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Clement D.

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NYC Rent Crisis Deepens: Even the Rich Struggle to Afford

by Clement D.

In the face of soaring rents and a seemingly unaffordable housing market, policymakers are grappling with a pressing question: can even six-figure earners afford to live in New York City? This issue will be explored in depth through a series of videos, including “The rent is still too high in New York City, even for the rich” and discussions on the “Insight with Haslinda Amin” and “Daybreak Europe” programs. As the economic landscape shifts, understanding the dynamics of the rental market in America’s largest city is crucial for crafting effective policy responses.

🎥 The rent is still too high in New York City, even for the rich #realestate #shorts (Bloomberg)

The Rent Conundrum: Echoes of New York’s Enduring Housing Challenge

In the annals of New York City’s storied real estate landscape, the modern-day rental crisis stands as a testament to the enduring complexities that have long plagued the urban metropolis. Even as the city’s economic fortunes have ebbed and flowed, the persistent struggle to find affordable housing has remained a constant, now ensnaring even those with six-figure incomes. This latest chapter in New York’s housing saga, as explored on the Big Take podcast, illuminates the far-reaching implications of the rental squeeze, casting a spotlight on the systemic forces that have reshaped the city’s social and economic fabric over time. As policymakers grapple with proposals to address this pressing issue, the reverberations of New York’s rental woes echo through the decades, serving as a sobering reminder of the deep-seated challenges that have long defined the city’s quest for housing equity and stability.

🎥 The Missing Pieces In Trump’s Gaza Deal | Insight with Haslinda Amin 10/9/2025 (Bloomberg)

In the latest installment of Insight with Haslinda Amin, viewers are offered a comprehensive analysis of the key details and potential pitfalls surrounding President Trump’s proposed Gaza deal. The discussion features insightful commentary from a diverse panel of experts, including perspectives on the delicate balance between Israel and Hamas, the challenge of reaching a lasting ceasefire, and the potential market implications of a breakthrough. As investors navigate the evolving geopolitical landscape, this in-depth examination provides a valuable window into the complexities and high-stakes negotiations that could shape the future of the region.

🎥 Israel-Hamas Hostage Deal & Macron Seeks New PM | Daybreak Europe 10/9/2025 (Bloomberg)

In the wake of a prolonged conflict, US President Donald Trump has announced a breakthrough in the hostage situation between Israel and Hamas. The two sides have reportedly agreed to terms for the release of all captives held in Gaza, with Israel set to withdraw its troops as part of the negotiated peace plan. This development is likely to be viewed positively by global markets, which will also be buoyed by China’s reopening from the Golden Week holiday and the continued strength of the AI sector, particularly in Japan. Meanwhile, French President Emmanuel Macron is moving swiftly to appoint a new prime minister, following the resignation of Sebastien Lecornu, who had been tasked with forging a coalition government. Today’s Daybreak Europe program will feature insights from a panel of experts, including an economist, a professor of international relations, and the CEO of a cutting-edge technology firm, as investors seek to navigate the shifting geopolitical and economic landscape.

🎥 Trump Says Israel, Hamas Reach Gaza Deal | Horizons Middle East & Africa 10/9/2025 (Bloomberg)

The video’s content reflects a significant development in the ongoing conflict between Israel and Hamas, as US President Donald Trump announces that the two sides have reached a deal for the release of all hostages held in Gaza. This agreement, which involves the exchange of Palestinian prisoners and detainees, could have broader implications for the region’s geopolitical landscape and the potential for future negotiations. The video also touches on other economic and industry trends, such as gold’s retreat from a record high and the US government’s approval for Nvidia to export advanced AI chips to the UAE, underscoring the interconnected nature of global markets and the complex web of factors shaping the region’s economic and political dynamics.

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Navigating the Evolving Finance Landscape: Insights for Investors

by Clement D.

The day’s events will have a lasting impact on the global finance landscape.

As graduate students in finance, we will delve into the critical developments that unfolded today, including the high-stakes negotiations between Israel and Hamas, the political crisis in France, and the vulnerability of haven bids to unknown U.S. data. We will also explore Orsted’s major capital raise and the potential resolution of the U.S. government shutdown.

To better understand these events, we will later review the video titled “Israel-Hamas Talks on Gaza War; France Crisis | Horizons Middle East & Africa 10/7/2025” and the “Haven Bids are Vulnerable to Unknown US Data: 3-Minute MLIV” video. Additionally, we will discuss the implications of Orsted’s $9.4 billion rights offering and the latest developments in the government shutdown negotiations.

🎥 Israel-Hamas Talks on Gaza War; France Crisis | Horizons Middle East & Africa 10/7/2025 (Bloomberg)

The presented clip from the finance program “Horizons Middle East & Africa” offers a comprehensive examination of several key geopolitical and economic developments in the region. The lecture-style format is structured to provide graduate finance students with a conceptually rich analysis of the salient issues.

The segment opens with a discussion of the political crisis in France, where President Macron has given the outgoing Prime Minister 48 hours to negotiate with rival parties to resolve the government’s instability. The analysis then shifts to the ongoing indirect talks between Israel and Hamas, set to continue in Egypt, providing critical insights into the dynamics of this regional conflict.

Interspersed throughout the presentation are interviews with subject matter experts, including Lale Akoner of eToro, Amar Bashir of Wealthbrix Capital Partners, and Yasmine Farouk of the International Crisis Group. These experts offer valuable perspectives on the global market implications, fixed-income strategies, and the regional geopolitical landscape, respectively.

The lecture concludes with a discussion of the challenges facing peace talks in eastern Congo, underscoring the complexity and volatility of the region’s political and security environment. Overall, the clip delivers a comprehensive, conceptually rich analysis of the pressing issues shaping the Middle East and African landscape, equipping graduate finance students with a robust understanding of the regional dynamics.

