Python financial market analysis

Saved searches

Use saved searches to filter your results more quickly

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.

Exploratory analysis, visualization of stock market data along with predictions made on it using different techniques.

Ztrimus/Stock-Market-Analysis

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Sign In Required

Please sign in to use Codespaces.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching Xcode

If nothing happens, download Xcode and try again.

Launching Visual Studio Code

Your codespace will open once ready.

There was a problem preparing your codespace, please try again.

Latest commit

Git stats

Files

Failed to load latest commit information.

README.md

Investment Bankers, CA’s, Hedge Fund / Portfolio Managers, Forex traders, Commodities Analysts.
These have been historically considered to be among the most coveted professions of all time. Yet, if one fails to keep up with the demands of the day, one would find one’s skills to be obsolete in this era of data analysis. Data Science has inarguably been the hottest domain of the decade, asserting its need in every single sphere of corporate life. It was not long ago when we discovered the massive potential of incorporating ML/AI in the financial world. Now, the very idea of the two being disjointed sounds strange. Data Science has been incremental in providing powerful insights ( which people didn’t even know existed ) and helped massively increase efficiency, helping everyone from a scalp trader to a long term debt investor. Accurate predictions, unbiased analysis, powerful tools that run through millions of rows of data in the blink of an eye have transformed the industry in ways we could’ve never imagined.

1. Data Analysis using Pandas

2. Data Visualization and Technical Analysis

3. Fundamental analysis using Regression

4. Trade Call Prediction using Classification

5. Modern Portfolio Theory

6. Clustering for Diversification analysis

If you are new to Python,This YouTube series is best for you.

• Jupyter notebook is a very popular and flexible development environment which lets us write and execute python code, display the output and any kind of visualization or plot, etc. in the same document. however, it is always advisable to install Python with Anaconda environment on your system as well.
• Link for Introduction to Jupyter notebook:
  • Quick Beginner Guide
  • Introduction to Jupyter notebook
  • Jupyter Notebook Users Manual
  • Markdown for Jupyter notebooks cheatsheet
  • In the coming modules, we will be doing various tasks to analyze and make predictions on allotted selected company stock. You may need to learn about the underlying markets.
    • Basics of Financial Markets:
      The very first step in solving a problem is understanding the problem. To tackle the questions and problem statements that await us, it’s suggested that you first go through above link (it’s a PDF «Basics of Financial Markets» documentation) and get introduced to the basic concepts that we’d coming across in the following modules to come.
    • Introduction to Stock Markets:
      Now read and understand Chapter 6,7 and 8 from this link.
    • The Stock Market Documentary:
      If you Don’t know what is stock markets, how it works and want to know about it for fun. Then check out this documentary.

    Steps to Follow Before Start:

    1. Select an Industry: Go to the All Stock Data directory.
    2. In this directory, you’ll be able to browse through the different industries. You can choose an industry which is related to your discipline or any industry of your liking.
    3. On doing so, download a CSV file of stock data of a company belonging to the industry you chose. I have chosen Infosys Stock from Large-cap.
    4. To know more about this large-cap, mid-cap and small-cap data go to Readme_all_stock_data.md file.

    . ├── . ├── Module # Each Module/Topic/Chapter │ ├── Questions # Queries/Questions related to chapter | | ├── .ipynb # Questions Jupyter notebook │ ├── Solutions # Solved Queries - Answers to all Questions | | ├── .ipynb # Questions Jupyter notebook | | ├── .csv # CSV file / Dataset for stocks | ├── images # Images used in Jupyter notebook, Just for seek of representation │ └── Resource Matrial # Different reference matrial(txt, doc, pdf) └── . 

    Saurabh Zinjad

    All other known bugs and fixes can be sent to «zinjadsaurabh1997@gmail.com» with the subject «stock market analysis Suggestion». Reported bugs/fixes will be submitted to correction.

    About

    Exploratory analysis, visualization of stock market data along with predictions made on it using different techniques.

    Источник

    Python for finance: automated analysis of the financial markets

    A highly automated method to analyse the performance of the financial markets by business sector.

    The Python script presented in this article has been used to analyse the impact of COVID-19 on the various business sectors of the S&P 500 index, but can also be easily adapted to any other analysis of financial markets.

