Hi,

I think that a few CEFs, such as the BlackRock CII fund, don't exist in PortfoliosLab. Rather than sending you the missing ISINs, I thought I'd just send you all ISINs of all Closed-End Funds. I hope you can bulk check these and add the ones that are missing.

ACP US0030571063

ACV US92840N1000

ADX US0062121043

AEF US00301W1053

AFB US01864U1060

AGD US00302M1062

AIO US92838Y1001

AOD US00326L1008

ARDC US04014F1021

ASGI US00326W1062

ASG US5299001025

AVK US00764C1099

AWF US01879R1068

AWP US00302L1089

BANX US8617801043

BBN US09248X1000

BCAT US09260U1097

BCV US0596951063

BCX US09257A1088

BDJ US09251A1043

BFK US09248F1093

BFZ US09248E1029

BGB US09257R1014

BGH US06760L1008

BGR US09250U1016

BGT US0919411043

BGX US09257D1028

BGY US0925241079

BHK US09249E1010

BHV US0924811005

BIT US09258A1079

BKN US09247D1054

BKT US09247F2092

BLE US09249N1019

BLW US09249W1018

BME US09250W1071

BMEZ US09260E1055

BMN US09262G1085

BNY US09248L1061

BOE US0925011050

BRW US78518H2022

BSL US09256U1051

BSTZ US09260K1016

BTA US09250B1035

BTO US4097352060

BTT US09257P1057

BTZ US0925081004

BUI US09248D1046

BWG US10537L1044

BXMX US6706ER1015

BYM US0924791041

CAF US6174681030

CCD US12811V1052

CCIF US92535C1045

CEE US1534361001

CEF CA85208R1010

CEV US27826F1012

CGO US1281181065

CHI US1281171082

CHN US1693731077

CHW US12811L1070

CHY US12811P1084

CIF US59318T1097

CII US09256A1097

CIK US2249161066

CLM US21924B3024

CMU US59318E1029

CPZ US12812C1062

CRF US21924U3005

CSQ US1281251017

CXE US59318D1046

CXH US59318B1089

DBL US2586231076

DFP US33848W1062

DHF US09660L1052

DHY US22544F1030

DIAX US67075F1057

DLY US25862D1054

DMA US25065A5020

DMB US09662W1099

DMO US95790B1098

DNP US23325P1049

DPG US26433C1053

DSM US09662E1091

DSU US09255R2022

DTF US23334J1079

DXYZ US25063F1075

EAD US94987B1052

ECAT US09262F1003

ECF US2890741067

EDD US6174771047

EDF US86164T1079

EEA US2987681028

EFR US27828Q1058

EFT US2782791048

EHI US95766B1098

EIM US27827X1019

EMD US95766A1016

EMF US8801911012

EMO US18469P2092

ENX US27827Y1091

EOD US94987C1036

EOI US2782741050

EOS US2782771081

EOT US27829L1052

ERC US94987D1019

ERH US94987E1091

ETB US27828X1000

ETG US27828S1015

ETJ US27829G1067

ETO US27828U1060

ETV US27828Y1082

ETW US27829C1053

ETX US27829U1051

ETY US27828N1028

EVF US27826S1033

EVG US27828V1044

EVM US27828A1007

EVN US27826U1088

EVT US27828G1076

EVV US27828H1059

EXG US27829F1084

FAX US0030098679

FCO US0030131090

FCT US33733U1088

FFA US3373181094

FFC US3384781007

FINS US03464A1007

FLC US3384791089

FMN US31423P1084

FMY US33734E1038

FOF US19248P1066

FPF US33718W1036

FRA US09255X1000

FSCO US30290Y1010

FTF US35472T1016

FTHY US33741Q1076

FT US3551451038

FUND US85208J1097

GAB US3623971013

GAM US3688021043

GBAB US4016641072

GCV US36240B1098

GDL US3615701048

GDO US95790C1071

GDV US36242H1041

GF US6444651060

GGN US36465A1097

GGT US36239Q1094

GGZ US36249W1045

GHY US69346J1060

GLO US18914E1064

GLQ US18914C1009

GLU US36242L1052

GLV US18913Y1038

GNT US36465E1010

GOF US40167F1012

GRF US2694511005

GRX US36246K1034

GUG US40170T1060

GUT US36240A1016

HEQ US47804L1026

HFRO US43010E4044

HGLB US43010T1043

HIO US95766K1097

HIX US95766J1025

HNW US7236531015

HPF US41013X1063

HPI US41013W1080

HPS US41021P1030

HQH US87911J1034

HQL US87911K1007

HTD US41013V1008

HYI US95768B1070

HYT US09255P1075

IAE US92912J1025

IAF US0030111035

IDE US92912X1019

IFN US4540891037

IGA US92912R1041

