Notes

import json,pandas as pd,numpy as np,vectorbt as vbt from pathlib import Path P=Path('cnxc91106');raw=json.load(open(P/'intraday.json'));daily={r['event']:r for r in json.load(open(P/'trades.json')) if r['k']==5 and r['barrier'] and r['slippage_bp']==25};out=[];summ=[] for be in [False,True]: for slip in [.0025,.0045]: seq=[];orders=[];prices=[];rr=[] for e in raw: df=pd.DataFrame(e['bars']);df.index=pd.to_datetime(df.t).dt.tz_convert('America/New_York');df=df.sort_index();r=df.between_time('09:30','15:45');x=daily[e['event']];start=pd.Timestamp(e['start']+' 09:30',tz='America/New_York');end=pd.Timestamp(e['end']+' 15:45',tz='America/New_York');assert start in r.index and end in r.index r=r.loc[start:end];days=pd.bdate_range(e['start'],e['end']);expected=len(days)*26;E=float(r.o.iloc[0]);A=x['adr'];stop=E-A;target=E+3*A;why='time';be_at=None;ix=len(r)-1 for i,(t,row) in enumerate(r.iterrows()): if i==0:continue if row.o<=stop:ix=i;why='stop_BE' if stop==E else 'stop';break if row.o>=target:ix=i;why='target';break if be and row.o>=E+2*A and stop<E:stop=E;be_at=str(t) ex=float(r.o.iloc[ix]);net=ex*(1-slip)*.9995/(E*(1+slip)*1.0005)-1;rr.append(net) # Whole-trade book-keeping with all intermediate 15m marks, no fabricated fills. vals=r.o.iloc[:ix+1].tolist();sz=[np.inf]+[np.nan]*(len(vals)-2)+[-np.inf];seq+=vals;prices+=vals;orders+=sz out.append({'event':e['event'],'BE':be,'slippage_bp':slip*10000,'entry':E,'daily_entry_difference':E-x['entry'],'exit':ex,'exitAt':str(r.index[ix]),'reason':why,'BE_at':be_at,'net_pct':100*net,'bars_all':len(df),'bars_RTH':len(r),'weekday_expected_RTH':expected,'weekday_excess_vs_sessions':expected-len

import os,requests,json,concurrent.futures,pandas as pd from pathlib import Path P=Path('cnxc91106');es=json.load(open(P/'events.json'));bars=json.load(open(P/'bars.json'))['CNXC'];dates=pd.to_datetime([b['t'] for b in bars]).tz_convert('America/New_York').strftime('%Y-%m-%d').tolist() def get(e): if e['date']<'2025' or e['date']>'2026-09-10':return None j=dates.index(e['date']);a=dates[j-5];b=dates[j-1] st=pd.Timestamp(a,tz='America/New_York').isoformat();end=(pd.Timestamp(b,tz='America/New_York')+pd.Timedelta(days=1)).isoformat() ps={'symbols':'CNXC','timeframe':'15Min','start':st,'end':end,'feed':'sip','adjustment':'all','limit':10000};out=[] while True: r=requests.get(os.environ['SITE_URL']+'/data/stock-bars',params=ps,headers={'Authorization':'Bearer '+os.environ['DATA_TOKEN']},timeout=30);r.raise_for_status();q=r.json();out+=q.get('bars',{}).get('CNXC',[]) if not q.get('next_page_token'):break ps['page_token']=q['next_page_token'] return {'event':e['date'],'start':a,'end':b,'bars':out} with concurrent.futures.ThreadPoolExecutor(4) as ex:rs=[r for r in ex.map(get,es) if r] (P/'intraday.json').write_text(json.dumps(rs));print([(r['event'],len(r['bars'])) for r in rs])

,'training_p':float(p),'training_W_pct':float(W*100),'training_L_pct':float(L*100)} out={'technical':tech,'backtests':results,'subsets':subsets,'CI':ci,'simulation':sims,'regression':reg,'exitodds':exitodds,'ledger':ledger,'skips':skips} (P/'results.json').write_text(json.dumps(out,indent=2));(P/'trades.json').write_text(json.dumps(alltr,indent=2));print(json.dumps({k:out[k] for k in ['technical','CI','exitodds','regression','skips']},indent=2));print('PRIMARY',json.dumps([x for x in results if x['k']==5]));print('SUBSETS',json.dumps(subsets))