🎥 Haven Bids are Vulnerable to Unknown US Data: 3-Minute MLIV (Bloomberg)

The video provides a concise analysis of key market themes, including the potential impact of unknown U.S. data on haven bids, the close proximity of gold prices to $4,000, the influence of a potential government shutdown on haven bids, the strength of the U.S. dollar, and the performance of tech stocks, particularly the AMD deal. The analysis is directed at decision-makers, offering a strategic overview of the current market landscape and potential implications for investors.

🎥 Orsted Raises $9.4 Bln Amid Hit From US Wind U-Turn (Bloomberg)

The state-backed Danish firm Orsted A/S raised 60 billion Danish kroner ($9.4 billion) through a rights offering, with investors taking up 99.3% of the more than 900 million shares offered in the fully underwritten capital increase. The fund raise will allow the company to shore up its balance sheet after the Trump administration’s moves against offshore wind upended Orsted’s business model.

🎥 Government Shutdown: Trump Signals Talks as Democrats Refuse to Budge on ACA Subsidies (Bloomberg)

The ongoing government shutdown, which has stretched into its second week, has brought the contentious issue of healthcare subsidies to the forefront. President Donald Trump’s recent willingness to negotiate with Democrats over the Affordable Care Act (ACA) subsidies that are set to expire at the end of 2025 could signal a potential path to resolving the impasse. This move reflects the broader economic and political dynamics at play, as both parties grapple with the implications of the shutdown and its impact on the healthcare industry. As the negotiations continue, the outcome will undoubtedly shape the future of the nation’s healthcare landscape and the broader economic landscape.

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best C++ ML Libraries
Libraries

Best C++ ML Libraries

by Clement D.

This article explores the best C++ ML libraries, ranging from general-purpose frameworks to specialized toolkits for deep learning, linear algebra, and probabilistic modeling. Whether you’re building a high-frequency trading model, deploying AI on edge devices, or integrating ML into performance-critical systems, these libraries give you the flexibility of C++ combined with the power of modern machine learning.

1.Tensorflow

TensorFlow is one of the most widely used machine learning frameworks, originally designed with Python as its primary interface. However, it also provides a C++ API that allows developers to build and deploy ML models directly in performance-critical environments.

The C++ interface is lower-level compared to Python but offers significant advantages: reduced overhead, faster execution, and tighter integration into existing C++ systems. It is commonly used in high-performance computing, trading platforms, embedded systems, and real-time inference pipelines where every microsecond counts.

While training models in C++ is possible, it is often more practical to train in Python (using TensorFlow/Keras) and then export the model as a SavedModel or GraphDef. The C++ API is then used to load and run inference on that model.

TensorFlow’s C++ API provides tools for:

  • Loading computational graphs.
  • Executing inference sessions.
  • Managing tensors efficiently.
  • Running models on CPU or GPU with minimal overhead.

Because it is lower-level, error handling and debugging are more complex than in Python. However, once integrated, it can achieve extremely fast inference speeds.

Here’s a simple C++ snippet that demonstrates loading a TensorFlow graph and running inference:

#include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h"

using namespace tensorflow;

int main() {
    Session* session;
    Status status = NewSession(SessionOptions(), &session);

    // Load a pre-trained model
    GraphDef graph_def;
    ReadBinaryProto(Env::Default(), "model.pb", &graph_def);
    session->Create(graph_def);

    // Prepare input tensor
    Tensor input(DT_FLOAT, TensorShape({1, 784})); // e.g., MNIST image

    // Run session
    std::vector<Tensor> outputs;
    session->Run({{"input_node", input}}, {"output_node"}, {}, &outputs);

    std::cout << outputs[0].matrix<float>() << std::endl;
}






2.Pytorch





PyTorch is one of the best C++ ML libraries, and its C++ distribution (LibTorch) brings its power to performance-critical applications. Unlike TensorFlow, which feels more graph-centric in C++, LibTorch offers an eager execution model very close to its Python counterpart.





LibTorch is commonly used when you need fast inference in C++ applications — for example, in trading engines, robotics, self-driving pipelines, and real-time computer vision systems. Developers can either:





    

  1. Train models in Python and export them via TorchScript for deployment in C++, or
    2. 




  2. Train and run models directly in C++ using LibTorch’s API.
    3. 






Key features include:





    

  • Seamless use of autograd in C++.
    • 




  • GPU acceleration with CUDA out of the box.
    • 




  • Tensor operations identical to Python PyTorch.
    • 




  • Integration with TorchScript for portable inference.
    • 






Compared to TensorFlow C++, PyTorch’s API feels more “native” and developer-friendly. It offers flexibility while maintaining high performance, making it a strong choice for production inference pipelines.





Here’s a small LibTorch snippet:





#include <torch/torch.h>
#include <iostream>

struct Net : torch::nn::Module {
    torch::nn::Linear fc{nullptr};
    Net() { fc = register_module("fc", torch::nn::Linear(784, 10)); }
    torch::Tensor forward(torch::Tensor x) { return torch::relu(fc->forward(x)); }
};

int main() {
    Net net;
    auto input = torch::randn({1, 784});
    auto output = net.forward(input);
    std::cout << output << std::endl;
}










3. MLPack





mlpack is a C++-native machine learning library designed for speed, scalability, and ease of use. Unlike TensorFlow and PyTorch, which are deep learning frameworks, mlpack specializes in classical ML algorithms such as regression, clustering, dimensionality reduction, and nearest neighbors.





Its design philosophy emphasizes:





    

  • High performance (optimized C++ code, often faster than Python equivalents).
    • 




  • Simplicity (intuitive, consistent API).
    • 




  • Flexibility (usable as a C++ library or via CLI/Python/Julia bindings).
    • 






mlpack shines in scenarios where deep learning isn’t necessary but you still want production-quality performance — e.g., finance, anomaly detection, recommendation systems, and embedded devices.





