Concepts
The Shuhari.Capital engine is a multi-strategy portfolio system. Instead of picking one approach to investing for all market conditions, it combines five different strategies, each designed to do well in different environments. We then assign a sub-portfolio weight to each strategy and combine them into a single portfolio that rebalances every month.
Some strategies are defensive, some are aggressive, and we are working to balance them to adapts to what's happening in the economy and markets.
Personal Context
Personally, I started learning more about "tactical asset allocation"(TAA) in contrast to stock-picking while experimenting with Quantopian (later QuantConnect) about 10 years ago. It's a quantitative research and trading engine that can be programmed in Python or C# and got me hooked on the idea of systematic investing.
The idea of making deliberate, temporary shifts away from a long term "core" strategy to reap returns or sidestep some risk along the way really resonated with me. Mebane T. Faber's research paper "A Quantitative Approach to Tactical Asset Allocation" (2006, The Journal of Wealth Management, Spring 2007) set me off to spend the coming years venturing into this field as a weekend hobby.
When then developing the Shuhari.Capital engine, one of my main drivers was to cap the downside through TAA-typical momentum mechanics, which wave historically shown around 50% reduction in drawdowns. Putting real funds on the line is stressful even in good times. Limiting the exposure to buy-and-hold drawdowns I realised will strongly reduce the likelihood of me making mistakes, even if it comes at the cost of higher returns.
The Five Strategies
The portfolio is split into five sub-portfolios. The "Alpha Overlay" strategy is fixed at 10% of capital and is the only stock-selection overlay rather than a directional allocation, that is aimed at adding alpha from either "value" or "momentum", based on the identified regime.
The other four strategies are weighted dynamically each month based on their recent risk contribution (more on that below). Here is what each one does.
1. Stability Core
What it does: Holds a diversified mix of global equities, bonds, commodities, gold, and a small Bitcoin position. As the "core" foundation, this part of the portfolio should always invested and broadly diversified. Exceptions could be global commodity shock regimes with correlation breakdowns that can trigger cash positions as a last resort.
What makes it tactical: The default allocation is 60% mixed global equities / 20% bonds / 10% commodities / 7.5% gold / 2.5% Bitcoin. But the system monitors macro signals like yield curve inversions, credit spreads widening, or major indices falling below rolling average prices to shift weight toward defensive assets when conditions deteriorate. When things improve, it shifts back.
The baseline temperature is set to "risk-on", the shifts are gradually increasing in intensity with every additional economic indicators trigger, resulting in defensive overweight or the extreme "pause" in cash.
2. Momentum Chaser
What it does: Ranks a universe of ETFs by their recent price (multi-) momentum (3–12 months) and concentrates capital in the strongest trends. If nothing is trending up, it moves to cash or bonds.
Why it works: Momentum is one of the most well-documented selection criteria in financial research. Assets that have shifted into a directional momentum beyond mean reversion bands tend to show continued momentum that one can capitalise one. This time limited "continuation" has been identified over decades of data across every asset class and geography. The academic literature used in our engine to on this topic includes Jegadeesh and Titman (1993, 2001), Asness, Moskowitz & Pedersen (2013), Moskowitz, Ooi & Pedersen (2012), Fama & French (1996), Novy-Marx (2012) and Asness, Frazzini & Pedersen (2019), which all have confirmed underlying concepts repeatedly.
We're not predicting here, we find momentum that is already forming and "chase" it. When the move goes flat, we paddle back to a risk-off baseline.
3. Sector Navigator (split US and "Rest of the World")
What it does: Instead of buying the broad market, this strategy picks a set of the strongest sectors based on multi-momentum and relative strength. It overweights winners and avoids losers.
Why two versions: The allocation is split evenly between US sectors and global sectors. Same methodology, different universe. This keeps geographic diversification even within a concentrated sector bet and allows to play different strengths/weaknesses depending on geopolitical developments. This is about avoiding crises as much as it is riding domestic trends while allowing for global trends to result in overlapping picks.
