Author

Clement D.

Geopolitics, Oil, and Risk: 3-Minute MLIV Insights for Investors

by Clement D. January 8, 2026

Geopolitical Tensions and Market Volatility: Key Themes for Finance Professionals

In today’s complex global landscape, finance professionals must stay attuned to the interplay between geopolitics and market dynamics. As discussed in the “Geopolitics Needs Policy to Move Risk: 3-Minute MLIV” video, factors such as the ongoing situation in Venezuela and the potential impact of Iran protests require careful analysis and strategic consideration.

Additionally, the “US To Control Venezuelan Oil ‘Indefinitely’; Iran Protests | Horizons Middle East & Africa 1/8/2026” video sheds light on the U.S. government’s plans to exert control over Venezuela’s oil exports, a development that could have significant implications for global energy markets.

Furthermore, the “ICE Minneapolis Shooting & Trump Demands More Defense Spending | Daybreak Europe 1/8/2026” video highlights the potential for geopolitical events, such as the ICE shooting in Minneapolis, to influence market sentiment and volatility.

To round out the discussion, the “Fun Quant Interview Question #quantinterviews” video presents a thought-provoking quantitative challenge that finance professionals may encounter in their career development.

🎥 Geopolitics Needs Policy to Move Risk: 3-Minute MLIV (Bloomberg)

The video’s thoughtful examination of the interplay between geopolitics and economic policy offers valuable insights for analysts and investors navigating today’s complex market landscape. By breaking down key themes such as the impact of Trump policy changes, the influence of AI and tech stocks on inflation, and China’s regulatory decisions, the panel provides a nuanced perspective on the multifaceted factors shaping global markets. As the world grapples with heightened geopolitical tensions, this insightful discussion underscores the critical importance of tailored policy responses to effectively manage and mitigate emerging risks.

🎥 US To Control Venezuelan Oil ‘Indefinitely’; Iran Protests | Horizons Middle East & Africa 1/8/2026 (Bloomberg)

The U.S. plans to indefinitely control future sales of Venezuelan oil, with President Donald Trump stating that the country will use the revenue only to purchase U.S.-made goods. Additionally, Bloomberg News has learned that Russia targeted South African video gamers as part of a recruitment drive for its war in Ukraine. In Iran, the president has ordered security forces not to target peaceful protesters, while Saudi Arabia’s Tadawul All Share Index jumped as much as 2.5% on Wednesday. Furthermore, Samsung reported that its fourth-quarter profit more than tripled to a record high, thanks to AI’s impact on the memory market.

🎥 ICE Minneapolis Shooting & Trump Demands More Defense Spending | Daybreak Europe 1/8/2026 (Bloomberg)

In the latest episode of Bloomberg’s Daybreak Europe, the program tackled several key financial themes with significant implications. President Trump’s demand for a more than 50% increase in the U.S. military budget to $1.5 trillion, along with his threat to defense companies over shareholder payouts, underscores the administration’s focus on bolstering national defense. Meanwhile, the Trump administration’s plan to control future sales of Venezuelan oil and hold the proceeds in U.S. accounts signals a continued effort to exert financial pressure on the Maduro regime. Additionally, the protests in Minneapolis following the shooting of a woman by an Immigration and Customs Enforcement officer have deepened the ongoing divisions in the country, with the mayor calling on ICE to leave the city. These developments, combined with updates on the global markets and insights from industry experts, provide a comprehensive overview of the financial landscape.

🎥 Fun Quant Interview Question #quantinterviews (Quant Prof)

In this strategic memo, we present a concise analysis of a video that addresses a common quantitative interview question. The video offers a thought-provoking challenge, providing decision-makers with valuable insights into the problem-solving abilities of potential candidates. The content is presented in a formal, succinct manner, highlighting the key elements that would be of interest to finance professionals evaluating prospective hires. This video serves as a useful tool in the assessment of quantitative skills, enabling informed decision-making in the recruitment process. By addressing this type of interview question, organizations can better identify candidates with the analytical acumen required to succeed in the dynamic finance industry.

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January 8, 2026 0 comments

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Equities Rise, Greenland Alerts, Nvidia’s Chips: Digest Key Finance News

by Clement D. January 6, 2026

The Rise of the New Financial Frontier: Navigating the Evolving Landscape

The finance world has long been a bastion of tradition, but the tides are turning as we enter a new era of unprecedented change. This article will delve into the emerging trends and transformative forces that are reshaping the very foundations of the industry, setting the stage for a future where the rules of the game are being rewritten.

[Reference to the videos: The insights and analysis presented in the videos “Equities to Rise Not Just on Asia Tech: 3-Minute MLIV,” “Greenland on Alert After US Venezuela Move & Danish PM NATO Warning | 1/6/2026,” “Lebanon’s Economy & Trade Minister; Nvidia’s Rubin Chips | Horizons Middle East and Africa 1/6/2026,” and “Nvidia’s CES AI Reveal Fuels Global Tech Optimism | Insight with Haslinda Amin 01/06/2026” will provide a valuable context for understanding the broader trends and implications discussed in this article.]

🎥 Equities to Rise Not Just on Asia Tech: 3-Minute MLIV (Bloomberg)

In a concise and data-driven analysis, the Bloomberg MLIV team, including Anna Edwards, Guy Johnson, Tom Mackenzie, and Skylar Montgomery Koning, unpacks the key themes impacting analysts and investors on “The Opening Trade.” The discussion covers the broader market outlook, with a focus on the potential for equities to rise beyond just the Asian tech sector. The experts delve into the 2026 growth projections and the dynamics of the dollar’s performance against the euro, providing valuable insights for market participants.

🎥 Greenland on Alert After US Venezuela Move & Danish PM NATO Warning | 1/6/2026 (Bloomberg)

The Danish Prime Minister has warned that a U.S. attack on Greenland would lead to the end of the NATO alliance. This comes after the recent raid on Caracas, which has resulted in Delcy Rodríguez being sworn in as the acting president of Venezuela. Nicolás Maduro has also pleaded not guilty to drug charges in his first court appearance in the U.S. Meanwhile, Nvidia’s CEO has announced that the company’s highly-anticipated new artificial intelligence chips are nearing release, which are expected to speed up AI development. The report also covers the ongoing unrest in Venezuela, the potential impact on the U.S.-GCC relationship, and the strong performance of Asian stocks.

🎥 Lebanon’s Economy & Trade Minister; Nvidia’s Rubin Chips | Horizons Middle East and Africa 1/6/2026 (Bloomberg)

As an investment strategist, I would highlight the key risks, opportunities, and market sentiment surrounding the topics covered in this video. While Venezuela’s political instability and the US charges against Maduro pose significant risks, the potential restructuring of Ethiopia’s bonds could present a strategic investment opportunity. Additionally, the impending release of Nvidia’s highly-anticipated Rubin data center chips may generate positive market sentiment, particularly within the technology sector. However, the ongoing challenges in Lebanon’s economy, as discussed by the country’s Economy and Trade Minister, could create uncertainty for investors. Overall, this video offers a diverse range of financial and geopolitical developments that warrant close monitoring by investors seeking to navigate the complex landscape of the Middle East and Africa region.

🎥 Nvidia’s CES AI Reveal Fuels Global Tech Optimism | Insight with Haslinda Amin 01/06/2026 (Bloomberg)

Nvidia’s CES AI Reveal Fuels Global Tech Optimism

The video segment from Insight with Haslinda Amin provides a quantitative insight into the impact of Nvidia’s latest AI announcements at CES 2026. The data highlights a clear pattern of Asian stocks rallying, led by a surge in the tech sector, as investors respond positively to Nvidia’s unveiling of new AI tools for autonomous vehicles and robotics. This statistical signal suggests that Nvidia’s advancements are fueling broader optimism in the global tech landscape, potentially signaling opportunities for investors to capitalize on the AI boom.

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January 6, 2026 0 comments

Greeks

Calculate Delta by Finite Differences

by Clement D. January 2, 2026

How to calculate delta by finite differences? In quantitative finance, Delta measures how sensitive an instrument’s value is to changes in the underlying price.

where $V$ is the value of the instrument and $S$ is the price of the underlying.

In practical terms, Delta answers a simple question:

If the underlying price moves slightly, how much does the price of my instrument move?

A Delta of 0.5 means that a one-unit increase in the underlying price increases the instrument’s value by roughly half a unit. A Delta close to 1 behaves like the underlying itself, while a Delta near 0 barely reacts at all.

This interpretation makes Delta central to both pricing and risk management. Traders use it to hedge exposure, risk systems use it to aggregate sensitivities, and quants use it to reason about how models respond to market moves.

So while Delta is written as a simple derivative, it represents a very concrete financial quantity: exposure to price moves.

1.What are finite differences?

In mathematics, a derivative measures how a quantity changes in the limit of an infinitely small move. In code, however, we never have infinitesimal changes — only finite ones.
A finite difference approximates a derivative by evaluating a function at nearby points and measuring the change.

Formally, the derivative of a function $f(x)$ f(x) can be approximated as:

$\frac{df}{dx}(x) \;\approx\; \frac{f(x + h) – f(x)}{h}$

where h is a small but finite step.

Instead of asking how a function behaves at an exact point, finite differences ask a more practical question: what happens if I move the input slightly and recompute the output? This turns abstract calculus into something directly computable using repeated function evaluations.

This idea is at the heart of many numerical methods used in quantitative finance, from Greeks to PDE solvers.

Common Finite Difference Approximations

Method	Formula	Accuracy	Notes
Forward difference	$\frac{f(x+h)-f(x)}{h}$	$O(h)$	Simple, biased
Backward difference	$\frac{f(x)-f(x-h)}{h}$	$O(h)$	Used near boundaries
Central difference	$\frac{f(x+h)-f(x-h)}{2h}$	$O(h^2)$	Most common in practice

Here is an illustration of the 3 methods:

2.Apply Finite Differences to Greeks Calculation: The Case of Delta

Delta is defined as the derivative of an instrument’s value with respect to the underlying price. Using finite differences, this derivative can be approximated by bumping the underlying price slightly and re-pricing the instrument. Instead of relying on an analytical expression for Delta, we observe how the price changes in response to a small move in the underlying.

In practice, this means evaluating the pricing function at two nearby prices and measuring the difference:

$\Delta \;\approx\; \frac{V(S+h) – V(S-h)}{2h}$
This central difference approximation is widely used because it is more accurate than one-sided alternatives and symmetric around the current price. It works for any pricing model and any payoff structure, which makes it especially attractive in production systems where models evolve faster than analytical formulas.

Central difference has a second-order truncation error, meaning the approximation error decreases proportionally to $h^2$ as the bump size becomes smaller. In contrast, forward and backward differences only achieve first-order accuracy, with errors proportional to $h$ h.

This faster convergence allows central differences to reach a given accuracy with larger bump sizes, which is numerically beneficial in floating-point arithmetic. Larger bumps reduce sensitivity to rounding errors and pricing noise, making the estimate more stable in practice.

In short, central differences strike a better balance between accuracy and numerical robustness. They converge faster, are less biased, and are therefore the default choice for Delta calculations in most quantitative systems.

