Best Algorithmic Trading Platforms for 2025
Comprehensive analysis and comparison of top automated trading solutions
Overview of Algorithmic Trading Platforms
Algorithmic trading platforms have transformed the investment landscape, enabling traders to execute strategies with precision, consistency, and speed that would be impossible to achieve manually. These platforms range from user-friendly solutions for retail traders to sophisticated environments for professional quants and institutions.
Modern algorithmic trading systems offer various levels of automation, from assisting with trade execution to fully autonomous strategy deployment. They incorporate features like backtesting engines, optimization tools, risk management systems, and market data integration to create a complete environment for developing and deploying quantitative strategies.
In this comprehensive guide, we evaluate the most effective algorithmic trading platforms across three categories:
- Professional Algorithmic Trading Platforms: Comprehensive solutions with advanced features for professional traders, quants, and institutions
- Retail-Focused Algorithmic Trading Platforms: Accessible platforms designed for individual traders with varying levels of programming experience
- Cloud-Based Algorithmic Trading Services: Modern solutions offering algorithm development and deployment through cloud infrastructure
Our evaluation methodology examines each platform's strategy development capabilities, backtesting accuracy, market connectivity, execution quality, programming flexibility, data access, and overall value proposition. We've tested each platform extensively to provide insights into which tools best serve different trader profiles and strategy types.
Professional Algorithmic Trading Platforms
QuantConnect
Overview: QuantConnect stands at the forefront of professional algorithmic trading platforms, offering an institutional-grade infrastructure for strategy development, backtesting, and deployment. Its open-source LEAN engine provides a robust foundation for sophisticated quantitative research across multiple asset classes.
Key Features:
- Powerful cloud-based algorithm development environment
- Support for C#, Python, and F# programming languages
- Multi-asset class coverage (equities, futures, options, forex, crypto)
- Tick, second, and minute-level historical data
- Advanced backtesting with detailed performance analytics
- Live trading connectivity to multiple brokers
- Alpha marketplace for algorithm licensing
- Robust community and extensive documentation
Pricing:
Free tier with basic features; Research license ($20/month); Professional license ($100/month); Institutional license (custom pricing)
Pros:
- Institutional-grade architecture and performance
- Extensive market data library with fine granularity
- Exceptional community and educational resources
- Open-source core engine with transparent operation
Cons:
- Steeper learning curve than retail platforms
- Requires programming knowledge
- Higher cloud computing costs for complex strategies
Ideal For:
Professional quants, serious algorithmic traders, and institutions requiring institutional-grade infrastructure for research and deployment. Particularly valuable for multi-asset strategies that need high-frequency data and precise execution across markets.
Interactive Brokers API
Overview: Interactive Brokers' API ecosystem provides one of the most comprehensive interfaces for algorithmic trading, offering direct market access across global exchanges with exceptional execution quality. While not a standalone platform, its APIs (REST, WebSocket, FIX, and native libraries) form the foundation for many custom algorithmic trading solutions.
Key Features:
- Comprehensive market access to 150+ global exchanges
- Multiple API options (REST, WebSocket, FIX, native libraries)
- Language support for Python, Java, C++, C#, and more
- Advanced order types and smart routing technology
- Real-time streaming market data
- Historical data access for backtesting
- Exceptionally low commission structure
- Integration with third-party platforms and libraries
Pricing:
No additional cost beyond standard IBKR commissions and market data fees; $0 account minimum for IBKR Lite; $2,000 minimum for IBKR Pro
Pros:
- Exceptional execution quality and market access
- Comprehensive API functionality across asset classes
- Low trading costs and efficient capital usage
- Institutional-grade infrastructure and reliability
Cons:
- Requires external development environment
- No built-in strategy backtesting
- Steeper learning curve than integrated platforms
Ideal For:
Experienced developers and professional traders who want to build custom algorithmic trading systems with superior execution quality. Particularly valuable for those requiring global market access, complex order types, and the flexibility to develop in their preferred environment.
TradeStation
Overview: TradeStation offers a comprehensive ecosystem for algorithmic trading through its proprietary EasyLanguage and advanced platform capabilities. With decades of experience serving professional traders, the platform combines robust strategy development tools with reliable execution infrastructure.
