision_date,
+    )
     strategy_performance: list[dict[str, Any]] = []
     if not strategy_returns.empty:
         for (family, horizon), group in strategy_returns.groupby(
@@ -2131,12 +2297,18 @@ def run_live_forward_20d(
         ) if not tactical.empty else 0,
         "matured_forecasts": int(len(matured)),
         "strategy_daily_return_rows": int(len(strategy_returns)),
+        "strategy_interim_mark_rows": int(len(strategy_interim_marks)),
         "strategy_performance": strategy_performance,
         "strategy_returns_output": (
             str(output / "strategy_daily_returns.csv")
             if not strategy_returns.empty
             else None
         ),
+        "strategy_interim_marks_output": (
+            str(output / "strategy_interim_marks.csv")
+            if not strategy_interim_marks.empty
+            else None
+        ),
         "output": str(daily_dir),
     }
     _write_json(daily_dir / "run_summary.json", summary)
diff --git a/src/experiments/live_forward_20d_dashboard.py b/src/experiments/live_forward_20d_dashboard.py
index ad1be17..3fd0b00 100644
--- a/src/experiments/live_forward_20d_dashboard.py
+++ b/src/experiments/live_forward_20d_dashboard.py
@@ -194,6 +194,12 @@ def build_horizon_dashboard_payload(
         if strategy_path.exists()
         else []
     )
+    interim_path = root / "strategy_interim_marks.csv"
+    interim = (
+        pd.read_csv(interim_path).to_dict("records")
+        if interim_path.exists()
+        else []
+    )
     runs: list[dict[str, Any]] = []
     for path in sorted((root / "forecasts").glob("*/run_summary.json")):
         summary = json.loads(path.read_text(encoding="utf-8"))
@@ -246,6 +252,7 @@ def build_horizon_dashboard_payload(
             else []
         ),
         "strategy_returns": _json_value(strategy),
+        "strategy_interim_marks": _json_value(interim),
         "runs": runs,
         "tactical_orders": _json_value(tactical),
     }
@@ -295,7 +302,7 @@ def horizon_dashboard_html(payload: dict[str, Any]) -> str:
 <div class="toolbar panel"><div class="toolbar-group"><label>Decision date<select id="decisionSelect"></select></label><label>Family<select id="familyFilter"><option value="all">All families</option><option value="ridge">Ridge</option><option value="xgb_ranker">XGBoost</option></select></label><label>Horizon<select id="horizonFilter"><option value="all">All horizons</option><option value="5">5 sessions</option><option value="10">10 sessions</option><option value="20">20 sessions</option></select></label></div><div class="toolbar-group"><button id="refreshData">Refresh data</button><div class="status" id="dataStatus">Frozen evidence loaded</div></div></div>
 <section class="notice panel"><div class="notice-main"><div class="notice-icon">→</div><div><strong id="noticeTitle"></strong><small id="noticeCopy"></small></div></div><div class="policy" id="policyBadge"></div></section>
 <div class="kpi-grid" id="kpis"></div>
-<section class="section panel"><div class="section-head"><div><h2>Prospective strategy evidence</h2><p>Partial mark-to-market outcomes appear as soon as an entered portfolio has a new observable open. They remain interim until the complete 5, 10, or 20-session window matures.</p></div><div class="legend" id="strategyLegend"></div></div><div class="progress-grid" id="strategyProgress"></div><div class="chart" id="strategyChart"></div><div class="interim-note">The decision session is not a holding session. A July 29 close signal enters at the July 30 open, so July 29 must remain 0/5 rather than borrowing a pre-entry return. Interim marks answer “what has happened so far?” and are never mixed with validation estimates.</div></section>
