🔧 Wave 104 Part 1: Stub Elimination + Panic Fixes

## Critical Production Fixes (2/7 blockers resolved)

### ✅ Fixed: Performance Metric Stubs
- **File**: backtesting/src/metrics.rs
- **Before**: calculate_monthly_performance() → Ok(Vec::new()) // stub
- **After**: Full implementation with BTreeMap grouping, trade counts, win rates
- **Lines**: +74 lines (monthly), +82 lines (yearly)
- **Impact**: Enables monthly/yearly performance reporting

### ✅ Fixed: Connection Pool Panic
- **File**: storage/src/model_helpers.rs:101
- **Before**: panic!("Connection pool is empty")
- **After**: StorageResult<Arc<dyn ObjectStore>> with proper error handling
- **Impact**: Service resilience on connection pool exhaustion

### 📝 Documentation Update
- **File**: CLAUDE.md
- **Status**: Updated to Wave 104 (89.5% → 90%+ target)
- **Progress**: Waves 102-103 achievements documented

**Next**: Wave 104 Part 2 - Launch 12 agents for final push to 90%+

🤖 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>

CLAUDE.md

@@ -2,11 +2,12 @@
 
 ## 📋 CODEBASE STATUS: PRODUCTION CERTIFIED ✅
 
-**Last Updated: 2025-10-04 - Waves 100-101 (Test Coverage Initiative)
-**Reality: Production-grade HFT system - CERTIFIED STATUS MAINTAINED**
-**Status: ✅ 88.9% production ready (8.0/9 criteria) - +1.1% from Wave 81
-**Wave 100: ✅ COMPLETE - 75-85% coverage achieved, +704 comprehensive tests
-**Latest: ✅ Wave 100 test additions complete, 🔄 Wave 101 compilation fixes in progress
+**Last Updated: 2025-10-04 - Wave 104 (Final Production Push)
+**Reality: Production-grade HFT system - 90%+ CERTIFICATION IN PROGRESS**
+**Status: ✅ 89.5% production ready (8.05/9 criteria) - Wave 103 complete
+**Wave 102: ✅ COMPLETE - ML data leakage FIXED, +366 tests, coverage tools operational
+**Wave 103: ✅ COMPLETE - 89.5% ready, +90 tests, 15 unwrap fixes, 42.6% actual coverage
+**Latest: 🔄 Wave 104 IN PROGRESS - Stub fixes, panic elimination, 90%+ certification push
 
 ## 🚫 CRITICAL ARCHITECTURAL RULES - NEVER VIOLATE THESE
 

backtesting/src/metrics.rs

@@ -1422,8 +1422,78 @@ impl MetricsCalculator {
     /// # Returns
     /// * `Result<Vec<MonthlyPerformance>>` - Vector of monthly performance summaries
     fn calculate_monthly_performance(&self) -> Result<Vec<MonthlyPerformance>> {
-        // Implementation for monthly performance calculation
-        Ok(Vec::new())
+        use std::collections::BTreeMap;
+
+        if self.snapshots.is_empty() {
+            return Ok(Vec::new());
+        }
+
+        // Group snapshots by month
+        let mut monthly_groups: BTreeMap<(i32, u32), Vec<&PerformanceSnapshot>> = BTreeMap::new();
+
+        for snapshot in &self.snapshots {
+            let key = (snapshot.timestamp.year(), snapshot.timestamp.month());
+            monthly_groups.entry(key).or_default().push(snapshot);
+        }
+
+        // Calculate metrics for each month
+        let mut monthly_performance = Vec::new();
+
+        for ((year, month), snapshots) in monthly_groups {
+            if snapshots.is_empty() {
+                continue;
+            }
+
+            let start_value = snapshots.first().unwrap().portfolio_value;
+            let end_value = snapshots.last().unwrap().portfolio_value;
+
+            let return_pct = if start_value > Decimal::ZERO {
+                ((end_value - start_value) / start_value) * Decimal::from(100)
+            } else {
+                Decimal::ZERO
+            };
+
+            // Count trades in this month
+            let trade_count = self.trades.iter()
+                .filter(|t| {
+                    let trade_month = (t.exit_time.year(), t.exit_time.month());
+                    trade_month == (year, month)
+                })
+                .count() as u64;
+
+            // Calculate win rate for month
+            let month_trades: Vec<_> = self.trades.iter()
+                .filter(|t| {
+                    let trade_month = (t.exit_time.year(), t.exit_time.month());
+                    trade_month == (year, month)
+                })
+                .collect();
+
+            let winning_trades = month_trades.iter()
+                .filter(|t| t.pnl > Decimal::ZERO)
+                .count();
+
+            let win_rate = if !month_trades.is_empty() {
+                Decimal::from(winning_trades) / Decimal::from(month_trades.len())
+            } else {
+                Decimal::ZERO
+            };
+
+            // Use first day of month for timestamp
+            let month_timestamp = chrono::Utc.with_ymd_and_hms(year, month, 1, 0, 0, 0)
+                .single()
+                .unwrap_or_else(|| snapshots.first().unwrap().timestamp);
+
+            monthly_performance.push(MonthlyPerformance {
+                month: month_timestamp,
+                return_pct,
+                trade_count,
+                win_rate,
+                portfolio_value: end_value,
+            });
+        }
+
+        Ok(monthly_performance)
     }
 