    The code has been developed in Python programming language, and can be found in my GitHub repository (link below) as a Jupyter notebook.

    r-poli/FinancialMarketAnalysis

    This Jupyter notebook enables the user to perform an automated stock market analysis for the sector or sub-sector of…

    The script makes use of standard Python packages (i.e. pandas, bokeh, math) as well as the yfinance API (Application Programming Interface), that is used to download the S&P 500 stock prices. The API is free to use and it is public, meaning that the user does not need an individual API key.

    # Import libraries
    import yfinance as yf
    import pandas as pd
    from bokeh.plotting import figure
    import bokeh.models as bmo
    from bokeh.palettes import Paired11
    from bokeh.io import show
    from bokeh.models import ColumnDataSource, HoverTool
    import math

    In the first section of the script, the user needs to define the variable called “depth” to defines the level of detail of the analysis. This can either be set to “sector” or “sub_sector”. Choosing “sector” will produce a plot such as the one in Figure 1, whereas “sub_sector” will produce a plots similar to the ones in Figure 2 and Figure 3. In this last case, the user has to also specify the “filter”, that is the sector of interest. The available values of filter are: Communication Services, Consumer Discretionary, Consumer Staples, Energy, Financials, Health Care, Industrials, Information Technology, Materials, Real Estate and Utilities.

    # Example of input definition
    depth = 'sub_sector'
    filter = 'Information Technology'

    All the other inputs such as the list of S&P 500 stocks, and the date to compare current market performance against (beginning of 2020) are automatically set.

    index_name = 'SP_500'
    companies = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies', flavor='bs4')[0]

    Источник

    Saved searches

    Use saved searches to filter your results more quickly

    You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.

    financial-analysis

    Here are 234 public repositories matching this topic.

    fmilthaler / FinQuant

    A program for financial portfolio management, analysis and optimisation.

    constverum / Quantdom

    Python-based framework for backtesting trading strategies & analyzing financial markets [GUI ]

    amphibian-dev / toad

    ESC Team’s credit scorecard tools.

    timkpaine / pyEX

    Python interface to IEX and IEX cloud APIs

    twelvedata / twelvedata-python

    Twelve Data Python Client — Financial data API & WebSocket

    kristpapadopoulos / seriesnet

    Time series prediction using dilated causal convolutional neural nets (temporal CNN)

    gauss314 / Bursatil-Argentina-Python

    Guia de ejemplos didácticos en python temática finanzas bolsa trading argentina usa

    alpacahq / pipeline-live

    Pipeline Extension for Live Trading

    pikers / piker

    e2e FOSS trading for non-tinas

    awslabs / sagemaker-explaining-credit-decisions

    Amazon SageMaker Solution for explaining credit decisions.

    whoiskatrin / financial-statement-pdf-extractor

    Python script to extract as much structured information as possible from annual/quarterly reports.

    bukosabino / streamlit-demo-financial-eda

    Automated Exploration Data Analysis on a financial dataset

    knipknap / stocklist

    Stock data collection and analysis

    DavidCico / Enhanced-Event-Driven-Backtester

    In this repository, an event-driven backtester is implemented based on QuantStart articles. The backtester is programmed in Python featuring numerous improvements, in terms of coding structure, data handling, and simple trading strategies.

    Aman22sharma / JPMorgan-Chase-Virtual-Internship

    JPMorgan Chase & Co. Software Engineering Virtual Internship tasks [ patch files submission ] solutions

    kyhorne / trading_sim

    📈 📆 Backtest trading strategies concurrently using historical chart data from various financial exchanges.

    ymyke / tessa

    tessa – simple, hassle-free access to price information of financial assets 📉 🤓 📈

    kafkasl / hpc.fassr

    High-Performance Computing. Fundamental Analysis Stock Screener and Ranker

    Erfaniaa / financial-indexes-correlation

    Analyze financial data correlations

    EasyAI / Python-Charting-Indicators

    This repository contains a python file with is used to calculate charting indicators.

    Improve this page

    Add a description, image, and links to the financial-analysis topic page so that developers can more easily learn about it.

    Add this topic to your repo

    To associate your repository with the financial-analysis topic, visit your repo’s landing page and select «manage topics.»

    Источник