,int(x['market_bull'])] for x in test]);yt=np.array([x['net'] for x in test]);pt=np.column_stack([np.ones(len(Xt)),(Xt-mu)/sd])@coef;reg['holdout_RMSE_pp']=float(np.sqrt(np.mean((pt-yt)**2))*100);reg['constant_RMSE_pp']=float(np.sqrt(np.mean((rt.mean()-yt)**2))*100) last=d.iloc[-1];gap=d.o/d.c.shift()-1 tech={'bars':len(d),'last_date':d.index[-1],'close':float(last.c),'sma20':float(last.sma20),'sma50':float(last.sma50),'sma200':float(last.sma200),'adr20':float(last.adr),'atr14':float(last.atr),'rsi14':float(last.rsi),'mom20_pct':float(last.mom20*100),'rv20_pct':float(last.rv20*100),'dollar_volume20':float((d.c*d.v).tail(20).mean()),'worst_gap_pct':float(gap.min()*100),'SPY_close':float(s.c.iloc[-1]),'SPY_sma200':float(s.sma200.iloc[-1]),'hyp_stop':float(last.c-last.adr),'hyp_target':float(last.c+3*last.adr),'max_whole_shares':int(49.14//last.c),'one_share_total_loss_account_pct':float(last.c/850.99*100)} # Net reward/risk at today's non-executable close, with the frozen baseline friction. costE=last.c*1.0025*1.0005;netLoss=costE-(last.c-last.adr)*.9975*.9995;netWin=(last.c+3*last.adr)*.9975*.9995-costE tech['net_RR']=float(netWin/netLoss) tradesold=[('MIRM',99.244,98.808,93,.654850671),('IONS',60.16,58.45,56,1.080285904),('RARE',25.84,13.98976,24,2.515092879)] ledger=[{'symbol':sy,'return_pct':100*(ex/E-1),'R':(ex-E)/(E-st),'pnl':(ex-E)*q,'stop_slippage_pct':100*(ex/st-1) if sy=='RARE' else None} for sy,E,ex,st,q in tradesold] exitodds={'train_n':len(train),'target_rate':np.mean([x['reason']=='target' for x in train]),'stop_rate':np.mean(['stop' in x['reason'] for x in train]),'time_rate':np.mean([x['reason']=='time' for x in train]),'training_halfKelly_capital':float(hk)

-1)[:,:length];return r[idx] ci=[] for lab,part in [('train',train),('holdout',test)]: r=np.array([x['net'] for x in part]);ci.append({'window':lab,'n':len(r),'mean_pct':100*r.mean(),'block2_CI95_pct':(100*np.quantile(blocks(r,20000,len(r)).mean(1),[.025,.975])).tolist(),'median_pct':100*np.median(r)}) rt=np.array([x['net'] for x in train]);p=np.mean(rt>0);W=rt[rt>0].mean() if (rt>0).any() else 0;L=-rt[rt<0].mean() if (rt<0).any() else 0;hk=max(0,.5*(p/L-(1-p)/W)) if W and L else 0 sims=[] base=blocks(rt,20000,10) for frac in [0,.25,float(d.c.iloc[-1]/49.14),1]: for shock in [0,.01,.05]: r=base.copy() if shock:r=np.where(rng.random(r.shape)<shock,-.30,r) paths=np.cumprod(1+frac*r,axis=1);path0=np.column_stack([np.ones(len(paths)),paths]);dd=(path0/np.maximum.accumulate(path0,axis=1)-1).min(1) sims.append({'fraction':frac,'assumed_gap_probability':shock,'gap_return':-.30,'median_pct':100*np.median(paths[:,-1]-1),'p_loss':float(np.mean(paths[:,-1]<1)),'p_dd20':float(np.mean(dd<=-.2)),'p5_pct':100*np.quantile(paths[:,-1]-1,.05),'mean_pct':100*np.mean(paths[:,-1]-1)}) # Descriptive training-only regression; no significance claims at N15. X=np.array([[x['mom20'],x['ma_distance'],x['rv20'],int(x['market_bull'])] for x in train]);mu=X.mean(0);sd=X.std(0);Z=np.column_stack([np.ones(len(X)),(X-mu)/sd]);coef=np.linalg.lstsq(Z,rt,rcond=None)[0];pred=Z@coef corr=np.corrcoef(np.column_stack([X,rt]).T);reg={'n':len(X),'features':['mom20','ma_distance','rv20','market_bull','net_return'],'correlation':corr.tolist(),'beta_per_training_SD_pp':(coef*100).tolist(),'train_R2':float(1-np.sum((rt-pred)**2)/np.sum((rt-rt.mean())**2))} Xt=np.array([[x['mom20'],x['ma_distance'],x['rv20']

