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Clement D.

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Financial News: Prediction Markets, Sports Investing, and AI

by Clement D.

“The yield curve is no longer inverted—it’s steepening, a historical signal that often precedes a recession,” says renowned economist Paul Krugman. As institutional investors navigate the complexities of the current market landscape, this strategic memo aims to provide key insights that can inform your investment decisions.

In this article, we will explore the evolving dynamics of the prediction market, delving into the perspectives of Tristan Thompson, NBA champion and Tracy AI chief content officer, who shares his insights on the industry’s growth and future potential. Additionally, we will examine the business of sports investing, featuring insights from Ian Charles, Arctos Co-Managing Partner & Co-Founder, who discusses his company’s stakes in various franchises.

Furthermore, we will examine Tesla’s shareholder-initiated proposal to invest in xAI, with commentary from Tesla Board Chair Robyn Denholm, who emphasizes the need to address the “misunderstanding of AI in the marketplace.”

These video resources will provide a comprehensive understanding of the trends and opportunities shaping the finance landscape, empowering you to make informed strategic decisions.

🎥 Tristan Thompson on AI Ventures, Prediction Markets (Bloomberg)

In a video interview, Tristan Thompson, the NBA champion and chief content officer at Tracy AI, discusses the rapid growth of the prediction market, which has now become a billion-dollar industry. He suggests that this trend is spreading globally, with the potential to expand into the sports industry. Thompson’s insights highlight the increasing significance of prediction markets and their potential market implications.

🎥 The Business of Sports Investing (Bloomberg)

Ian Charles, Arctos Co-Managing Partner & Co-Founder, joined Bloomberg’s Dani Burger in Dallas to discuss his company’s stakes in over twenty sports franchises, including the Golden State Warriors, the Los Angeles Dodgers, the Pittsburgh Penguins, and the Chicago Cubs. The discussion highlighted the growing trend of institutional investors entering the sports industry, attracted by the potential for steady returns and the diversification benefits of sports team ownership. Charles provided insights into Arctos’ investment strategy, which focuses on acquiring minority stakes in well-managed teams with strong fan bases and growth potential. The presentation explored the market implications of this trend, suggesting that the increased availability of capital could lead to higher valuations and more opportunities for team owners to monetize their assets.

🎥 Tesla board chair on potential investment in xAI #shorts #xai #artificialintelligence #tesla #musk (Bloomberg)

In the annals of corporate history, the role of artificial intelligence in shaping the future of industry has been a subject of intense debate and speculation. It is against this backdrop that we find the recent remarks by Robyn Denholm, the esteemed Chair of Tesla’s Board, addressing the company’s potential investment in xAI. Denholm’s comments, shared in an interview with Edward Ludlow, shed light on a broader trend that has been unfolding over the past decade – the growing recognition of the transformative power of AI and the need for a more nuanced understanding of its implications within the financial and business spheres.

Denholm’s emphasis on the “misunderstanding of AI in the marketplace” underscores the challenges that have accompanied the rapid advancement of this technology. As the global economy navigates the uncharted waters of the 21st century, the ability to harness the potential of AI has become a critical strategic imperative for corporations seeking to maintain their competitive edge. Tesla’s exploration of investment opportunities in xAI, as highlighted in this discourse, reflects a broader shift in corporate priorities, where the long-term implications of technological innovation are being carefully weighed against the potential risks and rewards.

🎥 Yield Curve Says Recession—But the Job Market Disagrees (Capital Trading)

In a policy briefing, the unusual divergence between the inverted yield curve, a classic recession signal, and the still-strong U.S. labor market is examined. While falling leading indicators and a weak manufacturing PMI suggest an impending downturn, the resilience of the job market presents a puzzling contrast. This video explores the regulatory, macroeconomic, and systemic implications of this clash between economic indicators, probing whether this time is truly different or if the inevitable recession is merely being delayed. Investors are cautioned about the high-risk nature of CFDs and the need to carefully consider their individual financial circumstances and objectives before engaging in such complex financial instruments.

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News

Navigating Finance Trends: Insights for Investors

by Clement D.

“Tragedy Strikes the Political Landscape: The Assassination of Charlie Kirk Sparks Reflections on Violence and the State of America”

In a shocking turn of events, the recent assassination of prominent conservative figure Charlie Kirk has sent shockwaves through the nation, raising urgent questions about the state of political discourse and the growing threat of extremism. As the investigation unfolds, with law enforcement providing updates on the manhunt (LIVE: FBI, Utah police give update on Charlie Kirk shooter as manhunt continues — 9/11/2025), this tragedy serves as a somber reminder of the fragility of our democratic institutions and the need to confront the underlying societal issues that breed such senseless acts of violence.

Beyond the immediate details of the case, the broader economic and historical implications of this event cannot be ignored. As Bill Nygren, Oakmark Funds portfolio manager, notes in his analysis (The S&P isn’t the broad-based index it used to be, says Oakmark Fund’s Bill Nygren), the shifting dynamics within the stock market and the broader economy are inextricably linked to the social and political landscape. In this climate of heightened polarization and mistrust, the calls for a renewed focus on “more spirituality in America today and less hate,” as echoed by Robert Kraft, Kraft Group CEO and chairman and New England Patriots owner, take on a renewed urgency.

🎥 Trump Addresses Killing of Charlie Kirk (Bloomberg)

In the wake of the tragic assassination of conservative commentator Charlie Kirk, President Donald Trump has released a video addressing the nation. Speaking with the candor and gravity befitting a market strategist’s morning call, the former president urges all Americans, including the media, to confront the sobering reality that demonization and vitriol can have devastating consequences. With flags across the country lowered to half-staff for several days, this sober and forward-looking message from Trump underscores the need for unity and civility, even in the face of deep political divisions.

🎥 The S&P isn’t the broad-based index it used to be, says Oakmark Fund’s Bill Nygren (CNBC Television)

In a policy briefing, the regulatory, macroeconomic, and systemic implications of the video’s content are emphasized. The portfolio manager from Oakmark Funds highlights that the S&P 500 index, often considered a broad-based representation of the U.S. stock market, has become increasingly concentrated in a handful of large technology companies. This shift raises concerns about the index’s ability to accurately reflect the broader economy and the potential risks it may pose to investors and policymakers. The discussion also touches on the lingering effects of the 9/11 attacks and their impact on the financial markets, underscoring the need for robust regulatory frameworks and macroeconomic policies to enhance the resilience of the financial system.

🎥 LIVE: FBI, Utah police give update on Charlie Kirk shooter as manhunt continues — 9/11/2025 (CNBC Television)

The FBI and Utah law enforcement officials have provided an update on the ongoing manhunt for the suspect responsible for the assassination of conservative political activist Charlie Kirk during his appearance at Utah Valley University on September 11th, 2025. The press briefing comes after the release of two individuals of interest who were previously detained in connection with the shooting. The investigation remains active as authorities continue to pursue leads and gather evidence to apprehend the perpetrator of this tragic incident.

