The Best Performing Quant Trading Funds and How You Can Use AI to Invest Like Them

Quantitative trading (aka algorithmic trading) has made many millionaires and several quant trading funds have shown remarkable success. In this article, I’ll show you some of the most successful quant trading funds in history, and how you can use AI to help build your own quant trading strategies.

So what is quant trading? Quantitative trading, often shortened to "quant trading," is a type of investing strategy that uses mathematical models and algorithms to make decisions. Instead of relying solely on traditional analysis methods (like reviewing financial statements or market news), quant traders use powerful computers to analyze large volumes of data, such as historical price trends or financial indicators. The goal is to identify patterns or relationships that can predict future price movements. From there, the system can make trades automatically based on these predictions. In essence, quant trading is a high-tech, data-driven approach to investing, offering the potential to find profitable opportunities that might be missed by human traders.

If you're looking for the best quant trading platforms, click here.

1. Citadel Advisors

Founded by Kenneth Griffin in 1990, Citadel Advisors is a dominant force in the world of quantitative trading (1). The firm has consistently produced strong returns for its investors, boasting an impressive annualized return of approximately 19% from 1990 to 2021 (2). Such performance doesn't happen by accident; it's the result of a finely-tuned blend of advanced technology, comprehensive quantitative research, and in-depth fundamental analysis.

As a testament to its adaptability and prowess, Citadel has evolved into a global investment institution managing over $38 billion in investment capital (3). The firm’s diverse portfolio spans a broad range of assets, including equities, fixed income, commodities, and foreign exchange, allowing it to seize opportunities wherever they arise (4).

A standout aspect of Citadel's trading strategy is its reliance on high-frequency trading. The firm is among the top high-frequency traders, which significantly contributes to daily trading volumes across various exchanges worldwide (5). Through a complex web of algorithms and mathematical models, Citadel's high-frequency trading systems can execute thousands of orders in fractions of a second, giving it an edge in a market where speed is crucial.

Moreover, Citadel distinguishes itself with its cutting-edge use of technology and relentless pursuit of innovation. The firm's robust technological infrastructure enables it to swiftly process vast amounts of market data, thereby informing its trading strategies and facilitating informed decision-making (6).

2. Bridgewater Associates

Bridgewater Associates, the brainchild of Ray Dalio founded in 1975, stands as one of the most successful and influential hedge funds in the world (7). Under the stewardship of Dalio, Bridgewater has consistently provided robust returns to its clients, with its flagship Pure Alpha fund boasting an impressive annualized return of approximately 12% since its inception (8).

Operating with over $150 billion in assets under management (9), Bridgewater Associates showcases an investing philosophy rooted in understanding how markets work. The firm's success is based on its innovative risk parity approach, encapsulated in its All Weather portfolio. The All Weather strategy is designed to perform well across all economic environments by balancing risk allocation across four economic scenarios: rising growth, declining growth, rising inflation, and declining inflation (10).

Another significant factor contributing to Bridgewater's success is its culture of radical transparency and idea meritocracy. This culture enables the firm to foster an environment where the best ideas rise to the top, irrespective of their source (11). The result is a more inclusive decision-making process, leading to more comprehensive risk assessment and better investment choices.

Bridgewater is also noteworthy for its application of technology. It uses algorithms and data analysis to confirm or challenge investment theses, enhancing the robustness of its investment process (12).

3. AQR Capital Management

AQR Capital Management, co-founded in 1998 by Cliff Asness along with partners David Kabiller, John Liew, and Robert Krail (13), has carved out a distinctive niche in the world of quant trading. The firm, known for its innovative and academic approach to investing, has yielded significant returns for its clients. As of 2021, AQR manages approximately $140 billion in assets across various strategies (14).

AQR stands out for its pioneering work in factor investing, which involves targeting specific drivers of return across asset classes, such as value, momentum, and quality (15). Factor investing has been academically proven to deliver excess returns over time, and AQR has been at the forefront of translating these academic insights into practical investment strategies (16).

Moreover, the firm stands by the principles of transparency and openness. It routinely shares its research findings with the public, a practice that not only underlines AQR's commitment to knowledge sharing but also helps cultivate trust with its clientele (17).

In terms of technology, AQR utilizes sophisticated algorithms and data analysis tools to identify and capitalize on these factors. This strong emphasis on technology and systematic, data-driven strategies makes AQR a trailblazer in the quant trading landscape (18).

4. D.E. Shaw

Founded in 1988 by David E. Shaw, a former Columbia University computer science professor, D.E. Shaw Group has been a pioneer in the quant trading space (19). With a consistent track record of strong returns, the firm reported an impressive net annual return of 13.9% over its lifetime until 2021 (20)

Managing over $50 billion in investment capital (21), D.E. Shaw has built its reputation on an interdisciplinary approach to investment, blending sophisticated mathematical modeling, cutting-edge technology, and expert financial analysis. It's recognized for its proprietary algorithms that can sift through massive amounts of data to identify investment opportunities that would otherwise go unnoticed (22).

The D.E. Shaw Group is notable for its innovative use of technology and its early adoption of high-frequency trading strategies. By leveraging the power of advanced technology and the speed of high-frequency trades, the firm has achieved an unprecedented level of efficiency and precision in executing its investment strategies (23).

5. Renaissance Technologies

Renaissance Technologies, founded in 1982 by former codebreaker James Simons (24), stands as an embodiment of quantitative trading success. The firm's renowned Medallion Fund, known for its exclusivity and extraordinarily high returns, has delivered an average annual return of 39.1% after fees from 1988 to 2020 (25). Such exceptional performance has firmly placed Renaissance Technologies among the most successful hedge funds globally.

