Is the research free?

Yes. All research is published free on Substack. Macro briefs, equity deep dives, geopolitical reads, and central bank analysis are all available at no cost. No paywall, no spam.

What is LiJie Guo's investment track record?

As of October 2026, LiJie Guo has a 100% hit rate across 9 closed investment calls with an average return of +32.7% per closed call. Closed calls include: Oracle (ORCL) +74.3%, IREN Limited +36.4%, Lemonade (LMND) +34.7%, Reddit (RDDT) +32.5%, Marvell Technology (MRVL) +31.9%, SandDisk (SNDK) +32.3%, AST SpaceMobile (ASTS) +28.8%, IBM +18.7%, and Adeia (ADEA) +4.8%.

What kind of research does Aeon Nimbus publish?

Aeon Nimbus publishes: (1) Formal equity investment calls with entry, target, stop-loss, and full thesis before the outcome is known. (2) Macro research briefs covering global markets, central bank policy, and geopolitical dynamics. (3) Systematic trading strategies — 12 quantitative models across equity and FX. (4) A dynamic macro model portfolio with a simulated +28.4% return since inception. (5) Open-source financial tools: DCF, Black-Scholes, Monte Carlo, and portfolio construction.

What systematic strategies does Aeon Nimbus offer?

Aeon Nimbus has developed 13 fully systematic strategies across equity and FX markets, covering momentum, mean-reversion, and macro regime models. These include strategies across 6 FX pairs and 7 equity models combining quantitative signals. All strategy performance is available on the website.

LiJie Guo is a London-based independent macro and equity research analyst and the founder of Aeon Nimbus Research. He holds a Triple MSc — MSc in Management (Finance Track) from emlyon Business School, MSc in Quantitative Finance from Politecnico di Milano, and MSc in Finance (Asset Management) from Bayes Business School (formerly Cass) — and is a CFA Level I candidate. His career spans equity research on African listed equities at Hamilcar Capital (a London-Paris-Nairobi boutique specialising in African special situations), long/short equity in Madrid, quantitative research at Crandon Capital, macro strategy at Santomera Bay Capital, and Big Four advisory at PwC. He publishes all investment calls pre-outcome on Substack with a fully auditable public track record.

Does Aeon Nimbus Research have free financial tools?

Yes. Aeon Nimbus Research provides six open-source financial tools available free in the browser: a DCF valuation engine, a Black-Scholes options pricing calculator, a Monte Carlo portfolio simulator, a Kelly Criterion position sizer, a bond analytics calculator, and a multi-factor correlation matrix. All tools require no login and have no data limits.

Is Aeon Nimbus Research a hedge fund?

No. Aeon Nimbus Research is an independent research and analysis platform. It is not a regulated investment firm, does not manage external capital, and does not solicit investors. The Research Lab and model portfolio are simulated research vehicles for developing and displaying quantitative methodology. All investment calls and research content are published for transparency and educational purposes only.

Where did LiJie Guo work before founding Aeon Nimbus Research?

LiJie Guo's professional experience includes: Hamilcar Capital (equity research analyst covering African listed equities and special situations, London-Paris-Nairobi boutique), Jupiter Asset Management, Crandon Capital Management (private credit and real estate), Santomera Bay Capital (macro strategy and private equity portfolio management), PwC (Big Four M&A financial due diligence), SpinLab — the HHL Accelerator, and Antler (early-stage venture capital in Berlin). He also worked at a leading independent fund platform in Spain running long/short equity and macro strategies.

How does LiJie Guo's investment approach differ from other analysts?

LiJie Guo publishes every formal investment call before the outcome is known — including entry price, stop-loss, position size, and full thesis. This pre-commitment creates an auditable, tamper-proof track record. Most analysts publish views retrospectively or select only their winning calls. Aeon Nimbus records all calls including any future losses, permanently and unedited.

What is the Oracle ORCL investment call by Aeon Nimbus?

The Oracle (ORCL) call was Aeon Nimbus Research's first formal investment call, published on 8 April 2026 at an entry price of $143.36. The thesis identified a $98 billion cloud backlog invisible to consensus — AI demand had pulled Oracle's 5-year infrastructure runway into 18 months. The position was closed on 1 June 2026 at $249.88, delivering a +74.3% return. LiJie Guo subsequently added to ORCL at $138.07, $124.65, and $115.50 across three additional lots.

What is LiJie Guo's average investment return?

Across nine closed investment calls, LiJie Guo has achieved a 100% hit rate with an average return of +32.7% per closed position. Individual returns: Oracle (ORCL) +74.3%, IREN Limited +36.4%, Lemonade (LMND) +34.7%, Reddit (RDDT) +32.5%, Marvell Technology (MRVL) +31.9%, SandDisk (SNDK) +32.3%, AST SpaceMobile (ASTS) +28.8%, IBM +18.7%, and Adeia (ADEA) +4.8%. All calls were published before the outcome was known.

Is Aeon Nimbus Research available on Substack?

Yes. Aeon Nimbus Research publishes all research free on Substack at aeonnimbus.substack.com. This includes formal equity investment calls with full thesis, macro research briefs, geopolitical market analysis, and central bank policy commentary. No subscription fee, no paywall. The Substack serves as the primary timestamp record for all pre-outcome investment calls.

What financial tools does Aeon Nimbus Research offer for free?

Aeon Nimbus Research provides six open-source financial tools free in the browser with no login required: (1) DCF Valuation Engine for discounted cash flow analysis, (2) Black-Scholes Options Pricing Calculator, (3) Monte Carlo Portfolio Simulator running 10,000 iterations, (4) Kelly Criterion Position Sizer for optimal bet sizing, (5) Bond Analytics Calculator for yield and duration, and (6) Multi-Factor Correlation Matrix for portfolio construction. All tools are built from scratch and open-sourced.

What markets does Aeon Nimbus Research cover?

Aeon Nimbus Research covers: (1) US and international equities — single-stock fundamental analysis with entry, target, and stop-loss; (2) Global macro — central bank policy, monetary regime analysis, inflation dynamics, and interest rate forecasting; (3) Geopolitical market dynamics — how political events affect asset prices; (4) FX and commodity markets through 5 systematic quantitative strategies; (5) Systematic equity — 7 quantitative equity models combining momentum, mean-reversion, and macro signals.

Who should follow Aeon Nimbus Research?

Aeon Nimbus Research is valuable for: individual investors who want to see how a professional analyst builds and tracks investment theses; finance students and CFA candidates studying fundamental equity research and macro analysis; allocators evaluating independent research talent with a verified track record; and quantitative researchers interested in systematic strategy development across equity and FX markets.

How can I contact LiJie Guo or Aeon Nimbus Research?

LiJie Guo can be contacted through the contact form on aeonnimbus.com, via LinkedIn at linkedin.com/in/lijieguo-es/, or by subscribing to the Substack at aeonnimbus.substack.com. For allocator inquiries, institutional research partnerships, or media requests, use the contact page on the website.

LiJieGuo.

Q: How does LiJie Guo publish investment calls?

Every investment call is published on Substack and recorded on aeonnimbus.com before the outcome is known. Each call includes: the entry price, stop-loss level, position size as a percentage of portfolio, investment horizon, and the full fundamental or macro thesis. Results — including any losses — are recorded publicly on the Track Record page.

Q: What is LiJie Guo's educational background?

LiJie Guo holds a Triple MSc across three leading European business schools: MSc in Management (Finance Track) from emlyon Business School, MSc in Quantitative Finance from Politecnico di Milano, and MSc in Finance with specialization in Asset Management from Bayes Business School (formerly Cass Business School, City University of London). He is also a CFA Level I candidate.

Independent Macro & Equity Research · London

9 for 9. Every investment call published with full thesis, entry, stop-loss, and position size before the outcome is known — and closed profitable. Average return per closed call: +32.7% · Hit rate: 100% · All nine permanently on the record.

Try AI Pitcher

Full equity research report · Aeon Nimbus methodology · Free

Education

Triple MSc — emlyon · Politecnico di Milano · Bayes (Cass)

Designation

CFA Level I Candidate

Experience

L/S Equity · Quant Research · Macro Strategy

Prior Firms

Hamilcar Capital · Jupiter AM · Crandon AM · Santomera Bay · PwC · SpinLab & Antler ↗

↓ Q2 2026 Investor Letter

#	Instrument	Thesis	vs. Street	Published	Period	Return
01	ORCL Oracle Corporation	$98B cloud backlog invisible to the street — AI demand pulled Oracle's 5-year infrastructure runway into 18 months ↗	Buy · Missed $98B backlog	8 Apr 2026	Apr – Jun 2026	+74.3%
02	IREN IREN Limited	Market priced macro fear, not the moat — proprietary power assets at $47 for a business that compounds on the energy cost curve ↗	Uncovered · Macro fear mispriced	20 May 2026	May – Jun 2026	+36.4%
03	LMND Lemonade, Inc.	Loss ratio cracked below 70% — reinsurance treaty reset cleared the structural overhang on a business priced for failure.	Street Sell · Missed loss-ratio turn	2 Jul 2026	Jul 2026	+34.7%
04	RDDT Reddit, Inc.	AI's training feedstock, not its victim — Reddit's unique human corpus is the data infrastructure every major lab depends on ↗	Street Mixed · Missed AI data angle	27 May 2026	May – Jun 2026	+32.5%
05	MRVL Marvell Technology	Custom silicon cycle severely underpriced — hyperscaler ASIC commitments locked in multi-year revenue that consensus hadn't modeled.	Street Buy · Missed ASIC cycle	9 Jun 2026	Jun 2026	+31.9%
06	SNDK SandDisk Corporation	Storage cycle recovery mispriced — NAND pricing inflection embedded in results before consensus updated forward estimates.	Consensus Neutral · Missed cycle turn	19 Jul 2026	Jul – Aug 2026	+32.3%

+0%

Avg. Return · Closed Calls

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Avg. Alpha vs. S&P 500 · Per Call

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Closed Calls · All Profitable

0

Open Positions · Live

Portfolio Simulation · Closed Calls

Cumulative P&L: —

Portfolio AUM

$

Allocation per call (%)

— —

Capital Deployed

—

Total Gain

—

Final Portfolio

—

Return on AUM

—

Avg per Call

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Research delivered to your inbox.

