CF

Vibe Code a Generative AI Finance App with Python and LangChain

7h 36m 39s
English
Paid

Vibe Code a Generative AI Finance App with Python and LangChain is a 66-lesson 7 hours 36 minutes self-paced course by Zero To Mastery. Learn how AI and finance work together as you build a real app with Python and LangChain .

Course facts

Lessons
66
Duration
7 hours 36 minutes
Level
All levels
Language
English
Updated
Instructor
Zero To Mastery
Price
Premium

Learn how AI and finance work together as you build a real app with Python and LangChain. You follow clear steps and see how each part fits into a full workflow.

What You Build

You start with setup and data work. You load data, clean it, and shape it for your app. You study key financial metrics and KPIs that guide solid investment choices.

You then move into core finance ideas. You code the Modern Portfolio Theory and the Black–Litterman model. You turn formulas into clear Python code you can use in real projects.

How You Learn

You write code, run tests, and see results right away. You learn why each model works and where it fails. You also see how AI can help you improve these models.

What You Gain

By the end, you have a working finance app. You know how to talk about AI, finance, and Python with ease. This course fits both newcomers to fintech and developers who want to build smarter finance tools.

Who teaches Vibe Code a Generative AI Finance App with Python and LangChain? Zero To Mastery

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Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

What lessons are included in Vibe Code a Generative AI Finance App with Python and LangChain?

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#1: Introduction - Let's Build!
All Course Lessons (66)
#Lesson TitleDurationAccess
1
Introduction - Let's Build! Demo
06:58
2
Game Plan for Setup and Gains
04:18
3
Setup
06:06
4
Fetch FX Rates
06:41
5
Cache FX Rates
03:42
6
Download Financial Data
06:09
7
Computing Gains and Losses
07:08
8
Computing Totals
07:42
9
Updates to Current Portfolio
12:54
10
Additions to Portfolio
09:31
11
Exporting the CSV File
05:25
12
Download and Setup Cursor
04:36
13
Virtual Environment and Dependencies
05:18
14
Formatting the App
10:30
15
Running the App Locally
04:52
16
Setting Up Tab 1
05:28
17
Finalizing Tab 1
11:30
18
Setting Up Asset Updates
23:47
19
Adding New Assets Setup
05:16
20
Adding New Assets Wrap Up
09:39
21
Debugging Tab 1
07:21
22
Debugging Tab 2
07:56
23
Exporting Data
13:20
24
Testing Tab 2
01:32
25
Introduction to Finance
07:54
26
Top Fundamental KPIs for Stock Analysis
08:03
27
Top Trading KPIs for Stock Analysis
08:41
28
Get Historical Prices
03:40
29
Compute Moving Averages
06:43
30
Plot Moving Averages
04:03
31
Volatility
06:53
32
Plotting Volatility
07:17
33
PE Ratio
05:41
34
Plotting PE Ratios
02:36
35
Beta
04:23
36
Sharpe Ratio
07:34
37
RSI
06:16
38
Plotting RSI
02:54
39
MACD Crossover
05:23
40
Plotting MACD Crossover
03:00
41
Setting Up the New Script
06:17
42
Moving Averages
11:22
43
Improving the App Design
05:24
44
Volatility
06:52
45
PE Ratio and Betas
07:59
46
Sharpe Ratio, RSI and MACD
05:55
47
Testing and Debugging
10:02
48
Adding Key Points to KPIs
03:03
49
Game Plan for GenAI Recommendations
03:52
50
OpenAI and Langchain Setup
07:05
51
What is LangChain?
05:32
52
System Recommendations
03:58
53
Combine KPIs
08:46
54
AI Recommendations
06:06
55
Setup
04:01
56
.env and API Key
13:05
57
Finalizing the AI Recommendations
07:25
58
Game Plan for Improving the Portfolio and Deployment
03:07
59
Calculating New KPIs
06:55
60
AI Recommendations with New Instruments
05:02
61
Setup
04:56
62
Finalizing Tab 6
11:28
63
Versioning the Libraries
03:35
64
Github
06:52
65
Deployment
07:10
66
From Diogo: How I Invest
12:10
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Frequently asked questions

What are the prerequisites for enrolling in this course?
The course is designed for both newcomers to fintech and developers who want to build smarter finance tools. A basic understanding of Python will be beneficial as you will be coding key financial models and using libraries like LangChain. Familiarity with financial concepts could help but is not mandatory, as the course covers fundamental KPIs and models such as the Modern Portfolio Theory and Black–Litterman model.
What will I build during the course?
You will build a generative AI finance app using Python and LangChain. The project involves setting up the environment, fetching and processing financial data, and implementing financial models like the Modern Portfolio Theory and Black–Litterman model. You will also integrate AI to make system recommendations based on financial KPIs. By the end of the course, you will have a functioning finance app capable of providing investment insights.
What specific tools and platforms are used in this course?
The course utilizes Python for coding the financial models and LangChain for AI integration. You will work with financial datasets, fetching FX rates, and implementing KPIs for stock analysis. The course also guides you through setting up a virtual environment and dependencies necessary for running the app locally, as well as using OpenAI for AI recommendations.
How does the depth and scope of this course compare to other finance app development courses?
This course offers a unique combination of AI and finance by focusing on both the implementation of core financial models and the integration of AI to enhance these models. It goes beyond standard app development by teaching you to calculate and combine financial KPIs with AI-driven recommendations, providing a comprehensive understanding of how AI can optimize financial decision-making.
How much time should I expect to commit to this course?
The course consists of 66 lessons. While the total runtime is not specified, you should be prepared to spend time coding, testing, and debugging, as well as understanding financial concepts and AI integration. The hands-on approach of the course means you will actively write code and run tests, which requires a time commitment for both learning and practice.
What important topics are not covered in this course?
While the course covers foundational financial models and AI integration, it does not delve into advanced machine learning techniques or deep learning algorithms. The focus is on practical applications of AI in finance rather than theoretical exploration of AI technologies. Additionally, deployment of the app to production environments is only briefly mentioned, with no extended focus on cloud services.
How can the skills learned in this course be applied to other courses or careers?
The skills acquired in this course, such as coding in Python, understanding financial KPIs, and integrating AI with LangChain, are highly transferable. They can be applied to other fintech projects or courses that involve financial data analysis and AI applications. The ability to implement and improve financial models with AI is valuable for careers in finance, data science, and AI development.