CS50 pset9: Using flask API to create a web application to exchange stocks' shares.

Overview

C$50 Finance

In this guide we want to implement a website via which users can “register”, “login” “buy” and “sell” stocks, like below:

Picture of dashboard

Background

If you’re not quite sure what it means to buy and sell stocks (i.e., shares of a company), head here for a tutorial.

We’re about to implement C$50 Finance, a web app via which you can manage portfolios of stocks. Not only will this tool allow us to check real stocks’ actual prices and portfolios’ values, it will also let you buy and sell stocks by querying IEX for stocks’ prices.

Indeed, IEX lets you download stock quotes via their API (application programming interface) using URLs like https://cloud.iexapis.com/stable/stock/nflx/quote?token=API_KEY.

Before getting started on this project, we’ll need to register for an API key in order to be able to query IEX’s data. To do so, follow these steps:

  • Visit iexcloud.io/cloud-login#/register/.
  • Select the “Individual” account type, then enter your email address and a password, and click “Create account”.
  • Once registered, scroll down to “Get started for free” and click “Select Start” to choose the free plan.
  • Once you’ve confirmed your account via a confirmation email, visit (https://iexcloud.io/console/tokens).
  • Copy the key that appears under the Token column (it should begin with pk_).
  • In a terminal window execute:
export API_KEY=value

where value is that (pasted) value, without any space immediately before or after the =. You also may wish to paste that value in a text document somewhere, in case you need it again later.

Install requirements

This guide wrote for Windows Terminal and if you have another OS you may change it.

Before we start, you should clone this GitHub repo and then install the dependencies.

git clone https://github.com/magnooj/CS50-finance.git
cd CS50-fincance
pip install -r requirements.txt

Through the files

Now, we are ready to run and test our project. By running ls you can see these files:

Flask API

The first step in building APIs is to think about the data we want to handle, how we want to handle it and what output we want with our APIs. In our example, we want users can register, log in, log out and buy, sell and qout stocks; Finally, see the history of their transactions.

The main HTML file in our app is layout.html. We created a template that other HTML files cand extend that.

In this example, we create Flask eight routs so that we can serve HTTP traffic on that route.

  • / or index : Is the homepage of our app. If user loged in, it display the user’s current cash balance along with a grand total (i.e., stocks’ total value plus cash). But, if user didn.t log in, it displays the login page.
  • register : It has a form that user can register by filling it.
  • buy : In this route, users can input a stock’s symbol and buy some shares.
  • sell : In this page, users can SELECT from theis stocks’ symbol and sell their shares.
  • qoute : Users can lookup the price each share in a stock’s symbol.
  • history : It displays an HTML table summarizing all of a user’s transactions ever, listing row by row each and every buy and every sell.
  • login and logout : These routes start and terminate user’s session.

Of course there is some files like apology.html that displays the error to the user. You can check other files.

Now, We cheked our files and sqw how our app is working. To run the app, when you are in CS50-finance directory, enter this command in the terminal:

flask run

I hope you enjoyed how to stocks' exchange web application using flask. if you have any comments please do not hesitate to send me an e-mail.

Regards,

Ali Ganjizadeh

Extract data from a wide range of Internet sources into a pandas DataFrame.

pandas-datareader Up to date remote data access for pandas, works for multiple versions of pandas. Installation Install using pip pip install pandas-d

Python for Data 2.5k Jan 09, 2023
A utility for functional piping in Python that allows you to access any function in any scope as a partial.

WithPartial Introduction WithPartial is a simple utility for functional piping in Python. The package exposes a context manager (used with with) calle

Michael Milton 1 Oct 26, 2021
Titanic data analysis for python

Titanic-data-analysis This Repo is an analysis on Titanic_mod.csv This csv file contains some assumed data of the Titanic ship after sinking This full

Hardik Bhanot 1 Dec 26, 2021
INF42 - Topological Data Analysis

TDA INF421(Conception et analyse d'algorithmes) Projet : Topological Data Analysis SphereMin Etant donné un nuage des points, ce programme contient de

2 Jan 07, 2022
A data analysis using python and pandas to showcase trends in school performance.

A data analysis using python and pandas to showcase trends in school performance. A data analysis to showcase trends in school performance using Panda

Jimmy Faccioli 0 Sep 07, 2021
Random dataframe and database table generator

Random database/dataframe generator Authored and maintained by Dr. Tirthajyoti Sarkar, Fremont, USA Introduction Often, beginners in SQL or data scien

Tirthajyoti Sarkar 249 Jan 08, 2023
PyPDC is a Python package for calculating asymptotic Partial Directed Coherence estimations for brain connectivity analysis.

Python asymptotic Partial Directed Coherence and Directed Coherence estimation package for brain connectivity analysis. Free software: MIT license Doc

Heitor Baldo 3 Nov 26, 2022
A neural-based binary analysis tool

A neural-based binary analysis tool Introduction This directory contains the demo of a neural-based binary analysis tool. We test the framework using

Facebook Research 208 Dec 22, 2022
Deep universal probabilistic programming with Python and PyTorch

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

7.7k Dec 30, 2022
Anomaly Detection with R

AnomalyDetection R package AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the pre

Twitter 3.5k Dec 27, 2022
Clean and reusable data-sciency notebooks.

KPACUBO KPACUBO is a set Jupyter notebooks focused on the best practices in both software development and data science, namely, code reuse, explicit d

Matvey Morozov 1 Jan 28, 2022
Hidden Markov Models in Python, with scikit-learn like API

hmmlearn hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models. For supervised learning learning of HMMs and

2.7k Jan 03, 2023
MIR Cheatsheet - Survival Guidebook for MIR Researchers in the Lab

MIR Cheatsheet - Survival Guidebook for MIR Researchers in the Lab

SeungHeonDoh 3 Jul 02, 2022
Statsmodels: statistical modeling and econometrics in Python

About statsmodels statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics an

statsmodels 8k Dec 29, 2022
Instant search for and access to many datasets in Pyspark.

SparkDataset Provides instant access to many datasets right from Pyspark (in Spark DataFrame structure). Drop a star if you like the project. 😃 Motiv

Souvik Pratiher 31 Dec 16, 2022
PyIOmica (pyiomica) is a Python package for omics analyses.

PyIOmica (pyiomica) This repository contains PyIOmica, a Python package that provides bioinformatics utilities for analyzing (dynamic) omics datasets.

G. Mias Lab 13 Jun 29, 2022
Vaex library for Big Data Analytics of an Airline dataset

Vaex-Big-Data-Analytics-for-Airline-data A Python notebook (ipynb) created in Jupyter Notebook, which utilizes the Vaex library for Big Data Analytics

Nikolas Petrou 1 Feb 13, 2022
Conduits - A Declarative Pipelining Tool For Pandas

Conduits - A Declarative Pipelining Tool For Pandas Traditional tools for declaring pipelines in Python suck. They are mostly imperative, and can some

Kale Miller 7 Nov 21, 2021
vartests is a Python library to perform some statistic tests to evaluate Value at Risk (VaR) Models

gg I wasn't satisfied with any of the other available Gemini clients, so I wrote my own. Requires Python 3.9 (maybe older, I haven't checked) and opti

RAFAEL RODRIGUES 5 Jan 03, 2023
Parses data out of your Google Takeout (History, Activity, Youtube, Locations, etc...)

google_takeout_parser parses both the Historical HTML and new JSON format for Google Takeouts caches individual takeout results behind cachew merge mu

Sean Breckenridge 27 Dec 28, 2022