🎥 Robert Kraft: We need more spirituality in America today and less hate (CNBC Television)

As an investment strategist, I would frame this video as an intriguing opportunity to gain insight into the perspectives of a prominent business leader and sports franchise owner. While the tragic circumstances surrounding the discussion may present some inherent risks, the potential upside lies in the chance to better understand Kraft’s views on the role of spirituality and the need to address societal divisions in America today. The market sentiment, as evidenced by the interview’s placement on a popular financial news program, suggests a growing interest in the intersection of business, sports, and broader social issues. Investors would be wise to approach this video with an open mind, seeking to extract valuable insights that could inform their decision-making and potentially uncover emerging trends that may shape the investment landscape in the years ahead.

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News

Oil Prices Outlook: IEA Warns of Supply Surplus

by Clement D.

Oil Prices: Market Can’t Absorb Increase in Supplies, IEA’s Bosoni Says

As oil prices remain volatile, the International Energy Agency’s head of oil markets, Toril Bosoni, warns that the market may not be able to absorb the projected record oil surplus for next year. This uncertainty surrounding the supply-demand dynamics presents both risks and potential opportunities for investors navigating the finance landscape.

The video “Oil Prices: Market Can’t Absorb Increase in Supplies, IEA’s Bosoni Says” delves deeper into the IEA’s outlook, providing valuable insights that can inform investment strategies and risk management approaches.

Amidst this backdrop of market volatility, the article will also explore the potential implications of the Trump ally Charlie Kirk’s tragic shooting, as reported in the “Trump Ally Charlie Kirk Killed In Shooting | Daybreak Europe 9/11/2025” video. Additionally, the analysis will touch on Citi’s reassuring outlook, as detailed in the “Citi: No US Recession On The Horizon | Horizons Middle East & Africa 09/11/2025” video, and the potential for a shake-up in the Euro-Dollar-Yen currency trio, as discussed in the “Euro-Dollar-Yen Are Due for a Shake-Up: 3-Minute MLIV” video.

By framing the day’s news in terms of risk, opportunity, and uncertainty, this article aims to provide a concise, analytical overview to help readers navigate the complex and dynamic finance landscape.

🎥 Oil Prices: Market Can’t Absorb Increase in Supplies, IEA’s Bosoni Says (Bloomberg)

According to the International Energy Agency’s head of oil markets, Toril Bosoni, the oil market is unable to absorb the projected increase in supplies. The IEA’s latest report forecasts a record oil surplus of 3.33 million barrels per day in 2026, around 360,000 more per day than previously anticipated. This surplus is driven by OPEC+ continuing to revive production and the group’s rivals growing their output. The report’s findings suggest a challenging outlook for oil prices, as the market struggles to balance the significant increase in global supply.

🎥 Trump Ally Charlie Kirk Killed In Shooting | Daybreak Europe 9/11/2025 (Bloomberg)

On September 11, 2025, Bloomberg Daybreak Europe reported on a pivotal development in the United States. Conservative activist and Donald Trump ally Charlie Kirk has been shot dead. Kirk was credited with energizing America’s youth vote, paving the way for the President’s return to the White House. The program also covered other key financial and geopolitical events, including traders bracing for a critical U.S. inflation report that could impact the Federal Reserve’s policy decisions, the European Central Bank’s plan to maintain interest rates, and Poland’s call for more air defense support from NATO allies after a Russian drone incursion. The show featured interviews with industry experts such as Pilar Gomez-Bravo, co-Chief Investment Officer of Fixed Income at MFS Investment Management, and Chad Edwards, co-founder and CEO of Cusp.AI.

🎥 Citi: No US Recession On The Horizon | Horizons Middle East & Africa 09/11/2025 (Bloomberg)

In an exclusive conversation with Citigroup CEO Jane Fraser, the bank’s growth strategy and outlook for the Middle East region were discussed. The interview highlighted the regulatory, macroeconomic, and systemic implications of the bank’s operations, providing a policy briefing-style summary. Notably, the segment also covered the recent controversy surrounding the Israeli Prime Minister’s statements regarding Qatar’s hosting of the Hamas office, as well as the tragic shooting of conservative activist Charlie Kirk in Utah. The discussion featured insights from former US Ambassador to Saudi Arabia, Robert W. Jordan, and Haig Bathgate, CEO of Callanish Capital, offering a comprehensive analysis of the region’s economic and geopolitical landscape.

🎥 Euro-Dollar-Yen Are Due for a Shake-Up: 3-Minute MLIV (Bloomberg)

The video “Euro-Dollar-Yen Are Due for a Shake-Up: 3-Minute MLIV” analyzes the current market trends and implications for the major global currencies. Hosted by Bloomberg’s Anna Edwards, Guy Johnson, Valerie Tytel, and Paul Dobson, the discussion covers the potential for an interest rate cut by the Federal Reserve, the upcoming European Central Bank decision, and the anticipated volatility in the euro, dollar, and yen. The video also touches on the possibility of a hike by the Bank of Japan, suggesting significant shake-ups across the major currency pairs. The analysis provides valuable insights for finance analysts and investors seeking to navigate the current market landscape.

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News

Navigating Finance: C++ DSA, Labubu Resale, and Job Data Insights

by Clement D.

The permutation problem in C++ is a fundamental concept in data structures and algorithms, with significant implications for quantitative finance. Today’s finance stories explore the intersection of this technical subject and the broader business landscape, from the rise of Labubu plush toys to concerns surrounding the reliability of US jobs data. Later in this article, we will delve into a detailed video tutorial on C++ permutation and backtracking algorithms, as well as insights from industry leaders on the NFL’s global growth and big-ticket deals.

🎥 C++ DSA Permutation | Recursion & Backtracking | Data Structures and Algorithms (Learn Coding)

In the annals of computer science, the study of permutations has long held a place of prominence, serving as a cornerstone in the development of efficient algorithms and problem-solving strategies. This captivating video from the esteemed “Learn Coding” channel delves into the intricacies of Permutations in C++, employing the powerful technique of Backtracking to unravel the complexities of this ubiquitous concept.

Tracing the evolution of this topic, the video guides viewers through a comprehensive exploration, from the fundamental principles to the application of permutations in real-world problem-solving. With a keen eye for detail, the presenter meticulously dissects the step-by-step logic, offering a deep dive into the underlying mechanisms that govern the generation of permutations. The inclusion of LeetCode problems further cements the practical relevance of this subject, equipping viewers with the necessary tools to excel in the competitive programming landscape.