Some popular algorithms include:





    

  • k-Nearest Neighbors, k-Means, Gaussian Mixture Models.
    • 




  • Decision Trees and Random Forests.
    • 




  • Logistic and Linear Regression.
    • 




  • Collaborative Filtering.
    • 






It’s also header-only, so integration into existing C++ projects is straightforward.





Here’s a small mlpack example, training and evaluating logistic regression:





#include <mlpack/methods/logistic_regression/logistic_regression.hpp>
#include <armadillo>
#include <iostream>

int main() {
    arma::mat X; // Features
    arma::Row<size_t> y; // Labels

    X.randu(100, 10);    // 100 samples, 10 features
    y = arma::randi<arma::Row<size_t>>(100, arma::distr_param(0,1));

    mlpack::regression::LogisticRegression<> model(X, y, 0.001);

    arma::Row<size_t> predictions;
    model.Classify(X, predictions);

    std::cout << "Accuracy: " 
              << arma::accu(predictions == y) / double(y.n_elem) 
              << std::endl;
}











4.DLib





Dlib is a modern C++ toolkit containing machine learning algorithms, optimization tools, and computer vision functions. It is best known for its face detection and facial landmark recognition, but its scope is much broader, making it one of the most versatile C++ ML libraries.





Key strengths include:





    

  • Classical ML algorithms (SVMs, decision trees, k-means, regression).
    • 




  • Deep learning support with a clean C++ API for building neural nets.
    • 




  • Computer vision utilities (HOG detectors, object tracking, image processing).
    • 




  • Optimization solvers for linear and nonlinear problems.
    • 






Unlike TensorFlow or PyTorch, Dlib is less about large-scale deep learning and more about practical ML for real-world tasks. It’s widely used in embedded systems, robotics, finance anomaly detection, and face recognition applications.





Dlib is also header-only, making it easy to integrate into C++ projects without heavy dependencies. Its API is clean and expressive, leveraging C++11 templates and modern design.





Here’s a small example of training a Support Vector Machine (SVM) classifier with Dlib:





#include <dlib/svm_threaded.h>
#include <iostream>

int main() {
    typedef dlib::matrix<double,2,1> sample_type;
    dlib::svm_c_trainer<dlib::linear_kernel<sample_type>> trainer;
    std::vector<sample_type> samples = {{0,0}, {1,1}, {1,0}, {0,1}};
    std::vector<double> labels = {-1, 1, -1, 1};

    auto decision_function = trainer.train(samples, labels);
    sample_type test; test = 0.9, 0.9;
    std::cout << "Prediction: " << decision_function(test) << std::endl;
}


									            

        


		            

				                    

						                            
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Tech Bubble Burst? Navigating Finance with Bloomberg’s MLIV

						

			by 										Clement D.
									
					

					    


    

        

            

									
Tech Stocks Face Scrutiny Amid Market Volatility: A Strategist’s Morning Note


As global markets navigate the current landscape, today’s key themes warrant close attention from analysts and investors. The MLIV team breaks down the potential implications of the tech stock landscape, while Bloomberg explores innovative solutions to power the growing demands of AI data centers. Additionally, the rise of OpenAI’s remarkable valuation underscores the transformative impact of emerging technologies. In the realm of personal wellness, an intriguing electro muscle stimulation suit showcases the intersection of technology and human performance. Amidst these diverse developments, our strategist’s morning note aims to provide a comprehensive overview to guide market participants in the days ahead.


🎥 So What If Tech Stocks Are in a Bubble?: 3-Minute MLIV (Bloomberg)


As a risk analyst, the video highlights several potential exposures and vulnerabilities in the global financial markets. The rally in global stocks following the US government shutdown suggests a degree of resilience, but the potential bursting of the tech stock “bubble” could pose significant risks. The sale of OpenAI shares and concerns over Japanese government bonds and government bonds in France, Spain, and the UK further underscore the fragility of the current market environment. Additionally, the potential for mass layoffs of federal workers due to the shutdown could have broader economic implications. While the video covers a range of topics, the overall message is one of caution, as the financial system continues to navigate a complex and uncertain landscape.






🎥 Would you wear a suit that zaps you with electricity? (Bloomberg)


Utilizing an electro muscle stimulation suit, an innovative approach to fitness and recovery, has garnered significant attention in the finance world. This video showcases the personal experience of Chris Rovzar, who explored the potential benefits of this technology after sustaining a few injuries. The video delves into the unique features of the suit, which aims to expedite the recovery process and potentially enhance muscle development. As the finance industry increasingly embraces cutting-edge solutions, the insights shared in this video underscore the growing significance of novel technologies in the realm of personal health and wellness, ultimately highlighting their relevance and impact within the broader financial landscape.






🎥 OpenAI Becomes World’s Largest Startup With $500 Bln Valuation (Bloomberg)


The recent transaction involving current and former OpenAI employees selling approximately $6.6 billion worth of stock to investors has propelled the ChatGPT owner’s valuation to a staggering $500 billion, making it the world’s largest startup. This significant boost in the company’s price tag, well beyond its previous $300 billion level during a SoftBank-led financing round earlier this year, highlights the growing financial significance of artificial intelligence technology and its potential impact on the global market.






🎥 AI Data Centers Need More Power: Could Oil Could Be the Answer? (Bloomberg)


The growing power demand from artificial intelligence data centers has sent wholesale electricity prices to record highs, posing potential vulnerabilities for the finance sector. Francisco Blanch, head of commodities and derivatives research at Bank of America, highlights the resilience factor of oil, which accounts for a third of the energy consumed globally. “Even though it doesn’t really play out that much into power generation, it could come back if prices stay this low,” Blanch explains during an interview on Bloomberg Television, suggesting oil as a potential solution to mitigate the exposure of AI data centers’ insatiable appetite for power.






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Navigating Trade Wars and Tariffs: Expert Insights for Investors

						

			by 										Clement D.
									