Macro briefs, equity deep dives, geopolitical reads — each with a formal investment call published before the outcome is known. Every result on the record.

Subscribe on Substack →

Free · No spam · Unsubscribe anytime

Investment Philosophy

The market misprices
divergence.

Every position begins with a single question: what does the market believe, and why is that belief wrong? The answer is rarely about sectors. It is about the gap between narrative and fundamental reality — the moment when consensus pricing embeds a story that the underlying business, macro backdrop, or structural dynamic no longer supports.

The approach is bottom-up in execution and top-down in framing. A company's unit economics, balance sheet inflection, or competitive moat matters most — but it matters when the macro backdrop creates the catalyst that makes the market look. Patience to wait for that convergence, discipline to size asymmetrically, and the willingness to publish the full thesis before the outcome is known — that is the practice.

01 — EDGE

Narrative vs. Reality

Markets price stories. When the story diverges from what the numbers actually show — on unit economics, competitive position, or structural change — the gap is the opportunity. Sector is irrelevant. The divergence is not.

02 — CONSTRUCTION

Asymmetric Risk · Defined Loss

Every call enters with a defined stop. The maximum loss is known before the position is opened; the upside is uncapped and thesis-driven. Sizing is disciplined — enough to matter, never enough to compromise the portfolio if the thesis is wrong.

03 — PROCESS

Catalyst-Driven Timing

Being right too early is the same as being wrong. Each thesis requires a specific near-term catalyst — an earnings inflection, a regulatory event, a macro regime shift — that forces the market to reprice. The catalyst defines the horizon; the fundamental gap defines the magnitude.

04 — ACCOUNTABILITY

Published Before the Outcome

Full thesis, entry price, stop-loss, and position size go public before the position plays out. Nothing is revised retrospectively. The record — wins and future losses alike — stays permanently public. Accountability is the only honest framework for trust.

Research. Systems. Tools.

Three parts of one independent research practice — all free, all public, all permanently on the record.

Research Lab

12 Systematic Strategies

A live macro model portfolio built on 12 fully systematic strategies across equity and FX — each backtested to 99% real-tick data. Free access, no paywall, no login.

EQ MomentumFX Trend Macro RegimeVol Targeting Mean ReversionFactor Alpha EM RotationRate Sensitivity L/S EquityCarry Momentum×VolTail Risk

Research

Pre-Outcome Investment Calls

Every formal call published before the outcome — full thesis, entry, stop, and sizing. Losses included. Nothing deleted.

Jupiter Asset Management

Equity · Initiating · Jul 2026

Open

Dollar Squeeze — EM Positioning

Macro · Brief · Jun 2026

—

Oracle Corporation

Equity · Closed · Mar 2026

+74.3%

Tools

Six Analyst Tools — Free

Practitioner-grade financial tools built in the browser. No login, no limits, no paywall. Used by analysts in 40+ countries.

DCF(FCF, r, g)

DCF Valuation Engine

Q(thesis, catalyst, edge)

Investment Idea Screener

N(μ, σ²) × 10⁴

Monte Carlo Simulator

f* = (bp − q) / b

Kelly Criterion Sizer

"

Exactly the kind of transparent, pre-outcome research I've been looking for. Every call published with a real thesis before it plays out.

— Allocator, London

"

The ORCL call at 143 was the clearest asymmetric setup I'd seen in 2026. The WACC build and scenario table were institutional-grade.

— Independent Portfolio Manager

"

Finally an analyst who puts calls on record before the outcome. Six for six with full thesis each time — that's a real track record.

— Portfolio Manager, Family Office

Coverage Universe

Sectors & Themes Under Active Coverage

Technology

AI Infrastructure
Semiconductors
Cloud & SaaS
Custom Silicon
Data Centres
Cybersecurity

Financials

Insurtech
Digital Banking
Capital Markets
Alt. Asset Mgmt
Credit
Fintech

Consumer & Media

Social Platforms
Digital Media
E-Commerce
Consumer Tech
Gaming
Marketplaces

Energy & Infra

Digital Mining
Power & Utilities
Energy Transition
Data Infra
Storage
Grid Tech

Global Macro

Central Bank Policy
FX & Rates
EM Markets
Geopolitics
Credit Cycles
Commodities

Systematic

EQ Momentum
FX Trend
Macro Regime
Mean Reversion
Vol Targeting
Carry & Factor

Featured Research

Recent Investment Calls & Macro Briefs

All Research ↗

Equity · Closed +74.3%

Oracle Corporation — The $98B Backlog the Market Couldn't See

AI demand compressed Oracle's 5-year infrastructure runway to 18 months. Entry $143.36 · Exit $249.88 · Published 8 Apr 2026 before the outcome.

Read thesis ↗

Equity · Closed +36.4%

IREN Limited — The Power Is the Moat

Market priced macro fear into a business with proprietary power infrastructure compounding on the energy cost curve. Entry $47.88 · Published 20 May 2026.

Read thesis ↗

Equity · Closed +32.5%

Reddit — Not AI's Victim. Its Feedstock.

Reddit's unique human corpus is the data infrastructure every major AI lab depends on. The street called it a loser in the AI era. Entry $140.88 · Published 27 May 2026.

Read thesis ↗

Modelling Lab · Case Study No. 01

Oracle Corporation — Interactive DCF

Stress-test the thesis published 8 Apr 2026. Adjust growth rates, terminal margin, and discount rate — the implied share price updates in real time. Base inputs reflect the published call's assumptions.

Assumptions

Revenue CAGR — Yr 1–322%

5%40%

Revenue CAGR — Yr 4–514%

3%30%

Terminal EBIT Margin35%

20%50%

WACC9.5%

7.0%14.0%

Terminal Growth Rate4.0%

1.0%6.0%

Base revenue (FY2025): $56.4B

Net debt: $87.0B

Shares outstanding: 2.73B

D&A 3.5% of rev · CapEx 5.0% of rev · Tax 17%

Margin glides 28% → terminal over 5 years

Implied Share Price

—

Entry $143.36 · Exit $249.88

Upside vs. Entry

—

Year	Revenue ($B)	EBIT Margin	Free Cash Flow ($B)	PV of FCF ($B)

Sensitivity — Implied Price · WACC (rows) × Terminal Growth (cols)

Model assumptions: FY2025 base revenue $56.4B · Net debt $87.0B · Shares 2.73B · D&A 3.5% of revenue · CapEx 5.0% of revenue · Effective tax rate 17% · EBIT margin glides from 28% (Year 1) to the terminal input by Year 5. The published call (8 Apr 2026) was entered at $143.36 and closed at $249.88 on 1 Jun 2026 (+74.3%). The realised return reflects both intrinsic value convergence and multiple expansion as cloud revenue accelerated ahead of consensus. For illustrative and educational purposes only. Not investment advice.

Sum-of-the-Parts · Oracle Corporation

Segment Valuation — NTM Multiples

Adjust the revenue or EBITDA multiple per segment. The implied share price updates alongside the DCF above — SOTP on NTM numbers typically captures a static floor; the DCF growth assumptions capture the incremental value the thesis was betting on.

Segment	NTM Revenue ($B)	Basis	Multiple	Implied EV ($B)
Cloud Infrastructure (OCI)	13.5	Revenue	15×	202.5
Cloud Applications (SaaS)	9.2	Revenue	6×	55.2
Database & License Support	$16.3B EBITDA (65% margin on $25.0B rev)	EBITDA	14×	228.2
Hardware	3.4	Revenue	0.8×	2.7
Services & Other	11.1	Revenue	1.2×	13.3
Enterprise Value				501.9
Less: Net Debt				(87.0)
Equity Value				414.9
Implied Price (2.73B shares)				$152

NTM revenue estimated from FY2025 actuals: OCI ($9.0B × 1.50), SaaS ($8.0B × 1.15), DB/License ($25.0B × 1.03), Hardware ($3.5B × 0.97), Services ($10.9B × 1.02). Database EBITDA assumes 65% margin on NTM license support revenue. Net debt $87.0B. Shares 2.73B. SOTP on NTM numbers is a static floor — the DCF captures growth value beyond Year 1. Illustrative only. Not investment advice.

Verified Investment Track Record

Every investment call published by Aeon Nimbus Research is timestamped on Substack before the position plays out. Returns are calculated from the published entry price to exit. The record below represents all closed calls as of September 2026.

Ticker	Company	Entry	Exit	Return	Period	Published
ORCL	Oracle Corporation	$143.36	$249.88	+74.3%	Apr – Jun 2026	8 Apr 2026
IREN	IREN Limited	$47.88	$65.30	+36.4%	May – Jun 2026	20 May 2026
LMND	Lemonade Inc.	$59.56	$80.23	+34.7%	Jul 2026	2 Jul 2026
RDDT	Reddit Inc.	$140.88	$186.68	+32.5%	May – Jun 2026	27 May 2026
MRVL	Marvell Technology	$245.05	$323.26	+31.9%	Jun 2026	9 Jun 2026

9 closed calls · 100% hit rate · +32.7% average return · All calls published before outcome on Substack · All positions including future losses remain permanently on record.

Who Is LiJie Guo?

LiJie Guo is a macro and equity research analyst based in London. He holds a Triple MSc — MSc in Management (Finance Track) from emlyon Business School, MSc in Quantitative Finance from Politecnico di Milano, and MSc in Finance (Asset Management) from Bayes Business School (formerly Cass) — and is a CFA Level I candidate. His professional career spans multiple institutions across three countries, covering long/short equity, quantitative research, macro strategy, private equity, private credit, and Big Four financial advisory.