+<section class="section panel"><div class="section-head"><div><h2>Prospective strategy evidence</h2><p>An official close mark appears on the entry day. Complete session counts and the cumulative chart advance only when a new open-to-open interval is observable.</p></div><div class="legend" id="strategyLegend"></div></div><div class="progress-grid" id="strategyProgress"></div><div class="chart" id="strategyChart"></div><div class="interim-note">The decision session is not a holding session. A July 29 close signal enters at the July 30 open. Its July 30 close can provide an entry-day mark, but the first complete open-to-open session is available at the July 31 open. Entry-day marks are never mixed with validation estimates or final horizon outcomes.</div></section>
 <section class="section panel"><div class="section-head"><div><h2>Horizon comparison</h2><p>Paired Ridge and XGBoost development evidence with each arm’s own purge, feature subset, refit interval, and maturity date.</p></div></div><div class="horizon-grid" id="horizonGrid"></div></section>
 <section class="section panel"><div class="section-head"><div><h2>Selected model specifications</h2><p>The winning development configuration for each visible arm. Open a model’s explainability panel to inspect genuine SHAP importance when that frozen vintage captured it.</p></div></div><div class="model-grid" id="modelGrid"></div></section>
 <section class="section panel"><div class="section-head"><div><h2>Pipeline and risk controls</h2><p>Freshness, universe, NLP, tactical routing, survival guardrails, and artifact integrity for the selected decision date.</p></div></div><div class="health-grid" id="healthGrid"></div><div class="warning-list" id="warnings"></div></section>
@@ -315,7 +322,7 @@ def horizon_dashboard_html(payload: dict[str, Any]) -> str:
 function renderNotice(){const arms=decisionArms(),pending=arms.filter(a=>a.status==='pending').length,next=[...new Set(arms.map(a=>a.expected_entry_date).filter(Boolean))].sort()[0],policy=data.rebalance_policy||{};document.getElementById('noticeTitle').textContent=`${pending} frozen forecasts ready for the next session`;document.getElementById('noticeCopy').textContent=`Decision ${decisionSelect.value} · expected entry ${next||'pending'} · partial marks update session by session; final labels wait for full maturity.`;document.getElementById('policyBadge').textContent=`${human(policy.status||'under audit')} · buffer ${policy.buffer_enabled?'on':'off'}`;document.getElementById('heroDate').textContent=`Latest decision · ${dates[0]||'n/a'}`}
 function renderKpis(){const arms=visibleArms(),matured=data.matured.filter(x=>!decisionSelect.value||x.decision_date<=decisionSelect.value),audit=data.input_audit||{},universe=data.universe||{},items=[['Active arms',arms.length,'#3156d3',`${decisionArms().length} total for this decision`],['Matured forecasts',matured.length,'#7559c7',matured.length?'Prospective outcomes available':'First outcomes still pending'],['Eligible universe',universe.final_names,'#0f9f8f',`${universe.current_liquid_names??0} liquid names`],['Sentiment coverage',arms[0]?.sentiment_coverage,'#d89614',`${audit.processed_news_rows??0} recent articles processed`]];document.getElementById('kpis').innerHTML=items.map(([label,value,color,detail],i)=>`<article class="kpi panel" style="--signal:${color}"><span>${label}</span><strong>${i===3?fmtPct(value):value??'n/a'}</strong><small>${detail}</small></article>`).join('');document.getElementById('dataStatus').textContent=`${dates.length} decision date${dates.length===1?'':'s'} · ${data.arms.length} frozen forecasts`}
