Algo trading: Financial foundations
Apply Algo trading through value, risk, markets.
Advanced / Structured learning draft / Version 0.9
Structured learning draftAI & Algorithmic Trading
Build a clearer, auditable financial workflow. You will finish with a fictional financial model with assumptions and checks.
This open learning manuscript is undergoing course-specific editorial review. It does not provide certification. Progress and notes are stored only in this browser.
Finance & fintechTool stack
PythonTensorFlow
12guided lessons
4focused modules
1reviewable project
PDFoffline workbook
The problem
What this course helps you do
AI & Algorithmic Trading is a structured, practical course covering Algo trading, ML, Backtesting, QuantLib, Signals. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
Who it is for
Learners studying financial technology and analytical methods.
Prerequisites
- Basic percentages and spreadsheet skills
Portfolio-ready project
Produce a reviewable AI & Algorithmic Trading project using Algo trading, ML, Backtesting.
Deliverable: A working AI & Algorithmic Trading artefact plus an evidence-based self-review.
Practice fileCash-flow practice fileFictional receipts and expenses with dates and KES amounts.Planning template · MarkdownProject briefDefine the user, outcome, boundaries, evidence and acceptance criteria before building.Printable workbook · PDFComplete course workbookA4-ready lesson prompts, project worksheets, evidence logs and references.
Downloads open without an account. Practice data is fictional; review each file before using it in another system.
Definition of done
- The output matches the stated outcome.
- Inputs and decisions are reproducible.
- Boundary and failure evidence is included.
- Limitations and responsibility considerations are explicit.
Course plan
From first concept to finished work
ML: Tools and data
Apply ML through records, analysis, controls.
Backtesting: Applied decisions
Apply Backtesting through scenarios, evaluation, reporting.
QuantLib: Risk and governance
Apply QuantLib through security, regulation, review.
Source-led learning
Authoritative references
Course claims are grounded in primary documentation and recognized public guidance. Recheck version-sensitive information before professional use.
- National Payments SystemCentral Bank of Kenya
- Financial educationOECD