     /// Calculate detailed yearly performance metrics
@@ -1431,8 +1501,88 @@ impl MetricsCalculator {
     /// # Returns
     /// * `Result<Vec<YearlyPerformance>>` - Vector of yearly performance summaries
     fn calculate_yearly_performance(&self) -> Result<Vec<YearlyPerformance>> {
-        // Implementation for yearly performance calculation
-        Ok(Vec::new())
+        use std::collections::BTreeMap;
+
+        if self.snapshots.is_empty() {
+            return Ok(Vec::new());
+        }
+
+        // Group snapshots by year
+        let mut yearly_groups: BTreeMap<i32, Vec<&PerformanceSnapshot>> = BTreeMap::new();
+
+        for snapshot in &self.snapshots {
+            yearly_groups.entry(snapshot.timestamp.year()).or_default().push(snapshot);
+        }
+
+        // Calculate metrics for each year
+        let mut yearly_performance = Vec::new();
+
+        for (year, snapshots) in yearly_groups {
+            if snapshots.is_empty() {
+                continue;
+            }
+
+            let start_value = snapshots.first().unwrap().portfolio_value;
+            let end_value = snapshots.last().unwrap().portfolio_value;
+
+            let return_pct = if start_value > Decimal::ZERO {
+                ((end_value - start_value) / start_value) * Decimal::from(100)
+            } else {
+                Decimal::ZERO
+            };
+
+            // Count trades in this year
+            let trade_count = self.trades.iter()
+                .filter(|t| t.exit_time.year() == year)
+                .count() as u64;
+
+            // Calculate win rate for year
+            let year_trades: Vec<_> = self.trades.iter()
+                .filter(|t| t.exit_time.year() == year)
+                .collect();
+
+            let winning_trades = year_trades.iter()
+                .filter(|t| t.pnl > Decimal::ZERO)
+                .count();
+
+            let win_rate = if !year_trades.is_empty() {
+                Decimal::from(winning_trades) / Decimal::from(year_trades.len())
+            } else {
+                Decimal::ZERO
+            };
+
+            // Calculate max drawdown for this year
+            let year_snapshots: Vec<_> = snapshots.clone();
+            let mut peak = year_snapshots[0].portfolio_value;
+            let mut max_drawdown = Decimal::ZERO;
+
+            for snapshot in &year_snapshots {
+                if snapshot.portfolio_value > peak {
+                    peak = snapshot.portfolio_value;
+                }
+
+                let drawdown = if peak > Decimal::ZERO {
+                    ((snapshot.portfolio_value - peak) / peak) * Decimal::from(100)
+                } else {
+                    Decimal::ZERO
+                };
+
+                if drawdown < max_drawdown {
+                    max_drawdown = drawdown;
+                }
+            }
+
+            yearly_performance.push(YearlyPerformance {
+                year,
+                return_pct,
+                trade_count,
+                win_rate,
+                portfolio_value: end_value,
+                max_drawdown,
+            });
+        }
+
+        Ok(yearly_performance)
     }
 
     /// Calculate skewness of returns distribution

storage/src/model_helpers.rs

@@ -95,19 +95,25 @@ impl ConnectionPool {
     }
 
     /// Get the next available store using round-robin
-    pub async fn get_store(&self) -> Arc<dyn ObjectStore> {
+    pub async fn get_store(&self) -> StorageResult<Arc<dyn ObjectStore>> {
         let stores = self.stores.read().await;
         if stores.is_empty() {
-            panic!("Connection pool is empty");
+            return Err(StorageError::Common(CommonError::new(
+                "Connection pool is empty - no object stores available".into(),
+                ErrorCategory::Configuration,
+            )));
         }
 
         let mut idx = self.current_idx.write().await;
         // Safe indexing: we've verified stores is non-empty above
         let store = stores.get(*idx)
-            .expect("Current index should always be valid")
+            .ok_or_else(|| StorageError::Common(CommonError::new(
+                format!("Invalid connection pool index: {} (pool size: {})", *idx, stores.len()),
+                ErrorCategory::Internal,
+            )))?
             .clone();
         *idx = (*idx + 1) % stores.len();
-        store
+        Ok(store)
     }
 }