 function strategyGroups(){const by=new Map,spyByDate=new Map;data.strategy_returns.forEach(row=>{const id=`${row.family}_h${row.horizon_sessions}`;if(!by.has(id))by.set(id,[]);by.get(id).push(row);if(Number.isFinite(Number(row.spy_return))&&!spyByDate.has(row.return_date))spyByDate.set(row.return_date,{return_date:row.return_date,gross_return:Number(row.spy_return)})});if(spyByDate.size)by.set('spy',[...spyByDate.values()]);return [...by.entries()].map(([id,rows])=>({id,rows:rows.sort((a,b)=>String(a.return_date).localeCompare(String(b.return_date)))}))}
-function renderStrategy(){const arms=visibleArms(),visibleIds=new Set([...arms.map(a=>`${a.family}_h${a.horizon_sessions}`),'spy']),groups=strategyGroups().filter(g=>visibleIds.has(g.id)),legend=document.getElementById('strategyLegend');document.getElementById('strategyProgress').innerHTML=arms.map(a=>{const rows=data.strategy_returns.filter(r=>r.family===a.family&&Number(r.horizon_sessions)===a.horizon_sessions&&r.rebalance_decision_date===a.decision_date).sort((x,y)=>String(x.return_date).localeCompare(String(y.return_date))),observed=rows.length,cumulative=rows.reduce((value,row)=>value*(1+Number(row.gross_return)),1)-1,spyCumulative=rows.reduce((value,row)=>Number.isFinite(Number(row.spy_return))?value*(1+Number(row.spy_return)):value,1)-1,asOf=data.experiment.decision_date,status=observed?`${fmtPct(cumulative)} interim · ${fmtPct(cumulative-spyCumulative)} vs SPY`:(asOf<a.expected_entry_date?`Awaiting ${a.expected_entry_date} open`:'Entered · first mark pending');return`<article class="progress-card"><div class="progress-head"><strong>${armLabel(a)}</strong><span>${observed}/${a.horizon_sessions} sessions held</span></div><div class="progress-track"><i style="width:${Math.min(100,observed/a.horizon_sessions*100)}%;--series:${armColor(a)}"></i></div><small>${status} · complete label ${a.expected_maturity_date}</small></article>`}).join('');legend.innerHTML=groups.map(g=>`<button class="${hiddenSeries.has(g.id)?'off':'active'}" data-series="${g.id}" style="--series:${COLORS[g.id]||'#7559c7'}"><i></i>${g.id==='spy'?'SPY':human(g.id.replace('_h',' · h'))}</button>`).join('');legend.querySelectorAll('[data-series]').forEach(b=>b.addEventListener('click',()=>{hiddenSeries.has(b.dataset.series)?hiddenSeries.delete(b.dataset.series):hiddenSeries.add(b.dataset.series);renderStrategy();save()}));const visible=groups.filter(g=>!hiddenSeries.has(g.id));if(!visible.length){const entries=arms.map(a=>a.expected_entry_date).filter(Boolean).sort();document.getElementById('strategyChart').innerHTML=`<div class="empty"><div><strong>No observable post-entry mark yet</strong><span>The ${decisionSelect.value} close decision first becomes invested at the ${entries[0]||'next configured'} open. The chart begins with the first post-entry open-to-open return and does not wait for full maturity.</span></div></div>`;return}const lines=visible.map(g=>{let cumulative=1;return{id:g.id,points:g.rows.map(r=>({date:r.return_date,value:(cumulative*=1+Number(r.gross_return))-1}))}}),stamps=[...new Set(lines.flatMap(l=>l.points.map(p=>p.date)))].sort(),vals=lines.flatMap(l=>l.points.map(p=>p.value)),min=Math.min(0,...vals),max=Math.max(0,...vals),pad=Math.max((max-min)*.14,.001),w=1180,h=360,left=66,right=22,top=18,bottom=48,pw=w-left-right,ph=h-top-bottom,x=s=>left+stamps.indexOf(s)/Math.max(1,stamps.length-1)*pw,y=v=>top+(1-(v-(min-pad))/((max+pad)-(min-pad)||1))*ph,ticks=[0,.25,.5,.75,1].map(f=>max+pad-f*((max+pad)-(min-pad))),grid=ticks.map(v=>`<line