Statistical arbitrage of cointegrating currencies with pair trading where the signal for the next day is predicted using LSTM
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Updated
Sep 24, 2020 - Python
Statistical arbitrage of cointegrating currencies with pair trading where the signal for the next day is predicted using LSTM
A quantitative trading strategy backtester with an interactive dashboard. Enables users to implement, test, and visualise trading strategies using historical market data, featuring customisable parameters and key performance metrics. Developed with Python and Polars.
The goal of this project is to develop a statistical arbitrage strategy for cryptocurrencies using Python
OpenFintech is a financial analysis library designed for Python developers and financial analysts. It provides powerful tools for conducting both trend following and mean reversion analyses, utilizing financial market data. This project aims to make complex financial algorithms accessible and easy to use.
Closed-form solutions and fast calibration & simulation for SABR-based models with mean-reverting stochastic volatility
This script implements a mean reversion strategy for a given stock. It calculates the z-scores for the stock's price and generates entry and exit signals based on predefined thresholds. The script also performs a backtest on the strategy and visualizes the returns.
Quick calculation for profit loss of trades.
Predict price reversion signals for mean reverting stocks on NSE
Crypto trading bot that utilizes a Binance API for data
Algorithmic Trading Project
Project of Group 6 - CS408 - APCS, HCMUS - 2025
This is a project exploring dual-strategy trading algorithm using mean reversion and momentum for VN30F1M futures market. Project of Group 7 - CS408 - APCS, HCMUS - 2025
Hybrid Trading Strategy for VN30F1M Futures Combines SMA-100 and RSI-14 indicators to exploit trend-following and mean-reversion opportunities. Project of Group 9 - CS408 - APCS, HCMUS - 2025
A Python project that finds cointegrated asset pairs and generates Z-score based trading signals. Simulates a portfolio backtest.
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