This calculation is usually part of a bigger picture in the Black-Scholes equation giving a theoretical price value:

3. A Practical Example to Illustrate the Calculation

Assume we are pricing a European call option and observe the following prices from our pricing function:

Underlying Price $S$	Option Price $V(S)$
99.00	4.52
100.00	5.00
101.00	5.49

We choose a bump size of: $h = 1.0$

Using the central finite difference formula for Delta: $\Delta \;\approx\; \frac{V(S+h) – V(S-h)}{2h}$

we plug in the observed values: $\Delta \;\approx\; \frac{5.49 – 4.52}{2 \times 1.0} = \frac{0.97}{2} = 0.485$
A Delta of 0.485 means that, around $S = 100$ S=100, a one-unit increase in the underlying price increases the option value by approximately 0.49 units. This aligns with the intuition for a near-the-money call option, whose Delta typically lies between 0.4 and 0.6.

4. An Implementation in C++

In practice, the pricing function used to compute Delta is rarely a simple formula. It is often a reusable component shared across pricing, risk, and valuation workflows. Finite differences work particularly well in this setting because they require no changes to the pricing logic itself.

Consider a simple European call option priced using the Black–Scholes formula. We expose the pricing logic as a callable object and reuse it unchanged to compute Delta.

#include <cmath>

struct BlackScholesCall
{
    double strike;
    double rate;
    double volatility;
    double maturity;

    double operator()(double spot) const
    {
        const double sqrtT = std::sqrt(maturity);
        const double d1 =
            (std::log(spot / strike)
             + (rate + 0.5 * volatility * volatility) * maturity)
            / (volatility * sqrtT);

        const double d2 = d1 - volatility * sqrtT;

        return spot * normal_cdf(d1)
             - strike * std::exp(-rate * maturity) * normal_cdf(d2);
    }
};

The Delta computation itself remains generic:

template<typename Pricer>
double delta(Pricer&& price, double spot, double h)
{
    return (price(spot + h) - price(spot - h)) / (2.0 * h);
}

We can now compute Delta by simply bumping the spot price and reusing the same pricing function:

BlackScholesCall call{100.0, 0.01, 0.20, 1.0};

double spot = 100.0;
double bump = 0.5;

double d = delta(call, spot, bump);

5. Alternative Approaches to Calculate Delta

The appropriate method depends on the pricing model, the complexity of the payoff, and the performance requirements of the system. The table below summarises the most common approaches used in quantitative finance.

Method	How Delta Is Obtained	Accuracy	Performance	Typical Use Cases
Analytical formulas	Closed-form derivative of the pricing formula	Exact (within model)	Very high	Vanilla options, simple models
Finite differences	Bump underlying and reprice	Approximate	Medium	General-purpose risk systems
PDE-based methods	Local slope on a pricing grid	Approximate	High	Equity and rates models
Adjoint (AAD)	Reverse differentiation of the pricer	High	Very high	Large Monte Carlo portfolios

Finite differences sit at the centre of this spectrum. They are not the most accurate method, nor the fastest, but they are often the most practical. Their simplicity, model-independence, and ease of implementation make them a natural default in many production systems.

Understanding how Delta is computed — and the numerical trade-offs involved — is essential for interpreting risk numbers correctly. A Delta is not just a derivative on paper; it is the result of a numerical procedure, shaped by design choices that matter in practice.

January 2, 2026 0 comments

News

Starfighters CEO Expands Spaceport and Satellite Operations for Data Boost

by Clement D. January 1, 2026

Are investors prepared for the unexpected turbulence ahead? The day’s trading session was marked by sharp swings and heightened volatility, underscoring the pressing need for a robust risk management strategy.

In our [video: Starfighters CEO Says Looking to Add Spaceport, Aircrafts], we delve into StarFighters’ ambitious plans to scale up their low-orbit satellite operations, including the addition of a new spaceport. This could have far-reaching implications for the data center industry, a topic we’ll explore in greater depth later in the article.

Meanwhile, in [video: Mamdani Takes Reins of New York City], we witness the historic inauguration of Zohran Mamdani as the 112th mayor of New York City. This transition of power is sure to have significant ripple effects on the financial landscape of the nation’s economic hub.

Stay tuned for more updates from the [video: Today on NYSE Live | Federal Reserve Minutes Reveal Policymakers Were Split on December Rate Cut] and our [video: Mad Money 12/31/25 | Audio Only] as we navigate the day’s events and uncover the key factors shaping the market’s direction.

🎥 Starfighters CEO Says Looking to Add Spaceport, Aircrafts (Bloomberg)

In the video, Rick Svetkoff, the founder and CEO of StarFighters, discusses his company’s plans to scale up low-orbit satellite operations. He highlights the company’s efforts to add another spaceport, which could potentially support the growing demand for data centers. Svetkoff’s insights suggest that StarFighters’ initiatives could have significant market implications, particularly in the data infrastructure and satellite technology sectors.

🎥 Mamdani Takes Reins of New York City (Bloomberg)

The clip depicts Zohran Mamdani’s assumption of the office of New York City’s 112th mayor, marking the culmination of his improbable and historic rise to power over the past year. The narrative surrounding this event underscores the remarkable nature of Mamdani’s journey, as he has defied expectations and overcome significant challenges to reach this pivotal milestone in his political career.

🎥 Today on NYSE Live | Federal Reserve Minutes Reveal Policymakers Were Split on December Rate Cut (New York Stock Exchange)

In a surprising turn of events, the recently released Federal Reserve minutes have shed light on a significant divide among policymakers during the December meeting. The data reveals that officials were split on the decision to cut interest rates, with some advocating for a more dovish approach while others pushed for a more hawkish stance. This insight into the central bank’s internal deliberations provides a quantitative snapshot of the complex decision-making process that ultimately shaped the Fed’s monetary policy. Analyzing the patterns and anomalies within the minutes offers valuable clues about the factors that influenced the final outcome, potentially signaling future shifts in the Fed’s approach to managing the economy.

🎥 Mad Money 12/31/25 | Audio Only (CNBC Television)

The featured video provides an audio-only recording of Jim Cramer’s “Mad Money” program, offering viewers a personal guide through the complexities of Wall Street investing. The presentation aims to navigate the opportunities and pitfalls of the financial markets, with the primary objective of helping viewers make informed investment decisions. The content is structured around key takeaways, presented in an objective and precise manner, providing a research-oriented summary for finance enthusiasts. Viewers can access the live and exclusive video content from CNBC by subscribing to CNBC PRO, further enhancing their understanding of the evolving financial landscape.

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January 1, 2026 0 comments

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2025 Trading Insights: Navigating the Markets with Cramer’s Advice

by Clement D. December 30, 2025

The Significance of Year-End Trading Dynamics: A Didactic Exploration

As the curtain falls on another eventful year in the world of finance, it is crucial for graduate students in the field to understand the nuanced dynamics that shape the final days of trading. In this structured introduction, we will delve into the key factors influencing the current market landscape, with a particular focus on the interplay between investor sentiment, macroeconomic indicators, and industry-specific trends.

To provide a comprehensive overview, we will later in the article present a series of informative videos, including “Final Days of 2025 Trading Officially Get Underway,” which offers a timely analysis of the ongoing market events, and “Mad Money 12/29/25,” a valuable audio-only resource featuring the insights of renowned financial expert Jim Cramer. Additionally, we will explore Cramer’s perspectives on the benefits of long-term investing and the criteria he recommends for stock selection, as outlined in his book “How to Make Money in Any Market.”

By delving into these diverse sources, graduate students will gain a deeper understanding of the intricate factors shaping the final trading sessions of the year, equipping them with the knowledge necessary to navigate the complex world of finance with confidence and strategic acumen.

🎥 Final Days of 2025 Trading Officially Get Underway (New York Stock Exchange)

The presentation commences with a succinct overview of the current state of the financial markets, emphasizing the pivotal juncture that marks the final days of the trading year 2025. Attendees are guided through a meticulous analysis of the key factors driving market dynamics, including the interplay of macroeconomic indicators, geopolitical developments, and investor sentiment.

The discussion then delves into the intricate mechanisms underlying the trading activities during this critical period, exploring the nuanced strategies employed by seasoned market participants. Particular attention is devoted to the complex interactions between asset classes, volatility patterns, and risk management frameworks, equipping the audience with a comprehensive understanding of the multifaceted nature of end-of-year trading.

Finally, the presentation concludes with a forward-looking perspective, highlighting the potential implications of the current market trends and their significance for the forthcoming year. Attendees are encouraged to engage in a robust Q&A session, fostering a dynamic exchange of insights and facilitating a deeper comprehension of the subject matter.

🎥 Mad Money 12/29/25 | Audio Only (CNBC Television)

The latest episode of “Mad Money” hosted by Jim Cramer offers a quantitative insight into the complex world of Wall Street investing. Analyzing patterns, anomalies, and statistical signals, Cramer navigates through the jungle of opportunities and pitfalls, providing a personal guide to help viewers make informed financial decisions. This audio-only presentation delves into the nuances of the market, empowering investors with the knowledge they need to succeed in the ever-evolving landscape of Wall Street.

🎥 Jim Cramer talks the benefits of long-term investing (CNBC Television)

In an age where the siren song of short-term gains and speculative frenzy has lured many an unwary investor, the sage counsel of veteran financial commentator Jim Cramer stands as a beacon of reason and foresight. Drawing upon his decades-long experience navigating the ebb and flow of the markets, Cramer’s treatise on the virtues of long-term investing, encapsulated in his work “How to Make Money in Any Market,” offers a timely and invaluable perspective. Amidst the cacophony of voices advocating for rapid portfolio churning and chasing the latest trends, Cramer’s measured approach serves as a reminder that the true path to sustainable wealth lies in the patient cultivation of one’s assets, weathering the storms of volatility and capitalizing on the gradual yet inexorable march of economic progress. In an era where the temptation to succumb to the allure of instant gratification is ever-present, Cramer’s insights provide a vital counterpoint, urging investors to cultivate the discipline and fortitude required to reap the bountiful rewards that long-term, prudent stewardship of one’s financial resources can yield.

🎥 Jim Cramer says you want to pick stocks that meet two criteria (CNBC Television)

The presentation opens with a discussion of the two key criteria Cramer emphasizes for successful stock selection. Firstly, the stock must exhibit strong momentum, demonstrating consistent upward price movement and robust trading volume. This momentum signals the market’s confidence in the underlying company’s prospects. Secondly, the stock should be undervalued relative to its intrinsic worth, offering investors an attractive entry point. Cramer underscores the importance of identifying this mismatch between market price and fundamental value, as it presents a lucrative opportunity for capital appreciation. He then delves into the genesis of his book “How to Make Money in Any Market,” highlighting his decades-long experience in navigating volatile market conditions and distilling his investment philosophy into a practical guide for retail investors. The presentation concludes by emphasizing Cramer’s belief that by adhering to these two principles, investors can successfully navigate the complexities of the financial markets and generate consistent returns, even in the face of prevailing economic uncertainty.

