Learning QuantConnect - Part I: Account Setup & Securities (Python) 🧨🚀👨🚀
If I have learned one thing over the past years in algorithmic trading it’s this: If your backtests aren’t realistic, strategies won’t perform well when you put them to the test.
So I decided to invest more time into learning one of the most realistic, feature-rich platforms out there: QuantConnect. This platform packs a truck load of useful algo features like realistic backtesting with tick data, slippage simulation, and broker models, advanced strategy features like portfolio management, universe selection, access to high quality data sets and a scalable live trading architecture with ML integration.
Due to it’s complexity, QuantConnect has a steep learning curve. That’s why it’s not as popular among retail traders as let’s say TradingView. The QuantConnect Bootcamp is an okay introduction providing a cookbook of how to implement a few use cases.
However, in my tutorials I want to structure things a little differently: I am planning to review each concept of the QuantConnect SDK in detail, e.g. Securities, Universes, Trading, Indicators, Reality Modeling, Machine Learning. Each tutorial will explain the concept and provide specific code samples of how to work with this concept. This way, you should have a full understanding of how to use the concepts at the end of the series and start building your own strategies. 🚀🚀🚀
But enough of the chatter! Let’s get started with the first part of the series: Account Setup & Securities.
This story is solely for general information purposes, and should not be relied upon for trading recommendations or financial advice. Source code and information is provided for educational purposes only, and should not be relied upon to make an investment decision. Please review my full cautionary guidance before continuing.
What is QuantConnect?
QuantConnect is an open-source, cloud-based algorithmic trading platform, founded in 2011 by Jared Broad. It allows users to design, test, and deploy trading strategies across various financial markets including equities, FX, futures, options, and cryptocurrencies. The platform caters to a diverse audience ranging from individual traders to hedge funds and brokerages.
The key features of QuantConnect include extensive market data for backtesting strategies, the Lean Algorithmic Trading Engine for local development and customization, and support for Python and C# coding. Additionally, its Alpha Streams feature lets users license their trading strategies to funds. While basic backtesting is free, live trading and advanced features require a subscription.
QuantConnect combines comprehensive data resources, a robust trading engine, and a supportive community, making it a valuable tool for those in the field of quantitative finance and algorithmic trading.
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