yutiansut/QUANTAXIS

Features

• Quantitative Trading Platforms - Provides an integrated environment for developing, backtesting, and executing algorithmic trading strategies across global markets.
• Backtesting Simulations - Provides a high-fidelity simulation environment using event-driven architecture to verify strategy performance before live deployment.
• Technical Indicator Calculators - Includes technical indicator calculators to generate trading signals from historical price and volume data.
• Account Management - Provides a consistent interface to manage investment accounts across different global markets and languages.
• Automated Risk Management - Monitors unrealized loss and asset concentration to automatically reduce or close risky positions.
• Automated Trading Execution - Executes automated trading strategies across multiple market gateways and accounts using production-ready logic.
• Live Trading Execution - Executes trades in real-time markets to maintain positions and monitor live performance.
• Investment Portfolio Tracking - Tracks real-time balances, available cash, and order history across multiple global markets.
• Position Limits - Enforces strict constraints on asset sizes and capital allocation percentages for every trade.
• Real-time Portfolio Tracking - Provides real-time calculation of floating profit, loss, and margin usage based on live price updates.
• Stop-Loss Strategies - Automatically closes positions when market prices hit predefined percentage stop-loss or profit thresholds.
• Strategy Performance Analyzers - Calculates key quantitative metrics such as annual return and win rate to evaluate strategy effectiveness.
• Trading Strategy Backtesters - Simulates automated trading strategies using historical market data to calculate drawdowns and performance.
• Trading Strategy Frameworks - Provides a comprehensive framework with base classes and event hooks for developing diverse quantitative trading strategies.
• Trading Record Persistence - Implements persistence for account states and trade records to maintain historical data across sessions.
• Real-Time Data Processors - Processes live financial data feeds in real-time to retrieve current prices, spreads, and changes.
• Market Data Providers - Implements interfaces for fetching historical and real-time financial market data, including bars and ticks.
• Unified Data Provider Interfaces - Provides a standardized communication layer to translate generic requests across diverse financial data providers.
• Backtesting Engines - Ships an event-driven simulation environment to evaluate strategy performance and risk using historical data.
• Data Engineering Pipelines - Implements automated workflows for ingesting, normalizing, and storing large-scale multi-asset financial datasets.
• Event-Driven - Implements event-driven logic to trigger specific handler functions via a subscription and emission system.
• Trading Infrastructure Orchestration - Provides a containerized infrastructure model using Kubernetes to manage strategy pods, databases, and message brokers.
• Message Brokers - Utilizes a message broker to facilitate asynchronous communication and decoupling between strategy and execution services.
• Trading Gateways - Provides a connectivity layer for routing trades across various exchange APIs with integrated risk controls.
• Alpha Strategy Management - Provides a framework to develop and manage alpha-seeking strategies designed to outperform market benchmarks.
• Financial Computation Libraries - Provides a Rust-accelerated engine using Apache Arrow for high-performance technical indicator calculation and data processing.
• Indicator Computation Engines - Includes a high-performance indicator computation engine accelerated by Rust for low-latency financial math.
• Multi-Factor Research Models - Ships a dedicated systematic base class for developing quantitative factors and research modules.
• Financial Analysis Tools - Ships professional financial analysis tools for quantitative research and statistical market trend identification.
• Rust-Accelerated Computations - Executes performance-critical mathematical operations and technical indicators using compiled Rust binaries.
• Trading Risk Management - Validates all outgoing orders against margin requirements and position limits to prevent excessive risk.
• Asynchronous Messaging - Implements non-blocking architectural patterns for routing high-volume data between system components.
• Backtest to Live Transitions - Enables the transition of strategy code from backtesting to live environments with integrated execution.
• Event-Driven Architectures - Implements an event-driven architecture to trigger trading logic based on real-time market events.
• Multi-Market Order Routers - Provides a multi-market execution gateway for routing orders across diverse exchange interfaces.
• Zero-Copy Mechanisms - Uses Apache Arrow to transfer large financial datasets between memory formats without redundant copying.
• Portfolio Metric Trackers - Calculates financial portfolio metrics such as annual return, Sharpe ratio, and maximum drawdown.
• Overfitting Debuggers - Implements overfitting debuggers and statistical techniques to prevent quantitative models from fitting noise.
• Portfolio Optimization Algorithms - Employs quantitative algorithms to track and optimize asset allocations and overall portfolio performance.
• Convex Optimization Solvers - Determines ideal asset allocations by balancing risk and return using convex optimization.
• Factor Analysis - Implements a standardized research class for evaluating the performance of individual financial factors.
• Market Data Sources - Implements connectors for acquiring and managing market data from multiple sources for quantitative analysis.