Professional Experience

Hamilcar Capital — equity research analyst covering African listed equities and special situations; London-Paris-Nairobi investment boutique
Jupiter Asset Management — investment research and analysis
Crandon Capital Management (Crandon AM) — quantitative research, private credit, and real estate investment analysis
Santomera Bay Capital (Santomera Bay) — macro strategy and private equity portfolio management at a leading independent fund platform in Spain
PwC — Big Four M&A financial due diligence and transaction advisory
SpinLab — HHL Accelerator — early-stage venture capital exposure in European tech ecosystem
Antler — the world's most active early-stage VC, Berlin operations

Academic Credentials

MSc in Management (Finance Track) — emlyon Business School
MSc in Quantitative Finance — Politecnico di Milano
MSc in Finance, Asset Management — Bayes Business School (formerly Cass), City University of London
CFA Level I Candidate (Chartered Financial Analyst program)

What Is Aeon Nimbus Research?

Aeon Nimbus Research is an independent macro and equity research platform founded by LiJie Guo in London. It is not a hedge fund, not a registered investment adviser, and does not manage external capital. It is a research publication platform with a rigorous, verifiable methodology.

The defining feature of Aeon Nimbus Research is its pre-outcome commitment framework: every formal investment call is published in full — entry price, stop-loss level, position size as a percentage of portfolio, target price, investment horizon, and complete fundamental or macro thesis — before the trade plays out. This makes the track record tamper-proof. Nothing is revised, deleted, or selected after the fact.

The platform publishes research across four domains: single-stock equity analysis, global macro, geopolitical market dynamics, and systematic quantitative strategies.

Equity Research — formal investment calls on US and international equities, with full thesis published before outcome
Global Macro — central bank policy analysis, monetary regime forecasting, inflation dynamics, and interest rate impact on markets
Geopolitical Market Analysis — how political events and power shifts translate into market dislocations
Systematic Strategies — 16 proprietary quantitative models across 7 equity, 6 FX/commodity and 3 Tenkai index/gold strategies, backtested on real tick data across 12+ years of full market cycles

Frequently Asked Questions

Is LiJie Guo's research free?

Yes. All research published by Aeon Nimbus Research is available free on Substack at aeonnimbus.substack.com. There is no paywall, no subscription fee, and no premium tier. Macro briefs, equity calls, geopolitical reads, central bank analysis, and systematic strategy commentary are all published at no cost.

How does Aeon Nimbus Research compare to other independent research platforms?

Most independent research platforms publish views after the fact, cherry-pick winning calls, or do not disclose entry and exit levels. Aeon Nimbus Research publishes every formal call before the outcome is known, with full parameters — entry, stop-loss, target, size, and thesis. This creates a verifiable, auditable record that cannot be retroactively edited.

What was the Oracle ORCL investment thesis?

Published 8 April 2026 at $143.36. The thesis: Oracle's $98 billion cloud backlog was invisible to Wall Street consensus. AI infrastructure demand had compressed Oracle's five-year cloud buildout into an 18-month window, creating a revenue inflection that consensus models had not captured. The position was closed at $249.88 on 1 June 2026, returning +74.3%.

What was the Reddit RDDT investment thesis?

Published 27 May 2026 at $140.88. The thesis: Reddit is AI's training feedstock, not its victim. Reddit's unique corpus of human-generated, context-rich text is the data infrastructure that every major AI lab depends on. The market was pricing Reddit as if AI would displace it — the opposite was true. Closed at $186.68 on 4 June 2026, returning +32.5%.

What is the Aeon Nimbus macro model portfolio?

The Aeon Nimbus macro model portfolio is a dynamic, regime-driven allocation framework. It adjusts weights across equities, fixed income, gold, commodities, and cash based on the prevailing macro regime — including inflation trajectory, central bank policy stance, credit cycle position, and geopolitical risk level. It has returned +28.4% since inception and is a research vehicle, not a live fund.

Who is the target audience for Aeon Nimbus Research?

Aeon Nimbus Research is designed for individual investors who want to see how a professional constructs and tracks investment theses with full transparency; finance students and CFA candidates studying fundamental equity analysis and macro research methodology; family offices and allocators evaluating independent research talent with a verified, pre-outcome track record; and quantitative researchers interested in systematic strategy construction across equity and FX markets.

Is there a Q2 2026 investor letter from Aeon Nimbus Research?

Yes. The Q2 2026 investor letter is available as a PDF download on aeonnimbus.com. It covers the closed positions from Q2 2026, the investment philosophy, methodology, and outlook for open positions.

Legal Disclaimer — Aeon Nimbus Research is an independent research and analysis platform operated by LiJie Guo, London, United Kingdom. All content published on this website and associated channels is for informational and educational purposes only. Nothing on this site constitutes investment advice, a solicitation, a recommendation, or an offer or invitation to buy or sell any financial instrument or security. Past performance, including any track record presented, is not indicative of future results. Investment decisions are solely the reader's own responsibility. LiJie Guo is not a regulated financial adviser and Aeon Nimbus Research is not a regulated investment firm. For enquiries: LinkedIn ↗

TripleMSc · 3 Schools

CFALevel I Candidate

4+Institutions · 3 Countries

12Systematic Strategies

£51bnAUM Research Scope

Investment Philosophy

"The world is always mispriced somewhere. Capital follows narratives. Narratives follow power. Most investors read the price. This is about reading what moves it."

Aeon Nimbus is an independent macro and equity research platform built on a single principle: every formal investment call is published with full thesis, entry, stop-loss, and position size before the outcome is known. No retrospective commentary. No curated track records. The research covers global macro, single-stock equity, geopolitical market dynamics, and central bank policy — combined with proprietary algorithmic strategies across 6 FX/commodity pairs and 7 systematic equity models.

Career Progression

Independent Fund Platform · Spain

Long/Short Equity & Macro

Portfolio research and trade execution across L/S equity and macro strategies at a leading independent fund platform in Spain.

Crandon

Private Credit & Real Estate

Credit analysis and real estate investment across private debt and direct property strategies.

Santomera Bay · Barcelona

Private Equity & Strategy

Portfolio management and Strategy & Operations at a private family office in Barcelona, covering direct PE positions and strategic advisory.

PwC

M&A Financial Due Diligence

Financial due diligence on M&A transactions, covering financial modelling, quality of earnings, and deal structuring analysis.

SpinLab (HHL) · Antler · Berlin

Venture & Early-Stage VC

Early exposure to Europe's venture ecosystem through the HHL Accelerator (SpinLab) and Antler, the world's most active early-stage VC, in Berlin.

Hamilcar Capital · London–Paris–Nairobi Current

Equity Research Analyst · African Listed Equities

Equity research and valuation of listed African companies for institutional and private clients at a London-Paris-Nairobi investment boutique specialising in African listed and private equity special situations. Building DCF, comparable company analysis and EV-to-equity bridge models with country-risk-adjusted discount rates and explicit currency scenario modelling. Authoring full equity research reports and designing AI-driven automation for the research and data workflow.

Aeon Nimbus Research · London Current

Independent Macro & Equity Research

Publishing independent macro and equity research with a public, pre-outcome track record. 100% hit rate across 9 closed calls, avg +32.7% return. 12 systematic strategies. Open-source financial tools.

Education

He holds a Triple MSc — MSc in Management (Finance Track) from emlyon, MSc in Quantitative Finance from Politecnico di Milano, and MSc in Finance (Asset Management) from Bayes Business School (formerly Cass) — and is a CFA Level I candidate.

EM

emlyon Business School

MSc in Management · France

PM

Politecnico di Milano

MSc · GSOM · Italy

BS

Bayes Business School

MSc in Finance · City, University of London

Subscribe — Research Free Download CV →

Current Research

Jupiter Asset Management · London · FTSE 250

Equity Return Predictability Across Japanese, US & European Markets

An industry research project at Jupiter Asset Management (£51bn AUM) investigating cross-market equity return predictability using factor models and statistical tests. The project is jointly supervised by an Asset Pricing professor at Bayes Business School, a Systematic Equity Portfolio Manager at Jupiter AM, and a Computational Finance professor at Paris Dauphine University.

Economic framework

Hypothesis construction

Python · Factor models

Statistical tests

Availability

Open to research collaborations

Open to institutional enquiries

Considering full-time roles

Selective on advisory roles

Research Methodology

How I Build an Investment Thesis

01 · SCREENING

Macro-first, then single-stock

Every idea starts with a macro read: what is the prevailing regime, where is the central bank in its cycle, and which sectors benefit from or are exposed to the current geopolitical configuration. Only then do I screen for single stocks within that macro frame — avoiding sector bets that fight the regime.

02 · FUNDAMENTAL ANALYSIS

Identify what the street is missing

I look for one key variable the consensus has mispriced: a structural inflection that hasn't yet shown up in earnings, a balance sheet item the market is treating incorrectly, or a demand driver that sell-side models have not incorporated. Each call requires a specific, falsifiable thesis — not directional sentiment.

03 · POSITION CONSTRUCTION

Size, stop, and time horizon first

Before publishing, I set three parameters: position size as % of portfolio (typically 3–10%), a hard stop-loss at the level that would invalidate the thesis, and an investment horizon (not a price target timeline). Sizing is driven by conviction and correlation — I will not concentrate across correlated names.

04 · PRE-OUTCOME PUBLICATION

The record is written before the result

Every call is published on Substack and recorded on aeonnimbus.com with a timestamp before the market closes on the entry date. The full thesis, entry price, stop-loss, and size are all visible before the outcome is known. Nothing is retroactively edited or deleted — the record stands permanently regardless of result.

05 · SYSTEMATIC OVERLAY

Quantitative signals as a second opinion

Discretionary calls are stress-tested against the 12 systematic strategies running in parallel. If the quant models flatly contradict a discretionary thesis, I either reduce size or re-examine my fundamental assumptions. The systematic portfolio is not a replacement for fundamental thinking — it is a check on behavioural bias and timing.

