A step-by-step guide to implementing Mean Reversion algorithmic trading strategies in python for beginners to experts.
Welcome to the first issue of Quant Fridays — a weekly series where we analyze and implement algorithmic trading strategies in python.
Algorithmic trading, also known as automated or black box trading, refers to the use of computer programs to execute trades based on a predetermined set of rules. This type of trading has become increasingly popular in recent years due to its ability to analyze vast amounts of data and make trades at a faster speed and with greater accuracy than human traders.
In this first article of the new series “Quant Fridays”, we will be taking a deeper look into a common strategy used in algorithmic trading, mean reversion, which is based on the idea that prices will tend to return to their average over time. This strategy can be applied in various markets, including stocks, currencies, and commodities.
In this article, we will delve into the details of mean reversion trading and how it can be applied using advanced techniques. We will then create an advanced mean reversion model from scratch in python and discuss the potential risks and benefits of this strategy while providing some tips for implementing it effectively.
What is Mean Reversion Trading?
Mean reversion is a statistical concept that refers to the tendency of a variable, such as a stock price, to return to its average value over time. This average value is known as the mean or the expected value.
Mean reversion trading involves identifying when a security’s price has deviated significantly from its mean and then positioning oneself to profit from the eventual return to the mean. This can be done through the use of technical analysis, which involves analyzing historical price data and using indicators to identify trends and patterns.
There are several ways to calculate the mean in mean reversion trading, including using the simple average of a security’s price over a certain period of time or using more advanced techniques such as the moving average or Bollinger Bands.
Advanced Techniques for Mean Reversion Trading
One advanced technique for implementing a mean reversion strategy is the use of a z-score. The z-score measures how many standard deviations a security’s price is from its mean. A high z-score indicates that the security’s price is significantly above or below its mean and may be due for a mean reversion.
Another advanced technique is the use of pairs trading, which involves simultaneously taking a long position in one security and a short position in another that is highly correlated with the first security. This can be done in order to profit from the mean reversion of the spread between the two securities.
Practical Application of Mean Reversion Pairs Trading
Pairs trading is a popular strategy used in algorithmic trading that involves simultaneously taking a long position in one security and a short position in another that is highly correlated with the first security. The goal of this strategy is to profit from the mean reversion of the spread between the two securities.
In order to create a real-life algorithmic trading model that implements this strategy, we first need to access the historical and/or live data for the securities we want to trade. Although there are multiple data sources we could use, I always recommend Yahoo Finance, because of the richness and ease of accessing available financial data. After we successfully download the data we will store it in a pandas DataFrame.
Next, we need to calculate the spread between the two securities. The spread is simply the difference between the closing prices of the two securities. We can do this using the following code:
Now that we have the spread between the two securities, we can calculate the mean and standard deviation of the spread. These values will be used to calculate the z-score of the current spread, which is a measure of how many standard deviations the current spread is from the mean.
With the mean and standard deviation calculated, we can now calculate the z-score of the current spread. To do this, we simply subtract the mean spread from the current spread and divide the result by the standard deviation of the spread.
Now that we have the z-score of the current spread, we can use it to decide when to enter and exit trades. To do this, we need to set the threshold values for entering and exiting trades. In this example, we will set the entry threshold to 1.0, which means that we will enter a trade if the z-score is above 1.0. We will set the exit threshold to 0.5, which means that we will exit a trade if the z-score falls below 0.5.
Now that we have the threshold values set, we can use them to decide when to enter and exit trades. To do this, we will use an if statement to check if the z-score is above the entry threshold or below the exit threshold. If it is above the entry threshold, we will enter a long position in security A and a short position in security B. If it is below the exit threshold, we will close the positions.
Finally, we can output the current positions in securities A and B to see what our current trades are.
That’s it! This is a basic example of how to implement a pairs trading strategy in Python. Of course, there are many ways to customize and improve upon this strategy. For example, you could use different threshold values, add risk management techniques, or incorporate other technical indicators.
