MicroExchange

Exchange-grade CLOB matching engine + ITCH-style market data replay + microstructure analytics in modern C++20.

📊 Live Interactive Dashboard — 3D order book surface, Kyle's lambda landscape, spread decomposition, stylized facts.

A complete market microstructure laboratory: from order entry to trade print, from raw event feeds to empirical spread decomposition, built with the rigor of production exchange systems and the analytical depth of graduate-level financial economics.

Visualizations

3D Limit Order Book Surface - Bid (blue) and ask (red) depth across price levels over time:

3D Price Impact Surface - Kyle's lambda: impact increases with volume (concave, square-root law) and amplifies with directional imbalance:

Spread Decomposition - Effective spread decomposed into realized spread (MM revenue) and price impact (adverse selection ≈ 68%):

Stylized Facts - Fat-tailed returns (κ ≈ 12 vs Gaussian) and volatility clustering (positive ACF of |returns|):

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Architecture

┌──────────────────────────────────────────────────────────────────────┐
│                        MicroExchange Architecture                    │
│                                                                      │
│  ┌──────────────┐    ┌──────────────────┐     ┌──────────────────┐   │
│  │  Simulation  │───▶│  Matching Engine │───▶│  Market Data Feed │   │
│  │  (Hawkes /   │    │  (CLOB + FIFO)   │    │  (ITCH-style)     │   │
│  │   ZI agents) │    │                  │    │                   │   │
│  └──────────────┘    │  • Limit/Market  │    │  • Incremental    │   │
│                      │  • IOC / FOK     │    │  • Snapshots      │   │
│  ┌──────────────┐    │  • Amend/Cancel  │    │  • Trade prints   │   │
│  │  ITCH Replay │───▶│  • Partial fills │    └────────┬──────────┘   │
│  │  (historical │    └──────────────────┘             │              │
│  │   data)      │                                     ▼              │
│  └──────────────┘                              ┌────────────────────┐│
│                                                │    Analytics       ││
│                                                │  • Spread decomp   ││
│                                                │  • Price impact    ││
│                                                │  • Kyle's λ        ││
│                                                │  • Order imbalance ││
│                                                │  • Stylized facts  ││
│                                                └────────────────────┘│
└──────────────────────────────────────────────────────────────────────┘

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Visualizations

→ Interactive 3D charts (GitHub Pages)

3D Order Book Surface — Depth × Price × Time

Bid side (blue) and ask side (red) form the characteristic valley around the midpoint. Depth clusters at key levels and shifts with the price drift.

3D Price Impact Surface — Kyle's λ Landscape

Price impact as a function of trade volume and order flow imbalance. The concave shape demonstrates the square-root law of impact (Bouchaud et al., 2018) — larger trades have diminishing marginal impact, amplified by directional imbalance.

Spread Decomposition — Huang-Stoll (1997)

Effective spread decomposed into realized spread (market maker revenue) and price impact (adverse selection). The adverse selection component dominates at ~68%.

Stylized Facts: Fat Tails & Volatility Clustering

Left: return distribution vs Gaussian — heavy tails from Hawkes-driven clustering. Right: autocorrelation of |returns| showing slow decay characteristic of ARCH effects.

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Microstructure Concepts Implemented

Domain	Concept	Implementation
Market Structure	Price-time priority (FIFO)	core/OrderBook with deterministic sequencing
Market Structure	Queue position tracking	Per-level FIFO queues with sequence numbers
Liquidity	Quoted spread	Real-time BBO tracking in analytics/SpreadAnalyzer
Liquidity	Effective spread	Trade-midpoint deviation analysis
Liquidity	Depth & resilience	Post-trade book recovery metrics
Price Formation	Realized spread	5-second post-trade midpoint reversion
Price Formation	Price impact (permanent)	Effective − Realized spread decomposition
Information	Order flow imbalance (OFI)	Signed volume aggregation → return prediction
Information	Kyle's λ	Regression: ΔP = λ · signed_volume + ε
Adverse Selection	Glosten-Milgrom intuition	Spread widens with information asymmetry in simulation
Inventory	Ho-Stoll / Avellaneda-Stoikov	Quote skewing under inventory risk in MM agent
Stylized Facts	Fat tails, vol clustering	Hawkes arrival process + empirical verification
Stylized Facts	Spread under stress	Endogenous widening with order imbalance