					

					    


    

        

            

									
The recent volatility in financial markets signals a profound shift in the underlying technological and structural dynamics that shape the global economy. As quants and data analysts grapple with these changes, it is crucial to understand how geopolitical tensions, trade policies, and regulatory uncertainty are transforming the landscape.


In our upcoming articles, we will explore several key developments that are shaping the future of finance. First, we will examine the potential implications of China’s stance on Taiwan and how it could impact trade relations with the United States, as discussed in the video “Opinion: Will US Trade Taiwan for TikTok and Tariffs?”. We will then delve into the impact of the latest tariffs imposed by the Trump administration on timber, lumber, and kitchen cabinets, as detailed in the video “Trump Orders Tariffs on Timber, Lumber, Kitchen Cabinets”.


Additionally, we will analyze the potential market implications of the looming government shutdown in the United States, as covered in the video “Markets on Edge As US Shutdown Looms | Bloomberg: The Asia Trade, 9/30/25”. Finally, we will explore the proposed Gaza peace plan and its potential impact on global financial stability, as discussed in the video “Gov. Shutdown Looms; Proposed Gaza Peace Plan | Balance of Power: Late Edition 9/29/2025”.


By understanding these technological and structural transformations, finance professionals can better navigate the complexities of the modern market landscape and make informed decisions in the face of unprecedented challenges.


🎥 Opinion: Will US Trade Taiwan for TikTok and Tariffs? (Bloomberg)


In the video, Bloomberg Opinion Columnist Karishma Vaswani discusses the potential shift in the US-China relationship and its implications for Taiwan. Vaswani highlights China’s efforts to challenge the longstanding “strategic ambiguity” in Washington’s approach, urging the US to align with Beijing in opposing Taiwan’s independence. This change in wording, Vaswani argues, is a matter of survival for the self-ruling island and raises the risk of the US being perceived as an unreliable partner in the Asia-Pacific region. The discussion touches on the broader economic and geopolitical dynamics at play, underscoring the delicate balance the US must maintain in its relations with China and Taiwan.






🎥 Trump Orders Tariffs on Timber, Lumber, Kitchen Cabinets (Bloomberg)


The analysis presents the key findings from a video report on US President Donald Trump’s order to impose tariffs on imports of softwood timber, lumber, and kitchen cabinets. The order, effective from October 14 with some increases on January 1, is expected to significantly impact Canada as the largest wood supplier to the US. The report highlights the economic and trade implications of the tariffs, providing a concise and formal overview of the central points covered in the video.






🎥 Markets on Edge As US Shutdown Looms | Bloomberg: The Asia Trade, 9/30/25 (Bloomberg)


As the US government faces the prospect of a shutdown, investors are closely monitoring the potential regulatory, macroeconomic, and systemic implications. The video highlights the ongoing tensions between the Trump administration and Democrats over budget negotiations, with the looming shutdown posing risks to financial markets. Discussions around a potential peace plan for the Gaza conflict and the impact of US tariffs on the global economic outlook further underscore the complex web of geopolitical and economic factors shaping the current market landscape. Analysts and industry leaders provide insights on the challenges and opportunities facing investors as they navigate this uncertain environment.






🎥 Gov. Shutdown Looms; Proposed Gaza Peace Plan | Balance of Power: Late Edition 9/29/2025 (Bloomberg)


In a concise and formal strategic memo to decision-makers, the video “Balance of Power: Late Edition 9/29/2025” covers several key political and economic developments. (R) Sen. Ron Johnson argues that the government shutdown drama is unnecessary, while Former Deputy Assistant Secretary of Defense Evelyn Farkas discusses a proposed Gaza peace plan from President Trump and Netanyahu. Republican Strategist Marc Short warns of the threat of job cuts raising the stakes for a potential government shutdown. Additionally, Harvard Kennedy School’s Jeanne Sheehan Zaino and Stonecourt Capital Partner Rick Davis comment on Eric Adams’ decision to exit the NYC mayoral race. The video provides a comprehensive update on the intersection of politics and global business, offering insights for decision-makers.






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Navigating the Financial Landscape: Expert Insights for Optimal SEO

						

			by 										Clement D.
									
					

					    


    

        

            

									
In the annals of financial history, one question has long puzzled investors and economists alike: what happens when a pointer is double deleted? This conundrum, explored in the captivating video “What happens when pointer is double deleted” from the esteemed CppNuts channel, holds the key to unlocking a deeper understanding of the intricate workings of finance.


As the financial landscape continues to evolve, the demand for skilled professionals capable of navigating the complexities of the industry has never been more pressing. The video “The Only Reason I Can ACTUALLY Build With AI Now” delves into the transformative power of AI, unveiling how tools like Drawbridge are revolutionizing the way we approach user interface design and coding workflows.


Moreover, the ongoing worker shortage in the manufacturing sector has prompted a closer examination of the role of legal immigrants in sustaining economic growth. In “America’s Worker Shortage: Why Manufacturing Companies Need Immigrants,” we explore the strategies employed by North Dakota companies, such as Cardinal Glass, to address this critical challenge.


For those aspiring to join the ranks of the financial elite, the video “How to Finding a Quant Job” provides invaluable insights into the rigorous requirements and competitive landscape of the quantitative finance industry. As students navigate the path to a successful career, this resource offers a glimpse into the dedication and expertise necessary to thrive in this dynamic field.


🎥 What happens when pointer is double deleted (Cpp nuts)


This research summary examines the implications of double-deleting a pointer in C/C++ programming. The key takeaways are as follows: First, the video demonstrates that double-deleting a pointer can lead to undefined behavior, which can result in memory corruption, segmentation faults, or other unpredictable outcomes. Second, it highlights the importance of carefully managing memory allocation and deallocation to avoid such issues. Finally, the video emphasizes the need for thorough error handling and defensive programming practices to ensure the stability and reliability of C/C++ applications. Overall, this presentation provides valuable insights into a common programming pitfall and the strategies developers can employ to mitigate its risks.






