Strategy Framework

The Strategy Framework provides the structure for creating, validating, and executing trading strategies within World Fund. At its core is the Strategy Factory and Strategy Selector (SFSS) system that ensures strategies meet rigorous standards before deployment.

Strategy Factory and Strategy Selector (SFSS) System

The SFSS system represents the core architecture for strategy generation, validation, and selection. It follows a structured workflow to ensure only robust, validated strategies reach deployment and are dynamically selected based on current market conditions.

Strategy Factory

The Strategy Factory is responsible for strategy creation through multiple pathways:

• User-Defined Strategies: Created through the Fund Builder's natural language interface

• Quant Agent Generation: AI-generated strategies based on market patterns and opportunities

• Template Modification: Customization of pre-existing strategy templates

• Expert Design: Manually coded strategies by professional quantitative analysts

Strategy Selector

The Strategy Selector dynamically chooses the most appropriate strategies based on current market conditions, optimizing performance across different regimes:

• Market Regime Detection: Identifies distinct market environments (trending, ranging, volatile) using a combination of statistical pattern recognition and machine learning

• Dynamic Strategy Allocation: Automatically adjusts capital allocation across strategies based on their historical performance in similar market conditions

The Strategy Selector uses machine learning to dynamically allocate capital across the strategy pool based on recent performance and detected market regimes.

The allocation process works as follows:

1. Historical performance of each strategy is analyzed across different market conditions

2. Market regime detection identifies the current market environment

3. An optimal allocation of weights is calculated for the upcoming period

This process can be represented mathematically as follows:

$\text{Given performance } s^i_t \text{ of strategy } i \text{ at time } t$$\text{Calculate weights } (w_1, w_2, \ldots, w_n) \text{ for period } [T, T+1]$$\text{Subject to: } 0 \leq w_j \leq 1 \text{ and } \sum_{j=1}^{n} w_j \leq 1$

• Performance Metrics: Comprehensive evaluation beyond simple returns using a multi-factor analysis framework (Sharpe, Sortino, Calmar ratios, drawdowns, recovery periods, etc.)

• Robustness Tests: Implements walk-forward analysis, Monte Carlo simulation with over 1,000 iterations, and stress testing against historical market crashes

• Market Regime Analysis: Segments and evaluates performance across different market conditions using hierarchical clustering and regime-switching models

• Correlation Assessment: Utilizes advanced statistical techniques to measure strategy uniqueness compared to existing library, enforcing maximum correlation thresholds

• Risk Profile Validation: Ensures strategy risk characteristics match stated objectives through multi-dimensional risk assessment

Example Validated Strategies

The Strategy Framework has validated several sophisticated strategies, including:

• Adaptive Moving Average Crossover: A strategy that dynamically adjusts moving average periods based on market volatility, validated across bull, bear, and sideways markets since 2020. This strategy automatically determines optimal MA periods using a proprietary volatility ratio, preventing both excessive trading in high-volatility environments and missed opportunities in low-volatility periods.

• Volatility Breakout with Volume Confirmation: Detects price breakouts from consolidation patterns, confirmed by abnormal trading volume, with proven effectiveness in range-bound to trending transitions. This approach uses a combination of Bollinger Band width contraction followed by expansion, coupled with volume surge filters to minimize false breakouts.

• Multi-Timeframe Momentum: Analyzes momentum across multiple timeframes (hourly, daily, weekly) to generate high-conviction entry signals with clearly defined risk parameters. This strategy requires momentum alignment across at least two timeframes, utilizing RSI, MACD, and proprietary momentum oscillators with regime-specific thresholds.

Decoupled Execution Architecture

The Strategy Framework implements a decoupled event-driven architecture for strategy execution:

Signal Generator

The Signal Generator processes validated strategies and generates trading signals:

• Receives market data and on-chain events in real-time

• Applies strategy logic to generate precise trading signals

• Calculates position sizing and risk parameters

• Emits structured signal events to the execution layer

Trader Agent

The Trader Agent focuses exclusively on optimizing order execution:

• Receives signal events from the Signal Generator

• Determines optimal execution approach based on market conditions

• Manages slippage and minimizes market impact

• Handles position entry, modification, and exit

• Reports execution quality metrics back to the system

This complete separation between strategy processing (Signal Generator) and order execution (Trader Agent) allows each component to specialize in its specific function, creating more efficient and reliable performance.

Strategy Representation

Strategies in the framework are represented through a standardized format:

• Metadata: Strategy identification, version, creator, and description

• Parameters: Configurable inputs with valid ranges and defaults

• Logic Definition: The core algorithm expressed in a standardized format

• Risk Parameters: Defined risk controls including position sizing, stop-loss, and exposure limits

• Market Requirements: Specific data inputs and market conditions needed for proper execution

This comprehensive Strategy Framework creates a complete ecosystem for strategy development, validation, and that only well-tested strategies with genuine edge reach deployment while maintaining a clear separation of concerns between signal generation and trade execution.