From 833d31a86dafdfeb4d649f2f83db092417851aac Mon Sep 17 00:00:00 2001 From: medusa Date: Tue, 20 Feb 2024 22:51:49 +0000 Subject: [PATCH] Add docs/financial_docs/Algo-Trading-Toolkit.md --- docs/financial_docs/Algo-Trading-Toolkit.md | 56 +++++++++++++++++++++ 1 file changed, 56 insertions(+) create mode 100644 docs/financial_docs/Algo-Trading-Toolkit.md diff --git a/docs/financial_docs/Algo-Trading-Toolkit.md b/docs/financial_docs/Algo-Trading-Toolkit.md new file mode 100644 index 0000000..ce4d586 --- /dev/null +++ b/docs/financial_docs/Algo-Trading-Toolkit.md @@ -0,0 +1,56 @@ +# Algo-Trading Toolkit + +A comprehensive list of tools and libraries tailored for different components of algorithmic trading, focusing on APIs, backtesting, trade management, and reporting metrics. + +## API Interaction + +- **`ccxt` (Python)** + - **Description**: A cryptocurrency trading library with support for many cryptocurrency exchange markets and trading APIs. + - **Use Case**: Fetching live market data, executing trades, and managing portfolios across multiple cryptocurrency exchanges. + +- **`Oanda's REST API` (Various Languages)** + - **Description**: Offers programmatic access to Oanda's trading engine for fetching historical data, real-time market data, and executing trades. + - **Use Case**: Ideal for forex trading, providing a robust interface for data collection and automated trade execution on Oanda's platform. + +## Backtesting + +- **`Backtrader` (Python)** + - **Description**: A powerful Python framework for backtesting trading algorithms with an emphasis on ease of use and flexibility. + - **Use Case**: Testing trading strategies over historical data to assess their performance before live deployment. + +- **`QuantConnect` (C#, Python)** + - **Description**: An algorithmic trading platform that provides backtesting and live trading capabilities, supporting equities, forex, options, futures, and cryptocurrencies. + - **Use Case**: For traders looking to use both cloud-based backtesting and live trading with support for multiple asset classes. + +## Trade Management Systems + +- **`MetaTrader 5` (MQL5)** + - **Description**: A platform for trading Forex, analyzing financial markets, and using Expert Advisors for automated trading. + - **Use Case**: Suitable for traders who prefer a ready-made platform with built-in trade management features, including automated trading through Expert Advisors. + +- **`Alpaca` (Python, JavaScript)** + - **Description**: An API-first brokerage platform that allows algorithmic trading with commission-free stock and ETF trading. + - **Use Case**: Best for U.S. equities trading with a focus on API-driven automated trading systems. + +## Reporting Metrics & Visualization + +- **`Dash` (Python)** + - **Description**: A Python framework for building analytical web applications ideal for creating interactive, web-based dashboards. + - **Use Case**: Visualizing trading performance metrics, real-time data feeds, and strategy backtesting results in customizable dashboards. + +- **`Grafana`** + - **Description**: An open-source platform for monitoring and observability, which can connect to virtually any data source, including SQL and NoSQL databases. + - **Use Case**: Creating real-time analytics dashboards and graphs for monitoring trading system metrics, market data, and alerting on specific events. + +## Comprehensive Platforms + +- **`QuantConnect` (C#, Python)** + - **Already Mentioned Under Backtesting**: Additionally, QuantConnect serves as a comprehensive platform offering a complete suite for strategy development, backtesting, and live trading across multiple asset classes. + +- **`TradingView`** + - **Description**: A cloud-based charting and social networking platform where traders can analyze financial markets and share trading ideas. + - **Use Case**: Ideal for technical analysis, with powerful charting tools and a vast library of indicators and strategies shared by the community. + +--- + +This toolkit represents a selection of purpose-built tools for various facets of algorithmic trading. The choice of tools depends on specific needs, trading strategies, preferred programming languages, and the financial markets of interest.