Diversification Low Correlation Sharpe Ratio Portfolio for Non-US-Correlated Portfolio With AI

Building a Bullet-Proof, Non-US-Correlated Portfolio With AI

Table of Contents

1. Introduction: Why “Decouple From America” Is More Than a Slogan

2. Understanding Correlation Risk in 2025

3. Choosing a Truly Global Opportunity Set

4. Volatility as a Compass, Not a Scarecrow

5. Data Plumbing: Where the Numbers Really Come From

6. Back-Testing vs. Forward Simulation: The Philosophy

7. Designing the $100,000 Sandbox

8. Strategy Frameworks for Each Instrument

9. Programming the AI Engine

10. First Pass: The 14-Day Baseline Run

11. Diagnosing the Initial Results

12. The Aluminum Experiment

13. Full-Basket Stress-Test and Why It Failed

14. Pruning the Garden: Cutting Cocoa, Bitcoin & Aluminum

15. Going “All-In” on Brent Crude—Madness or Method?

16. Risk Management Beyond Simple Stops

17. Position Sizing With Kelly, CVaR, and Real-Time Greeks

18. Execution Microstructure: Slippage, Tick Size, and Fees

19. Psychology of Trading a One-Asset “Portfolio”

20. Automating the Daily Re-Simulation Loop

21. Building a Robust Monitoring Dashboard

22. From Research Notebook to Production Code

23. Auditing, Compliance, and Reg-Tech Considerations

24. Edge Decay: How Long Will This Trade Live?

25. Scenario Analysis: War Premiums, Supply Shocks & Black Swans

26. Scaling Up: From $100K to $10M Without Moving the Market

27. Common Pitfalls and How to Dodge Them

28. Key Takeaways for Traders, Quants, and Asset Managers

29. Final Thoughts: The Future of AI-Driven Portfolio Design

    
    

1. Introduction: Why “Decouple From America” Is More Than a Slogan

TRIPLE ALGO TRADER PRO PACKAGE: YOUR COMPLETE TRADING SYSTEM

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Why Diversification + Low Correlation = A Higher Sharpe Ratio

Diversification Low Correlation Sharpe Ratio

1. Diversification spreads risk. Holding positions that react to different economic forces smooths out the bumps in your equity curve.

2. Low correlation is the engine of diversification. Two assets can be volatile on their own, yet if they zig and zag at different times, the portfolio’s overall volatility falls.

3. The Sharpe ratio measures the payoff of that smoother ride. It takes the extra return you earn above the risk-free rate and divides it by the volatility you endured to get it.

   
   

Put simply, a portfolio of low-correlated assets can deliver the same—or even higher—returns than a single “hero” trade, but with far less stomach-churning variance. The result is a better Sharpe ratio, meaning more reward for each unit of risk taken.

In the pages that follow, we’ll use these principles to build a $100 K, non-US-correlated portfolio. We’ll test, prune, and re-optimize positions until the mix offers the highest risk-adjusted return our data can justify.

The United States remains the gravitational center of global finance, but gravity also creates black holes. When the S&P 500 sneezes, high-beta equities, EM carry trades, and even some commodities catch pneumonia. For investors worried about an impending U.S. recession, the obvious question is: “Can I build a portfolio that survives—or even thrives—if the American consumer shuts down?”

That was the animating question behind the YouTube video that sparked this article. In sixty short seconds viewers saw an AI agent rip through terabytes of historical futures prices and live option-chain data, then spit out a 14-day simulation that crowned Brent crude as the lone hero in a $100,000 sandbox.

Here we expand that bite-sized demo into a full-length investigation. We’ll dissect the math, the code, the economic intuition, the mistakes, and the insights that surfaced when we tried to do the unthinkable: make money while ignoring the world’s largest economy.

2. Understanding Correlation Risk in 2025

Correlation is a shape-shifter. In periods of calm, asset classes that look uncorrelated can snap together under stress and rout even the most “diversified” books. The pandemic shock of 2020, the energy spike of 2022, and the rapid-fire rate hikes of 2023 all taught the same lesson: correlations go to one when the VIX goes to thirty.

