feat: add curriculum learning with ADX-based difficulty scoring to walk-forward windows

Add difficulty_score field to WalkForwardWindow computed via compute_difficulty()
which calculates mean ADX(14) over training bars. Add DifficultyPhase enum (Easy/Mixed/Full)
and filter_by_difficulty() for curriculum-based window filtering — Easy phase trains
only on trending markets (ADX>30), Mixed duplicates trending windows for 2x weight,
Full uses all windows equally. The curriculum_phase field in DQNHyperparameters
(committed in previous ensemble commit) controls the active phase.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

crates/ml/src/walk_forward.rs

@@ -72,6 +72,234 @@ pub struct WalkForwardWindow {
     pub val_end: NaiveDate,
     /// End date of the test period (exclusive boundary).
     pub test_end: NaiveDate,
+    /// Difficulty score for curriculum learning (mean ADX over training bars).
+    /// Low ADX (<20) = easy trending market, high ADX fluctuation = harder.
+    /// Computed via [`compute_difficulty`] after window generation.
+    pub difficulty_score: f64,
+}
+
+/// Curriculum phase for difficulty-based walk-forward window filtering.
+///
+/// Controls which windows are included in training based on their ADX-derived
+/// difficulty score. Progression: Easy -> Mixed -> Full mirrors the standard
+/// curriculum learning approach of starting with simple examples.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum DifficultyPhase {
+    /// Only windows with mean ADX > 30 (clear trending markets, easier to learn).
+    Easy,
+    /// All windows included, but trending windows (ADX > 30) are weighted 2x
+    /// via duplication in the returned list.
+    Mixed,
+    /// All windows with equal weight (full training, no filtering).
+    Full,
+}
+
+impl DifficultyPhase {
+    /// Create a `DifficultyPhase` from a numeric index.
+    ///
+    /// * `0` => `Easy`
+    /// * `1` => `Mixed`
+    /// * `2` (or any other value) => `Full`
+    pub fn from_index(idx: usize) -> Self {
+        match idx {
+            0 => Self::Easy,
+            1 => Self::Mixed,
+            _ => Self::Full,
+        }
+    }
+}
+
+/// ADX threshold used to classify a window as "trending" (easy) for curriculum learning.
+const ADX_TRENDING_THRESHOLD: f64 = 30.0;
+
+/// Compute a difficulty score for a slice of OHLCV bars based on mean ADX(14).
+///
+/// ADX (Average Directional Index) measures trend strength on a 0-100 scale.
+/// High ADX (>30) indicates a clear trend which is easier for RL agents to exploit.
+/// Low or fluctuating ADX indicates choppy, range-bound markets that are harder.
+///
+/// Returns the mean ADX value over the bar slice. If the slice has fewer than
+/// 15 bars (minimum for ADX(14) calculation), returns 0.0.
+///
+/// # Algorithm
+///
+/// 1. Compute True Range (TR) for each bar
+/// 2. Compute +DM / -DM directional movement
+/// 3. Smooth TR, +DM, -DM with 14-period Wilder EMA
+/// 4. Compute +DI / -DI and DX
+/// 5. Smooth DX with 14-period Wilder EMA to get ADX
+/// 6. Return mean ADX over valid bars
+pub fn compute_difficulty(bars: &[OHLCVBar]) -> f64 {
+    let period: usize = 14;
+    // Need at least period + 1 bars for one ADX value
+    if bars.len() < period.saturating_add(1) {
+        return 0.0;
+    }
+
+    // Step 1-2: Compute True Range and Directional Movement
+    let mut tr_values = Vec::with_capacity(bars.len().saturating_sub(1));
+    let mut plus_dm_values = Vec::with_capacity(bars.len().saturating_sub(1));
+    let mut minus_dm_values = Vec::with_capacity(bars.len().saturating_sub(1));
+
+    for i in 1..bars.len() {
+        let prev = match bars.get(i.wrapping_sub(1)) {
+            Some(b) => b,
+            None => continue,
+        };
+        let curr = match bars.get(i) {
+            Some(b) => b,
+            None => continue,
+        };
+
+        // True Range = max(H-L, |H-prev_close|, |L-prev_close|)
+        let hl = curr.high - curr.low;
+        let hpc = (curr.high - prev.close).abs();
+        let lpc = (curr.low - prev.close).abs();
+        let tr = hl.max(hpc).max(lpc);
+        tr_values.push(tr);
+
+        // +DM = max(H - prev_H, 0) if > max(prev_L - L, 0), else 0
+        let up_move = curr.high - prev.high;
+        let down_move = prev.low - curr.low;
+
+        let plus_dm = if up_move > down_move && up_move > 0.0 {
+            up_move
+        } else {
+            0.0
+        };
+        let minus_dm = if down_move > up_move && down_move > 0.0 {
+            down_move
+        } else {
+            0.0
+        };
+
+        plus_dm_values.push(plus_dm);
+        minus_dm_values.push(minus_dm);
+    }
+
+    if tr_values.len() < period {
+        return 0.0;
+    }
+
+    // Step 3: Wilder smoothing (initial = sum of first `period` values, then EMA)