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December 30, 2025 0 comments

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ETF Trends, Innovations, and Insights: A Guide to 2025 and Beyond

by Clement D. December 29, 2025

Will the AI Boom Continue? Navigating the Risks and Opportunities of the Magnificent Seven in 2026

As the AI-driven rally propels the tech titans known as the “Magnificent Seven” to soaring heights, a crucial question looms: can this extraordinary momentum be sustained in the year 2026? This analytical overview will delve into the surge of these industry behemoths, their skyrocketing valuations, and the underlying factors that may shape their future trajectory, framing the discussion in terms of risk, opportunity, and the inherent uncertainty that lies ahead.

[Reference to the video “Will the AI Boom Continue? The Fate of the Magnificent Seven in 2026”]

Additionally, this piece will explore the essential AI skills that professionals will need to stay ahead of the curve in 2026, drawing insights from the video “Essential AI Skills For 2026.” [Reference to the video]

Amidst the rapid advancements in the AI landscape, the finance world faces a crossroads, where navigating the risks and seizing the opportunities will be paramount for those seeking to thrive in the years to come.

🎥 NYSE 2025 ETF Wrap-Up: Trends, Takeaways, and What’s Next (New York Stock Exchange)

As the markets open this morning, we turn our attention to the key takeaways from the 2025 ETF Wrap-Up, a comprehensive look at the transformative year experienced by the exchange-traded fund (ETF) industry. Tim Reilly, the Head of Exchange Traded Solutions at the NYSE, joins us to provide his expert insights on the trends that shaped the market, the strategic adaptations employed by the exchange, and the promising outlook for the ETF landscape in the year ahead.

Reilly’s analysis highlights the record-breaking pace of new ETF launches, reflecting the growing investor demand for innovative products across diverse asset classes. He also delves into the shifts in investor preferences, underscoring the NYSE’s proactive approach in positioning itself to cater to these evolving needs. As we look ahead to 2026, Reilly offers a forward-looking perspective, outlining the strategies and opportunities that are poised to drive the continued expansion and adoption of ETFs in the coming year.

🎥 Trump Praises Zelenskiy After Talks With No Breakthrough (Bloomberg)

The presentation begins with an overview of the key macroeconomic factors influencing the geopolitical landscape. The discussion then transitions to an analysis of the strategic implications of the ongoing negotiations between the leaders of the United States and Ukraine. Students are encouraged to consider the potential economic ramifications of any prospective peace deal, particularly in terms of its impact on foreign direct investment, trade dynamics, and currency markets. The lecture emphasizes the importance of nuanced interpretation of diplomatic rhetoric, cautioning against overconfident extrapolation from the cordial tone of the press conference. Finally, the presentation concludes by highlighting the need for continued monitoring of the situation, as the absence of a tangible breakthrough suggests the presence of unresolved complexities that may shape future economic outcomes in the region.

🎥 Essential AI Skills For 2026 (Tina Huang)

The video “Essential AI Skills For 2026” provides a concise and analytical overview of the key skills needed to stay ahead in the rapidly evolving field of artificial intelligence. The presenter, a former data scientist at Meta, highlights the importance of mastering AI agent fundamentals, building custom AI agents, and familiarizing oneself with the latest AI language models like ChatGPT and Perplexity. The video emphasizes the market implications of these skills, suggesting that individuals who acquire them will be well-positioned to capitalize on the growing demand for AI-driven solutions across various industries. The presenter also shares valuable resources, including links to relevant tutorials and a waitlist for an upcoming AI Agent Bootcamp, further enhancing the video’s utility for viewers seeking to enhance their AI expertise.

🎥 Will the AI Boom Continue? The Fate of the Magnificent Seven in 2026 (Capital Trading)

The AI-driven rally has powered the Magnificent Seven stocks to massive gains, but can the boom continue in 2026? In this video, we examine the surge of these tech giants, rising valuations, and concerns about a potential AI bubble. We also discuss CAPE ratios, earnings growth forecasts, and whether these companies can deliver the returns investors are pricing in. The success of AI ROI could shape Wall Street’s performance for the year ahead.

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December 29, 2025 0 comments

Interview Performance

C++ Shared Pointers: Top shared_ptr Quant Interview Questions

by Clement D. November 30, 2025

C++ shared pointers come up again and again in quant interviews, and for good reason: they sit at the intersection of memory management, performance, ownership semantics, and real-time system reliability, all skills quants are expected to master. In modern C++ codebases used across trading desks, risk engines, and pricing libraries, std::shared_ptr is everywhere, yet many candidates only know the surface-level behavior. Interviewers use shared pointer questions to test whether you understand what’s really happening under the hood: control blocks, atomic reference counting, cache effects, and the subtle performance pitfalls that matter in low-latency environments. They also want to see if you can reason about ownership graphs, detect leaks caused by cycles, and choose correctly between shared_ptr, unique_ptr, and raw pointers in high-frequency workloads. What are the top shared_ptr questions?

Question 1: “What is a Shared Pointer? Give A Quantitative Finance Example”

A shared_ptr is a reference-counted smart pointer that enables shared ownership of a dynamically allocated object, automatically deleting it when the last owner goes away.

In many pricing engines, several components need access to the same yield-curve snapshot without copying it. A shared_ptr is ideal here because it lets each module share ownership safely. Here’s a minimal example:

#include <iostream>
#include <memory>
#include <vector>

struct YieldCurve {
    std::vector<double> tenors;
    std::vector<double> discountFactors;

    YieldCurve() {
        std::cout << "YieldCurve built\n";
    }
    ~YieldCurve() {
        std::cout << "YieldCurve destroyed\n";
    }
};

int main() {
    auto curve = std::make_shared<YieldCurve>();

    std::cout << "Ref count initially: " << curve.use_count() << "\n";

    {
        // Risk model shares the same curve
        auto riskModelCurve = curve;
        std::cout << "Ref count after risk model uses it: "
                  << curve.use_count() << "\n";
    } // riskModelCurve dies, curve stays alive

    std::cout << "Ref count after model finished: "
              << curve.use_count() << "\n";
}

What This Example Demonstrates

1. Shared Ownership of a Core Market Object

In real pricing systems, many components—pricing engines, risk calculators, scenario generators—must all access the same yield curve. Using std::shared_ptr ensures the curve persists as long as at least one module still uses it, without forcing expensive deep copies.

2. Reference Counting Behind the Scenes

Each time the shared_ptr is copied (e.g., when the risk model takes a reference), the strong reference count increases. When copies go out of scope, the count decreases. Only when the count reaches zero does the object get destroyed. This is exactly what happens to the YieldCurve instance across scopes in the example.

3. Automatic Lifetime Management (RAII)

You never call delete on the yield curve. Its lifetime is tied to the lifetime of the owning shared_ptr instances.
This reduces the classic risks in large quant codebases: dangling pointers, double deletes, and lifetime mismatches between pricing components.

Question 2: “How does `shared_ptr` manage reference counting?“

std::shared_ptr uses a separate control block to track how many owners an object has. Every time a shared_ptr is copied, the control block increments a strong reference count. Every time a shared_ptr is destroyed or reset, that count is decremented. When the strong count reaches zero, the managed object is automatically deleted.

Under the hood, the control block stores:

A strong reference count
(number of active shared_ptr owning the object)
A weak reference count
(number of weak_ptr observing the object)
The managed pointer
(Optionally) a custom deleter and allocator

All reference count updates are atomic, which makes shared_ptr safe to use across threads—though more expensive than unique_ptr. In practice, this mechanism ensures that shared market objects (like yield curves, volatility surfaces, or trade graphs) live exactly as long as the last component using them, with no need for manual delete and no risk of premature destruction. One of the top shared_ptr questions!

Question 3: “What causes a memory leak with `shared_ptr`?“

A memory leak with std::shared_ptr happens when two or more objects form a cyclic reference, meaning each holds a shared_ptr to the other, so their reference counts never drop to zero and their destructors never run.

For example, if struct A has std::shared_ptr<B> b; and struct B has std::shared_ptr<A> a;, creating the cycle a->b = b; and b->a = a; will leak both objects because each keeps the other alive. The fix is to use std::weak_ptr on one side of the relationship.

struct B;

struct A { std::shared_ptr<B> b; };
struct B { std::shared_ptr<A> a; }; // ← this creates a cycle and leaks

auto a = std::make_shared<A>();
auto b = std::make_shared<B>();
a->b = b;
b->a = a; // reference counts never reach 0 → leak

Here’s the same idea but fixed using std::weak_ptr so the cycle doesn’t keep the objects alive:

#include <memory>
#include <iostream>

struct B;

struct A {
    std::shared_ptr<B> b;
    ~A() { std::cout << "A destroyed\n"; }
};

struct B {
    std::weak_ptr<A> a;  // weak_ptr breaks the cycle
    ~B() { std::cout << "B destroyed\n"; }
};

int main() {
    auto a = std::make_shared<A>();
    auto b = std::make_shared<B>();

    a->b = b;
    b->a = a;  // does NOT increase refcount

    return 0;  // both A and B are destroyed normally
}

That's one of the top shared_ptr questions.

Question 4: `make_shared` vs. `shared_ptr<T>(new T)`: what’s the difference?

std::make_shared<T>() and std::shared_ptr<T>(new T) both create a shared_ptr, but they differ in performance, memory layout, and exception-safety:

make_shared is faster and uses one allocation: it allocates the control block and the object in a single heap allocation, improving cache locality.
shared_ptr<T>(new T) uses two allocations: one for the control block and one for the object, making it slower and more memory hungry.
make_shared is exception-safe: if constructor arguments throw, no memory is leaked. With shared_ptr<T>(new T), if you add custom deleters or wrap logic incorrectly, leaks can occur.
make_shared is preferred except when you need a custom deleter or want separate lifetimes for control block and object (rare—e.g., weak-to-shared resurrection edge cases).

Example:

auto p1 = std::make_shared<MyObject>();        // one allocation, safe
auto p2 = std::shared_ptr<MyObject>(new MyObject());  // two allocations

Question 5: Why is `shared_ptr` slower?

std::shared_ptr is slower because it performs atomic reference counting, extra bookkeeping, and sometimes extra allocations to manage shared ownership. Every copy of a shared_ptr must atomically increment the control block’s reference count, and every destruction must atomically decrement it; these atomic operations create contention, inhibit compiler optimizations, and add CPU overhead. A shared_ptr also maintains both a strong and weak count, uses a control block to track them, and may require separate heap allocations (unless created via make_shared). This combination of atomic ops + bookkeeping + heap activity makes shared_ptr significantly slower than a raw pointer or even a unique_ptr, which performs no reference counting at all.

November 30, 2025 0 comments

IDE Libraries

Clang Formatting for C++: An Overview of Clang-Format

by Clement D. November 23, 2025

Maintaining consistent C++ style across a large codebase is one of the simplest ways to improve readability, reduce onboarding time, and prevent unnecessary merge conflicts. Yet many C++ teams, especially in quantitative finance, where codebases grow organically over years still rely on manual style conventions or developer-specific habits. The result is familiar: inconsistent indentation, mixed brace styles, scattered spacing rules, and code that “looks” different depending on who touched it last. Clang-Format solves this problem. What is Clang formatting for C++?

Part of the Clang and LLVM ecosystem, clang-format is a fast, deterministic, fully automated C++ formatter that rewrites your source code according to a predefined set of style rules. Instead of arguing about formatting in code reviews or spending time manually cleaning up diffs, quant developers can enforce a single standard across an entire pricing or risk library automatically and reproducibly.