06 · EXIT DISCIPLINE

Rules-based, not emotional

Positions are closed when: the stop-loss is triggered, the original thesis is invalidated by new data, a better risk-adjusted opportunity presents itself, or the position reaches a point where continued holding requires a new thesis rather than the original one. Hitting a price target is not automatically a close trigger — the thesis must be reassessed at every major inflection.

↓ Download Q2 Letter

Published research

Research Notes

Equity deep dives · Macro briefs · Geo/market wraps · Central bank analysis

Date	Category	Title	Read

01

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02

Track Record

Every formal call published with entry, target, and full thesis. Wins and losses — all on the record.

View full record →

03

Interactive Tools

Black-Scholes, DCF, portfolio construction, and more — free, open-source, in-browser tools.

Open toolkit →

Audited performance

Track Record

Every call published publicly before the outcome is known. Losses included. Never deleted.

↓ Q2 2026 Investor Letter (PDF)

Total calls

—

— open · — closed

Hit rate

—

Closed positions only

Avg. return · closed

—

Realised P&L

Capital deployed

—

Open position sizing

Alpha vs. S&P 500

—

Avg. per closed call vs S&P over the same holding period · S&P since first call: —

Live prices · alpha = call return − S&P 500 return between each call's publish and exit dates · methodology ↓

Performance Statistics

Metric	Aeon Nimbus Apr 2026 – present	S&P 500¹ Same period
Return
Closed calls — hit rate²	100%	n/a
Average return per closed call³	+32.7%	—
Average excess return vs S&P (alpha)³	—	—
Portfolio-weighted return⁴	—	—⁵
Risk
Best closed call	+74.3% (ORCL)	—
Worst closed call⁶	+4.8% (ADEA)	—
Losing trades	0 of 8	—
Avg hold period⁷	~5–8 weeks	—
Process
Publication timing⁸	Before outcome — every call	—
Deleted or edited calls	0	—

Worst closed call

+4.8%

ADEA · Adeia Inc. — Published 4 May 2026, closed 4 Aug 2026 at $31.44 (entry $30.00). Thesis partially played out — licensing renewals confirmed but at a slower cadence than modelled. Position exited when the original 12-month thesis no longer justified continued holding at the new risk/reward. The floor of the track record is a positive return, not a loss — but it is recorded and disclosed without qualification.

Floor of track record

Footnotes

¹ S&P 500 index (^GSPC) daily closes, price return (dividends excluded). For each closed call the benchmark is measured over that call's own window: close on or before the publish date to close on or before the exit date.
² Hit rate = closed positions with a positive return ÷ total closed positions.
³ Simple (unweighted) averages across closed calls. Alpha = average of (call return − S&P 500 return over the same holding period). Not a TWRR or portfolio-level return.
⁴ Weighted by published position size: each call's return × its published size as % of AUM, summed across all closed calls. Simulated on a hypothetical AUM — not a live fund.
⁵ S&P 500 price return from the first call's publish date to the latest close. Displayed for context only; not comparable to per-call returns.
⁶ Worst closed call by return. The floor is a gain, not a loss. The track record includes no losing closed positions as of September 2026.
⁷ Approximate average time from publication date to exit date across 9 closed calls. Ranges from 7 days (RDDT) to ~3 months (ADEA).
⁸ All calls published on Substack with full thesis, entry price, stop-loss, position size, and horizon before the position is exited. Timestamped and permanently on record.

Capital Simulation

Simulated AUM

$

Realised P&L

—

Return on AUM

—

across — closed calls at published position sizing

Per-call contribution bars scaled to largest gain

Simulated for illustration — real published returns and position sizes applied to a hypothetical AUM. Not a live fund, not investment advice.

Open Book · Live Prices

Realized

—

+

Unrealized

—

=

Total P&L

—

Closed Positions

Thesis Catalysts

Loading…

Full Record

Date	Ticker	Direction	Entry	Target	Stop	Size	Horizon	Status	Return	Last Price	ROI

Investor Letters

2026

Q2 2026 · Apr – Jun

Aeon Nimbus Investor Letter

Performance review · portfolio positioning · macro outlook

PDF ↓

Methodology & Disclosures

Methodology: Every call is published on Substack with entry price, stop-loss, position size, and full thesis — before the outcome is known. Returns are calculated from the published entry price to exit price. Open P&L uses live prices. S&P 500 benchmark starts from 08 Apr 2026, the date of the first call. All positions, including losses, remain on the record and are never edited or deleted.

⚠ A 100% hit rate carries a caveat: nine trades closed, none reaching a full stop-loss trigger. The worst closed call returned +4.8% (ADEA), which means the track record floor is positive — but the streak has not yet been tested by a realised loss. Treat this as a live, transparent starting point — not a mature track record.

Built & open-sourced by Aeon Nimbus

Open Source Tools

Six analyst tools — Black-Scholes to DCF to portfolio construction — built from scratch by Aeon Nimbus, open-sourced on GitHub, and free to use in the browser.

Quant

QM

Quantitative Models

Black-Scholes · Monte Carlo · Bond Analytics · Volatility Engine

Open Source · GitHub ↗

Open Tool

Core

VE

Complete Valuation Engine

DCF · WACC/CAPM · 3-way sensitivity · Peer comps · Football field · Reverse DCF

Open Source · GitHub ↗

Open Tool

PM Skill

PC

Portfolio Construction

Efficient frontier · Sharpe optimisation · Correlation matrix · Risk attribution

Open Source · GitHub ↗

Open Tool

PM Skill

PS

Position Sizing Calculator

Kelly criterion · Fixed fractional · Max drawdown simulation · R-multiple

Open Source · GitHub ↗

Open Tool

PM Skill

CA

Cross-Asset Correlation

Macro regime detection · Correlation heatmap · Risk-on / risk-off scoring

Open Source · GitHub ↗

Open Tool

Analyst Skill

IS

Investment Idea Screener

Catalyst scoring · Sentiment overlay · EV/EBITDA · FCF yield · Momentum rank

Open Source · GitHub ↗

Open Tool

Core

SP

Sum-of-the-Parts Builder

Segment valuation · Revenue / EBITDA / EBIT multiples · Implied price · Contribution waterfall

Open Source · GitHub ↗

Open Tool

Markets

ER

Earnings Radar

Upcoming earnings · Beat probability · Implied move · Consensus EPS

Opens in Markets tab

Go to Markets →

Markets

CT

Congress Tracker

Political disclosures · Party breakdown · Sector clustering · Overlap alerts

Opens in Markets tab

Go to Markets →

Markets

SH

S&P 500 Heatmap

Individual stocks · Squarified treemap · Sector filter · 1D / 1W / 1M / YTD

Opens in Markets tab

Go to Markets →

New

AA

Analyst Alpha Signal

Consensus direction · Broker target vs price-implied · AN contrarian read · Conviction score

Free · No login

Open Tool

New

TW

Trade Exposure

Revenue geography · Supply chain risk · Tariff sensitivity · War vs Peace positioning

Free · No login

Open Tool

QM

Quantitative Models Original

Aeon Nimbus · Black-Scholes · Monte Carlo · Bond Analytics · Volatility Engine

Quant⬡ GitHub

AEON NIMBUS · ORIGINAL IMPLEMENTATIONS · ALL COMPUTATIONS IN-BROWSER · MATHEMATICALLY VERIFIED

Inputs

Spot Price S

Strike K

Time to Expiry T (yrs)

Volatility σ %

Risk-free Rate r %

Option Type

Results

Option Price

—

Delta Δ

—

∂V/∂S

Gamma Γ

—

∂²V/∂S²

Theta Θ

—

per calendar day

Vega ν

—

per 1% vol move

Rho ρ

—

per 1% rate move

Payoff Diagram — Intrinsic value & option premium vs spot at expiry

Implied Volatility — Newton-Raphson

Market Price

Implied Volatility

—

GBM Parameters

Spot Price S₀

Annual Drift μ %

Annual Vol σ %

Simulations

Horizon (days)

Simulated Paths — up to 200 displayed · S(t+dt) = S(t)·exp((μ−σ²/2)dt + σ√dt·Z)

Terminal Distribution Statistics

Metric	Value
Mean terminal price	—
Std deviation	—
5th percentile	—
Median (50th pct)	—
95th percentile	—
P(profit) — above S₀	—
P(> +20%)	—
P(< −20%)	—

Bond Parameters

Face Value ($)

Coupon Rate %

Coupon Frequency

YTM %

Maturity (years)

Accrued days since last cpn

Results

Clean Price

—

% of face value

Dirty Price

—

Clean + accrued int.

Macaulay Duration

—

years

Modified Duration

—

% price chg / 1% YTM

Convexity

—

2nd-order rate sensitivity

DV01

—

$ per 1bp move in YTM

Price–Yield Curve · Tangent line at current YTM shows duration approximation

Cashflow Schedule

Period	Cashflow	PV of CF	Wt (t×PV/P)	Cumulative Wt

Parameters

Spot Price

Annualised Vol σ %

Annual Drift μ %

Horizon (days)

Target Price (P(hit))

Volatility Cone — GBM uncertainty bands over time

Terminal Distribution — Log-normal density

Expected Terminal

—

S₀·e^(μT)

Median Terminal

—

S₀·e^((μ-σ²/2)T)

P(hit target)

—

probability S_T ≥ target

1σ range at T

—

±1 std dev band

Implied Volatility Surface — 5×5 B-S IV heatmap (strike × expiry)

VE

Complete Valuation Engine

DCF · WACC/CAPM · 3-way sensitivity · Peer comps · Football field · Reverse DCF

Core⬡ GitHub

Company

Name

Ticker

Share price ($)

Shares out. (M)

Net debt ($M)

Market cap ($M)

Financials ($M)

Revenue TTM

EBITDA TTM

EBITDA margin %

D&A ($M)

CapEx ($M)

Tax rate %

Growth assumptions

Rev growth Y1 %

Rev growth Y2 %

Rev growth Y3 %

Rev growth Y4 %

EBITDA margin Y5 %

Terminal growth %

Exit EV/EBITDA (x)

CapEx % rev Y5

WACC / CAPM

Risk-free rate %

Equity risk prem %

Beta

Country risk prem %

Cost of debt %

Debt weight %

Risk-free rate (Rf)

—

US 10Y yield

β × Equity risk prem

—

Systematic risk component

Country risk premium

—

EM adjustment

Cost of equity (Ke)

—

CAPM result: Rf + β×ERP + CRP

Ke × equity weight

—

Equity contribution to WACC

Kd(AT) × debt weight

—

Debt contribution to WACC

Capital structure

—

Equity % / Debt %

WACC

—

Discount rate used in DCF

Metric ($M)	Y1	Y2	Y3	Y4	Y5
Click Run Complete Valuation in the Inputs tab first.

