Summary
I built a concurrent producer-consumer pipeline designed to handle high frequency market data (HFT) streams smoothly and reliably.
At its core is a hybrid form of a queue which uses two separate mutex locks (one for the head, one for the tail) to avoid the usual deadlock headache you get with a single lock design. Furthermore it tracks its size by using atomic operations for fast reads and supports batch pops so the consumer can grab multiple items at once instead of going one by one.
The whole system was tested using real-time SPY price data ( a frozen quote at $695.42), feeding the queue through a Shannon entropy calculator to prove it could handle realistic market workloads without dropping a single tick.
This is a behavioral measurement framework, not a trading strategy!
Core Contributions as the sole creator.
Verified components:
Shannon Entropy Engine
This is the calculator or metric / framework where the trader actions take place, the actions are the common ( BUY, SELL, HOLD) and spits out entropy in bits from 0.0 ( which equates to everyone doing the exact same thing) all the way to 1.585 ( which constitutes perfect chaos across all three actions) Every calculation matches the math.Adaptive Sliding Window
The window size dynamically scales between 50 to 500 observations, based on the entropy change rate. And shrinks back down during stable periods, this keeps the analysis both robust and responsive.Concurrent Market Pipeline
A Producer–consumer setup that keeps data flowing fast even when the entropy gets math heavy using the base 2 logarithms over H, ingestion never waits for the analysis, so it does not miss ticks during volatility spikes.Optimized Queue with Backpressure
Is the high performance queue that was designed to decouple the producer and consumer operations for smooth data flow in market pipelines. It uses dual mutexes ( one for the head and one for the tail ) which prevents the common issues found in single lock queues and eliminates potential deadlocks. The queue tracks its size with atomic counters, allowing fast and contention free reads to monitor capacity without blocking worker threads. Producer-consumer based, it supports batch pops so consumers can retrieve multiple items effeciently, reducing lock overhead. Backpressure then signals producers to slow when the queue is full, preventing overload during heavy loads
Behavioral Interpretation
Entropy acts as a market disorder index:
Low entropy (0–0.5 bits)
High consensus among traders; strong directional conviction.Medium entropy (0.5–1.2 bits)
Mixed behavior; liquid markets without dominant control.High entropy (1.2+ bits)
Maximum uncertainty; diverse and erratic behavior, often seen during consolidation or before regime changes.
Entropy measures structural unpredictability, not price magnitude.
Validation Results
Mathematical correctness:
- Unit tests validate entropy against known probability distributions
- Edge cases handled (empty windows, single-action windows, uniform distributions)
- Theoretical maximum (~1.585 bits for 3 states) was confirmed
Live SPY pipeline:
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Interpretation:
Live data produces entropy levels consistent with mixed trader behavior and no dominant directional regime.
Performance Notes:
Includes a short synthetic high-frequency benchmark (~5000 events)
Throughput estimates (millions of ops/sec on some machines) = micro-benchmark only - these are extrapolations
No claims are made about sustained production throughput or sub-millisecond guarantees
Needs dedicated benchmarking on target hardware, and is required for performance validation
Technical Validation
Language: C++17 (standard library + pthread)
Queue: Mutex-based and hybrid optimized queue
Concurrency: Thread-safe producer–consumer model
Entropy Range: 0.0 – 1.585 bits (3-state system)
Data Source: Simulated live SPY feed (±0.02% random walk)
Backpressure: Producer throttling at 90% queue capacity
Current Status
Validated:
Correct Shannon entropy computation
Adaptive window behavior
Concurrent pipeline stability
Backpressure handling under load
End-to-end live SPY integration
Unvalidated / ongoing research:
Long-horizon volatility correlation
Predictive capability
Real exchange order flow integration
Sustained performance characteristics
Lock-free queue necessity