As the C++ DSA series continues to captivate audiences, this particular video stands as a testament to the ongoing commitment of the “Learn Coding” team to provide learners with a robust foundation in the ever-evolving field of data structures and algorithms. By seamlessly integrating theory, implementation, and problem-solving strategies, this resource promises to be an invaluable asset for students, professionals, and enthusiasts alike, solidifying its place as a must-watch in the annals of computer science education.

🎥 Meet The Teacher Who Wakes Up At 3:30 AM For His Labubu Business (Business Insider)

The video examines the case of Ellis Stephens, a Bay Area teacher who has found financial success in customizing and reselling Labubu plush toys. Fueled by the blind-box collectibles craze driven by the rise of Pop Mart, the Labubu resale market has exploded globally, with rare figures selling for up to $30,000 each. Pop Mart’s valuation now exceeds that of industry giants Mattel and Hasbro combined. The video explores the Labubu phenomenon, including its popularity among celebrities, concerns about a potential collapse similar to Beanie Babies, and the emergence of a lucrative resale market. Through interviews and data analysis, the video provides a comprehensive overview of this niche but thriving business opportunity.

🎥 Can US jobs data be trusted? #bls #shorts #jobs #jobsdata ##jobsreport (Bloomberg)

In this concise research summary, the key takeaways from the video are presented in an objective and structured manner. The analysis examines the growing concerns surrounding the credibility of US jobs data, as reported by the Bureau of Labor Statistics (BLS). The video delves into the potential issues underlying the jobs numbers, offering insights that challenge the reliability of this economic indicator. Through a precise and informative approach, the presentation synthesizes the core findings and implications explored within the video, providing readers with a succinct yet comprehensive understanding of the topic at hand.

🎥 The NFL’s Next Play: Global Growth & Big Deals (Bloomberg)

The NFL’s expansion plans, private equity investments, and the evolving media landscape were the focal points of a strategic discussion between Miami Dolphins CEO Tom Garfinkel and Ares CEO Michael Arougheti. The executives, interviewed at Bloomberg Power Players in New York, highlighted the league’s global growth ambitions, the potential for new streaming rights deals, and the increasing role of private equity in shaping the future of professional football. The insights provided by these industry leaders offer decision-makers a concise yet comprehensive understanding of the strategic priorities and emerging trends shaping the business of the NFL.

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quantitative developers
Jobs

What are Quantitative Developers?

by Clement D.

What are quantiative developers? In modern finance, the role of the Quantitative Developer (often shortened to Quant Dev) has become essential. Sitting at the intersection of software engineering and quantitative research, quant developers are the bridge between mathematical models and real-world trading systems.

1. What do they do?

A quant developer’s day-to-day work involves:

  • Model Implementation: Translating mathematical models into robust C++, Python, or Java code.
  • Performance Optimization: Making sure algorithms run within microseconds for low-latency trading or scale to millions of rows in risk simulations.
  • Data Engineering: Ingesting, cleaning, and structuring terabytes of market data for model training and backtesting.
  • Integration with Systems: Connecting models to execution platforms, risk engines, or reporting pipelines.
  • Tooling and Libraries: Writing reusable libraries for derivatives pricing, yield curve construction, Monte Carlo simulation, or time series analysis.

Quant developers need a unique blend of skills:

  • Programming Expertise: Strong command of C++ (for performance-critical systems), Python (for prototyping and data analysis), and sometimes Java or Scala.
  • Mathematical Understanding: Comfort with linear algebra, probability, statistics, and financial mathematics.
  • Financial Knowledge: Understanding of derivatives, risk metrics, pricing conventions, and portfolio management.
  • Systems Knowledge: Familiarity with high-performance computing, parallelization, distributed systems, and databases.

2. Who do they work for?

Quantitative developers work across a wide range of financial institutions. In investment banks, they are often responsible for building risk engines and pricing libraries that support traders and risk managers. Hedge funds and proprietary trading firms rely on them to implement high-performance trading strategies and execution algorithms where microseconds matter.

Asset managers employ quant devs to optimize portfolio analytics, factor models, and reporting systems, while insurance companies and corporate treasury departments need their expertise for pricing structured products and managing hedging strategies. Beyond traditional finance, they also play an important role in fintech startups, crypto exchanges, and DeFi platforms, where new forms of trading and risk management require robust technical foundations. Quant developers are also employed by exchanges, clearing houses, regulators, and even central banks, helping to ensure resilient, transparent, and compliant systems. S

ome work in financial software companies and data vendors, creating the analytical libraries used by traders and analysts around the world. Others operate as consultants or freelancers, delivering highly specialized development services for trading desks and quantitative research groups. In every setting, the common thread is the same: they sit at the intersection of mathematics, finance, and software engineering, turning theory into production-ready systems that directly impact how markets function.

3. How much do they make?

Quantitative developers are some of the best-paid engineers in finance, with compensation varying by location, experience, and employer. In London, junior quant devs often start between £70,000 and £90,000, while mid-level developers earn £120,000 to £180,000. Senior positions at investment banks, hedge funds, or proprietary trading firms frequently exceed £200,000, with contractors sometimes billing £700 to £1,200 per day outside IR35. In New York, salaries are even higher: entry-level roles typically fall in the $100,000 to $130,000 range, mid-level developers earn $150,000 to $200,000, and senior hires at top hedge funds can reach $250,000 to $400,000+.


Total compensation is usually boosted by bonuses, which can be very significant in high-performing funds or trading desks. While banks generally offer slightly lower pay in exchange for stability, hedge funds and prop shops provide the highest upside, and fintechs often compete with equity packages instead of cash. Contractors benefit from flexibility and higher daily rates but give up job security and benefits. Within the field, specialization also plays a role: C++ developers working on low-latency trading systems command the strongest salaries, while Python-focused developers, though still well paid, typically earn a bit less. Across regions, U.S. pay tends to outpace European markets, but in every case, quant developers consistently earn well above typical software engineering salaries, reflecting the critical role their work plays in moving global markets.

4. How is the job market for them?

global financial hubs. Investment banks continue to hire them to build and maintain risk engines and pricing platforms, while hedge funds and proprietary trading firms aggressively expand their low-latency trading teams. At the same time, fintech startups and crypto/DeFi firms are creating new demand for developers who can combine finance, data, and engineering skills.

Demand is strongest in cities like London, New York, Hong Kong, and Chicago, with Zurich, Frankfurt, Singapore, and Tokyo also offering strong markets for risk and trading specialists. Employers are especially eager to find C++ developers skilled in performance and latency optimization, while Python developers remain in high demand for data pipelines, prototyping, and machine learning applications.

Experience with distributed systems, cloud platforms, and real-time data processing makes candidates even more competitive. The rise of machine learning in finance has also created hybrid roles that blend software engineering with data science. Job stability tends to be highest at banks, while the biggest upside is offered by hedge funds and prop shops, which often compete fiercely for top candidates by raising salaries.

