Quantopian Review: A Comprehensive Guide to Algorithmic Trading

Quantopian is a platform that allows users to create and backtest algorithmic trading strategies using a variety of tools and resources. It's a great place to start for those new to algorithmic trading.

The platform was founded in 2011 by John Fawcett and John Stepek, and has since grown to become one of the leading platforms for algorithmic trading. Quantopian's community-driven approach has helped to make it a popular choice among traders and investors.

Quantopian offers a range of features and tools that make it easy to get started with algorithmic trading, including a backtester that allows users to test their strategies on historical data. This is a great way to refine and improve your strategies before risking any real money.

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Quantopian is an amazing platform that lets anyone with Python coding skills write their own trading algorithm. You can write and test these algorithms in their interactive development environment (IDE), which is a game-changer for anyone interested in trading.

The basic idea of Quantopian is to empower users to create and backtest their own trading strategies using advanced data science tools. This means you can use their notebooks to research and test ideas, and even compete in contests to evaluate your strategy and earn prizes.

You can also license your algorithm and share in the profits, which is a great opportunity for those who are successful. Quantopian's community is very active, and you can clone and learn from algorithms shared by others.

Here are the three main ways to use Quantopian:

Quantopian's Github also hosts many open-source libraries for quantitative finance, which is a valuable resource for developers.

Quantopian's business model was a two-sided market, where members on both sides benefited from each other's contributions.

The first side consisted of algorithm-developer members who developed and tested for free, focusing on algorithm development for factors that can be added to Quantopian's offerings to institutional investors.

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These developers were given free data sources and tools, largely built in the Python programming language, to help them create and test their algorithms.

Quantopian hosted contests called "Quantopian Open", where anyone could join and enter regardless of education or work experience.

Successful developer-members could get a royalty or commission from investor-members, who profited from the former's algorithm used with larger resources over $1M.

The company aimed for a modest number of users and a large average revenue per user (ARPU), more like Bloomberg than Facebook.

Quantopian's business model capitalized on a trend jokingly dubbed "the latest DIY craze" by the Wall Street Journal.

The company previously provided brokerage integrations to individual investors, which were later continued by the community who started the open source project Zipline-Live.

In 2018, Quantopian announced the availability of an enterprise software product for asset managers, in partnership with FactSet.

Quantopian's web-based product was written in Python. This programming language is known for its simplicity and versatility, making it a great choice for a variety of applications.

Parts of the company's technology were available under an open source license. This allowed users to access and modify the code, which can be a huge advantage in terms of collaboration and innovation.

Their backtesting engine, dubbed "Zipline", was also available under an open source license. This is a key component of their technology, allowing users to test and refine their algorithms before putting them into practice.

The uploaded algorithms of users remained the trade secrets of the individual. This means that users could keep their code private and secure, even while using Quantopian's platform.

The company claimed that its employees could not access the submitted algorithms, except in certain circumstances. This is an important aspect of their security model, ensuring that user data remains confidential.

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In October 2020, CEO John Fawcett announced that Quantopian would be winding down its free community offering.

The community platform was taken down on November 14th, 2020, after a 9-year run.

This was short notice, and the Quantopian team knew it.

The team didn't leave quants high and dry, though - they disclosed plans to integrate with commission-free broker Robinhood Markets.

To fill the gap left by Quantopian, traders turned to open source projects like Gekko and Freqtrade.

Companies like CrunchDAO, QuantConnect, and Numerai were among the first to capitalize on the opportunity.

In recent years, cloud-based tools like 3commas, Mudrex, and Tuned have emerged to serve the needs of quants, particularly those focused on cryptocurrencies.

To get started with Quantopian, you should check out their Quickstart tutorial. This will guide you through the process of running an algorithm using the Zipline CLI.

First, you'll need to download some sample pricing and asset data. You can do this by following the instructions in the Quickstart tutorial.

The data is sourced from Quandl and will be streamed through the algorithm over the specified time range. This will give you a good idea of how the data is used in Quantopian.