1.What is the Clang and LLVM ecosystem?

The Clang and LLVM ecosystem is a modern, modular compiler toolchain used for building, analyzing, and optimizing C++ (and other language) programs. Clang is the front-end: it parses C++ code, checks syntax and types, produces highly readable diagnostics, and generates LLVM’s intermediate representation (IR). LLVM is the backend: a collection of reusable compiler components that optimize the IR and generate machine code for many architectures (x86-64, ARM, etc.). Unlike monolithic compilers like GCC, the Clang/LLVM stack is built as independent libraries, which makes it incredibly flexible.

This design allows developers to build tools such as clang-format, clang-tidy, source-to-source refactoring engines, static analyzers, and custom compiler plugins. The ecosystem powers modern IDE features, code intelligence, and even JIT-compiled systems.

Because of its modularity, fast compilation, modern C++ standard support, and rich tooling, Clang/LLVM has become the backbone of many large C++ codebases, including those used in finance, gaming, scientific computing, and operating systems like macOS.

2.Clang-Format: The Modern Standard for C++ Code Formatting

Clang-format has become the default choice for formatting C++ code across many industries, from finance to large-scale open-source projects. Built on top of the Clang and LLVM ecosystem, it provides a fast, deterministic, and fully automated way to enforce consistent style rules across an entire codebase.

Instead of relying on ad-hoc conventions or individual preferences, teams can define a single .clang-format configuration and apply it uniformly through editors, CI pipelines, and pre-commit hooks. The result is cleaner diffs, fewer formatting discussions in code reviews, and a more maintainable codebase—crucial benefits for large C++ systems such as pricing engines, risk libraries, or high-performance trading infrastructure.

3.Installation

How to start using Clang formatting for C++? Let’s start with installation.

I’m using mac, and it’s as simple as:

➜  ~ brew install clang-format

==> Fetching downloads for: clang-format
✔︎ Bottle Manifest clang-format (21.1.6)            [Downloaded   12.7KB/ 12.7KB]
✔︎ Bottle clang-format (21.1.6)                     [Downloaded    1.4MB/  1.4MB]
==> Pouring clang-format--21.1.6.sonoma.bottle.tar.gz
🍺  /usr/local/Cellar/clang-format/21.1.6: 11 files, 3.4MB
==> Running `brew cleanup clang-format`...

With linux, it would also be as simple as:

➜  ~ sudo apt-get install clang-format

To get a general overview of the tool, just run the –help command:

➜  ~ clang-format –help

OVERVIEW: A tool to format C/C++/Java/JavaScript/JSON/Objective-C/Protobuf/C# code.

If no arguments are specified, it formats the code from standard input
and writes the result to the standard output.
If <file>s are given, it reformats the files. If -i is specified
together with <file>s, the files are edited in-place. Otherwise, the
result is written to the standard output.

USAGE: clang-format [options] [@<file>] [<file> …]

OPTIONS:

Clang-format options:

  –Werror                       – If set, changes formatting warnings to errors
  –Wno-error=<value>            – If set, don’t error out on the specified warning type.
    =unknown                     –   If set, unknown format options are only warned about.
                                     This can be used to enable formatting, even if the
                                     configuration contains unknown (newer) options.
                                     Use with caution, as this might lead to dramatically
                                     differing format depending on an option being
                                     supported or not.
  –assume-filename=<string>     – Set filename used to determine the language and to find
                                   .clang-format file.
                                   Only used when reading from stdin.
                                   If this is not passed, the .clang-format file is searched
                                   relative to the current working directory when reading stdin.
                                   Unrecognized filenames are treated as C++.
                                   supported:
                                     CSharp: .cs
                                     Java: .java
                                     JavaScript: .js .mjs .cjs .ts
                                     Json: .json .ipynb
                                     Objective-C: .m .mm
                                     Proto: .proto .protodevel
                                     TableGen: .td
                                     TextProto: .txtpb .textpb .pb.txt .textproto .asciipb
                                     Verilog: .sv .svh .v .vh
  –cursor=<uint>                – The position of the cursor when invoking
                                   clang-format from an editor integration
  –dry-run                      – If set, do not actually make the formatting changes
  –dump-config                  – Dump configuration options to stdout and exit.
                                   Can be used with -style option.
  –fail-on-incomplete-format    – If set, fail with exit code 1 on incomplete format.
  –fallback-style=<string>      – The name of the predefined style used as a
                                   fallback in case clang-format is invoked with
                                   -style=file, but can not find the .clang-format
                                   file to use. Defaults to ‘LLVM’.
                                   Use -fallback-style=none to skip formatting.
  –ferror-limit=<uint>          – Set the maximum number of clang-format errors to emit
                                   before stopping (0 = no limit).
                                   Used only with –dry-run or -n
  –files=<filename>             – A file containing a list of files to process, one per line.
  -i                             – Inplace edit <file>s, if specified.
  –length=<uint>                – Format a range of this length (in bytes).
                                   Multiple ranges can be formatted by specifying
                                   several -offset and -length pairs.
                                   When only a single -offset is specified without
                                   -length, clang-format will format up to the end
                                   of the file.
                                   Can only be used with one input file.
  –lines=<string>               – <start line>:<end line> – format a range of
                                   lines (both 1-based).
                                   Multiple ranges can be formatted by specifying
                                   several -lines arguments.
                                   Can’t be used with -offset and -length.
                                   Can only be used with one input file.
  -n                             – Alias for –dry-run
  –offset=<uint>                – Format a range starting at this byte offset.
                                   Multiple ranges can be formatted by specifying
                                   several -offset and -length pairs.
                                   Can only be used with one input file.
  –output-replacements-xml      – Output replacements as XML.
  –qualifier-alignment=<string> – If set, overrides the qualifier alignment style
                                   determined by the QualifierAlignment style flag
  –sort-includes                – If set, overrides the include sorting behavior
                                   determined by the SortIncludes style flag
  –style=<string>               – Set coding style. <string> can be:
                                   1. A preset: LLVM, GNU, Google, Chromium, Microsoft,
                                      Mozilla, WebKit.
                                   2. ‘file’ to load style configuration from a
                                      .clang-format file in one of the parent directories
                                      of the source file (for stdin, see –assume-filename).
                                      If no .clang-format file is found, falls back to
                                      –fallback-style.
                                      –style=file is the default.
                                   3. ‘file:<format_file_path>’ to explicitly specify
                                      the configuration file.
                                   4. “{key: value, …}” to set specific parameters, e.g.:
                                      –style=”{BasedOnStyle: llvm, IndentWidth: 8}”
  –verbose                      – If set, shows the list of processed files

Generic Options:

  –help                         – Display available options (–help-hidden for more)
  –help-list                    – Display list of available options (–help-list-hidden for more)
  –version                      – Display the version of this program

➜  ~ clang-format --help

OVERVIEW: A tool to format C/C++/Java/JavaScript/JSON/Objective-C/Protobuf/C# code.

If no arguments are specified, it formats the code from standard input
and writes the result to the standard output.
If <file>s are given, it reformats the files. If -i is specified
together with <file>s, the files are edited in-place. Otherwise, the
result is written to the standard output.

USAGE: clang-format [options] [@<file>] [<file> ...]

OPTIONS:

Clang-format options:

  --Werror                       - If set, changes formatting warnings to errors
  --Wno-error=<value>            - If set, don't error out on the specified warning type.
    =unknown                     -   If set, unknown format options are only warned about.
                                     This can be used to enable formatting, even if the
                                     configuration contains unknown (newer) options.
                                     Use with caution, as this might lead to dramatically
                                     differing format depending on an option being
                                     supported or not.
  --assume-filename=<string>     - Set filename used to determine the language and to find
                                   .clang-format file.
                                   Only used when reading from stdin.
                                   If this is not passed, the .clang-format file is searched
                                   relative to the current working directory when reading stdin.
                                   Unrecognized filenames are treated as C++.
                                   supported:
                                     CSharp: .cs
                                     Java: .java
                                     JavaScript: .js .mjs .cjs .ts
                                     Json: .json .ipynb
                                     Objective-C: .m .mm
                                     Proto: .proto .protodevel
                                     TableGen: .td
                                     TextProto: .txtpb .textpb .pb.txt .textproto .asciipb
                                     Verilog: .sv .svh .v .vh
  --cursor=<uint>                - The position of the cursor when invoking
                                   clang-format from an editor integration
  --dry-run                      - If set, do not actually make the formatting changes
  --dump-config                  - Dump configuration options to stdout and exit.
                                   Can be used with -style option.
  --fail-on-incomplete-format    - If set, fail with exit code 1 on incomplete format.
  --fallback-style=<string>      - The name of the predefined style used as a
                                   fallback in case clang-format is invoked with
                                   -style=file, but can not find the .clang-format
                                   file to use. Defaults to 'LLVM'.
                                   Use -fallback-style=none to skip formatting.
  --ferror-limit=<uint>          - Set the maximum number of clang-format errors to emit
                                   before stopping (0 = no limit).
                                   Used only with --dry-run or -n
  --files=<filename>             - A file containing a list of files to process, one per line.
  -i                             - Inplace edit <file>s, if specified.
  --length=<uint>                - Format a range of this length (in bytes).
                                   Multiple ranges can be formatted by specifying
                                   several -offset and -length pairs.
                                   When only a single -offset is specified without
                                   -length, clang-format will format up to the end
                                   of the file.
                                   Can only be used with one input file.
  --lines=<string>               - <start line>:<end line> - format a range of
                                   lines (both 1-based).
                                   Multiple ranges can be formatted by specifying
                                   several -lines arguments.
                                   Can't be used with -offset and -length.
                                   Can only be used with one input file.
  -n                             - Alias for --dry-run
  --offset=<uint>                - Format a range starting at this byte offset.
                                   Multiple ranges can be formatted by specifying
                                   several -offset and -length pairs.
                                   Can only be used with one input file.
  --output-replacements-xml      - Output replacements as XML.
  --qualifier-alignment=<string> - If set, overrides the qualifier alignment style
                                   determined by the QualifierAlignment style flag
  --sort-includes                - If set, overrides the include sorting behavior
                                   determined by the SortIncludes style flag
  --style=<string>               - Set coding style. <string> can be:
                                   1. A preset: LLVM, GNU, Google, Chromium, Microsoft,
                                      Mozilla, WebKit.
                                   2. 'file' to load style configuration from a
                                      .clang-format file in one of the parent directories
                                      of the source file (for stdin, see --assume-filename).
                                      If no .clang-format file is found, falls back to
                                      --fallback-style.
                                      --style=file is the default.
                                   3. 'file:<format_file_path>' to explicitly specify
                                      the configuration file.
                                   4. "{key: value, ...}" to set specific parameters, e.g.:
                                      --style="{BasedOnStyle: llvm, IndentWidth: 8}"
  --verbose                      - If set, shows the list of processed files

Generic Options:

  --help                         - Display available options (--help-hidden for more)
  --help-list                    - Display list of available options (--help-list-hidden for more)
  --version                      - Display the version of this program

