Hey CQF Delegates January 25 Cohort Batch/CQF Exam June 25 cohort batch admission/MBA Ranadeb/BBA Ranadeb
CQF Exam Schedule for the June 2025 CQF Program
Exam Released Submission Deadline
Level 1Exam 1(Module 2)07 August 2025 21 August 2025
Level 1 Exam 2 (Module 3)11 September 202525 September 2025
Level 2 Exam 3(Module4/partially5) 07 October 2025 21October 2025
Level 2 Final Project 25 November 202520 January 2026
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CQF Ready final Projects solutions guide available instantly to your Inbox in the areas mentioned below : Credit Spread for a Basket Product (CR) coding in Python.
Deep Learning for Financial Time Series (DL) coding in Python.
Pairs Trading Strategy Design & Back test (TS) coding in python.
Portfolio Construction using Black-Litterman Model and Factors (PC) coding in python.
Blending Ensemble for Classification (ML) coding in python.
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Credit Spread for a Basket Product (CR)
Deep Learning for Financial Time Series (DL)
Pairs Trading Strategy Design & Back test (TS)
Portfolio Construction using Black-Litterman Model and Factors (PC)
Optimal Hedging with Advanced Greeks (DH)
Blending Ensemble for Classification (ML)
Algorithmic Trading for Reversion and Trend-Following (AL)
Deep Neural Networks for Solving High Dimensional PDEs (DN)
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Certificate in Quantitative Finance Final Project Brief
January 2025 Cohort
This document outlines topics available for this cohort. No other topics can be submitted. Each topic has by-step instructions to give you a structure (not limit) as to what and how to implement.
Marks earned will strongly depend on your coding of numerical techniques and presentation of how you explored and tested m,a quantitative model (report in PDF or HTML). Certain numerical methods are too involved or auxiliary to the model, for example, do not recode optimisation or RNs generation. Code adoption allowed if the code fully modified by yourself.
A capstone project requires own study and ability to work with documentation on packages that implement numerical methods in your coding environment e.g., Python, R, Matlab, C#, C++, Java. You do not need to pre-approve the coding language and use of libraries, including very specialised tools such as Scala, kdb+ and q. However, software like EViews is not coding.
To complete the project, you must code the model(s) and its numerical techniques form one topic from the below options and write an analytical report. If you continue from a previous cohort, please review topic description because tasks are regularly reviewed. It is not possible to submit past topics.
Credit Spread for a Basket Product (CR)
Deep Learning for Asset Prediction (DL)
Pairs Trading Strategy Design & Back test (TS)
Portfolio Construction using Black-Litterman Model and Factors (PC)
Optimal Hedging with Advanced Data Modelling (DH)
Blending Ensemble for Classification (ML)
Algorithmic Trading for Reversion and Trend-Following (AL)
Deep Neural Networks for Solving High Dimensional PDEs (DN)
Project Report and Submission
First recommendation: do not submit Python Notebook ’as is’ – there is work to be done to transform it into an analytical report. Remove printouts of large tables/output. Write up mathematical sections (with LaTeX markup). Write up analysis and comparison for results and stress-testing (or alike). Explain your plots. Think like a quant about the computational and statistical properties: convergence/accuracy/variance and bias. Make a table of the numerical techniques you coded/utilised.
Project Report must contain sufficient mathematical model(s), numerical methods and an adequate conclusiondiscussing pros and cons, further development.
There is no set number of pages. Some delegates prefer to present multiple plots on one page for comparability, others choose more narrative style.
It is optimal to save Python Notebook reports as HTML but do include a PDF with page numbers
– for markers to refer to.
Code must be submitted and working.
Submission date for the project is Thursday 21st August 2025, 23.59 BST
There is no extension time to Final Project.
Projects without a hand-signed declaration or working code are incomplete.
Failure to submit ONE report file and ONE zip file according to the naming instructions means such a project will miss an allocation for grading.
Coding for Quant Finance
Choose programming environment that has appropriate strengths and facilities to implement the topic (pricing model). Common choice is Python, Java, C++, R, Matlab. Exercise judgement as a quant: which language has libraries to allow you to code faster, validate easier.
Use of R/Matlab/Mathematica is encouraged. Often there a specific library in Matlab/R gives fast solution for specific models in robust covariance matrix/cointegration analysis tasks.
Project Brief give links to nice demonstrations in Matlab, and Webex sessions demonstrate Python notebooks {does not mean your project to be based on that ready code.
Python with pandas, matplotlib, sklearn, and tensorow forms a considerable challenge to Matlab, even for visualization. Matlab plots editor is clunky, and it is not that difficult to learn various plots in Python.
