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Data Primary sources Updated Aug 2026

Data hub: every figure on this site, with its source and date

Every number used anywhere on this site appears below with its value, unit, primary source, document identifier, publication date and freshness status. Nothing is averaged and nothing is estimated. Where two credible sources disagree, both appear as separate rows. Where we looked for a source and could not find one, the row says so. The table is the site's working method made visible: if a claim on any page cannot be traced from here, it should not be on that page.

Published 27 August 2026 · Updated 28 August 2026 · AI Trading Book Editorial

Status key
  • Current — the latest published version of the source that we could locate.
  • Historical — describes a specific past period and will not be superseded.
  • Verify before use — changes frequently. Vendor pricing and tier limits in particular.
  • Proposal — published but not in force. Consultations and draft rules.
  • Data not found — we searched for a primary source and did not locate one.

Regulation

Table 1. Regulatory facts — United Kingdom, Australia and New Zealand
FactValueSourceDateStatus
FCA position on AI-specific rulesNo extra AI regulation plannedFCA AI approach pageupd. 13 Feb 2026Current
UK algorithmic trading regimeMAR 7A + onshored RTS 6FCA HandbookIn forceCurrent
RTS 6 conformance testingArticle 6Onshored RTS 6In forceCurrent
RTS 6 annual self-assessmentArticle 9Onshored RTS 6In forceCurrent
RTS 6 stress testingArticle 10Onshored RTS 6In forceCurrent
RTS 6 kill functionalityArticle 12Onshored RTS 6In forceCurrent
FCA algorithmic controls review10 firms; adequate pre-trade controls, weak understanding of third-party algorithmsFCA multi-firm review21 Aug 2025Historical
UK retail CFD rulesIn forceFCA PS19/18; COBS 22.51 Aug 2019Current
FCA AI Lab announcedFCA9 Jun 2025Historical
FCA AI Lab testing beganFCAOct 2025Historical
FCA AI Lab showcaseFCA28–29 Jan 2026Historical
FCA AI Lab second cohortFCAfrom late Apr 2026Current
ASIC AI use-case review624 use cases / 23 licensees; 61% planning growth; 92% of genAI cases 2022–23ASIC REP 79829 Oct 2024Historical
ASIC algorithmic rules modernisationTrading Algorithm definition; immediate-suspension controlsASIC CP 38627 Aug 2025Proposal
CP 386 submissions lodgedLaw Council of Australia; AFMAOct 2025Historical
AU product intervention order in effectASIC 20-254MRfrom 29 Mar 2021Current
AU order extended to23 May 2027ASIC 22-082MR2022Current
AU leverage cap — major FX30:1ASIC product intervention order29 Mar 2021Current
AU leverage cap — minor FX, gold, major indices20:1ASIC product intervention order29 Mar 2021Current
AU leverage cap — other commodities10:1ASIC product intervention order29 Mar 2021Current
AU leverage cap — crypto CFDs2:1ASIC product intervention order29 Mar 2021Current
AU margin close-out50% of initial marginASIC product intervention order29 Mar 2021Current
AU binary options banRetail clientsASIC product intervention orderfrom 3 May 2021Current
AU algorithmic share — listed equitiesapproximately 85%ASIC, with CP 38627 Aug 2025Current
AU algorithmic share — SPI 200 futuresapproximately 94%ASIC, with CP 38627 Aug 2025Current
AU algorithmic share — 3-year bond futuresapproximately 46%ASIC, with CP 38627 Aug 2025Current
NZ advice licensingFAP licence + dispute resolution schemeFMA licensing pagesupd. 1 Jul 2026Current
NZ discretionary managementDIMS, licensed separatelyFinancial Markets Conduct Act 2013In forceCurrent
NZ robo-advice exemptionGranted 2018, amended 2020; superseded by FSLAA regimeFMA2018 / 2020Historical
NZX Participant Rules editionNZX19 Feb 2026Current
NZX proceduresNZX17 Nov 2025Current
NZ retail leverage capData not found
IOSCO report on AI in capital marketsIOSCOPD788IOSCO12 Mar 2025Current
IOSCO agentic AI reportIOSCOPD823 (FR/02/2026)IOSCOMay 2026Current
EU AI ActRegulation (EU) 2024/1689EUR-Lexin force 1 Aug 2024Current

