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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Competition Name: The Nature Conservancy Fisheries Monitoring
Method Name:
Pre-processing: Median Filtering, Histogram Equalization
Segmentation: K-Means Clustering
Feature Extraction: Oriented FAST and Rotated BRIEF (ORB)
Classifier:K-Nearest Neighbour
Dataset Description:
The dataset compiled by The Nature Conservancy in partnership with Satlink, Archipelago Marine Research, the Pacific Community, the Solomon Islands Ministry of Fisheries and Marine Resources, the Australia Fisheries Management Authority, and the governments of New Caledonia and Palau.
The Train and Test dataset consists of images having only one fish category out of eight different categories mentioned as follows:
1. Albacore tuna
2. Bigeye tuna
3. Yellowfin tuna
4. MahiMahi
5. Opah
6. Sharks
7. Other (i.e. fish present but not the defined categories)
8. No Fish (i.e. no fish is in the image)
Method Used:
Algorithm
Input: Training Set and Test Set of Images
Output: Classification Score
1. Read the input RGB image
2. Perform median filtering for noise removal
3. Perform histogram equalization for image enhancement
4. Perform the K-Means clustering for segmentation of pre-processed image
5. Applying Oriented FAST and Rotated BRIEF (ORB) method for feature extraction
6. Apply K-Nearest Neighbour (KNN) classifier for recognition
The algorithm initiates into reading the input RGB images from the training set and store them into array. A 5X5 window-based 2D convolution operation is applied on the input images for median filtering. In this process, the neighboring pixels are ordered according to their intensity values and the median value becomes the resulting output for the central pixel of the defined window. Median filters can handle efficiently with noise, in particular impulse noise in which some individual pixels have extreme values while preserving the contrast within the images.
Contrast is a significant image element that can be defined as a ratio between the highest and the lowest pixel intensities of an image. As the images usually suffer from poor image quality, degradation in contrast and happening of shading and artefacts, the lack in centering pixel intensity, poor lightening, specimen spotting are important factors that affect the images which leads to enhance the contrast. In our methodology, histogram equalization is applied for image enhancement. Histogram of images provides a pixel-wise intensity distribution and description of the image appearance globally. The equalization process mapping histogram distribution to a wider and more uniform distribution of intensity values, as a result the intensity values are spread over the whole range of intensity distribution of the images.
The pre-processed images are segmented based on K-Means clustering approach for extraction of the region of interest. K-Means is an unsupervised learning process which assigns and groups the image pixels into a predefined number of clusters based on the calculated similarity for each pixel associate with the nearest center pixel. When all image pixels have been assigned, this iterative process continues with the new calculated center pixels. The process initiates the center pixels corresponding to the clusters randomly and keeps reassigning until criterion is met:
The K-Means objective function:
where is a chosen distance measure between a data point and the cluster centre , is an indicator of the distance of the n data points from their respective cluster centres.
After applying segmentation process, the Oriented FAST and Rotated BRIEF (ORB) method is used to extract significant features. This method combines FAST（Feature from Accelerated Segment test) keypoint detector and BRIEF (Binary Robust Independent Elementary Features) descriptor approach. This method initiates with identification of FAST corner keypoints within the image region based on comparison of the intensity threshold value between the center pixel and those in neighborhood around that pixel. Harris corner extracts the first target N number of FAST keypoints in order to employ them for further processing. A scale pyramid process is incorporated in order to extract N number of FAST keypoints at each level in the pyramid. The intensity centroid value is also measured for each corner orientation for achieving the direction of corresponding keypoints. BRIEF extracts descriptors around selected key feature points through binary coding.
Where, p(x) is the pixel intensity at that point x in image region
p(y) is the pixel intensity at that point y in image region
a set of points can uniquely identify one binary detection τ
K-nearest Neighbor algorithm incorporates the extracted key feature points in order to classify the test image data based on the trained information.
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