x1="${left}" x2="${left+pw}" y1="${y(v)}" y2="${y(v)}" stroke="#e6ebf2"/><text x="${left-9}" y="${y(v)+4}" text-anchor="end" fill="#69768a" font-size="10">${fmtPct(v)}</text>`).join(''),paths=lines.map(line=>`<path d="${line.points.map((p,i)=>`${i?'L':'M'}${x(p.date)},${y(p.value)}`).join(' ')}" fill="none" stroke="${COLORS[line.id]}" stroke-width="${line.id==='spy'?2:3}" stroke-dasharray="${line.id==='spy'?'6 5':''}" stroke-linecap="round"/>`).join('');document.getElementById('strategyChart').innerHTML=`<svg viewBox="0 0 ${w} ${h}">${grid}<line x1="${left}" x2="${left+pw}" y1="${y(0)}" y2="${y(0)}" stroke="#9aa7b8" stroke-dasharray="5 5"/>${paths}</svg>`}
+function renderStrategy(){const arms=visibleArms(),visibleIds=new Set([...arms.map(a=>`${a.family}_h${a.horizon_sessions}`),'spy']),groups=strategyGroups().filter(g=>visibleIds.has(g.id)),legend=document.getElementById('strategyLegend');document.getElementById('strategyProgress').innerHTML=arms.map(a=>{const rows=data.strategy_returns.filter(r=>r.family===a.family&&Number(r.horizon_sessions)===a.horizon_sessions&&r.rebalance_decision_date===a.decision_date).sort((x,y)=>String(x.return_date).localeCompare(String(y.return_date))),mark=(data.strategy_interim_marks||[]).find(r=>r.family===a.family&&Number(r.horizon_sessions)===a.horizon_sessions&&r.rebalance_decision_date===a.decision_date),observed=rows.length,cumulative=rows.reduce((value,row)=>value*(1+Number(row.gross_return)),1)-1,spyCumulative=rows.reduce((value,row)=>Number.isFinite(Number(row.spy_return))?value*(1+Number(row.spy_return)):value,1)-1,asOf=data.experiment.decision_date,status=observed?`${fmtPct(cumulative)} interim · ${fmtPct(cumulative-spyCumulative)} vs SPY`:mark?`Entry-day close mark ${fmtPct(mark.gross_mark_to_close)} · ${fmtPct(mark.active_mark_vs_spy)} vs SPY`:(asOf<a.expected_entry_date?`Awaiting ${a.expected_entry_date} open`:'Entered · first mark pending');return`<article class="progress-card"><div class="progress-head"><strong>${armLabel(a)}</strong><span>${observed}/${a.horizon_sessions} complete sessions</span></div><div class="progress-track"><i style="width:${Math.min(100,observed/a.horizon_sessions*100)}%;--series:${armColor(a)}"></i></div><small>${status} · complete label ${a.expected_maturity_date}</small></article>`}).join('');legend.innerHTML=groups.map(g=>`<button class="${hiddenSeries.has(g.id)?'off':'active'}" data-series="${g.id}" style="--series:${COLORS[g.id]||'#7559c7'}"><i></i>${g.id==='spy'?'SPY':human(g.id.replace('_h',' · h'))}</button>`).join('');legend.querySelectorAll('[data-series]').forEach(b=>b.addEventListener('click',()=>{hiddenSeries.has(b.dataset.series)?hiddenSeries.delete(b.dataset.series):hiddenSeries.add(b.dataset.series);renderStrategy();save()}));const visible=groups.filter(g=>!hiddenSeries.has(g.id));if(!visible.length){const entries=arms.map(a=>a.expected_entry_date).filter(Boolean).sort(),marks=arms.map(a=>(data.strategy_interim_marks||[]).find(r=>r.family===a.family&&Number(r.horizon_sessions)===a.horizon_sessions&&r.rebalance_decision_date===a.decision_date)).filter(Boolean),message=marks.length?'Official entry-day close marks are shown above. The cumulative chart begins after the first complete open-to-open session.':`The ${decisionSelect.value} close decision first becomes invested at the ${entries[0]||'next configured'} open.