IGD US92912T1007

IGI US95790A1016

IGR US12504G1004

IHD US92912P1084

IIF US61745C1053

IIM US46132P1084

IQI US46133G1076

ISD US69346H1005

JCE US67090X1072

JEQ US00306J1097

JFR US67072T1088

JGH US67075G1031

JHI US4101421034

JHS US4101231037

JLS US6707351093

JMM US67075J1079

JOF US47109U1043

JPC US67073B1061

JPI US67075A1060

JQC US67073D1028

JRI US67074Y1055

JRS US67071B1089

KF US5006342092

KIO US48249T1060

KTF US2333681094

KYN US4866061066

LDP US19248C1053

LEO US05588W1080

LGI US52106W1036

MCI US06759X1072

MCN US5574371002

MCR US5527271093

MEGI US56064Q1076

MFM US5527381066

MGF US5529391006

MHD US09253N1046

MHF US95766N1037

MHN US09255C1062

MIN US55273C1071

MIY US09254V1052

MMD US56064K1007

MMT US5527371083

MMU US95766M1053

MPA US09255G1076

MPV US06761A1034

MQT US09254G1085

MQY US09254F1003

MSD US61744H1059

MUA US09254J1025

MUC US09254L1070

MUE US09254C1071

MUJ US09254X1019

MVF US09253R1059

MVO US5538591091

MVT US09253T1016

MXE US5928341057

MXF US5928351023

MYD US09253W1045

MYI US09254E1038

MYN US09255E1029

NAC US67066Y1055

NAD US67066V1017

NAN US67066X1072

NAZ US67061W1045

NBB US67074C1036

NBH US64124P1012

NBXG US64133Q1085

NCA US67062C1071

NCV US92838X8056

NCZ US92838U8018

NDMO US67079X1028

NEA US6706571055

NFJ US92840R1014

NHS US64128C1062

NIE US92841M1018

NIM US67061T1016

NKX US6706511085

NMAI US6707501085

NMCO US6706631032

NMI US67062J1025

NML US64129H1041

NMS US6707341029

NMT US67061E1047

NMZ US6706821039

NNY US67062M1053

NOM US67060Q1085

NPCT US67080D1037

NPFD US67080R1023

NPV US67064R1023

NQP US6709721087

NRK US6706561072

NRO US64190A1034

NUV US6709281009

NUW US6706951059

NVG US67071L1061

NXC US67063R1032

NXDT US65340G2057

NXG US2316472073

NXJ US67069Y1029

NXN US67063V1044

NXP US67062F1003

NZF US67070X1019

OCCI US67111Q1076

OIA US46132X1019

OPP US76882G1076

OXLC US6915431026

PAI US95766T1007

PAXS US72203T1007

PCF US42968F1084

PCM US69323T1016

PCN US72200U1007

PCQ US72200N1063

PDCC US70476Q1004

PDI US72201Y1010

PDO US69355M1071

PDT US41013T1051

PDX US69346N1072

PEO US00548F1057

PFD US3384801060

PFL US72201H1086

PFN US72201J1043

PFO US33848E1064

PGP US7220111030

PGZ US74255X1046

PHD US72369J1025

PHK US7220141078

PHT US72369H1068

PIM US7469091000

PML US72200W1062

PMM US7468231037

PMO US7469221037

PPT US7468531006

PSF US19248Y1073

PTA US19249X1081

PTY US72201B1017

QQQX US6706991071

RA US1128301041

RCG US7597201059

RCS US72200X1046

RFI US19247R1032

RFM US76883H1041

RFMZ US76883Y1073

RGT US78081T1043

RIV US76881Y1091

RLTY US19249Q1031

RMI US76883F1084

RMM US76882H1059

RMMZ US76882M1045

RMT US7809151043

RNP US19247X1000

RQI US19247L1061

RSF US76882B1089

RVT US7809101055

SABA US8801981064

SBI US9584351095

SCD US50208A1025

SDHY US69355J1043

SOR US8361443034

SPE US84741T1043

SPXX US6706EW1000

SRV US2316313004

STEW US1015071012

STK US19842X1090

SWZ US8708751016

TDF US88018T1016

TEAF US27901F1093

TEI US8801921094

THQ US8791051043

THW US87911L1089

TSI US8723401043

TWN US8740361063

TY US8954361031

USA US5301581048

UTF US19248A1097

UTG US7561581015

VBF US46132L1070

VCV US46132H1068

VFL US24610T1088

VGI US92829B1017

VGM US46131M1062

VKI US46132E1038

VKQ US46131J1034

VLT US46131F1012

VMO US46132C1071

VPV US46132K1097

VTN US46131T1016

VVR US46131H1077

WDI US95790K1097

WEA US9576641057

WIA US95766Q1067

WIW US95766R1041

XFLT US98400T1060

ZTR US92835W1071

Thanks as always.