Three forces will likely dominate correlation structure through 2025–2026:

1. U.S. Fiscal Reckoning: Ballooning deficits plus presidential-cycle policy uncertainty.

2. De-Dollarization Drip: Central banks quietly diversifying reserves into non-USD assets.

3. Commodity Greenflation: The hard-asset spending spree needed for energy transition.

To sidestep the first force entirely, we restrict ourselves to instruments whose primary fundamental drivers reside outside the continental United States.

3. Choosing a Truly Global Opportunity Set

Our universe begins with seven tickers across five macro buckets:

• Currencies: CHF (Swiss franc), CAD (Canadian dollar), AUD (Australian dollar)• Energy: Brent Crude Oil• Softs: Cocoa• Base Metals: Aluminum• Digital Assets: Bitcoin (reference rate XBTUSD)

Why these?

• CHF offers a classic safe-haven bid with negative correlation to risk assets.

• CAD marries G7 credit quality with petro-currency upside.

• AUD is a China-levered proxy whose volatility is often underpriced.

• Brent Crude settles in London and reflects North Sea/Brent Blend fundamentals rather than WTI’s Cushing logistics.

• Cocoa futures trade off West African weather—about as orthogonal to U.S. payrolls as it gets.

• Aluminum is the workhorse metal for EVs, solar frames, and aircraft.

• Bitcoin adds a censorship-resistant layer with weekend liquidity.

4. Volatility as a Compass, Not a Scarecrow

Many traders treat high volatility like a haunted house—fun to talk about, terrifying to enter. We flip the script. Volatility tells us where optionality is cheap or rich.

Annualized historical volatilities (one-year look-back) came in as follows at the June 13 snapshot:

• Cocoa: 51%• Bitcoin: 49%• Brent: 34%• Aluminum: 28%• AUD: 11%• CAD: 9%• CHF: 8%

With vol as our compass, we expect Cocoa and Bitcoin to offer fat tails; Brent and Aluminum reside in the medium zone; the three currencies are the low-vol ballast.

5. Data Plumbing: Where the Numbers Really Come From

Garbage in, garbage out. We fused three layers of data:

1. Continuous Futures Back-Adjustments (Quandl and direct exchange feeds)

2. Real-Time Option Chains (via an OCC-compliant market data vendor)

3. High-Frequency Spot FX Ticks (rolled up into 5-minute bars)

All symbols were normalized to USD notional, then synced to a single business-time index to eliminate look-ahead bias.

6. Back-Testing vs. Forward Simulation: The Philosophy

Most retail traders do a simple thing: slice the past, optimize on P&L, deploy in the future. The problem? Markets don’t cooperate. Instead of a naïve backtest, we used a hybrid Monte Carlo + bootstrapped residual approach:

• Step 1: Fit regime-switching GARCH models to each return series.• Step 2: Draw 50,000 correlated paths, keeping co-variance anchored to the trailing 90-day window.• Step 3: Overlay live option-derived implied vol to re-scale the shocks.• Step 4: Evaluate P&L under strategy rules for each path, then take the median.

The result is a “middle-ring” forecast—less optimistic than a pure backtest, less pessimistic than worst-case VaR.

7. Designing the $100,000 Sandbox

Capital constraints matter. With $100K, position sizing must honor contract specs:

• Brent Crude (ICE) mini contract ≈ $10,000 notional per lot• CME Micro FX futures ≈ $10,000 notional• Cocoa (ICE) ≈ $20,000 per lot• LME Aluminum mini lot via CME ALI ≈ $7,500• CME BTC micro futures ≈ $2,000

Margin requirements averaged 7–12% across instruments, freeing us to deploy synthetic option trades without breaching capital ceilings.

Initial equal-risk weights produced this dollar allocation:

• Brent 25%• CHF 15%• CAD 15%• AUD 15%• Aluminum 10%• Cocoa 10%• Bitcoin 10%

8. Strategy Frameworks for Each Instrument

1. Brent Crude: 67% delta-long futures, 33% 25-delta OTM puts (weekly expiry) for downside cover.

2. Currencies: Cash-and-carry futures arbitrage—short front-month, long spot via FX swap, harvest term premium.

3. Cocoa: Mean-reversion band using Bollinger + options strangles to collect theta.

4. Aluminum: Calendar-spread bias trade (long 3-mo, short 12-mo) betting on backwardation persistence.

5. Bitcoin: Momentum overlay with 10/40 EMA and synthetic covered calls.

Each strategy consumes margin and option premium differently, so the AI’s job is to juggle them within the $100K leash.

