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These tend to be the strongest performers so you will get better results than you would have in real life. For stocks: Is your data the right frequency? There can also be some difficulty in backtesting high frequency trading strategies with low frequency data which I have talked about previously. This is because stock prices are an amalgamation of prices coming from multiple different exchanges. For these intraday systems, you will need more granular data such as 1-minute data.
For stocks: Is the data point-in-time accurate? If you are using fundamental data as part of your trading strategy then it is crucial that the data is point-in-time accurate. Finding accurate point-in-time data for fundamentals can be difficult. But there are options available from providers like Compustat and FactSet.
For futures: How is the data being reproduced? Futures markets are comprised of individual contracts with set lifespans that end on specific delivery months. This can be OK for intraday trading and for seeing where a futures contract traded in the past. But it means there are price gaps where contracts roll over. This produces a realistic representation of the market and is generally better for backtesting.
However, the downside to this approach is that historical price levels are not accurate. Markets in backwardation can end up with negative prices due to the back-adjustment calculation and these prices may not be adequately shown on some charts. For example, the back-adjusted Soybeans chart below shows negative prices between and late The Soybeans chart shows negative price values between due to the back-adjustment. System calculations such as those using multiplication and division can be thrown off by negative prices or prices that are close to zero.
Therefore, you need to be careful using these calculations in your formulas. Make sure back-adjusted prices are not giving off false signals. For forex: Where is the data from and what does it show? There is no centralized exchange in forex so historical data can differ between brokers.
Usually the difference is small but it can still have an impact on your mean reversion forex strategy. A general rule is to only use historical data supplied by the broker you intend to trade with. Doing so means your backtest results are more likely to match up with your live trading results.
In addition, forex quotes are often shown in different formats. Some providers show the bid, some the ask and some a mid price. If you intend to backtest this data you need to know what you are dealing with. Otherwise your mean reversion forex strategy will fail as soon as you go live. You can add a couple of pips of slippage to reflect the spread that you typically get from your broker.
You want your backtest trades to match up with your live trades as closely as possible. Remember that spreads can increase during important events or high volatility. Is the data kept clean and up to date? Maintaining a database for hundreds or thousands of stocks, futures contracts or forex markets is a difficult task and errors are bound to creep in. Is there enough data to make meaningful conclusions?
A lot of trading mistakes can come from not having enough data in the first place. There are no hard rules but ideally you want to see a good sample of trades. For a mean reversion strategy that trades daily bars you will typically want at least eight to ten years of data covering different market cycles and trading conditions.
Bare in mind, however, that good trading strategies can still be developed with small sample sizes. They are simply harder to prove with the typical stress testing techniques available. You will get more out of the process if you have some clear aims in mind. Some key questions to ask are: What markets do you want to focus on? What timeframes do you want to test? What ideas will you test? What kind of tests will you run? What parameters will you test? What metrics will you use to measure success?
When will you give up on an idea? When I sit down to do analysis, I try to focus on markets that are more suited to my trading style. I look for markets that are liquid enough to trade but not dominated by bigger players. I want to test markets that will allow me to find an edge. In terms of timeframes I usually focus on end-of-day trading and I try to start off with a logical idea or pattern that I have observed in the live market. I like to only test a couple of trading rules at first and I want to see a large sample of results, usually over trades.
My biggest concern is to avoid curve fit results and find strategies that have a possible explanation or behavioural reason for why they would work. No matter what type of analysis I do I always reserve a small amount of out-of-sample data which I can use at a later to date to evaluate the idea on.
If I have only a small amount of data then I will need to see much stronger results to compensate. I will always compare this to a simple benchmark like buy and hold and I like to see some consistency between in-sample and out-of-sample results. I know that these factors will affect me mentally when I trade the system live so I need to be comfortable with what is being shown.
When it comes to backtesting a mean reversion trading strategy, the market and the trading idea will often dictate the backtesting method I use. If the idea is based on an observation of the market, I will often simply test on as much data as possible reserving 20 or 30 percent of data for out-of-sample testing. This allows me to see the maximum number of trade results. If the idea has adjustable parameters or I am only testing one single instrument, I will often use a walk-forward method.
The walk-forward method will work to overcome the smaller sample of trades that comes from trading just one market. I will often put a time limit on my testing of an idea. This is easier said than done though so you need to be disciplined.