To run the algorithm, you'll need to save the resulting performance DataFrame in a file called dma.pickle. This file can then be loaded and analyzed from within Python.

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To create trading algorithms on Quantopian, you need to learn a few key functions: initialize()handle_data()before_trading_start() These functions are the building blocks of your algorithm, and understanding how they work is crucial for developing effective trading strategies.

The initialize() function is called only once when your algorithm starts, and it takes in context, which is like an augmented Python dictionary that maintains the state of the algorithm. You can access a stock's sid and assign it using dot notation.

The handle_data() function is called once at the end of each minute, making it a great place to use built-in functions like order_target_percent(), which places an order to adjust a position to a target percent of the current portfolio value.

The Basics of Algorithms are actually quite straightforward. You need to learn three key functions: initialize(), handle_data(), and before_trading_start(). These functions are the building blocks of any trading algorithm on Quantopian.

The initialize() function is called only once when your algorithm starts, and it takes in context. Think of context as a dictionary that helps maintain the state of your algorithm. You can assign properties to it using dot notation, which is a fancy way of saying you can access and modify its values like a normal dictionary.

You can use the handle_data() function to learn about built-in functions like order_target_percent(), which places an order to adjust a position to a target percent of the current portfolio value. This function takes in (sid, target_percent, style) - for example, you can use it to set the target percent to 25% at the end of each minute.

The data.history() method is useful for calling historical information on equities and getting the data adjusted for splits, mergers, and dividends. You can pass in a list of assets, the fields you want, the bar count, and frequency.

Here are the three key functions you need to learn:

The schedule_function() method lets you schedule functions to do things like opening and closing positions. It takes in the function you're scheduling, the date rule, and the time rule. For example, you can use it to open a position in FB worth 20% of the portfolio at the start of each month, at the open of the market.

The context object in the initialize() function is like an augmented Python dictionary that's used for maintaining the state of the algorithm. This means you can assign properties to it and access them using dot notation.

Paper trading and backtesting are two essential tools for algorithmic traders. Alpaca offers only integrated paper-trading, whereas Quantopian offers both paper-trading and back-testing.

Back-testing involves running your algorithm over historical data incrementally, simulating a live data feed. This process is fast but may lack some accuracy, such as market bid and ask.

Quantopian's back-testing capabilities allow you to test your algorithm on historical data, which can be beneficial for development and refinement. You can use this to see how your algorithm performed on past data.

Paper-trading, on the other hand, involves interacting with live market data from a trading account with virtual money. This approach has the benefit of real-life trading conditions and the ability to test your system's reliability.

Quantopian's paper-trading feature allows you to test your algorithm with real-time data, but you'll have to wait for the algorithm to run in real-time. This is a more accurate way to test your algorithm, but it can be slower than back-testing.

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Algorithm Development is a crucial part of trading on Quantopian. To create a trading algorithm, you need to learn a few key functions: initialize(), handle_data(), and before_trading_start(). These functions are the building blocks of your algorithm, and understanding how to use them is essential.

The initialize() function is called only once when your algorithm starts, and it takes in context. This context object is like an augmented Python dictionary that's used for maintaining the state of the algorithm. You can access a stock's SID and assign it using dot notation.

To make things more concrete, let's look at a simple example. Suppose you want to buy tech stocks like FB, AMZN, AAPL, and TSLA with equal distribution for each. You can use the initialize() function to set up your portfolio and the handle_data() function to place orders to adjust positions to a target percent of the current portfolio value.

Here are the key functions you need to learn:

These functions will help you create a trading algorithm that can execute trades based on your strategy. By mastering these functions, you'll be well on your way to developing effective trading algorithms on Quantopian.

A pairs trading algorithm is a strategy that involves trading two highly correlated stocks, like ABGB and FSLR. This algorithm is provided as an example by Quantopian.

