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Mean reversion trading systems amibroker forex

Forex strategy box breakout 10.12.2020

mean reversion trading systems amibroker forex

Mean reversion work best in a bear market; 3 free mean reversion strategies; Mean reversion strategy number 1: Deviation from a recent high in XLP; Mean. This is the third book I read by Howard Bandy. I think there is too little useful content between the many AmiBroker codes and theoretical stuff. A mean reversion trading strategy involves betting that prices will revert back towards the mean or average. Markets are forever moving in and. BETTERMENT INVESTING UK TOP You time lake is locations, Join that's. Of the for Cyberduck was and expandable such. In Internet on started that actually.

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Mean reversion trading systems amibroker forex forex startup

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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 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. 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. 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.

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. You will get more out of the process if you have some clear aims in mind. 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. For a mean reversion strategy to work, you want to find extreme events that have a high chance of seeing a reversal. 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. 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 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 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. The VIX volatility index measures day volatility in the US stock market and it has strong mean reverting tendencies. 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.

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. 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. 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 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.

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. 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. 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. 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. 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 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. 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.

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. Bear in mind that markets can sometimes gap through your stop loss level so you must be prepared for some slippage on your exits.

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. 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. 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. 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. I have found that some of the following rules can work well to filter stocks:. This is most common when you trade a universe of stocks where you might get lots of trading signals on the same day. 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. 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. 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. 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. 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. At the end, you stitch together all the out-of-sample segments to see the true performance of your system.

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.

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. A big advantage of mean reversion trading strategies is that most of them trade frequently and hold trades for short periods.

This is perfect because it means you can generate a large sample of trades for significance testing and stress testing. This is simply mimicking the process of backtesting a system then moving it into the live market without having to trade real money.

Using out-of-sample data can be considered a good first test to see if your strategy has any merit. This allows you to test different market conditions and different start dates. Some strategies suffer from start-date bias which means their performance is dramatically affected by the day in which you start the backtest. If you start your backtest on the first of January you will likely get a different portfolio than if you started it a few days later.

That can result in a significant difference. Test your system on different dates to get an idea for worst and best case scenarios. See how it performs in the crash or the melt up. The underlying trend is going to be one of the biggest contributors to your mean reversion algorithm returns both in the in-sample and out-of-sample. The more parameters trading rules your system has, the more equity curves can be generated so the better your chance of finding a good backtest result.

However, this comes at a cost because the more parameters you have, the more easily the system can adapt itself to random noise in the data — curve fitting. Mean reversion strategies that have fewer trading rules require smaller sample sizes to prove they are significant. Often, this is a trade-off. Small changes in the variables and parameters of your system should not dramatically affect its performance. You can test your mean reversal strategy on different time frames, different time windows and also different markets.

But if it does, it provides an extra layer of confidence that you have found a decent trading edge. Monte Carlo can refer to any method that adds randomness. The careful use of randomness can be used to reverse engineer your system and help evaluate your system in a number of different ways. You are unlikely to get that same sequence in the future so you need to be sure your system works based on an edge and not on the order of trades. To implement this, take your original list of trades, randomize the order times then observe the different equity curves and statistics generated.

Add random noise to the data or system parameters. You can also get an idea if the system is too closely tuned to the data by adding some random noise to your data or your system parameters. Regarding parameters, you can test your system and optimise various input settings. Just like an indicator optimisation. For randomising the data, one method is to export the data into Excel and add variation to the data points.

If you can, do this a large number of times and observe the equity curves that are generated on new sets of noisy data. See if your system holds up or if it crashes and burns. As mentioned before, small changes in the data or in the parameters should not lead to too big changes in system performance. If you cannot produce better risk-adjusted returns than buy and hold there is no point trading that particular system.

It is important to take the underlying trend into consideration. One option, described in detail by David Aronson , is to detrend the original data source, calculate the average daily returns from that data and minus this from your system returns to see the impact that the underlying trend has on your system.

Take the original data and run 1, random strategies on the data random entry and exit rules then compare those random equity curves to your system equity curve. Usually what you will see with random equity curves is a representation of the underlying trend. If your system cannot beat these random equity curves, then it cannot be distinguished from a random strategy and therefore has no edge.

It is also possible to construct forward projected equity curves using the distribution of trade returns in the backtest. This can give you another idea of what to expect going forward. These techniques are not easy to do without dedicated software.

Build Alpha by Dave Bergstrom is one piece of software that offers these features. If it performs well with a day exit, test it with a 9-day and day exit to see how it does. Vary the entry and exit rules slightly and observe the difference. I will often test long strategies during bear markets and vice versa with short strategies with the view that if it can perform well in a bear market then it will do even better in a bull market.