Step Four — Buy And Sell Rules For a mean reversion strategy to work, you want to find extreme events that have a high chance of seeing a reversal. There are plenty of buy and sell rules to choose from: Standard Deviation Standard deviation measures dispersion in a data series so it is a good choice to use in a mean reversion strategy to find moments of extreme deviation.
To implement this effectively you can: Calculate the average daily returns of your intended instrument Organise the daily returns into regular intervals bins Plot the intervals as a histogram and calculate probability distribution Develop trading strategies that trade when movement is more than an average standard deviation sigma.
Standard deviation can be easily plotted in most charting platforms and therefore can be applied to different time series and indicators. Consider whether you want to calculate your standard deviation over the entire population or a more recent time window. These means market conditions do not stay the same for long and high sigma events happen more often than would be expected.
When RSI 3 is under 15, it is often a good place to enter a mean reversion trade. When a stock becomes extremely oversold in a short space of time short sellers will take profits. Longs will also throw in the towel or have their stops hit. This can trigger a quick rebound in price. Profits can be taken when the indicator breaks back above 50 or Strategies based on this indicator have worked well on stocks and ETFs in the past.
Bollinger Bands Bollinger Bands plot a standard deviation away from a moving average. A close under the bottom Bollinger Band or above the top Bollinger Band can be an extreme movement and therefore a good opportunity to go the other way.
A value more than 0. A value of 1 means the stock finished right on its highs. For mean reversion strategies I will often look for a value below 0. This is a good indicator to combine with other technical trading rules. This makes it a useful choice for incorporating into a mean reversion system. For example, when VIX is heavily oversold, volatility is low, and that can sometimes indicate complacency. This can be a good time to short stocks since investors are not prepared for a jump in vol.
When VIX is high, there may be a lot of fear in the market and that can indicate a chance to go long. Historically, big spikes in the VIX have coincided with attractive buying opportunities. We have a system in our program that has a very high win rate using this method.
However, bear in mind that volatility particularly low volatility can go on for long periods. PE Ratios A different approach to mean reversion is to look at financial ratios. Buying a stock when the PE drops very low and selling when it moves higher can be a good strategy for value investing.
Some value investors have been known to seek out PE ratios under 10, under 5, even under 1. Investor Sentiment As mentioned previously, sentiment indicators can also identify turning points. When too many investors are pessimistic on a market it can be a good time to buy. This can be part of a longer term strategy or used in conjunction with other rules like technical indicators.
Stop Losses The more a market moves against you, the stronger the mean reversion signal becomes. Therefore stop losses can be logically inconsistent for mean reversion systems and they can harm performance in backtesting. However, stop losses should still be used to protect against large adverse price movements especially when using leverage where there is a much higher risk of ruin. Statistics such as maximum adverse excursion can help show the best placement of fixed stop losses for mean reversion systems.
Fixed stop losses will usually reduce performance in backtesting but they will keep you from ruin in live trading. Trailing Stops And Profit Targets Trailing stops work well for momentum systems but they can be hard to get right for mean reversion strategies.
I have never found that trailing stops work any better that fixed stops but they may be more effective when working on higher frequency charts. Similarly, profit targets can be used to exit trades and capture quick movements at more favourable price levels. If using a profit target, it is a good idea to have a target that adjusts to the volatility of the underlying instrument.
For example, a target based on two or three times the ATR average true range. Overall, I have found that profit targets are better than trailing stops but the best exits are usually made using logic from the system parameters.
Time-Based Stops Now and again you will get a mean reversion trade that never rebounds. Instead of a quick reversal, the stock keeps going lower and lower. These are the worst type of trades for mean reversion strategies because you can be kept stuck in a losing trade for what seems an eternity. In these cases, a time-based stop can work well to get out of your losing position and free up your capital for another trade. I have found that 10 or 12 days can be enough to get out of a position that continues to drift against you.
GE stocks has drifted lower and lower and is not a good candidate for mean reversion. Step Five — Initial Testing Once you have some basic trading rules set up you need to get these programmed into code so that you can do some initial testing on a small window of in-sample data. You must be careful not to use up too much data because you want to be able to run some more elaborate tests later on. At this point you are just running some crude tests to see if your idea has any merit.