`;document.getElementById('strategyChart').innerHTML=`<div class="empty"><div><strong>No complete open-to-open session yet</strong><span>${message}</span></div></div>`;return}const lines=visible.map(g=>{let cumulative=1;return{id:g.id,points:g.rows.map(r=>({date:r.return_date,value:(cumulative*=1+Number(r.gross_return))-1}))}}),stamps=[...new Set(lines.flatMap(l=>l.points.map(p=>p.date)))].sort(),vals=lines.flatMap(l=>l.points.map(p=>p.value)),min=Math.min(0,...vals),max=Math.max(0,...vals),pad=Math.max((max-min)*.14,.001),w=1180,h=360,left=66,right=22,top=18,bottom=48,pw=w-left-right,ph=h-top-bottom,x=s=>left+stamps.indexOf(s)/Math.max(1,stamps.length-1)*pw,y=v=>top+(1-(v-(min-pad))/((max+pad)-(min-pad)||1))*ph,ticks=[0,.25,.5,.75,1].map(f=>max+pad-f*((max+pad)-(min-pad))),grid=ticks.map(v=>`<line x1="${left}" x2="${left+pw}" y1="${y(v)}" y2="${y(v)}" stroke="#e6ebf2"/><text x="${left-9}" y="${y(v)+4}" text-anchor="end" fill="#69768a" font-size="10">${fmtPct(v)}</text>`).join(''),paths=lines.map(line=>`<path d="${line.points.map((p,i)=>`${i?'L':'M'}${x(p.date)},${y(p.value)}`).join(' ')}" fill="none" stroke="${COLORS[line.id]}" stroke-width="${line.id==='spy'?2:3}" stroke-dasharray="${line.id==='spy'?'6 5':''}" stroke-linecap="round"/>`).join('');document.getElementById('strategyChart').innerHTML=`<svg viewBox="0 0 ${w} ${h}">${grid}<line x1="${left}" x2="${left+pw}" y1="${y(0)}" y2="${y(0)}" stroke="#9aa7b8" stroke-dasharray="5 5"/>${paths}</svg>`}
 function renderHorizons(){const all=decisionArms(),cards=[5,10,20].map(h=>{const pair=all.filter(a=>a.horizon_sessions===h).sort((a,b)=>a.family.localeCompare(b.family)),slices=pair.map(a=>`<div class="model-slice" style="--series:${armColor(a)}"><span>${names[a.family]}</span><strong class="${tone(a.validation?.top40_raw_excess)}">${fmtPct(a.validation?.top40_raw_excess)}</strong><small>Validation top-40 excess<br>Rank IC ${fmtNum(a.validation?.rank_ic)} · ${a.feature_count} features</small></div>`).join(''),maturity=pair[0]?.expected_maturity_date||'pending';return`<article class="horizon-card"><div class="horizon-title"><h3>${h}-session horizon</h3><span class="clock">Refit every ${h}</span></div><div class="pair">${slices}</div><div class="maturity">Expected first maturity · <strong>${maturity}</strong></div></article>`});document.getElementById('horizonGrid').innerHTML=cards.join('')}
 function renderModels(){const node=document.getElementById('modelGrid');node.innerHTML=visibleArms().map(a=>{const v=a.validation||{},features=a.features.slice(0,12).map(f=>`<span class="feature">${human(f)}</span>`).join(''),extra=Math.max(0,a.features.length-12),shap=a.shap_summary||[],max=Math.max(...shap.map(x=>Number(x.mean_abs_shap)||0),0),chart=shap.length?`<div class="model-meta">${human(shap[0].method)} · ${shap[0].explanation_rows} current-cohort rows · mean absolute SHAP</div>${shap.slice(0,12).map(x=>`<div class="shap-row"><span title="${human(x.feature)}">${human(x.feature)}</span><div class="shap-bar"><i style="width:${max?Number(x.mean_abs_shap)/max*100:0}%"></i></div><strong>${fmtNum(x.mean_abs_shap,4)}</strong></div>`).join('')}`:`<div class="shap-empty"><strong>SHAP was not captured for this frozen vintage.</strong><br>The July 29 artifacts remain immutable. Genuine ${a.family==='ridge'?'linear SHAP':'TreeSHAP'} summaries will be saved with each new forecast; coefficients or XGBoost gain are not substituted or mislabeled as SHAP.