Security, Compliance and Risk Controls
Why this matters for HFT engineers (beginner-friendly)
- In HFT, an unchecked order can cause market, financial, or regulatory harm in milliseconds. Think of your system like a basketball play: the
feed handlerpasses the ball, thestrategydrives to the rim, and theorder gatewaymust be the coach saying "no" when the shot is bad. A pre-trade check is that coach.
High-level flow (ASCII diagram)
[Exchange] ---> [NIC] ---> [Feed Handler] ---> [Strategy] ---> [Order Gateway] | | | <-- pre-trade -- | | risk checks, | | throttling, | | auditing |
Core controls you'll implement and test in labs
- Pre-trade risk checks:
max_order_size,max_notional,allowed_symbols,only_market_hours. - Order throttling / rate limiting: per-client token bucket or leaky bucket to prevent bursts.
- Auditing: immutable, append-only logs of every order decision (
accept/reject+ reason + trace id). - Circuit breakers: disable live trading on severe rule breaches or exchanges errors.
- Compliance hooks: exportable audit events and sequence numbers for regulators.
Trade-offs and pragmatic advice (for folks with C++, Python, Java, C, JS backgrounds)
- C++: implement fast, allocation-free checks on the hot path. Use preallocated structures, plain arrays, and
enumreasons. - Python/JS: great for prototyping checks quickly, but watch allocations and the GIL/event loop — keep hot path tiny and push heavy work to background threads/processes.
- Java/C#: good middle-ground — use non-blocking queues and careful GC tuning.
- Always keep the decision (accept/reject) cheap and deterministic.
What an audit entry should include (minimal hot-path fields)
timestamp_ns,client_id,order_id,symbol,size,price,decision,reason,trace_id
Hands-on demo (C++):
- The code below simulates a tiny
Order Gatewayimplementing simple pre-trade checks, a per-client token-bucket throttler, and an audit ring buffer. - It prints decisions and a summary so you can tinker with thresholds and see effects immediately.
Try these challenges after running the demo:
- Change
MAX_ORDER_SIZEto a smaller value and re-run — how many orders are rejected? - Lower
TOKEN_RATEto throttle clients more; simulate a burst by increasingBURST_ORDERS. - Replace the C++ token bucket logic with a Python
asynciocoroutine (exercise for Python practice). - Add a
blacklistofclient_ids and ensure blacklisted clients are always rejected with reasonblacklisted.
Short notes on compliance and production hardening
- Make audit logs tamper-evident: append-only files with rotation, checksums, and offsite replication.
- Expose health and safety endpoints (read-only):
GET /health,GET /stats,POST /pause(operator-controlled circuit breaker). - Unit test the rule set and simulate time drift / replays in your backtest environment.
Ready? Run the C++ example below (main.cpp). Modify the constants to explore behaviors and think how you'd implement the same in Python or Java.
xxxxxxxxxx}using namespace std;using ns = chrono::nanoseconds;using steady = chrono::steady_clock;struct Order { string client; string symbol; int size; double price; uint64_t order_id;};// Simple token bucket for rate limiting (tokens measured as "orders")struct TokenBucket { double tokens = 0.0; double rate_per_sec = 1.0; // tokens added per second double capacity = 10.0; steady::time_point last = steady::now();