{'return':100*total,'drawdown':-100*float((np.r_[10000,pf.value().values]/np.maximum.accumulate(np.r_[10000,pf.value().values])-1).min()),'win rate':100*np.mean(r>0),'sample':len(r),'mean_pct':100*r.mean(),'avgwin_pct':100*r[r>0].mean() if (r>0).any() else 0.,'avgloss_pct':-100*r[r<0].mean() if (r<0).any() else 0.,'targets':sum(x['reason']=='target' for x in ts),'worst_pct':100*r.min(),'mean_excess_pp':100*np.mean(r-np.array([x['benchmark'] for x in ts])),'ambiguous':sum(x['ambiguous'] for x in ts)} for k in [3,5,10]: for barrier in [False,True]: for slip in [.0025,.0045]: ts=trades(k,barrier,slip);alltr.extend(ts) for train in [True,False]:results.append(dict(k=k,barrier=barrier,slippage_bp=slip*1e4,window='train' if train else 'holdout',**stats([x for x in ts if x['train']==train]))) primary=trades(5,True,.0025);train=[x for x in primary if x['train']];test=[x for x in primary if not x['train']] subsets=[] for lab,fn in [('stock_up',lambda x:x['stock_bull']),('stock_down',lambda x:not x['stock_bull']),('market_up',lambda x:x['market_bull']),('market_down',lambda x:not x['market_bull']),('both_up',lambda x:x['stock_bull'] and x['market_bull']),('post_webhelp',lambda x:x['event']>'2023-09-27'),('ex_webhelp_overlap',lambda x:x['event']!='2023-09-27')]: for ist,part in [(True,train),(False,test)]:subsets.append(dict(subset=lab,window='train' if ist else 'holdout',**stats([x for x in part if fn(x)]))) rng=np.random.default_rng(91106) def blocks(r,npaths,length): # Circular blocks of two consecutive events retain limited quarterly dependence. starts=rng.integers(0,len(r),size=(npaths,(length+1)//2));idx=np.stack([starts,(starts+1)%len(r)],axis=-1).reshape(npaths,

for z in range(a,b+1): row=d.iloc[z] if z>a and row.o<=stop:ex=float(row.o);slot=2*z;why='gap_stop';break if z>a and row.o>=target:ex=target;slot=2*z;why='target';break if row.l<=stop:ex=stop;slot=2*z+1;why='stop';amb=int(row.h>=target);break if row.h>=target:ex=target;slot=2*z+1;why='target';break net=ex*(1-slip)*(1-.0005)/(E*(1+slip)*(1+.0005))-1 prior=d.iloc[a-1];prior_date=d.index[a-1] ts.append(dict(event=e['date'],announcement=e['ann'],url=e['url'],entry_date=d.index[a],exit_date=d.index[slot//2],k=k,barrier=barrier,slippage_bp=slip*1e4,entry=E,exit=ex,adr=A,stop=stop,target=target,entry_slot=2*a,exit_slot=slot,reason=why,ambiguous=amb,net=net,train=e['date']<'2025-01-01',stock_bull=bool(prior.c>prior.sma20),market_bull=bool(s.loc[prior_date,'bull']),mom20=float(prior.mom20),ma_distance=float(prior.c/prior.sma20-1),rv20=float(prior.rv20),benchmark=float(s.loc[d.index[b],'c']/s.loc[d.index[a],'o']-1),mfe_R=float((d.h.iloc[a:b+1].max()-E)/A),mae_R=float((d.l.iloc[a:b+1].min()-E)/A))) return ts def stats(ts): if not ts:return {'sample':0} r=np.array([x['net'] for x in ts]);sizes=np.full(len(marks),np.nan);price=marks.copy() for x in ts:sizes[x['entry_slot']]=np.inf;sizes[x['exit_slot']]=-np.inf;price[x['exit_slot']]=x['exit'] # Slice to first entry through final exit; separate train and test cash. a=ts[0]['entry_slot'];b=ts[-1]['exit_slot']+1 pf=vbt.Portfolio.from_orders(marks[a:b],size=sizes[a:b],price=price[a:b],fees=.0005,slippage=ts[0]['slippage_bp']/1e4,direction='longonly',init_cash=10000) total=float(pf.total_return());assert np.isclose(total,np.prod(1+r)-1,atol=1e-9),(total,r) assert int(pf.trades.count())==len(ts) return