PV of FCFs (Y1–Y5)

—

Discounted at WACC

Terminal value

—

Exit multiple × Y5 EBITDA

PV of terminal value

—

TV share of total EV

Enterprise value

—

PV FCFs + PV TV

Net debt (–)

—

Subtracted from EV

Equity value

—

EV minus net debt

Implied share price

—

Equity value ÷ shares

Upside / downside

—

vs current price

Run valuation to see reverse DCF analysis — what the current price implies the market is assuming.

Bear case (20%)

—

WACC +2%, exit multiple −1x

Base case (60%)

—

Your model assumptions

Bull case (20%)

—

WACC −1%, exit multiple +1x

Expected value (probability-weighted: Bear 20% / Base 60% / Bull 20%)—

Each cell shows the implied share price under that WACC and exit EV/EBITDA multiple combination. Base case highlighted in gold. Green = material upside. Amber = near current. Red = downside.

Run valuation in Inputs tab to generate sensitivity tables.

Company	EV/EBITDA	EV/Revenue	FCF Yield	Rev Growth	EBITDA Margin	Implied Price
Run valuation first.

Peer median EV/EBITDA

—

→ implied price

Peer median EV/Revenue

—

→ implied price

Multi-method average

—

DCF + 2 peer methods

DCF vs peer convergence

—

Alignment check

The football field plots all valuation methods on a single axis. The red line is the current share price. The wider the range of methods above the red line, the more asymmetric the upside.

Run valuation first.

PC

Portfolio Construction

Build a mock fund · P&L attribution · Long/short exposure · Risk budget discipline

PM Skill⬡ GitHub

Build a mock portfolio to demonstrate portfolio-level thinking — the skill that separates analysts from PMs. Add positions below. The tool calculates total exposure, P&L, long/short split, and remaining risk capacity.

TickerEntry $Current $SharesDirP&L

Total market value

—

Total unrealised P&L

—

Portfolio return

—

Long / Short exposure

—

Add positions and calculate to see risk summary.

Calculate portfolio in the Builder tab first.

Total Capital ($)

Max Single Position %

Max Total Deployed %

TickerDirectionConv.Entry $Target $Stop $Alloc %

Portfolio Simulation Results

Total Capital

—

Capital Deployed

—

Remaining Cash

—

Positions

—

Expected Gain (target)

—

Max Loss (all stopped)

—

Expected Return %

—

Risk/Reward

—

Position Breakdown

Psychological & Risk Assessment

Run simulation to see psychological assessment.

PS

Position Sizing Calculator

Kelly Criterion · Fixed-risk (1R) · Conviction-weighted — the skill that separates analysts from PMs

PM Skill⬡ GitHub

How much capital you allocate to each idea is as important as the idea itself. This tool implements three methods used by professional PMs. Full Kelly maximises theoretical log-wealth; modified Kelly (½ or ¼) controls for estimation error. Fixed-risk anchors size to your stop-loss distance. Conviction-weighted blends both.

Kelly Criterion

f* = (p·b − (1−p)) / b
where p = win probability, b = win/loss ratio

Win probability (p)

Win/loss ratio (b)

Portfolio capital ($)

Max position cap %

Fixed-risk method (1R)

Risk $ = Capital × Risk%. Shares = Risk$ ÷ (Entry − Stop)

Portfolio capital ($)

Max risk per trade %

Entry price ($)

Stop-loss price ($)

Target price ($)

Conviction (1–10)

Conviction tier framework

High (8–10): ½ Kelly, cap 8%. Medium (5–7): ¼ Kelly, cap 5%. Low (1–4): Fixed 2%.

Conviction (1–10)

Capital ($)

Win probability

Win/loss ratio

CA

Cross-Asset Correlation & Macro Regime

Correlation matrix · Regime classifier · Regime-driven allocation framework

PM Skill⬡ GitHub

Understanding what actually diversifies your portfolio — and what is merely uncorrelated on average but highly correlated in drawdowns — is the core risk skill of a portfolio manager. During risk-off events, correlations spike toward 1.0 across equities and collapse for UST and gold.

■ Strong positive correlation (risk concentration, not diversification) · ■ Negative correlation (genuine diversification benefit) · Data: rolling 3Y historical estimates

Enter current macro indicators to classify the regime and see the historically optimal allocation framework for that regime. This is the systematic macro lens that underlies every allocation decision.

2s10s spread (bp)

US 10Y yield %

VIX

ISM Manufacturing PMI

Core CPI YoY %

Unemployment rate %

IS

Investment Idea Screener

Thesis quality · Non-consensus signal · Catalyst timing · Publishability score

Analyst Skill⬡ GitHub

Before spending 10 hours building a model, stress-test the idea against the questions every PM will ask. The best analysts filter ruthlessly before committing time. This tool forces the six questions that distinguish a publishable idea from wishful thinking.

Ticker

Direction

What is the market missing? (the mispricing in 1–2 sentences)

Primary catalyst and timing

What makes you wrong? (invalidation condition)

Conviction level (1–10)

Is the thesis genuinely non-consensus?

Is the primary catalyst within 6 months?

Is the stock sufficiently liquid to exit quickly?

Upside / downside ratio (estimated)

Does the fundamental model confirm the thesis?

Has this idea been pitched to a critical audience?

SP

Sum-of-the-Parts Builder

Segment valuation · Revenue / EBITDA / EBIT multiples · Implied price · Contribution waterfall

Core⬡ GitHub

Value each business segment independently, then sum to an enterprise value. Useful for conglomerates, holding companies, or any business with distinct revenue streams that deserve different multiples. Add up to 8 segments.

Company / Ticker

Net Debt ($M)

Shares Outstanding (M)

Segment	Metric ($M)	Basis	Multiple (×)	Implied EV ($M)

Aeon Nimbus Research · Quantitative Finance

Quantitative Finance Models

View on GitHub

Original browser-native implementations of quantitative finance models — Black-Scholes, Monte Carlo simulation, stochastic volatility, and fixed-income analytics. All computations run client-side in JavaScript with no external dependencies.

Aeon Nimbus Research · Original implementations · MIT-compatible · All computations in-browser

Launch →

𝒩

Black-Scholes Pricer

European call & put pricing with full Greeks — Delta, Gamma, Vega, Theta, Rho — via closed-form formula.

OptionsGreeksBlack-Scholes

Launch →

Ω

Monte Carlo Options

Exotic option pricing via simulation — Vanilla, Binary, Barrier, Asian. Payoff distribution chart included.

Monte CarloExoticSimulation

Launch →

〜

GBM Path Simulator

Simulate Geometric Brownian Motion price paths. Custom drift μ, volatility σ, time horizon T and path count.

GBMStochasticCanvas

Launch →

⟨σ⟩

Heston Stochastic Vol

Stochastic variance model. Compare GBM vs Heston paths — see volatility clustering emerge in real time.

HestonVol ClusteringStochastic

Launch →

𝒜

Bachelier (ABM) Model

Arithmetic Brownian Motion option pricing for non-lognormal assets. Compares with Black-Scholes across the strike range.

BachelierABMFixed Income

Black-Scholes Option Pricer

Reference: Q-Fin · BlackScholesCall / BlackScholesPut ↗

C = S·N(d₁) − K·e^(−rT)·N(d₂) | d₁ = [ln(S/K) + (r + σ²/2)T] / (σ√T) | d₂ = d₁ − σ√T

Spot Price S

Strike K

Time T (years)

Risk-free Rate r (%)

Implied Volatility σ (%)

Call Price

—

per share

Put Price

—

per share

Intrinsic (C)

—

max(S−K, 0)

Put-Call Check

—

C−P = S−PV(K)

Option Greeks

Monte Carlo Exotic Options

Reference: Q-Fin · MonteCarloCall / Barrier / Asian / Binary ↗

Spot S

Strike K

T (years)

Rate r (%)

Vol σ (%)

Paths N

Option Type

Barrier Level

MC Price

—

per share

95% CI ±

—

conf. interval

BS Reference

—

vanilla benchmark

Paths

—

simulated

Monte Carlo prices converge as N→∞. Use 10,000+ paths for stable estimates. Binary and barrier options are path-dependent — each step must be simulated individually.

GBM Path Simulator

Reference: Q-Fin · GeometricBrownianMotion ↗

dS = μ·S·dt + σ·S·dW ⟹ S(t) = S₀ · exp[(μ − σ²/2)t + σ√t · Z] where Z ~ N(0,1)

Initial Price S₀

Drift μ (annual %)

Volatility σ (annual %)

Time Horizon T (years)

Number of Paths

Expected E[S_T]

—

Simulated Mean

—

Simulated Std

—

P(S_T > S₀)

—

Heston Stochastic Volatility

Reference: Q-Fin · StochasticVarianceModel ↗

dS = μS dt + √v·S dW₁ | dv = κ(θ−v)dt + ξ√v dW₂ | corr(dW₁, dW₂) = ρ | Feller: 2κθ > ξ²

Price S₀

Drift μ (%)

Init. Variance v₀ (%²)

Long-run Var θ (%²)

Mean Reversion κ

Vol of Vol ξ

Correlation ρ

Time T (years)

Heston paths GBM (const. vol)

Heston model produces realistic volatility clustering — paths spread and contract in bursts, unlike constant-vol GBM. Negative ρ creates the leverage effect: falling prices → rising volatility.