9. Programming the AI Engine

We employed a modular stack:

graph LR

A[Jupyter] --> B[Python 3.11]

B --> C[Polars DataFrames]

C --> D[pytorch / LSTM]

D --> E[cvxpy Optimization]

E --> F[FastAPI Microservice]

A reinforcement-learning wrapper (proximal policy optimization) updates allocations daily, maximizing a custom reward = median(P&L) ÷ CVaR_95.

10. First Pass: The 14-Day Baseline Run

On June 13 the simulator projected:

• Expected P&L: +$102• Median Win Ratio: 44%• Annualized Return: 73% (simple straight-line)• Max Drawdown (sim): –1.2%

Not breathtaking, but directionally positive given the low risk.

11. Diagnosing the Initial Results

The culprit for low expectancy? Small positive skew in Brent was diluted by flattish FX arbitrage and choppy softs/metal trades. Bitcoin’s noisiness didn’t help either.

Trade-level breakdown (median per path):

Asset	Avg Win	Avg Loss	Win %	Contribution
Brent	$22.4	–$6.1	62%	+$148
CHF	$3.8	–$3.7	48%	+$2
CAD	$3.5	–$4.0	43%	–$12
AUD	$2.9	–$3.3	45%	–$8
Aluminum	$5.1	–$7.9	38%	–$47
Cocoa	$6.7	–$8.2	41%	–$39
Bitcoin	$12.1	–$18.4	40%	–$86

Aluminum, Cocoa, and Bitcoin dragged the basket into mediocrity.

12. The Aluminum Experiment

We hypothesized that Aluminum’s negative drag stemmed from a temporary oversupply narrative priced in by Chinese smelter restart rumors. Dropping Aluminum and re-running the paths improved expected P&L from $102 to $451, nearly quadrupling edge while bumping win-ratio to 53%.

Lesson: one laggard with high marginal volatility can nullify half a dozen modest winners.

13. Full-Basket Stress-Test and Why It Failed

Curiosity led us to re-insert every asset at full weight—call it the “kitchen-sink” approach. Result: –$383 expected loss and a win-ratio collapse to 37%. Correlation spikes under stress, particularly between AUD, CAD, and Brent during energy price swings, produced lethal downside clusters.

14. Pruning the Garden: Cutting Cocoa, Bitcoin & Aluminum

Systematic pruning left four survivors: Brent, CHF, CAD, AUD. The slimmed basket restored positive expectancy to +$102 with a healthier Sharpe. Still, the FX trio’s gains barely covered Brent’s risk.

15. Going “All-In” on Brent Crude—Madness or Method?

At this point the AI proposed the nuclear option: allocate 100% of trading capital to Brent but only deploy 25% of notional exposure, letting implied vol do the heavy lifting while protective puts cap tail risk.

Key metrics on the Brent-only setup:

• Notional Exposure: $25,000• Option Premium Spent: $450 (rolled weekly)• Expected 14-Day Return: +$600• Stat Prob of Loss > –2%: 5.7%• Win Ratio: 100% across median path set

The decision boils down to philosophy: do you prefer diversified mediocrity or concentrated excellence with an insurance overlay?

16. Risk Management Beyond Simple Stops

Stops on futures are blunt instruments amid overnight gaps. Instead, we lean on:

1. Dynamic Delta Hedging: Re-balancing option strikes to keep net delta ≈ 0.7

2. Gamma Scalping: Monetizing convexity when Brent rallies sharply.

3. Volatility Targeting: Scaling position size inversely with 10-day realized vol.

17. Position Sizing With Kelly, CVaR, and Real-Time Greeks

Kelly sizing on a skewed distribution can recommend absurd leverage. We compromise by:

• 0.5× Kelly fraction on expected log-utility

• Cap CVaR_95 at 1.5% of portfolio NAV

• Limit option vega to 0.3 per $1K capital

18. Execution Microstructure: Slippage, Tick Size, and Fees

ICE Brent mini ticks in $0.01 increments ($10 per tick). Average bid-ask during London hours: 1–2 ticks. We built a “smart child order” router that slices the 25% lot into five TWAP buckets across the first 45 minutes of EU open.

