Build and Transform Fintech Applications using GPT

Explore cash flow fundamentals, including inflows, outflows, the formula for net cash flow NCF = TCI - DCO, and reading the cash flow statement for liquidity.

Explore types of cash flow, including operating, investing, and financing flows; learn how CFO, CFI, and financing activities reveal a company's liquidity, investment, and capital structure.

Analyze walmart's cash flow statement, covering operating, investing, and financing activities, and explain how a positive cash flow resulted in a $1.09 billion increase in cash.

Explore how cash flow drives both operations and financial analysis. Apply metrics such as NPV, DCF, IRR, liquidity, cash flow yield, CF ratio, cash conversion ratio, and capital expenditures.

The statement of cash flows reveals how cash from operating activities compares to net income to assess earnings quality, and shows losses funded by debt or equity for investors.

Explore Flask essentials from installation and quick start to templates, Jinja context, testing with pytest, app factories, test client, and CLI commands for building fintech web apps.

Set up Firebase for fintech apps by configuring the Firebase console, enabling analytics, authentication, Cloud Firestore, storage hosting, and obtaining credentials for project integration.

Develop a Flask backend for authentication and user roles, creating signup and login endpoints with role-based access for admin, accountant, and viewer, using JWT token management.

Initialize a Flask app with bcrypt, Firebase, and JWT, configure roles, and implement a sign-up route that processes JSON input for email, password, and a default viewer role.

Register a user by creating an account with email and password, store the user data in the database under users, assign a role, and return a success or error JSON.

Authenticate users with email and password, fetch their role from the database, and issue a jwt access token containing the user id and role for admin routes.

learn to implement protected routes for admin and accountant roles using JWT authentication, returning access denied when a user lacks the required role and welcoming authorized admins and accountants.

Learn to test APIs in Google Colab using Postman or Python requests, perform sign-up with json data (email, password, role admin), post to 127.0.0.1:5000, and print the response json.

Demonstrates testing a login flow by posting credentials to a local login URL and printing the JSON response, using email admin@example.com and password admin 123.

Learn to test the admin route by using a jwt token in the authorization header and sending a get request to localhost:5000/admin, then print the response as json.

Implement a Flask API for income tracking by building endpoints to add, fetch all, get by category, update, and delete income, and continue the app in the next lesson.

Create the flask app by configuring flask, request and json handling with firebase and jwt extended, enabling access token creation and jwt identity retrieval, plus date and time utilities.

Initialize a flask app, set up firebase and JWT secret key, configure app with JWT manager, and define income categories such as salary, sales, investments, other, and ad income.

Extend income categories by creating new income records via the add income API route, validating JWT identity and parsing amount, category, source, date, and recurring fields.

Validate income category and return errors for invalid categories. Create and push a new income record with amount, source, date, and name, then return the income id or an error.

Fetch all income records for a user by validating jwt and retrieving the user id, returning the income list or a no income records found message.

Fetch incomes by category via the get income by category route, validate the category, and return filtered incomes or errors like invalid income category or no income records.

Learn how to jsonify filtered income data, handle messages and no-record scenarios, and return a json file or error 400 in fintech applications.

Update income records in fintech applications through a secured app route using JWT to identify the user. Update the data at the path db.child('income').child(userId).child(incomeId).update(data).

Update income in a fintech app manages income records, validates user identity with JWT, and returns 400 errors or success messages such as income updated successfully or income deleted successfully.

Test add income by posting to a local API with jwt bearer token, sending amount 1500, category salary, source company, and recurrence, using google colab, postman, or python requests.

Test get all income and test get income by category using the local http://127.0.0.1:5000 endpoints, print the response.json to verify the results.

Test income APIs by hitting the get income endpoint, updating income by id with amount 2000 via a put request, and deleting income by id, and inspect the response JSON.

Add and categorize expenses like rent, utilities, payroll, marketing, and miscellaneous, with recurring options, reminders, and payment tracking; fetch transactions, filter by date or category, and update or delete records.

Build a Flask API for expense tracking within fintech applications, implementing core operations to add, fetch all, fetch by category, update, and delete expenses.

Create a Flask app by importing Flask, request, and Firebase, and initialize Firebase with a config to connect the database. Configure the JWT manager for authentication and handle request JSON.

Implement a Flask add expense endpoint secured with JWT authentication, validate expense categories, and capture amount, category, description, date, recurring, and due date for fintech expenses.

Learn how to add expenses in a fintech app by validating categories, generating an expense id in a real-time database, and returning a JSON success message with error handling.

Fetch all expenses by user id using JWT identity. Return a json with the expenses or a no expenses message, and 400 on error.

Fetches expenses by category by extracting user id from JWT, normalizing category to lowercase, validating against allowed categories, and retrieving expenses from the database.

Filter expenses by expense id and return a json response, handling no records for this category and exceptions as E. Update expense records as part of the workflow.

Update an expense record via the app route using a put method, validate jwt identity, and update the expense in the database, returning success or error 400.

Delete an expense record via the app route using expense id and jwt-derived user id, remove it from the expenses database, and return a success or error message.

Test add expense APIs using Google Colab, Postman, or Python requests; send data with JWT bearer token and headers for amount, category rent, description apartment rent, date, and due date.

Test get all expenses and test get expenses by category against the local API, using the given URL, and print the response JSON from the headers.

Test update expense and test delete expense endpoints by sending requests with an expense ID, amount, and headers, and verify the JSON responses.

Master cash flow forecasting by fetching historical transactions from Firebase, visualizing cash flow with pandas and seaborn, forecasting future cash flow, and applying AI-based trends and scenario analysis for budgeting.

Fetch historical transactions and configure Firebase, while importing pandas, numpy, matplotlib.pyplot, seaborn, and statsmodels for Holt winters exponential smoothing with DateTime utilities.

Learn to initialize Firebase, connect to the Firebase database, and implement get_transactions to fetch historical transactions for a user and assemble them into a dataframe with date, amount, and type.

Create a pandas data frame using pd.DataFrame, assign data, sort by date and place, and return the results.

Visualize historical cash flow by plotting date on the x-axis and amount on the y-axis with a line plot, including axis labels, a title, an income legend, and a grid.

Forecasting future cash flow uses exponential smoothing to predict 30 days of net cash flow. Forecasts include seasonal periods of seven in the fitted model.

Explore scenario analysis in fintech applications by generating best case, worst case, and average case scenarios from the forecasted cash flow, and plot them with labeled legends for clear visualization.

Explore AI-based trends and recommendations by applying machine learning and linear regression to detect financial patterns, analyze cash flow, and assess trend strength in fintech data.

Implement a Flask API with JWT authentication to fetch user-specific forecast cash flow data, return it as dict oriented to records, and show a no data available message if empty.

Explore retrieval qa to ask questions against a custom knowledge base; the retriever finds relevant chunks, and the llm answers using context, with embedding-based queries and a q chain run.

Learn to use generative AI utilities for knowledge base summarization and quiz generation with GPT, including practical journal summarization and discussion of LLMs, embeddings, and vector data.

Generate a five-question, four-option self-study quiz from the knowledge base using a GPT prompt, include correct answers, simple wording, and an answer key.