🎥 The Only Reason I Can ACTUALLY Build With AI Now (AI Lab)


In this insightful tutorial, the presenter showcases how AI agents can revolutionize the process of UI editing through Drawbridge, an open-source tool that batch-fixes interface issues efficiently. The video highlights the key financial implications of this innovative approach, which outperforms traditional prompt engineering methods by allowing artificial intelligence to process multiple changes simultaneously.


The video compares the performance of Cursor AI and Claude Code, revealing that while Cursor sometimes struggles with file detection, Claude delivers flawless results, underscoring the importance of selecting the right AI automation tool for optimal development workflows. Two transformative use cases are explored: a Landing Page Builder that empowers non-technical users to create prototypes by describing their desired components, and a Client Collaboration feature that streamlines the revision process by letting clients visually select elements for the AI to implement.


The presenter also demonstrates how to integrate design systems for consistent styling, ensuring the AI assistant follows specific design languages. This visual approach to AI-driven coding eliminates the back-and-forth typically required when fixing multiple UI issues, revolutionizing the way developers and teams work with artificial intelligence.






🎥 America’s Worker Shortage: Why Manufacturing Companies Need Immigrants (Bloomberg)


In a recent video, Bloomberg examines the critical worker shortage facing the manufacturing industry in the United States. As companies like Cardinal Glass in North Dakota struggle to fill essential roles, they have turned to legal immigrant workers from countries such as South Sudan and Haiti. However, with tightening immigration policies, the future of this labor pipeline remains uncertain. Institutional investors should closely monitor how manufacturing firms adapt to this challenge and the potential impact on their operations and profitability.






🎥 How to Finding a Quant Job (Dimitri Bianco)


In this insightful video, the presenter delves into the nuanced world of finding a quant job, highlighting the statistical signals and patterns that aspiring quants should be mindful of. The video emphasizes the importance of a master’s degree, a standout resume, and a deep understanding of quant finance topics as the key prerequisites for securing a coveted quant role. Additionally, the presentation underscores the value of networking, attending conferences, and conducting informational interviews to uncover hidden opportunities and stay ahead of the competition. By framing the content through a quantitative lens, the video provides a data-driven perspective on the challenges and strategies involved in navigating the highly competitive quant job market.






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Navigating Finance Trends: AI, Chips, and Quantum Breakthroughs

						

			by 										Clement D.
									
					

					    


    

        

            

									
The Great Depression of the 1930s taught us that economic crises can reverberate for decades, shaping the path of nations and markets. Today, as the world grapples with the aftermath of the AI-driven rally, we find ourselves at a crossroads reminiscent of those tumultuous times.


In this article, we’ll explore the key market events unfolding, from Witkoff’s predictions of a Gaza breakthrough to Intel’s pursuit of an Apple investment, as captured in the videos “Witkoff Predicts Gaza Breakthrough; Intel Seeks Apple | Horizons Middle East & Africa 9/25/2025” and “Intel Seeks Apple Investment & HSBC Quantum Breakthrough | Daybreak Europe 9/25/2025.” 


Additionally, we’ll delve into the profound impact of AI and drone technology on modern warfare, as showcased in the video “How AI, drones in Ukraine have changed warfare #tech #politics.” Finally, we’ll examine the potential ripple effects of Trump’s visa chaos on global investments, as discussed in the interview “Trump’s Visa Chaos Risks Billions in Investments | Bloomberg: Insight with Haslinda Amin, 9/25/25.”


By connecting these timely events to broader economic and historical themes, we aim to provide our readers with a comprehensive understanding of the forces shaping the financial landscape.


🎥 Witkoff Predicts Gaza Breakthrough; Intel Seeks Apple | Horizons Middle East & Africa 9/25/2025 (Bloomberg)


In an ever-evolving global market landscape, the latest episode of Horizons Middle East & Africa offers valuable insights for investors. The report highlights several key developments that could impact the region’s financial landscape. Notably, US equity-index futures have inched higher in Asian trading, signaling a potential rebound after a brief pause in the AI-driven rally. This suggests cautious optimism among market participants, though the underlying risks and volatility in the sector remain a concern. Additionally, the segment explores Taiwan’s decision to step back from chip export curbs on South Africa, a move that could have significant implications for the semiconductor industry and global supply chains. Equally intriguing is the prediction by US envoy Steve Witkoff of a potential breakthrough in the Israel-Hamas Gaza war, which could have far-reaching geopolitical and economic consequences. With expert commentary from industry leaders, such as Ray Sharma-Ong and Frances Ames, this episode of Horizons Middle East & Africa provides a comprehensive analysis of the region’s financial landscape, enabling investors to navigate the opportunities and risks with greater clarity.






🎥 Intel Seeks Apple Investment & HSBC Quantum Breakthrough | Daybreak Europe 9/25/2025 (Bloomberg)


In this strategic memo, we summarize the key highlights from the Bloomberg Daybreak Europe program, which covers the latest developments in global finance and economics.


The report begins with a recap of US futures edging higher after Wall Street’s recent AI-driven rally. The Federal Reserve’s Mary Daly suggests more rate cuts are coming, but cautions the central bank should move cautiously. Notably, Bloomberg has learned that Intel has approached Apple about a potential investment, as part of Intel’s efforts to stage a comeback after the US government took a stake in the struggling chipmaker.


On the trade front, the US has formally cut tariffs on EU cars to 15%, with the lower rate backdated to August 1st. However, the US is also setting the stage for levies on robotics. Separately, HSBC has achieved a breakthrough in deploying quantum computing for financial markets, using IBM’s quantum processor to attain a 34% improvement in predicting bond trade prices.


Overall, this program provides valuable insights on key developments impacting the global financial landscape.






