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The best forex breakdown strategies

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To find the optimal parameters for both the tick intervals and the time lags, we then employed the multi-linear regression analysis for limit orders see 2. In the following subsections, we described the detailed methodology of the identification of the optimal parameters. We first quantified the timescale of trend-following behavior of each trader by studying the correlation between historical price trends and future limit-order price changes by traders.

Let us look at the two sample trajectories of limit orders issued by two different traders, which illustrate the variety of the limit-order response speed to the change of transaction prices Fig 2 A. For example, let us compare the interpretation based on the blue and red lines in Fig 2 B.

The blue line is based on 3 maximum time lag with 1 tick coarse-graining and indicates downward trends. On the other hand, the red line is based on 3 maximum time lag with 4 tick coarse-graining and indicates upward trends though the given transaction time series is the same. It is therefore necessary to determine i the timescale for coarse-graining and ii the maximum time lag for each trader. A , Sample trajectories of limit orders issued by two FTs during six minutes: the lifetimes of ask and bid orders red and blue lines, respectively , and a trajectory of transaction prices black line.

B , Schematic of the difference interpretation of trends from a single price trajectory. If a trader sees short-term price changes, the prices are in a down-trend the blue curved arrow , whereas if a trader sees longer-term price changes, prices are in an up-trend the red curved arrow. Different timescales lead to the different interpretations to historical trends. C , Relationship between the limit-order price change and trends. The historical periods over which to take an average are 1 tick orange , 3 ticks light-blue , and 8 ticks violet.

The hyperbolic tangent relation between them, empirically shown in early works [ 10 , 11 ] focusing on the last single tick price change, also establishes price changes over several ticks. D , Three sample-normalized weights of regressors obtained by Eq 2 left side and the weights of FTs after scaling right side. The inset plots the scaled weights on a log scale.

Although there are deviations around the distribution tail, the overall trend is well captured by the exponential function. In this paper, we determined such strategy parameters by maximizing the correlation between the historical market price changes and the future limit-order price changes of the trader. Here is the mid-price of the best ask price and the best bid price. P t is the transaction price at time t. When there is no bid ask quote, is substituted by the last bid ask quote price, and extreme limit-order price changes more than tpip are excluded from the following analysis.

Correspondingly, is calculated on the basis of the 1 tick coarse-graining and 5 maximum time lags orange , the 3 tick coarse-graining and 10 maximum time lags light-blue , and the 8 tick coarse-graining and 8 maximum time lags violet. We found that these three examples can be well-approximated by the hyperbolic tangent curves denoted by the black line.

It is worth noting that this relationship is a straightforward generalization of the formula found in Refs. On the basis of this relation, we retroactively incremented the number of time lags under multiple time-coarse-graining and optimize the parameter set to maximize the correlation between the historical market price change and the future quoted price of a trader.

We reduced this non-linear equation to a linear equation using the inverse function of hyperbolic tangent and then performed a multi-linear regression analysis. We next found that coefficients w i k approximately decays exponentially, whereby the characteristic timescale of trend-following can be defined by the decay timescale in Fig 2 D. After determining the maximum time lag K i and the time-coarse-graining such that the adjusted coefficient of determination takes a maximum, we show three examples of coefficients of the regressors with the approximate exponential functions Fig 2 D.

We note that we could not identify the function form for the tail in the absence of sufficient number of data points. Indeed, the typical maximum time lag is five and is not sufficient to conclude whether the true tail obeys other functions such as a power-law tail or not. Fortunately, however, the body part of the weight function is the most important to measure trends and thus we employed the exponential fitting function for simplicity in this paper.

This result shows the direct evidence that the EMA is a typical metrics to measure market price trends [ 12 ]. We excluded from these plots data of traders for which the sum of the squared errors SSE of the prediction normalized by the d i exceeds the 0.

We explain the way to determine both c i and K i introduced in Eqs 2 and 3. We performed the following iteration method with a given coarse-graining time interval j ranging from 1 to 20 ticks. We describe how to determine the time interval referred to by traders in making a decision to issue market orders.

