v2.0 to v2.0.0

---

name: broker-side-order-throttle-detection

description: Use when operating high-frequency or high-volume trading bots to measure

order acknowledgment round-trip latency (ACK RTT), detect silent broker-side order

throttling during market volatility using Exponentially Weighted Moving Average

(EWMA) and Variance (EWMVar), and dynamically back off order dispatch using an AIMD

congestion control engine.

domain: algorithmic-trading

subdomain: broker-integration

tags:

- broker-integration

- order-throttle

- latency-monitoring

- ack-rtt

- silent-throttling

- aimd-backoff

- ewma-anomaly-detection

brokers_frameworks:

- Broker Throttle Detector

- Python High-Frequency Engine

author: algo-trading-skills-contributors

license: Apache-2.0

---

## When to Use

Invoke this skill when executing order flow on broker APIs during high market volatility or news events. Brokers often silently throttle order processing (delaying order acknowledgments from <20ms to >800ms) without returning HTTP 429 rate limit error codes. Unmonitored silent throttling causes order queue buildup, stale fills, and slippage. This skill measures ACK round-trip time (RTT), detects statistical latency anomalies dynamically via EWMA and EWMVar ($Z \ge 3.0$), and applies Additive Increase, Multiplicative Decrease (AIMD) backoff.

## Prerequisites

- High-precision order submission timestamping and ACK event callback listeners.

- Understanding of smoothing factors (alpha) for exponential weighting.

## Workflow

1. **Record Order ACK Latency**:

- Capture submission timestamp $t_{\text{sub}}$ and acknowledgment timestamp $t_{\text{ack}}$. Compute $\text{RTT} = (t_{\text{ack}} - t_{\text{sub}}) \times 1000$ ms.

2. **Calculate Baseline Latency Statistics (EWMA, EWMVar)**:

- Continuously update EWMA and EWMVar for latency to adjust to structural network shifts quickly without $O(N)$ memory requirements.

3. **Detect Silent Throttle Anomalies**:

- Flag `SILENT_THROTTLE` if current RTT exceeds threshold:

$$\text{RTT} > \text{EWMA} + 3 \times \text{EWMStd} \quad \text{or} \quad \text{RTT} > 500\text{ms}$$

4. **Apply AIMD Order Dispatch Backoff**:

- When silent throttling is flagged, multiply the backoff delay (Multiplicative Decrease of dispatch rate). When normal, linearly decrease the backoff (Additive Increase of dispatch rate).

> Full procedure: see `references/workflows.md`.

> Standards reference: see `references/standards.md`.

> Printable pre-flight checklist: see `assets/checklist.md`.

## Common Pitfalls

- **Confusing Network Jitter with Broker Throttling**: Treating isolated single-packet network delays as systemic broker throttling. EWMVar handles jitter better than simple sliding windows.

- **Ignoring Minimum Variance Clamping**: Micro-bursting in a highly deterministic network can drop variance to near-zero, causing false positives on tiny 1ms delays. A variance clamp fixes this.

- **Static Latency Thresholds**: Using hardcoded latency limits that don't adjust to changing network conditions across trading sessions.

## Verification

- Simulate baseline RTT (15ms) followed by a 600ms latency spike and verify `SILENT_THROTTLE` classification.

- Confirm adaptive backoff delay applies AIMD logic correctly (multiplicative jump, additive decay).

- Run `python scripts/test_throttle_detector.py` and confirm 100% pass rate.

## Related Skills

- `multi-broker-rate-limit-handling`

- `tick-buffering-burst-handling`

- `structured-logging-for-post-incident-forensics`