[Research][Testing] Trailing Stop vs Fixed Take Profit Analysis

[iAmGiG]

Summary

Investigate whether trailing stops capture more upside than fixed take profit targets for momentum strategies.

Background

Current exit strategy uses fixed 8% take profit which caps upside. This has a documented gap:

Gap: Momentum strategies (like TSMOM) rely on "letting winners run." A fixed 8% Take Profit caps your upside on a stock that might run 50%.

Why It Matters: Trailing stops are essential to capture the "fat tails" of momentum distributions. Fixed targets truncate the right tail of returns, reducing overall expectancy.

Objective

Create trailing_stop_analysis.py to compare:

• Fixed TP/SL (current: 8% TP / 5% SL)
• Trailing stop only (no fixed TP)
• Hybrid: Trailing stop with minimum TP floor

Trailing Stop Methods to Test

Method	Description
Percentage Trail	Trail by fixed % from high (e.g., 5% from peak)
ATR Trail	Trail by ATR multiple from high
Chandelier Exit	ATR-based trail from highest high
Parabolic SAR	Accelerating trail (standard parameters)

Test Scenarios

1. Strong Trend (synthetic 30%+ move)
2. Choppy Market (synthetic sideways with noise)
3. Real Data (AAPL 2024 bull run, SPY Jan 2025 pullback)

Metrics to Capture

• Maximum Favorable Excursion (MFE)
• Maximum Adverse Excursion (MAE)
• Average win size (fixed vs trailing)
• Win rate comparison
• Sharpe ratio
• Profit factor

Acceptance Criteria

• Script created at scripts/research/exit_strategy_analysis/trailing_stop_analysis.py
• Compare at least 3 trailing methods vs fixed TP
• Test on trending vs choppy synthetic data
• Test on real 2024-2025 data
• Document when trailing outperforms fixed (regime-specific guidance)

Related

• Documented gap in scripts/research/exit_strategy_analysis/README.md (Section 2: Trailing Stops vs Fixed Targets)
• Issue Implement Dynamic Trailing Stop Logic #321 (Trailing Stops implementation)
• Issue [Research] Optimal Partial Exit Strategies - Academic Literature Review #417 (Partial Exit Strategies)