Key Features:
- Proprietary EasyLanguage for strategy development
- Advanced charting with embedded strategy testing
- RadarScreen for real-time market scanning
- Portfolio Maestro for multi-strategy portfolio testing
- Extensive historical data for backtesting
- Real-time strategy monitoring and performance tracking
- Options strategy testing and automation
- Strategy automation with no additional hosting costs
Pricing:
$10/month platform fee (waived with sufficient activity); Standard per-trade commissions; $2,000 minimum account balance
Pros:
- All-in-one solution from development to execution
- EasyLanguage is accessible for non-programmers
- Excellent historical data library
- Robust execution infrastructure
Cons:
- EasyLanguage limited compared to full programming languages
- Higher commissions than some competitors
- Desktop-centric approach with less cloud functionality
Ideal For:
Active traders transitioning from discretionary to algorithmic trading who value an integrated environment. Particularly well-suited for futures and equity traders who want to automate existing strategies without extensive programming knowledge.
Retail-Focused Algorithmic Trading Platforms
TradingView Pine Script
Overview: TradingView's Pine Script language makes algorithmic trading accessible to a broad audience, combining the platform's popular charting capabilities with a simplified scripting language. While primarily focused on strategy development and backtesting, it also offers automation through broker integrations.
Key Features:
- Intuitive Pine Script language for strategy development
- Integrated visual backtesting environment
- Extensive technical indicator library
- Rich visualization capabilities for strategy results
- Strategy alerts with automation capabilities
- Community sharing of strategies and indicators
- Growing broker integration ecosystem
- Cloud-based platform accessible from any device
Pricing:
Free tier with basic features; Pro ($14.95/month); Pro+ ($29.95/month); Premium ($59.95/month) with increasing capabilities
Pros:
- Exceptionally user-friendly for beginners
- Excellent visualization and charting integration
- Strong community with shared scripts and ideas
- No separate infrastructure required
Cons:
- Limited to chart-based strategies
- Simplified language lacks full programming flexibility
- Restricted to daily/hourly data for longer timeframes in lower tiers
Ideal For:
Technical analysis enthusiasts and beginning algorithmic traders who want to automate chart-based strategies. Particularly valuable for those transitioning from manual trading to basic automation without a steep programming learning curve.
MetaTrader 5
Overview: MetaTrader 5 has established itself as the industry standard for retail forex and CFD algorithmic trading. Its MQL5 programming language, strategy marketplace, and widespread broker adoption make it a complete ecosystem for developing, testing, and deploying automated trading systems.
Key Features:
- MQL5 programming language for strategy development
- Strategy Tester with optimization capabilities
- Integrated development environment (IDE)
- MQL5 Market for buying and selling algorithms
- Copy trading functionality
- Integration with hundreds of brokers worldwide
- Virtual Hosting for 24/7 strategy operation
- Multi-asset trading (forex, stocks, futures, options)
Pricing:
Free platform; Virtual Hosting from $5/month; Marketplace signal subscriptions vary
Pros:
- Comprehensive ecosystem with wide broker support
- Strong community and marketplace
- Integrated hosting solution
- Accessible for intermediate-level programmers
Cons:
- Limited strategy sophistication compared to professional platforms
- MQL5 not transferable to other programming contexts
- Backtesting has known limitations
Ideal For:
Retail forex and CFD traders looking for a comprehensive ecosystem for automated trading. Particularly valuable for those trading across multiple brokers who want to develop custom strategies or leverage the marketplace of pre-built systems.
NinjaTrader
Overview: NinjaTrader specializes in futures and forex algorithmic trading, offering a robust platform for strategy development, backtesting, and automation. Its C#-based NinjaScript language provides a good balance between accessibility and programming power.
Key Features:
- NinjaScript (C#-based) for strategy development
- Advanced charting and market analytics
- Strategy Analyzer with walk-forward testing
- Trade Performance analytics
- Simulation environment for practice
- Extensive third-party add-on ecosystem
- Direct market access for futures trading
- Custom indicator development
Pricing:
Free for charting and strategy development; Leased license ($1,099 one-time or from $50/month); Lifetime license ($1,099)
Pros:
- Excellent platform for futures trading automation
- C#-based language offers good programming flexibility
- Strong backtesting and optimization capabilities
- Robust execution and order routing
Cons:
- Higher cost for full automation capabilities
- Limited asset class coverage compared to some competitors
- Desktop-based architecture requires dedicated hardware
Ideal For:
Futures and forex traders with some programming knowledge who need reliable execution and sophisticated strategy development tools. Particularly well-suited for day traders and swing traders focusing on technical analysis-based systems.