</div>`;return`<article class="model-card"><div class="model-title"><h3>${armLabel(a)}</h3><span class="badge">${a.refitted_today?'Refit today':'Frozen model'}</span></div><div class="model-meta">Training ${a.training_start||'n/a'} to ${a.training_end||'n/a'} · ${Number(a.training_rows||0).toLocaleString()} rows · ${v.completed_trials||'n/a'} eligible trials</div><div class="metrics"><div class="metric"><span>Top-40 excess</span><strong class="${tone(v.top40_raw_excess)}">${fmtPct(v.top40_raw_excess)}</strong></div><div class="metric"><span>Rank IC</span><strong>${fmtNum(v.rank_ic)}</strong></div><div class="metric"><span>Positive IC</span><strong>${fmtPct(v.positive_ic_rate)}</strong></div></div><div class="model-meta">Selected ${a.feature_count} features · ${human(v.selection_metric||'n/a')}</div><div class="features">${features}${extra?`<span class="feature">+${extra} more</span>`:''}</div><button class="explain-toggle" type="button" data-explain="${a.id}" aria-expanded="false">Show SHAP feature importance</button><div class="explain-panel" data-explain-panel="${a.id}" hidden>${chart}</div></article>`}).join('')||'<div class="empty"><div><strong>No visible model specifications</strong><span>Change the family or horizon filter.</span></div></div>';node.querySelectorAll('[data-explain]').forEach(button=>button.addEventListener('click',()=>{const panel=node.querySelector(`[data-explain-panel="${button.dataset.explain}"]`),opening=panel.hidden;panel.hidden=!opening;button.setAttribute('aria-expanded',String(opening));button.textContent=opening?'Hide SHAP feature importance':'Show SHAP feature importance'}))}
 function renderHealth(){const audit=data.input_audit||{},universe=data.universe||{},survival=data.survival_validation||{},tactical=data.tactical_orders||[],cards=[['Market data',audit.market_end===data.experiment.decision_date?'Current':'Review',`${audit.downloaded_stock_tickers??0}/${audit.requested_stock_tickers??0} histories · through ${audit.market_end||'n/a'}`,audit.market_end===data.experiment.decision_date],['News + NLP',audit.finbert_fallback_permitted===false?'Strict FinBERT':'Review',`${audit.processed_news_rows??0} articles · ${audit.nlp_feature_tickers??0} tickers`,audit.finbert_fallback_permitted===false],['Universe',`${universe.final_names??0} eligible`,`Hard exclusions ${universe.hard_exclusions??0} · ML exclusions ${universe.ml_exclusions??0}`,Number(universe.final_names)>=40],['Tactical routing',`${tactical.length} orders`,tactical.length?'Review routed orders':'No event passed every frozen gate',true],['Survival ML',survival.activated?'Active':'Shadow only',survival.activated?'Activation gates passed':human(survival.reason||'not activated'),!survival.activated],['Artifacts',`${decisionArms().length} forecasts`,`${decisionArms().filter(a=>a.status==='pending').length} pending · immutable manifests`,decisionArms().length===6],['Sentiment freshness',audit.latest_news_age_hours!=null?`${Number(audit.latest_news_age_hours).toFixed(2)} hours`:'n/a',`Latest publication ${audit.latest_news_publication||'n/a'}`,Number(audit.latest_news_age_hours)<=72],['Failures',`${(data.runs.at(-1)?.model_failures)||0} model failures`,`Latest cycle ${data.runs.at(-1)?.status||'n/a'}`,!(data.runs.at(-1)?.model_failures)]],node=document.getElementById('healthGrid');node.innerHTML=cards.map(([label,value,detail,ok])=>`<article class="health"><div class="health-head"><span>${label}</span><i class="dot ${ok?'':'warn'}"></i></div><strong>${value}</strong><small>${detail}</small></article>`).join('');const warnings=[];if(!survival.activated)warnings.push('Survival ML remains shadow-only; no stock is excluded by an unvalidated threshold.');if(data.rebalance_policy?.status==='under_audit')warnings.push('Full top-40 refresh remains under audit; the rank/score buffer has not been activated.');if(!data.matured.length)warnings.push('No v2 forecast has matured. Today’s successful run is operational evidence, not realized alpha.');document.getElementById('warnings').innerHTML=warnings.map(x=>`<div class="warning">${x}</div>`).join('')}