Sample data points for which market orders are issued by two real traders were plotted Fig 3 A , and traders also seemingly have different responses to price trends due to the similar reason explained in limit-order analysis. Note that traders are allowed to attach the acceptable transaction price to market orders.

If the current best price is worse than that price, a market order fails. To analyze how traders respond to trends, we used logistic regression in the parallel method to analyze limit orders. A , Sample trajectories of market orders by two FTs during six minutes. Upward downward triangles are sell buy orders. The colour of triangles represents the status of market orders: orange gray signifying filled missed. The black line is the trajectory of transaction prices.

B , Weights w i in Eq 3 of traders obtained from a logistic analysis Eq 5 are plotted at j i k. To obtain the optimal w i , we follow the same procedure with the limit-order analysis except for using the SSE, not. C , The horizontal and vertical axis mean the historical trends and the probabilities controlling the direction of market orders, respectively. The black is the standard logistic function. On top of that, we depict the magnitude of the historical trends for two traders as cross-marks, which is obtained by the Eq 5.

The market orders issued to the buy sell side are depicted by the cross-marks at 1 0. We set the threshold of the p -value at 0. Note that despite the weaker threshold employed in this section, this criteria is generally accepted in the field of statistics [ 13 ].

After determining both the time-coarse-graining and the maximum time lag for each trader, we plotted the coefficients obtained by the multi-logistic regression for traders Fig 3 B. Most of the coefficients are positive, but a few are negative. We classify their strategies based on the sign of. We next show the fitting result based on our logistic regression method. The horizontal and vertical axis of Fig 3 C respectively indicate the historical trends and the probabilities controlling the direction of market orders i.

The black line in this figure is the standard logistic function. In addition, we marked the magnitude of historical trends as cross-marks for two traders when market orders are issued, which are calculated according to Eq 5. Given the vertical axis showing the probabilities controlling the direction of market orders, the top bottom graph shows a trader weakly strongly motivated by historical trends. To understand financial markets as a market ecology, we are interested in the typical differences of limit-order strategies, rather than the detailed differences of them in this paper.

We thus cluster the limit-order strategies by the similarity of trend-following timescales, and then track the differences of the limit-order activities back to the differences of their limit-order book shapes, which has been a topic of study of late [ 10 , 11 , 15 — 19 ]. Fig 4 A shows the distribution of the reference times. Using the k -means method, we classified the reference times into three clusters: the short-time typically 4 ticks; 30 sec , intermediate-time typically 20 ticks; 2.

To determine the cluster size, we employ the silhouette method [ 20 ] and compared clusters ranging from size 2 to 5. We conclude that three clusters form an optimal size in terms of both the silhouette coefficient and the thickness of clusters.

A , Distribution of the trend-following reference time of FTs. There are three typical clusters ranging from 1 tick to 10 ticks short-time cluster, marked in orange , from 11 ticks to 23 ticks intermediate-time cluster, in light-blue , and from 24 ticks to 50 ticks long-time cluster, in violet , all which are obtained using the k -means method.

They typically correspond to half, three, and six minutes given the average transaction interval is 9 seconds in this week. Two samples around 60 ticks were excluded as exceptions. B , The average number of limit orders red and transactions as limit orders blue for each cluster. The gradations in the plot bars presents a heat map of the ascending number of limit orders and that of transactions as limit orders by a trader in each cluster.

The short-time long-time trend-followers submit the most least frequently, whereas the number of transactions for intermediate-time trend-followers is least despite a relatively large number of submissions. C , Probability density functions of the limit-order distributions PDFs conditional on the limit-order strategies.

The peak of PDF of intermediate-time trend-followers lies far behind the best prices compared with other trend-followers, which reduce the transaction frequencies of intermediate-time trend-followers. D , Time-series of the ratio for the number of limit orders in the order book issued by each cluster.

Each bar represents the hourly average ratio. The clock in the figure starts from am to pm for each standard time. Dark-gray bars represent the fraction of limit orders issued by LFTs. What does this timescale difference imply? To answer this question, we studied the average number of limit-order submissions and that of transactions as limit orders for each cluster Fig 4 B. Although the number of submissions has a trivial correlation in that short-time long-time trend-followers submit the most least frequently, the number of transactions has a nontrivial correlation; the number of transactions for intermediate-time trend-followers is least despite a relatively large number of submissions.