Contracting opportunities remain strong in London, with high day rates and flexibility, although these roles come with less security. Overall, the market is robust and demand consistently outpaces supply, making skilled quant developers highly valuable in a world where speed, accuracy, and innovation are central to financial success.

5. Conclusion

In conclusion, the role of the quantitative developer has become indispensable in modern finance.
They sit at the intersection of mathematics, software engineering, and financial markets, transforming theory into production-ready systems.
Their work underpins trading platforms, risk engines, pricing libraries, and portfolio analytics.
Without quant developers, many of the models designed by quantitative analysts would remain purely academic.
The profession combines deep technical expertise with financial intuition, a rare and valuable mix.
It is also one of the few roles where C++ mastery continues to provide a strong competitive edge.
At the same time, Python has cemented its place as the language of choice for rapid prototyping and data analysis.
The career offers exceptional compensation, with salaries and day rates far exceeding traditional software roles.
But beyond money, it provides intellectual challenge and direct market impact.
Each line of code written by a quant dev has the potential to influence millions in PnL or mitigate significant risks.
The job market remains strong, with demand outpacing supply across banks, hedge funds, and fintechs.
Global hubs like London, New York, Hong Kong, and Chicago will continue to attract top talent.
Meanwhile, the rise of machine learning, cloud computing, and decentralized finance is expanding the scope of opportunities.
Quant developers are no longer confined to traditional institutions—they are shaping the future of fintech and digital assets.
Those who excel combine low-level performance engineering with an ability to adapt to new paradigms.
The best quant devs are lifelong learners, constantly updating their skills in algorithms, systems, and finance.
For aspiring professionals, it is a career that rewards curiosity, rigor, and resilience.
For the industry, it is a role that ensures innovation and stability in equal measure.
Ultimately, quantitative developers are the engineers behind modern markets, ensuring that complex ideas can operate at scale.
Their contribution is both foundational and forward-looking, making them central to the evolution of global finance.

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Multithreading in C++
LibrariesPerformance

Multithreading in C++ for Quantitative Finance

by Clement D.

Multithreading in C++ is one of those topics that every developer eventually runs into, whether they’re working in finance, gaming, or scientific computing. The language gives you raw primitives, but it also integrates with a whole ecosystem of libraries that scale from a few threads on your laptop to thousands of cores in a data center.

Choosing the right tool matters: what are the right libraries for your quantitative finance use case?

Multithreading in C++

1. Standard C++ Threads (Low-Level Control)

Since C++11, <thread>, <mutex>, and <future> are part of the standard. You manage threads directly, making it portable and dependency-free.

Example: Parallel computation of moving averages in a trading engine

#include <iostream>
#include <thread>
#include <vector>

void moving_average(const std::vector<double>& data, int start, int end) {
    for (int i = start; i < end; i++) {
        if (i >= 2) {
            double avg = (data[i] + data[i-1] + data[i-2]) / 3.0;
            std::cout << "Index " << i << " avg = " << avg << "\n";
        }
    }
}

int main() {
    std::vector<double> prices = {100,101,102,103,104,105,106,107};
    std::thread t1(moving_average, std::ref(prices), 0, 4);
    std::thread t2(moving_average, std::ref(prices), 4, prices.size());

    t1.join();
    t2.join();
}


2. Intel oneTBB (Task-Based Parallelism)

oneTBB (Threading Building Blocks) provides parallel loops, pipelines, and task graphs. Perfect for HPC or financial risk simulations.

Example: Monte Carlo option pricing

#include <tbb/parallel_for.h>
#include <vector>
#include <random>

int main() {
    const int N = 1'000'000;
    std::vector<double> results(N);

    std::mt19937 gen(42);
    std::normal_distribution<> dist(0, 1);

    tbb::parallel_for(0, N, [&](int i) {
        double z = dist(gen);
        results[i] = std::exp(-0.5 * z * z); // toy payoff
    });
}

3. OpenMP (Loop Parallelism for HPC)

OpenMP is widely used in scientific computing. You add pragmas, and the compiler generates parallel code.

#include <vector>
#include <omp.h>

int main() {
    const int N = 500;
    std::vector<std::vector<double>> A(N, std::vector<double>(N, 1));
    std::vector<std::vector<double>> B(N, std::vector<double>(N, 2));
    std::vector<std::vector<double>> C(N, std::vector<double>(N, 0));

    #pragma omp parallel for
    for (int i = 0; i < N; i++)
        for (int j = 0; j < N; j++)
            for (int k = 0; k < N; k++)
                C[i][j] += A[i][k] * B[k][j];
}

4. Boost.Asio (Async Networking and Thread Pools)

Boost.Asio is ideal for low-latency servers, networking, and I/O-heavy workloads (e.g. trading gateways).

#include <boost/asio.hpp>
using boost::asio::ip::tcp;

int main() {
    boost::asio::io_context io;
    tcp::acceptor acceptor(io, tcp::endpoint(tcp::v4(), 12345));

    std::function<void()> do_accept = [&]() {
        auto socket = std::make_shared<tcp::socket>(io);
        acceptor.async_accept(*socket, [&, socket](boost::system::error_code ec) {
            if (!ec) {
                boost::asio::async_read_until(*socket, boost::asio::dynamic_buffer(std::string()), '\n',
                    [socket](auto, auto) {
                        boost::asio::write(*socket, boost::asio::buffer("pong\n"));
                    });
            }
            do_accept();
        });
    };

    do_accept();
    io.run();
}


5. Parallel STL (<execution>)

C++17 added execution policies for standard algorithms. This makes parallelism easy.

#include <algorithm>
#include <execution>
#include <vector>

int main() {
    std::vector<int> trades = {5,1,9,3,2,8};
    std::sort(std::execution::par, trades.begin(), trades.end());
}



6. Conclusion

Multithreading in C++ offers many models, each fit for different workloads. Use std::thread for low-level control of system tasks. Adopt oneTBB or OpenMP for data-parallel HPC simulations. Leverage Boost.Asio for async networking and trading engines. Rely on CUDA/SYCL for GPU acceleration in Monte Carlo or ML. Enable Parallel STL (<execution>) for easy speed-ups in modern code. Try actor frameworks (CAF/HPX) for distributed, message-driven systems.

Compiler flags also make a big difference in multithreaded performance. Always build with -O3 -march=native (or /O2 in MSVC). Use -fopenmp or link to TBB scalable allocators when relevant. Prevent false sharing with alignas(64) and prefer thread_local scratchpads. Mark non-aliasing pointers with __restrict__ to help vectorization. Consider specialized allocators (jemalloc, TBB) in multi-threaded apps. Profile with -fsanitize=thread to catch race conditions early.