🎥 How AI, drones in Ukraine have changed warfare #tech #politics (Bloomberg)


The ongoing conflict in Ukraine has served as a pivotal moment in the evolution of modern warfare, catalyzing a profound shift in the strategic and economic dynamics that underpin global military operations. The emergence of advanced technologies, such as artificial intelligence and drone systems, has fundamentally altered the landscape of combat, introducing new capabilities and challenges that redefine the very nature of military engagement. This transformation, as articulated by industry experts like Alex Ferrara of Bessemer Venture Partners, underscores the remarkable adaptability of warfare in the face of technological disruption, a phenomenon that will undoubtedly continue to shape the trajectory of geopolitical power dynamics in the years to come. As the world grapples with the implications of these emerging technologies, the lessons learned from the Ukrainian theater will undoubtedly serve as a crucial reference point for military strategists and policymakers alike, as they navigate the complex and ever-evolving landscape of modern warfare.






🎥 Trump’s Visa Chaos Risks Billions in Investments | Bloomberg: Insight with Haslinda Amin, 9/25/25 (Bloomberg)


The video “Trump’s Visa Chaos Risks Billions in Investments” by Bloomberg Insight with Haslinda Amin examines the significant financial implications of the Trump administration’s policies on immigration and visa regulations. The program delves into the concerns raised by South Korean companies facing uncertainty over their U.S. investment projects due to visa issues, as well as the views of immigration lawyer Charles Kuck, who argues that the administration’s contradictory agendas on immigration and investments are jeopardizing billions of dollars in potential investments. The video also features insights from industry leaders, including the CEO of Macquarie Group and the Head of Investment Services at BNP Paribas Wealth Management, Asia, who discuss the broader impact of these policies on technology-driven infrastructure investments and the AI industry.






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							                                best time series database                        
					                

			
			
    

		            
DatabasesPerformance

		
        
Best Time Series Database: An Overview of KDB+

						

			by 										Clement D.
									
					

					    


    

        

            

									

In modern quantitative finance, data is everything. Trading desks and research teams rely on vast streams of tick data, quotes, and market events, all arriving in microseconds. What is the best time series database? Managing, storing, and querying this firehose efficiently requires more than a generic database: it demands a system built specifically for time series.





Enter kdb+, a high-performance columnar database created by KX. Known for its lightning-fast queries and ability to handle terabytes of historical data alongside real-time feeds, kdb+ has become the industry standard in financial institutions worldwide. From high-frequency trading to risk management, it powers critical systems where speed and precision cannot be compromised.





What sets kdb+ apart is its unique combination of a time-series optimized architecture with the expressive q language for querying. It seamlessly unifies intraday streaming data with historical archives, giving quants the ability to backtest, analyze, and act without switching systems.













1.What is KDB+?





KDB+ is a high-performance time-series database created by Kx Systems and built in C++. It was designed to handle massive volumes of structured data at extreme speed, making it ideal for environments where both real-time and historical analysis are critical. Unlike traditional row-based databases, KDB+ stores data in a columnar format, which makes scanning, aggregating, and analyzing large datasets much faster and more memory-efficient. At its core, it is not only a database but also a complete programming environment, paired with a powerful vector-based query language called q. q combines elements of SQL with array programming, allowing concise expressions tailored for time-series queries such as joins on timestamps, rolling windows, or as-of joins on top of a tabular data structure.










This combination enables KDB+ to ingest streaming data while simultaneously providing access to years of history within the same system. The result is a platform capable of processing billions of rows in milliseconds, which is why it has become the gold standard in finance for trading, risk, and PnL systems. Hedge funds, investment banks, and exchanges rely on KDB+ to analyze tick data, price instruments, monitor risk, and support algorithmic trading strategies. Although it has found applications beyond finance, such as in telecoms and IoT, its deepest adoption remains on trading floors where latency and accuracy are paramount. 





Example in q (KDB+ query language):





trade:([] time:09:30 09:31 09:32;
          sym:`AAPL`AAPL`MSFT;
          price:150.2 150.5 280.1;
          size:200 150 100)





This defines a table trade with 3 columns (time, sym, price, size) and 3 rows.





You can then run a query like:





select avg price by sym from trade





Result:





symavg price
AAPL150.35
MSFT280.1





The main trade-off is cost: licenses are expensive, but in industries where milliseconds translate to millions, its efficiency and reliability make KDB+ irreplaceable.










2. Why is KDB+ so efficient for quantitative finance?





KDB+ is exceptionally efficient in quantitative finance because it was designed from the ground up to deal with the challenges of financial time-series data. At its core, it uses a columnar storage model, which means that data for each column is stored contiguously in memory. This structure drastically speeds up operations like scanning, aggregating, and filtering on a single field. For example, computing average prices or bid-ask spreads across billions of ticks. The system also runs entirely in memory by default, avoiding the I/O bottlenecks of disk-based databases, while still allowing persistence for longer-term storage. On top of this, the q language gives quants and developers a concise, vectorized way to query and transform data. Instead of writing long SQL or Python loops, q lets you express complex analytics in just a few lines, which not only improves productivity but also reduces latency.










KDB+ further integrates real-time and historical data seamlessly, so the same query engine can process both a live market feed and decades of stored data. This is invaluable for trading desks that need to backtest strategies, monitor risk, and react instantly to new market conditions. Its efficiency also comes from its extremely lightweight runtime, capable of handling billions of rows in milliseconds without the overhead of more general-purpose systems like Spark or relational databases. 





kdb Insight SDK is a unified platform for building real-time analytics applications at scale. Instead of stitching together a patchwork of tools like Kafka, Spark, and Redis, it provides everything you need—streaming, storage, and query—in a single technology stack.










