Artifex Holding Ltd. operates exclusively proprietary trading. The company’s treasury function is responsible for managing excess cash flows that are not committed to long-term uses, with the objective of capital preservation alongside risk-adjusted return generation.

The investment activity is built on two complementary pillars: a discretionary mandate and a systematic mandate.

Discretionary Mandate

Under the discretionary mandate, the treasury actively allocates capital across major asset classes, including:

• government bonds
• equities

In addition, the portfolio may take smaller, opportunistic positions over shorter time horizons in instruments such as:

• volatility products (e.g. VIX)
• commodities
• foreign exchange

This approach allows the company to flexibly capture market opportunities based on prevailing macroeconomic and market conditions.

Systematic Mandate – SmoothAlpha

The systematic investment activity is based on the SmoothAlpha Diversified Trading Strategy, the result of more than 10 years of academic research and development, implemented as a fully automated, rule-based trading system without ad-hoc decision-making.

The strategy is built on the premise that financial markets continuously move toward equilibrium, while ever-changing economic conditions repeatedly disrupt this balance. The resulting:

• disequilibria
• trends
• mispricings

create persistent opportunities that can be systematically captured .

SmoothAlpha is a diversified Managed Futures program that:

• trades liquid global futures markets
• is active across 50+ futures markets (commodities, equity indices, fixed income, FX) 
• provides daily liquidity 

The strategy combines multiple well-established approaches:

• Momentum (trend-following)
• Carry (capturing supply–demand imbalances)
• Value (exploiting under/overvaluation)
• Reversal (mean reversion)
• Defensive (risk-based allocation)

Portfolio allocation across strategies is dynamic and adaptive, incorporating recent performance of both individual strategies and asset classes. The framework includes multi-layer optimization, with elements of machine learning to adjust weights while maintaining robust constraints to avoid overfitting.

The entire investment process is automated end-to-end, from data processing and signal generation to risk management and execution.

The objective of the strategy is to deliver a market-independent (alpha) return stream, with low correlation to traditional asset classes.