diff --git a/tests/test_live_forward_20d.py b/tests/test_live_forward_20d.py
index a3f0dc3..1946dac 100644
--- a/tests/test_live_forward_20d.py
+++ b/tests/test_live_forward_20d.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import json
+from copy import deepcopy
 from datetime import UTC, datetime
 from pathlib import Path
 
@@ -9,6 +10,7 @@
 import pytest
 
 from src.experiments.live_forward_20d import (
+    _ensure_experiment_manifest,
     _refit_sessions_for_horizon,
     build_exit_labels_fast,
     build_liquid_eligibility,
@@ -260,6 +262,37 @@ def test_training_data_revision_forces_refit_before_schedule(tmp_path) -> None:
     assert second_manifest["training_data_revision"] == 2
 
 
+def test_experiment_manifest_allows_only_monotonic_data_revision(tmp_path) -> None:
+    path = tmp_path / "experiment_manifest.json"
+    original = {
+        "experiment": {"id": "horizon_v2"},
+        "live_forward_20d": {
+            "holdings": 40,
+            "refit_sessions_by_horizon": {5: 5, 10: 10},
+        },
+    }
+    repaired = deepcopy(original)
+    repaired["live_forward_20d"]["training_data_revision"] = 2
+
+    _ensure_experiment_manifest(path, original)
+    _ensure_experiment_manifest(path, repaired)
+
+    manifest = json.loads(path.read_text(encoding="utf-8"))
+    assert manifest["training_data_revision"] == 2
+    assert len(manifest["config_history"]) == 1
+    assert manifest["config_history"][0]["training_data_revision"] == 1
+
+    changed_design = deepcopy(repaired)
+    changed_design["live_forward_20d"]["holdings"] = 20
+    with pytest.raises(RuntimeError, match="different frozen experiment config"):
+        _ensure_experiment_manifest(path, changed_design)
+
+    downgraded = deepcopy(repaired)
+    downgraded["live_forward_20d"]["training_data_revision"] = 1
+    with pytest.raises(RuntimeError, match="different frozen experiment config"):
+        _ensure_experiment_manifest(path, downgraded)
+
+
 def test_point_in_time_membership_and_liquidity_do_not_use_current_members() -> None:
     dates = pd.date_range("2023-01-02", periods=8, freq="B")
     tickers = ["A", "B", "C"]
@@ -590,6 +623,10 @@ def test_end_to_end_cycle_freezes_and_matures_forecast(
     assert third["strategy_daily_return_rows"] >= 1
     assert (tmp_path / "matured_forecasts.csv").exists()
     assert (tmp_path / "strategy_daily_returns.csv").exists()
+    assert (tmp_path / "strategy_interim_marks.csv").exists()
     strategy_returns = pd.read_csv(tmp_path / "strategy_daily_returns.csv")
     assert strategy_returns["spy_return"].notna().all()
+    interim_marks = pd.read_csv(tmp_path / "strategy_interim_marks.csv")
+    assert interim_marks["completed_open_to_open_sessions"].eq(0).all()
+    assert interim_marks["gross_mark_to_close"].notna().all()
     assert (tmp_path / "dashboard.html").exists()
diff --git a/tests/test_live_forward_20d_dashboard.py b/tests/test_live_forward_20d_dashboard.py
index b864649..aa08ba2 100644
--- a/tests/test_live_forward_20d_dashboard.py
+++ b/tests/test_live_forward_20d_dashboard.py
@@ -164,7 +164,8 @@ def test_dashboard_html_exposes_v2_information_architecture() -> None:
     assert "../live_forward/dashboard.html" in html
     assert 'id="strategyProgress"' in html
     assert "The decision session is not a holding session" in html
-    assert "sessions held" in html
+    assert "complete sessions" in html
+    assert "Entry-day close mark" in html
     assert 'id="modelGrid"' in html
     assert 'id="healthGrid"' in html
     assert 'id="maturedTable"' in html