Regardless of how you choose to customize your strategy, Python is a powerful language that offers a wide range of tools and libraries for implementing algorithmic trading strategies. By using Python, you can analyze vast amounts of data, make trades at a faster speed and with greater accuracy than human traders, and potentially increase your chances of success in the markets.
Risks and Benefits of Mean Reversion Trading
Like any trading strategy, mean reversion carries its own set of risks and potential rewards. One potential risk is that the security’s price may not return to its mean as expected, resulting in a loss for the trader. Additionally, mean reversion strategies can be vulnerable to market shocks and events that cause prices to deviate significantly from their means.
On the other hand, mean reversion strategies can be profitable in trending markets, as well as in markets that are experiencing extreme overbought or oversold conditions. They can also be less risky than other types of trading strategies, as they seek to profit from small price movements rather than large price swings.
Tips for Implementing Mean Reversion Trading
If you are interested in implementing a mean reversion strategy, there are a few key things to keep in mind:
- Start with a well-defined trading plan: Clearly define your entry and exit points and stick to them.
- Use risk management techniques: It is important to carefully manage your risk when implementing any trading strategy, and this is especially true for mean reversion strategies, which can be vulnerable to market shocks. Use stop-loss orders and other risk management techniques to protect your capital.
- Use a robust trading platform: Choose a trading platform that is reliable, user-friendly, and offers a wide range of tools.
- Monitor the markets regularly: Keep an eye on market conditions and be prepared to adjust your trading strategy as needed. This may involve adjusting your mean, using different technical indicators, or switching to a different trading strategy altogether.
Conclusion
Mean reversion trading is a popular strategy that can be applied to a variety of markets and can offer the potential for profits in both trending and range-bound markets. By using advanced techniques such as the z-score and pairs trading, and by following good risk management practices, traders can potentially increase their chances of success with this strategy. However, it is important to be aware of the potential risks and to carefully monitor the markets to ensure that the strategy is working as expected.
Overall, mean reversion trading can be a powerful tool in the algorithmic trader’s toolkit, but it is important to have a solid understanding of the strategy and to approach it with caution. By following the tips outlined in this article, traders can potentially improve their chances of success with mean reversion trading.
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FAQs
Is mean reversion trading profitable? ›
Mean reversion is a useful market concept to understand, but it doesn't assure profitable trading. While prices do tend to revert to the mean over time, we can't know for sure, in advance, when that will happen. Prices can continue moving away from the mean for longer than expected.
Can you make money from algorithmic trading? ›Algorithmic trading can make an extremely profitable career.
However, it is not without risk. Algorithmic traders must have a deep understanding of the markets they trade and the strategies they use. They must also be able to effectively backtest their trading systems to ensure that they are robust.
Mean reversion, or reversion to the mean, is a theory used in finance that suggests that asset price volatility and historical returns eventually will revert to the long-run mean or average level of the entire dataset.
What is the success rate of mean reversion strategy? ›Mean reversion trading strategy (the results)
Winning rate: 86.84%
Trend following strategies, when followed correctly of course, are the safest and arguably the most profitable trading strategies out there. They perform best when used over the long-term, as trends take weeks and months to develop, and may potentially last for years or even decades.
What time frame is best for mean reversion? ›The time frame is extremely important when it comes to mean reversion. Just like various markets, each time frame has its own way of moving. In fact, I have discovered over the years that the 10 and 20 exponential moving averages work the best on the four hour and daily time frames.
Why does algo trading fail? ›Algorithmic HFT is a notable contributor to exaggerated market volatility, which can stoke investor uncertainty in the near term and affect consumer confidence over the long term. As the markets move lower, more stop-losses are activated, and this negative feedback loop creates a downward spiral.
How long does it take to learn algorithmic trading? ›| Course Features | Executive Programme in Algorithmic Trading (EPAT) |
|---|---|
| Course duration | 6 months via weekend lectures |
| Course modules | 14 modules |
| Faculty members | 15+ |
| Part-time | Yes |
Calculus. Calculus is one of the main concepts in algorithmic trading and was actually termed as infinitesimal calculus, which means the study of values that are really small to be even measured.
