Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data

A simple way to keep track of an Exponential Moving Average (EMA) version of your pytorch model

Implementation of Mega, the Single-head Attention with Multi-headed EMA architecture that currently holds SOTA on Long Range Arena

The collections of simple, weighted, exponential, smoothed moving averages.

Calculate an exponential moving average from an array of numbers.

Fastest Technical Indicators written in typescript, Supports: Browser, NodeJS, ES6, CommonJS. More than +100 indicators(SMA, EMA, RSI, MACD, ...)

tools for finding/selecting options using the e*trade developer API

Modified Extended Kalman Filter with generalized exponential Moving Average and dynamic Multi-Epoch update strategy (MEKF_MAME)

A python package to extract historical market data of cryptocurrencies and to calculate technical price indicators.

A simple, customizable EMA Crossover Forex trading algorithm made with Oanda's Rest v20 API.

Forecasting Time Series with Moving Average and Exponential Smoothing

Online statistics implementations, including average, variance and standard deviation; exponentially weighted versions as well.

A Stock Prices Analytics Dashboard, comprising of python codes for price predictions, technical indicators, and dashboard hosting

iOS iBeacon based indoor location application

Testing the profitability of an algo-trading algorithm which uses exponential moving averages

mic_py : Python 3 code for successful use of microphone on windows. stdev_ema.py : Python 3 code for calculation of standard deviation and exponential moving average of stock data.

This code is part of the "Comparison of K-Means and Model-Based Clustering methods for drill core pseudo-log generation based on X-Ray Fluorescence Data" written by researchers of the Directory of Geology and Mineral Resources from the Geological Survey of Brazil – CPRM.

Analyzed historical monthly sales data of a company. Created multiple forecast models for two different products of a particular Wine Estate and recommended the optimum forecasting model to predict monthly sales for the next 12 months along with appropriate lower and upper confidence limits

This project is dedicated to forecasting 1-hour EURUSD exchange rates through the strategic amalgamation of advanced deep learning techniques. The incorporation of key technical indicators—RSI, MA, EMA, and VWAP—enhances the model's grasp of market dynamics

This repository focuses on optimizing a trend-based trading strategy for the EURUSD currency pair. By combining PSO and GA, the goal is to maximize returns while minimizing risk. The strategy considers buy and sell signals based on Supertrend and EMA conditions, with a fixed commission of 3 pips per trade.