Comparison of Trading Strategies - return percent.
-
Updated
May 9, 2023 - Jupyter Notebook
Comparison of Trading Strategies - return percent.
Python Bollinger Bands algorithmic trading strategy backtest
Analyzing and predicting the stock prices,multiple machine learning models, including LSTM (Long Short-Term Memory), Prophet, and others
Multithreaded stock market simulation that allows for different trading bots running various financial strategies and technical indicators. Creates a Market Making Bot running off of AAPL data.
I AM NOT A DAYTRADER (i just did this to learn the code) You know those girls who only talk about zodiac signs? I have more faith in their quantitative ability than i do 4 guys who use technical analysis for anything besides understanding market behavior & implementing their own personal strategy to mitigate the risk of emotional trading decisions
Strategy based based on the price range (bands) indicators.
FOREX data for various currency pairs is used and different methods are used to analyse trend of these pairs.Future prediction of these currency pairs and trading strategies to maximize returns
An EA based on Bollinger Band breakout.
Cryptocurrency trading
Strategy based on SVE Bollinger Bands indicator
How to build serverless and event-driven cloud-native applications with Azure in Java.
The algorithm for various trading strategies using different technical indicators and comparing all the different strategies on various parameters.
The MATLAB code analyses stock prices of a company and predicts the closing price. The algorithms implemented for predicting closing price are: (a)Kalman Filter (b)Kalman Multiple Linear Regression The algorithms implemented for analysing the trends in a stock (c) Bollinger bands (d). Chaikin Oscillator Output - 1. Graphs showing the predicted a…
Bollinger Bands Width Stock Scanner - NYSE, NASDAQ & B3 (Brazil)
Dashboard to view a stock's basic information, RSI, Bollinger bands, EMA, SMA, sentiment analysis via Python
TMA Band True (Contrarian) Strategy
An RSI and Bollinger Bands-based trading strategy, yielding a 3103% cumulative return for provided backtesting setting
This repository is a collection of open-source use-case examples of algorithmic or quant trading in Python.
Stock market momentum analysis using averaging of 7 weighted technical indicators
Add a description, image, and links to the bollinger-bands topic page so that developers can more easily learn about it.
To associate your repository with the bollinger-bands topic, visit your repo's landing page and select "manage topics."