The platform is designed to handle billions of events per day while keeping both real-time and historical data accessible through the same interface. At the core is the Data Access Process (DAP), which exposes data from memory, intraday, and historical stores through one API. Whether you prefer q, SQL, or Python (via PyKX), the query experience is consistent and efficient.





A lightweight service layer coordinates execution: the Service Gateway routes requests, the Resource Coordinator identifies the best processes to handle them, and the Aggregator combines results into a unified response.





With kdb Insight SDK, you can ingest, transform, and analyze streaming data without the complexity of multi-tool pipelines. The result is a simpler, faster way to power mission-critical, real-time analytics.





3. Some Examples





You want to get 5-minute realized volatility per symbol? 
Here’s a clean q snippet you can drop in:





/ assume 1-second bars for brevity; w=00:05
w:00:05;
bars:select time,sym,px:price by sym from trades;
bars:update ret:log px%prev px by sym from bars;
select rv:sqrt 252*sum ret*ret % (count ret) by sym from bars where time within (last time





You want the last quote for AAPL at or before a specific timestamp T? 
Use an as-of join like this:





/ Pick the timestamp of interest
T:.z.P + 0D00:00:03;

/ Return the last quote at/before T for AAPL
aj[`sym`time; ([] sym:`AAPL; time:T); quotes]





You want 1-minute OHLCV per symbol? 
Here’s a tidy q snippet:





/ Assume `trades` has: time, sym, price, size

/ 1) Bucket timestamps to 1-minute bins
tr: update mtime:1 xbar time from trades;

/ 2) Compute OHLCV per (sym, minute)
select
  open:first price,
  high:max price,
  low:min price,
  close:last price,
  vol:sum size
by sym, mtime
from tr










4. Conclusion





KDB+ remains the gold standard for time-series analytics when latency and scale matter. With kdb Insight SDK, you get streaming, storage, and query in one coherent stack: no glue code. Real-time and historical data live behind a single API (q/SQL/Python), simplifying everything. The columnar, in-memory design delivers millisecond analytics on billions of events. Our snippets showed the essentials: VWAP, as-of joins, OHLCV bars, and realized volatility. Interoperability is straightforward: PyKX for Python, C API/C++ for tight integration. Operationally, Insight’s gateway, coordinator, and aggregator remove orchestration pain. This translates to faster iteration cycles and fewer production surprises. Trade-offs exist (licensing, expert skills), but ROI is clear for mission-critical systems. If you’re in quant finance or any latency-sensitive domain, KDB+ is hard to beat. Your next step: spin up a local process, load dummy trades, and run the queries.
Then wire a small Python or C++ client and time your end-to-end path. When ready, try Insight SDK to scale from laptop to cluster without re-architecture. Measure p95/p99 latencies and storage footprints to validate the fit for your workload.
If the numbers hold, you’ve found your real-time analytics platform.

									            

        


		            

				                    

						                            
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News

		
        
Disruption in Fertility Tech: Insights for Finance Professionals

						

			by 										Clement D.
									
					

					    


    

        

            

									
Institutional Investors: Navigating the Evolving Fertility Landscape, AI Disruption, and the Fed’s Monetary Policy


As institutional investors, it is crucial to stay informed on the latest developments shaping the finance and technology sectors. In this strategic memo, we will explore a range of insights, from the disruption in the fertility industry to the implications of the Nvidia-OpenAI deal and the Federal Reserve’s rate-cutting cycle.


First, we delve into the rise of Kindbody, a fertility chain that has experienced rapid growth, as highlighted in the Bloomberg report “IVF Disrupted: Investigating the quick rise of fertility chain Kindbody.” This investigation provides valuable insights into the broader challenges and opportunities within the largely unregulated fertility industry.


Next, we turn our attention to the Nvidia-OpenAI deal, where Nvidia will invest up to $100 billion to support new data centers and AI infrastructure, as covered in the “Nvidia-OpenAI Deal; France Recognizes Palestine | Horizons Middle East & Africa 9/23/2025” video. This strategic partnership holds significant implications for the future of artificial intelligence and its impact on various industries.


Finally, we examine the Federal Reserve’s rate-cutting cycle, with insights from Marathon Asset Management CEO, co-founder, and chairman Bruce Richards, as featured in the “Fed Cuts Only at Halfway Mark, Marathon’s Richards Says” video. Richards’ perspective on the Fed’s remaining 125 basis-points of cuts sheds light on the central bank’s monetary policy and its potential effects on the broader economy.


Additionally, we briefly touch on the US judge’s decision to allow the construction of Orsted’s wind farm off the coast of Rhode Island, as reported in the “Orsted’s Rhode Island Wind Farm Given Go-Ahead by Judge” video, highlighting the ongoing developments in the renewable energy sector.


As institutional investors, it is crucial to stay informed on these strategic topics to make well-informed decisions and navigate the evolving landscape of finance and technology.


🎥 IVF Disrupted: Investigating the quick rise of fertility chain Kindbody #tech (Bloomberg)


The Bloomberg reporter’s investigation into Kindbody, a rapidly growing fertility chain in the U.S., highlights broader issues within the largely unregulated fertility industry. The report provides a deep dive into the company’s rise, illustrating the complex challenges and regulatory gaps faced by patients navigating this sensitive and often costly healthcare service. The podcast episode, “IVF Disrupted: The Kindbody Story,” available on various platforms, offers decision-makers a concise and informative overview of the industry’s dynamics and the implications for consumers seeking fertility treatments.






🎥 Nvidia-OpenAI Deal; France Recognizes Palestine | Horizons Middle East & Africa 9/23/2025 (Bloomberg)


Nvidia’s ambitious $100 billion investment in OpenAI aims to bolster the companies’ AI infrastructure and capabilities, underscoring the financial significance of this strategic partnership. Additionally, France’s formal recognition of Palestine as an independent state and Congo’s decision to lift its cobalt export ban highlight the evolving geopolitical and economic dynamics in the Middle East and Africa. Prominent industry experts, including Ayesha Tariq, Yezid Sayigh, and Thomas Pramotedham, provide insightful analysis on these developments and their implications for the region’s financial landscape.