To investigate this nontrivial correlation, we studied the limit order book shape for each cluster, representing the typical depth of order placements Fig 4 C. These order-book profiles provide clear answers to the nontrivial behaviour. The short-time and long-time trend-followers maintain their orders near the best prices, leading to frequent transactions.

The non-EMA trend-followers also transact frequently because they leave their orders without price modifications. However, the intermediate-time trend-followers maintain their orders relatively far from the best prices compared with other trend-followers and therefore are less likely to transact.

We remark on the intraday pattern of limit-order strategies. Fig 4 D is the hourly limit-order component ratio in the order book. In Tokyo, trend-following of short duration is the dominant strategy during the daytime, whereas in New York it is of intermediate duration.

Given the order-book shape in Fig 4 C , Tokyo New York traders are bullish bearish on transactions at current best prices in the daytime. We report the detail properties of market-order strategies. Fig 5 A is the distribution of market-order strategies of FTs, which is quantified by : positive negative implies that the i th trader is a trend-follower contrarian , who issues buy orders during positive negative trends, and sell orders during negative positive trends.

In our market-order analysis, we found several FTs were contrarians but most were trend-followers. Note that traders showing no significant correlation with trends were classified within the random cluster. A , Distribution of quantifying the average strength of trend-following for market orders. The original samples w i are shown in Fig 4. A positive negative denotes a i th trader is a trend-follower contrarian , and represented by a green pink plot bar.

B , Average number of market orders red and that of transactions as market orders blue for each cluster. The gradation in plot bars presents a heat map of the ascending number of market orders and that of transactions as market orders by a trader in each cluster. Contrarians are active despite their small size. C,D , Failure probabilities of market orders in transactions C and the probabilities in which market orders are issued at prices better than the current best prices D.

The green gray bars and circles represent the strategic properties of trend-followers random traders. Trend-follower may be attempting to obtain better prices than current best prices by submitting market orders in advance. E , Time-series of the ratio for the number of market orders issued by each cluster. The dark-gray bars signify the fraction of market orders issued by LFTs.

To extract features of the strategies, we studied the number of market orders and that of transactions as market orders for each cluster Fig 5 B. We found that the contrarians are overwhelmingly active despite their small size. Indeed, the first and second most frequent traders were contrarians in our dataset.

Notably, a previous study [ 14 ] reports the existence of contrarians at the trader group level. Another feature is the difference in the degree of contributions to transactions. Given the large number of market orders trend-followers issue, the transaction count is relatively small compared with that for random traders.

To clarify this imbalance, we defined the failure probability as the fraction of failed market orders to total market orders see S1 Appendix. One of our conjectures is that trend-followers may aim the latency during price-matching processes we have another conjecture based on pinging strategies [ 14 , 21 — 24 ] which is illustrated in S2 Appendix. Given this latency, a good strategy may be to hit in advance better prices than the current best prices following their trend prediction.

Note that the individual Tokyo traders in the daytime behave as contrarians, which is consistent with a previous work [ 25 ] indicating that contrarian behaviour is the favoured and profitable strategy in Japan. This figure shows the following two characteristics: one is the immense contribution to submissions and transactions by the short-time trend-followers for limit orders and the trend-followers for market orders i.

The other characteristic is the tendency that there are many traders who submit mainly either limit or market orders i. This characteristic implies that they might be specialized in either limit or market order strategy. A , The frequencies of order submissions and that of transactions issued by traders employing strategies using various combinations of limit and market orders.

The box size represents the number of traders. The blank elements indicates the absence of traders adopting corresponding combination strategies. We confirm i the immense contributions to submissions and transactions by the short-time cluster for limit order strategies and the trend-follower cluster for market-order strategies surrounded by a chain line, and ii a large population of traders tend to mainly submit either limit orders or market orders given the size of boxes surrounded by a dotted line.