Retail losses and consumer harm

Table 2. Published loss rates, intervention effects and scam figures
FactValueSourceDateStatus
AU retail CFD investors losing money, FY202468%ASIC REP 82820 Jan 2026Current
UK CFD customers losing moneyapproximately 80%FCACurrent
UK loss-making accounts, sampled82%FCA CP16/40 sampleHistorical
IG retail loss disclosure70%IG, under COBS 22.5Verify before use
Interactive Brokers UK loss disclosure69%IBKR UK, under COBS 22.5Verify before use
NZ retail loss rateData not found
Reduction in aggregate AU retail net loss after intervention91%ASIC REP 724Historical
Reduction in loss-making AU accounts per quarter51%ASIC REP 724Historical
Scam websites removed, 202511,964ASIC 26-063MR8 Apr 2026Current
Scam websites removed, prior 12 months6,270ASIC 26-063MR8 Apr 2026Historical
Year-on-year increase in takedowns90%ASIC 26-063MR8 Apr 2026Current
Social media scam advertisements removedmore than 1,100ASIC 26-063MR8 Apr 2026Current
Scam sites removed since 2023more than 25,000ASIC 26-063MR8 Apr 2026Current
Fabricated brand advertised returns85% and 374% ("Deep Blue")ASIC 26-063MR8 Apr 2026Current
Fabricated brand named"Quantum AI"ASIC 24-180MRHistorical
Celebrity-impersonation scam lossesAUD 7.4mASIC 26-195MR17 Aug 2026Current
Scams removed, celebrity-impersonation actionmore than 19,400ASIC 26-195MR17 Aug 2026Current
Total AU reported scam losses, 2025AUD 2.18bnNational Anti-Scam Centre2025Current
AU investment scam losses, 2025AUD 837.7mNational Anti-Scam Centre2025Current
Total AU reported scam losses, 2024AUD 2.03bn (−25.9%)National Anti-Scam Centre2024Historical
AI-branded share of investment scam lossesData not found

Performance, accuracy and adoption

Table 3. Fund performance, prediction accuracy, methodology and adoption
FactValueSourceDateStatus
AI hedge fund index annualised return9.8% vs 13.7% S&P 500Eurekahedge AI Hedge Fund IndexDec 2009 – Jul 2024Historical
Cumulative return comparison115% vs 210% S&P and 133% MSCI WorldEurekahedgeJan 2011 – Jan 2020Historical
Worst month since 2010 launch−3.38% vs −1.11%EurekahedgeJan 2022Historical
Review of ML equity experiments27 experiments; no conclusive evidence of returns at scaleBuczynski, Cuzzolin & Sahakian, Int. J. Data Science & Analytics 11(3)Apr 2021Current
Recommended t-statistic thresholdabove 3.0Harvey, Liu & Zhu, Review of Financial Studies 29(1)Jan 2016Current
Deflated Sharpe RatioAdjusts for trials, track length, non-normalityBailey & López de Prado2014Current
Probability of Backtest OverfittingMethodBailey & López de PradoCurrent
Assessment of published backtestsA large proportion may be misleadingBailey, Borwein, López de Prado & Zhu, Notices of the AMS2014Current
Directional accuracy anchor58.07%Yu & YanHistorical
Directional accuracy anchor64.21%Ding et al.Historical
Transformer directional accuracyapproximately 69.1%ScienceDirect study2025Historical
Deep learning vs linear, high-frequency65–75% vs 57–67%, 500 NASDAQ stocksSirignano & Cont, Quantitative Finance2019Historical
Source of 80–90%+ accuracy claimsLook-ahead bias from shuffled splitsarXiv 2407.09831 (preprint)Current
UK firms using AI75% of 118 surveyedBank of England & FCA21 Nov 2024Current
Planning adoption within 3 years10%Bank of England & FCA21 Nov 2024Current
Use cases with automated decisioning55%Bank of England & FCA21 Nov 2024Current
Use cases fully autonomous2%Bank of England & FCA21 Nov 2024Current
Use cases implemented by third parties33%Bank of England & FCA21 Nov 2024Current
Firms using foundation models17%Bank of England & FCA21 Nov 2024Current
Firms with an accountable person for AI84%Bank of England & FCA21 Nov 2024Current
Complete / partial understanding of AI used34% / 46%Bank of England & FCA21 Nov 2024Current
Leading use casesProcess 41%, cyber 37%, fraud 33%Bank of England & FCA21 Nov 2024Current
Use-case materiality62% low / 22% medium / 16% highBank of England & FCA21 Nov 2024Current
Provider concentration, top threeCloud 73%, model 44%, data 33%Bank of England & FCA21 Nov 2024Current
Systemic risk observationCorrelated positions amplify shocksBank of England, Financial Stability in Focus9 Apr 2025Current
IOSCO use-case surveyClient communications 66.7%, algorithmic trading 63.3%, robo-advising 60%, surveillance 53.3%, productivity 50%IOSCO2024Historical
Algorithmic share, developed equity markets60–75%; plateau 70–80%Select USACurrent
US algorithmic share, historical~15% (2003) → over 70% (2010)Select USAHistorical
FX orders algorithmic~25% (2006) → ~80% (2016)Historical
HFT share of US equity50–55%SEC2023Historical
Regulator assessment of AI tool profitabilityData not found

