Our Methodology

Our Approach

At Apex Equity, we believe investment decisions should be driven by data, not emotions or market commentary. Our strategies are built on a systematic, quantitative approach that analyzes decades of historical fund performance to identify recurring seasonal patterns.

We don't predict the future. We identify patterns that have persisted across multiple market cycles — bull markets, bear markets, recessions, and recoveries — and build allocation rules that capitalize on these patterns.

Backtesting Methodology

Data Collection

We collect and maintain historical daily price data for all funds in our coverage universe:

• TSP Funds: C, S, I, F, and G Fund daily share prices from 2003-present (20+ years)
• Fidelity Funds: 11 mutual funds including FXAIX, FCNTX, FBGRX, FSPGX, FSMAX, FSSNX, FTIHX, FXNAX, FBALX, FIOFX, and SPAXX
• Vanguard Funds: Mutual funds including VFIAX, VTSAX, VIGAX, VBTLX, and others

Data is sourced from official fund providers and verified for accuracy. We account for dividends, distributions, and fund splits.

Strategy Generation

Our scanner engine systematically generates and tests thousands of possible allocation strategies:

1. Define the allocation universe: Which funds can be held in each calendar month
2. Generate strategy candidates: Every possible monthly fund rotation combination
3. Apply realistic constraints: Account for transfer limits (TSP allows 2 interfund transfers/month), settlement periods, and execution timing
4. Backtest against historical data: Simulate each strategy's performance over the full historical period

Performance Metrics

Every strategy is evaluated across multiple dimensions, not just raw returns:

• CAGR (Compound Annual Growth Rate): The annualized rate of return over the test period
• Maximum Drawdown: The largest peak-to-trough decline — measures worst-case scenario
• Volatility: Standard deviation of returns — measures consistency
• Sharpe Ratio: Risk-adjusted return (return per unit of risk taken)
• Composite Score: Our proprietary weighted score combining all metrics to rank strategies

Strategy Filtering

Not all strategies make the cut. We apply strict filters to ensure quality:

• CAGR filter: Strategy must exceed the buy-and-hold benchmark by a meaningful margin
• Drawdown filter: Maximum drawdown must be within acceptable limits
• Consistency filter: Strategy must perform well across multiple sub-periods, not just one lucky stretch
• Practicality filter: Strategy must be executable within fund transfer rules and realistic timing

Why Seasonality Works

Seasonal patterns in financial markets aren't random — they're driven by well-documented, recurring factors:

• Tax-Loss Harvesting (November-January): Investors sell losing positions in December for tax benefits, then reinvest in January, creating predictable selling pressure followed by buying pressure
• Institutional Fund Flows: Mutual funds, pension funds, and endowments rebalance on predictable quarterly and annual schedules
• Corporate Earnings Cycles: Quarterly earnings seasons create predictable volatility patterns
• Federal Reserve Calendar: FOMC meetings follow a fixed schedule, influencing market behavior around announcement dates
• Behavioral Finance: Human psychology creates repeating patterns — optimism in spring, vacation-driven low volume in summer, year-end window dressing

These factors are structural, not random. They've persisted across decades of data because they're driven by institutional rules, tax codes, and human behavior — none of which change quickly.

Technology Stack

Our backtesting and scanning platform is built in-house using:

• Strategy Scanner: PHP-based engine that generates, backtests, and ranks thousands of strategies
• AI Analysis: Large language model integration for generating strategy insights and risk assessments
• Real-Time Alerts: Automated notification system that sends allocation change alerts via email
• Interactive Backtester: Browser-based tool that lets users test custom strategies against historical data

About the Team

Data Sources & References

Primary Data Sources

Academic Research on Seasonality

Regulatory & Disclosure