Proprietary
Algorithms
Rules-based, quantitative trading systems engineered to exploit market inefficiencies, volatility regimes, and interest rate dynamics — generating uncorrelated, risk-adjusted returns across all market conditions.
1.65
Alcazar 5 Sharpe Ratio
2.79
Calmar Ratio
772.9%
Backtested Return
40%
Portfolio Weight
Engineering Alpha Through Systematic Precision
Elevate Capital's quantitative strategies are built on a foundational belief: markets are not fully efficient, and systematic, rules-based approaches can reliably exploit structural inefficiencies that discretionary investors cannot access at scale.
Our algorithms are developed by a team with direct experience at BlackRock's ETF management division — where Richard Dedu managed approximately $350 billion in ETF AUM — combined with deep engineering expertise in signal processing, machine learning, and high-frequency execution.
The quantitative book represents 40% of total portfolio allocation and is designed to be structurally uncorrelated with the long market strategy, providing genuine diversification rather than the illusory diversification of correlated assets.
Rules-Based Execution
Algorithms execute without emotional bias, following pre-defined rules that have been rigorously backtested and validated across multiple market regimes.
Regime Adaptability
Dynamic regime detection enables the algorithms to adapt their behavior to changing market conditions — from risk-on to risk-off, from low to high volatility.
HFT-Grade Execution
High-frequency trading capabilities enable speed-optimized entry and exit, minimizing market impact and slippage on all algorithm-driven trades.
Risk-First Design
Every algorithm is designed with capital preservation as the primary objective. Hard drawdown limits and volatility-scaling ensure risk is always within defined parameters.
Alcazar 5
Developed by Richard Dedu, Ken Luu, and Dereck Nielsen. Designed to exploit rapid shifts in market volatility and interest rate regimes through dynamic ETF rotation.
Alcazar 5 is a rules-based, dynamic ETF rotation strategy that operates on a proprietary signal engine designed to identify regime shifts in market volatility and interest rate environments. When the signal detects a shift, the algorithm rotates the portfolio into the optimal ETF configuration for the new regime.
The strategy maintains a variable risk profile — it can operate in both risk-on and risk-off configurations, including leveraged long and inverse ETF positions. This flexibility enables Alcazar 5 to generate positive returns in both rising and falling markets, subject to the accuracy of its regime detection signals.
Backtested results demonstrate a cumulative return of 772.9% with a Sharpe Ratio of 1.65 and a Calmar Ratio of 2.79 — metrics that reflect both the magnitude and quality of returns relative to the risk taken.
Backtested Performance Metrics
ETF Universe
Alcazar 5 dynamically rotates across the following instruments based on proprietary regime signals:
How It Works
Proprietary signal engine monitors volatility indices, yield curve dynamics, and cross-asset correlations in real time.
When a regime shift is detected, the algorithm scores each ETF in the universe against the new regime parameters.
The portfolio rotates into the highest-scoring ETF configuration, with position sizing determined by signal confidence and risk budget.
Execution is handled by the HFT infrastructure, minimizing market impact and ensuring speed-optimized fills.
Risk Parity
A complementary quantitative strategy designed to allocate risk equally across asset classes, providing structural diversification and drawdown protection.
The Risk Parity algorithm is inspired by the pioneering work of Bridgewater Associates' All Weather strategy and AQR Capital Management's risk parity research — adapted and refined by Elevate's quantitative team for a dynamic, ETF-based implementation.
Rather than allocating capital equally across asset classes, Risk Parity allocates risk equally — meaning that each asset class contributes the same amount of volatility to the portfolio. This approach naturally reduces concentration in high-volatility assets and increases allocation to lower-volatility, diversifying assets.
The result is a portfolio that is more resilient across different economic environments — performing well in both inflationary and deflationary regimes, in both growth and recession scenarios. This makes it an ideal complement to the directional exposure of Alcazar 5.
Equal Risk Contribution
Each asset class contributes equally to total portfolio volatility, preventing concentration in any single risk factor.
Dynamic Rebalancing
Volatility estimates are updated continuously, triggering rebalancing when risk contributions drift beyond defined thresholds.
Leverage Management
Modest leverage is applied to lower-volatility assets to achieve target risk levels, managed within strict drawdown constraints.
All-Weather Design
The strategy is designed to perform across all four economic quadrants: growth, recession, inflation, and deflation.
AI-Augmented Quantitative Systems
LLM Sentiment Analysis
Large Language Models process earnings calls, SEC filings, news feeds, and social sentiment in real time, generating structured signals that augment traditional quantitative inputs. Inspired by the AI integration approaches of Two Sigma and Renaissance Technologies.
Machine Learning Forecasting
Ensemble machine learning models — including gradient boosting, neural networks, and random forests — generate multi-horizon return forecasts across the ETF universe, providing additional signal inputs to the Alcazar 5 rotation engine.
HFT Execution Engine
A proprietary high-frequency execution infrastructure enables microsecond-level order routing, smart order splitting, and real-time market impact minimization — ensuring that algorithmic signals translate into optimal execution quality.
Why Latency, Algorithms, and Quantum Computing
Define the Next Era of Alpha
The Latency Arms Race
In modern financial markets, the difference between profit and loss is measured not in seconds, but in microseconds. A 2013 study published by the National Bureau of Economic Research demonstrated that high-frequency traders with co-location advantages — placing servers physically adjacent to exchange matching engines — can execute trades in under 500 microseconds, capturing price discrepancies that exist for only fractions of a second before the market corrects. According to research published in Operations Research, a single millisecond of additional latency can cost a high-frequency trading firm between $4 million and $100 million annually depending on strategy and volume.