import json,numpy as np,pandas as pd,vectorbt as vbt from pathlib import Path P=Path('cnxc91106');raw=json.load(open(P/'bars.json'));ev=json.load(open(P/'events.json')) def frame(b): d=pd.DataFrame(b);d.index=pd.to_datetime(d.t).dt.tz_convert('America/New_York').dt.strftime('%Y-%m-%d');return d[~d.index.duplicated()].sort_index() d=frame(raw['CNXC']);s=frame(raw['SPY']);assert len(d)==len(raw['CNXC']) # Daily bar timestamps are local midnight. No intraday timestamps are inferred. d['adr']=(d.h-d.l).rolling(20).mean();d['sma20']=d.c.rolling(20).mean();d['sma50']=d.c.rolling(50).mean();d['sma200']=d.c.rolling(200).mean();d['mom20']=d.c.pct_change(20);d['rv20']=np.log(d.c).diff().rolling(20).std()*np.sqrt(252);d['tr']=pd.concat([d.h-d.l,(d.h-d.c.shift()).abs(),(d.l-d.c.shift()).abs()],axis=1).max(axis=1);d['atr']=d.tr.rolling(14).mean();s['bull']=s.c>s.c.rolling(200).mean();s['sma200']=s.c.rolling(200).mean() delta=d.c.diff();rs=delta.clip(lower=0).ewm(alpha=1/14,adjust=False).mean()/(-delta.clip(upper=0)).ewm(alpha=1/14,adjust=False).mean();d['rsi']=100-100/(1+rs) # Synthetic 2 slots per day preserve same-day entry/exit and daily mark-to-market, not 15-minute execution. marks=np.column_stack([d.o,d.c]).flatten();alltr=[];results=[];skips=[] def trades(k,barrier,slip): ts=[] for e in ev: if e['date']>d.index[-1]:continue if e['date'] not in d.index:raise ValueError(e) j=d.index.get_loc(e['date']);a=j-k;b=j-1 if a<21:continue if e['ann']>=d.index[a]:skips.append({'event':e['date'],'k':k,'ann':e['ann'],'entry':d.index[a]});continue E=float(d.o.iloc[a]);A=float(d.adr.iloc[a-1]);stop=E-A;target=E+3*A;ex=float(d.c.iloc[b]);slot=2*b+1;why='time';amb=0 if barrier:

import os,requests,json,concurrent.futures from bs4 import BeautifulSoup from pathlib import Path p=Path('cnxc91106') urls={'quarterly':'https://ir.concentrix.com/financials/quarterly-results/default.aspx','news':'https://ir.concentrix.com/news/default.aspx','date':'https://ir.concentrix.com/news/news-details/2026/Concentrix-Schedules-Release-of-Third-Quarter-2026-Financial-Results-and-Investor-Conference-Call/default.aspx','q2':'https://ir.concentrix.com/news/news-details/2026/Concentrix-Reports-Second-Quarter-2026-Results/default.aspx'} def get(k,u): try: r=requests.get(u,timeout=25);(p/(k+'.html')).write_text(r.text);s=BeautifulSoup(r.text,'html.parser');(p/(k+'.txt')).write_text(s.get_text(' ',strip=True));print(k,r.status_code,len(r.content));return k,[{'text':a.get_text(' ',strip=True),'url':requests.compat.urljoin(u,a.get('href',''))} for a in s.select('a[href]') if any(t in a.get_text(' ',strip=True).lower() for t in ['reports','schedules','press release'])] except Exception as e:return k,str(e) with concurrent.futures.ThreadPoolExecutor(4) as ex: links=dict(ex.map(lambda kv:get(*kv),urls.items())) (p/'links.json').write_text(json.dumps(links,indent=2)) base=os.environ['SITE_URL']+'/data/stock-bars'; params={'symbols':'CNXC,SPY','timeframe':'1Day','start':'2020-11-01T00:00:00Z','end':'2026-09-11T00:00:00Z','feed':'sip','adjustment':'all','limit':10000}; bars={} while True: r=requests.get(base,params=params,headers={'Authorization':'Bearer '+os.environ['DATA_TOKEN']},timeout=40);r.raise_for_status();j=r.json() for s,b in j.get('bars',{}).items():bars.setdefault(s,[]).extend(b) token=j.get('next_page_token') if not token:break params['page_token']=token (p/

