Mercury010's repositories
machine-learning-from-scratch
This repository contains the implementation from scratch of some of the most used Machine Learning algorithms
ChatGPT_Trading_Bot
This is the code for the "ChatGPT Trading Bot" Video by Siraj Raval on Youtube
pnl
FIFO PnL trading calculator in Python
d6tstack
Quickly ingest messy CSV and XLS files. Export to clean pandas, SQL, parquet
sec-edgar-downloader
📈 Download filings from the SEC EDGAR database using Python
sec-edgar
Download all companies periodic reports, filings and forms from EDGAR database.
algorithmic-trading-python
The repository for freeCodeCamp's YouTube course, Algorithmic Trading in Python
bitcoinbook
Mastering Bitcoin 2nd Edition - Programming the Open Blockchain
Follow-the-Big-Money-13f-files-SEC-USA
Our goal is to provide a simple tool to simplify the investment decision process for anybody. The tool helps replicate the portfolios of the most attractive stocks in the capital markets without paying any management fees.
ML-For-Beginners
12 weeks, 24 lessons, classic Machine Learning for all
Mercury010
Config files for my GitHub profile.
blockchain-demo
A web-based demonstration of blockchain concepts.
py
Repository to store sample python programs for python learning
best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
csvtotable
Simple command-line utility to convert CSV files to searchable and sortable HTML table.
TIA
Your Advanced Twitter stalking tool
Real-time-Sentiment-Tracking-on-Twitter-for-Brand-Improvement-and-Trend-Recognition
A real-time interactive web app based on data pipelines using streaming Twitter data, automated sentiment analysis, and MySQL&PostgreSQL database (Deployed on Heroku)
trading-pnl
FIFO Trading Model for PnL Calculation
stock-visualizer
A Python application that visualizes stock data using professional candlestick charts.
Twitter-moods-as-stock-price-predictors-on-Nasdaq
An attempt to predict next day's stock price movements using sentiments in tweets with cashtags. Six different ML algorithms were deployed (LogReg, KNN, SVM etc.). Main libraries used: Pandas & Numpy