What makes it different from Momentum Chaser: Momentum Chaser picks across asset classes (equities, bonds, commodities). Sector Navigator stays within equities but rotates between industries. They're complementary — one diversifies across "what to own," the other "where within equities.
4. Alpha Overlay (10%)
What it does: This is the stock-picking layer to put "icing on the cake". Within the winning sectors identified by the Sector Navigator, the system scores individual stocks on six dimensions and selects the highest-ranked names.
The six dimensions:
| Dimension | What it measures |
|---|---|
| Growth | Earnings and revenue acceleration |
| Momentum | Price trend strength and consistency |
| Quality | Profitability, returns on capital, financial health |
| Value | Whether the stock is cheap relative to its fundamentals and peers |
| Revisions | Whether analysts are upgrading their estimates |
| Sentiment | News tone (using AI-based natural language processing) |
Why only 10%: Individual stocks carry idiosyncratic risk, which has alpha potential, but decisively increases risk. While I do think there is big potential in fundamentally analysing individual stocks in-depth and with an edge in niche know-how, capping this at 10% of the portfolio means stock-level surprises can't blow up the whole thing. We're not spending weeks studying reports here, this is automated and higher-level.
5. Vault / Deep Dive (Currently in Development)
What it does: A qualitative analysis layer that will complement the quantitative Alpha Overlay. Think DCF valuation, insider activity tracking, peer comparison andthe kind of fundamental analysis that a human analyst would do, structured into a systematic framework with the result being manually reviewed.
The intention here is to add a longer-term holding view to the otherwise tactically shifting portfolio. This layer will make full-on long term investment decisions rather than firing allocation signals.
Status: This is the newest component and still being built out. I'll write about it as it develops.
From Fixed Weights to Risk Parity
The original version of this system used fixed capital allocations: Stability Core got 33.3%, Momentum Chaser 28.3%, Sector Navigator 28.3% (split evenly between US and Global), and Alpha Overlay 10%. Simple and easy to explain.
The problem is that capital weight is not the same as risk contribution.
If the Momentum Chaser happens to be running at twice the volatility of Stability Core, it contributes more than half of the portfolio's total risk despite holding less than a third of the capital. The fixed percentages gave me a a false sense of balance, which I realised once volatility/the VIX started escalating at the end of 2025 / early 2026. I thought we were diversified, but in practice one strategy dominated the portfolio's swings.
This showed up clearly when comparing the strategies across different market regimes. Momentum Chaser and Sector Navigator are both trend-following equity strategies with higher correlation. In a momentum-driven bull market they reinforce each other nicely, but needed a mechanism to break correlation in corrections. Their combined weight (56.6% of capital) meant they were contributing far more than 56.6% of the portfolio's risk.
The fix: Equal Risk Contribution (ERC) weighting.
Instead of assigning each strategy a fixed percentage of capital, ERC works backwards from the target: give each strategy a share of capital so its contribution to total portfolio variance is equal. We give more volatile strategies and strategies that move in sync with others smaller weights than the ones that really diversifies with less correlation.
The math (Maillard, Roncalli & Teïletche, Journal of Portfolio Management, 2010) is a bit more complex to implement than a fixed split, but the idea was immediately intuitive to me. Every strategy should pull its own weight, and no single strategy should dominate portfolio risk just because of how its volatility.
In practice, the engine now computes the 90-day covariance matrix across the four main strategies before each monthly rebalance, solves for ERC weights, and applies the floors (5%) and ceilings (55%) to each strategy so we prevent any strategy from fully dominant. The exception to this: Alpha Overlay stays fixed at 10% as it is a stock-selection overlay without a stable directional return series.
The initial fixed weights (33.3% / 28.3% / 28.3% / 10%) remain the fallback if the price history needed to compute the covariance is unavailable.