• High-Performance Account Processing - Uses high-performance components to accelerate account-related operations and financial calculations.
• CTA Strategy Implementation - Provides built-in callbacks for executing and completing commodity trading strategies.
• Multi-Strategy Capital Allocation - Allocates capital across several distinct strategies and combines results into a unified portfolio.
• Trading Order Monitors - Tracks the full lifecycle of financial trades across stocks, futures, and options using a thread-safe interface.
• Account Hierarchies - Distributes and tracks orders across master and child accounts to manage complex organizational trading structures.
• Derivative Pricing Models - Implements mathematical models for calculating the fair value of options and complex derivative instruments.
• Stock Price Trackers - Provides real-time monitoring of asset prices and market depth for specific stock symbols.
• Trade Profitability Recorders - Monitors holdings and computes real-time floating and realized profit and loss for trades.
• Incremental Backtesting - Optimizes backtesting by processing only new data since the last run while maintaining strategy state.
• Performance Visualization - Generates equity curves and drawdown charts to identify trends and risk patterns in trading results.
• Financial Instrument Listing - Fetches comprehensive lists of available financial instruments to identify tradable assets.
• Margin - Tracks balances and positions across different assets using stocks and futures margin models.
• Cross-Language Data Protocols - Utilizes the Apache Arrow format for high-performance, zero-copy data sharing across Python, Rust, and C++.
• Strategy Observers - Provides specialized components for tracking and visualizing live financial strategy metrics and order events.
• Data Pipeline Orchestration - Provides data pipeline orchestration to execute sequences of processing tasks via dependency graphs.
• Tick Data Retrieval - Retrieves high-frequency, trade-by-trade transaction records for historical analysis and real-time monitoring.
• Data Storage Optimizers - Optimizes query latency for large datasets using indexing and columnar storage formats.
• Cross-Sectional Transformations - Performs cross-sectional transformations and analysis on assets by grouping them by industry.
• Financial Market Visualizers - Provides financial market visualizers to represent price data and trends through graphical charts.
• Financial Data Format Translation - Translates data between tables, JSON, and arrays to facilitate movement between analysis and storage.
• Financial Data Processing - Organizes raw financial data into structured formats that support efficient filtering by time and security.
• Market Value Calculators - Computes total and floating market values by combining asset prices with share counts.
• Cryptocurrency - Includes interfaces for fetching historical and real-time cryptocurrency market updates via WebSocket.
• Index Data Fetching - Provides capabilities to retrieve daily, minute-level, and real-time price data for various market indices.
• International Market Data Access - Enables retrieval of historical daily and minute-level data for international stock markets including US and Hong Kong.
• Shared Memory Data Exchange - Employs shared memory data exchange for high-throughput inter-process communication of real-time market data.
• Market Data - Persists large-scale market data in optimized databases designed for rapid quantitative analysis.
• Time Series Indexing - Generates minute or hour-level time indices to align financial bar data for analysis.
• Time Series Resampling - Converts high-frequency tick or minute data into standardized lower-frequency hourly or daily bars.
• Backtesting Workload Distribution - Distributes indicator calculations and backtesting tasks across multiple CPU cores to improve simulation speed.
• Container Deployment - Provides container-based deployment workflows to ensure consistent quantitative environments across Kubernetes.
• Containerized Packaging - Packages the quantitative environment in containers to ensure consistent deployment across infrastructures.
• Deployment Infrastructure - Orchestrates the underlying technical stack including data storage, message queuing, and execution engines.
• Kubernetes Orchestration - Orchestrates strategy pods, databases, and web servers using Kubernetes for scalable analysis.
• PubSub Messaging Systems - Utilizes pub-sub messaging systems to decouple components by routing signals through a message broker.
• Asynchronous Processing - Implements asynchronous processing patterns to handle concurrent I/O and real-time data streams.
• Corporate Action Price Adjustments - Applies forward and backward adjustments to stock prices to correctly account for corporate actions.
• Financial Ratio Analysis - Computes financial ratio analysis and company health indicators such as return on equity.
• Parallel Processing - Distributes financial instrument computations across multiple CPU cores to accelerate market analysis.
• Risk Assessment Metrics - Computes critical risk metrics including position value, margin utilization, and exposure ratios.
• Strategy Parameter Optimization - Identifies robust strategy configurations by testing parameter combinations across training and out-of-sample periods.
• Task Orchestration Engines - Ships a task orchestration engine that executes computational sequences using directed acyclic graphs.
• Parallel Task Executors - Executes computations across multiple threads and processes using parallel task executors.
• System Health Monitors - Tracks real-time trading metrics and account balances to trigger alerts based on equity thresholds.
• Backtesting Engines - Comprehensive framework for strategy development and research.

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