Cloud-Based Algorithmic Trading Services
Alpaca
Overview: Alpaca has revolutionized algorithmic trading with its developer-friendly API and commission-free structure. The platform combines modern RESTful and WebSocket APIs with comprehensive documentation and community resources, making it ideal for developers building custom trading applications.
Key Features:
- Clean, modern REST and WebSocket APIs
- Commission-free trading for U.S. stocks and ETFs
- Paper trading environment for testing
- Support for Python, JavaScript, and other languages
- Polygon.io market data integration
- Webhook-based automation
- OAuth integration for third-party applications
- Growing fractional shares and crypto offerings
Pricing:
Commission-free trading; Premium data packages available from $25/month; No minimum account balance for retail users
Pros:
- Modern API design with excellent documentation
- No commissions for U.S. equities trading
- Strong community and third-party integration
- Developer-friendly approach
Cons:
- Limited asset classes (primarily U.S. equities)
- No built-in strategy development environment
- Premium data required for best execution
Ideal For:
Software developers and fintech entrepreneurs building custom trading applications or services. Particularly valuable for those creating web-based or mobile trading platforms who need clean API access with minimal trading costs.
QuantRocket
Overview: QuantRocket delivers a professional-grade algorithmic trading platform with a focus on research, backtesting, and multi-broker deployment. Its containerized architecture allows deployment flexibility while providing institutional-quality tools for serious quantitative traders.
Key Features:
- Docker-based deployment (cloud or local)
- Jupyter notebooks integration for research
- Extensive historical data sources
- Zipline-based backtesting engine
- Multi-strategy portfolio support
- Integration with Interactive Brokers and Alpaca
- Command line interface for automation
- Python-based research and trading environment
Pricing:
Basic ($50/month); Standard ($100/month); Professional ($200/month); Enterprise ($400/month) with increasing data and capabilities
Pros:
- Flexible deployment options (cloud or local)
- Professional-grade research and backtesting tools
- Excellent historical data integration
- Strong focus on reproducible research
Cons:
- Higher technical barrier to entry
- Requires Python programming knowledge
- Higher cost structure than some retail platforms
Ideal For:
Serious quantitative traders and small funds who need research-focused tools with deployment flexibility. Particularly valuable for Python programmers who want a balance between institutional-quality tools and reasonable costs.
Quantower
Overview: Quantower offers a modern, modular trading platform with algorithmic capabilities and multi-broker connectivity. Its combination of advanced charting, order flow analysis, and automation features appeals to both discretionary and algorithmic traders across multiple asset classes.
Key Features:
- Multi-broker connectivity (20+ supported brokers)
- Advanced charting with DOM and order flow
- Algorithmic strategy builder
- C# API for custom indicator and strategy development
- Multi-asset trading across global markets
- Highly customizable workspace and layout
- Advanced scanner and screening tools
- Volumetric analysis capabilities
Pricing:
Free basic version; Advanced package ($40/month); Ultimate package ($90/month); Additional data packages available
Pros:
- Excellent broker connectivity options
- Strong balance of discretionary and algorithmic tools
- Modern interface with extensive customization
- Good value for feature set
Cons">
Ideal For:
Traders who combine discretionary and algorithmic approaches and need multi-broker connectivity. Particularly well-suited for those trading futures, forex, and stocks who want advanced order flow analysis alongside automation capabilities.