Johann

All Closed-End Funds

We just made it easier to find better ETFs and mutual funds in the same category. Instead of opening a dozen tabs and running screeners, you can now see all the relevant alternatives ranked by the things that actually matter — like expense ratio, risk-adjusted returns, and drawdown — right on the fund's page.

### What we added

Every ETF and mutual fund on PortfoliosLab now has an **Alternatives** page. Open any fund — say, [VOO](/symbol/VOO/alternatives) — and you'll see a breakdown of similar funds from the same category, ranked by the things that actually matter when choosing between them.

<img height="412" width="696" src="https://portfolioslab.com/static/user-content/1/f0ahvv66ifgewz9r8k7e5bx6.png" />

The page is split into five sections, each answering a different question:

**Cheapest alternatives** — same category, sorted by expense ratio. If you're paying 0.50% and there's a nearly identical fund at 0.03%, you should know about it.

**Best risk-adjusted alternatives** — ranked by PortfoliosLab's combined score that accounts for volatility, drawdown, and return consistency over one year. Not just "what went up the most" but "what went up without falling off a cliff."

**Top performing (YTD)** — straightforward year-to-date returns. Useful for spotting momentum, less useful on its own.

**Lowest volatility** and **lowest drawdown** — for when your goal is sleeping through a correction, not chasing returns.

Below the ranked tables, you'll also see funds from the same issuer and a list of what other investors commonly compare with the fund you're looking at — based on real usage data, not editorial picks.



<img height="302" width="692" src="https://portfolioslab.com/static/user-content/1/q4gdr87fspmly06qqijbz9wf.png" />

The alternatives page starts from the fund you're already researching and surfaces what you'd find if you spent an hour digging through screeners — ranked, filtered to the same category, and ready to compare side by side. Each row links directly to a full head-to-head comparison if you want to go deeper.

### A few ways to use it

You hold a broad-market ETF and want to check whether a cheaper option exists in the same category. Open its alternatives page and look at the expense ratio table.

You're evaluating a fund someone recommended and want to see how it stacks up on risk. Check the risk-adjusted and drawdown tables to see where it falls relative to peers.

You want to understand what else is out there before committing to a position. Scan the full page, then compare directly with anything that catches your eye.

We just made it easier to find better ETFs and mutual funds in the same category with a dedicated alternatives comparison page.

Find Better ETFs and Mutual Funds Without the Guesswork

When you're looking at a portfolio, knowing it returned 12% last year isn't enough. You want to know how it behaves relative to the market. Was your strategy just riding the market or did it have a real edge? The block shows five metrics at a glance: Alpha, Beta, R², Upside Capture, and Downside Capture.

<img height="254" width="706" src="https://portfolioslab.com/static/user-content/1/s8z19ssdg4dcuxaedeod2m9i.png" />

To make it more useful, we also added a few insights alongside numbers. Raw numbers alone can mislead. A beta of 0.5 looks defensive, but only means something if R² is high enough. A portfolio capturing 200% of upside and 106% of downside is technically "favorable" but that's not the whole story. The block catches these cases and calls them out directly.

When R² is too low for the metrics to be meaningful, it tells you to pick a better benchmark rather than letting you draw the wrong conclusions.

Available on all portfolio pages where a benchmark is selected and in the [Portfolio Analysis tool](https://portfolioslab.com/tools/portfolio-analysis).

We added a Benchmark Metrics block to all portfolio pages and to the portfolio analysis tool results.