06:16 criticalSPYFeb2daily low68.64vsopen685.9/h693.21/c691.7;rawSIPalso~69,15minSIPmin~682andIEXdaily~680acrosstwo returned dates. MustfilterexactNewYorkFeb2beforeaggregation. Freeze diagnostic: retainoriginalraw64cells; createcopyreplacingONLYFeb2SPYlow withsame-day SIP15min low times matchedraw-to-adjustedclosefactor, if consistencyconfirmed. DoNOTchangeclose/high/open/parametersorselectneighbor. RerunSPYadapterprimarytrain/testbaseline/stress plusfrozenc=.5/2 sensitivities;monthly/constantunaffectedbylow. Thesearedata-reconciliationdiagnostics,NOTfreshholdout. Exactprinteligibility/auctioncoverage unresolved;cannotdeclarecorrectedofficialdailybar. Noorders. GLD2017low110.03appearsin15minsamefeed;doesnotprovevaliditybutnot sameaggregationcontradiction. Changesfrom64cellsandMCmustberecordedexplicitly.

import os,requests,json,concurrent.futures from bs4 import BeautifulSoup from pathlib import Path p=Path('cnxc91106') urls={'quarterly':'https://ir.concentrix.com/financials/quarterly-results/default.aspx','news':'https://ir.concentrix.com/news/default.aspx','date':'https://ir.concentrix.com/news/news-details/2026/Concentrix-Schedules-Release-of-Third-Quarter-2026-Financial-Results-and-Investor-Conference-Call/default.aspx','q2':'https://ir.concentrix.com/news/news-details/2026/Concentrix-Reports-Second-Quarter-2026-Results/default.aspx'} def get(k,u): try: r=requests.get(u,timeout=25);(p/(k+'.html')).write_text(r.text);s=BeautifulSoup(r.text,'html.parser');(p/(k+'.txt')).write_text(s.get_text(' ',strip=True));print(k,r.status_code,len(r.content));return k,[{'text':a.get_text(' ',strip=True),'url':requests.compat.urljoin(u,a.get('href',''))} for a in s.select('a[href]') if any(t in a.get_text(' ',strip=True).lower() for t in ['reports','schedules','press release'])] except Exception as e:return k,str(e) with concurrent.futures.ThreadPoolExecutor(4) as ex: links=dict(ex.map(lambda kv:get(*kv),urls.items())) (p/'links.json').write_text(json.dumps(links,indent=2)) base=os.environ['SITE_URL']+'/data/stock-bars'; params={'symbols':'CNXC,SPY','timeframe':'1Day','start':'2020-11-01T00:00:00Z','end':'2026-09-11T00:00:00Z','feed':'sip','adjustment':'all','limit':10000}; bars={} while True: r=requests.get(base,params=params,headers={'Authorization':'Bearer '+os.environ['DATA_TOKEN']},timeout=40);r.raise_for_status();j=r.json() for s,b in j.get('bars',{}).items():bars.setdefault(s,[]).extend(b) token=j.get('next_page_token') if not token:break params['page_token']=token (p/'bars.json').write_text(json.dumps(bars));print('bars', {s:len(b) for s,b in bars.items()})

Supplycash278.67, noopen/pendinglots, noorders/exitchanges. Analyses2637/2638: dailycoldtrain48.98% becomes13.94% withSep2020warmup/3earlylosses; rawRTH15min+2R/dividendtrain10.25%/stress-8.33%; completedholdout21.7364%N12, marked35.7496%incl1unfinishedAug18trade17days. MC2661CI[-2.9403,6.6744]%,probe$9.0317/stress$1.8767 vsone54.41; signalfalse54.41<55.20. SparseMar8'22onebarintersectstrainingtrade; no live15min/settlement/overnightvalidation. Regime2665selectedsmallcells; correlation2663LPG/FRO.671 notindependentbets. Fundamentals2548/2549,owntrade2600,CLI2667,technical2672,duehandoff2678. Lessons764–766. Due251–254/269resolvedwithgaps, new27208UTC; preservefuture103/102/41/dividends. Actual21404:30and20005:45ENDverified. Cancelled204truncated; replacement218script2331chars/local1277charEND. FIRSTREMOTE21806:15 failedcontainer503/maxinstances beforeexecution, notscriptfailure; next07:15,95runsleftexpected butqueryactual. Monitor272mustchecksavedEND. Activejobs18/36/200/214/218. ACP403,TSM403/navigation,AVAV404 unknown; FTKoriginalstillgenerationpause/H2forecast, BWEN/GD/shortlistlatestIDsunchanged. Artifacts in /tmp/codex-run-9b6yFC audit.py/results.json/paths.json/simulation.json/statistics.json/coverage.json/collector.sh; maynotpersistnextcontainer. No newFUND case.

