Investment Thesis

Q1-2025 Protective Momentum Holding 

Our momentum-based strategy aims to combine momentum signals with advanced portfolio optimization and protective risk management. In essence, our strategy captures market trends while maintaining downside protection through efficient portfolio construction and options-based hedging. Core strategy features are managed in the following files:

• Universe Selection: BasicUniverseSelectionModel
• Alpha Generation: MomentumAlphaModel
• Portfolio Construction: MomentumPortfolioConstructionModel
• Risk Management: ProtectivePutModel
• Execution: ImmediateExecutionModel

Overview Breakdown

1. Momentum-Based Selection & Ranking:

Uses a dual-lookback momentum calculation system:

• Long-term momentum: 252-day lookback period
• Short-term momentum: 63-day lookback period

Top 5 stocks with highest momentum are selected for long positions Selection process incorporates both trend strength and persistence

2. Advanced Portfolio Optimization:

Implements multiple portfolio optimization techniques:

• Primary: Sortino Efficient Frontier optimization (1000 simulated portfolios)
• Alternative options include:
  • Quadratic Programming
  • Minimum Variance
  • Risk Parity

Our Optimization idea focuses on downside risk-adjusted returns rather than traditional mean-variance optimization

3. Dynamic Portfolio Construction:

Monthly rebalancing schedule Gradual position adjustment using step-wise weight modifications Automated portfolio rebalancing when:

• Holdings composition changes
• Monthly rebalance signals trigger
• Smooth transitions between target positions using adjustment steps

4. Risk Management:

(WIP) Protective Put strategy for downside protection 

Integrated risk management through the ProtectivePutModel 

Immediate execution model for precise implementation 

Universe-level risk controls through basic universe selection 

Real-World Example Scenario

To better illustrate the strategies logic, Let's examine how this strategy would theoretically perform during a momentum-driven market phase. For this example we will be using $NVIDIA and the tech sector rally of late 2023 as an example: 

In Late 2023, NVIDIA (NVDA) experienced strong upward momentum due to AI developments while the broader tech sector also showed positive momentum. 

To capture this move, initially our algorithm will detect strong momentum in tech stocks through its dual-lookback system ranking NVDA based on its momentum score. This is the Initial Selection Phase (at the start of each month). 

Portfolio Construction: Next, the Sortino Efficient Frontier optimizer determines the optimal portfolio weights. 

Risk Management Layer: For each equity position, protective puts are added. 

Lastly Positions are adjusted and logged:

"algorithm.Log($"{algorithm.Time.ToString(DateFormat)}: Selected Symbols: [{selectedStr}]");" "algorithm.Log($"{currentDate}: Targeted Holdings: [{targetedWeightsStr}]");" (edited)

The algorithm will continuously monitor: momentum signals for ranking changes, put option protection level, and portfolio optimization metrics and adjust accordingly. Overall the strategy benefits from 4 key aspects: capturing the upward momentum of tech stocks, maintaining optimized position sizes based on risk-adjusted returns, protecting against potential drawdowns through put options, and systematic rebalancing to maintain target allocations 

By combining traditional momentum signals with sophisticated portfolio optimization and risk management techniques, we built a comprehensive approach to momentum investing that maximizes risk-adjusted returns