We show pie charts quantifying the overall balance between liquidity providers and consumers. Each component is highlighted to illustrate trading performances as measured by the Sharpe ratio see S3 Appendix. As one may notice, there exists the strong correlation between the Sharpe ratios and liquidity consumption probabilities 0.

This correlation suggests the traders consuming providing the liquidity are likely to exhibit good bad trading performances as they take on risk for not for their sake. This result is consistent with the analysis concerning the inventory risk for liquidity providers to the decline in asset prices [ 26 ]. A The pie charts quantify the overall balance between the liquidity provision and consumption of the cluster.

Here the liquidity provision consumption is measured as the total volumes transacted as makers takers. Each cluster is classified as either a liquidity provider or consumer through a statistical test on the significance of the imbalance between liquidity provision P and consumption C. In addition, clusters are colour coded red, yellow, or blue to mark their trading performances as measured by the Sharpe ratio. The breakdown of trading performances and trading profits of clusters the high performance clusters coded by a brown and green line are further investigated in Fig 8.

This positive correlation implies that more frequently traders transact as takers, better performances traders are likely to exhibit. It would be interesting to explore why the two opposite types of clusters surrounded by a brown line typically high frequency traders HFTs and a green line LFTs in Fig 7 exhibit high trading performances.

We therefore provide the breakdown properties of these two clusters as a case study. After aggregating traders at the bank level, we plotted the distributions of trading profits calculated every 20 minutes, the total trading profits in this week, and the Sharpe ratios Fig 8 A , 8 B and 8 C , respectively.

Given the previous study highlighting that HFTs are highly profitable by taking advantage of response speed, this result indicates counterintuitively that strategies of HFTs and LFTs seem equilibrium-balanced by optimization according to different metrics at least in our dataset. A,B,C Trading profits and trading performances of banks, the traders in the high-performance cluster of which are aggregated. We exclude from the aggregation traders with transaction counts below in the week.

Specifically, A — C plots the trading profit distributions per trader calculated every 20 minutes during a week, the cumulative profit distributions at the end of the week, and the distribution of the trading performances measured by the Sharpe ratio. In summary, focusing on the historical market trends, we classified the timescale of the limit-order trend-following and the response pattern for market-order strategy to the trends.

The differences in the timescale of the limit-order trend-following are closely related to the limit-order book shape. The traders with the short and long trend-following timescales are bullish to transact with the current best price, while traders with intermediate time are bearish. The differences in the response pattern of market orders to trends have a close link to failure probabilities; how many market orders are finally transacted out of all submitted market orders.

The failure probabilities of trend-followers are quite high while those of random traders are low. This, in turn, helps them hold overnight positions. Typically, swing traders enter and exit market positions based on the momentum indicators that provide buy and sell signals to find overbought or oversold markets for their own interest.

Scalping strategies involve identifying extremely short and rapid price movements for quick profits. If a strategy has a small expected gain, traders can increase leverage to make the strategy very profitable even if each trade only results in small gains on a percentage basis.

The key is developing a robust trading system to capitalize on these opportunities. The most common scalping strategies are centered on news flow-related volatility. For example, non-farm payrolls or preliminary GDP announcements can have a significant impact on currency pairs that include the USD. Many traders wait for an extreme reaction and then enter a long or short position in the opposite direction to benefit from mean reversion.

Traders may also create complex strategies involving technical indicators and automate them using tools like Metatrader. Since trades may be fast and frequent, automation helps ensure that they are executed on a timely basis without the involvement of human emotion.

The catch is that any mistakes are instantly amplified, so it's important to test the system. One of the most unpopular forex strategies, owing to the kind of high risk it entails, news trading is typically practiced only by advanced or experienced traders with deep pockets.

A trading practice based on fundamental and technical analysis helps traders benefit purely from the notable volatility often seen in the forex market after key news releases. News traders monitor economic calendars for key data releases, watch the market closely before the event and then determine the key support and resistance levels. This, hence, helps them react quickly after the event based on the results. It must be noted that news traders need to maintain strict discipline when managing their currency positions during such fast markets and often place stop-loss to take profit orders in the market.

Even before we get to the question of how to pick the best forex trading strategies, it must be understood that no strategy comes without the risk of losing money. All strategies pose a considerable threat just like they offer a potential gain.