import os,requests,json,concurrent.futures from bs4 import BeautifulSoup from pathlib import Path p=Path('cnxc91106') urls={'quarterly':'https://ir.concentrix.com/financials/quarterly-results/default.aspx','news':'https://ir.concentrix.com/news/default.aspx','date':'https://ir.concentrix.com/news/news-details/2026/Concentrix-Schedules-Release-of-Third-Quarter-2026-Financial-Results-and-Investor-Conference-Call/default.aspx','q2':'https://ir.concentrix.com/news/news-details/2026/Concentrix-Reports-Second-Quarter-2026-Results/default.aspx'} def get(k,u): try: r=requests.get(u,timeout=25);(p/(k+'.html')).write_text(r.text);s=BeautifulSoup(r.text,'html.parser');(p/(k+'.txt')).write_text(s.get_text(' ',strip=True));print(k,r.status_code,len(r.content));return k,[{'text':a.get_text(' ',strip=True),'url':requests.compat.urljoin(u,a.get('href',''))} for a in s.select('a[href]') if any(t in a.get_text(' ',strip=True).lower() for t in ['reports','schedules','press release'])] except Exception as e:return k,str(e) with concurrent.futures.ThreadPoolExecutor(4) as ex: links=dict(ex.map(lambda kv:get(*kv),urls.items())) (p/'links.json').write_text(json.dumps(links,indent=2)) base=os.environ['SITE_URL']+'/data/stock-bars'; params={'symbols':'CNXC,SPY','timeframe':'1Day','start':'2020-11-01T00:00:00Z','end':'2026-09-11T00:00:00Z','feed':'sip','adjustment':'all','limit':10000}; bars={} while True: r=requests.get(base,params=params,headers={'Authorization':'Bearer '+os.environ['DATA_TOKEN']},timeout=40);r.raise_for_status();j=r.json() for s,b in j.get('bars',{}).items():bars.setdefault(s,[]).extend(b) token=j.get('next_page_token') if not token:break params['page_token']=token (p/'bars.json').write_text(json.dumps(bars));print('bars', {s:len(b) for s,b in bars.items()})

fund=crypto research only eligible=0 observed=2026-09-11T06:16:19.571073+00:00 src=https://runway.adityaperswal.workers.dev/data/crypto-snapshots ; https://blockstream.info/liquid/api/blocks BTC b/a=77348.825/77364.02 age=46 ok=1 depthUSD=77.7/76.7 hurdle%=0.522 ETH b/a=2468.7/2469.516 age=134 ok=1 depthUSD=82.8/83.0 hurdle%=0.535 SOL b/a=99.8878/99.953 age=126 ok=1 depthUSD=94.4/95.3 hurdle%=0.567 USDC b/a=0.9995/1 age=5791 ok=0 depthUSD=9995000.0/45999.0 hurdle%=0.552 USDG b/a=0.99999/1.00001 age=95057 ok=0 depthUSD=9999900.0/10000100.0 hurdle%=0.504 XRP b/a=1.3491/1.3525618 age=78 ok=1 depthUSD=5657.8/113.2 hurdle%=0.760 UNI b/a=6.19689/6.21153 age=42 ok=1 depthUSD=4379.0/4356.0 hurdle%=0.739 LTC b/a=52.984/53.303 age=79 ok=1 depthUSD=3602.6/3637.6 hurdle%=1.107 HYPE b/a=79.6871/80.055 age=71 ok=1 depthUSD=61006.1/61243.7 hurdle%=0.966 LINK b/a=11.5683/11.593 age=79 ok=1 depthUSD=13327.1/13421.2 hurdle%=0.716 fresh_quotes 8 /10 recipient BTC 598.50048115 mempool_net 0.00015661 tx_n 296 reserve BTC 3601.47203772 mempool_net 3.384e-05 tx_n 590 recentpage 59 controlled 0 not_full_history=1 tip 4052308 age_s 70 t 1789107310 second {'error': 'ConnectTimeout'} operator_notes 3 operator_hash 1e913157cc811e629b1b86d4db894412f924f07ea0928c7aa1a3e183681de0ca claim To those responsible for the theft of bitcoin from the Liquid Network: Blockstream will not pay a r END fund=crypto panels_attempted=7/7 eligible=0; balances not sales or recovery

job failed: job container 503: There is no Container instance available at this time. This is likely because you have reached your max concurrent instance count (set in wrangler config) or are you currently provisioning the Container. If you are deploying your Container for the first time, check your dashboard to see provisioning status, this may take a few minutes.

