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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Welcome to the thrilling world of quantitative finance, where numbers dance, algorithms rule, and fortunes are made and lost in the blink of an eye. Whether you’re a math whiz or someone who thinks a derivative is just a fancy word, fear not! We’ve got the ultimate guide to help you conquer the exciting realm of quantitative finance. So, buckle up and get ready for an adventure in numbers!
Build a Math Fortress
Start with the basics: Calculus, Linear Algebra, Probability. Make them your best friends. Construct your Math Fortress strong enough to withstand the financial storms ahead.
2. Finance, Not Just for Bankers
Dive into the financial sea and discover the wonders of asset pricing, portfolio management, and risk management. Understand financial derivatives – they’re not as mysterious as they sound!
3. Quantify Your Dreams with Education
Enroll in a quantitative finance program or a related field to level up your skills. Certifications like CFA or FRM are your treasure maps to the golden realms of finance.
4. Speak the Language of Machines
Learn the language of machines: Python, R, MATLAB. They’re not just for geeks; they’re your financial allies. Get cozy with libraries like NumPy and pandas. They’ll be your companions on this epic journey.
5. Quantum Leap into Models and Strategies
Tinker with models like Black-Scholes and CAPM. It’s like being a financial wizard. Dive into the world of algorithmic trading. It’s like teaching your money to dance.
6. Tech Trends – Your Crystal Ball
Keep an eye on tech trends. Machine learning and AI are the cool kids in this financial playground. Unleash the power of technology in your financial models. It’s not cheating; it’s called being smart.
7. The Social Network – Finance Edition
Network like your financial future depends on it – because it does! Attend conferences, join forums, and connect with the wizards of quantitative finance.
8. The Art of Financial Kung Fu
Develop ninja-level critical thinking skills. Question assumptions like a financial detective. Refine your models and strategies with the precision of a financial blacksmith.
9. Quantum Challenges and Competitions
Participate in challenges. Kaggle is your arena – where brains clash and brilliance shines. Solve real-world problems and emerge victorious. It’s like being a superhero of finance.
10. Learn Like Your Career Depends on It
Embrace lifelong learning. Read, attend workshops, and keep evolving. In the world of quantitative finance, the only constant is change. Stay ahead, and you’ll be the financial superhero of tomorrow! In conclusion, welcome to the exhilarating world of quantitative finance. With your Math Fortress, financial wisdom, and tech-savvy sidekick, you’re ready for anything this dynamic field throws your way. Get out there and conquer the financial universe – it’s waiting for your quantitative genius!
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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.
Keyword: ChatGPT, Artificial Intelligence, Content Creation, Creativity, AI Human Collaboration, Education, Tutoring, Ethical Considerations
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