The implications for institutional investors extend beyond HFT firms. As Citadel Securities, Virtu Financial, and Jane Street have demonstrated, the firms that invest most aggressively in low-latency infrastructure — fiber optic networks, co-location, FPGA-based order routing — consistently outperform on execution quality. For a fund like Elevate Capital, this means that our HFT execution engine is not merely a technology investment; it is a structural risk management tool that reduces slippage, minimizes market impact, and ensures that the signals generated by Alcazar 5 are translated into fills at the intended price.
A 2024 survey of hedge fund managers by Datos Insights found that over 78% of funds with assets above $1 billion now operate algorithmic execution infrastructure, with 41% citing latency reduction as their single largest technology investment priority. The convergence of cloud computing, co-location services, and FPGA acceleration has democratized access to sub-millisecond execution — but the firms that deploy these tools most intelligently retain a durable edge.
Algorithmic Risk Management: The Institutional Standard
The most sophisticated risk management frameworks in the world — those deployed by Bridgewater Associates, Renaissance Technologies, and Two Sigma — share a common architecture: they are algorithmic, rules-based, and emotionless. Ray Dalio's foundational insight, articulated across decades of Bridgewater's research, is that human judgment is systematically biased by emotion, recency bias, and overconfidence. The solution is not to eliminate human judgment, but to encode it into systematic rules that execute without deviation.
Jim Simons, the mathematician who founded Renaissance Technologies and generated average annual returns of 66% over a decade through the Medallion Fund, built his entire investment philosophy on a single premise: markets move in patterns that can be identified and exploited by algorithms. His approach — hiring physicists, mathematicians, and computer scientists rather than traditional investors — produced the most successful trading operation in financial history. The lesson for institutional allocators is clear: systematic, quantitative approaches to risk management consistently outperform discretionary alternatives over long time horizons.
A June 2024 report by the U.S. Senate Homeland Security Committee found that hedge funds deploying AI and machine learning strategies outperformed their non-AI peers by an average of 12% on a risk-adjusted basis. The report highlighted that the primary source of outperformance was not return generation, but risk management — AI-driven systems demonstrated superior ability to identify and exit positions before drawdowns materialized.
Quantum Computing: The Next Frontier
34%
Improvement in bond trading optimization
HSBC & IBM Quantum Trial, 2025
HSBC and IBM demonstrated the world's first quantum-enabled algorithmic trading experiment, achieving a 34% improvement in bond portfolio optimization versus classical computing methods.
$47M
New alpha generated via quantum models
Goldman Sachs Quantum Research, 2025
Goldman Sachs reported generating $47 million in incremental alpha through quantum-enhanced portfolio optimization models, deployed in live trading environments.
10,000x
Speed advantage for specific optimization problems
IBM Quantum & World Economic Forum, 2025
Quantum computers can solve certain portfolio optimization and risk modeling problems up to 10,000 times faster than classical computers, enabling real-time scenario analysis at previously impossible scale.
Quantum computing represents the most significant technological discontinuity in financial markets since the introduction of electronic trading. While classical computers process information in binary bits — either 0 or 1 — quantum computers leverage qubits that can exist in multiple states simultaneously through superposition. This enables quantum systems to evaluate millions of portfolio configurations, risk scenarios, and optimization solutions in parallel, solving problems that would take classical computers years in a matter of seconds.
The World Economic Forum's 2025 Banking in the Quantum Era report identifies three strategic applications with near-term commercial viability: portfolio optimization across large asset universes, real-time risk modeling under stress scenarios, and fraud detection at transaction scale. IBM's collaboration with HSBC and Vanguard — published in December 2025 — demonstrated that quantum-enhanced optimization algorithms can identify superior portfolio allocations that classical mean-variance optimization systematically misses, particularly in high-dimensional, constrained optimization problems common in multi-asset hedge fund portfolios.
Elevate Capital's technology roadmap incorporates quantum-ready infrastructure design, ensuring that as quantum computing transitions from experimental to commercial deployment — a transition that leading institutions including Goldman Sachs, JPMorgan, and Citigroup are actively preparing for — our quantitative systems can integrate quantum-enhanced optimization without architectural rebuilding. The firms that prepare today will capture the quantum advantage first.
Expert Perspectives
Leading economists, fund managers, and technologists on the future of algorithmic trading, risk management, and quantum computing in financial markets.
Jim Simons — Founder, Renaissance Technologies
Renaissance Technologies: Trading Strategies Revealed
A rare documentary detailing how the world's most successful quantitative fund developed its algorithmic trading strategies over decades.
Ray Dalio — Founder, Bridgewater Associates
Ray Dalio on Risk Management & Investment Principles
Bridgewater's founder explains the systematic, rules-based approach to risk management that underpins one of the world's largest hedge funds.
IBM Quantum — Financial Services Division
IBM's Perspective on Quantum Computing in Finance
IBM's quantum computing team explains the near-term applications of quantum technology in portfolio optimization, risk modeling, and trading.
Backtested performance results are hypothetical and do not reflect actual trading results. Past performance is not indicative of future results. All performance metrics referenced are based on backtested simulations and are subject to the inherent limitations of backtesting. Elevate Capital Management — Confidential — For Institutional Use Only.