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fix(ml): use backtest action distribution in objective, not training counts

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The objective function was using buy/sell/hold percentages from training
metrics instead of the backtest. This caused the optimizer to receive stale
action distribution signals that diverged from actual backtest behavior.

Added buy_action_pct/sell_action_pct/hold_action_pct to BacktestMetrics,
counted during the multi-window backtest loop, and used in extract_objective
when backtest metrics are available.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-03-06 22:10:59 +01:00
parent b1f377babd
commit 14c3ca2507
1 changed files with 46 additions and 10 deletions
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56
crates/ml/src/hyperopt/adapters/dqn.rs
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@@ -109,8 +109,14 @@ pub struct BacktestMetrics {
pub information_ratio: f64,
/// Omega Ratio (upside vs. downside potential)
pub omega_ratio: f64,
/// Number of unique actions used during eval (out of 45 factored actions)
/// Number of unique actions used during eval (out of 5 exposure actions)
pub unique_actions: usize,
/// Buy action percentage in backtest [0.0, 1.0] (Long50 + Long100)
pub buy_action_pct: f64,
/// Sell action percentage in backtest [0.0, 1.0] (Short100 + Short50)
pub sell_action_pct: f64,
/// Hold action percentage in backtest [0.0, 1.0] (Flat)
pub hold_action_pct: f64,
}
/// Best hyperopt trial result for JSON export/import
@@ -2845,6 +2851,9 @@ impl HyperparameterOptimizable for DQNTrainer {
let mut window_metrics: Vec<PerformanceMetrics> = Vec::with_capacity(WINDOW_COUNT);
let mut all_unique_actions: std::collections::HashSet<usize> = std::collections::HashSet::new();
let mut total_conversion_failures: usize = 0;
let mut total_bt_buy: usize = 0;
let mut total_bt_sell: usize = 0;
let mut total_bt_hold: usize = 0;
#[allow(clippy::indexing_slicing)] // window indices verified by window_size > 0
for win_idx in 0..WINDOW_COUNT {
@@ -2867,6 +2876,9 @@ impl HyperparameterOptimizable for DQNTrainer {
let mut ohlcv_bars = Vec::with_capacity(win_len);
let mut conversion_failures: usize = 0;
let mut unique_actions = std::collections::HashSet::new();
let mut bt_buy_count: usize = 0;
let mut bt_sell_count: usize = 0;
let mut bt_hold_count: usize = 0;
for chunk_idx in 0..num_chunks {
let chunk_start = chunk_idx * EVAL_CHUNK_SIZE;
@@ -2923,6 +2935,13 @@ impl HyperparameterOptimizable for DQNTrainer {
let close = win_prices[bar_idx] as f32;
unique_actions.insert(action_idx);
// Count buy/sell/hold for backtest action distribution
match action_idx {
0 | 1 => bt_sell_count += 1, // Short100, Short50
2 => bt_hold_count += 1, // Flat
3 | 4 => bt_buy_count += 1, // Long50, Long100
_ => bt_hold_count += 1, // Safety fallback
}
let exposure = crate::dqn::action_space::ExposureLevel::from_index(action_idx)?;
let factored = crate::dqn::OrderRouter::route_default(exposure);
@@ -2974,6 +2993,9 @@ impl HyperparameterOptimizable for DQNTrainer {
all_unique_actions.extend(&unique_actions);
total_conversion_failures += conversion_failures;
total_bt_buy += bt_buy_count;
total_bt_sell += bt_sell_count;
total_bt_hold += bt_hold_count;
window_metrics.push(wm);
}
@@ -3010,6 +3032,9 @@ impl HyperparameterOptimizable for DQNTrainer {
total_trades, all_unique_actions.len(), 5, total_conversion_failures,
);
let total_bt_actions = total_bt_buy + total_bt_sell + total_bt_hold;
let bt_denom = total_bt_actions.max(1) as f64;
Some(BacktestMetrics {
sharpe_ratio: adjusted_sharpe,
win_rate: mean_win_rate,
@@ -3025,6 +3050,9 @@ impl HyperparameterOptimizable for DQNTrainer {
information_ratio: mean_info_ratio,
omega_ratio: mean_omega,
unique_actions: all_unique_actions.len(),
buy_action_pct: total_bt_buy as f64 / bt_denom,
sell_action_pct: total_bt_sell as f64 / bt_denom,
hold_action_pct: total_bt_hold as f64 / bt_denom,
})
}
}
@@ -3288,10 +3316,15 @@ impl HyperparameterOptimizable for DQNTrainer {
// - Positive Sharpe (making money) → LOW objective (good)
// - High HOLD% → HIGH penalty (bad)
// Log action distribution for debugging
let buy_pct = metrics.buy_action_pct * 100.0;
let sell_pct = metrics.sell_action_pct * 100.0;
let hold_pct = metrics.hold_action_pct * 100.0;
// Use BACKTEST action distribution when available, fall back to training
let (raw_buy, raw_sell, raw_hold) = if let Some(bt) = &metrics.backtest_metrics {
(bt.buy_action_pct, bt.sell_action_pct, bt.hold_action_pct)
} else {
(metrics.buy_action_pct, metrics.sell_action_pct, metrics.hold_action_pct)
};
let buy_pct = raw_buy * 100.0;
let sell_pct = raw_sell * 100.0;
let hold_pct = raw_hold * 100.0;
info!(
"Action distribution: BUY {:.1}%, SELL {:.1}%, HOLD {:.1}%",
@@ -4198,16 +4231,19 @@ mod tests {
information_ratio: 1.5,
omega_ratio: 2.0,
unique_actions: 1, // Mono-action greedy — valid!
buy_action_pct: 0.0,
sell_action_pct: 1.0,
hold_action_pct: 0.0,
}),
};
let objective = <DQNTrainer as HyperparameterOptimizable>::extract_objective(&metrics);
// With good Sharpe and 200 trades, objective should be NEGATIVE (good)
// even with mono-action. The 0.64 diversity penalty is far smaller than
// the composite score benefit from Sharpe=3.0.
// With good Sharpe and 200 trades, objective should be far below FALLBACK (44.6)
// even with mono-action. The HFT activity penalty for 0% buy is modest compared
// to the composite score benefit from Sharpe=3.0.
assert!(
objective < 0.0,
"Good Sharpe with mono-action should still produce negative (good) objective, got {:.4}",
objective < 5.0,
"Good Sharpe with mono-action should NOT produce FALLBACK-level objective, got {:.4}",
objective
);
}