4.Usage

Imagine a messy piece of C++ code calculating DVA with formatting problems all over:

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

double computeDVA(const std::vector<double>& exposure,
 const std::vector<double>& pd,
   const std::vector<double> lgd, double discount)
{
double dva=0.0;
for (size_t i=0;i<exposure.size();i++){
double term= exposure[i] * pd[i] * lgd[i] *discount;
   dva+=term;
}
 return dva; }

int   main() {

std::vector<double> exposure = {100,200,150,120};
 std::vector<double> pd={0.01,0.015,0.02,0.03};
  std::vector<double> lgd = {0.6,0.6,0.6,0.6};
double discount =0.97;

double dva = computeDVA(exposure,pd,lgd,discount);

 std::cout<<"DVA: "<<dva<<std::endl;

return 0;}

This respects the general DVA formula (from the XVA family):

Let’s format it with clang-format using the LLVM style, I run:

clang-format -i -style=LLVM dva.cpp

with:

-i = overwrite the file in place
-style=LLVM = apply the LLVM formatting style

It becomes sweet and nice:

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

double computeDVA(const std::vector<double> &exposure,
                  const std::vector<double> &pd, const std::vector<double> lgd,
                  double discount) {
  double dva = 0.0;
  for (size_t i = 0; i < exposure.size(); i++) {
    double term = exposure[i] * pd[i] * lgd[i] * discount;
    dva += term;
  }
  return dva;
}

int main() {

  std::vector<double> exposure = {100, 200, 150, 120};
  std::vector<double> pd = {0.01, 0.015, 0.02, 0.03};
  std::vector<double> lgd = {0.6, 0.6, 0.6, 0.6};
  double discount = 0.97;

  double dva = computeDVA(exposure, pd, lgd, discount);

  std::cout << "DVA: " << dva << std::endl;

  return 0;
}

5. A List and Comparison of The Clang Styles

Formatting styles in clang-format come from real, large-scale C++ codebases: LLVM, Google, Chromium, Mozilla, and others. Each style reflects the conventions of the organization that created it, and each emphasizes different priorities such as readability, compactness, or strict consistency. While clang-format supports many styles, they all serve the same purpose: enforcing a predictable, automated layout for C++ code across complex projects. Here is an overview of Clang formatting for C++ via a list of styles available:

Style	Origin / Used By	Brace Style	Indentation	Line Length	Notable Traits
LLVM	LLVM/Clang project	Stroustrup-like	2 spaces	80	Clean, minimal, modern; default for clang-format
Google	Google C++ Style Guide	Allman/Google	2 spaces	80	Very consistent; strong whitespace rules
Chromium	Chromium/Google Chrome	K&R	2 spaces	80	Optimized for very large codebases
Mozilla	Firefox	Allman	2 or 4 spaces	99	Slightly looser than Google; readable
WebKit	WebKit / Safari	Stroustrup	4 spaces	120	Widely spaced; readable for UI and engine code
GNU	GNU coding standard	GNU style	2 spaces	79	Uncommon now; unusual brace placements
Microsoft	Microsoft C++/C#	Allman	4 spaces	120	Familiar to Windows devs; wide spacing
JS	JavaScript projects	K&R	2 spaces	80	For JS/TS/CSS formatting, not C++
File	Custom `.clang-format`	—	—	—	User-defined rules; highly flexible

Among all available clang-format styles, LLVM stands closest to a true industry standard for modern C++ development. Its clean, neutral layout makes it easy to read, easy to maintain, and suitable for teams of any size: from open-source contributors to quant developers in large financial institutions. Unlike more opinionated styles such as Google or GNU, LLVM avoids strong stylistic constraints and focuses instead on clarity and consistency.

This neutrality is exactly why so many projects adopt it as their base style or use it directly without modification. For quant teams working on pricing engines, risk libraries, or low-latency infrastructure, LLVM offers a stable, widely trusted foundation that integrates seamlessly into automated workflows and CI pipelines.

If you need a formatting standard that “just works” across diverse C++ codebases, LLVM is the safest and most broadly compatible choice.

6. Manage Clang Formating in your Codebase

he easiest way to standardize formatting across an entire C++ codebase is to create a .clang-format file at the root of your project. This file acts as the single source of truth for your formatting rules, ensuring every developer, editor, and CI job applies exactly the same style. Once the file is in place, running clang-format becomes fully deterministic: every file in your project will follow the same indentation, spacing, brace placement, and wrapping rules.

A .clang-format file can be as simple as one line—BasedOnStyle: LLVM—or it can define dozens of customized options tailored to your team. Developers don’t need to memorize or manually enforce formatting conventions; the file encodes all rules, and clang-format applies them automatically. Most editors (VSCode, CLion, Vim, Emacs) pick up the configuration instantly, and CI pipelines can run clang-format checks to prevent unformatted code from entering the repository.

An example of .clang-format file:

BasedOnStyle: LLVM

# Indentation & Alignment
IndentWidth: 2
TabWidth: 2
UseTab: Never

# Line Breaking & Wrapping
ColumnLimit: 100
AllowShortIfStatementsOnASingleLine: false
AllowShortFunctionsOnASingleLine: Empty

# Braces & Layout
BreakBeforeBraces: LLVM
BraceWrapping:
  AfterNamespace: false
  AfterClass: false
  AfterControlStatement: false

# Includes
IncludeBlocks: Regroup
SortIncludes: true

# Spacing
SpaceBeforeParens: ControlStatements
SpacesInParentheses: false
SpaceAfterCStyleCast: true

# C++ Specific
Standard: Latest
DerivePointerAlignment: false
PointerAlignment: Left

# Comments
ReflowComments: true

# File Types
DisableFormat: false

Put your file inside your project directory, example of structure:

my-project/
  .clang-format
  src/
    dva.cpp
    pricer.cpp

Once the .clang-format file is in place:

No need to specify -style
No need to pass config flags
clang-format automatically uses your project’s style rules

Just run:

clang-format -i myfile.cpp

And your team stays fully consistent.

7. Include clang-format in. a pre-commit hook

You might want to do more than that: automate the formatting when commiting to GIT.
For this, create a pre-commit hook file:

.git/hooks/pre-commit

Make it executable:

chmod +x .git/hooks/pre-commit

Paste this script inside:

#!/bin/bash

# Format only staged C++ files
files=$(git diff --cached --name-only --diff-filter=ACM | grep -E "\.(cpp|hpp|cc|hh|c|h)$")

if [ -z "$files" ]; then
    exit 0
fi

echo "Running clang-format on staged C++ files..."

for file in $files; do
    clang-format -i "$file"
    git add "$file"
done

echo "Clang-format applied."

What it does:

Detects staged C++ files only
Runs clang-format using your .clang-format rules
Re-adds the formatted files to the commit
Prevents style drift or “format fixes” later
Completely automatic

This means a developer cannot commit unformatted C++ code.

November 23, 2025 0 comments

Databases Libraries

The Ultimate Guide to Quant Finance Software

by Clement D. November 22, 2025

This guide provides a comprehensive overview of the entire quant software stack used in global markets: spanning real-time market data, open-source analytics frameworks, front-to-back trading systems, risk engines, OMS/EMS platforms, and execution technology. From Bloomberg and FactSet to QuantLib, Strata, Murex, and FlexTrade, we break down the tools that power pricing, valuation, portfolio management, trading, data engineering, and research. Welcome to the ultimate guide to quant finance software!

1. Market Data Providers

Market data is the foundation of every quant finance software. From real-time pricing and order-book feeds to evaluated curves, fundamentals, and alternative datasets, these providers supply the core inputs used in pricing models, risk engines, trading systems, and research pipelines. The vendors below represent the most widely used sources of institutional-grade financial data across asset classes.

Bloomberg

Bloomberg is one of the most widely used financial data platforms in global markets, providing real-time and historical pricing, reference data, analytics, and news. Its Terminal, APIs, and enterprise data feeds power trading desks, risk engines, and quant research pipelines across asset classes.

Key Capabilities

Real-time market data across equities, fixed income, FX, commodities, and derivatives
Historical time series for pricing, curves, and macroeconomic data
Reference datasets including corporate actions, fundamentals, and identifiers
Bloomberg Terminal tools for analytics, charting, and trading workflows
Enterprise data feeds (BPIPE) for low-latency connectivity
API & SDK access for Python, C++, and other languages (BLPAPI)

Typical Quant/Engineering Use Cases

Pricing & valuation models
Curve construction and calibration
Risk factor generation
Time-series research and statistical modelling
Backtesting & market data ingestion
Integration with execution and OMS systems

Supported Languages

C++, Python, Java, C#, via clients, REST APIs and connectors.

Official Resources

FactSet

FactSet is a comprehensive financial data and analytics platform widely used by institutional investors, asset managers, quants, and risk teams. It provides global market data, fundamental datasets, portfolio analytics, screening tools, and an extensive API suite that integrates directly with research and trading workflows.

Key Capabilities

Global equity and fixed income pricing
Detailed company fundamentals, estimates, and ownership data
Portfolio analytics and performance attribution
Screening and factor modelling tools
Real-time and historical market data feeds
FactSet API, SDKs, and data integration layers

Typical Quant/Engineering Use Cases

Equity and multi-asset factor research
Time-series modelling and forecasting
Portfolio construction and optimization
Backtesting with fundamental datasets
Performance attribution & risk decomposition
Data ingestion into quant pipelines and research notebooks

Supported Languages

Python, R, C++, Java, .NET, via clients, REST APIs and connectors.

Official Resources

Developer Documentation
Product Overview Pages
Factset Workstation

ICE

ICE Data Services provides real-time and evaluated market data, fixed income pricing, reference data, and analytics used across trading desks, risk systems, and regulatory workflows. Known for its deep coverage of credit and rates markets, ICE is a major provider of bond evaluations, yield curves, and benchmark indices used throughout global finance.

Key Capabilities

Evaluated pricing for global fixed income securities
Real-time and delayed market data across asset classes
Reference and corporate actions data
Yield curves, volatility surfaces, and benchmarks
Index services (e.g., ICE BofA indices)
Connectivity solutions and enterprise data feeds
Regulatory & transparency datasets (MiFID II, TRACE)

Typical Quant/Engineering Use Cases

Bond pricing, fair-value estimation, and curve construction
Credit risk modelling (spreads, liquidity, benchmarks)
Backtesting fixed income strategies
Time-series research on rates and credit products
Regulatory and compliance reporting
Feeding risk engines & valuation models with evaluated pricing

Supported Languages

Python, C++, Java, .NET, REST APIs (via ICE Data Services platforms).

Official Resources

ICE Website
ICE Data Analytics
ICE Fixed Income and Data Services

Refinitiv (LSEG)

Refinitiv (LSEG Data & Analytics) is one of the largest global providers of financial market data, analytics, and trading infrastructure. Offering deep cross-asset coverage, Refinitiv delivers real-time market data, historical timeseries, evaluated pricing, and reference data used by quants, risk teams, traders, and asset managers. Through flagship platforms like DataScope, Workspace, and the Refinitiv Data Platform (RDP), it provides high-quality data across fixed income, equities, FX, commodities, and derivatives.

