High-Frequency Trading in Volatile Markets

Traditional HFT systems are structurally price-agnostic optimized for latency, inventory control, and spread capture. Directionality is often considered noise. But that boundary is fading. By blending medium-frequency signals those operating on 1–5 minute horizons into the microstructure layer, we steer passive flow toward statistically favorable outcomes. No need to cross spreads or sacrifice queue priority. Just soft biasing of reservation price, quote asymmetry, and inventory targets, all driven by predictive structure. This backtest reconstructs L2 books tick-by-tick and simulates fill probabilities using probabilistic queue models. There’s no market impact modeled by necessity but for small clips, the simulation closely approximates the real mechanics. It's realistic enough to evaluate how signal shapes flow, not just returns. I’ve put this strategy live today. The real test begins now seeing how these MFT-informed passive quotes behave under real market pressure. Results will unfold over the coming days. And for context: several major HFT hedge funds already run multi-frequency desks, routing predictive signals into execution engines. This is part of a broader convergence forecast meets fill logic.

Volatility is far more predictable than returns. Returns are noisy and prone to sudden regime shifts. Volatility follows patterns. It clusters together when markets are turbulent. It reverts to long-term averages after extreme periods. It behaves differently across daily, weekly, and monthly timeframes. And it responds predictably to market shocks and announcements. Verdad tested multiple volatility forecasting models across major asset classes. The HAR (Heterogeneous Autoregressive) model, which combines short, medium, and long-term volatility measures was the clear winner. Different participants operate on different timeframes. High-frequency traders react to intraday movements. Hedge funds might trade weekly around events. Pension funds and sovereign wealth funds move monthly or quarterly. The model captures how volatility propagates across these different time horizons. In their analysis spanning 1996 to 2025, HAR consistently outperformed alternatives. It beat simple averages, trailing volatility measures, exponentially weighted averages, and even the more complex GARCH models. The outperformance was consistent across currencies, commodities, equities, and bonds. Verdad enhanced the basic HAR model by adding forward-looking information from options markets. This HAR-IV model incorporates the VIX to capture market expectations of future volatility. Adding implied volatility improved performance across all evaluation metrics. The enhancement proves particularly valuable during regime shifts or before major macro events. Options markets often price in risks that backward-looking measures haven't captured yet. Simple models often beat complex ones. The HAR model uses basic linear regression with lagged volatility measures. Despite its simplicity, it outperforms more sophisticated alternatives. Complexity does not always equals superiority in financial modeling. https://lnkd.in/eM_wktsH

Even with best-in-class #HFT infrastructure, we’re consistently losing money, I've been told. And this is how we fixed it👇 This was the concern a Managing Director at a proprietary HFT firm shared with me. Despite cutting-edge DMA, #Rust-optimized systems, and colocation setups, they were still struggling. As their executive advisor, I worked with their leadership team to uncover the root causes of their situation and engineer a complete turnaround. Here’s what we found and how we fixed it: 1️⃣ Culprit #1: Infrastructure Optimization Gaps. Latency metrics looked strong on paper, but after creating a framework to measure every single part, the team revealed queue inefficiencies and unoptimized execution paths that needed fine-tuning. We established a quantifiable baseline and uncovered critical bottlenecks that, once addressed, significantly improved trade timing. 2️⃣ Culprit #2: Lack of Analytics for Performance Tracking The firm lacked a system to systematically track and analyze trading and risk performance. I help their team to developed an analytic framework to provide real-time insights, enabling data-driven decision-making and early detection of inefficiencies. 3️⃣ Culprit #3: Inefficient Market Microstructure Exploitation I helped their team enhance strategies by dynamically adjusting order placement and queue positioning, leveraging order book imbalances and latency arbitrage. Predictive models were also implemented to anticipate shifts in market microstructure, securing an edge in fleeting opportunities. 4️⃣ Building Advanced Execution Capabilities I guided their executives in overhauling their execution pipeline with sub-millisecond response times, integrating low-latency signal processing. Custom OMS optimizations improved order fill rates, ensuring their strategies operated at peak efficiency. The Results? In just ten months: > Latency improved by 12%, optimizing execution and queue positions. > Two new alpha strategies developed by the research team delivered 30% better returns compared to legacy systems. > Losses transformed into a 25% increase in quarterly profitability, with consistent positive returns across market conditions. Takeaway: Identifying the root causes of inefficiencies—whether technical or strategic—is the first step to unlocking profitability. Is your team having similar challenges? we can explore these or other techniques. Every situation is unique and needs its unique attention. #hft #trading #investmentbanking