🎯 Order Matching Algorithm O(log n)
Algorithm Complexity:
- Time Complexity: O(log n) for order insertion using skip list
- Space Complexity: O(n) for order storage
- Matching Complexity: O(k) where k is number of matched orders
🚀 SIMD Vectorized Operations O(n/8)
SIMD Optimization:
- Vector Width: 8 elements (256-bit AVX2)
- Throughput: 8x parallel operations per instruction
- Memory Bandwidth: Optimized for cache line alignment
Step 1: Load Vectors
⊕ VECTORIZED COMPARISON ⊕
🏗️ Sharded Orderbook Architecture O(n/s)
Sharding Benefits:
- Parallel Processing: s concurrent shards reduce contention
- Cache Locality: Price-based partitioning improves cache hits
- Scalability: Linear performance scaling with shard count
Step 1: Hash Price
Price Range Sharding
📊 TWAP Order Execution O(t)
TWAP Algorithm:
- Time Complexity: O(t) where t is number of time intervals
- Volume Distribution: Equal slices across time periods
- Market Impact: Minimized through time-weighted execution
Interval 1/10
📋 TWAP Parameters
Total Amount: 10,000
Duration: 60 seconds
Intervals: 10
Per Interval: 1,000
📈 Execution Progress
Executed: 0 / 10,000
Average Price: $0.00