Market size estimates

Eight estimates spanning roughly 22 times. Published in full because the spread is definitional rather than statistical — the reasoning is on the market size page.

Table 4. Commercial market-size estimates, as issued
FirmBaseForecastCAGRStatus
Fortune Business InsightsUSD 2.53bn (2025)USD 4.33bn (2034)6.0%Verify before use
Coherent Market InsightsUSD 3.59bn (2026)USD 6.68bn (2033)9.3%Verify before use
DatainteloUSD 4.2bnUSD 9.8bn9.8%Verify before use
IMARC GroupUSD 18.8bnUSD 43.2bn9.39%Verify before use
ReAnInUSD 20.58bnUSD 43.27bn (2032)11.2%Verify before use
Grand View ResearchUSD 21.06bn (2024)USD 42.99bn (2030)12.9%Verify before use
Straits ResearchUSD 57.65bnUSD 150.36bn (2033)12.73%Verify before use
TechnavioGrowth of USD 23.94bn (2025–30)16.7%Verify before use
Official statistical seriesData not found

Platform pricing

All captured from vendor pages in August 2026. Every row is verify before use — tariffs, tier limits and promotional pricing change often, and a stale price is worse than no price.

Table 5. Platform pricing and limits, captured August 2026
PlatformPricing (USD)Notable limitsStatus
TradingView0 / 12.95 / 29.95 / 59.95 / 199.95 per month, annual billingCharts per tab 2/4/8/16; indicators 5/10/25/50; alerts 20/100/400/1000Verify before use
TrendSpider14-day trial 19–49; then 52.38 / 56.42 / 73.20 / 128.40 promotionalWorkspaces 5/10/15/20; bots 5/10/50/100; add-ons for futures data and backtest depthVerify before use
Trade IdeasBasic 89/mo or 1,068/yr; Premium 178/mo or 2,136/yrCharts 10/20Verify before use
Cryptohopper0 / 29 / 69 / 129Positions 80/80/200/500; coins 15/15/50/75; page changed 26 Jun 2026Verify before use
3CommasPage shows 15/20, 38/50, 105/140 without a stated billing periodDCA·signal·grid bots 5·2·2 / 20·20·10 / 1000·1000·1000Verify before use
PionexNo subscription; spot maker/taker 0.050%/0.050%Withdrawal tiers by KYC: 0 / 20,000 / 1,000,000 USDTVerify before use
QuantConnectFree research tier; live from approximately 60/mo; datasets 5–350+/moLEAN open source; Python 3.11 / C#; 20+ brokersVerify before use
Interactive Brokers APIWeb / FIX / TWS; 170 markets in 40 countries (vendor statement)Verify before use
Capitalise.aiFree for eligible IBKR clientsVerify before use
AlpacaCommission-free US equities and optionsFINRA broker-dealer since 2018; 30+ countriesVerify before use
ComposerSEC-registered; pricing not capturedData not found

Documented incidents

Table 6. Failures and incidents cited across the site
IncidentDetailDateStatus
Knight Capitalapproximately USD 440m lost in about 45 minutes; 4,026,087 executions across 154 securities; approximately 397m shares; shares −75% in two days; SEC penalty USD 12m under the Market Access Rule1 Aug 2012Historical
Flash CrashJoint CFTC/SEC reportevent 6 May 2010; report 30 Sep 2010Historical
3Commas — first unauthorised tradesFTX accounts, DMG pairs; escalated 21 Oct20 Oct 2022Historical
3Commas — platform updateKeys assessed as not taken from its database; many affected keys never connectedDec 2022Historical
3Commas — mass revocationCirculated key dataset confirmed genuine; exchanges asked to revoke28 Dec 2022Historical
3Commas — verified total lossData not found
OKX spot-grid behaviourBelow the lower bound the bot stops buying while the asset continues fallingCurrent

Where the data does not exist

Twelve gaps recur across the site. Each was searched for and each is published as a gap rather than filled.