The key: match the concurrency model + compiler setup to your workload for maximum speed.

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What is dv01
Risk

What Is DV01? An Implementation in C++

by Clement D.

What is DV01? In fixed income markets, DV01 (Dollar Value of 01) is one of the most important risk measures every quant, trader, and risk manager needs to understand. DV01 tells you how much the price of a bond, swap, or portfolio changes when interest rates shift by just one basis point (0.01%). This tiny movement in yield can translate into thousands or even millions of dollars in profit or loss for large portfolios.

In other words, DV01 measures interest rate sensitivity in dollar terms. If you’ve ever wondered “What is DV01 in bonds?”, think of it as the financial system’s ruler for measuring how prices react to micro-changes in rates.

For quants, DV01 is the foundation for hedging strategies, scenario analysis, and stress testing. For developers, it’s a key calculation baked into trading systems and risk engines. In this article, we’ll explore what DV01 really is, explain the math behind it, and provide a clean, modern C++ implementation to compute DV01 for single bonds and entire portfolios.

What is DV01?

DV01, short for Dollar Value of 01, measures the dollar change in a bond’s price when its yield changes by one basis point (0.01%). It’s derived directly from the bond’s price–yield relationship: when yields rise, bond prices fall, and DV01 quantifies that sensitivity. Mathematically, DV01 is the negative derivative of price with respect to yield, scaled by 1 basis point:

[math] \Large dv01 = – \frac{dP}{dy} \times 0.0001 [/math]

Where:

  • P = price of the bond (in dollars).
  • y = yield to maturity (as a decimal, e.g. 0.05 for 5%).
  • [math] \frac{dP}{dy} [/math] = the rate of change of the bond price with respect to yield (the slope of the price–yield curve).
  • 0.0001 = one basis point expressed as a decimal (1bp = 0.01% = 0.0001).

Example:
A 5-year bond priced at $102 with a modified duration of 4.5 has:

[math] DV01 = 4.5 \times 102 \times 0.0001 = 0.0459 [/math]

This means that if yields go up by just 1bp, the bond’s price will drop by 4.59 cents.

A C++ Implementation

Here is an implementation of a dv01 calcualtion in C++:

#include <iostream>
#include <vector>
#include <cmath>

// --- Bond structure ---
struct Bond {
    double face;      // Face value (e.g. 100)
    double coupon;    // Annual coupon rate (as decimal, e.g. 0.05 for 5%)
    int maturity;     // Maturity in years
    double yield;     // Yield to maturity (as decimal)
};

// --- Price a plain-vanilla annual coupon bond ---
double priceBond(const Bond& bond) {
    double price = 0.0;
    for (int t = 1; t <= bond.maturity; ++t) {
        price += (bond.face * bond.coupon) / std::pow(1 + bond.yield, t);
    }
    price += bond.face / std::pow(1 + bond.yield, bond.maturity); // Add principal repayment
    return price;
}

// --- DV01 using the bump method ---
double dv01(const Bond& bond) {
    constexpr double bp = 0.0001; // One basis point
    double basePrice = priceBond(bond);

    Bond bumpedBond = bond;
    bumpedBond.yield += bp; // bump yield by 1bp

    double bumpedPrice = priceBond(bumpedBond);

    return basePrice - bumpedPrice; // DV01 in dollars
}

// --- Example usage ---
int main() {
    Bond bond = {100.0, 0.05, 5, 0.04}; // Face=100, 5% coupon, 5-year, 4% yield
    double price = priceBond(bond);
    double dv01Value = dv01(bond);

    std::cout << "Bond Price: $" << price << std::endl;
    std::cout << "DV01: $" << dv01Value << std::endl;

    return 0;
}

This simple struct holds the key attributes of a plain-vanilla fixed-rate bond:

  • Face: The amount the bond will pay back at maturity.
  • Coupon: Annual interest rate the bond pays.
  • Maturity: Number of years until final repayment.
  • Yield: Market-required return, used for discounting future cashflows.

To calculate the dv01 here we:

  • Takes the original bond price.
  • Bumps the yield by one basis point (0.0001).
  • Re-prices the bond using the bumped yield.
  • Subtracts the bumped price from the original price.

This gives the DV01 the measures how much the price changes when yields shift by 1bp: this “bump method” is exactly what traders and risk systems use.

Compile and Run

Create dv01.cpp containing the code above, as well as a CMakeLists.txt like:

cmake_minimum_required(VERSION 3.10)
project(dv01)
set(CMAKE_CXX_STANDARD 17)
add_executable(dv01 ../dv01.cpp)

Then run the compilation:

mkdir build
cd build
cmake ..
make

Then run the program:

./dv01
Bond Price: $104.452
DV01: $0.0457561

The code of the application is accessible here:

https://github.com/cppforquants/dv01

Alternative Implementations

When moving from educational examples to real-world analytics, most teams don’t maintain their own pricing code: they turn to professional libraries. In C++, the dominant choice is QuantLib, an open-source framework used by banks, hedge funds, and trading desks worldwide. QuantLib offers several advantages for calculating bond price sensitivity:

  • It handles all the details you would otherwise code by hand, including calendars, settlement dates, and day count conventions.
  • It includes a wide range of pricing engines, so the same approach works for fixed-rate bonds, floaters, swaps, and even more complex instruments.
  • It allows you to shift the yield curve directly and reprice instantly, so bumping rates for sensitivity tests is just a matter of swapping in a different term structure.
  • It ensures consistency with market standards, which is critical if your numbers need to match the desk’s systems.

For a teaching example, writing the pricing loop yourself is helpful… But for production, using QuantLib means fewer bugs, faster development, and calculations that match what traders and risk managers expect.

#include <ql/quantlib.hpp>
#include <iostream>

using namespace QuantLib;

int main() {
    try {
        // 1. Set the evaluation date
        Calendar calendar = TARGET();
        Date today(26, July, 2025);
        Settings::instance().evaluationDate() = today;

        // 2. Define bond parameters
        Date maturity(26, July, 2030);
        Real faceAmount = 100.0;
        Rate couponRate = 0.05; // 5% annual coupon

        // 3. Build the bond schedule
        Schedule schedule(today, maturity, Period(Annual), calendar,
                          Unadjusted, Unadjusted, DateGeneration::Forward, false);

        // 4. Create the fixed-rate bond
        FixedRateBond bond(3, faceAmount, schedule,
                           std::vector<Rate>{couponRate},
                           ActualActual(ActualActual::ISDA));

        // 5. Build the flat yield curve at 4%
        Handle<YieldTermStructure> curve(
            boost::make_shared<FlatForward>(today, 0.04, ActualActual(ActualActual::ISDA)));