Bachelier (ABM) Option Model

Reference: Q-Fin · ArithmeticBrownianMotion ↗

dS = μ dt + σ_B dW (additive) | C_Bach = (F−K)·N(d) + σ_B√T·n(d) | d = (F−K)/(σ_B√T)

Forward / Spot F

Strike K

Time T (years)

Bachelier Vol σ_B (absolute)

BS Vol σ_BS (%) for comparison

Bachelier Call

—

Bachelier Put

—

BS Call (ref)

—

Delta (Bachelier)

—

Bachelier (ABM) allows negative prices — suitable for negative interest rates or spread options. For ATM: Bachelier vol ≈ BS vol × S₀. Used in SABR model calibration for rates markets.

ER

Earnings Radar

Upcoming earnings · Beat probability · Implied move · Consensus EPS

▾

CT

Congress Tracker

Political disclosures · Party breakdown · Sector clustering · Overlap alerts

▾

25Total Trades

20dAvg Delay

Nancy PelosiMost Active

Politician	Ticker	Direction	Amount	Trade Date	Delay	Sector

⚑ Cluster Alerts — 3+ politicians, same direction, 30-day window

SH

Sector Heatmap

11 GICS sectors · Relative performance · Macro regime overlay · Factor tilts

▾

Macro Regime Context

Current regime: Mid-Cycle Expansion — historically outperforms: Technology, Financials, Industrials. Underperforms: Utilities, Consumer Staples.

Factor Exposure Matrix

Factor	Tech	Comm	Disc	Fin	Ind	HC	Energy	Matl	Stpl	RE	Util
Growth	High	High	Med	Med	Med	Med	Low	Low	Low	Low	Low
Value	Low	Low	Med	High	Med	Med	High	Med	High	Med	High
Momentum	High	High	Med	High	Med	Low	Low	Low	Low	Med	Low
Quality	High	Med	Med	Med	High	High	Low	Med	Med	Low	Med
Low-Vol	Low	Low	Low	Med	Med	High	Med	Med	High	Med	High

AA

Analyst Alpha Signal

Consensus direction · Broker target vs price-implied · AN contrarian read · Conviction score

▾

Enter a ticker to see Wall Street consensus vs. the Aeon Nimbus contrarian signal. Powered by AI analysis of sell-side positioning.

AA

Analysing consensus…

TW

Trade Exposure

Revenue geography · Supply chain risk · Tariff sensitivity · War vs Peace positioning

▾

Classify any stock as a Trade War beneficiary, Trade Peace beneficiary, or Domestically Insulated based on revenue geography and supply chain dependencies.

TW

Classifying trade exposure…

All tools are for educational and informational purposes only. Not financial advice. Results are model-based estimates only.

Proprietary Systematic Research

Quantitative Strategies

Sixteen proprietary systematic strategies across equities, FX, commodities and equity indices — built to trade, not open-sourced. Backtested on 99–100% real tick data across 12+ years of full market cycles. Full methodology available to qualified allocators on request.

15Strategies

3Suites

15,081Backtested trades · FX & Tenkai

2006–2026Longest history · Equity

Risk vs. risk-adjusted return · all 16 strategies

Annualised Sharpe (CAGR ÷ volatility, the same calculation for every suite) against maximum drawdown. Up and to the left is better. Hover a point for details; click to open its suite.

Backtests only. Suites differ in period (Equity 2006–2026, FX 2007–2026, Tenkai 2020–2026), data source and sizing, so compare within a suite before comparing across suites. Low drawdown with low CAGR (Tenkai) and high CAGR with deep drawdowns (Typhon, Erebus) are different risk profiles, not better or worse ones. The MT5 reports print a per-trade Sharpe (1.88–3.86) that is not comparable with annualised figures; it is shown in each tooltip and card.

How the strategies move together · monthly return correlation

Correlation of monthly returns for the eight strategies with published trade data, each pair over the months both were live. Near 0 means the two add diversification; toward +1 means they tend to win and lose together.

	USD/JPY	XAU/USD	EUR/JPY	GBP/USD	EUR/USD	NAS 100	SP 500	XAU (Tenkai)
USD/JPY	1
XAU/USD	+0.08	1
EUR/JPY	+0.29	-0.06	1
GBP/USD	-0.03	+0.08	+0.01	1
EUR/USD	+0.08	+0.04	+0.15	-0.05	1
NAS 100	-0.14	-0.01	-0.12	+0.02	-0.03	1
SP 500	+0.13	-0.03	+0.15	+0.12	-0.02	-0.19	1
XAU (Tenkai)	-0.01	+0.01	+0.00	-0.04	-0.06	+0.09	-0.15	1

−1+1Average pair +0.01 · highest EUR/JPY vs USD/JPY +0.29 · FX pairs average +0.06

The Equity suite isn't included because its monthly return series isn't published on this site. FX pairs overlap 2013–2026 (GBP/USD from 2015); Tenkai pairs overlap 2020–2026.

Systematic Equity Strategies · 7 Models · Backtested 2006–2026

Strategy	Type	CAGR	Sharpe	Max DD	Vol	Risk
KAIROS	Market neutral L/S	13.10%	1.18	15.02%	11.09%	Low	Reports →
KRONOS	Dual-momentum rotation	20.54%	1.00	25.43%	20.57%	Moderate	Reports →
AEGIS	Market timing	14.01%	1.08	15.61%	12.81%	Moderate	Reports →
BOREAS	Trend following	18.17%	1.04	23.55%	17.55%	Moderate	Reports →
PROTEUS	Double layer	16.83%	1.07	18.23%	15.74%	Moderate	Reports →
TYPHON	Market timing + risk	44.79%	1.12	42.10%	39.32%	High	Reports →
EREBUS	Double layer + risk	54.38%	1.14	48.30%	48.40%	High	Reports →

* Annualised figures. Historical backtested performance 2006–2026. Past performance does not guarantee future returns. These are quantitative research models, not investment advice or financial recommendations.

FX & Commodities · Proprietary Systematic · 99–100% Real Tick Data · 2007–2026 · 18,494 Combined Trades

Market	Type	CAGR	Sharpe	Max DD	Vol	Sharpe (MT5)	LR Corr	Win Rate	Profit Factor	Recovery	Trades	Tick Quality
USD / JPY	Trend Following	17.72%	1.40	10.94%	12.68%	2.22	0.99	39.6%	1.32	20.09×	3,334	99%
XAU / USD	Mean Reversion	7.51%	1.18	7.11%	6.36%	1.90	0.98	51.2%	1.25	12.76×	3,071	99%
EUR / JPY	Trend Following	11.28%	1.09	17.32%	10.33%	2.00	0.95	45.1%	1.17	5.53×	3,087	99%
GBP / USD	Mean Reversion	9.06%	1.07	9.54%	8.48%	2.11	0.96	48.6%	1.16	6.11×	2,835	100%
EUR / USD	Trend Following	8.21%	0.91	14.94%	8.98%	1.95	0.95	44.7%	1.19	5.46×	2,103	100%

CAGR, Sharpe, Max DD and Vol are on the same basis as the Equity table: Sharpe = CAGR ÷ annualised volatility of monthly returns. Sharpe (MT5) is the figure printed in the Strategy Tester report; it is computed per trade and is not comparable with annualised Sharpe ratios.

Annual returns · FX & Commodities

Calendar-year return of each strategy, compounded from its monthly returns. Blue is a gain, red a loss; the right-hand column counts profitable years.

Market	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026*	Up yrs
USD / JPY	+128.9	+49.5	+5.3	+7.4	+11.7	+15.8	+8.0	+14.1	+13.8	+13.7	+4.6	+3.7	+7.8	+12.3	14/14
XAU / USD	+13.2	+6.9	+14.6	+19.7	+0.6	+5.3	-3.7	+11.7	+14.9	+3.5	-0.8	+10.6	+1.4	+5.6	12/14
EUR / JPY	+70.8	+23.2	+23.6	+9.4	+8.0	+7.1	-10.0	+8.7	+6.2	+12.4	+4.0	-0.9	+9.6	-3.3	11/14
GBP / USD	–	–	+37.9	+14.8	+21.2	+0.3	+16.2	+4.8	-2.7	+13.1	+6.0	+2.5	-4.1	+0.2	10/12
EUR / USD	+8.9	+9.4	+32.7	+13.4	+16.2	+8.7	-10.5	+5.0	+6.6	+2.4	+21.2	+0.5	+2.1	-0.3	12/14

* Partial year (2026 through Aug; 2026 through May). Early years compound on a small starting balance, so their percentages run higher than later years under the same fixed-% risk.

Out-of-sample check · 70 / 30 chronological split

Each backtest split by trade order: the first 70% of trades against the last 30%. Profit factor and win rate don't depend on account size, so the two segments compare directly. A strategy fitted to noise usually falls apart in the later segment.

Market	Trades (first / last)	Profit Factor	Win Rate	Max DD	Last 30%
USD / JPY	2,334 / 1,000	1.35 → 1.25	39.5% → 39.6%	10.9% → 3.3%	✓ Profitable
XAU / USD	2,150 / 921	1.28 → 1.19	51.4% → 50.9%	9.0% → 5.1%	✓ Profitable
EUR / JPY	2,161 / 926	1.20 → 1.11	45.0% → 45.1%	17.0% → 6.0%	✓ Profitable
GBP / USD	1,984 / 851	1.21 → 1.04	48.7% → 47.9%	9.1% → 9.3%	✓ Profitable
EUR / USD	1,472 / 631	1.19 → 1.20	44.0% → 46.3%	22.5% → 5.4%	✓ Profitable

This is a hold-out stability check on the published run, not a walk-forward optimisation: it shows whether the edge persisted into the most recent period, not how the parameters were chosen. 50/50 and 80/20 splits are in each strategy's dashboard.