Table 7. Verified absences — searched for, not located
What is missingWhere it would appear
NZ retail loss rateNZ regulation; CFD statistics
NZ retail leverage capNZ regulation
NZ algorithmic share of turnoverNZ regulation; market size
NZ regulator study of AI adoptionNZ regulation
Any FMA publication on algorithmic or AI trading conductNZ regulation
Count of UK firms authorised for algorithmic tradingUK regulation
UK-specific algorithmic share of turnoverUK regulation; market size
Any regulator assessment of AI trading tool profitabilityUK, AU and NZ regulation; evidence
Loss statistics split by trading methodCFD statistics
Multi-year cohort study of retail tradersCFD statistics
Official AI trading market statisticMarket size
Audited live accuracy for any retail AI productPrediction accuracy
Composition of this fact base, August 2026 73 tracked entries Verified figures — 61 Named source, identifier where one exists, date Verified absences — 12 Searched for, not located, published as gaps Our own count of the tables on this page.
The twelve absences are part of the data, not omissions from it.

How this table is maintained

A figure enters only with a named source, a retrievable identifier where the source type has one, and a date. Where two credible sources disagree, both get rows — retail loss rates and market-size estimates are the two places this happens most. Nothing is averaged, harmonised or rounded to a convenient value.

Rows marked verify before use are re-checked before being cited anywhere new; vendor pricing is the main category. Rows marked historical describe a fixed past period and will not change. When a source publishes a new version, the row updates and the change is logged on the corrections page rather than applied silently. The full standard is set out in the editorial policy.

If you find an error, or a primary source for one of the twelve gaps, the corrections page is the route. A document number or URL is more useful than a description.

Frequently asked questions

What is the data hub?

A single table containing every figure used anywhere on this site, with its value, unit, primary source, document identifier where one exists, publication date and a freshness status. It exists so that any claim on any page can be checked in one place.

What does the freshness status mean?

Current means the source is the latest published version we could locate. Historical means the figure describes a specific past period and will not be updated. Verify before use means the value changes frequently — vendor pricing in particular — and should be re-checked at the source before being relied on.

How are figures selected for inclusion?

A figure appears only if it has a named source, a retrievable identifier such as a report or media release number, and a date. Where two credible sources disagree, both are listed with their own rows rather than averaged or reconciled.

Why are some cells marked data not found?

Because we looked for a primary source and could not locate one. It means the figure could not be verified, not that it does not exist. Publishing the gap is more useful than filling it with an estimate borrowed from a neighbouring jurisdiction.

Can I reuse these figures?

Yes, and preferably by citing the original source listed in the row rather than this page. Each entry names the regulator publication, paper or vendor page it came from precisely so that it can be traced past us.

Which figures conflict with each other?

Retail loss rates range from 68% to 82% across different measures, and market-size estimates span roughly 22 times. In both cases the conflict is definitional — different populations, periods and boundaries — so both are published in full rather than resolved.

How current is the platform pricing?

Captured from vendor pages in August 2026 and marked verify before use. Platform tariffs, tier limits and promotional pricing change frequently, so any pricing figure should be re-checked at the vendor's own page before it is relied on.

How often is this page updated?

When a source publishes a new version, when a figure we marked verify before use is re-checked, or when a correction is made. Changes are logged on the corrections page rather than applied silently.

What is the largest gap in this data?

New Zealand. Of fourteen comparable regulatory data points tracked across three markets, we located New Zealand primary sources for three, against thirteen for the United Kingdom and twelve for Australia.

Do any of these figures measure whether AI trading is profitable?

Only indirectly. The Eurekahedge AI Hedge Fund Index comparison and the peer-reviewed review of 27 machine-learning experiments are the closest available evidence. No regulator publishes an assessment of AI trading tool performance.

How do I report an error?

Through the corrections page, ideally with the document number or URL of the source that contradicts what is published here. Corrections are logged with the date and what changed, rather than edited in silently.

About this page

Compiled by AI Trading Book Editorial from the primary sources named in each row. Figures are reproduced as published, without averaging or harmonisation. Conflicting values appear as separate rows with their own sources. The composition count in the chart is our own tally of the tables on this page. Published 27 August 2026. Changes are logged on the corrections page.

Informational research only. Nothing on this page is personal financial, legal, tax or investment advice. Regulatory positions, vendor pricing and published statistics change; verify at the named source before relying on any figure.