Feature Comparison
| Platform | Programming Language | Asset Classes | Backtesting | Data Quality | Live Trading | Learning Curve | Starting Cost |
|---|---|---|---|---|---|---|---|
| QuantConnect | C#, Python, F# | Equities, Futures, Forex, Options, Crypto | Excellent | Excellent | Multiple brokers | Steep | Free/$20 mo |
| Interactive Brokers API | Multiple | All major markets | External only | Excellent | IBKR only | Steep | $0* |
| TradeStation | EasyLanguage | Equities, Futures, Forex, Options | Strong | Strong | TradeStation only | Moderate | $10/mo* |
| TradingView | Pine Script | All charted markets | Good | Good | Limited integration | Gentle | Free/$14.95 mo |
| MetaTrader 5 | MQL5 | Forex, CFDs, Futures, Stocks | Good | Moderate | Many brokers | Moderate | Free |
| NinjaTrader | NinjaScript (C#) | Futures, Forex, Stocks | Strong | Strong | Selected brokers | Moderate | Free/$50 mo |
| Alpaca | Python, JavaScript, etc. | US Stocks, ETFs, Crypto | External only | Good | Alpaca only | Moderate | Free |
| QuantRocket | Python | Stocks, Futures, Forex, Options | Excellent | Excellent | IBKR, Alpaca | Steep | $50/mo |
| Quantower | C# | Stocks, Futures, Forex, Crypto | Moderate | Good | 20+ brokers | Moderate | Free/$40 mo |
* Additional broker commissions and/or data fees may apply
Specialized Recommendations
For Professional Quants
Best Choice: QuantConnect
Professional quantitative traders and funds will find QuantConnect's institutional-grade infrastructure and research capabilities unmatched. Its combination of multi-asset class data, flexible programming environment, and sophisticated backtesting provides the necessary tools for developing complex strategies. The platform's cloud architecture enables seamless scaling from research to production without infrastructure concerns, while the LEAN open-source engine ensures transparency and extensibility.
For Custom Solution Developers
Best Choice: Interactive Brokers API
Developers building custom algorithmic trading systems will benefit most from Interactive Brokers' comprehensive API ecosystem. The combination of outstanding execution quality, global market access, and diverse language support provides the foundation for sophisticated trading applications. While requiring more development effort than turnkey solutions, the IBKR API offers unparalleled flexibility for those with the technical capabilities to leverage it effectively.
For Technical Strategy Automation
Best Choice: TradingView Pine Script
Technical analysts looking to automate their chart-based strategies will find TradingView's Pine Script the most accessible solution. Its integration with TradingView's popular charting platform and simplified syntax makes the transition from manual to automated trading smoother than any alternative. The visual backtesting environment and growing broker integration ecosystem provide everything needed for technical traders to implement their systems without extensive programming knowledge.
For Forex Algorithmic Trading
Best Choice: MetaTrader 5
Forex traders will benefit from MetaTrader 5's widespread adoption among forex brokers and its comprehensive ecosystem. The platform's extensive marketplace of ready-made strategies, signal services, and custom indicators provides options for traders of all experience levels. The virtual hosting service ensures reliable 24/7 operation essential for forex markets, while the global community offers abundant resources for strategy development and troubleshooting.
For Futures Day Traders
Best Choice: NinjaTrader
Active futures traders will appreciate NinjaTrader's specialized focus on futures markets with excellent execution and advanced order types. The platform's C#-based NinjaScript offers a good balance between power and accessibility, while the comprehensive backtesting environment helps validate strategies before deployment. The extensive marketplace of third-party indicators and add-ons provides additional tools for specific trading approaches, particularly for order flow and market profile analysis.
For Beginner Algo Traders
Best Choice: Alpaca
Beginning algorithmic traders, especially those with some programming experience, will find Alpaca's commission-free structure and user-friendly API ideal for learning. The platform's excellent documentation, active community, and paper trading environment provide a supportive ecosystem for developing initial strategies. The growing library of open-source examples and third-party integrations offers valuable learning resources, while the lack of commissions removes financial pressure during the learning process.
Expert Perspectives
"The democratization of algorithmic trading technology has been one of the most significant developments in retail trading over the past decade. Platforms that once would have cost millions in infrastructure are now accessible to individual traders. However, this accessibility comes with both opportunity and risk—the technology barrier has lowered, but the knowledge requirement for successful algorithmic trading remains substantial."— Dr. Thomas Wilson, Director of Quantitative Research, Millennium Partners
"The most common mistake I see among new algorithmic traders is an exclusive focus on entry signals while neglecting position sizing, risk management, and exit conditions. The best platforms facilitate a holistic approach to strategy development—not just identifying opportunities but managing them properly. Look beyond backtested returns to metrics like drawdown, risk-adjusted return, and robustness across market regimes."— Jennifer Chen, CFA, Algorithmic Trading Consultant and Former Quantitative Strategist at Two Sigma
"Cloud-based algorithmic trading platforms represent the future of the industry. The ability to develop locally but deploy globally with minimal infrastructure concerns has transformed the economics of algorithmic trading. This shift allows traders to focus on strategy development rather than technology management, while also enabling levels of computational power and data access previously available only to institutions."— Robert Martinez, Founder, Algorithmic Trading Institute
Our Evaluation Methodology
Our comprehensive assessment of algorithmic trading platforms is based on a rigorous methodology that examines multiple dimensions of functionality, performance, and value. Each platform receives a score based on the following criteria:
Strategy Development Capabilities (20%)
We evaluate the tools, programming languages, and development environments provided for creating trading algorithms. This includes assessing the flexibility of the programming interface, availability of built-in functions, debugging capabilities, and overall ease of translating trading ideas into executable code. Platforms offering more powerful and flexible development options receive higher scores.
