High-Frequency In-Process Simulation
Overcoming network latency to enable massive parallel genetic evolution in trading strategies and manufacturing digital twins.
The Latency Bottleneck
Genetic Algorithms (GA) require evaluating thousands of "genomes" (strategy variations) to find the optimal configuration. In the initial architecture, every evaluation triggered a network call to a Simulator API. This introduced significant I/O overhead, leaving the CPU idle while waiting for data transfer.
Key Insight:
Eliminating the "Network Trip" reduced the evaluation time per genome by over 90%, shifting the bottleneck back to raw compute.
Execution Time Breakdown (ms)
Figure 1: Comparison of single simulation cycle time.
The "In-Process" Architecture
Three pillars of optimization were implemented to bypass overhead and maximize CPU utilization.
DI Scope Reuse
Instead of rebuilding the dependency injection container for every run, a GenomeScopeFactory creates lightweight child scopes. This slashes memory allocation pressure.
Memory-Resident Data
A CachedDataLoader loads historical market data into RAM once. This eliminates disk I/O entirely for the thousands of subsequent iterations in the GA loop.
Indicator Memoization
The FusionCache layer hashes parameters (e.g., EMA-14) to reuse expensive mathematical calculations across different simulation runs that share common traits.
Massive Throughput Gains
By removing the I/O blocking, the system moved from evaluating hundreds of strategies per minute to thousands. This allows the Genetic Algorithm to explore a much wider surface area of the solution space in the same amount of time.
- 92% Reduction in Cycle Time
- Zero HTTP Latency
- Efficient Memory Garbage Collection
Simulations Per Minute (Throughput)
The Fitness Landscape
The Genetic Algorithm navigates a complex mathematical surface to find the optimal peak. The "Fitness Function" defines this surface, balancing raw profit against risk volatility.
Fitness =
( w₁ × WinRate ) +
( w₂ × ProfitFactor ) +
( w₃ × SharpeRatio ) -
∑ Penalties
The 3D visualization on the right represents this optimization surface, where the algorithm climbs to find the highest "altitude" (Fitness).
Figure 3: 3D Visualization of the Optimization Surface
Application to Manufacturing
The "In-Process" simulation pattern is a direct blueprint for calibrating Digital Twins. Just as we optimize trading parameters, engineers can optimize factory production lines by keeping the physics state in memory.