Buy and Hold / Equities

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Overall Progress: Completed

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Boot Camp Lessons

Lesson Overview

Buy and hold may be the simplest algorithm possible yet covers many aspects of building an algorithm. In this lesson we walk through the steps of initializing an algorithm to run a buy and hold strategy along with some nuances of getting the data you want.

What You'll Learn

Initializing algorithms date range and starting portfolio value.
Requesting asset data and configuring your data resolution.
Differences in data normalization techniques and how to request raw data.
Accessing algorithm portfolio properties.

Lesson Requirements

A good working knowledge of Python or C# is recommended.

Lesson Content

	Status

Initializing Algorithms	Status
Set Starting Cash
Set Date Range

Assets and Data Selection	Status
Manually Selecting Data
Set Data Normalization Mode

Managing Your Portfolio	Status
Checking Holdings
Placing Orders

About the Author

QuantConnect Team

Selected group of pseudo-humans that can type without looking at the keyboard and use a super advanced neural network to transform coffee and orange juice into code.

Have a Question?

QuantConnect Q&A

What brokerages do you support? Do you have live trading?

We support live trading with Interactive Brokers, Alpaca, GDAX, FXCM, OANDA, and Bitfinex. We also allow you to do paper trading with Equity, Crypto, Forex and CFD data.

What libraries are white-listed for use in QuantConnect?

See a full list of white listed libraries in our Documentation/Supported Libraries section.

If your library is not there and you want it added please let us know.

I can't code, do you have a visual editor to design algorithms?

We do not have any plans to build a visual algorithm designer. We believe the only way to make money in the markets is with the most powerful, flexible tools available. We recently designed the Algorithm Framework which allows relatively easily assembly of coding blocks to design an algorithm but you still need to have a strong foundation in code. Its this belief which has powered many of the design decisions behind QuantConnect. We were algorithmic traders ourselves for 3 years and built hundreds of algorithms. They ranged from the simple, to incredibly complex. The common theme among them was the need for flexibility which can only be achieved through raw code.

How do I get started learning algorithmic trading or quant finance?

Learning quantitative trading is especially difficult as there is so little public information available. We have sought to address this through the educational materials we make available. We provide a Bootcamp for those unfamiliar with programming or trading concepts, a series of tutorials on implementing a quantitative strategy, and our YouTube channel which is intended to guide you on using the QC API. Inside the Algorithm Lab have the QC University which is a collection of 100+ demonstration algorithms to help you get started.

The QuantConnect community and the algorithms they share are also a great way to get started. There are thousands of members just like yourself who share their journey.

How do I know my intellectual property and algorithm source code are secure?

We take protecting your intellectual property very seriously. We limit the access to the database to key team member who need it, and ensure they have no conflict of interest from trading on our personal accounts.

For those especially concerned we have open sourced the LEAN engine, allowing you to do algorithmic trading entirely on your own servers - proving we're truly trying to empower your trading.

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