Having said that, there also isn't any one single-best and most profitable forex trading strategy, it really all comes down to what suits one's own needs; what may work for someone else may be a disaster for you. Always conduct extensive personal research to identify your secret soup. However, one of the key aspects to consider is a time frame for your trading style.

The forex market has become extremely popular among active traders due to its extended hours, deep liquidity, and availability of leverage. However, cryptocurrencies are quickly becoming another alternative market for the same trader demographic with greater volatility. Traders can use many of the same strategies in these new markets.

Traders interested in the forex trading crypto markets should be mindful of some key differences between the two. Crypto markets tend to be much less liquid than forex markets, which means that some strategies may not directly translate. Leverage may also be less available than in the forex markets, particularly among larger brokerages and trading platforms. ZenLedger helps crypto traders remain current and accurate with the IRS' unique requirements by aggregating transactions across multiple exchanges and wallets and auto-populating IRS forms, such as Form Schedule D and Form Try ZenLedger out for free today!

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Some traders will focus on two particular levels, while others will trade "bands" or "areas" - for example, if you identified 1. Only focusing on that particular level might mean you will lose out on good trading opportunities, as price can often reverse before hitting it. The ADX has low readings most of the time, and we can see that the price has often bounced off the Trend trading strategies involve identifying trade opportunities in the direction of the trend.

The idea behind it is that the trading instrument will continue to move in the same direction as it is currently trending up or down. When prices are consistently rising posting higher highs , we are talking about an uptrend.

Vice-versa, declining prices the trading instrument is making lower lows will indicate a downtrend. Except when looking at the price action, traders can use supporting tools to identify the trend. Moving averages are one of the most popular ones.

Traders might simply look whether the price is trading above or below a moving average the DMA is a popular and widely watched one or use MA crossovers. To use moving average crossovers which can also be used as entry signals , you will have to set a fast MA and a slow MA. The day moving average crossing above the day moving average could indicate the beginning of an uptrend, and vice-versa.

The goal of position trading is to capture profits from long-term trend moves, while ignoring the short-term noise occurring day to day. Traders that utilise this type of trading style might hold positions open for weeks, months and in rare cases — even years. Along with scalping, it is one of the more difficult trading styles.

It requires a trader to remain highly disciplined, able to ignore noise and remain calm even when a position moves against them for several hundred pips. Imagine for example, that you had a bearish outlook on stocks in early While you would have enjoyed the price movements at the beginning and the end of the year, the rally from March to September could have been a painful experience.

Only few traders have the discipline to keep their positions running for such a long-time period. Day traders usually do not hold trades only for seconds, as scalpers do. However, their trading day also tends to be focused on a specific session or time of the day, when they try to act on opportunities.

While scalpers might use a M1 chart to trade, day traders tend to use anything from the M15 up to the H1 chart. Scalpers tend to open more than 10 trades per day some highly active traders might end up with even more than per day , while day traders usually take it a bit slower and try to find good opportunities per day. Day trading could suit you well if you like to close your positions before the trading day ends, but do not want to have the high level of pressure that comes with scalping.

When scalping, traders are trying to take advantage of small intraday price moves. Some even have a target of only 5 pips per trade, and the trade duration could vary from from seconds to a few minutes. Scalpers need to be good with numbers and be able to make decisions quickly, even when under pressure. They also usually spend more time in front of the screen, and tend to focus on one or a few specific markets e.

The advantage of being a scalper can be that it allows you to focus on the market in a specific timeframe, and you do not have to worry about holding your positions overnight or interpreting long-term fundamentals. However, scalping comes with a lot of pressure as you need to be fully focused during your trading session. Furthermore, it is easier to make mistakes and react emotionally when your trades are running only for minutes. It may therefore not be the best trading style for beginners to first start with.

Swing trading is a term used for traders who tend to hold their positions open for multiple days. They might use anything from a H1 to a D1 chart, or even weekly. Popular trading strategies include trend following, range trading or breakout trading. Traders who choose this type of trading style need patience and discipline. It might take days for a quality opportunity to show up, or you might end up holding a trade open for a week or more while running an open loss. Some traders do not have the necessary patience, and close their trades too early.