New Portfolio Metrics: Alpha, Beta, R², Upside and Downside Capture

Great stuff Dmitry, as you know I working a lot with PRT for the actual investing. Therefore as you wanted to you could refine the walk-forward approach by implementing anchored and non-anchored and calculating WFE. (the following is quoted from the public guide, [https://www.prorealtime.com/en/pdf/probacktest.pdf)](https://www.prorealtime.com/en/pdf/probacktest.pdf\)) 

Walk Forward analysis is an essential part of developing a trading strategy. It allows you to optimize a trading system and to validate its robustness and stability over time. First, the walk forward analysis optimizes a set of variables on an initial period called "in-sample data" or the "optimization period", then it tests the best parameters on the following period called "out of sample data" or the "test period", and repeats the process by shifting forward the time windows. This automated process can be repeated as many times as necessary. The optimization periods (in-sample) may have a single starting point (Anchored mode), or different starting points (Non-anchored mode). NON-ANCHORED MODE ANCHORED MODE By comparing the results of these 2 periods, the method allows you to: Check that the performance of the strategy on the optimization periods (in-sample data: shown in blue) are consistent with the test periods (out-of-sample data: shown in grey). This avoids the risk of "overfitting" by projecting the strategy on a period of time not used itself in the optimization. Check how the strategy behaves in changing market conditions Test the strength of the strategy on past data To determine if a strategy is robust or not, we use the WFE ratio (walk forward efficiency ratio). The walk forward efficiency ratio is a qualitative indicator of the optimization process. It compares the annualized gain of the test period to the annualized gain of the optimization period. This measure of robustness is a critical part of walk forward analysis. WFE Ratio \= Annualized gain of the test period Annualized gain of the optimization period Annualized gains are the gains realized over a given period, expressed in annual terms. They let us compare the gains of different periods on a common basis. Typically, the optimization period (in-sample/blue in the image above) represents 70% of the total analyzed period, and the test period (out of sample/grey) 30%. To measure the reliability of the analysis, the results of the two periods need to be evaluated with a common basis. Studies of this method indicate that if at least 3/5 test periods display a WFE ratio greater than 50%- 60%, the strategy can be considered to be robust. After the walk forward method, it is interesting to analyze the results of several points. It is necessary to examine the regularity of gains from post-optimization periods. If the results show that the strategy shows a risk of being over-optimized (\= the gains from test periods are much lower than those of optimization periods), the platform lets you adjust different parameters (variables, stop and target levels, trading periods) and launch a walk forward analysis again, as many times as needed, to obtain a robust strategy

How strategy backtests usually work. You input your portfolio positions and ask the optimizer to find an allocation with best performance. The optimizer then looks at the historical data, picks a few stocks that did well, drops the rest and presents you with a portfolio that would have crushed it over the last years. When comparing with the original allocation the backtest looks great. Of course it does. The optimizer already knew what happened.

This is called lookahead bias, and it makes backtests almost useless for predicting real-world performance. So we've added a way to test your portfolio strategy without it.

### What we built

In [Portfolio Optimization](/tools/portfolio-optimization), there's now a **Reoptimize Frequency** setting with three modes: Once, Quarterly, and Yearly.

Set it to Quarterly or Yearly, and the tool will re-run the optimization at the start of each period — using only the data available at that point in time — then measure how the resulting portfolio actually performed over the next period. This repeats across the entire timeline, giving you a realistic picture of how the strategy would have played out if you'd been running it live.

This is different from the **Optimization Date** setting, which works in the default "Once" mode. That one splits your data at a specific point: the optimizer trains on everything before that date, then you see how the resulting weights performed afterward. Useful, but it's still a single optimization — one set of weights tested against one future period.

Walk-forward takes it further by re-optimizing repeatedly, so every period gets weights that were calculated without any knowledge of what came next.

<img height="74" width="637" src="https://portfolioslab.com/static/user-content/1/ffzakrfu7msavorg3oe3ew43.png" />

### Why it matters

A single-point optimization tells you what worked in the past. Walk-forward optimization tells you whether your strategy is robust enough to keep working as the market changes.

If the out-of-sample results hold up across multiple rebalancing periods — through rate hikes, drawdowns, recoveries — you have something. If they fall apart, better to know that here than after you've deployed capital.

<img height="402" width="637" src="https://portfolioslab.com/static/user-content/1/ryec7uikdlm7acfynu12vmvd.png" />

*Harry Browne's Permanent Portfolio with a periodic optimization vs the original static allocation.*

### A few ways to use it

Set Reoptimize Frequency to Yearly and compare the walk-forward result against your original allocation. If the optimized weights consistently outperform through different market regimes, that's a meaningful signal about the strategy's durability.

Try different training windows alongside it. A 1-year window will react faster to recent market shifts; a 3-year window produces more stable weights. Walk-forward backtesting helps you see which one actually generalizes instead of just fitting the past.

### Availability

Walk-forward backtesting is available on Plus and Pro plans. Single-point optimization (the default "Once" setting) works the same as before for all users.

Portfolio Optimization now supports walk-forward backtesting. Re-optimize your portfolio quarterly or yearly using only past data.

Platform Updates

Discussions by tag fund