Trading Engine & Metrics Pipeline

A C++17 trading‐engine prototype with:

• Limit & Market orders with price–time priority
• TCP ingestion of CSV orders (orderId,accountId,symbol,side,type,price,quantity,timestamp\n)
• REST API (Boost.Beast) for order‐book snapshots and recent trades
• CORS support so any frontend can fetch /book/{symbol} and /trades/{symbol}
• Realtime metrics (order latency & throughput) → Kafka → InfluxDB → Grafana
• Simple JavaScript dashboard polling the REST API

Features

• Limit, Market & Cancel orders (price–time priority)
• Multi-symbol order-books
• Concurrent TCP order intake (port 9000)
• REST snapshot API (port 8080) for book & trades
• Kafka topics: orders, trades, metrics
• Metrics: order latency & throughput → InfluxDB
• Grafana dashboard for real-time visualization
• Unit tests (Catch2) covering core logic

Prerequisites

• C++17 toolchain (GCC ≥ 9 / Clang ≥ 10)
• CMake ≥ 3.15
• Boost.System (for Asio)
• librdkafka & librdkafka++ (C++ Kafka client)
• nlohmann/json (single-header)
• moodycamel::ConcurrentQueue (single-header)
• Docker & Docker Compose
• Python 3.7+ (for metrics consumer)

1. System prerequisites

• C++ toolchain: clang-14 or gcc-9+, CMake ≥ 3.15
• Boost (headers + System)
• librdkafka (for Kafka C++ producer)
• Python 3 (for feeders & metrics bridge)
• Docker & docker-compose

For example, with Mac:

brew update
brew install cmake boost librdkafka librdkafka++ pkg-config
brew install nlohmann-json
brew install docker docker-compose

(alternative setups for other OSes are definitely possible, but I haven't done them)

2. Bring up the data platform

docker-compose up -d

This will launch:

• Zookeeper @2181
• Kafka @9092
• InfluxDB 2.x @8086 (admin/admin, bucket=metrics, org=myorg)
• Grafana @3000 (default admin/admin)

3. Build & run the engine

mkdir build && cd build
cmake ..
make
./src/engine

• Listens for orders over TCP 9000
• Serves REST on HTTP 8080

4. Build & Run

mkdir -p build && cd build
cmake ..
make
ctest --output-on-failure
./src/engine

5. Start the metrics bridge

Create a virtualenv (optional):

python3 -m venv .venv
source .venv/bin/activate
pip install kafka-python influxdb-client
chmod +x ../metrics_consumer.py

Then:

./metrics_consumer.py

Consumes the Kafka metrics topic and writes into InfluxDB.

6. Drive the engine with simulated orders

a) Via nc

echo "1001,1,AAPL,0,0,150.00,5,1650000000000" | nc localhost 9000
echo "1002,2,AAPL,1,0,150.00,3,1650000000100" | nc localhost 9000

b) Automated script

./feed_orders.py --host localhost --port 9000 \
                 --symbols AAPL,GOOG,TSLA \
                 --rate 5 --limit 100
chmod +x feed_orders.py

Streams random orders (5 Hz, 100 total by default).

7. Launch the dashboard

Serve the web/ folder on port 8000:

cd web
python3 -m http.server 8000

Open http://localhost:8000 in your browser:

• Symbol dropdown (AAPL | GOOG | TSLA)
• Order‐book snapshot (top 10 bids)
• Recent trades (last 10)
• Auto-refresh every 2 s