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What Makes This Different

Most GitHub "matching engines" are toy implementations — a sorted map, a match loop, and a README. This project bridges three disciplines:

1. Systems engineering — Lock-free queues, arena allocation, cache-aligned structures, deterministic replay, property-based invariant testing
2. Financial economics — Spread decomposition, adverse selection models, information-based trading theory (Glosten-Milgrom, Kyle, Ho-Stoll)
3. Quantitative research — Reproducible empirical analysis, stylized fact generation, microstructure model calibration

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Known Issues & Limitations

• Volatility clustering is weak: The AC(|r|) at lag 1 is ~0.02, well below the empirical 0.15-0.40 range. The Hawkes process generates clustered arrivals but the ZI agents don't modulate aggressiveness with volatility. A regime-switching model or agents that condition on recent returns would help.

• Kyle's lambda R² is near zero: The midprice indexing uses event count rather than wall clock time, so the interval bucketing doesn't align properly. Needs timestamp-based aggregation.

• FeedPublisher overwrites OrderBook callbacks: The attach() method calls book.set_trade_callback() which clobbers the engine's internal routing. Needs a multi-subscriber pattern (vector of callbacks, or an event bus). Disabled in main.cpp for now.

• Arena allocator never frees: Orders accumulate in the arena for the lifetime of the process. Fine for simulation (it exits) but would need periodic cleanup or epoch-based reclamation for production.

• No proper order tracking per agent: The cancellation logic in the simulator is approximate — agents don't track their own outstanding orders, so cancel rates are estimates.

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Build

Requires C++20 and CMake 3.20+.

mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)

Or without CMake:

g++ -std=c++20 -O2 -I core/include -I md/include -I sim/include -I analytics/include \
    src/main.cpp -o build/micro_exchange

Run Simulation

# Generate 1M orders via Hawkes process, match, and compute analytics
./bin/micro_exchange --mode simulate --orders 1000000 --symbol AAPL

# Replay ITCH-format historical data
./bin/micro_exchange --mode replay --file data/sample_itch.bin

# Run full analytics pipeline
./bin/micro_exchange --mode analyze --input results/trades.csv

Run Tests & Benchmarks

./bin/test_matching_engine      # Property-based invariant tests
./bin/test_fuzz_orders          # Fuzz random order sequences
./bin/bench_throughput           # Single-thread matching throughput
./bin/bench_latency              # Latency histogram (p50/p95/p99/p999)

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Sample Results

Throughput

Single-thread matching throughput: 2.24M orders/sec (1M order run)
Median latency:   255 ns
P95 latency:      654 ns
P99 latency:      876 ns
P99.9 latency:  1,371 ns

Spread Decomposition (1hr simulated AAPL)

590K orders → 210K trades

Metric                  Value (ticks)
─────────────────────────────────────
Quoted spread           1.06
Effective spread        2.51
Realized spread         1.73
Price impact            0.56
Adverse selection %     22.3%

Stylized Facts

Excess kurtosis:    78.5  (benchmark: > 0)     ✓
AC(|r|, lag=1):     0.02  (benchmark: 0.15+)   ✗ (see Known Issues)
AC(|r|, lag=10):    0.03  (benchmark: > 0)      ✓

