You might stumble upon supposedly ** quantitative trading strategies** based only on anecdotal evidence in the vast ocean of online resources. However, untested methods and strategies offer little more than hollow promises. We believe in providing examples of strategies that have been thoroughly scrutinized, backtested, and “proven” to deliver consistent results. It doesn’t need to be advanced – just look at the quantitative trading strategies below. As a matter of fact, trading should be done as simple as possible!

We have been quantitative traders, both retail and proprietary, for over two decades since we started in 2001, and below we provide you with **8 quantitative trading strategies** that might help you trade better. It’s not investment advice, far from it, but it shows how you can develop simple ideas into a portfolio of trading strategies. All strategies were developed many years ago**, **some were published on this website as far back as 2012, and the quant trading strategies have proven to hold up well after publication.

All strategies in the article are backtested and have many years of out of sample backtsting.

If you are new to quantitative and automated trading, you might find these articles helpful:

- Short-term trading systems that work
- Is backtesting worth it? (Backtesting quantitative strategies)
- Performance metrics for systematic trading
- How to calculate win rate in quantitative trading
- Why is max drawdown important?
- Risk-adjusted returns in quantitative trading

We also remind you that you can get the code for all the strategies below if you become a member.

## What are quantitative trading strategies?

Quantitative trading is rule-based models and calculations to predict future returns. We can call it a systematic trading approach that uses strict statistical trading methods to find odds and probabilities. You want to have a strategy that has a positive expectancy.

When one quantitative trading strategy is found sound and robust (even better if you have many strategies), you have an automated trading system you can trade via a computer. Automation is power; you can trade almost unlimited strategies via your computer or VPS.

Let’s go to our first quantitative trading system:

## Russell rebalancing strategy

Our first quantitative trading strategy trades just once a year. Yes, just once a year, but still, it might serve a beneficial purpose in a portfolio of many trading strategies (portfolio diversification is extremely important for quantitative trading systems). This is a seasonal trading strategy, a type of strategy we like a lot, and it has worked well for decades.

Russell 2000 rebalances their holdings once per year on the fourth Friday of June, and during this period, Russell 2000 has performed very well. Not only Russell 2000, but also the broader market, like S&P 500, for example. Thus, we might argue the outperformance is explained mainly by the small-cap effect. Nevertheless, we like good results, whatever the cause, and let’s look at the trading rules:

- Buy on the close of the first trading day after the 23rd of June.
- Sell on the close on the first trading day of July.

We backtested the cash index, RUT, and got the following equity curve:

The trading performance metrics look like this:

- Average gain per trade: 1.34%
- Win ratio: 76%
- Average winner: 2.3%
- Average loser: 1.8%
- Max drawdown: 6%
- Profit factor: 4.1

Not bad for such a simple concept!

Let’s go to the second trading strategy:

## Rubber Band trading strategy

Our second strategy was published as early as 2012 on this website. The strategy can be used on many indices and assets, but we backtest the S&P 500 and the ETF with the ticker code SPY, the oldest ETF still trading.

The trading rules read like this:

- Calculate a 5-day average of the (High minus Low – (H-L)). That is the “ATR”.
- Calculate the High of the last 5 days.
- Calculate a band 2.5 times below the 5-day High using the average from point number 1 (ATR).
- If it closes below the band in number 3, then go long at the close.
- Exit when the close is higher than yesterday’s high.

The average gain per trade is 0.66%, the win rate is 77%, and the annual return is 6.4% despite being invested only 14% of the time.

## MFI indicator strategy

MFI is an abbreviation for Money Flow Index and resembles the much more famous RSI indicator.

The Money Flow Index (MFI) is a momentum indicator designed to gauge the inflow and outflow of funds within a security over a specific time frame. By looking at both price and volume, it tries to gain insights into the market dynamics. Oscillating between 0 to 100, the MFI indicates overbought and oversold conditions, serving as a tool for identifying potential market reversal points.

This sounds well, but does it work? We make the following trading rules:

- If the two-day MFI is below 10, we buy at the close
- We sell at the close when the close ends higher than yesterday’s high
- We have a time stop of 10 trading days

We backtested the trading rules on Nasdaq 100 by using the ETF with the ticker code QQQ and got the following equity curve:

The average gain per trade is 0.46%, the win rate is 70%, and the annual return is 11.1% despite being invested only 34% of the time.

## S&P 500, gold, and bonds rotation momentum strategy

Meb Faber, the famous money manager, published an article in 2015 where he rotated between gold, stocks, and bonds. It’s a momentum strategy, and the trading rules are simple.

Here is all there is to it:

Three asset classes: Stocks, bonds, gold.

Invest equally in whatever is going up (defined as 3 month SMA > 10 month SMA).

We assume the capital is allocated equally, depending on how many signals we get. For example, if one month we have two positive signals, we allocate 50% to each position, if we have three signals, we use 33.33% each, and 100% if only one signal.