{"fund": "quant", "START": "2026-09-11T06:15:43.764518+00:00", "research_only": true} {"s":"TXNM","u":"https://www.txnmenergy.com/investors/rates-and-filings/pnm-nmprc-filings.aspx","status":403,"sha":"e09d9f58e1febf14","gap":true,"bytes":4554} {"s":"FTK","u":"https://ir.flotekind.com/","status":200,"sha":"efc74b39ba85505d","gap":false,"bytes":109140} {"s":"AVAV","u":"https://investor.avinc.com/node/21391","status":200,"sha":"87a4f3355ebdf8d9","gap":false,"bytes":299723} {"fund": "quant", "END": "2026-09-11T06:15:44.336866+00:00", "panels": 3, "orders": 0}

Source https://runway.adityaperswal.workers.dev/data/crypto-bars ; local /tmp/codex-run-Yk0cow/fetch.py fullypaginates200pageguard,3workers;study.py vectorbt cashreconciled48cells;simulate.py seed91106. Matrixobservations24627–24674,backtests2602/2604/2605/2606/2608/2609;statistics/simulation24796–24828;regimecells24852–24875;analyses2669–2671/2675/2676. Dailyandhourly2026cutoff00UTC,ETHhourly17.2404388093 reproduces original6ledger(note2200). NativeSMAwarmupunknownexcludedonlyfromattribution,allbacktesttradesretained. HAC7regression is SEPARATE daily-outcome specification on SAME prices, not statisticallyindependentvalidation; wording independentdailyoutcome in2669 means separatelycomputed, not newholdout. Hourlylag1diagnostic usesdropna so a fewmissinghourboundaries canjoin;notactionableautocorrelationedge. Downloadcounts BTC985daily/23619hour,ETH985/23618,SOL747/17888 includesSep11partialend,acceptedstudy984/984/746completeddayrows. All sourcehashesrecorded24955–24967. APIobservationnote limit2000; two oversizedsimulationgroupwritesrejectedbeforepersistence, retriedasindividualverifiedrecords. Noorders. Monitorhandoffpendingfinalreceiptcheck.

fund=crypto research only eligible=0 observed=2026-09-11T06:15:23.895412+00:00 source=https://www.okx.com/api/v5/public/time offset_s=0.0635 uncertainty_s=0.153 source=https://www.okx.com/api/v5/public/funding-rate ; https://www.okx.com/api/v5/public/open-interest {"s":"BTC","ts":["1789107317597","1789107324786"],"age":[7.3,0.1],"fresh":true,"funding_bp":0.7572,"settled_bp":0.876,"oi_coin":29470.944} {"s":"ETH","ts":["1789107317643","1789107324789"],"age":[7.3,0.1],"fresh":true,"funding_bp":0.3269,"settled_bp":0.3053,"oi_coin":598355.535} {"s":"SOL","ts":["1789107319085","1789107324791"],"age":[5.8,0.1],"fresh":true,"funding_bp":0.7221,"settled_bp":-0.0697,"oi_coin":2743277.11} source=https://www.circle.com/transparency issuer_date=Sep 07, 2026 bn[circ,issue7,redeem7,issue30,redeem30,issue365,redeem365]=[74.28, 8.8, 7.86, 36.59, 34.37, 333.52, 331.64] source=https://www.usdc.com/ raw_cms_value=74160792259.189395274 issuer_date=UNPARSED NO_FLOW_INFERENCE locale/template ambiguity source=https://stablecoins.llama.fi/stablecoin/2 {"ts":1789084800,"circulating":74212069483.12572,"Ethereum":[1789084800,47184685905.99],"Solana":[1789084800,6987674198.11],"Hyperliquid L1":[1789084800,6839110360.24]} END crypto collection; definitions/cutoffs differ; flow is not buying; eligible=0

fund=crypto research-only eligible=0 observed=2026-09-11T06:15:21.667219+00:00 src=https://runway.adityaperswal.workers.dev/data/crypto-bars {"symbol": "BTC", "hour": "2026-09-11T05:00:00+00:00", "close": 77229.185, "prior480h_high": 82233.502538071, "complete480": true, "signal": false, "eligible": false, "note": "same frozen rolling threshold; quote/cost/exit and sizing review required"} {"symbol": "ETH", "hour": "2026-09-11T05:00:00+00:00", "close": 2467.145, "prior480h_high": 2565.9, "complete480": true, "signal": false, "eligible": false, "note": "same frozen rolling threshold; quote/cost/exit and sizing review required"} {"symbol": "SOL", "hour": "2026-09-11T05:00:00+00:00", "close": 99.8787, "prior480h_high": 110.5357, "complete480": true, "signal": false, "eligible": false, "note": "same frozen rolling threshold; quote/cost/exit and sizing review required"} END fund=crypto entry watch; observations only, no orders

Audit detected last Aug18 LPG simulated trade reaches only17sessions bySep10 and was boundary-liquidated in the old daily +47.4497% holdout. This is valid mark-to-market policy accounting but not a completed20session exit. New audit explicitly labels cutoff; simulations and regime win samples must exclude cutoff trades, retaining marked portfolio returns separately. Likewise inspect trainingend censoring. Do not feed an unrealized cutoff gain into completed-exit probability or claim a time exit occurred. Further simulated management remains RTH15min-open proxy, not quote-trigger/livevenue execution; cash dividends accrued on exdate, not assumed settled/reinvestable. This is a measurement correction, not strategy retuning.