        // 6. Attach a discounting engine
        bond.setPricingEngine(boost::make_shared<DiscountingBondEngine>(curve));

        // 7. Compute base price
        Real basePrice = bond.cleanPrice();

        // 8. Bump the yield curve by 1bp (0.01%) and reprice
        Handle<YieldTermStructure> bumpedCurve(
            boost::make_shared<FlatForward>(today, 0.0401, ActualActual(ActualActual::ISDA)));
        bond.setPricingEngine(boost::make_shared<DiscountingBondEngine>(bumpedCurve));

        Real bumpedPrice = bond.cleanPrice();

        // 9. Compute and print sensitivity
        Real priceChange = basePrice - bumpedPrice;

        std::cout << "Base Price: " << basePrice << std::endl;
        std::cout << "Price Change for 1bp shift: " << priceChange << std::endl;
    }
    catch (std::exception& e) {
        std::cerr << "Error: " << e.what() << std::endl;
        return 1;
    }

    return 0;
}

To run the example, install QuantLib (for example, via your system package manager or by building from source) and compile with a standard C++17 or later compiler:

g++ -std=c++17 -I/usr/include/ql -lQuantLib sensitivity_example.cpp -o sensitivity_example
./sensitivity_example

This produces the base bond price and the change in price after a 1bp shift in the yield curve. From there, you can expand the approach:

  • Price a portfolio of bonds by looping through multiple instruments and summing their sensitivities.
  • Swap in a different term structure (e.g., a real yield curve from market data) to see how results change under new scenarios.
  • Experiment with different bond types like floaters or callable bonds by just changing the instrument class and pricing engine.

These extensions show how the same core idea can scale from a single-bond demo into a risk engine component that handles thousands of securities and multiple yield environments.

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Calculate theta for options in C++
Greeks

How to Calculate Theta for Options in C++

by Clement D.

In quantitative finance, understanding how an option’s value changes over time is critical for risk management and trading. This is where Theta, the Greek that measures time decay, plays a central role. In this article, you’ll learn how to calculate Theta for options in C++ with a simple yet powerful approach to estimate how much an option’s price erodes as it approaches expiration. Whether you’re building a pricing engine or just deepening your understanding of the Greeks, this guide will walk you through a practical C++ implementation step-by-step.

Theta for Options Explained

1. What is Theta?

Theta is one of the most important Greeks in options trading. It measures the rate at which an option loses value as time passes, assuming all other factors remain constant. This phenomenon is known as time decay.

Mathematically, Theta is defined as the partial derivative of the option price V with respect to time t:

[math]\large \Theta = \frac{\partial V}{\partial t}[/math]

In most practical cases, Theta is negative for long option positions meaning the value of the option decreases as time progresses. This reflects the diminishing probability that the option will end up in-the-money as expiration nears.

Imagine you hold a European call option on a stock priced at $100, with a strike at $100 and 30 days to expiry. If the option is worth $4.00 today, and $3.95 tomorrow (with no change in volatility or the stock price), then Theta is approximately:

[math]\large \Theta \approx \frac{3.95 – 4.00}{1} = -0.05[/math]

This means the option loses 5 cents per day due to the passage of time alone.

Key Characteristics of Theta:

  • Short-term options have higher Theta (faster decay).
  • At-the-money options typically experience the highest Theta.
  • Theta increases as expiration approaches, especially in the final weeks.
  • Call and put options both exhibit Theta decay, but not symmetrically.

2. The Black-Scholes Formula

Under the Black-Scholes model, we can calculate Theta analytically for both European call and put options. These closed-form expressions are widely used for pricing, calibration, and risk reporting.

Black-Scholes Theta Formulas

European Call Option Theta:

[math]\large \Theta_{\text{call}} = -\frac{S \cdot \sigma \cdot N'(d_1)}{2\sqrt{T}} – rK e^{-rT} N(d_2)[/math]

European Put Option Theta:

[math]\large \Theta_{\text{put}} = -\frac{S \cdot \sigma \cdot N'(d_1)}{2\sqrt{T}} + rK e^{-rT} N(-d_2)[/math]

Where:

  • [math]S[/math] = spot price of the underlying
  • [math]K[/math] = strike price
  • [math]T[/math] = time to maturity (in years)
  • [math]r[/math] = risk-free interest rate
  • [math]\sigma[/math] = volatility
  • [math]N(d)[/math] = cumulative normal distribution
  • [math]N'(d)[/math] = standard normal density (i.e., PDF)
  • [math]d_1 = \frac{\ln(S/K) + (r + \sigma^2/2)T}{\sigma \sqrt{T}}[/math]

3. An Implementation in C++

Here’s a minimal, self-contained example that calculates Theta using QuantLib:

#include <ql/quantlib.hpp>
#include <iostream>

using namespace QuantLib;

int main() {
    // Set evaluation date
    Calendar calendar = TARGET();
    Date today(19, July, 2025);
    Settings::instance().evaluationDate() = today;

    // Option parameters
    Option::Type optionType = Option::Call;
    Real underlying = 100.0;
    Real strike = 100.0;
    Rate riskFreeRate = 0.01;     // 1%
    Volatility volatility = 0.20; // 20%
    Date maturity = calendar.advance(today, 90, Days); // 3 months
    DayCounter dayCounter = Actual365Fixed();

    // Construct option
    ext::shared_ptr<Exercise> europeanExercise(new EuropeanExercise(maturity));
    ext::shared_ptr<StrikedTypePayoff> payoff(new PlainVanillaPayoff(optionType, strike));
    VanillaOption europeanOption(payoff, europeanExercise);

    // Market data (handle wrappers)
    Handle<Quote> spot(ext::shared_ptr<Quote>(new SimpleQuote(underlying)));
    Handle<YieldTermStructure> flatRate(ext::shared_ptr<YieldTermStructure>(
        new FlatForward(today, riskFreeRate, dayCounter)));
    Handle<BlackVolTermStructure> flatVol(ext::shared_ptr<BlackVolTermStructure>(
        new BlackConstantVol(today, calendar, volatility, dayCounter)));

    // Black-Scholes-Merton process
    ext::shared_ptr<BlackScholesMertonProcess> bsmProcess(
        new BlackScholesMertonProcess(spot, flatRate, flatRate, flatVol));

    // Set pricing engine
    europeanOption.setPricingEngine(ext::shared_ptr<PricingEngine>(
        new AnalyticEuropeanEngine(bsmProcess)));

    // Output results
    std::cout << "Option price: " << europeanOption.NPV() << std::endl;
    std::cout << "Theta (per year): " << europeanOption.theta() << std::endl;
    std::cout << "Theta (per day): " << europeanOption.theta() / 365.0 << std::endl;

    return 0;
}

This example prices a European call option using QuantLib’s AnalyticEuropeanEngine, which leverages the Black-Scholes formula to compute both the option’s price and its Greeks, including Theta. The method theta() returns the annualized Theta value, representing the rate of time decay per year; dividing it by 365 gives the daily Theta, which is more intuitive for short-term trading and hedging. You can easily experiment with other scenarios by changing the option type to Option::Put or adjusting the maturity date to observe how Theta behaves under different conditions.