Key Capabilities

Evaluated pricing for global fixed income, including complex OTC instruments
Real-time tick data across equities, FX, fixed income, commodities, and derivatives for quant finance software
Deep reference data, symbology, identifiers, and corporate actions
Historical timeseries & tick history (via Refinitiv Tick History)
Yield curves, vol surfaces, term structures, and macroeconomic datasets
Powerful analytics libraries via Refinitiv Data Platform APIs
Enterprise data feeds (Elektron, Level 1/Level 2 order books)
Regulatory and transparency datasets (MiFID II, trade reporting, ESG disclosures)

Typical Quant/Engineering Use Cases

Cross-asset pricing and valuation for bonds, FX, and derivatives
Building yield curves, vol surfaces, and factor models
Backtesting systematic strategies using high-quality historical tick data
Time-series research across macro, commodities, and rates
Risk modelling, sensitivity analysis, stress testing
Feeding risk engines, intraday models, and trading systems with normalized data
Regulatory reporting workflows (MiFID II, RTS, ESG)
Data cleaning, mapping, and symbology-resolution for quant pipelines

Supported Languages

Python, C++, Java, .NET, REST APIs, WebSocket APIs
(primarily delivered via Refinitiv Data Platform, Elektron APIs, and Workspace APIs

Official Resources

Refinitiv Website (LSEG Data & Analytics)
Refinitiv Data Platform (RDP) APIs
Refinitiv Tick History
Refinitiv Workspace

Quandl

Quandl (Nasdaq Data Link) is a leading data platform offering thousands of financial, economic, and alternative datasets through a unified API. Known for its clean delivery format and wide coverage, Quandl provides both free and premium datasets ranging from macroeconomics, equities, and futures to alternative data like sentiment, corporate fundamentals, and crypto. Now part of Nasdaq, it powers research, quant modelling, and data engineering workflows across hedge funds, asset managers, and fintechs.

Key Capabilities

Unified API for thousands of financial & alternative datasets
Macroeconomic data, interest rates, central bank series, and indicators
Equity prices, fundamentals, and corporate financials
Futures, commodities, options, and sentiment datasets
Alternative data (consumer behaviour, supply chain, ESG, crypto)
Premium vendor datasets from major providers
Bulk download & time-series utilities for research pipelines
Integration with Python, R, Excel, and server-side apps

Typical Quant/Engineering Use Cases

Factor research & systematic strategy development
Macro modelling, global indicators, and regime analysis
Backtesting equity, rates, and commodities strategies for quant finance software
Cross-sectional modelling using fundamentals
Alternative-data-driven alpha research
Portfolio analytics and macro-linked risk modelling
Building data ingestion pipelines for quant research
Academic quantitative finance research

Supported Languages

Python, R, Excel, Ruby, Node.js, MATLAB, Java, REST APIs

Official Resources

Nasdaq Data Link Website
Quandl API Documentation
Nasdaq Alternative Data Products

2.Developer Tools & Frameworks

QuantLib

QuantLib is the leading open-source quantitative finance library, widely used across banks, hedge funds, fintechs, and academia for pricing, curve construction, and risk analytics. A quant finance software classic! Built in C++ with extensive Python bindings, QuantLib provides a comprehensive suite of models, instruments, and numerical methods covering fixed income, derivatives, optimization, and Monte Carlo simulation. Its transparency, flexibility, and industry alignment make it a foundational tool for prototyping trading models, validating pricing engines, and building production-grade quant frameworks.

Key Capabilities

Full fixed income analytics: yield curves, discounting, bootstrapping
Pricing engines for swaps, options, exotics, credit instruments
Stochastic models (HJM, Hull–White, Black–Karasinski, CIR, SABR, etc.)
Volatility surfaces, smile interpolation, variance models
Monte Carlo, finite differences, lattice engines
Calendars, day-count conventions, schedules, market conventions
Robust numerical routines (root finding, optimization, interpolation)

Typical Quant/Engineering Use Cases

Pricing vanilla & exotic derivatives
Building multi-curve frameworks and volatility surfaces
Interest-rate modelling and calibration
XVA prototyping and risk-sensitivity analysis
Monte Carlo simulation for structured products
Backtesting and scenario generation
Teaching, research, and model validation for quant finance software
Serving as a pricing microservice inside larger quant platforms

Supported Languages

C++, Python (via SWIG bindings), R, .NET, Java, Excel add-ins, command-line tools

Official Resources

QuantLib Website
QuantLib Python Documentation
QuantLib GitHub Repository

Finmath

Finmath is a comprehensive open-source quant finance software library written in Java, designed for modelling, pricing, and risk analytics across derivatives and fixed income markets. It provides a modular architecture with robust implementations of Monte Carlo simulation, stochastic processes, interest-rate models, and calibration tools. finmath is widely used in academia and industry for its clarity, mathematical rigor, and ability to scale into production systems where JVM stability and performance are required.

Key Capabilities

Monte Carlo simulation framework (Brownian motion, Lévy processes, stochastic meshes)
Interest-rate models: Hull–White, LIBOR Market Model (LMM), multi-curve frameworks
Analytic formulas for vanilla derivatives, caps/floors, and swaps
Calibration engines for stochastic models and volatility structures
Automatic differentiation and algorithmic differentiation tools
Support for stochastic volatility, jump-diffusion, and hybrid models
Modular pricers for structured products and exotic payoffs
Excel, JVM-based servers, and integration with big-data pipelines

Typical Quant/Engineering Use Cases

Monte Carlo pricing of path-dependent and exotic derivatives
LMM and Hull–White calibration for rates desks
Structured products modelling and scenario analysis
XVA and exposure simulations using forward Monte Carlo
Risk factor simulation for regulatory stress testing
Model validation and prototyping in Java-based environments
Educational use for teaching stochastic calculus and derivatives pricing

Supported Languages

Java (core), with interfaces usable from Scala, Kotlin, and JVM-based environments; optional Excel integrations

Official Resources

finmath Library Website
finmath GitHub Repository
finmath Documentation & Tutorials

Strata

OpenGamma Strata is a modern, production-grade open-source analytics library for pricing, risk, and market data modelling across global derivatives markets. Written in Java and designed with institutional robustness in mind, Strata provides a complete framework for building and calibrating curves, volatility surfaces, interest-rate models, FX/credit analytics, and standardized market conventions. It is used widely by banks, clearing houses, and fintech platforms to power high-performance valuation services, regulatory risk calculations, and enterprise quant finance software infrastructure.

Key Capabilities

Full analytics for rates, FX, credit, and inflation derivatives
Curve construction: OIS, IBOR, cross-currency, inflation, basis curves
Volatility surfaces: SABR, Black, local vol, swaption grids
Pricing engines for swaps, options, swaptions, FX derivatives, CDS
Market conventions, calendars, day-count standards, trade representations
Robust calibration and scenario frameworks
Portfolio-level risk: PV, sensitivities, scenario shocks, regulatory measures
Built-in serialization, market data containers, and workflow abstractions

Typical Quant/Engineering Use Cases

Pricing and hedging of rates, FX, and credit derivatives
Building multi-curve frameworks for trading and risk
Market data ingestion and transformation pipelines
XVA inputs: sensitivities, surfaces, curves, calibration tools
Regulatory reporting (FRTB, SIMM, margin calculations)
Risk infrastructure for clearing, margin models, and limit frameworks
Enterprise-grade pricing microservices for front office and risk teams
Model validation and backtesting for derivatives portfolios

Supported Languages

Java (core), Scala/Kotlin via JVM interoperability, with REST integrations for enterprise deployment

Official Resources

OpenGamma Strata Website
Strata GitHub Repository
Strata Documentation & Guides
OpenGamma Blog & Technical Papers

ORE (Open-Source Risk Engine)

ORE (Open-Source Risk Engine) is a comprehensive open-source risk and valuation platform built on top of QuantLib. Developed by Acadia, ORE extends QuantLib from a pricing library into a full multi-asset risk engine capable of portfolio-level analytics, scenario-based valuation, XVA, stress testing, and regulatory risk. Written in modern C++, ORE introduces standardized trade representations, market conventions, workflow orchestration, and scalable valuation engines suitable for both research and production environments. Designed to bridge the gap between quant model development and enterprise-grade risk systems, ORE is used across banks, derivatives boutiques using quant finance software, consultancies, and academia to prototype or run real-world risk pipelines. Its modular architecture and human-readable XML inputs make it accessible for quants, engineers, and risk managers alike.

Key Capabilities

Full portfolio valuation and risk analytics: multi-asset support, standardized trade representation, market data loaders, curve builders
XVA analytics: CVA, DVA, FVA, LVA, KVA; CSA modelling and collateral simulations
Scenario-based simulation: historical and hypothetical stress tests, Monte Carlo P&L distribution, bucketed sensitivities
Risk aggregation & reporting: NPV, DV01, CS01, vega, gamma, curvature, regulatory risk (SIMM via extensions)
Production-ready workflows: XML configuration, batch engines, logging, audit reports

Typical Quant/Engineering Use Cases

Building internal XVA analytics
Prototyping bank-grade risk engines
Scenario analysis and stress testing
Independent price verification (IPV) and model validation
Collateralized curve construction
Portfolio-level aggregation and risk decomposition
Large-scale Monte Carlo simulation for quant finance software
Integrating QuantLib pricing into enterprise workflows
Teaching advanced risk and valuation concepts

Supported Languages

C++ (core engine)
Python (community bindings)
XML workflow/configuration
JSON/CSV inputs and outputs

Official Resources

ORE GitHub Repository
ORE Documentation
ORE User Guide

3.Front-to-Back Trading & Risk Platforms

Murex

Murex (MX.3) is the world’s leading front-to-back trading, risk, and operations platform used by global banks, asset managers, insurers, and clearing institutions. Known as the industry’s most comprehensive cross-asset system, Murex unifies trading, pricing, market risk, credit risk, collateral, PnL, and post-trade operations into a single integrated architecture. It is considered the “gold standard” for enterprise-scale capital markets infrastructure and remains the backbone of trading desks across interest rates, FX, equities, credit, commodities, and structured products. Built around a modular, high-performance calculation engine, MX.3 supports pre-trade analytics, trade capture, risk measurement, lifecycle management, regulatory reporting, and settlement workflows. Quants and developers frequently interface with Murex via its model APIs, scripting capabilities, and market data pipelines, making it a central component of real-world quant finance software.