If you like to analyse the markets without any rush, and are comfortable with running positions for days or even weeks — swing trading might be the right trading style for you. It also gives you the opportunity to include fundamental analysis trying to anticipate monetary policy moves or political developments — which is futile to do when scalp trading.

A trader using a carry trade strategy will try to profit from the difference in interest between the two different currencies that make up a currency pair. A trader would go buy a currency with a high interest rate and sell a currency with low interest rate. By doing so, the trader will receive an interest rate payment based on the size of their position. The benefits of a carry trade strategy is that you can earn substantial interest from just holding a position.

Of course, you need the right market environment for this to work. Carry trades perform well in a bullish market environment when traders are seeking high risk. The Japanese Yen is a traditional safe haven, which is why many carry trades involve being short on the Yen against another "risk-on" currency. However, you should also be familiar with the characteristics of the currency you are buying. For example, the Australian Dollar will benefit from rising commodity prices, the Canadian Dollar has a positive correlation with oil prices and so on.

A breakout strategy aims to enter a trade as soon as the price manages to break out of its range. Traders are looking for strong momentum and the actual breakout is the signal to enter the position and profit from the market movement that follows. Traders may enter the positions at market, which means they will have to closely monitor the price action, or by placing buy stop and sell stop orders.

They will usually place the stop just below the former resistance level or above the former support level. News trading is a strategy in which the trader tries to profit from a market move that has been triggered by a major news event. This could be anything from a central bank meeting and an economic data release to an unexpected event natural disaster or geopolitical tensions escalating. News trading can be very risky as the market tends to be extremely volatile during those times.

You will also find that the spread of the affected trading instruments may widen significantly. Due to liquidity evaporating, you are also at risk of slippage - meaning your trade could be executed at a significantly worse price than expected or you may struggle getting out of your trade at the level you had in mind. First of all, you need to determine which event you want to trade and which currency pair s it will affect the most. A meeting of the European Central Bank will certaintly impact the Euro the most.

However, which specific currency pair should you pick? If you are expecting a hawkish ECB that will signal rate hikes, it would make sense to pick a low-yielding currency, such as the Japanese Yen. Furthermore, you can approach news trading either with a bias or no bias at all.

It means that you have an idea where you think the market might move depending on how the event unfolds. On the other hand, news trading without a bias means that you will try to capture the big move regardless of its direction.

Retracement trading includes temporary changes in the direction of a certain trading instrument. Retracements should not be confused with reversals - while reversals indicate a major change of the trend, retracements are just temporary pullbacks. By trading retracements, you are still trading in the direction of the trend.

You are trying to capitalise on short-term price reversals within a major price trend. There are several ways you can trade retracements. For example, you could use trendlines. Let's have a look at the chart of the US below. The index is in a clear uptrend and the rising trendline could have been used as a buying opportunity once the price tests the actual trendline.

Fibonacci retracements are another popular tool to trade retracements - particularly the Grid trading involves placing multiple orders above and below a certain price. The idea behind it is to profit from volatility by placing both buy and sell orders at regular intervals above and below the set price level for example, every 10 pips above and below. If the price moves into one direction, your position gets larger and so does your floating PnL. The risk is of course, that you will get false breakouts or a sudden reversal.

Each trader should try to identify their own edge. This might be a set of skills that the trader possesses. For example, some traders might have a short attention span but are quick with numbers and can handle the stress of intraday trading extremely well. Whereas a trader with a different trading style may not be able to function efficiently in this kind of environment, but could instead be a skilled strategist who can always keep sight of the bigger picture. There are many benefits of forex trading so it's up to you to compare the strategies which may be better suited.

Test them out in a demo environment with virtual funds. When you get a feeling for which one suits you the best, you can consider testing it out in a live environment. But this isn't true. The biggest downfall with breakout trading is that there are too many false breakouts. The strategy differentiates a false breakout and a genuine breakout.

We have tested many technical indicators to develop the best breakout trading strategy. No matter how much backtesting we have done, one technical indicator always comes first. Before we move forward, we must define this mysterious technical indicator.