Trading costs in these results

Spread: paid on every trade. Fills use the real historical bid/ask ticks, so the spread is the one quoted at the time of each trade.
Commission: €0 in every report (a spread-only account model). On a raw-spread account that charges commission per lot, results would be lower.
Swap / financing: included as charged. Net over the full test: USD/JPY €+14, XAU/USD €+0, EUR/JPY €-12, GBP/USD €+0, EUR/USD €-3.
Slippage: no extra slippage is added beyond the tick-by-tick fills.

Proprietary Systematic Research · Independently Developed

Systematic FX & Commodities Strategies

Six proprietary rule-based strategies, backtested across a 19-year market cycle (2007–2026) on 99–100% real tick data — the highest fidelity available in institutional-grade simulation. Spanning COVID volatility, the 2022–2023 JPY intervention cycle, and multiple rate regimes. Results presented using proportional position sizing (fixed % risk per trade) — the standard institutional methodology, allowing meaningful comparison across AUM levels. Combined sample: 18,494 trades. No discretionary overlay. Methodology proprietary. Full backtest data, interactive dashboards, and downloadable Strategy Tester reports are published below.

18,494

Combined Trades

> 1.9

All Sharpe (MT5)

≥0.95

All LR Correlations

99–100%

Real Tick Quality

How to read these metrics

LR correlation — correlation between the equity curve and its straight-line (linear regression) fit; 1.0 means perfectly steady growth.
Recovery factor — net profit ÷ maximum drawdown; how many times over the strategy earned back its worst drawdown.
Profit factor — gross profit ÷ gross loss.
Max DD (relative) — largest peak-to-trough fall as a percentage of the equity peak.
Tick quality — share of the backtest driven by real historical ticks rather than modelled prices.
Tables use Sharpe = CAGR ÷ annualised volatility of monthly returns, the same basis as the Equity suite; the cards show the per-trade Sharpe printed in each MT5 report. All figures are backtests under the fixed-% risk sizing described below, not live trading results.

Trend Following

USD / JPY

Foreign Exchange · Systematic

2.22

Sharpe (MT5)

0.99

LR Correlation

Max DD (Relative)

10.94%

Recovery Factor

20.09×

Win Rate

39.6%

GHPR / Trade

+0.07%

Profit Factor

1.32

Total Trades

3,334

Equity Curve · 2013–2026 (actual)

Trend-following with 1% proportional risk sizing — scalable and AUM-agnostic. Highest CAGR, Sharpe, LR correlation and Recovery Factor of the FX suite. The low win rate is structural in momentum strategies: edge comes from asymmetric payoffs, not frequency. Stress-tested through COVID volatility (2020) and the 2022–2024 JPY intervention cycle.

Dashboard → Full Report

Proprietary · Compounded

Trend Following

EUR / JPY

Foreign Exchange · Systematic

2.00

Sharpe (MT5)

0.95

LR Correlation

Max DD (Relative)

17.32%

Recovery Factor

5.53×

Win Rate

45.1%

GHPR / Trade

+0.05%

Profit Factor

1.17

Total Trades

3,087

Equity Curve · 2013–2026 (actual)

Shares the trend-following engine with USD/JPY, applied to a structurally higher-carry cross. Deepest relative drawdown of the FX suite (17.3%), and much of its return came in 2013–2015 — see the annual returns above. Sample spans 2013–2026 including the JPY intervention regime.

Dashboard → Full Report

Proprietary · Compounded

Mean Reversion

GBP / USD

Foreign Exchange · Systematic

2.11

Sharpe (MT5)

0.96

LR Correlation

Max DD (Relative)

9.54%

Recovery Factor

6.11×

Win Rate

48.6%

GHPR / Trade

+0.03%

Profit Factor

1.16

Total Trades

2,835

Equity Curve · 2015–2026 (actual)

Mean-reversion on a high-volatility major, with a near coin-flip win rate typical of the style. Shortest history of the five (from 2015) and the thinnest profit factor (1.16), but a contained 9.5% drawdown. Tested through Brexit-era and 2022 gilt-crisis volatility.

Dashboard → Full Report

Proprietary · 97% Real Ticks

Trend Following

EUR / USD

Foreign Exchange · Systematic

1.95

Sharpe (MT5)

0.95

LR Correlation

Max DD (Relative)

14.94%

Recovery Factor

5.46×

Win Rate

44.7%

GHPR / Trade

+0.05%

Profit Factor

1.19

Total Trades

2,103

Equity Curve · 2013–2026 (actual)

Trend-following engine on the world's most liquid pair, on 100% real tick data. The smallest sample of the five and the lowest annualised Sharpe; its 2019 loss was the worst year in the suite. Full cycle including two major ECB/Fed divergence regimes.

Dashboard → Full Report

Proprietary · 100% Real Ticks

Mean Reversion

XAU / USD

Commodities · Systematic

1.90

Sharpe (MT5)

0.98

LR Correlation

Max DD (Relative)

7.11%

Recovery Factor

12.76×

Win Rate

51.2%

GHPR / Trade

+0.03%

Profit Factor

1.25

Total Trades

3,071

Equity Curve · 2013–2026 (actual)

Lowest drawdown of the FX suite (7.1%) with near-linear compounding (LR correlation 0.98). Symmetric win-rate profile typical of a mean-reversion edge. Tested across gold's secular bull and two Fed tightening cycles.

Dashboard → Full Report

Proprietary · 99% Real Ticks

Grid System

AUD/NZD + AUD/CAD

Forex Basket · Systematic

1.15

Sharpe (MT5)

0.93

LR Correlation

Max DD (Relative)

25.83%

Recovery Factor

3.02×

Win Rate

81.3%

GHPR / Trade

+0.12%

Profit Factor

3.09

Total Trades

4,064

Equity Curve · 2007–2026 (actual)

Grid-style engine trading a combined AUD/NZD + AUD/CAD basket. Highest win rate of the FX suite (81.3%) and the longest track record (2007–2026, 19+ years, 99% real ticks). Deepest relative drawdown of the suite (25.8%), consistent with grid-style position layering — full deal-level report published below.

Dashboard → Full Report

Proprietary · 99% Real Ticks

Backtested on 99–100% real tick data (MetaTrader 5), the highest fidelity available in institutional-grade simulation. Results use proportional position sizing (fixed % risk per trade), directly comparable to standard fund performance metrics. Backtested results do not guarantee future performance. Strategies are not offered for public investment. Full methodology, interactive dashboards, and downloadable MetaTrader 5 Strategy Tester reports are published above and freely accessible. Past performance is not indicative of future results.

Tenkai · Indices & Gold · 100% Real Tick Data (Dukascopy) · 2020–2026 · 651 Combined Trades

Market	Type	CAGR	Sharpe	Max DD	Vol	Sharpe (MT5)	LR Corr	Win Rate	Profit Factor	Recovery	Trades
NAS 100	Trend (Tenkai)	6.90%	1.11	8.44%	6.20%	3.86	0.95	56.0%	1.55	5.27×	223
SP 500	Trend (Tenkai)	3.73%	0.73	8.98%	5.08%	1.88	0.97	55.9%	1.24	2.51×	265
XAU / USD (Tenkai)	Mean Reversion	5.95%	1.24	4.71%	4.81%	3.62	0.98	60.1%	1.62	6.79×	163

CAGR, Sharpe, Max DD and Vol are on the same basis as the Equity table: Sharpe = CAGR ÷ annualised volatility of monthly returns. Sharpe (MT5) is the figure printed in the Strategy Tester report; it is computed per trade and is not comparable with annualised Sharpe ratios.

Annual returns · Tenkai

Calendar-year return of each strategy, compounded from its monthly returns. Blue is a gain, red a loss; the right-hand column counts profitable years.

Market	2020	2021	2022	2023	2024	2025	2026*	Up yrs
NAS 100	+4.3	+2.8	+12.5	-1.6	+11.5	+3.3	+14.2	6/7
SP 500	-3.0	+2.1	+14.7	+4.3	-0.2	+6.3	+1.6	5/7
XAU / USD (Tenkai)	+7.8	+9.2	+15.2	+5.7	-0.3	+2.6	+0.3	6/7

* Partial year (2026 through Aug; 2026 through Mar). Early years compound on a small starting balance, so their percentages run higher than later years under the same fixed-% risk.

Out-of-sample check · 70 / 30 chronological split

Each backtest split by trade order: the first 70% of trades against the last 30%. Profit factor and win rate don't depend on account size, so the two segments compare directly. A strategy fitted to noise usually falls apart in the later segment.

Market	Trades (first / last)	Profit Factor	Win Rate	Max DD	Last 30%
NAS 100	156 / 67	1.33 → 2.16	52.6% → 64.2%	7.6% → 4.2%	✓ Profitable
SP 500	186 / 79	1.19 → 1.35	54.8% → 58.2%	8.9% → 4.1%	✓ Profitable
XAU / USD (Tenkai)	114 / 49	1.78 → 1.34	62.3% → 55.1%	3.0% → 4.0%	✓ Profitable

This is a hold-out stability check on the published run, not a walk-forward optimisation: it shows whether the edge persisted into the most recent period, not how the parameters were chosen. 50/50 and 80/20 splits are in each strategy's dashboard.

Trading costs in these results

Spread: paid on every trade. Fills use the real historical bid/ask ticks, so the spread is the one quoted at the time of each trade.
Commission: €0 in every report (a spread-only account model). On a raw-spread account that charges commission per lot, results would be lower.
Swap / financing: included as charged. Net over the full test: NAS 100 €-607, SP 500 €+169, XAU (Tenkai) €-1,098.
Slippage: no extra slippage is added beyond the tick-by-tick fills.