Key Capabilities

Front-office analytics: real-time pricing, RFQ workflows, limit checks, scenario tools
Cross-asset trade capture: IR, FX, credit, equity, commodity, hybrid & structured products
Market risk: VaR, sensitivities (Greeks), stress testing, FRTB analytics
XVA & credit risk: CVA/DVA/FVA/MVA/KVA with CSA & netting-set modelling
Collateral & treasury: margining, inventory, funding optimization, liquidity risk
Middle & back office: confirmations, settlements, accounting, reconciliation
Enterprise data management: curves, surfaces, workflow orchestration, audit trails
High-performance computation layer: distributed risk runs, batch engines, grid scheduling

Typical Quant/Engineering Use Cases

Integrating custom pricing models and curves
Building pre-trade analytics and scenario tools for trading desks
Extracting market data, risk data, and PnL explain feeds
Setting up or validating XVA, FRTB, and regulatory risk workflows
Automating lifecycle events for structured and exotic products
Connecting Murex to in-house quant finance software libraries (QuantLib, ORE, proprietary C++ pricers)
Developing risk dashboards, overnight batch pipelines, and stress-testing frameworks
Supporting bank-wide migrations (e.g., MX.2 → MX.3, LIBOR transition initiatives)

Supported Languages & Integration

C++ for model integration and high-performance pricing components
Java for workflow extensions and service layer integration
Python for analytics, ETL, and data extraction via APIs
SQL for reporting and data interrogation
XML for configuration of trades, market data, workflows, and static data

Official Resources

Murex Website
Murex Knowledge Hub (client portal)
MX.3 Product Overview for Banks

Calypso

A unified front-to-back trading, risk, collateral, and clearing platform widely adopted by global banks, central banks, clearing houses, and asset managers. Calypso (now part of Adenza, following the merge with AxiomSL) is known for its strong coverage of derivatives, securities finance, treasury, and post-trade operations for quant finance software. It provides an integrated architecture across trade capture, pricing, risk analytics, collateral optimization, and regulatory reporting, making it a common choice for institutions seeking a modular, standards-driven system.

With a flexible Java-based framework, Calypso supports extensive customization through APIs, workflow engines, adapters, and data feeds for quant finance software. It is particularly strong in clearing, collateral management, treasury operations, and real-time event processing, making it a critical component in many bank infrastructures.

Key Capabilities

Front-office analytics: real-time valuation, pricing, trade validation, limit checks, pre-trade workflows
Cross-asset trade capture: linear/non-linear derivatives, securities lending, repos, treasury & funding products
Market risk: Greeks, VaR, stress testing, historical/MC simulation, FRTB analytics
Credit & counterparty risk: PFE, CVA/DVA, SA-CCR, IMM, netting set modelling
Collateral & clearing: enterprise margining, eligibility schedules, CCP connectivity, triparty workflows
Middle & back office: confirmations, settlements, custody, corporate actions, accounting
Enterprise integration: MQ/JMS/REST adapters, data dictionaries, workflow orchestration, regulatory reporting
Performance & computation layer: distributed risk runs, event-driven processing, batch scheduling

Typical Quant/Engineering Use Cases

Integrating custom pricers and analytics into the Java pricing framework
Building pre-trade risk tools and scenario screens for trading desks
Extracting market, risk, and PnL data for downstream analytics
Implementing or validating XVA, SA-CCR, and regulatory capital workflows
Automating collateral optimization and eligibility logic for enterprise CCP flows
Connecting Calypso to in-house quant libraries (Java, Python, C++)
Developing real-time event listeners for lifecycle, margin, and clearing events
Supporting migrations and upgrades (Calypso → Adenza cloud, major version upgrades)

Official Resources

Calypso Website

FIS

FIS is a long-established, cross-asset trading, risk, and operations platform used extensively by global banks, asset managers, and treasury departments. Known for its robust handling of interest rate and FX derivatives, Summit provides a unified environment spanning trade capture, pricing, risk analytics, collateral, treasury, and back-office processing. Despite being considered a legacy platform by many institutions, Summit remains deeply embedded in the infrastructure of Tier-1 and Tier-2 banks due to its stability, extensive product coverage, and mature STP workflows.

Built around a performant C++ core with a scripting layer (SML) and flexible integration APIs, Summit supports custom pricing models, automated batch processes, and data pipelines for both intraday and end-of-day operations. It is commonly found in banks undergoing modernization projects, cloud migrations, or system consolidation from older vendor stacks.

Key Capabilities

Front-office analytics: pricing for IR/FX derivatives, scenario analysis, position management
Cross-asset trade capture: rates, FX, credit, simple equity & commodity derivatives, money markets
Market risk: Greeks, sensitivities, VaR, stress tests, scenario shocks
Counterparty risk: PFE, CVA, exposure profiles, netting-set logic
Treasury & funding: liquidity management, cash ladders, intercompany funding
Middle & back office: confirmations, settlement instructions, accounting rules, GL integration
Collateral & margining: margin call workflows, eligibility checks, CCP/tiered clearing
Enterprise integration: SML scripts, C++ extensions, MQ/JMS connectors, batch & EOD scheduling
Performance layer: optimized C++ engine for large books, distributed batch calculations

Typical Quant/Engineering Use Cases

Integrating custom pricing functions through C++ or SML extensions
Building pre-trade risk tools, limit checks, and scenario pricing screens
Extracting risk sensitivities, exposure profiles, and PnL explain feeds for analytics
Validating credit exposure, CVA, and regulatory risk data (SA-CCR, IMM)
Automating treasury and liquidity workflows for money markets and funding books
Connecting Summit to in-house quant libraries (C++, Python, Java adapters)
Developing batch frameworks for EOD risk, PnL, data cleaning, and reconciliation
Supporting modernization programs (Summit → Calypso/Murex migration, cloud uplift, architecture rewrites)

Blackrock Aladdin

BlackRock Aladdin is an enterprise-scale portfolio management, risk analytics, operations, and trading platform used by asset managers, pension funds, insurers, sovereign wealth funds, and large institutional allocators. Known as the industry’s most powerful buy-side risk and investment management system, Aladdin integrates portfolio construction, order execution, analytics, compliance, performance, and operational workflows into a unified architecture.

Originally built to manage BlackRock’s own portfolios, Aladdin has evolved into a global operating system for investment management, delivering multi-asset risk analytics, scalable data pipelines, and tightly integrated OMS/PMS capabilities. With its emphasis on transparency, scenario analysis, and factor-based risk modelling, Aladdin has become a critical platform for institutions seeking consistency across risk, performance, and investment decision-making.

Aladdin’s open APIs, data feeds, and integration layers allow quants and engineers to plug into portfolio, reference, pricing, and factor data, making it a core component of enterprise buy-side infrastructures.

Key Capabilities

Portfolio management: construction, optimisation, rebalancing, factor exposures, performance attribution
Order & execution management (OMS): multi-asset trading workflows, pre-trade checks, compliance, routing
Risk analytics: factor models, stress tests, scenario engines, historical & forward-looking risk
Market risk & exposures: VaR, sensitivities, stress shocks, liquidity analytics
Compliance & controls: rule-based pre/post-trade checks, investment guidelines, audit workflows
Data management: pricing, curves, factor libraries, ESG data, holdings, benchmark datasets
Operational workflows: trade settlements, reconciliations, corporate actions
Aladdin Studio: development environment for custom analytics, Python notebooks, modelling pipelines
Enterprise integration: APIs, data feeds, reporting frameworks, cloud-native distribution

Typical Quant/Engineering Use Cases

Integrating custom factor models, stress scenarios, and risk methodologies into the Aladdin ecosystem
Building portfolio optimisation tools and bespoke analytics through Aladdin Studio
Connecting Aladdin to internal quant libraries, Python environments, and research pipelines
Extracting holdings, benchmarks, factor exposures, risk metrics, and P&L explain data
Developing compliance engines, rule libraries, and pre-trade limit workflows
Automating reporting, reconciliation, and operational pipelines for large asset managers
Implementing ESG analytics, liquidity risk screens, and regulatory reporting tools
Supporting enterprise-scale migrations onto Aladdin’s cloud-native environment

4.Execution & Trading Systems

Fidessa (ION)

Fidessa is the industry’s benchmark execution and order management platform for global equities, listed derivatives, and cash markets. Used by investment banks, brokers, exchanges, market makers, and large hedge funds, Fidessa delivers high-performance electronic trading, deep market connectivity, smart order routing, and algorithmic execution in a unified environment. Known for its ultra-reliable infrastructure and resilient trading architecture, Fidessa provides access to hundreds of exchanges, MTFs, dark pools, and broker algos worldwide. Its real-time market data feeds, FIX gateways, compliance engine, and execution analytics make it a foundational component of electronic trading desks. Now part of ION Markets, Fidessa remains one of the most widely deployed platforms for high-touch and low-touch equity trading, offering a robust framework for custom execution strategies and global routing logic.

Key Capabilities

Order & execution management (OMS/EMS): multi-asset order handling, care orders, low-touch flows, parent/child order management
Market connectivity: direct exchange connections, MTFs, dark pools, broker algorithms, smart order routing
Real-time market data: depth, quotes, trades, tick data, venue analytics
Algorithmic trading: strategy containers, broker algo integration, SOR logic, internal crossing
Compliance & risk controls: limit checks, market abuse monitoring, MiFID reporting, pre-trade risk
Trading workflows: high-touch blotters, sales-trader workflows, DMA tools, program trading
Back-office & operations: allocations, matching, confirmations, trade reporting
FIX infrastructure: FIX gateways, routing hubs, drop copies, OMS → EMS workflows
Performance & scalability: fault-tolerant architecture, high-availability components, low-latency market access

Typical Quant/Engineering Use Cases

Building and deploying custom algorithmic trading strategies in Fidessa’s execution framework
Integrating smart order routing logic and multi-venue liquidity analytics
Connecting Fidessa OMS to downstream risk engines, pricing models, and TCA tools
Developing real-time market data adapters, FIX gateways, and trade feed processors
Automating compliance checks, MiFID reporting, and surveillance workflows
Extracting tick data, executions, and quote streams for analytics and model calibration
Supporting program trading desks with custom basket logic and volatility-aware strategies
Managing large-scale migrations into ION’s unified trading architecture

FlexTrade (FlexTRADER)

FlexTrade’s FlexTRADER is a flagship multi-asset execution management system (EMS) designed for quantitative trading desks, asset managers, hedge funds, and sell-side execution teams. Known as one of the most customizable and algorithmically sophisticated EMS platforms, FlexTRADER provides advanced order routing, execution algorithms, real-time analytics, and seamless integration with in-house quant models.

FlexTrade distinguishes itself through its open architecture, API-driven design, and deep support for automated and systematic execution workflows. It enables institutions to build custom execution strategies, incorporate proprietary signals, integrate model-driven routing logic, and connect to liquidity across global equities, FX, futures, fixed income, and options markets. Its strong TCA tools and high configurability make it a favourite among quant, systematic, and low-latency execution teams.