What markets are best for mean reversion? ›Mean reversion work best in a bear market
Contrary to what many believe, our experience is that mean reversion in the stock market works best during a bear market. It might sound counterintuitive, but the reason is increased volatility. And perhaps even more counterintuitive is that long works better than short!
What are the benefits of mean reversion? ›
The mean reversion model can be used as a statistical tool to evaluate an asset's value, stock prices, P/E ratio, etc. The main advantage of this theory is that it motivates investors to select an investment, despite the unfavorable conditions, with the confidence that they will make profits in the end.
What is Z score in mean reversion strategy? ›If the z score is positive, the current price of the security is above the mean. If the z score is negative, the current price of the security is below the mean. Hence, the z score is used to generate the mean reversion trading signals. The entry z score was set to 1 and the exit z score was set to 0.
Do hedge funds use mean reversion? ›Conclusions. Due to hedge fund mean reversion, future performance of the best and worst nominal performers of the past is similar. Re-processing nominal returns does not eliminate mean reversion. However, Sharpe ratio begins to identify future under-performers.
Does mean reversion work for Crypto? ›The trend-following and mean-reversion strategies are some of the most popular in quantitative finance. Additionally, they have been found quite effective across perhaps every asset class. Bitcoin and other cryptos are no exception.
Is mean reversion real? ›Mean reversion is a phenomenon that can be exhibited in a host of financial time-series data, from price data, earnings data, and book value. When the current market price is less than the average past price, the security is considered attractive for purchase, with the expectation that the price will rise.
How can I earn 1000 a day in trading? ›- Step 1 – Open a Trading Account and Transfer Funds. ...
- Step 2 – Pick Trending Stocks From Finance Websites/apps. ...
- Step 3 – Select 3 'Trending' Stocks for Trading. ...
- Step 4 – Read Price Charts of Selected Stocks.
Intro: 5-3-1 trading strategy
The numbers five, three and one stand for: Five currency pairs to learn and trade. Three strategies to become an expert on and use with your trades. One time to trade, the same time every day.
The 1% method of trading is a very popular way to protect your investment against major losses. It is a method of trading where the trader never risks more than 1% of his investment capital. The main motive behind this rule is in terms of protection – you are not risking anything other than what is available.
Is mean reversion better than trend following? ›Trend following and mean reversion both are two great trading forms and you really cannot say that one is better than the other. It all depends on the type of strategy and market you're attempting to trade, as well as your personal preferences.
What is the most accurate time frame for trading? ›Best time frames for day trading
Using 15-minute time frames is useful to day traders because their aim is to enter and exit positions multiple times per hour/day. The primary market trend can be established using 60-minute time frames. From there, time frames of 15 minutes can be used to establish short-term trends.
Is RSI mean reversion? ›
The 2 period RSI developed by Larry Connors is a mean reversion strategy which provides a short-term buy-sell signal. The strategy gives a probable buy signal when 2-period RSI goes below 10 (lower the better) which is regarded as highly oversold.
What are the disadvantages of algo trading? ›- Knowledge of the programming language- Formulating complex algorithms requires extensive know-how of coding software such as C+, C++, Java, Python, R, etc. ...
- Dependence on technology - Faulty algorithms have the potential to result in insurmountable losses for the trader.
Conclusion. The most significant risk of algorithmic HFT is that it can amplify systemic risk. Its propensity for growing market volatility has the potential to spread to other markets, fueling investor anxiety. Unusual market volatility on a regular basis could erode many investors' faith in the market's integrity.
Do banks use algo trading? ›It is widely used by investment banks, pension funds, mutual funds, and hedge funds that may need to spread out the execution of a larger order or perform trades too fast for human traders to react to.
What is the salary of an algorithmic trader? ›Algorithmic Trader salary in India ranges between ₹ 2.5 Lakhs to ₹ 91.2 Lakhs with an average annual salary of ₹ 8.5 Lakhs.