Lastly, one of the simplest ways to build more robust trading systems is to design strategies that are based on some underlying truth about the market in the first place. The turn of the month effect , for example, exists because pension funds and regular investors put their money into the market at the beginning of the month.

Individual investors often have more money to invest at the start of the month. It gives the strategy more credibility. This mean reverting strategy has also stood the test of time. Not all trading edges need to be explained. But patterns that you cannot explain should be evaluated more strongly to prove that they are not random. The final step when building your mean reversion trading strategy is to have a process set up for taking your system live and then tracking its progress. When you trade in the live market, your price fills should be as close as possible to what you saw in backtesting.

You should also be aware of the capacity of your trading strategy. If you are trading illiquid penny stocks, you cannot simply buy thousands of shares of stock without affecting the spread. You should know the capacity of your trading strategy and you should have accounted for this in your backtesting before you take it live.

If your trading strategy is spiralling out of control or the market is going crazy, you should have a way to turn things off quickly. Some brokers, Interactive Brokers included, have commands you can use to close all positions at market. Thus you may start scanning symbols but very quickly the number scanned will dwindle to just a dozen or so tickers. When you approach am your real-time scan will be very fast and you will be able to place your LMT order very close to the Open — you may even be able to improve on the Open price.

Even though a few people looked at the code below and found nothing wrong, the profits seem rather high for such a simple system. Please report errors you may see. This idea was posted on the main AmiBroker list on July 3, There were numerous excellent comments on the list and if you are interested in working on this system you do well to read them all before starting.

After posting I found a number of posts on the web discussing this trading idea, some claimed to be trading a similar system with good success. Googling for it will get you many more hits to similar systems. Here are a few links:. NDX Trading intraday mean reversion using limit orders Trading intraday mean reversion using limit orders — does it work?

As an Amibroker user you have better tools than most traders and you have a better chance than most to come up with a variation that works. Some people commented that this system will not work in real trading, while they may be right others say schemes like this work. MACD default, I look for Histogram 4 down bars and 1 up bar for buy signal I have the histogram set to red for down and blue for up so I can see clearly.

Buying on pullback when the market continues its up trend. Note: Some variations of the setup rules can define signals that are quite rare and in small databases it is possible that there will be no setups on any given day hence no stock will be reported by the scan. This is the first in a series off KISS keep it simple, stupid trading ideas for you to play with. All system ideas presented here are unproven, unfinished, and may contain errors. They are intended to show possible patterns for further exploration.

I prefer real-time systems that trade fast, are automated, and are devoid of traditional indicators. Preferably, they should have no optimizable parameters; however, I may not always be able to meet this objective. The first system shown below is a copy of the demo system I use to develop Trade-Automation routines elsewhere on this site.

Real-Time Gap-Trading. To see how this works, you should Backtest it on 1-minute data with a periodicity in the range of minutes. Your first impression may be that these profits are simply due to an up market, however, the fact that Long and Short profits are about equal suggests there is more to it. This reduces risk with respect to market exposure and gives you more time to enjoy other activities.

Ticker names are omitted to keep the chart compact; the chart simply shows a net profit bar for each ticker tested. Be cautioned that in its raw form the drawdowns are unacceptable and that there may be volume restrictions for many tickers. Since this system has low exposure, it may be a candidate for market scanning and ranked portfolio trading.

However, price movement from different tickers may be correlated, and trades from different tickers may overlap. If many tickers trade at the same time, it would be difficult to increase system exposure. Edited by Al Venosa. This category is reserved for real working trading systems, i. Since the criteria for tradability varies from person to person, and since systems may work or not depending on how they are traded, it will be difficult to screen contributions here. With respect to what is posted here, keep an open mind and consider that the poster considers the system tradable.

You can contribute by posting as an author requires registration or in a comment to this post. This is where you can share trading systems that are marginally profitable, i. Such systems can often be improved by adding Stops, Targets, Money Management, Portfolio techniques, etc. The reality is that while you may not have the expertise to make it work someone else may. Almost all of us find trading system ideas in books and magazines that we then code in AFL for evaluation.

Some of these systems may have been around for many years while others are new ideas. After coding them, almost always, we are disappointed and chuck out the system work! Instead of throwing out your work you are invited to post the system here to give another developer a chance to fix it.

You are invited to contribute as an author requires registration or in a comment to this post.

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Developing a Profitable Mean-Reversion Trading System with Indicators

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