Backtesting and Optimization (20%)
The quality of historical testing is critical for algorithmic trading success. We assess backtesting engine accuracy, historical data quality, simulation realism, optimization capabilities, and performance analytics. Special attention is paid to how platforms handle issues like look-ahead bias, slippage modeling, and transaction costs that impact the validity of backtesting results.
Market Data Quality and Access (15%)
We evaluate the breadth, depth, and quality of market data available for both research and live trading. This includes assessing data coverage across asset classes, historical data availability, data granularity (tick, minute, daily), and any additional costs for premium data. Platforms with more comprehensive and accurate data receive higher scores.
Execution Quality and Broker Integration (15%)
The ability to execute strategies effectively in live markets is essential. We assess the platform's connectivity to brokers and exchanges, execution latency, order routing capabilities, available order types, and deployment reliability. Platforms that offer superior execution quality and broader market access score higher in this category.
Platform Stability and Performance (10%)
We evaluate the technical robustness of each platform, including reliability during market volatility, resource efficiency, error handling, and monitoring capabilities. Cloud-based platforms are also assessed on their scalability and uptime guarantees, while desktop platforms are evaluated on their resource management and crash resistance.
User Experience and Learning Curve (10%)
The accessibility and usability of a platform significantly impact user success. We assess interface design, documentation quality, community support, educational resources, and the overall learning curve. Platforms that balance powerful features with usability receive higher scores in this category.
Cost Structure and Value (10%)
We evaluate the overall cost-effectiveness of each platform, including subscription fees, data costs, commission structures, and any additional expenses for deployment. This assessment considers both absolute costs and relative value compared to competitors with similar capabilities.
Our evaluation process includes hands-on testing of each platform over a minimum of 90 days by a team of experienced algorithmic traders with diverse backgrounds in quantitative finance, software development, and trading. We develop, backtest, and deploy sample strategies on each platform to assess real-world performance across different trading styles and asset classes.
Scores are updated semi-annually to account for new features, performance improvements, and competitive developments in this rapidly evolving space. Our methodology deliberately emphasizes both technical capabilities and practical usability, recognizing that the most powerful platform is of little value if users cannot effectively leverage its capabilities.
Key Considerations When Choosing a Platform
Trading Strategy Complexity
The complexity of your trading strategies should guide your platform choice. Simple technical analysis-based strategies can be implemented on retail-focused platforms like TradingView or MetaTrader. Multi-factor strategies integrating various data sources require more sophisticated platforms like QuantConnect or QuantRocket. Complex machine learning models or high-frequency approaches demand professional-grade infrastructure available through institutional platforms.
Technical Expertise
Be honest about your programming and quantitative skills when selecting a platform. Some solutions offer visual builders or simplified languages for those with limited coding experience, while others require proficiency in languages like Python, C#, or Java. Consider the learning curve required and whether the platform provides educational resources to help bridge knowledge gaps.
Asset Class Focus
Different platforms specialize in different markets. For equities trading, platforms with strong fundamental data integration like QuantConnect or Alpaca may be ideal. Futures traders often prefer NinjaTrader or TradeStation for their specialized order types and execution capabilities. Forex traders find the most support in the MetaTrader ecosystem, while multi-asset strategies may require Interactive Brokers API for comprehensive market access.
Infrastructure Requirements
Consider whether you prefer a cloud-based solution that handles infrastructure for you, or a desktop platform running on your hardware. Cloud platforms like QuantConnect offer scalability and 24/7 operation without infrastructure management, while desktop solutions like TradeStation provide greater control but require reliable hardware and connectivity. Hybrid approaches like QuantRocket allow deployment flexibility based on your needs.