What skills are required for algo trading? ›To get started with algorithmic trading, you must have computer access, network access, financial market knowledge, and coding capabilities.
Which platform is best for algorithmic trading? ›- Zerodha Streak.
- Zerodha Algoz.
- Algotrader.
- Robotrade.
- Robotrader.
But there is no fixed minimum amount required to start algo trading. How do you sell your successful stock trading algorithm? One suggestion might be to be a bit more specific in your language.
Which programming language is best for algorithmic trading? ›MatLab, Python, C++, JAVA, and Perl are the common programming languages used to write trading software.
What should I invest in when market is unstable? ›Consider including defensive assets for more stability
Defensive assets, such as cash and cash equivalents, Treasury securities and other U.S. government bonds, can help stabilize a portfolio when stocks are slipping.
What are mean reversion strategy indicators? ›
Mean reversion indicators (lagging) measure how far a price swing will stretch before a counter impulse triggers a retracement. Relative strength indicators (leading) measure oscillations in buying and selling pressure. Momentum indicators (leading) evaluate the speed of price change over time.
What is the difference between mean reversion and trend trading? ›Mean reversion tends to be a high probability system with low reward and high risk per trade. Conversely, trend following tends to be a low probability system with high reward and low risk per trade.
What is momentum mean reversion trading? ›Mean reversion is the opposite of momentum. Momentum relies on the long tails that sustained trends provide and the goal is to ride those trends for as long as possible. Mean reversion on the other hand relies on choppy random price action and preferably tight price tails so that price snaps back very quickly.
Is mean regression the same as mean reversion? ›Reversion to the mean, also called regression to the mean, is the statistical phenomenon stating that the greater the deviation of a random variate from its mean, the greater the probability that the next measured variate will deviate less far.
What is momentum trading? ›Momentum trading is a strategy that seeks to capitalize on momentum to enter a trend as it is picking up steam. Simply put, momentum refers to the inertia of a price trend to continue either rising or falling for a particular length of time, usually taking into account both price and volume information.
What is quantitative mean reversion trading? ›One of the key trading concepts in the quantitative toolbox is that of mean reversion. This process refers to a time series that displays a tendency to revert to its historical mean value. Mathematically, such a (continuous) time series is referred to as an Ornstein-Uhlenbeck process.
What is half life in mean reversion? ›The 'half life of mean reversion' is the average time it will take a process to get pulled half-way back to the mean.
Why is z-score better than mean? ›The value of the z-score tells you how many standard deviations you are away from the mean. If a z-score is equal to 0, it is on the mean. A positive z-score indicates the raw score is higher than the mean average. For example, if a z-score is equal to +1, it is 1 standard deviation above the mean.
What is 2 and 20 rule in hedge fund? ›"Two" means 2% of assets under management (AUM), and refers to the annual management fee charged by the hedge fund for managing assets. "Twenty" refers to the standard performance or incentive fee of 20% of profits made by the fund above a certain predefined benchmark.
What is the most profitable hedge fund strategy? ›Top hedge funds follow Equity Strategy, with 75% of the Top 20 funds tracking the same. Relative Value strategy is followed by 10% of the Top 20 Hedge Funds. Macro Strategy, Event-Driven, and Multi-Strategy make the remaining 15% of the strategy. Also, check out more information about Hedge Fund jobs.
What is the average ROI for hedge funds? ›
According to BarclayHedge, the average hedge fund generated net annualized returns of 7.2% with a Sharpe ratio of 0.86 and market correlation of 0.9 over the last five years through 2021.
Is mean reversion still profitable? ›Is mean reversion profitable? Yes, mean reversion works, but not in all markets. To our knowledge, it works best for stocks and less for other financial assets (for example, FOREX is more trending than mean-reverting).
What is the most profitable trading strategy in crypto? ›- Breakout trading. Breakout trading is a very popular crypto trading strategy thanks to its versatility. ...
- Moving averages crossovers. This is a very old and well-known strategy that often allows you to identify a trend change. ...
