A system for visualizing interesting stocks. Has powerful comparison capabilities and works seamlessly with your jupyter notebook. Written in QT with matplotlib.

This repository contains my portfolio of investment and finance-related data science projects. These projects are written and presented as Jupyter Notebooks (Python 3), and each link is followed by a short description of project goals, contributions, and dependencies used.

Predict tomorrow's S&P 500 index price using historical data. The data analysis has been preformed mainly using Jupyter Notebook and Python Machine Learning library of sklearn -RandomForestClassifier

In this notebook, I investigate how the various sectors performed on the stock market in 2022 by creating a market cap-weighted index of each sector (according to the GCIS), and plotting its returns over the year.

Analyze historical market data using Jupyter Notebooks

Python-based data cleaning & analysis project for BPCL stock prices from data.nasdaq.com. Utilizes libraries like Pandas, Matplotlib/Seaborn. Retrieves data via API, generates line graph. Gain insights into BPCL stock performance. Deliverables include Jupyter Notebook, findings summary, and complete source code.

A 3 part series of Jupyter notebooks to help one find alpha in the stock market with AI

Portfolio accounting and analysis using Emacs Org-mode and Jupyter Notebooks.

Neuro evolution agent to buy and sell stocks atumatically

Jupyter notebook to analyze stocks and generate graphs

A collection of notebooks I used in my Medium articles.

In this project I created a notebook that contains data preparation, analysis, and visualizations for all the time series data that a mock company would need to understand including: Visual depictions of seasonality (as measured by Google Search traffic) that are of interest to the company. An evaluation of how the company stock price correlates…

cnvar.cn及个人微信公众号【QuantInvest】里面提及的编程代码, 对股票各种研究和折腾分析A股市场的各种现象和投资机会，涉及编程、股票模型、分析研究、杂谈等，代码是python，以jupyter notebook展示。

This notebook provides some skills to perform financial analysis on economical data.

In today's world, people trade stocks through online with own decisions, based on the financial data and market information. This notebook presents several aspects of analyzing financial data using data science, combined both python and statistics to provide and extract insights from the stock data.

An example of aggregating OHLC stock data using Data-Forge Notebook

This Jupyter Notebook can be used for continuous monitoring of the status of a trading portfolio in Dhaka Stock Exchange.

A set of Jupyther notebooks for processing Market Indicators from the Web for predicting market movements. Julia, Python, TuriCreate DNN

A collection of notebooks and different prediction models that can predict the stock prices. Also a comparison of how all these models performed.

Algo Trading - Notebooks: Start to End of Algo Trading Development. Data:Stocks up till 2019 with all daily close data.