4. Compile and Run

You will need to install Quantlib first, on a mac:

brew install quantlib

and then prepare your CMakeList.txt:

cmake_minimum_required(VERSION 3.10)
project(theta)

set(CMAKE_CXX_STANDARD 17)

find_package(PkgConfig REQUIRED)
pkg_check_modules(QUANTLIB REQUIRED QuantLib)

include_directories(${QUANTLIB_INCLUDE_DIRS})
link_directories(${QUANTLIB_LIBRARY_DIRS})

add_executable(theta ../theta.cpp)
target_link_libraries(theta ${QUANTLIB_LIBRARIES})

Where theta.cpp contains the code from the former section.

Then:

mkdir build
cd build
cmake..
make

Once compiled, you can run the executable:

➜  build git:(main) ✗ ./theta
Option price: 4.65065
Theta (per year): -6.73569

Which gives you the value of theta.

You can clone the project here on Github to calculate Theta for options in C++:

https://github.com/cppforquants/theta

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non-overlapping intervals in C++
Interview

Non-Overlapping Intervals in C++: A Quantitative Developer Interview Question

by Clement D.

In quantitative developer interviews, you’re often tested not just on algorithms, but on how they apply to real-world financial systems. One common scenario: cleaning overlapping time intervals from noisy market data feeds. This challenge maps directly to LeetCode 435: Non-Overlapping Intervals. In this article, we’ll solve it in C++ and explore why it matters in quant finance from ensuring data integrity to preparing time series for model training. How to solve the problem of non-overlapping intervals in C++?

Let’s dive into a practical problem that tests both coding skill and quant context.

1. Problem Statement

You’re given a list of intervals, where each interval represents a time range during which a market data feed was active. Some intervals may overlap, resulting in duplicated or conflicting data.

Your task is to determine the minimum number of intervals to remove so that the remaining intervals do not overlap. The goal is to produce a clean, non-overlapping timeline: a common requirement in financial data preprocessing.

Input: intervals = {{1,3}, {2,4}, {3,5}, {6,8}}
Output: 1

Explanation: Removing {2,4} results in non-overlapping intervals: {{1,3}, {3,5}, {6,8}}.

2. The Solution Explained

To remove the fewest intervals and eliminate overlaps, we take a greedy approach: we always keep the interval that ends earliest, since this leaves the most room for future intervals.

By sorting all intervals by their end time, we can iterate through them and:

  • Keep an interval if it doesn’t overlap with the last selected one.
  • Remove it otherwise.

This strategy ensures we keep as many non-overlapping intervals as possible and thus remove the minimum necessary.

What is the time and space complexity for this approach?

Sorting dominates the time complexity. While the actual iteration through the intervals is linear (O(n)), sorting the n intervals takes O(nlogn), and that becomes the bottleneck.

Although we process all n intervals, we do so in place without allocating any extra data structures. We use just a few variables (last_end, removed), so the auxiliary space remains constant. That’s why the space complexity is O(1) assuming the input list is already given and we’re allowed to sort it directly.

AspectValueExplanation
Time ComplexityO(n log n)Due to sorting the intervals by end time
Space ComplexityO(1) (excluding input)Only a few variables used; no extra data structures needed

Now, let’s implement it in C++.

3. A C++ Implementation

Here is a C++ implementation of the solution:

#include <iostream>
#include <vector>
#include <algorithm>
#include <climits>
using namespace std;

// Greedy function to compute the minimum number of overlapping intervals to remove
int eraseOverlapIntervals(vector<vector<int>>& intervals) {
    // Sort intervals by end time
    sort(intervals.begin(), intervals.end(), [](const auto& a, const auto& b) {
        return a[1] < b[1];
    });

    int removed = 0;
    int last_end = INT_MIN;

    for (const auto& interval : intervals) {
        if (interval[0] < last_end) {
            // Overlapping — must remov.e this one
            removed++;
        } else {
            // No overlap — update last_end
            last_end = interval[1];
        }
    }

    return removed;
}

int main() {
    vector<vector<int>> intervals = {{1, 3}, {2, 4}, {3, 5}, {6, 8}};
    int result = eraseOverlapIntervals(intervals);
    cout << "Minimum intervals to remove: " << result << endl;
    return 0;
}

Let’s compile:

mkdir build
cmake ..
make

And run the code:

➜  build git:(main) ✗ ./intervals
Minimum intervals to remove: 1

4. Do It Yourself

To do it yourself, you can clone my repository to solve non-overlapping intervals in C++here:

https://github.com/cppforquants/overlappingintervals

Just git clone and run the compilation/execution commands from the Readme: 

# Overlapping Intervals

The overlapping intervals problem for quant interviews.

