For the intelligent retail investor

Institutional mathematics,
elegantly applied.

Optimize your portfolio using methods that span Markowitz mean-variance and risk parity through to machine learning, deep learning, and reinforcement learning. Alloq Alpha makes advanced portfolio construction, backtesting, scenario simulation, and rebalancing accessible from a single interface.

Alloq Alpha Terminal
Optimized Alpha
12.4%
Sharpe Ratio 1.84
Max Drawdown -8.2%

The Philosophy

Precision over intuition.

We abstract the complexity of quantitative finance into a clear, actionable interface. From classical optimisation theory to modern machine learning — let algorithms dictate your risk exposure, not emotion.

Multi-Paradigm Optimization

Select from Markowitz mean-variance, equal risk contribution, Hierarchical Risk Parity, or advanced models powered by neural networks and reinforcement learning — each tuned to your risk tolerance and return objectives.

Backtesting & Scenario Simulation

Stress-test any strategy against historical regimes and run Monte Carlo simulations across thousands of stochastic paths. Understand your portfolio's expected return distribution, tail risk, and probability of drawdown before committing capital.

Automated Rebalancing

Transition from your current allocation to the optimized target with minimal friction. The engine computes exact buy-sell schedules, accounts for drift thresholds, and generates a manifest you can execute directly through your broker.

Methodology

Optimize. Simulate. Rebalance.

Connect & Configure

Securely link your brokerage with read-only access or upload holdings directly. Define your constraints — asset classes, sector limits, risk tolerance, and target return — and select an optimization paradigm.

Optimize & Simulate

Choose from classical efficient frontier computation through to reinforcement-learning-based allocation policies. The engine optimizes, then runs historical backtests and Monte Carlo projections — surfacing risk-return profiles across thousands of future scenarios.

Review & Rebalance

Inspect the optimized allocation, projected performance, and scenario analysis. Approve a precisely calculated rebalancing manifest — with buy, sell, and hold instructions — that you execute directly through your broker.

Alloq Alpha Mobile

New Optimization

Configure Parameters

Risk Tolerance

Conservative Moderate Aggressive

Asset Classes

US Equities
Fixed Income
International Equity
Commodities
Optimization Paradigm Hierarchical Risk Parity

Simulation Results

1,024x Monte Carlo Paths
Expected Annual Return9.2%
95% CVaR (Worst-Case)-4.1%
95th Percentile (Best-Case)+14.3%
Sharpe Ratio1.84

Optimization Delta

+2.4%Expected Return
VTI
Buy $1.2k
TLT
Sell $800
GLD
Hold

Early Access

Register your interest.

We are building a unified platform for portfolio optimization — from classical models to reinforcement learning, with backtesting, Monte Carlo simulation, and automated rebalancing. Leave your details and you will be among the first to receive access when we launch.

Platform
iOS · Android · Web