The VWMA is one of the most underused technical indicators only professional traders use. VWMA looks like a moving average, but instead, it is based on volume. The VWMA is located on most trading platforms. Once it is applied to the chart, it should look like the figure below:. Like with any type of trading styles, breakout trading has its own unique advantages and disadvantages.

At the same time, if you have been screwed over and over again by false breakouts you can easily point out the other side of the coin. Because of the increased volatility created by the breakout setup, it can be challenging to enter the market without slippage. A good confirmation tool to evaluate the quality of a breakout is the MACD indicator or any other breakout trading indicators. Or, you can confirm breakout trading with volume profile.

If you want to learn how to confirm a stock breakout, then we suggest trying the volume profile indicator, which has more relevance in the stock market. However, the MACD indicator is more accessible and it is a great way for a trader to enter a breakout in the early stage of the breakout setup. Even if you have confidence and faith in a breakout setup, it will not save you from potential fakeouts.

You still need to have some sort of risk management technique and other ways to confirm breakouts. Our secret favorite way to identify breakout trades right before they happen is to look for clues from the price action.

The biggest thing we like to look after is tight consolidation at key support and resistance levels. When you see the range of the candlesticks getting smaller and smaller and compressed within a tight box-like price action, something has to give in and break under the pressure just created. Now, before we go any further, we always recommend taking a piece of paper and a pen.

Then note down the rules of the best Breakout trading strategy. The first step of the best breakout trading strategy requires identifying the price level. It can ultimately be your breakout trading level. This is the most important part when attempting breakout trading. This is why we only want to recognize significant and clear levels. Do you want to boost your knowledge in identifying these levels? This article will teach you methods to help identify the right support and resistance level.

The resistance level we have identified in the figure above is significant. We had strong rallies that quickly faded away. We need a breakout and breakout candle to close above our resistance level. This is a sign that the bulls are in control. We still need confirmation from the VWMA indicator. This will give us the green light to pull the trigger on this breakout trading. The final step of the best breakout trading strategy is the needed confirmation from the VWMA.

We need to visually see the VWMA stretch up. And the moving average needs to have a deeper inclination to the upside. This can be clearly visualized on the price chart. Prior to the breakout, the VWMA only gradually moved higher after the breakout happened.

We saw the VWMA aggressively moving higher, which showed a strong presence of volume behind the breakout. After we bought, we still needed to define where to place our protective stop loss. We also needed to know where to take profits. This brings us to the next step of the best breakout trading strategy. It was obvious to place our protective stop loss just below the breakout candle.

This is because once we break below the candle that initiates the breakout, it proves to us that this is a false breakout. No real buying is taking place, so we better back out of the trade. Our take profit technique is intuitive because a break below the VWMA suggests there are no more buyers to sustain the current rally. We want to book the profits at the early sign the market is ready to roll over. In the figure below, you can see an actual SELL trade example, using the best breakout trading strategy.

Breakout trading can be a very lucrative career to make quick profits. To be a successful breakout trader you need to learn the anatomy of a breakout and to be able to recognize false breakouts in order to avoid losses. Breakout trading works in any type of market conditions and if done correctly, breakout trading can lead to consistent results.

Some of the most effective types of breakouts are a result of trading range breakouts, channel breakouts, triangle breakouts, flag breakouts, support and resistance breakouts, etc. Breakout trading is better than swing trading because oftentimes a breakout can lead to large price swings or major price trends.

Swing trading breakout is usually done in harmony with the long-term trends. This means two things: instant gratification. We have one final tip. When you have the technicals and the fundamentals working for you, the trade success profitability increases.

Below is another strategy called trading volume in forex. Please leave a comment below if you have any questions about the Breakout Trading Strategy! Please Share this Trading Strategy Below and keep it for your own personal use! Thanks, Traders! We specialize in teaching traders of all skill levels how to trade stocks, options, forex, cryptocurrencies, commodities, and more. Our mission is to address the lack of good information for market traders and to simplify trading education by giving readers a detailed plan with step-by-step rules to follow.

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