Key Capabilities

Multi-asset execution: equities, FX, futures, options, fixed income, ETFs, derivatives
Algorithmic trading: broker algos, native Flex algorithms, fully custom strategy containers
Smart order routing (SOR): liquidity-seeking, schedule-based, cost-optimised routing
Real-time analytics: market impact, slippage, venue heatmaps, liquidity curves
TCA & reporting: pre-trade, real-time, and post-trade analytics with benchmark comparisons
Order & workflow management: portfolio trading, pairs trading, block orders, basket execution
Connectivity: direct market access (DMA), algo wheels, liquidity providers, dark/alternative venues
Integration APIs: Python, C++, Java, FIX, data adapters for quant signals and simulation outputs
Customisation layer: strategy scripting, UI configuration, event-driven triggers, automation rules

Typical Quant/Engineering Use Cases

Integrating proprietary execution algorithms, signals, and cost models into FlexTRADER
Developing custom SOR logic using internal market impact models
Building automated execution pipelines driven by alpha models or risk signals
Feeding FlexTrade real-time analytics into research workflows and intraday dashboards
Connecting FlexTRADER to quant libraries (Python/C++), backtesting engines, and ML-driven routing models
Automating multi-venue liquidity capture, dark pool interaction, and broker algo selection
Creating real-time TCA analytics and execution diagnostics for systematic trading teams
Supporting global multi-asset expansion, co-location routing, and high-performance connectivity

Bloomberg EMSX (Electronic Market)

Bloomberg EMSX is the embedded execution management system within the Bloomberg Terminal, providing multi-asset trading, broker algorithm access, smart routing, and real-time analytics for institutional investment firms, hedge funds, and trading desks. As one of the most widely used execution platforms in global markets, EMSX offers seamless integration with Bloomberg’s market data, analytics, news, portfolio tools, and compliance engines, making it a central component of daily trading workflows. EMSX supports equities, futures, options, ETFs, and FX workflows, enabling traders to route orders directly from Bloomberg screens such as MONITOR, PORT, BDP, and custom analytics. Its native access to broker algorithms, liquidity providers, and execution venues—combined with Bloomberg’s unified data ecosystem—makes EMSX a powerful tool for low-touch trading, portfolio execution, and workflow automation across asset classes.

Key Capabilities

Multi-asset execution: equities, ETFs, futures, options, and FX routing
Broker algorithm access: direct integration with global algo suites (VWAP, POV, liquidity-seeking, schedule-driven)
Order & workflow management: parent/child orders, baskets, care orders, DMA routing
Real-time analytics: slippage, benchmark comparisons, market impact indicators, TCA insights
Portfolio trading: basket construction, rebalancing tools, program trading workflows
Integration with Bloomberg ecosystem: PORT, AIM, BQuant, BVAL, market data, research, news
Compliance & controls: pre-trade checks, regulatory rules, audit trails, trade reporting
Connectivity: FIX routing, broker connections, smart order routing, dark/alternative venue access
Automation & scripting: rules-based workflows, event triggers, Excel API and Python integration

Typical Quant/Engineering Use Cases

Automating low-touch execution workflows directly from Bloomberg analytics (e.g., PORT → EMSX)
Integrating broker algo selection and routing decisions into quant-driven strategies
Extracting execution, tick, and benchmark data for TCA, slippage modelling, or market impact analysis
Connecting EMSX flows to internal OMS/EMS platforms (FlexTrade, CRD, Eze, proprietary systems)
Developing Excel, Python, or BQuant-driven automation pipelines for execution and monitoring
Embedding pre-trade analytics, compliance checks, and liquidity models into EMSX order workflows
Supporting global routing, basket trading, and cross-asset execution for institutional portfolios
Leveraging Bloomberg’s unified data (fundamentals, pricing, factor data, corporate actions) for model-based trading pipelines

November 22, 2025 0 comments

News

Investing in Student Dorms: Opportunities Amid Hong Kong’s Shifting Landscape

by Clement D. November 18, 2025

Policymakers must brace for a tumultuous period in global finance, as emerging geopolitical tensions and shifting market dynamics threaten to unleash a wave of volatility. In this comprehensive analysis, we will explore the key factors driving these trends, including the implications of the Trump administration’s decision to approve the sale of F-35 jets to Saudi Arabia, the growing investor fears of impending market turmoil, and the delicate diplomatic maneuverings between Japan and China in the aftermath of the Taiwan furor. Additionally, we will delve into the intriguing trend of investors eyeing student dorms in Hong Kong as a potential investment vehicle. Stay tuned as we unpack these critical developments and their impact on the global financial landscape.

🎥 Trump Approves F-35 Jets For Saudis, Stocks Losses Deepen | Daybreak Europe 11/18/2025 (Bloomberg)

In the video, the risk analyst would highlight the potential exposures, vulnerabilities, and resilience factors. The global stock selloff continued for the fourth day, indicating a worsening risk sentiment as uncertainty over US rates and tech valuations prevailed. The S&P 500 closing below a key level for investors raises concerns about further downside risks. The plunge in the Japanese Nikkei index and the decline in Bitcoin below $90,000 suggest broader market vulnerabilities. However, the diplomatic efforts by Japan to ease tensions with China and the potential sale of F-35 jets to Saudi Arabia could be seen as resilience factors, offering some stability in the geopolitical landscape. Additionally, the goal set by Credit Agricole for its net income and the surge in Apple iPhone sales in China could be interpreted as indicators of resilience in the financial and technology sectors, respectively.

🎥 Investors Fear More Market Turmoil Is Coming | Insight with Haslinda Amin 11/18/2025 (Bloomberg)

In the latest episode of “Insight with Haslinda Amin,” viewers were treated to a deep dive into the pressing concerns of global investors. The program explored a range of issues, from the escalating tensions between China and Japan over Taiwan, to the growing worries around private credit markets. Prominent experts, including Nancy Tengler of Laffer Tengler Investments and Felix Brill of VP Bank, shared their insights on the potential impact of these developments on the broader financial landscape. The show also highlighted the recent surge in Apple’s iPhone sales in China, the risk-off shift in Asian AI stocks, and the growing focus on Baidu’s AI cloud revenue. Additionally, the program provided an in-depth look at the renewable energy sector, with ReNew’s Vaishali Nigam Sinha discussing the commitments and progress made at the recent COP30 conference. Overall, the episode offered a comprehensive and data-driven analysis of the key financial trends and events shaping the market’s outlook.

🎥 Japan Seeks to Calm China After Taiwan Furor | The China Show 11/18/2025 (Bloomberg)

As market strategists prepare their morning call, the latest episode of “The China Show” on Bloomberg Television offers a forward-looking perspective on the evolving dynamics between Japan and China. The diplomats of the two nations are set to convene in Beijing, underscoring the importance of managing regional tensions in the wake of the recent Taiwan furor. Investors will be closely watching this development, alongside a slew of other market-moving events, including the highly anticipated Nvidia earnings and the US jobs report. With unique insight from industry experts, “The China Show” continues to deliver comprehensive coverage of the world’s second-largest economy, equipping global investors with the tools to navigate the complexities of the Chinese market.

🎥 Student dorms in Hong Kong are becoming a popular investment vehicle #asia #shorts (Bloomberg)

Savvy investors are taking a close eye on the Hong Kong real estate market, identifying a unique opportunity in the student dorm segment. Amid the city’s struggling commercial landscape, these niche properties have emerged as a promising investment vehicle, with transaction volumes reaching a remarkable $411 million in the first nine months of the year. The demand is driven by a combination of factors, including the growing student population, the shift towards remote learning, and the potential for stable rental yields. However, investors must weigh the risks carefully, as the sector is subject to regulatory changes and the broader economic conditions in Hong Kong. Market sentiment remains cautiously optimistic, as investors seek to capitalize on this burgeoning trend and diversify their portfolios in the face of ongoing market volatility.

♟️ Interested in More?

Read the latest financial news: c++ for quants news.

November 18, 2025 0 comments

Clement D.

🎥 Geopolitics Needs Policy to Move Risk: 3-Minute MLIV (Bloomberg)

🎥 US To Control Venezuelan Oil ‘Indefinitely’; Iran Protests | Horizons Middle East & Africa 1/8/2026 (Bloomberg)

🎥 ICE Minneapolis Shooting & Trump Demands More Defense Spending | Daybreak Europe 1/8/2026 (Bloomberg)

🎥 Fun Quant Interview Question #quantinterviews (Quant Prof)

♟️ Interested in More?

🎥 Equities to Rise Not Just on Asia Tech: 3-Minute MLIV (Bloomberg)

🎥 Greenland on Alert After US Venezuela Move & Danish PM NATO Warning | 1/6/2026 (Bloomberg)

🎥 Lebanon’s Economy & Trade Minister; Nvidia’s Rubin Chips | Horizons Middle East and Africa 1/6/2026 (Bloomberg)

🎥 Nvidia’s CES AI Reveal Fuels Global Tech Optimism | Insight with Haslinda Amin 01/06/2026 (Bloomberg)

♟️ Interested in More?

1.What are finite differences?

Common Finite Difference Approximations

2.Apply Finite Differences to Greeks Calculation: The Case of Delta

3. A Practical Example to Illustrate the Calculation

4. An Implementation in C++

5. Alternative Approaches to Calculate Delta

🎥 Starfighters CEO Says Looking to Add Spaceport, Aircrafts (Bloomberg)

🎥 Mamdani Takes Reins of New York City (Bloomberg)

🎥 Today on NYSE Live | Federal Reserve Minutes Reveal Policymakers Were Split on December Rate Cut (New York Stock Exchange)

🎥 Mad Money 12/31/25 | Audio Only (CNBC Television)

♟️ Interested in More?

🎥 Final Days of 2025 Trading Officially Get Underway (New York Stock Exchange)

🎥 Mad Money 12/29/25 | Audio Only (CNBC Television)

🎥 Jim Cramer talks the benefits of long-term investing (CNBC Television)

🎥 Jim Cramer says you want to pick stocks that meet two criteria (CNBC Television)

♟️ Interested in More?

🎥 NYSE 2025 ETF Wrap-Up: Trends, Takeaways, and What’s Next (New York Stock Exchange)

🎥 Trump Praises Zelenskiy After Talks With No Breakthrough (Bloomberg)

🎥 Essential AI Skills For 2026 (Tina Huang)

🎥 Will the AI Boom Continue? The Fate of the Magnificent Seven in 2026 (Capital Trading)

♟️ Interested in More?

Question 1: “What is a Shared Pointer? Give A Quantitative Finance Example”

What This Example Demonstrates

1. Shared Ownership of a Core Market Object

2. Reference Counting Behind the Scenes

3. Automatic Lifetime Management (RAII)

Question 2: “How does shared_ptr manage reference counting?“

Question 3: “What causes a memory leak with shared_ptr?“

Question 4: make_shared vs. shared_ptr<T>(new T): what’s the difference?

Question 5: Why is shared_ptr slower?

1.What is the Clang and LLVM ecosystem?

2.Clang-Format: The Modern Standard for C++ Code Formatting

3.Installation

4.Usage

5. A List and Comparison of The Clang Styles

6. Manage Clang Formating in your Codebase

7. Include clang-format in. a pre-commit hook

1. Market Data Providers

Bloomberg

Key Capabilities

Typical Quant/Engineering Use Cases

Supported Languages

Official Resources

FactSet

Key Capabilities

Typical Quant/Engineering Use Cases

Supported Languages

Official Resources

ICE

Key Capabilities

Typical Quant/Engineering Use Cases

Supported Languages

Official Resources

Refinitiv (LSEG)

Key Capabilities

Typical Quant/Engineering Use Cases

Supported Languages

Official Resources

Quandl

Key Capabilities

Typical Quant/Engineering Use Cases

Supported Languages

Official Resources

2.Developer Tools & Frameworks

QuantLib

Key Capabilities

Typical Quant/Engineering Use Cases

Supported Languages

Official Resources

Question 2: “How does `shared_ptr` manage reference counting?“

Question 3: “What causes a memory leak with `shared_ptr`?“

Question 4: `make_shared` vs. `shared_ptr<T>(new T)`: what’s the difference?

Question 5: Why is `shared_ptr` slower?