How has the strategy performed? Let’s look at the equity curve:

The average gain per trade is 0.77%, and the annual return is pretty good at 12%. The annual returns read like this:

Considering the simplicity of the strategy, we believe this is rather good. Compared to being only invested in stocks, the max drawdown is 26% vs. 55% for S&P 500.

## Weekly RSI quantitative trading strategy

Let’s make a strategy that uses the most famous indicator of them all: The Relative Strength Indicator, abbreviated RSI. We usually see little correlation between popularity and profits, but the RSI indicator doesn’t support that theory. Its logic is simple: buy oversold assets and sell overbought assets, but it works well for mean reversion assets like stocks (for example).

This strategy is backtested on the ETF that tracks Consumer Staples. The ticker code is XLP, and XLP is one of our favorite trading vehicles.

We make the following trading rules based on weekly bars:

- When the 2-week RSI crosses below 15, we go long at Friday’s close.
- We sell when the 2-weekly RSI crosses above 20.

In general, we prefer daily bars, but this is what the strategy performed like on weekly bars:

The average gain per trade is 1.2%, and the annual returns are 4.2%. However, please remember that the strategy is invested only 11% of the time, and we can argue the risk-adjusted return is 37% (annual returns divided by 0.11).

## The turn of the month strategy

Let’s turn to our favorite seasonality: the turn-of-the-month effect in stocks. It doesn’t work only for stocks but also for many other assets.

Research shows that stocks make almost all the gains during the last five trading days of the month and the first three trading days of the new month. Let’s see how this performed by backtesting the cash index of S&P 500 from 1960 until today.

We make the following trading rules:

We go long at the close on the fifth last trading day of the month, and we exit after seven days, ie. at the close of the third trading day of the next month. Thus, the strategy is invested around 33% of the time.

This very simple strategy has done very well:

The strategy made 0.6% per trade, 7% annually (Buy & Hold also 7%) while spending just 33% of the time invested. As a result, the max drawdown is just half of S&P 500’s 55%.

For comparison, this is the curve when you are invested in all other trading days of the month:

## Quantitative volatility trading strategy

Let’s backtest one of our premium strategies available for paying subscribers: a volatility strategy. It works on a wide range of assets but is best for stocks.

Because it’s a premium member strategy, we don’t reveal the trading rules (obviously).

The strategy has worked well, and the equity below is for S&P 500 (SPY):

The average gain is 1.1% for the 178 trades since 1993. Even though there are few trades, the annual return is 6.1% even though it’s invested only 8% of the time. Max drawdown is a very low 23%.

The same strategy performed even better for NASDAQ 100 (QQQ):

The average gain is 1.9%, and the total returns are significantly higher than Buy & Hold (11.6% vs. 8.6%).

## Treasury Bonds long and short strategy

One of the most essential things in trading is to trade many assets, both market directions, and different time frames.

To vary the strategies, we show you a strategy that trades both long and short Treasury Bonds. We used the ETF with the ticker code TLT as a proxy for Treasury Bonds.

This strategy is also for our paying members and is found here.

How has it performed? Pretty good!

The annual return is 9.8% (dividends reinvested) compared to Buy & Hold’s 4.5%. That’s more than twice the return despite being invested only 56% of the time.

## The pros and cons of quantitative trading (strategies)

Let’s provide a summary of the article by briefly discussing the advantages and disadvantages of quantitative trading:

When it comes to the pros and benefits, the following points are worth noting:

- Computer-driven execution: Your computer carries out trading activities, eliminating the need to constantly monitor the screen.
- Automation and strategy development: The ability to automate trading allows you to focus on continuously developing new strategies.
- Multiple strategy options: Quantitative trading enables you to trade using various strategies simultaneously.
- Efficient time management: The time spent remains the same regardless of trading one or fifty strategies.
- Psychological advantages: By introducing a layer between you and the actual trading, quantitative trading may reduce the likelihood of poor decisions, cognitive errors, and overriding signals.

However, it’s important to acknowledge that there are some downsides and disadvantages as well:

- Coding requirement: To engage in quantitative trading, you will need to learn coding skills.
- Experience for system discovery: Gaining experience is necessary to identify and utilize effective systems and strategies.
- Effort in finding new approaches: Actively seeking and developing new strategies and systems requires dedicated work.

Quantitative trading offers several advantages, such as automated execution, strategy diversification, and reduced psychological biases. On the other hand, it demands coding proficiency, experience in system discovery, and ongoing effort to find innovative approaches.

## 8 quantitative trading strategies – conclusion

This article has shown you the performance, returns, and statistics of **8 quantitative trading strategies** – six with complete trading rules and two strategies from our member’s area.

We believe that these data-driven trading techniques show you that anyone can develop a quantitative trading strategy and make money, given that you understand backtesting, follow the trading rules (and are not fooled by trading biases), and understand how markets work. It’s not rocket science, and quant trading doesn’t need to be advanced to work.