This is before you add any other fancy rules or position sizing. No money management, no position sizing, no commissions. I want to see if the idea is any good and worth continuing. If the idea does not look good from the start you can save a lot of time by abandoning it now and moving onto something else. So do some initial tests and see if your idea has any merit. Step Six — Position Sizing For Mean Reversion Trading If your mean reverting strategy passes initial testing, you can begin to take it more seriously and add components that will help it morph into a stronger model.
Position sizing is one of those crucial components to a trading system and there are different options available. Volatility Adjusted Position sizing based on volatility is usually achieved using the ATR indicator or standard deviation. The idea is that you buy more shares when volatility is low and fewer shares when volatility is high. This makes logical sense since volatility determines the trading range and profit potential of your trading rule. Volatility in stocks can change dramatically overnight.
For instance after an important piece of news. Equal Weighting Equal weighting is simply splitting your available equity equally between your intended positions. This is a simple method for position sizing which I find works well on stocks and is a method I will often use. Fixed Number Of Shares Or Contracts This approach involves trading a fixed number of shares or contracts every time you take a trade. This approach does not allow compounding which means you can get smaller drawdowns at the expense of larger gains.
This technique works well when trading just one instrument and when using leverage. It allows you to keep your risk at an even keel. As you gain confidence, you can increase the number of contracts and thereby dramatically improve your earning potential. Percent Risk To trade a percentage of risk, first decide where you will place your stop loss.
Then calculate the trade size that will allow your loss to be constrained to that percentage of your bankroll — if the stop loss is hit. This percentage is typically set at 0. Bear in mind that markets can sometimes gap through your stop loss level so you must be prepared for some slippage on your exits. Kelly Formula Using statistics from your trading strategy win rate and payoff the Kelly formula can be used to calculate the optimal amount of risk to take on each trade.
Since this is the optimal amount it can also lead to large drawdowns and big swings in equity. This is why many traders will halve or use quarter Kelly. Just being in the ballpark of Kelly is going to give you a good position size to apply to your trades so it is worth studying the formula. This is especially true if your estimates are based on simulations and not live trading. The Kelly Formula for position sizing. For example, if you have a mean reversion trading strategy based on RSI, you could buy more shares, the lower the RSI value gets.
The idea is that you buy more of a something when it better matches the logic of your system. Dynamic, factor weighted position sizing is something I have been looking more closely at and written about here. Step Seven — Adding More Rules Once you have your buy and sell rules sorted you will probably want to add some additional rules to improve the performance and logic of the system.
Market Timing Elements A good place to start is to identify some environments where your mean reversion system performs poorly in so that you can avoid trading in those conditions. There has been a lot written about the day moving average as a method to filter trades. This can be applied to the stock itself or the broader market. There are numerous other ways to use filters or market timing elements.
This is most common when you trade a universe of stocks where you might get lots of trading signals on the same day. Ranking for a mean reversion trading strategy might be linked to your buy rules. This ensures the logic of your system is consistent. Good trading systems can often be found by chance or with rules you would not have expected.
The important thing to remember is that ranking is an extra parameter in your trading system rules. A good backtest result might be caused entirely by your ranking method and not your buy and sell rules. Therefore you need to be careful that the ranking does not contribute to curve fit results. This is why I will often use a random ranking as well. Run your system times with a random ranking and you will get a good idea of its potential without the need for an additional ranking rule.
Other Timing Rules Once again, there are thousands of different rules and ideas to apply to your mean reversion trading strategy. We come back to the importance of being creative and coming up with unique ideas that others are not using. This may be your best bet to find a strategy that works.
Step Eight — Optimisations And Walk Forward Analysis The further you progress through the steps and the more rules you add to your trading system the more concern you need to pay against the dangers of curve fitting and selection bias. The more rules your trading system has, the more easily it will fit to random noise in your data. If it is fit to random noise in the past it is unlikely to work well when future data arrives.
Future data will be new and have its own characteristics and noisiness. Also, the more backtests you run, the more likely it is that you will come across a system that is curve fit in both the in-sample and out-of-sample period. Just because a system has performed well in a segment of out-of-sample data does not necessarily mean it is not a curve fit strategy. You can see a good out-of-sample result by chance as well. Optimisations Despite these drawbacks, there is still a strong case for using optimisations in your backtesting because it speeds up the search for profitable trade rules.