Portfolio Structure at a Glance
┌──────────────────────────────────────────────────────┐
│ ┌──────────────┐ ┌──────────────┐ │
│ │ Stability │ │ Momentum │ │
│ │ Core │ │ Chaser │ │
│ │ risk-based │ │ risk-based │ │
│ │ │ │ │ │
│ │ Macro ETF │ │ Trend ETF │ │
│ │ baseline │ │ overlay │ │
│ └──────────────┘ └──────────────┘ │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌────────────┐ │
│ │ Sector │ │ Sector │ │ Alpha │ │
│ │ Navigator │ │ Navigator │ │ Overlay │ │
│ │ US risk- │ │ Global risk- │ │ 10.0% │ │
│ │ based │ │ based │ │ (fixed) │ │
│ │ US sector │ │ Global sector│ │ Individual │ │
│ │ rotation │ │ rotation │ │ stocks │ │
│ └──────────────┘ └──────────────┘ └───────────┘ │
│ │
│ ERC weights recomputed monthly, 90-day covariance │
└──────────────────────────────────────────────────────┘
The Key Metrics and why we're tracking them
If you're going to evaluate a portfolio, these are the numbers that matter. Just the absolute return alone is a metric to aim for as an active Long-Short Portfolio Trader, not an automated allocation system like ours.
| Metric | What it tells you | Why it matters |
|---|---|---|
| CAGR | Compound annual growth rate | The true average return per year, accounting for compounding |
| Volatility | How much returns fluctuate | Lower is "better" (for us). High volatility means wild swings both ways, which we are trying to avoid in this portfolio. |
| Sharpe Ratio | Return per unit of risk | The standard for risk-adjusted performance. Above 1.0 is strong |
| Sortino Ratio | Return per unit of downside risk | Like Sharpe, but only penalises losses — not upside volatility |
| Max Drawdown | Worst peak-to-trough decline | The number that tells you "how bad can it get?" to evaluate if you can stand/afford it. |
| Calmar Ratio | CAGR ÷ Max Drawdown | Combines growth with worst-case pain. Higher = better |
| Beta | Sensitivity to the market | Beta of 0.4 means you capture ~40% of market moves (up and down) |
| Alpha | Excess return above what beta explains | Positive alpha means the portfolio adds value beyond market exposure |
The most important insight from these metrics: Two portfolios can have the same Sharpe ratio with completely different profiles. The S&P 500 and this portfolio both have a Sharpe of ~1.0 (as of March 2026), but the SPY gets there with high return and high volatility, while our Shuhari.Capital portfolio gets there with moderate return and low volatility. Same risk-adjusted performance, very different real-life experience.
The Philosophy
A few principles that have emerged to guide how I build this:
Rules over gut feel. Every decision the system makes is based on a defined rule with a defined threshold. I do not override the decisions, except from "pausing" the system when we're entering regimes I know I have not accounted for in the model. This removes the emotional aspect and makes our results measurable.
Diversify across strategies, not just assets. Owning 500 stocks doesn't help if they all go down together. This portfolio diversifies across approaches using macro allocation, trend following, sector rotation, and stock selection. Since different strategies work in different market regimes, true diversification also means ensuring that each strategy contributes equally to portfolio risk, not just to capital. (See "ERC" above.)
Respect the research. The strategies aren't invented from scratch or based on any sort of "magic formula" that I claim I have found. They're grounded in academic and practitioner research like tactical allocation, (dual-/multi-) momentum or the CANSLIM methodology. I've adapted them for our system where needed, but the foundations are documented, not invented.
Transparency over performance marketing. I will/have publish/ed backtests and results, the monthly portfolio and the economic data used. (Code TBD). If the model has a bad month, you'll see it.
What's Next
I'll be writing dedicated chapters on each strategy, including the specific signals, thresholds, and trade-offs. These will appear as blog posts and be linked from this page as they're published.
If you want to follow along: subscribe to the blog or check the Economic Pulse series for the macro data that feeds into the Stability Core.