🎥 Fed Cuts Only at Halfway Mark, Marathon’s Richards Says (Bloomberg)


According to Marathon Asset Management CEO and co-founder Bruce Richards, the Federal Reserve’s rate-cutting cycle is “only at the halfway mark,” indicating that it has 125 basis-points left to cut before reaching a neutral rate of 3%. Richards highlights the risks associated with weakening jobs data, despite higher inflation, which has prompted the Fed to embark on an easing cycle that is expected to last for some time. Regarding the opportunity, Richards anticipates that a “dove” will take over as the next Fed chair in eight months, who will likely bring rates down to the neutral rate of 3% almost immediately. However, investors should be mindful of the potential market sentiment shift as the Fed navigates this challenging economic environment.






🎥 Orsted’s Rhode Island Wind Farm Given Go-Ahead by Judge (Bloomberg)


In the annals of the renewable energy revolution, the recent ruling by a US judge permitting the continuation of Orsted’s wind farm project off the coast of Rhode Island stands as a pivotal moment. This decision, which quashed the efforts of the previous administration to halt the nearly-completed development, underscores the relentless march of clean energy technologies, even in the face of political headwinds. The Revolution Wind project, now poised to resume construction, represents a significant stride towards a more sustainable energy future, one that has been steadily gaining momentum over the past decades. As the global community grapples with the pressing challenges of climate change, this judicial victory for Orsted serves as a testament to the resilience and determination of the renewable energy sector, as it continues to reshape the economic and environmental landscape for generations to come.






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							                                C++26                        
					                

			
			
    

		            
LibrariesPerformance

		
        
C++26: The Next Big Step for High-Performance Finance

						

			by 										Clement D.
									
					

					    


    

        

            

									

C++ is still the backbone of quantitative finance, powering pricing, risk, and trading systems where performance matters most. The upcoming C++26 standard is set to introduce features that go beyond incremental improvements.
Key additions like contracts, pattern matching, executors, and reflection will directly impact how quants build robust, high-performance applications. For finance, that means cleaner code, stronger validation, and better concurrency control without sacrificing speed. This article highlights what’s coming in C++26 and why it matters for high-performance finance.


















1. Contracts





Contracts in C++26 bring native support for specifying preconditions and postconditions directly in the code. For quantitative finance, this means you can enforce invariants in critical libraries — for example, checking that discount factors are positive, or that volatility inputs are within expected ranges. Instead of relying on ad-hoc assert statements or custom validation layers, contracts give a standard, compiler-supported mechanism to make assumptions explicit. This improves reliability, reduces debugging time, and makes financial codebases more transparent to both developers and reviewers.





double black_scholes_price(double S, double K, double sigma, double r, double T)
    [[expects: S > 0 && K > 0 && sigma > 0 && T > 0]]
    [[ensures: return_value >= 0]]
{
}






Preconditions ([[expects: ...]]) ensure inputs like spot price S, strike K, and volatility sigma are valid.
Postcondition ([[ensures: ...]]) guarantees the returned option price is non-negative.










2. Pattern Matching





Pattern Matching is one of the most anticipated features in C++26. It provides a concise way to handle structured branching, similar to match in Rust or switch in functional languages. For quants, this reduces boilerplate in pricing logic, payoff evaluation, and instrument classification. Currently, handling multiple instrument types often requires long chains of if-else statements. Alternatively, developers rely on the visitor pattern, which adds indirection and complexity. Pattern matching simplifies this into a single, readable construct.





auto payoff = match(option) {
    Case(Call{.strike = k, .spot = s}) => std::max(s - k, 0.0),
    Case(Put{.strike = k, .spot = s})  => std::max(k - s, 0.0),
    Case(_)                            => 0.0  // fallback
};





This shows how a quant dev could express payoff rules directly, without long if-else chains or visitors.





3. Executors





Executors (std::execution) standardize async and parallel composition in C++26. They’re based on the Senders/Receivers model (P2300) that reached the C++26 working draft/feature freeze. Goal: make scheduling, chaining, and coordinating work composable and predictable. For quants, this means clearer pipelines for pricing, risk, and market-data jobs. You compose tasks with algorithms like then, when_all, let_value, transfer. Executors decouple what you do from where/how it runs (CPU threads, pools, IO).





// Price two legs in parallel, then aggregate — composable with std::execution
#include <execution>      // or <stdexec/execution.hpp> in PoC libs
using namespace std::execution;

auto price_leg1 = then(just(leg1_inputs),      price_leg);
auto price_leg2 = then(just(leg2_inputs),      price_leg);

// Fan-out -> fan-in
auto total_price =
  when_all(price_leg1, price_leg2)
  | then([](auto p1, auto p2) { return aggregate(p1, p2); });

// Run on a specific scheduler (e.g., thread pool) and wait for result
auto sched = /* obtain scheduler from your thread pool */;
auto result = sync_wait( transfer(total_price, sched) ).value();











4. Reflection





Reflection is about letting programs inspect their own structure at compile time. In C++26, the committee is moving toward standardized reflection facilities. The goal is to replace brittle macros and template tricks with a clean interface.
For quants, this means easier handling of large, schema-heavy systems. Think of trade objects with dozens of fields that must be serialized, logged, or validated. Currently, you often duplicate field definitions across code, serializers, and database layers.





struct Trade {
    int id;
    double notional;
    std::string counterparty;
};

// Hypothetical reflection API (syntax under discussion)
for (auto member : reflect(Trade)) {
    std::cout << member.name() << " = " 
              << member.get(trade_instance) << "\n";
}






This shows how reflection could automatically enumerate fields for logging, avoiding manual duplication of serialization logic.

									            

        


		            

				                    

						                            
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