If you trade the forex markets regularly, chances are that a lot of your trading is of the short-term variety; i. From my experience, there is one major flaw with this type of trading: h igh-speed computers and algorithms will spot these patterns faster than you ever will. When I initially started trading, my strategy was similar to that of many short-term traders. That is, analyze the technicals to decide on a long or short position or even no position in the absence of a clear trendand then wait for the all-important breakout, i. I can't tell you how many times I would open a position after a breakout, only for the price to move back in the opposite direction - with my stop loss closing me out of the trade. More often than not, the traders who make the money are those who are adept at anticipating such a breakout before it happens.

To identify the trading strategies, we focused on the response pattern of limit orders and market orders to historical trends. We first introduced the coarse-grained tick intervals to calculate market price changes and the time lags to measure trends. 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.

Under special assumptions, myopic portfolio policies are shown to be optimal and constant over time. In general, however, both optimal theoretical portfolios and current portfolio positions are subject to random movements so that periodic monitoring and rebalancing are necessary.

Transaction and monitoring costs create a tradeoff between the cost of not being at the optimal allocation tracking error and the cost of swapping the current portfolio for the optimal one. Optimal rebalancing results in the replacement of the optimal allocation with a no-trade region delimited by rebalance boundaries. The factors influencing the boundaries and the rebalancing decisions can be analytically and numerically explained. Popular rebalancing rules imply a substantial amount of excess trading costs, but they can generate positive net returns in the case of mean-reverting market regimes.

Keywords: trading strategies , asset management , intertemporal , monitoring , rebalancing , trading costs. Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter. Please, subscribe or login to access full text content. To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us.

All Rights Reserved. OSO version 0. University Press Scholarship Online. Sign in. Not registered? Sign up. Publications Pages Publications Pages.

Antivirus gateways for the vulnerability window. A different sounds make to publication realistic as controller during great for diagnostic imaging, memory is different devices, provide your. The deepest content in important resource.

Connect and to be within a the application following operating. A project uses Akismet is used.

Chart Growth Balance Profit. Pips Profit. Pips Profit Lots. Winners Vs. Losers Longs Vs. Shorts Longs Profit Vs. Shorts Profit Winners Profit Vs. Losers Losses. Pips Gain Profit. Trade Length: 11h 18m. Profit Factor: 1. Loading, please wait Hover over the desired column for a detailed explanation.

Data includes last transactions based on the analysed history. Trading Activity Change Profit Lots Pips. Monthly Analytics All Rights Reserved. Leverage creates additional risk and loss exposure. Before you decide to trade foreign exchange, carefully consider your investment objectives, experience level, and risk tolerance. You could lose some or all of your initial investment. Do not invest money that you cannot afford to lose. Educate yourself on the risks associated with foreign exchange trading, and seek advice from an independent financial or tax advisor if you have any questions.

Any data and information is provided 'as is' solely for informational purposes, and is not intended for trading purposes or advice. Past performance is not indicative of future results. All Quotes x. Dear User, We noticed that you're using an ad blocker. Myfxbook is a free website and is supported by ads. In order to allow us to keep developing Myfxbook, please whitelist the site in your ad blocker settings.

Thank you for your understanding! You're not logged in. This feature is available for registered members only. Registration is free and takes less than a minute. Click the sign up button to continue. Unless you're already a member and enjoying our service, then just sign in. Keep up to date with the markets. Enable notifications to receive real-time important market updates: Economic Calendar. These strategies will fit both short-term and long-term traders, who do not like the delay of the standard indicators and prefer to listen as the market is speaking.

Various candlestick patterns , waves, tick-based strategies, grid and pending position systems — they all fall into this category:. Fundamental Forex strategies are strategies based on purely fundamental factors that stand behind the bought and sold currencies. Various fundamental indicators, such as interest rates and macroeconomic statistics, affect the behavior of the foreign exchange market. These strategies are quite popular and will benefit long-term traders that prefer fundamental data analysis over technical factors:.

It is very important to test your trading strategy before going live with it. There are two ways to test your potential trading strategy: backtesting and forward testing. Backtesting is a kind of a strategy test performed on the past data. It can be either automated or manual. For automated backtesting, a special software should be coded.

Automated testing is more precise but requires a fully mechanical trading system to test. Manual testing is slow and can be rather inaccurate, but requires no extra programming and can be done without any special preparation process. Any backtesting results should be taken with a grain of salt as the tested strategy might have been created to fit particular backetsting historical data.

Forward testing is performed either on a demo account or on a very small micro live account. During such tests, you trade normally with your strategy as if you were trading your live account. As with backtesting, forward testing can also be automated. In this case, you would need to create a trading robot or expert advisor to execute your system. Of course, with discretionary strategy, you are limited solely to manual testing.

Forward testing results are considered to be more useful and representative than those of the backtests. Regardless of how you decide to test your strategy, you need to understand the results you get. Intuitively, you would want to judge the results according to strategy's profitability, but you should not forget about other important parameters of successful trading strategies.

They are: low drawdown sizes, short drawdown periods, high probability of winning, high average reward-to-risk ratios and big number of trades. Ideally, your system should earn equally well on bullish and bearish trades, the resulting balance curve should be consistent and uniform, without significant drops or long flat periods. If you are using MetaTrader for backtesting or forward testing, you can use our report analysis tool to better understand the strong and weak sides of your strategy.

If you want need information on currency trading strategies or need some additional examples of working strategies, you are welcome to browse our e-books section on strategies to learn from completely free downloadable e-books. You may also choose to read some articles from our strategy building section to improve your knowledge of the subject. If you want to share your Forex trading strategy with other traders, or want to ask some questions regarding the strategies presented here, please, join a discussion of the Forex strategies at the forum.

What Is Forex? Please disable AdBlock or whitelist EarnForex.

Leveraged trading in foreign currency or off-exchange products on margin carries significant risk and may not be suitable for all investors. We. Each trading strategy taught in this book comes with precise trade signals, entry and exit points and the right indicators to use. The New Forex Market Trading System is a trading system which helps forex traders to maximize profit on every trade signal generated by this new system.