‘Scripted solution’ means the ready functionality from toolboxes and libraries is called, but the amount of own coding of numerical methods is minimal or non-existent. This particularly applies to Matlab/R.
Projects done using Excel spreadsheet functions only are not robust, notoriously slow and do not give understanding of the underlying numerical methods. CQF-supplied Excel spreadsheets are a starting point and help to validate results but coding of numerical techniques/use of industry code libraries is expected.
The aim of the project is to enable you to code numerical methods and develop model prototypes in a production environment. Spreadsheets-only or scripted solutions are below the expected standard for completion of the project.
What should I code? Delegates are expected to re-code numerical methods that are central to the model and exercise judgement in identifying them. Balanced use of libraries is at own discretion as a quant.
Produce a small table in report that lists methods you implemented/adjusted. If using ready functions/borrowed code for a technique, indicate this and describe the limitations of numerical method implemented in that code/standard library.
It is up to delegates to develop their own test cases, sensibility checks and validation. It is normal to observe irregularities when the model is implemented on real life data. If in doubt, reflect on the issue in the project report.
The code must be thoroughly tested and well-documented: each function must be described, and comments must be used. Provide instructions on how to run the code.
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Exam 1(Module 2) January 24 Cohort released date is 7th March 2024 and Submission deadline is 21st March 2024. Exam 2 (Module 3) Released date is 15th April 24 and submission deadline is 29th April 2024. Exam 3 (Module 4/partially 5) Released date is 16th May 2024 and Submission Deadline is 30th May 2024. Final Project Released date is 27th June 2024 and submission deadline is 22nd August 2024. Final Optional Exam is Saturday 7th September 2024.
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Title: A Deep Dive into the Digital Frontier: Artificial Intelligence’s Development and Effects Introduction: Few inventions in the history of technology have so powerfully captivated the public’s imagination and aroused the collective consciousness as artificial intelligence (AI). Its conceptual underpinnings have been surpassed by this dynamic area, which combines computer science and cognitive research, to become a transformational force that is transforming industries, economies, and the very foundation of our daily lives. Overview of the Historical Development of AI The theoretical underpinnings of artificial intelligence were first developed in the middle of the 20th century by visionaries like Alan Turing and John McCarthy. McCarthy’s use of the phrase “Artificial Intelligence” and Turing’s groundbreaking work on computation created the foundation for a scientific and philosophical investigation into the possibility of creating artificial intelligence. However, the area did not come to life until the latter half of the 20th century when computer capacity and algorithmic expertise combined. Applications in Different Sectors Applications for AI are found in many fields, and they all benefit from its analytical abilities. AI algorithms are used in healthcare to identify diseases and forecast patient outcomes by analysing medical data. AI is used in the financial sector for algorithmic trading, risk assessment, and fraud detection. AI is used by autonomous vehicles to make quick decisions that increase road safety. AI-generated material has begun to appear in the entertainment sector, from scriptwriting to music production. AI’s beating heart is machine learning. Machine Learning (ML), a subset that enables systems to learn from experience, is at the heart of AI’s functioning. ML algorithms continuously improve their knowledge based on user interactions, whether it be recommendation algorithms on streaming platforms or personalised material on social media. Neural networks are used in Deep Learning, a sophisticated type of machine learning, to model human decision-making. This has led to advancements in natural language processing, image identification, and even strategic games like Go. Ethics in Artificial Intelligence As AI becomes more and more ingrained in our daily lives, ethical questions have arisen. Fairness concerns are raised by bias in algorithms, which is frequently a reflection of the data they are trained on, particularly in decision-making processes like hiring and lending. Concerns about privacy are also raised by the gathering and use of personal data for AI-driven insights. Discussions about responsibility and openness in algorithmic decision-making are becoming more popular as AI develops its autonomy. Human-AI Cooperation: A Win-Win Situation Contrary to dystopian stories about robots taking over the world, a more nuanced perspective sees AI as a partner rather than a rival. Routine tasks can be automated to free up human resources for more innovative and strategic projects. The speed with which AI can digest
enormous amounts of data improves human decision-making. AI improves the diagnostic skills of medical experts in industries like healthcare, resulting in interventions that are more
precise and timely. Opportunities and Challenges on the Horizon The path to the AI of the future is not without difficulties. The “black box” aspect of some AI systems makes it difficult to comprehend their decision-making processes as algorithms get increasingly complicated. It is essential to ensure the ethical development and application of AI in order to avoid unforeseen outcomes. A societal issue that needs to be addressed is the requirement for continuing education and upskilling to stay up with AI breakthroughs.