- MACD. ...
- Inter-Exchange arbitrage.
It is possible to earn a living by day trading in cryptocurrency, but it is also risky and highly speculative. Day trading in cryptocurrency involves buying and selling digital assets on a short-term basis, often within the same day, in an attempt to profit from price fluctuations.
What is linear mean reverting trading strategy? ›A mean reverting strategy is that, when you see the value is larger enough than its mean, you short the asset given that you know it will eventually return to its mean for your taking profit (hence the name of mean-reversion strategy), and vice versa when the value is much less than the mean value.
How do you know if a trend will reverse? ›For a trend reversal to happen, either the lower or upper trend line will be breached as the price starts to move in the opposite direction. For example, if there is a breakout with lower highs and lower lows, then you can expect an uptrend reversal.
What is the success rate of price action trading? ›However, price action strategies have been shown to be quite accurate, with many of the setups used by the price action trader showing a success rate of 75% or higher. The most accurate trading pattern used by a price action trader is the head and shoulders (or inverted head and shoulders) setup.
Does backtesting make you a better trader? ›Backtesting is one of the most important aspects of developing a trading system. If created and interpreted properly, it can help traders optimize and improve their strategies, find any technical or theoretical flaws, as well as gain confidence in their strategy before applying it to the real world markets.
Is high frequency trading profitable? ›A high-frequency trader will sometimes only profit a fraction of a cent, which is all they need to make gains throughout the day but also increases the chances of a significant loss. One major criticism of HFT is that it only creates “ghost liquidity” in the market.
Does mean reversion work in crypto? ›The trend-following and mean-reversion strategies are some of the most popular in quantitative finance. Additionally, they have been found quite effective across perhaps every asset class. Bitcoin and other cryptos are no exception.
What are the most profitable chart patterns? ›
The head and shoulders patterns are statistically the most accurate of the price action patterns, reaching their projected target almost 85% of the time. The regular head and shoulders pattern is defined by two swing highs (the shoulders) with a higher high (the head) between them.
Do professional traders use price action or indicators? ›Who Uses Price Action Trading? Since price action trading is an approach to price predictions and speculation, it is used by retail traders, speculators, arbitrageurs and even trading firms who employ traders. It can be used on a wide range of securities including equities, bonds, forex, commodities, derivatives, etc.
How long does it take to master price action trading? ›You will need to be patient and be ready to work hard. For learning swing trading, it takes at least 6 months and for intraday trading, at least a year. So don't get discouraged by the time required because this is a skill that will make you money for the rest of your life.
Is 100 trades enough for backtesting? ›The bigger the sample is the smaller the margin of error, but usually a sample date of 200 trades should be sufficient. If your trading system generates enough trades, then you should use 500 – 600 trades.
What are the drawbacks of backtesting? ›Shortcomings of Back-Testing
Another limitation is the inability to model strategies that would affect historic prices, and finally, back-testing is limited by potential curve fitting. Meaning, it is possible to find a strategy that would have worked well in the past, but will not work well in the future.
Backtesting a risk model, for instance, is typically done by checking if actual historical losses on a portfolio are very different from the losses predicted by the model. If actual losses are consistently higher, the model is underestimating risk. If they are lower, the model is overestimating risk.
Why do high-frequency traders never lose money? ›One strategy is to serve as a market maker, where the HFT firm provides liquidity on both the buy and sell sides. By purchasing at the bid price and selling at the ask price, high-frequency traders can make profits of a penny or less per share. This translates to big profits when multiplied over millions of shares.
What are the risks of algorithmic trading? ›But ALGO trading also entails risks stemming from potential failures of algorithms, IT systems and processes. In recent years, a number of major ALGO trading failures have resulted in substantial losses, fines and reputational damage for credit institutions and investment firms.
Which language is best for high-frequency trading? ›C++, a middle-level programming language, is a blessing for traders as the components of High-Frequency Trading (HFT), which are latency-sensitive, are usually developed in C++. This is because C++ is extremely efficient at processing high volumes of data.