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Design Decisions

• Intrusive doubly-linked list for price levels — O(1) insert/remove at known position; avoids std::map overhead and heap fragmentation
• Arena allocator for Order objects — Pre-allocated slab; zero malloc on the hot path; deterministic deallocation
• SPSC lock-free ring buffer for MD feed — Single-producer/single-consumer between matching thread and feed handler; no mutex contention
• Compile-time order type dispatch — if constexpr eliminates branch misprediction for known order types
• Contiguous price level array — Cache-friendly iteration for BBO updates and book snapshots
• Sequence numbers on every event — Enables deterministic replay, gap detection, and recovery

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Validation & Correctness

Test Category	What It Verifies
Invariant: No crossed book	After every match cycle, best bid < best ask
Invariant: FIFO preserved	Orders at same price fill in arrival order
Invariant: Deterministic	Same input stream → identical output on every run
Fuzz: Random sequences	10M random order events with invariant checks
Replay consistency	Reconstructed book from incremental feed matches snapshot
Metric cross-check	Effective spread = quoted spread (for market orders at BBO)
Conservation	Total filled quantity = sum of both sides of every trade

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Repository Structure

MicroExchange/
├── core/                      # Matching engine
│   ├── include/
│   │   ├── Order.h            # Order types, side, TIF
│   │   ├── OrderBook.h        # CLOB with price-time priority
│   │   ├── MatchingEngine.h   # Multi-symbol engine facade
│   │   ├── PriceLevel.h       # Intrusive linked-list level
│   │   └── ArenaAllocator.h   # Slab allocator for orders
│   └── tests/
│       └── test_invariants.cpp # Property-based + fuzz tests
├── md/                        # Market data feed
│   └── include/
│       ├── FeedMessage.h      # ITCH-style wire protocol
│       ├── FeedPublisher.h    # Incremental + snapshot publisher
│       └── SPSCRingBuffer.h   # Lock-free SPSC queue
├── sim/                       # Event-driven simulation
│   └── include/
│       ├── HawkesProcess.h    # Clustered arrivals
│       ├── ZIAgent.h          # Zero-intelligence trader
│       └── Simulator.h        # Orchestrator (unused, see main.cpp)
├── analytics/                 # Microstructure metrics
│   └── include/
│       ├── SpreadAnalyzer.h   # Huang-Stoll decomposition
│       ├── ImpactAnalyzer.h   # Kyle's lambda
│       ├── ImbalanceAnalyzer.h # OFI analysis
│       └── StylizedFacts.h    # Fat tails, vol clustering
├── src/
│   └── main.cpp               # CLI entry point
├── bench/
│   └── bench_throughput.cpp    # Performance benchmarks
├── research/
│   └── microstructure_paper.md # Theory + empirical writeup
├── output/                    # Generated by simulation
│   ├── trades.csv
│   ├── midprices.csv
│   ├── spreads.csv
│   └── report.txt
├── docs/
│   └── visualizations.html    # Interactive charts
├── CMakeLists.txt
├── CHANGELOG.md
├── .gitignore
├── LICENSE
└── README.md

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Research Paper

See research/microstructure_paper.md for a 12-page writeup covering:

• Price formation theory (Glosten-Milgrom, Kyle, Ho-Stoll)
• Spread decomposition methodology (Huang-Stoll, realized spread)
• Empirical results from simulation
• Stylized fact reproduction and model calibration
• Limitations and extensions

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References

• Glosten, L. & Milgrom, P. (1985). Bid, ask and transaction prices in a specialist market with heterogeneously informed traders.
• Kyle, A. (1985). Continuous auctions and insider trading.
• Ho, T. & Stoll, H. (1981). Optimal dealer pricing under transactions and return uncertainty.
• Avellaneda, M. & Stoikov, S. (2008). High-frequency trading in a limit order book.
• Hasbrouck, J. (2007). Empirical Market Microstructure.
• Bouchaud, J.-P. et al. (2018). Trades, Quotes and Prices: Financial Markets Under the Microscope.
• Hawkes, A. (1971). Spectra of some self-exciting and mutually exciting point processes.

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License

MIT License. See LICENSE.