Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

PyIndicators is a powerful and user-friendly Python library for financial technical analysis indicators, metrics and helper functions for pandas and polars dataframes. Written entirely in Python, it requires no external dependencies, ensuring seamless integration and ease of use.

Advanced stock market analysis using Python, Pandas, and Matplotlib with technical indicators, portfolio risk analysis, and financial data visualization.

Python project that fetches stock data, calculates technical indicators (MA, RSI, MACD), visualizes trends with plots, and generates CSV/text reports. Uses yfinance for data, pandas for analysis, and matplotlib for visualization. Ideal for stock analysis and learning financial indicators.

Repository containing Python scripts for financial market analysis. Includes exploratory data analysis, visualizations, technical indicators, strategy simulations, and financial data manipulation using libraries like Pandas, Matplotlib, yFinance, and more.

A real-time financial analytics dashboard built with Streamlit, Python, and Pandas. This tool provides stock price visualization, technical indicators, portfolio tracking, and customizable alerts.

This project explores stock price trends, technical indicators, volatility patterns, and risk-adjusted performance using **Python, Pandas, Matplotlib, and basic predictive modeling** to understand the challenges of forecasting financial time series.

Financial Dashboard – A real-time stock and crypto analysis dashboard built with Python, Streamlit, yFinance, Pandas, and Plotly. Features interactive charts, technical indicators (EMA, Bollinger Bands), and performance metrics with customizable timeframes

AI-powered agent made with Python, yFinance, Pandas, Matplotlib, and Ollama (Qwen2.5 model) for automated financial data processing. Handles user prompts to download/clean data, compute technical indicators (RSI, MACD, Bollinger Bands), perform EDA, generate comparative visualizations, and produce HTML reports for multiple financial assets.

Engineered a multi variate time-series forecasting system using Python and LSTM networks to predict price movements across multiple tickers simultaneously. Utilized Pandas for feature engineering of technical indicators and TensorFlow to perform hyperparameter tuning, optimizing model accuracy for high-volatility financial datasets.

Developed a trading algorithm in Python to predict buy and sell signals based on technical indicators such as Bollinger Bands, MACD, RSI, and ADX. Utilized Pandas and NumPy for data manipulation, Matplotlib for visualization, and Alpha Vantage API for real-time financial data retrieval.