Backtesting Capabilities
Accurate backtesting is fundamental to algorithmic strategy development. Assess whether a platform offers event-based or vectorized backtesting, how it handles market orders versus limit orders, if it properly accounts for survivorship bias in historical data, and whether it provides walk-forward testing and out-of-sample validation tools. These capabilities significantly impact your ability to develop robust strategies.
Total Cost Structure
Beyond platform subscription fees, consider all costs associated with algorithmic trading: market data fees, execution commissions, infrastructure costs for self-hosted solutions, and additional services like virtual hosting or premium data. Some platforms offer lower subscription fees but have higher execution costs or data fees, making a holistic assessment of the total trading cost essential.
Conclusion
Algorithmic trading platforms have transformed the investment landscape by enabling traders of all levels to implement systematic strategies with precision and consistency. From professional-grade solutions offering institutional capabilities to accessible platforms designed for retail traders, the range of options allows virtually anyone to benefit from automation in their trading approach.
When selecting an algorithmic trading platform, consider your specific needs, technical capabilities, and trading objectives. Professional quants and funds with complex multi-asset strategies will find the most value in comprehensive platforms like QuantConnect or QuantRocket. Traders with programming experience seeking maximum flexibility may prefer the Interactive Brokers API ecosystem. Those transitioning from discretionary to algorithmic trading often find the most success with accessible solutions like TradingView or TradeStation.
Remember that algorithmic trading success depends not just on the platform but on the quality of your strategy development, risk management, and ongoing monitoring. The best platform is the one that enables your specific approach while providing the tools to develop, test, and refine strategies with statistical validity. Many traders find value in starting with more accessible platforms and graduating to more sophisticated solutions as their skills and strategies evolve.
As algorithmic trading technology continues to evolve, we can expect greater accessibility through improved user interfaces, more sophisticated backtesting capabilities through machine learning, and enhanced integration with alternative data sources. The platforms that most effectively combine power and usability will continue to democratize systematic trading approaches once reserved for institutions.
Latest Updates
This analysis was last updated on May 10, 2025. We review and update our platform evaluations semi-annually to reflect new features, capabilities, and competitive developments.
Recent Platform Developments
- QuantConnect: Recently expanded their universe selection capabilities and introduced advanced risk management frameworks for institutional users.
- Interactive Brokers: Enhanced their API with new streaming data options and improved order routing algorithms for better execution quality.
- TradingView: Significantly upgraded Pine Script with functions and libraries support, bringing it closer to full programming language capabilities.
- Alpaca: Launched expanded market data options and added support for options trading through their API infrastructure.
- MetaTrader 5: Improved their backtesting engine to better handle limit orders and introduced new optimization techniques for strategy development.
Emerging Algorithmic Trading Trends
- Machine Learning Integration: Platforms are increasingly incorporating machine learning libraries and frameworks directly into their environments for more sophisticated strategy development.
- Alternative Data Access: Access to non-traditional data sources like satellite imagery, social sentiment, and web traffic is becoming more integrated into algorithmic trading platforms.
- Cloud-Native Architectures: Even traditionally desktop-based platforms are moving toward cloud-hybrid approaches that offer greater flexibility and accessibility.
- Cross-Asset Integration: The lines between traditional and digital asset trading are blurring, with more platforms adding cryptocurrency support alongside conventional markets.
- Collaborative Development: Community features that enable secure strategy sharing and collaborative development are becoming important differentiators among platforms.
Additional Resources
Educational Materials
These resources can help you build the foundational knowledge needed for algorithmic trading:
Related Platform Reviews
Explore our detailed analyses of related trading technologies:
Community Resources
Connect with other algorithmic traders through these communities:
- QuantConnect Forum
- Quantocracy
- Reddit r/algotrading
- Quantitative Finance Stack Exchange
- Nuclear Phynance
Recommended Books
Essential reading for algorithmic trading practitioners:
- "Algorithmic Trading: Winning Strategies and Their Rationale" by Ernest Chan
- "Inside the Black Box: A Simple Guide to Quantitative and High-Frequency Trading" by Rishi Narang
- "Quantitative Trading: How to Build Your Own Algorithmic Trading Business" by Ernest Chan
- "Trading Systems and Methods" by Perry Kaufman
- "Machine Learning for Algorithmic Trading" by Stefan Jansen