Average all-in round-trip cost: 0.04%, already deducted from sims.

19. Psychology of Trading a One-Asset “Portfolio”

Concentration amplifies emotional swings. Metrics to keep traders sane:

• Probability Wheel: real-time PDF of 1-day P&L

• Streak Tracker: number of consecutive losing days (alerts at 4)

• Variance Budget: running tally of vol consumed vs. allowed

20. Automating the Daily Re-Simulation Loop

We cron-schedule a 06:00 UTC job:

1. Pull fresh option chains.

2. Re-fit GARCH and correlation matrices.

3. Generate 10,000 new paths (fewer than research but fast).

4. Publish updated position sizes to Redis.

5. Trigger order-management if delta drift > 0.05.

21. Building a Robust Monitoring Dashboard

The stack:

• Streamlit front-end

• PostgreSQL for trade logs

• Plotly real-time P&L curves

• Dash alerts via Telegram bot for variance breaches

22. From Research Notebook to Production Code

Key lessons:

• Refactor notebooks into pure Python modules.

• Add unit tests for each data handler (pytest).

• Use Docker images pinned to Debian slim, Python 3.11, conda-forge libs.

• CI/CD via GitHub Actions deploying to an on-prem Kubernetes cluster.

23. Auditing, Compliance, and Reg-Tech Considerations

If you manage outside capital, MiFID II and CFTC recordkeeping rules demand:

• Audit trails of model versioning.

• Pre-trade risk checks (max order size, fat-finger).

• 5-year retention of electronic communications related to trade decisions.

24. Edge Decay: How Long Will This Trade Live?

Commodity markets price shocks quickly. Historical median half-life of similar Brent edges: ≈ 30–45 calendar days. Continual re-cal is mandatory.

25. Scenario Analysis: War Premiums, Supply Shocks & Black Swans

We injected deterministic shocks:

• +10% Brent supply disruption → +3.1× P&L

• CHF peg break (SNB event risk) → –0.9× (but we’re flat CHF now)

• Bitcoin regulatory ban → zero effect in Brent-only mode

26. Scaling Up: From $100K to $10M Without Moving the Market

ICE Brent mini ADV ~35,000 lots. A $10M book at 5× leverage equals ~500 mini lots—≈1.4% of ADV. Manageable if orders are sliced across sessions.

27. Common Pitfalls and How to Dodge Them

• Curve-Fitting Killer: Over-optimizing option strikes—use discrete grids.

• Greeks Drift: Not re-pricing options intraday; use 15-minute refreshes.

• Latency Mismatch: Futures fills vs. options fills can desync hedges—use IOC orders.

• Forget Funding: Crypto perpetual swaps have funding costs that eat edge.

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28. Key Takeaways for Traders, Quants, and Asset Managers

1. Diversification isn’t free—correlation spikes when you need it least.

2. Forward simulation beats static backtests for short-term horizons.

3. Concentration + hedging can outperform watered-down baskets.

4. Live option-chain data gives a reality check on volatility assumptions.

5. Daily re-calibration is vital; yesterday’s edge is today’s drag.

   
   

29. Final Thoughts: The Future of AI-Driven Portfolio Design

The experiment proves a bold idea: AI, armed with real-time derivatives data, can isolate pockets of asymmetric payoff in a world awash with noise. Today that pocket is Brent crude; tomorrow it might be Indonesian nickel or Malaysian palm oil. The key is an adaptive framework that learns, prunes, and reallocates—without human ego getting in the way.

As capital floods into “AI for alpha,” the bar rises. Those who treat machine intelligence as a sidecar will lag. Those who embed it at the core of their research, execution, and risk cycles will not merely survive the next U.S. downturn—they may profit handsomely from it.

So load up your data pipes, sharpen your models, and remember: the market owes you nothing—but the right code, pointed at the right asset, can make it feel like it does

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Want the source code, daily signals, and my full HFT/C++ eBook? Join the Elite Quant membership at quantlabs.net. See you on the inside, and happy (non-US) trading!