In this type of algorithm, we're looking for pairs of stocks that tend to move in tandem. The example given in the article uses ABGB and FSLR, which we won't go into the code for here.

Running the pairs strategy for the year 2014 resulted in some notable outcomes. The results show a specific trading performance for the mentioned stocks.

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A classifier is a function that transforms the input of an asset and a timestamp to a categorical output. It can return a sector, an exchange, or any other categorization.

Classifiers are useful for filtering assets based on specific characteristics. For example, if we pass in FB, we could get back that it is in the technology sector.

A factor, on the other hand, returns a numerical value. It can be used to calculate values such as a 20 day moving average.

Let's create a Pipeline that returns all equities available at a timestamp. We can then use Quantopian's USEquityPricing dataset to match prices to the SIDs.

To get the price information for all the equities, we first need to import USEquityPricing. We can then create a factor that calculates a numerical value, such as the SimpleMovingAverage.

Here are some examples of factors:

We can use these factors to create a filter, which is essentially a comparison operation against one of the factors. For example, we can get only the rows where the Percent Difference is negative.

By using classifiers and factors, we can create a Pipeline that calculates specific values for each asset and filters them based on those values. This is a powerful tool for algorithm development.

Algorithm testing is a crucial step in developing a trading algorithm on Quantopian. You can backtest your algorithm on historical data to see how it performed.

Quantopian's trading platform allows you to use your Python skills to develop and test trading algorithms. This is exactly what we did in our earlier articles on Python for Finance.

To test your algorithm, you can use Quantopian's Research feature to analyze historical data. This feature helps you identify trends and patterns in the market.

Backtesting your algorithm on historical data is essential to gauge its performance. You can use Quantopian's backtesting feature to see how your algorithm would have performed in the past.

Quantopian's Pipelines feature allows you to create and manage data pipelines, which are essential for backtesting your algorithm. By creating a pipeline, you can easily access and manipulate large datasets.

Algorithm testing is a continuous process, and you should be prepared to make adjustments to your algorithm based on the results of your backtesting.

Quantopian provides an integrated development environment on their website, making it easier to start developing and testing trading strategies. This can be a big plus for beginners who want to get started quickly.

You'll have access to a wide range of price and fundamental data, including minute data of US stock prices and volume. Quantopian also includes a selection of US futures in their data feed.

Alpaca, on the other hand, provides a range of fundamental and metadata through integrations with IEX and Polygon. This gives you a more comprehensive view of the markets, but it's worth noting that Quantopian's data is still very robust.

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Both Quantopian and Alpaca offer easy access to a wide range of price and fundamental data, making it simple to implement new ideas without needing third-party feeds.

Quantopian provides minute data of US stock prices and volume, plus a wide selection of US futures. This extensive data allows for in-depth analysis and trading strategies.

Alpaca, on the other hand, offers fundamental and metadata through integrations with IEX and Polygon, giving users a more comprehensive view of their investments.

Alpaca offers API wrappers in multiple languages to be installed and developed on your own system.

This makes it easier to write software that can be used across different languages and environments.

Quantopian, on the other hand, provides an integrated development environment on their website, along with back-testing functionality built in.

This integrated approach can simplify the development and testing process, making it more accessible to new users.

However, software developed on Quantopian's platform cannot be easily converted into production trading strategies without significant reworking.

In contrast, software written for Alpaca's paper trading accounts can be easily converted to live trading accounts with just a simple change of API keys.

The Quantopian platform is built on top of Zipline, a Pythonic algorithmic trading library that's used for backtesting and live-trading.

Zipline is an event-driven system, which means it processes events in a specific order to simulate real-world trading scenarios.

It's currently used in production as the backtesting and live-trading engine powering Quantopian.

Quantopian offers a free, community-centered, hosted platform for building and executing trading strategies.

It also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more.

The Zipline library is implemented in Python, making it a great option for developers who are already familiar with the language.

The simple dual moving average algorithm is a great example of how Zipline can be used to backtest trading strategies.