The Impact of AI on the Future The future of AI looks to be both fascinating and complicated. Improvements in Natural Language Processing (NLP) could result in more natural interactions with AI systems, making them more user-friendly and intuitive. Robotics and AI could revolutionise sectors like manufacturing and logistics and achieve previously unheard-of levels of efficiency. With its capacity to process information at speeds that are currently unthinkable, quantum computing could pave the way for new developments in problem-solving and optimisation.
Navigating the AI Landscape, Conclusion To sum up, artificial intelligence sits at the nexus of opportunity and accountability. The direction of AI’s progress depends on our capacity to appropriately and ethically use its power as a society. Embracing the potential of AI while protecting against its perils becomes crucial as we navigate the complex environment of technological innovation. The convergence of human brilliance and artificial intelligence is set to shape our destiny in this digital age.
Keywords: Artificial Intelligence, Evolution of AI, Impact of AI, Applications of AI, Human- AI Collaboration, Future Trends in AI
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ChatGPT is a tribute to the extraordinary advancements made in natural language processing in the dynamic field of artificial intelligence. ChatGPT, created by OpenAI, leverages the GPT-3.5 architecture’s capability while breaking down barriers and redefining how humans engage with technology. As we explore ChatGPT’s capabilities, it becomes clear that its influence goes beyond simple communication.
The Birth of
ChatGPT:
ChatGPT is the result of OpenAI’s unwavering quest to improve language models. The GPT (Generative Pre-trained Transformer) architecture is at its foundation and represents a substantial advancement in natural language generation and comprehension. ChatGPT has been trained on various datasets and has learned different linguistic nuances, allowing it to reply contextually and produce human-like text.
Conversational
Mastery:
At its core, ChatGPT is a master of conversation. It has risen to the top of applications like customer care and virtual assistance due to its meaningful and contextually appropriate communication capacity. Businesses use ChatGPT to speed up client interactions, give quick and precise answers, and improve user happiness. By linking the road between humans and machines, ChatGPT-powered virtual assistants enable fluid and natural interactions.
Extraordinary Writing
Companion:
Content producers, rejoice! In the field of writing, ChatGPT has become a crucial ally. ChatGPT contributes its unique flair to the writing process, whether for articles, blog posts, or marketing copy. Beyond simple grammar checks, the writing helper can actively participate in ideation, suggest changes, and stimulate the creative process. ChatGPT also offers comfort to programmers because it generates code snippets from descriptions in plain language, speeding up the coding process.
Educational
Empowerment:
With the introduction of ChatGPT, the educational landscape underwent a paradigm shift. No longer do students battle alone with homework problems or complex subjects. As a substitute for a real tutor, ChatGPT provides specialised guidance and explanations. The concept offers a dynamic platform for learners to practice conversational skills, receive immediate feedback, and improve their linguistic ability. This dynamic platform has excellent promise for language learning.
Developing Creativity: ChatGPT is a precursor to creativity, not limited to practical problems. ChatGPT turns into a partner in ideation for individuals struggling with the elusive spark of inspiration. ChatGPT injects a fresh stream of inspiration, working as a catalyst for invention, whether you’re a writer looking for new plot twists or an artist wishing for a fresh visual perspective.
Facilitating
Information Retrieval and Research:
ChatGPT is a valuable ally for academic and research settings. Due to its skill in information summarisation, sorting through big datasets is now a simple task. Researchers and students can use ChatGPT’s capacity to condense complicated information into summaries to accelerate the process of knowledge acquisition. The competence to quickly and precisely respond to user inquiries through the question-answering feature furthers the research procedure.
Ethical Considerations
and Challenges:
As we look at ChatGPT’s powers, discussing the ethical issues and problems associated with its use is essential. After being trained on various datasets, the model replicates the biases found in the data it learns from. Researchers and developers struggle to eliminate biases to guarantee equitable and fair interactions. Worries about the potential abuse of AI-generated content and the demand for strong security measures further highlight the ethical issues surrounding ChatGPT’s widespread use.
Future
Perspectives:
ChatGPT’s journey is far from over; it is only getting started. ChatGPT also develops along with technology. In subsequent iterations, contextual comprehension, problem-solving skills, and understanding of user intent can be improved. AI-human collaborations are anticipated to become more intense, establishing a relationship where the strengths of both parties are combined for optimal effectiveness and innovation.
Conclusion:
As a transformational force reshaping how we navigate the digital world, ChatGPT appears in the great tapestry of artificial intelligence as more than just a conversational agent. Its applications, from improving consumer interactions to encouraging creativity and advancing education, demonstrate its adaptability. But with great power comes great responsibility, and ChatGPT’s ethical issues serve as a timely reminder of the need for caution when using it. ChatGPT serves as a lighthouse, exposing the limitless possibilities that lie ahead, waiting to be explored and used for the benefit of humanity as we travel the vast landscape of AI.
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