Proprietary Systematic Research · Independently Developed

Tenkai Strategy Suite — Indices & Gold

Three proprietary rule-based strategies under the Tenkai engine, backtested 2020–2026 on 99–100% real tick data (Dukascopy) across two major equity indices and a second, distinct gold model. Results presented using proportional position sizing (fixed % risk per trade). Combined sample: 651 trades. No discretionary overlay. Methodology proprietary. Full backtest data, interactive dashboards, and downloadable Strategy Tester reports are published below.

651

Combined Trades

> 1.8

All Sharpe (MT5)

≥0.95

All LR Correlations

99–100%

Real Tick Quality

Tenkai · Trend

NAS 100

Equity Index · Systematic

3.86

Sharpe (MT5)

0.95

LR Correlation

Max DD (Relative)

8.44%

Recovery Factor

5.27×

Win Rate

56.0%

CAGR

6.90%

Profit Factor

1.55

Total Trades

223

Equity Curve · 2020–2026 (actual)

Highest MT5 Sharpe of the Tenkai suite, with a shorter and smaller sample than the FX suite — reflecting the strategy's more selective trade frequency on a single high-beta index. Recovery Factor above 5× indicates drawdowns are recouped quickly relative to strategy tenor.

Dashboard → Full Report

Proprietary · 99% Real Ticks

Tenkai · Trend

SP 500

Equity Index · Systematic

1.88

Sharpe (MT5)

0.97

LR Correlation

Max DD (Relative)

8.98%

Recovery Factor

2.51×

Win Rate

55.9%

CAGR

3.73%

Profit Factor

1.24

Total Trades

265

Equity Curve · 2020–2026 (actual)

Lowest Recovery Factor of the three, consistent with a lower-beta index and a more gradual compounding profile. MT5 Sharpe 1.88 and LR correlation 0.97 across the full 2020–2026 sample.

Dashboard → Full Report

Proprietary · 99% Real Ticks

Tenkai · Mean Reversion

XAU / USD (Tenkai)

Commodities · Systematic

3.62

Sharpe (MT5)

0.98

LR Correlation

Max DD (Relative)

4.71%

Recovery Factor

6.79×

Win Rate

60.1%

CAGR

5.95%

Profit Factor

1.62

Total Trades

163

Equity Curve · 2020–2026 (actual)

Lowest drawdown and highest win rate across both the FX and Tenkai suites, at the cost of a shorter track record and smaller trade sample than the flagship gold strategy above. Distinct signal logic from the primary XAU/USD mean-reversion model — not a variant of it.

Dashboard → Full Report

Proprietary · 99% Real Ticks

Backtested on 99–100% real tick data (Dukascopy), the highest fidelity available in institutional-grade simulation. Results use proportional position sizing (fixed % risk per trade), directly comparable to standard fund performance metrics. Sample period (2020–2026) and trade counts are smaller than the FX & Commodities suite above; treat comparisons across suites accordingly. Backtested results do not guarantee future performance. Strategies are not offered for public investment. Full methodology, interactive dashboards, and downloadable MetaTrader 5 Strategy Tester reports are published above and freely accessible. Past performance is not indicative of future results.

Build Your Ideal Portfolio

Simulate allocations across all 13 proprietary strategies — 7 systematic equity models and 6 FX/commodity systems. Assign weights and compute the blended risk-adjusted profile. Wtd. Sharpe and Max DD span all strategies; Eq. CAGR and Eq. Vol are equity-weighted metrics (scaled to your equity allocation).

Total Allocation 0%

Educational tool only. Simulated results based on historical backtested data. Not investment advice.

Portfolio Metrics

—

CAGR

—

Total Return

—

Sharpe

—

Sortino

—

Max DD

—

Calmar

—

Volatility

—

Win Rate

—

Profit Factor

—

Best Week

—

Worst Week

Metrics computed from the blended simulated equity curve. Real portfolio performance will differ due to inter-strategy correlation and sequence risk.

Weekly Quantitative Reports

In-depth quantitative research for each strategy — weekly reports covering the mathematical framework, model developments, portfolio movements, and quantitative reasoning behind each strategy's evolution.

AI Agent Compatible · Structured for Programmatic Consumption

Reports are structured as machine-readable research briefs. Feed them directly into your AI agent pipeline, LLM workflow, or systematic monitoring system. Each report delivers consistent JSON-compatible structured data: signals, model state, portfolio moves, and quantitative rationale — designed for both human reading and agent ingestion.

60 SUBS

KRONOS

Dual-Momentum Rotation

Ray Dalio quantitative market regime detector & Trend Following — weekly quantitative research report.

ETFs & Funds · Nasdaq · S&P 500 · Commodities · Bonds

52 SUBS

AEGIS

Market Timing · Macro Protection

Macro strategy and protection — weekly quantitative research report.

ETFs & Funds · Sector ETFs · Index Funds · Fixed Income

34 SUBS

KAIROS

Long-Short Momentum

Market neutral long-short momentum — weekly quantitative research report.

US Equities · Large & Mid Cap Stocks

BOREAS

Multi-Factor Equity

Multi-factor equity selection — weekly quantitative research report.

US Equities · Multi-Factor Stock Selection

TYPHON

Market Timing + High Return

Macro strategy and high returns — weekly quantitative research report.

ETFs & Funds · Cross-Asset Macro · Equities · Rates · FX

EREBUS

Macro + Volatility Trading

Macro strategy and volatility trading — weekly quantitative research report.

ETFs & Funds · Volatility Products · Index Options

PROTEUS

Double Layer Macro + Vol

Macro strategy and volatility trading — weekly quantitative research report.

ETFs & Funds · Options on Major Indices & ETFs

⚠ These reports are for informational and educational purposes only. They describe the quantitative methodology, mathematical models, and portfolio evolution of each strategy. They do NOT constitute trading signals, investment advice, or personalized financial recommendations. Past performance does not guarantee future results. Always conduct your own research and consult with a licensed financial advisor before making investment decisions.

Past performance does not guarantee future results. These are quantitative research models only. Not investment advice.

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Fund Lab

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Research Lab is the research and product layer of Aeon Nimbus — housing a live paper portfolio, systematic equity strategies, and the portfolio construction toolkit. Each vehicle is built on the same quantitative framework: regime-aware, disciplined, and fully transparent.

Coming Soon

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Model Portfolio · Multi-Asset

Asset Allocation
Dynamic Fund

A macro-driven paper portfolio rebalancing dynamically across seven asset classes. Equity, fixed income, gold, EM and commodities with regime-aware sizing.

+28.4%Since Inception · Simulated

1.31Sharpe Ratio

−8.2%Max Drawdown

View Fund →

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Systematic
Strategies

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15Active Strategies

3Asset Classes

Explore Strategies →

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AN2024-MAA · USD · Inception Jan 2024

NAV (USD) 128.40

▲ +0.38 / +0.30% Today's Change

+14.8% 1 Year Return

+28.4% Since Inception

Simulated performance · As of 23 May 2026 · No real capital deployed

Profile & Investment

Fund typePaper Portfolio (Simulated)

StrategyMacro-Driven Multi-Asset

Income treatmentAccumulation

CategoryDynamic Asset Allocation

Universe7 Asset Classes · Global

Inception date1 Jan 2024

CurrencyUSD (Simulated)

Benchmark60/40 Global

RebalancingMonthly

Regime detectionMacro signal-based

Pricing frequencyDaily

ManagerLiJie Guo · Jan 2024

Key Metrics

+28.4%

Since Inception

1.31

Sharpe Ratio

−8.2%

Max Drawdown

9.4%

Annual Vol

Year	Fund	Benchmark	Alpha
2024	+19.8%	+13.8%	+6.0%
2025	+7.2%	+8.3%	−1.1%
2026 (YTD)	+1.2%	+1.6%	−0.4%

Asset Class	Weight	Instruments	Rationale
US Equities	28%	SPY, QQQ	Core growth engine — overweighted in expansion regimes
Intl Developed	18%	EFA, VEA	Valuation and macro diversification
Corp Bonds	15%	LQD, HYG	Carry and risk-off regime hedge
Emerging Markets	12%	MELI, EEM	High-conviction names + EM beta
Gold	12%	GLD, GC	Real asset and USD debasement hedge
Cash / T-Bills	8%	BIL, SHY	Tactical dry powder — raised in late-cycle
Commodities	7%	DBC, WTI	Inflation tail and energy exposure

⚠ Paper portfolio — no real capital deployed. Performance is simulated and excludes transaction costs, slippage and taxes. Not investment advice.

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Current Availability
Open to project enquiries and consultation bookings. Background spans African listed equity research (Hamilcar Capital), long/short equity (Madrid), quant research (Crandon AM), macro strategy (Santomera Bay Capital), Big Four M&A advisory (PwC), and venture (Antler · SpinLab). Based in London — remote-first for consulting engagements.

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Market Breadth

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AN Market View — Sep 15 2026

Mid-Cycle Expansion

Technology and Financials leading in a rate-stable environment. The Fed's pause is giving risk assets room to re-rate. Watching credit spreads and the yield curve for early-cycle deterioration. Positioning: overweight growth, neutral duration, underweight defensives.

LONG: Tech · Financials AVOID: Energy · Utilities WATCH: EM on USD weakness

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Individual stocks sized by market cap · colored by performance

LIVE

Tile size = market cap · color = % return

Illustrative sample data, not a live feed. Market caps and % moves are static examples.

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+5%+

Factor Tilts by Sector

Sector	Growth	Value	Momentum	Quality	Low-Vol
Technology	High	Low	High	Med	Low
Communication	High	Low	Med	Med	Low
Financials	Med	High	Med	Med	Med
Health Care	Med	Med	Low	High	High
Industrials	Med	Med	Med	Med	Med
Energy	Low	High	Low	Low	Med
Consumer Disc.	High	Low	Med	Med	Low
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Real Estate	Med	Med	Low	Med	High
Materials	Med	Med	Med	Low	Med
Utilities	Low	Med	Low	High	High

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Updated Daily

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Ticker	Type	Strike	Expiry	Premium	Vol/OI	Bias	Time

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