By optimizing your trade rules you can quickly find out which settings work best and then you can zone in more closely on those areas building a more refined system as you go. The key is to recognise the limitations of optimising and have processes in place that can be used to evaluate whether a strategy is curve fit or robust. One of the simplest rules with optimising is to avoid parameters where the strong performance exists in isolation.
Instead, look for a range of settings where your system does well. For example, if you have a mean reversion trading strategy that buys day lows, it should also perform well on day lows, day lows, day lows, day lows etc. Walk Forward Analysis Another interesting method that can be used to optimise a trading strategy is called walk forward analysis, first introduced by Robert Pardo. This is where you separate your data out into different segments of in-sample and out-of-sample data with which to train and evaluate your model.
Your system trains itself on the in-sample data to find the best settings then you move it forward and test it once on the out-of-sample segment. Then you step forward to the next segment of in-sample data and repeat the process. Walk forward analysis example. Img thanks to Amibroker. Essentially, this method replicates the process of paper trading but sped up. You repeatedly test your rules on data then apply it to new data. The advantage of walk forward analysis is that you can optimise your rules without necessarily introducing curve fitting.
Give the system enough time and enough parameter space so that it can produce meaningful results. Step Nine — Stress Testing And Evaluation When you run a backtest, depending on your software platform, you will be shown a number of metrics, statistics and charts with which to evaluate your system. As I mentioned in step three, you should already know what metrics you are looking for at this point and how you want to evaluate your system. The first thing I will always look at is the overall equity curve as this is the quickest and best method for seeing how your mean reverting strategy has performed throughout the data set.
Each metric paints a different picture so it is important to look at them as a whole rather than focus on just one. This metric gives a good idea of the smoothness of an equity curve. However, in my experience, these two time frames are the most reliable when using mean reversion to identify buying or selling opportunities.
Therefore we can consider any other time frame as an exception to the rule. In other words, no clear direction or trend. This can be a short-term trend on the four-hour chart or a longer-term trend on the daily chart. Either way, a clear directional bias is needed to take full advantage of the use of mean reversion.
Notice in the USDJPY daily chart above, the market made two extended moves during which there was no reversion to the mean. In fact, the second rally totaled 1, pips. It all comes down to your style of trading — that is, your comfort level as a trader. You could opt to be more of a swing trader, which involves looking for reversions to the mean.
I consider myself a short to mid-term swing trader. Notice how the pair formed a bullish pin bar on a reversion to the mean. We also had former trend line resistance now acting as support. This is a great example of how you can use mean reversion, the pin bar trading strategy , trend lines and momentum in your favor.
So which time frame is best? This again depends on how you choose to trade and ultimately what your trading plan says. In fact, I consider this the preferred way to trade Forex price action. Summary I hope this lesson has presented you with a new way to use moving averages as a mean reversion tool. Just remember to always use the techniques discussed here in combination with other confluence factors to truly put the odds in your favor.
Do you currently use something similar to avoid market overextensions? Leave your feedback below.
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| Reversion to the mean forex | Does mean reversion work? The predicted returns are added to the rank array and then sorted by return. Use it to improve both your trading system and your backtesting process. A hundred or two hundred years may sound like long enough but if only https://opzet.xyz/irish-open-golf-2022-betting/4129-forex-time-indicator.php few signals are generated, the sample size may still be too small to make a solid judgement. If it works well during bull markets, see how it does in bear markets. |
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| Spreads on nba games today | Then both stocks are cointegrated with each other. Let it be said that there are many other ways that you could measure mean reversion so you are limited only in your imagination. The t-stats of the coefficients are The price must touch the lower line of the Bollinger band. Test your system on different dates to get an idea for worst and best case scenarios. Using these factors we use regression to predict the returns of the coming month. Back to top Mean Reversion in Pairs Trading Pairs trading is a fruitful option for mean reversion to the mean forex trades. |
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| Em dash on keyboard | These can act as good levels to enter and exit mean reversion trades. History [symbol], num, Resolution. This inherently helps increase the average win rate of the system. It applies well to the ideas of buying low and selling highby hoping to identify abnormal activity that will, theoretically, revert to a normal pattern. In reality, however, successful mean reversion traders know all about this issue and have developed simple rules to overcome it. It allows you to keep your risk at an even keel. |
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