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 handler passes the ball, the strategy drives to the rim, and the order gateway must 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 enum reasons.
• 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 Gateway implementing 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_SIZE to a smaller value and re-run — how many orders are rejected?
• Lower TOKEN_RATE to throttle clients more; simulate a burst by increasing BURST_ORDERS.
• Replace the C++ token bucket logic with a Python asyncio coroutine (exercise for Python practice).
• Add a blacklist of client_ids and ensure blacklisted clients are always rejected with reason blacklisted.

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.

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}

#include <iostream>

#include <vector>

#include <string>

#include <unordered_map>

#include <unordered_set>

#include <chrono>

#include <thread>

#include <random>

#include <sstream>

​

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();

​