Aeon Nimbus
Aeon Nimbus Research
Aeon Nimbus Research · London
LiJie
Guo.
Global markets. Unhedged views.

The world is always mispriced somewhere. Capital follows narratives. Narratives follow power. Most investors read the price. This is about reading what moves it.
About
Aeon Nimbus is an independent macro and equity research platform — publishing market views, thematic deep dives, geopolitical reads, and central bank analysis — alongside formal investment calls, each with entry, stop-loss, position size, and full thesis recorded before the outcome is known. LiJie has also developed proprietary algorithmic strategies for USDJPY and XAUUSD, combining quantitative signals with macro conviction. Every position, including the losses, is on the record.

His experience spans the capital structure — from long/short equity and macro at a leading independent fund platform in Spain, to Private Credit and Real Estate at Crandon, Private Equity portfolio management and Strategy & Operations at Santomera Bay's private office in Barcelona, and M&A financial due diligence at PwC. He gained early exposure to Europe's venture ecosystem through SpinLab — the HHL Accelerator — and Antler, the world's most active early-stage VC, in Berlin.

He holds a Triple MSc across emlyon Business School, Politecnico di Milano GSOM, and Bayes Business School, City University of London, and is a CFA Level I candidate.

Spanish-born and ethnically Chinese, he was shaped by two cultures — and drawn, from his first market investment at 18, to the intersection of global macro, technology, emerging markets, and asymmetric risk.

Not investment advice.
Education
emlyon Business School
MSc Management PGE
Finance Track
Politecnico di Milano
MSc Quantitative Finance
& FinTech · GSOM
Bayes Business School
MSc Finance
City, University of London
$VNET$10.51−2.3%· SPX5,614+0.29%· US10Y4.69%−2bp· GOLD$3,024+0.6%· VIX21.7−1.8%· WTI$67.82−0.4%· DXY103.4+0.2%· $MELI$2,161+1.2%· COPPER$4.42+1.1%· 2s10s−18bpINVERTED· $VNET$10.51−2.3%· SPX5,614+0.29%· US10Y4.69%−2bp· GOLD$3,024+0.6%· VIX21.7−1.8%· WTI$67.82−0.4%· DXY103.4+0.2%· $MELI$2,161+1.2%· COPPER$4.42+1.1%· 2s10s−18bpINVERTED
01
Research Notes

Equity deep dives, macro briefs, geo/market wraps, central bank analysis.

Read latest →
02
Track Record

Every call audited publicly. Entry, target, stop, position size, and outcome — wins and losses, fully timestamped.

View record →
03
Valuation Engine

Full DCF with WACC/CAPM, 3-way sensitivity, peer comps, football field. Pre-loaded with live examples.

Open tool →
04
PM Toolkit

Portfolio construction, Kelly position sizing, cross-asset correlation matrix, macro regime classifier, investment screener.

Open tools →
Free Aeon Nimbus Research — Every note published with full thesis, before the outcome. Subscribe on Substack
Approach
I publish independent research — macro views, equity deep dives, geopolitical analysis — and every formal investment call before the outcome is known, with entry, stop-loss, position size, and full thesis. Every result is recorded publicly. The research is the thinking. The track record is the proof.
Aeon Nimbus Research is an independent research platform. All content is for informational and educational purposes only and does not constitute investment advice, a solicitation, or an offer to buy or sell any security. Past performance is not indicative of future results. LiJie Guo · London · LinkedIn
Published research

Research Notes

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

Free Research · 3x/week
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Equity deep dives, macro briefs, geo/market wraps, and central bank analysis — sent directly to your inbox via Substack. Formal investment calls include entry, target, and full thesis, published before the outcome is known.
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Audited performance

Track Record

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

Why this matters: Any analyst can show you winners. The credibility is in publishing every call with position size, stop, and entry — before knowing the outcome — and recording losses with the same transparency as wins. This is what a fund will ask for. Every row here is timestamped on Substack the day it was published.

Total calls
0
No calls yet
Open
0
Active
Wins
0
Closed profitable
Losses
0
Stopped out
Avg return (closed)
0 closed positions
Hit rate
Closed only
DateTickerPost typeDirectionEntryTargetStopSizeHorizonStatusReturn
Proprietary Systematic Research · Independently Developed
Systematic FX & Commodities Strategies
Two proprietary rule-based strategies, backtested across a full 12-year market cycle (2013–2025) on 99% 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: 6,071 trades. No discretionary overlay. Methodology proprietary. Full backtest data available to qualified institutional allocators upon request.
6,071
Combined Trades
> 1.8
Both Sharpe Ratios
≥0.90
Both LR Correlations
99%
Real Tick Quality
Trend Following
USD / JPY
Foreign Exchange · Systematic
2.13
Sharpe Ratio
Exceptional
0.90
LR Correlation
Strong
Max DD (Relative)
15.83%
Recovery Factor
9.68×
Win Rate
39.6%
GHPR / Trade
+0.20%
Profit Factor
1.29
Total Trades
3,163
Equity Curve · 2013–2025
Trend-following with 1% proportional risk sizing — scalable and AUM-agnostic. The low win rate is structural in momentum strategies: edge comes from asymmetric payoffs, not frequency. Statistically robust sample. Stress-tested through COVID volatility (2020) and the 2022–2023 JPY intervention cycle.
Proprietary · Compounded
Mean Reversion
XAU / USD
Commodities · Systematic
1.82
Sharpe Ratio
Excellent
0.96
LR Correlation
Exceptional
Max DD (Relative)
15.54%
Recovery Factor
5.86×
Win Rate
51.2%
GHPR / Trade
+0.05%
Profit Factor
1.22
Total Trades
2,908
Equity Curve · 2013–2025
Smoothest equity curve of the two strategies — near-linear compounding confirmed by LR Correlation. Max drawdown less than half that of the USD/JPY strategy, despite identical position sizing. Symmetric win rate profile typical of a mean-reversion edge. Tested across gold’s secular bull (2018–2025) and two Fed tightening cycles.
Proprietary · 99% Real Ticks
Backtested on 99% 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, walk-forward analysis, and monthly P&L breakdown available to qualified institutional allocators upon request. Past performance is not indicative of future results.
Interactive analyst toolkit

Tools

Every tool a PM or equity research desk expects fluency in — live, free, open to all.

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
MetricValue
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
PeriodCashflowPV of CFWt (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)
📊
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)Y1Y2Y3Y4Y5
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%, terminal growth −1%
Base case (60%)
Your model assumptions
Bull case (20%)
WACC −1%, terminal growth +0.5%
Expected value (probability-weighted: Bear 20% / Base 60% / Bull 20%)

Each cell shows the implied share price under that WACC and terminal growth combination. Base case highlighted in gold. Green = material upside. Amber = near current. Red = downside.

Run valuation in Inputs tab to generate sensitivity tables.

CompanyEV/EBITDAEV/RevenueFCF YieldRev GrowthEBITDA MarginImplied 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.

🏗️
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.
⚖️
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
🔗
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 %
💡
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?
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 →
🎲
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
Aeon Nimbus · 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
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
Aeon Nimbus · 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
Aeon Nimbus · 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
Aeon Nimbus · 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.
All tools are for educational and informational purposes only. Not financial advice. Results are model-based estimates only.