letianzj/QuantResearch

Features

• Quantitative Trading Platforms - Provides an integrated environment for developing, backtesting, and validating systematic quantitative trading strategies.
• Trading Strategy Backtesters - Provides a comprehensive environment for evaluating quantitative trading strategies against historical market data.
• Financial Machine Learning Toolkits - Implements specialized machine learning toolkits for predicting asset prices using LSTM, RL, and GMMs.
• Financial Price Forecasting - Combines machine learning, deep RL, and time-series models to forecast asset prices and market trends.
• Portfolio Optimization Algorithms - Provides algorithms for optimizing asset allocation to maximize returns and manage risk.
• Efficient Frontier Construction - Generates a set of optimal portfolios that maximize return for different risk levels using numerical solvers.
• Half-Life Estimation - Estimates the time required for a price series to return halfway to its equilibrium level using linear regression.
• Portfolio Optimization - Provides a dedicated engine for calculating asset weights and risk metrics using Mean-Variance Optimization and the Efficient Frontier.
• Algorithmic Trading Simulators - Offers an event-driven simulation platform for evaluating the performance of automated investment strategies against historical data.
• Financial Time-Series Libraries - Provides a comprehensive library of tools for analyzing stationarity, cointegration, and volatility in financial time series.
• Time Series Analysis - Analyzes time series stationarity and trending behavior using Augmented Dickey-Fuller tests and Hurst Exponents.
• Stationarity Analysis - Provides stationarity testing for financial data series using the Augmented Dickey-Fuller unit root test.
• Time Series Modeling - Applies ARIMA, GARCH, and machine learning models to analyze financial volatility and predict price movements.
• Mean Reversion Calibration - Estimates Ornstein-Uhlenbeck or Vasicek models using Least Square and Maximum Likelihood methods.
• Asset Allocation Optimization - Computes optimal asset allocations using quadratic programming to maximize returns and minimize risk.
• Cointegration Tests - Implements statistical tests to identify mean-reverting portfolios of assets for statistical arbitrage.
• Dynamic Cointegration Estimation - Applies a Kalman Filter to perform Bayesian online training of linear regression models for pairs trading.
• Market Regime Detection - Identifies latent shifts in market states using Expectation-Maximization, Gaussian Mixture Models, and Markov Regime Switching Models.
• Pairs Trading Strategies - Implements quantitative strategies based on cointegration and z-score scaling for mean-reversion trading.
• Quadratic Programming Allocation - Employs quadratic programming solvers to calculate optimal asset weights for risk parity and minimum variance portfolios.
• Quantitative Financial Modeling - Implements statistical and predictive models to conduct quantitative research and validate trading strategies.
• Dynamic Hedge Ratio Estimation - Updates the cointegration relationship between financial assets over time using a Kalman Filter.
• Hedge Ratio Calculation - Computes linear relationships between assets via regression or eigenvectors to determine proportions for stationary spreads.
• Portfolio Risk Metrics - Calculates key financial risk metrics including Value at Risk and GARCH to optimize portfolio allocations.
• Backtesting Simulations - Implements an event-driven simulation environment to validate trading strategies using historical market data streams.
• Dynamic Linear Modeling - Tracks the evolution of latent states, such as intercept and slope, over time using a Kalman Filter.
• Bayesian Regressions - Implements Bayesian linear regression using conjugate models to incorporate prior knowledge into price predictions.
• Markov Chain Monte Carlo Sampling - Implements the Metropolis-Hastings algorithm for MCMC sampling to approximate posterior distributions for Bayesian regression.
• Market Regime Detection - Identifies latent market states by fitting Gaussian Mixture and Markov Regime Switching models to return data.
• Sharpe Ratio Maximization - Determines tangency portfolio weights that provide the highest excess return per unit of risk.
• Recurrent Neural Networks - Utilizes LSTM and GRU cells to model sequential financial time series for asset price forecasting.
• Derivative Pricing - Uses reinforcement learning agents to determine fair value and optimal exercise timing for financial derivative contracts.
• Statistical Arbitrage - Implements statistical arbitrage by identifying cointegrated asset pairs and executing mean-reversion strategies.
• Walk-Forward Validation - Prevents look-ahead bias by using rolling windows to re-estimate regression coefficients during backtesting.
• Trading Order Monitors - Tracks the lifecycle of trading orders from placement to filling, including slippage and commission simulation.
• Trade Visualization Tools - Generates interactive charts and volume profiles to visualize intraday trade flow and price levels.
• Performance Evaluation - Generates statistical reports and professional-grade performance analytics from historical trade data.
• Market Regime Detection - Uses Hidden Markov Models and Gaussian Mixture Models to identify shifts in market states and adapt trading logic.
• MCMC Sampling - Employs the Metropolis-Hastings algorithm for MCMC sampling to estimate parameters for linear regression models.
• Time Series Decomposition - Analyzes asset stationarity and mean reversion using Augmented Dickey-Fuller tests and Hurst Exponents.
• Order Execution Engines - Simulates brokerage order execution, including configurable commission fees and slippage costs.
• Risk Parity Implementation - Implements risk parity, maximum diversification, and minimum variance frameworks for asset allocation.
• Strategy Parameter Optimization - Iterates through parameter combinations to find settings that maximize specific performance targets for trading strategies.
• Kalman Filter Implementations - Uses Kalman filters to perform recursive Bayesian estimation of linear regression coefficients and cointegration relationships.
• Educational Resources - Quantitative analysis, strategy development, and backtesting resources.

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