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botvs/strategies

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Strategies

This project is a cross-language quantitative trading framework designed to implement and execute trading strategies consistently across Python, JavaScript, C++, and PineScript. It functions as a polyglot trading strategy translator and a multi-language algorithmic trading engine that maps high-level scripting and block-based logic to executable binaries.

The system features a financial domain-specific language parser that translates specialized trading syntax and visual programming blocks into a standardized internal representation. It includes a technical analysis pattern library providing standardized quantitative calculations to ensure identical numerical results across different programming runtimes.

The framework covers the algorithmic trading workflow, including quantitative trading development and technical analysis automation. It enables the translation of trading logic between diverse programming languages and the generation of multi-target code from a single strategy definition.

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Features

  • Algorithmic Trading Engines - Provides a core platform for executing automated trading strategies by mapping high-level logic to compiled binaries.
  • Quantitative Trading Platforms - Provides an integrated environment for developing, backtesting, and executing algorithmic financial trading strategies across languages.
  • Technical Indicator Calculators - Provides a standardized library of quantitative calculations to ensure consistent technical indicator results across different runtimes.
  • Technical Analysis - Provides tools for calculating technical indicators and automating the detection of chart patterns.
  • Trading Logic Mappings - Converts block-based programming structures into executable trading scripts via a direct mapping layer.
  • Block-to-Code Generators - Implements a system that translates graphical block structures into executable trading source code.
  • Cross-Language Semantic Standards - Ensures consistent evaluation and identical numerical results for quantitative patterns across different runtimes.
  • Domain Specific Language Parsers - Provides a parser that translates specialized financial scripting syntax into a structured tree for analysis.
  • Intermediate Representation Translation - Converts diverse trading language syntaxes into a target-neutral intermediate format to decouple parsing from execution.
  • Multi-Target Code Generation - Emits executable source code in multiple target languages from a single unified strategy definition.
  • Polyglot Execution Environments - Creates a bridge between high-level scripting languages and compiled binaries to run trading logic in diverse environments.
  • Trading Strategy Translators - Implements a polyglot translator that converts trading logic across Python, JavaScript, C++, and PineScript.
  • Algorithmic Trading - Provides the infrastructure for automating financial market analysis and executing quantitative investment models.
  • Cross-Language Implementations - Maintains strict logic and numerical parity for technical analysis calculations across multiple programming language ports.
  • Trading Strategy Definitions - Provides a framework for codifying financial logic and signal generation that can be targeted to multiple languages.
  • Trading Strategy Frameworks - Offers a specialized software framework for building and managing automated quantitative trading strategies.
  • Strategy Reference Implementations - Adapts trading logic across different languages like Python, JavaScript, and C++ for diverse platforms.
  • Trading Logic Interfaces - Implements node-based visual scripting and DSL translation for defining financial indicators and trading logic.
  • Strategy Frameworks - Repository of algorithmic trading strategies.
5,267 stars·1,697 forks·19 views

Star history

Star history chart for botvs/strategiesStar history chart for botvs/strategies

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does botvs/strategies do?

This project is a cross-language quantitative trading framework designed to implement and execute trading strategies consistently across Python, JavaScript, C++, and PineScript. It functions as a polyglot trading strategy translator and a multi-language algorithmic trading engine that maps high-level scripting and block-based logic to executable binaries.

What are the main features of botvs/strategies?

The main features of botvs/strategies are: Algorithmic Trading Engines, Quantitative Trading Platforms, Technical Indicator Calculators, Technical Analysis, Trading Logic Mappings, Block-to-Code Generators, Cross-Language Semantic Standards, Domain Specific Language Parsers.

Which projects share features with botvs/strategies?

Projects with overlapping indexed features include: shinnytech/tqsdk-python — tqsdk-python is a quantitative trading SDK and framework designed for developing automated strategies for futures,… stocksharp/stocksharp — StockSharp is an algorithmic trading platform and quantitative framework used for developing and deploying trading… ai4finance-foundation/finrl — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated… freqtrade/freqtrade-strategies — This is a library of cryptocurrency trading algorithms and technical analysis strategies designed for use with the… yutiansut/quantaxis — Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies… ai4finance-llc/finrl-library — FinRL-Library is a reinforcement learning trading framework and algorithmic trading library used to develop and…

Projects sharing features with Strategies

These projects share indexed features with Strategies. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • shinnytech/tqsdk-pythonshinnytech avatar

    shinnytech/tqsdk-python

    4,789View on GitHub↗

    tqsdk-python is a quantitative trading SDK and framework designed for developing automated strategies for futures, options, and stocks using Python. It functions as an algorithmic trading engine and financial market data API, providing the tools necessary to backtest strategies, analyze historical data, and execute live trades across multiple brokerage accounts. The project distinguishes itself through a specialized option analytics library that calculates Greeks, implied volatility, and volatility surfaces using the Black-Scholes model. It further supports complex order execution patterns, s

    Python
    View on GitHub↗4,789
  • stocksharp/stocksharpStockSharp avatar

    StockSharp/StockSharp

    10,126View on GitHub↗

    StockSharp is an algorithmic trading platform and quantitative framework used for developing and deploying trading robots across stock, forex, and cryptocurrency markets. It functions as a multi-asset trading gateway and a dedicated development environment for building, debugging, and scheduling automated strategies. The platform includes a visual strategy workflow editor that maps logic blocks to executable code and a simulation engine that replays historical tick data to validate trading logic. It utilizes a plugin-based broker integration system to normalize diverse exchange protocols into

    C#
    View on GitHub↗10,126
  • ai4finance-foundation/finrlAI4Finance-Foundation avatar

    AI4Finance-Foundation/FinRL

    13,964View on GitHub↗

    FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow

    Jupyter Notebookalgorithmic-tradingdeep-reinforcement-learningdrl-algorithms
    View on GitHub↗13,964
  • freqtrade/freqtrade-strategiesfreqtrade avatar

    freqtrade/freqtrade-strategies

    4,861View on GitHub↗

    This is a library of cryptocurrency trading algorithms and technical analysis strategies designed for use with the Freqtrade trading bot. The project provides a collection of pre-defined rules and mathematical indicators used to automate the buying and selling of digital assets. The repository focuses on algorithmic trading strategies and bot-driven asset management to remove manual execution from cryptocurrency trades. It enables quantitative trading analysis by allowing the development and testing of rule-based logic against historical market data. The system utilizes class-based strategy

    Pythonbitcoincryptocurrencyfreqtrade-strategies
    View on GitHub↗4,861
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