foxhunt/docs/plans/2026-03-05-hyperopt-degenerate-fix-design.md

Hyperopt Degenerate Trial Fix

Problem

7/9 DQN hyperopt trials produce degenerate policies (all-buy or all-hold) that generate 0-1 trades in backtest evaluation. All degenerates receive identical objective=1.25, creating a flat plateau that gives PSO zero gradient signal.

Root cause chain:

1. 45-action factored space collapses to 3 legacy actions (Buy/Sell/Hold) at eval time
2. EvaluationEngine treats consecutive same-direction actions as no-ops
3. Objective function can't distinguish between failure modes (all-buy vs all-hold vs insufficient trades)

Solution: Three-Part Fix

Part 1: Exposure-Aware Evaluation Engine

Add process_bar_factored(&FactoredAction) to EvaluationEngine that tracks continuous position exposure (-1.0 to +1.0):

• Long100→Long50 generates a partial close trade (delta=-0.5)
• Long50→Short100 generates a reversal trade (delta=-1.5)
• Long100→Long100 = no trade (delta=0)
• Transaction cost uses FactoredAction's order type (Market=0.15%, Limit=0.05%, IoC=0.10%)
• Position sizing: effective_size = target_exposure * kelly_fraction (multiplicative)

File: crates/ml/src/evaluation/engine.rs (~50 lines)

Part 2: Graduated Trade Insufficiency Penalty

Replace flat 1.25 plateau with smooth, differentiated penalties in extract_objective():

min_expected_trades = total_bars / 500  (~448 for 224K bars)

penalty = match total_trades {
    0        => 10.0,                                          // Model does nothing
    1..10    => 5.0 + 5.0 * (1.0 - trades/10.0),             // Near-degenerate
    10..min  => 2.0 * (1.0 - trades/min_expected),            // Insufficient activity
    _        => 0.0,                                           // Rely on Sharpe/Sortino
}

Additive on objective (higher=worse for PSO minimization). Each failure mode gets a unique value → PSO gets gradient signal.

File: crates/ml/src/hyperopt/adapters/dqn.rs (~25 lines in extract_objective())

Part 3: Wire Factored Eval into Hyperopt Backtest

Replace legacy action collapse in backtest loop:

// Before: factored → legacy → Action::Buy/Sell/Hold → engine.process_bar()
// After:  factored → engine.process_bar_factored()

File: crates/ml/src/hyperopt/adapters/dqn.rs (~3 lines in backtest loop)

Expected Impact

• Degenerate trials now produce different objectives (10.0 vs 5.0 vs 2.0 vs 1.25) → PSO can navigate
• Factored eval generates more trades from partial position changes → richer performance metrics
• Models that learn Long100/Long50 switching get credit instead of being penalized
• Combined: PSO should find viable configs in 5-8 initial trials instead of relying on luck

Files Changed

File	Change	~Lines
evaluation/engine.rs	process_bar_factored()	50
hyperopt/adapters/dqn.rs	extract_objective() penalty	25
hyperopt/adapters/dqn.rs	Backtest loop wiring	3
Tests	Unit tests	80
Total		~160