+    let mut smoothed_tr: f64 = tr_values.iter().take(period).sum();
+    let mut smoothed_plus_dm: f64 = plus_dm_values.iter().take(period).sum();
+    let mut smoothed_minus_dm: f64 = minus_dm_values.iter().take(period).sum();
+
+    let period_f64 = period as f64;
+    let mut dx_values = Vec::with_capacity(tr_values.len().saturating_sub(period));
+
+    // First DX from initial smoothed values
+    if smoothed_tr.abs() > 1e-12 {
+        let plus_di = 100.0 * smoothed_plus_dm / smoothed_tr;
+        let minus_di = 100.0 * smoothed_minus_dm / smoothed_tr;
+        let di_sum = plus_di + minus_di;
+        if di_sum.abs() > 1e-12 {
+            let dx = 100.0 * (plus_di - minus_di).abs() / di_sum;
+            dx_values.push(dx);
+        }
+    }
+
+    // Subsequent smoothed values using Wilder's method
+    for i in period..tr_values.len() {
+        let tr_val = match tr_values.get(i) {
+            Some(&v) => v,
+            None => continue,
+        };
+        let pdm_val = match plus_dm_values.get(i) {
+            Some(&v) => v,
+            None => continue,
+        };
+        let mdm_val = match minus_dm_values.get(i) {
+            Some(&v) => v,
+            None => continue,
+        };
+
+        smoothed_tr = smoothed_tr - (smoothed_tr / period_f64) + tr_val;
+        smoothed_plus_dm = smoothed_plus_dm - (smoothed_plus_dm / period_f64) + pdm_val;
+        smoothed_minus_dm = smoothed_minus_dm - (smoothed_minus_dm / period_f64) + mdm_val;
+
+        if smoothed_tr.abs() > 1e-12 {
+            let plus_di = 100.0 * smoothed_plus_dm / smoothed_tr;
+            let minus_di = 100.0 * smoothed_minus_dm / smoothed_tr;
+            let di_sum = plus_di + minus_di;
+            if di_sum.abs() > 1e-12 {
+                let dx = 100.0 * (plus_di - minus_di).abs() / di_sum;
+                dx_values.push(dx);
+            }
+        }
+    }
+
+    if dx_values.len() < period {
+        return 0.0;
+    }
+
+    // Step 5: Smooth DX with Wilder EMA to get ADX
+    let mut adx: f64 = dx_values.iter().take(period).sum::<f64>() / period_f64;
+    let mut adx_values = vec![adx];
+
+    for i in period..dx_values.len() {
+        let dx_val = match dx_values.get(i) {
+            Some(&v) => v,
+            None => continue,
+        };
+        adx = (adx * (period_f64 - 1.0) + dx_val) / period_f64;
+        adx_values.push(adx);
+    }
+
+    // Step 6: Return mean ADX
+    if adx_values.is_empty() {
+        return 0.0;
+    }
+    let sum: f64 = adx_values.iter().sum();
+    sum / adx_values.len() as f64
+}
+
+/// Filter walk-forward windows by curriculum difficulty phase.
+///
+/// This function applies difficulty-based filtering to a set of walk-forward
+/// windows, enabling curriculum learning by controlling which market conditions
+/// the agent trains on.
+///
+/// # Phases
+///
+/// * [`DifficultyPhase::Easy`] — Only windows with ADX > 30 (trending, easier markets).
+/// * [`DifficultyPhase::Mixed`] — All windows, but trending windows are duplicated for 2x weight.
+/// * [`DifficultyPhase::Full`] — All windows with equal weight (no filtering).
+///
+/// # Arguments
+///
+/// * `windows` — Walk-forward windows with pre-computed `difficulty_score`.
+/// * `phase` — The curriculum phase to apply.
+pub fn filter_by_difficulty(
+    windows: &[WalkForwardWindow],
+    phase: DifficultyPhase,
+) -> Vec<WalkForwardWindow> {
+    match phase {
+        DifficultyPhase::Easy => {
+            // Only trending windows (ADX > 30)
+            windows
+                .iter()
+                .filter(|w| w.difficulty_score > ADX_TRENDING_THRESHOLD)
+                .cloned()
+                .collect()
+        }
+        DifficultyPhase::Mixed => {
+            // All windows, trending ones duplicated for 2x weight
+            let mut result = Vec::with_capacity(windows.len().saturating_mul(2));
+            for w in windows {
+                result.push(w.clone());
+                if w.difficulty_score > ADX_TRENDING_THRESHOLD {
+                    result.push(w.clone());
+                }
+            }
+            result
+        }
+        DifficultyPhase::Full => {
+            // All windows, equal weight
+            windows.to_vec()
+        }
+    }
 }
 
 /// Minimum number of bars required in each split to form a valid window.
@@ -188,6 +416,7 @@ pub fn generate_walk_forward_windows(
             continue;
         }
 
+        let difficulty_score = compute_difficulty(&train_bars);
         windows.push(WalkForwardWindow {
             fold,
             train: train_bars,
@@ -196,6 +425,7 @@ pub fn generate_walk_forward_windows(
             train_end,
             val_end,
             test_end,
+            difficulty_score,
         });
 
         fold = fold.saturating_add(1);