Renaissance Technologies is best known for its heavy reliance on mathematical models and computational algorithms. Drawing on Simons' background as a renowned mathematician and codebreaker, the firm has developed complex mathematical models to predict and exploit patterns in financial markets (26).

The firm, managing over $60 billion in assets (27), employs an array of scientists, mathematicians, and physicists to build and refine its trading models. This interdisciplinary team approach enables Renaissance to explore the market from diverse perspectives, thereby gaining unique insights and giving it a competitive edge (28).

In addition, Renaissance is celebrated for its secrecy. Despite its success, the firm is tight-lipped about its investment strategies, contributing to its aura of mystery and allure (29).

6. Two Sigma Investments

Two Sigma Investments, founded in 2001 by John Overdeck and David Siegel (30), is renowned for its tech-driven, systematic approach to investing. Overdeck and Siegel, both with strong backgrounds in technology and finance, established Two Sigma with a vision to apply advanced technologies to financial markets. The firm, managing over $58 billion in assets as of 2021 (31), has achieved significant returns, with its flagship fund generating an annualized return of 15% over its lifetime (32).

Two Sigma stands out for its robust application of machine learning, artificial intelligence, and big data analytics. The firm employs a plethora of data scientists and engineers who develop sophisticated algorithms capable of analyzing and drawing insights from vast and diverse datasets (33). This approach, coupled with high-frequency trading techniques, allows Two Sigma to detect subtle patterns in market data and execute trades at lightning speed (34).

So how can you use AI to create your own quant trading strategies?

While the high-flying world of quant trading might seem exclusive, but with the help of AI you can create, test, and execute your own algorithmic quant trading strategies in minutes.

Step 1 - Head to Composer and click on the ‘Create’ section.

Composer is the tool we’ll use for this tutorial. It allows you to build trading algorithms with AI, backtest them, then execute—all in one platform. On the Create page you’ll have the option to “Create with AI”. Note: Composer offers stocks and ETF trading (no crypto at this time).

Why are we using Composer in this tutorial? Composer uses ChatGPT4 which is the latest AI model by OpenAI (released in March 2023). Composer has also invested heavily in finetuning the ChatGPT4 model specifically for the trading use case. Therefore you get a much better AI assistant for trading use cases.

Step 2 - Describe the type of trading strategy you want to create.

For some context, Chat GPT is a natural language processing (NLP) model so you interact with it in a question/answer style.

Start by asking the AI assistant what type of strategy you are looking for. The AI trading assistant will then create the code for your strategy and provide an explanation of what it does. Don’t worry if this code looks confusing (you won’t need to use it directly). This will be a very basic baseline which you will then use to tailor to your risk profile and trading requirements.

The example we'll use: "I think that AI is going to take off. Can you build me a strategy with exposure to AI?"

You can also ask it to create strategies like Ray Dalio’s All Weather Portfolio e.g. “Can you build me the All Weather Portfolio by Ray Dalio?”

Step 3 - Click “Backtest”.

This places the code that the AI assistant has created into the visual editor. Here it’s much easier to see how the logic works. You can also edit the strategy directly in this editor e.g. you may want to use the various indicators e.g. relative strength index or moving average to filter your assets.

If you scroll down, you’ll arrive at the backtester so you can use professional grade data to understand how the trading bot would have performed historically. There is also detailed performance metrics for the trading bot e.g. maximum drawdown, calmar ratio, sharpe ratio etc. You can also find how the bot would have allocated across assets each day.

In our example, the AI assistant has created a strategy with a basket of AI stocks, nice! But there's more work to do.

Step 4 - Continue the conversation and finetune your strategy.

Head back to the AI assistant chat box. Based on your own trading experience, the backtests, and your trading requirements, adjust the strategy as necessary. This could include asking the AI assistant to find ways to diversify the strategy, reduce the volatility, add a hedge, or change the strategy based on different market conditions. There are so many different paths you could take here. Once the AI assistant provides you with an updated strategy, insert it and backtest in the editor.

In our example, we can ask the chatbot: "this is cool, but I'd like to add a hedge in case AI doesn't do as well as I expect."

The AI assistant provides a 10% hedge in the form of Gold and Long Term Treasury Bonds. In the visual editor, you could also change the strategy to use other technical indicators on the Composer platform or rules-based logic e.g. If SPY is down by X, move to gold and long term treasury bonds, else stay in the tech stocks.

Step 5 - Invest with the click of a button.

Composer automatically executes your trades for you based on your rebalancing schedule. Composer is a Registered Investment Advisor (RIA) and they are regulated by FINRA and the SEC. Funds are protected up to $500k through SIPC and the custodian is BMO Harris bank.

As you can see, Composer's AI assistant with ChatGPT4 is a versatile tool that can assist you in building a personalised strategy based on your needs. The AI assistant can also help you change the rebalancing frequency of your investment strategies to ensure they follow the desired schedule. When it comes to identifying potential investment options, the AI assistant can provide recommendations for ETFs and stocks in particular sectors, such as technology, energy, or healthcare, according to your preferences. Another great use of the tool is finding and replace assets within existing strategies.

I hope I've provided some initial inspiration for you to create your own AI quant trading bot. I look forward to seeing what you create! If you want more inspiration on quant trading strategies you can create click here.

References

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