## Build

```bash
mkdir build
cd build
cmake ..
make
```

## Run
```
./intervals

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Memory management in C++
Performance

Memory Management in C++ High-Frequency Trading Systems

by Clement D.

High-Frequency Trading (HFT) systems operate under extreme latency constraints where microseconds matter. In this environment, memory management is not just an implementation detail. The ability to predict and control memory allocations, avoid page faults, minimize cache misses, and reduce heap fragmentation can directly influence trading success. What are the best tricks for memory management in C++?

C++ offers low-level memory control unmatched by most modern languages, making it a staple in the HFT tech stack. However, this power comes with responsibility: careless allocations or unexpected copies can introduce jitter, latency spikes, and subtle bugs that are unacceptable in production systems.

In this article, we’ll explore how memory management principles apply in HFT, the common patterns and pitfalls, and how to use modern C++ tools to build robust, deterministic, and lightning-fast trading systems.

1. Preallocation and Memory Pools

A common mitigation strategy is preallocating memory up front and using a memory pool to manage object lifecycles efficiently. This approach ensures allocations are fast, deterministic, and localized, which also improves cache performance.

Let’s walk through a simple example using a custom fixed-size memory pool.

C++ Example: Fixed-Size Memory Pool for Order Objects

#include <iostream>
#include <vector>
#include <bitset>
#include <cassert>

constexpr size_t MAX_ORDERS = 1024;

struct Order {
    int id;
    double price;
    int quantity;

    void reset() {
        id = 0;
        price = 0.0;
        quantity = 0;
    }
};

class OrderPool {
public:
    OrderPool() {
        for (size_t i = 0; i < MAX_ORDERS; ++i) {
            free_slots.set(i);
        }
    }

    Order* allocate() {
        for (size_t i = 0; i < MAX_ORDERS; ++i) {
            if (free_slots.test(i)) {
                free_slots.reset(i);
                return &orders[i];
            }
        }
        return nullptr; // Pool exhausted
    }

    void deallocate(Order* ptr) {
        size_t index = ptr - orders;
        assert(index < MAX_ORDERS);
        ptr->reset();
        free_slots.set(index);
    }

private:
    Order orders[MAX_ORDERS];
    std::bitset<MAX_ORDERS> free_slots;
};

Performance Benefits:

  • No heap allocation: All Order objects are stack-allocated as part of the orders array.
  • O(1) deallocation: Releasing an object is just a bitset flip and a reset.
  • Cache locality: Contiguous storage means fewer cache misses during iteration.

2. Object Reuse and Freelist Patterns

Even with preallocated memory, repeatedly constructing and destructing objects introduces CPU overhead and memory churn. In HFT systems, where throughput is immense and latency must be consistent, reusing objects via a freelist is a proven strategy to reduce jitter and improve performance via a simple trick of memory management in C++.

A freelist is a lightweight structure that tracks unused objects for quick reuse. Instead of releasing memory, objects are reset and pushed back into the freelist for future allocations: a near-zero-cost operation.

C++ Example: Freelist for Reusing Order Objects

#include <iostream>
#include <stack>

struct Order {
    int id;
    double price;
    int quantity;

    void reset() {
        id = 0;
        price = 0.0;
        quantity = 0;
    }
};

class OrderFreelist {
public:
    Order* acquire() {
        if (!free.empty()) {
            Order* obj = free.top();
            free.pop();
            return obj;
        }
        return new Order();  // Fallback allocation
    }

    void release(Order* obj) {
        obj->reset();
        free.push(obj);
    }

    ~OrderFreelist() {
        while (!free.empty()) {
            delete free.top();
            free.pop();
        }
    }

private:
    std::stack<Order*> free;
};

Performance Benefits:

  • Reusing instead of reallocating: Objects are reset, not destroyed — drastically reduces allocation pressure.
  • Stack-based freelist: LIFO behavior benefits CPU cache reuse due to temporal locality (recently used objects are reused soon).
  • Amortized heap usage: The heap is only touched when the freelist is empty, which should rarely happen in a tuned system.

3. Use Arena Allocators

When stack allocation isn’t viable — e.g., for large datasets or objects with dynamic lifetimes — heap usage becomes necessary. But in HFT, direct new/delete or malloc/free calls are risky due to latency unpredictability and fragmentation.

This is where placement new and arena allocators come into play.

  • Placement new gives you explicit control over where an object is constructed.
  • Arena allocators preallocate a large memory buffer and dole out chunks linearly, eliminating the overhead of general-purpose allocators and enabling bulk deallocation.

These techniques are foundational for building fast, deterministic allocators in performance-critical systems like trading engines and improve memory management in C++.

C++ Example: Arena Allocator with Placement new

#include <iostream>
#include <vector>
#include <cstdint>
#include <new>      // For placement new
#include <cassert>

constexpr size_t ARENA_SIZE = 4096;

class Arena {
public:
    Arena() : offset(0) {}

    void* allocate(size_t size, size_t alignment = alignof(std::max_align_t)) {
        size_t aligned_offset = (offset + alignment - 1) & ~(alignment - 1);
        if (aligned_offset + size > ARENA_SIZE) {
            return nullptr; // Out of memory
        }
        void* ptr = &buffer[aligned_offset];
        offset = aligned_offset + size;
        return ptr;
    }

    void reset() {
        offset = 0; // Bulk deallocation
    }

private:
    alignas(std::max_align_t) char buffer[ARENA_SIZE];
    size_t offset;
};

// Sample object to construct inside arena
struct Order {
    int id;
    double price;
    int qty;

    Order(int i, double p, int q) : id(i), price(p), qty(q) {}
};

Performance Benefits

  • Deterministic allocation: Constant-time, alignment-safe, no system heap calls.
  • Zero-cost deallocation: arena.reset() clears all allocations in one go — no destructor calls, no fragmentation.
  • Minimal overhead: Perfect for short-lived objects in bursty, time-sensitive workloads.

Ideal Use Cases in HFT

  • Message parsing and object hydration (e.g., FIX messages → Order objects).
  • Per-frame or per-tick memory lifetimes.
  • Temporary storage in pricing or risk models where objects live for microseconds.

4. Use Custom Allocators in STL (e.g., std::pmr)

Modern C++ introduced a powerful abstraction for memory control in the standard library: polymorphic memory resources (std::pmr). This allows you to inject custom memory allocation behavior into standard containers like std::vector, std::unordered_map, etc., without writing a full custom allocator class.

This is especially valuable in HFT where STL containers may be needed temporarily (e.g., per tick or per packet) and where you want tight control over allocation patterns, lifetime, and performance.

C++ Example: Using std::pmr::vector with an Arena

#include <iostream>
#include <memory_resource>
#include <vector>
#include <string>

int main() {
    constexpr size_t BUFFER_SIZE = 1024;
    char buffer[BUFFER_SIZE];

    // Set up a monotonic buffer resource using stack memory
    std::pmr::monotonic_buffer_resource resource(buffer, BUFFER_SIZE);

    // Create a pmr vector that uses the custom memory resource
    std::pmr::vector<std::string> symbols{&resource};

    // Populate the vector
    symbols.emplace_back("AAPL");
    symbols.emplace_back("MSFT");
    symbols.emplace_back("GOOG");

    for (const auto& s : symbols) {
        std::cout << s << "\n";
    }

    // All memory is deallocated at once when `resource` goes out of scope or is reset
}

Benefits for HFT Systems

  • Scoped allocations: The monotonic_buffer_resource allocates from the buffer and never deallocates until reset — perfect for short-lived containers (e.g., market snapshots).
  • No heap usage: Memory is pulled from the stack or a preallocated slab, avoiding malloc/free.
  • STL compatibility: Works with all std::pmr:: containers (vector, unordered_map, string, etc.).
  • Ease of integration: Drop-in replacement for standard containers — no need to write full allocator classes.

pmr Design Philosophy

  • Polymorphic behavior: Containers store a pointer to an std::pmr::memory_resource, enabling allocator reuse without changing container types.
  • Composable: You can plug in arenas, pools, fixed-size allocators, or even malloc-based resources depending on the use case.

Common pmr Resources

ResourceUse Case
monotonic_buffer_resourceFast, one-shot allocations (e.g., per tick)
unsynchronized_pool_resourceSmall object reuse with subpooling (no mutex)
synchronized_pool_resourceThread-safe version of above
CustomArena/slab allocators for domain-specific control

0 comments