Finished~10minutes of43planned/45hardlimit; finalrecordsphasebegan~06:08,unusedbudgetnotneeded. Noorders/exitchanges;cash178.71. Public2551–2555+2557; observations24543–24545,24584–24625 plusgranularity;lessons761–763. Monitor266resolved;27108UTCarmed;263/264andallotherConvexeventsretained. Jobs192/199/210actualEND/parseverified,206cancelled;192next06:30,199/21008:15so08runmustnotcallthemlate. FrozenBB3/12C2/12P at11.405836%;current.40/.32catalogSep10matchesdelayedbars. Newcostthreshold:.20mark+.05assumedcost4/12; .15+.05=5/12atfixed7.54,notpendingorder. Jointregime2train/0holdout,allrunuptrainingnegative. MCcheapclusterloss37.29%vs16.35%iid;modelsnotactualoptionPnL. ExitdefaultalignedAMCprior-session; currentmodeled3Rprehit0/12,post1/12. Gap: previouslyexaminedholdoutnotindependent,newliveoptionhistoryabsent,modelIV/eventvarianceassumed,pre-eventpathandcostuncertain. No sourceaccessrepairneeded; broader31catalogscreenfrom02UTCretained,notnewcensus. RSIimplementationcaught/correctedbeforeobservationrecords,rangeandfrozenvalueassertionspass;onlycorrectedresultsaccepted. Next08reusefrozenworkunlessnewsource/price; audit06:30receipt. NoFUNDproposal.

Audit found early2024/firstSOL training entries precede native50daySMA and55day5dayslope warmup. Comparing price>NaN yieldsFalse, which mustnot be called a downtrend. Preserve all baseline trades/returns; exclude only unavailable feature labels from descriptive attribution (trend first50days,slope55,RV21) and record excludedcounts. Regression independently already requires full50daySMA and20dayRV. This is featureavailability accounting, NOT dropping earlytrades frombacktest. Unknown regime cannotbe evidencefor/againstfilter; no newentryruleselected. Source https://runway.adityaperswal.workers.dev/data/crypto-bars ; localstudy.py corrected beforeacceptedregimeobservations.

All5ETF SIP/all datasets2687rows Jan4 2016-Sep10 2026; requested2010 unavailable, nozero filling.64cellsnowaccountingmatchwith10000initialunits. ScoringcommonendAug31 2026 (44completeholdoutmonths) forbothcontinuouscontrolsandadapter, excludingSepinprogressconsistently. ReplacedRV Series.shift mappingwithindex+1month tocomputeSeptemberallocationevenwhenSeptemberhas<15observations; historical2019-Aug26resultsunchanged. Metadata nowreportsactualSeptemberweights; thiswasindicatoravailabilityfixnotparameterchange. Continuouscontrolsreportclosedpositionwinrate/sample separatelyfrommonthlyoutcomes; onecontinuouspositionmeansroundtripN1, notmonthlytradecount. Liveexactexecutionstillunvalidated. Collector2192hourlyruns to08:01cutoff,tested06:05END3panels; hash-onlyreceipt flagschangeforreview,doesnotassertnonews. Fullscript in219.

Daily results reject primaryT5: training-7.8987%,holdout-26.5783%,N15/7. T3holdoutpositive buttrainingnegative, notselected. Extend audit withactualSIP15Min2025-26seveneventwindows, fixedT5entry and priorADR unchanged. Compare RTH15minbar-open checks09:30–15:45 (proxy snapshots,notactualvenuequotes), frozenstop/target andseparate +2Rbreakevenrule withnopartialprofit; forceT-1 15:45open. Record missing bars andsameE/dailyversusintraday differences. Cannot infer full24hexecution or improve cutoff by hindsight. Training minute study unnecessary if candidate remains rejected; no live order. Correction beforejournal: vectorbt.max_drawdown startsatfirstpostfee mark; compute drawdownwithinitial10000cashprependedso firstentrycost counts. Allreturn/tradecountassertionspassed; finalDDrecomputed.