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 1 Exam 1(Module 2) 07 August 2025 21 August 2025

Level 1 Exam 2 (Module 3) 11 September 2025 25 September 2025

Level 2 Exam 3(Module4/partially5) 07 October 2025 21October 2025

Level 2 Final Project 25 November 2025 20 January 2026

CQF Final Projects January 25 Cohort solutions guide is ready for instant delivery to your mailbox instantly after funds transfer and submission deadline is 21st August 2025 without extension. Interested Delegates looking for Final Projects January 25 Cohort Guidance or Assistance should contact us immediately for booking. First In First out basis. June 25 Cohort batch will start from 26th June 2025. Guaranteed success with our guidance. Discount option is available delegates booked all at a time. we accept payments via Paypal for overseas delegates, google pay or phone pay for Indian delegates. Free consultations or guidance available for booking. Reva University MBA tutors. Reva University BBA tutor s. Reva University MBA Final Year projects. Reva University BBA Hons Final Year Projects. Reva University Ranadeb. mba ranadeb. bba ranadeb. MBA Admission. BBA Admission.

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.

Other final Projects can also book well in advance. first in first out basis. No Bookings for final Projects January 25 Cohort after 25th July 25. CQF Exam June 25 Cohort starts from 26th June 25 onwards. Book Online today for CQF Exam solutions guide 1-3 and Final Projects solutions guide. Buy online cqf distinction exam solutions guide all past cohort at a reasonable prices. Distinction online test is on 6th September 2025 should contact us immediately for our guidance. 100% success rate with our guidance. Pls do contact us immediately via Whats App +91 8697669523.

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1. Credit Spread for a Basket Product (CR)
2. Deep Learning for Financial Time Series (DL)
3. Pairs Trading Strategy Design & Back test (TS)
4. Portfolio Construction using Black-Litterman Model and Factors (PC)
5. Optimal Hedging with Advanced Greeks (DH)
6. Blending Ensemble for Classification (ML)
7. Algorithmic Trading for Reversion and Trend-Following (AL)
8. Deep Neural Networks for Solving High Dimensional PDEs (DN)

Special Note : We are not Authorized by CQF nor Endorsed by CQF nor any official support of CQF. #findtutoronline.net is a separate legal entity having no connection with CQF at all. Regular 5000 visitors worldwide visiting our websites worldwide.

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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.

1. Credit Spread for a Basket Product (CR)
2. Deep Learning for Asset Prediction (DL)
3. Pairs Trading Strategy Design & Back test (TS)
4. Portfolio Construction using Black-Litterman Model and Factors (PC)
5. Optimal Hedging with Advanced Data Modelling (DH)
6. Blending Ensemble for Classification (ML)
7. Algorithmic Trading for Reversion and Trend-Following (AL)
8. 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 conclusion discussing 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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