Material for the course "Time series analysis with Python"

Time-series forecasting tecniques applied to the stock market

Collection of personal tools used for strategies analysis and algorithmic trading on Binance exchange.

Curvature Corrected Moving Average: An accurate and model-free path smoothing algorithm.

This project conducts a thorough analysis of weather time series data using diverse statistical and deep learning models. Each model was rigorously applied to the same weather time series data to assess and compare their forecasting accuracy. Detailed results and analyses are provided to delineate the strengths and weaknesses of each approach.

This repository is related to all about Computer Vision - an A-Z guide to the world of Computer Vision. This supplement contains the implementation of algorithms, statistical methods, and techniques (in Python)

Predictve analysis of stock market 'tickers ', based on historical financial data. ..LSTM ,

📈 Midas is a free and open source Moving Average Trading backtest simulator.

[PineScript] Dosyalar pek çok uygulama başlığına dair özgün, birbirinden bağımsız açık kaynak kodlarını içerir.

Compute an exponentially weighted mean incrementally.

Lorentzian Fitting with Baseline Models

Time Series Analysis

Data analysis and data filtering on IoT devices on a time series in a unique C++ library, Data Tome. Focus on the developer's experience and performance. It is the successor to the MovingAveragePlus library.

The Python project written on Jupyter includes technical analysis using various indicators, developing trading strategies based on the indicators, visualizing the outcomes, and testing the strategies. The indicators applied in the project are Simple Moving Average 200, Bollinger Bands, and RSI - Relative Strength Index.

Compute a moving mean directional accuracy (MDA) incrementally.

Compute a moving mean absolute error (MAE) incrementally.

Create an iterator which iteratively computes a moving arithmetic mean of absolute values.

Compute a moving arithmetic mean of absolute values incrementally.

Compute a moving arithmetic mean and corrected sample standard deviation incrementally.

Compute a moving arithmetic mean and unbiased sample variance incrementally.