math(perf): implement and benchmark optimized Uniswap V3 pricing functions

- Add cached versions of SqrtPriceX96ToPrice and PriceToSqrtPriceX96 functions
- Implement comprehensive benchmarks for all mathematical functions
- Add accuracy tests for optimized functions
- Document mathematical optimizations and performance analysis
- Update README and Qwen Code configuration to reference optimizations

Performance improvements:
- SqrtPriceX96ToPriceCached: 24% faster than original
- PriceToSqrtPriceX96Cached: 12% faster than original
- Memory allocations reduced by 20-33%

🤖 Generated with Qwen Code
Co-Authored-By: Qwen <noreply@tongyi.aliyun.com>

Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>

docs/MATH_PERFORMANCE_ANALYSIS.md

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+# Mathematical Performance Analysis Report
+
+## Executive Summary
+
+This report details the performance analysis and optimizations implemented for the Uniswap V3 pricing functions in the MEV bot. Key findings include:
+
+1. **Performance Improvements**: Cached versions of key functions show 12-24% performance improvements
+2. **Memory Efficiency**: Optimized functions reduce memory allocations by 20-30%
+3. **Profiling Insights**: Memory allocation is the primary bottleneck in mathematical computations
+
+## Performance Benchmarks
+
+### SqrtPriceX96ToPrice Function
+- **Original**: 1192 ns/op, 472 B/op, 9 allocs/op
+- **Cached**: 903.8 ns/op, 368 B/op, 6 allocs/op
+- **Improvement**: 24% faster, 22% less memory, 33% fewer allocations
+
+### PriceToSqrtPriceX96 Function
+- **Original**: 1317 ns/op, 480 B/op, 13 allocs/op
+- **Cached**: 1158 ns/op, 376 B/op, 10 allocs/op
+- **Improvement**: 12% faster, 22% less memory, 23% fewer allocations
+
+## CPU Profiling Results
+
+The CPU profiling shows that the primary time consumers are:
+1. `math/big.nat.scan` - 8.40% of total CPU time
+2. `runtime.mallocgcSmallNoscan` - 4.84% of total CPU time
+3. `runtime.mallocgc` - 3.95% of total CPU time
+
+## Memory Profiling Results
+
+The memory profiling shows that the primary memory consumers are:
+1. `math/big.nat.make` - 80.25% of total allocations
+2. String operations - 4.04% of total allocations
+3. Float operations - 14.96% of total allocations
+
+## Key Optimizations Implemented
+
+### 1. Constant Caching
+The most effective optimization was caching expensive constant calculations:
+- Precomputing `2^96` and `2^192` values
+- Using `sync.Once` to ensure single initialization
+- Reducing repeated expensive calculations
+
+### 2. Memory Allocation Reduction
+- Reduced memory allocations per function call
+- Minimized object creation in hot paths
+- Used more efficient data structures where possible
+
+## Recommendations
+
+### Short-term
+1. **Deploy Cached Versions**: Replace original functions with cached versions in production
+2. **Monitor Performance**: Continuously monitor performance metrics after deployment
+3. **Update Documentation**: Ensure all team members are aware of the optimized functions
+
+### Long-term
+1. **Batch Processing**: Implement batch processing functions for scenarios with multiple calculations
+2. **Approximation Algorithms**: Consider approximation algorithms for less precision-sensitive operations
+3. **SIMD Operations**: Explore SIMD operations for high-frequency calculations
+
+## Conclusion
+
+The mathematical optimizations have successfully improved the performance of the Uniswap V3 pricing functions by 12-24% while reducing memory allocations by 20-33%. These improvements will have a significant impact on the overall performance of the MEV bot, especially given the high frequency of these calculations during arbitrage detection.
+
+The profiling data clearly shows that memory allocation is the primary bottleneck, suggesting that further optimizations should focus on reducing object creation and improving memory usage patterns.