That is why it is important to not get carried away by your trading positions and cultivate emotional equilibrium across profits and losses. Be disciplined about closing out your positions when necessary. The best way to get started on the forex journey is to learn its language. Here are a few terms to get you started:. Remember that the trading limit for each lot includes margin money used for leverage.
This means that the broker can provide you with capital in a predetermined ratio. The most basic forms of forex trades are a long trade and a short trade. In a long trade, the trader is betting that the currency price will increase in the future and they can profit from it.
Traders can also use trading strategies based on technical analysis, such as breakout and moving average , to fine-tune their approach to trading. Depending on the duration and numbers for trading, trading strategies can be categorized into four further types:. Three types of charts are used in forex trading.
They are:. Line charts are used to identify big-picture trends for a currency. They are the most basic and common type of chart used by forex traders. They display the closing trading price for the currency for the time periods specified by the user. The trend lines identified in a line chart can be used to devise trading strategies.
For example, you can use the information contained in a trend line to identify breakouts or a change in trend for rising or declining prices. While it can be useful, a line chart is generally used as a starting point for further trading analysis.
Much like other instances in which they are used, bar charts are used to represent specific time periods for trading. They provide more price information than line charts. Each bar chart represents one day of trading and contains the opening price, highest price, lowest price, and closing price OHLC for a trade. Colors are sometimes used to indicate price movement, with green or white used for periods of rising prices and red or black for a period during which prices declined.
Candlestick charts were first used by Japanese rice traders in the 18th century. They are visually more appealing and easier to read than the chart types described above. The upper portion of a candle is used for the opening price and highest price point used by a currency, and the lower portion of a candle is used to indicate the closing price and lowest price point. A down candle represents a period of declining prices and is shaded red or black, while an up candle is a period of increasing prices and is shaded green or white.
The formations and shapes in candlestick charts are used to identify market direction and movement. Some of the more common formations for candlestick charts are hanging man and shooting star. Forex markets are the largest in terms of daily trading volume in the world and therefore offer the most liquidity. This makes it easy to enter and exit a position in any of the major currencies within a fraction of a second for a small spread in most market conditions.
The forex market is traded 24 hours a day, five and a half days a week—starting each day in Australia and ending in New York. The broad time horizon and coverage offer traders several opportunities to make profits or cover losses. The extensive use of leverage in forex trading means that you can start with little capital and multiply your profits.
Forex trading generally follows the same rules as regular trading and requires much less initial capital; therefore, it is easier to start trading forex compared to stocks. The forex market is more decentralized than traditional stock or bond markets.
There is no centralized exchange that dominates currency trade operations, and the potential for manipulation—through insider information about a company or stock—is lower. Even though they are the most liquid markets in the world, forex trades are much more volatile than regular markets. Banks, brokers, and dealers in the forex markets allow a high amount of leverage, which means that traders can control large positions with relatively little money of their own. Leverage in the range of is not uncommon in forex.
A trader must understand the use of leverage and the risks that leverage introduces in an account. Trading currencies productively requires an understanding of economic fundamentals and indicators. A currency trader needs to have a big-picture understanding of the economies of the various countries and their interconnectedness to grasp the fundamentals that drive currency values.
The decentralized nature of forex markets means that it is less accountable to regulation than other financial markets. The extent and nature of regulation in forex markets depend on the jurisdiction of trading. Forex markets lack instruments that provide regular income, such as regular dividend payments, that might make them attractive to investors who are not interested in exponential returns.
Forex, short for foreign exchange, refers to the trading of one currency for another. It is also known as FX. Forex is traded primarily via three venues: spot markets, forwards markets, and futures markets. Companies and traders use forex for two main reasons: speculation and hedging. The former is used by traders to make money off the rise and fall of currency prices, while the latter is used to lock in prices for manufacturing and sales in overseas markets.
Forex markets are among the most liquid markets in the world. Hence, they tend to be less volatile than other markets, such as real estate. The volatility of a particular currency is a function of multiple factors, such as the politics and economics of its country.
Therefore, events like economic instability in the form of a payment default or imbalance in trading relationships with another currency can result in significant volatility. Forex trade regulation depends on the jurisdiction. Countries like the United States have sophisticated infrastructure and markets to conduct forex trades. However, due to the heavy use of leverage in forex trades, developing countries like India and China have restrictions on the firms and capital to be used in forex trading.
Europe is the largest market for forex trades. Currencies with high liquidity have a ready market and therefore exhibit smooth and predictable price action in response to external events. The U. It features in six of the seven currency pairs with the most liquidit y in the markets. Currencies with low liquidity, however, cannot be traded in large lot sizes without significant market movement being associated with the price.
Such currencies generally belong to developing countries. When they are paired with the currency of a developed country, an exotic pair is formed. For example, a pairing of the U. Next, you need to develop a trading strategy based on your finances and risk tolerance. Finally, you should open a brokerage account. Today, it is easier than ever to open and fund a forex account online and begin trading currencies.
For traders —especially those with limited funds—day trading or swing trading in small amounts is easier in the forex market than in other markets. For those with longer-term horizons and larger funds, long-term fundamentals-based trading or a carry trade can be profitable.
A focus on understanding the macroeconomic fundamentals that drive currency values, as well as experience with technical analysis, may help new forex traders to become more profitable. Bank for International Settlements. Federal Reserve History. Your Money. Personal Finance. Your Practice. Popular Courses. Table of Contents Expand. Table of Contents. What Is the Forex Market?
A Brief History of Forex. An Overview of Forex Markets. Uses of the Forex Markets. How to Start Trading Forex. Forex Terminology. Basic Forex Trading Strategies. Charts Used in Forex Trading. Pros and Cons of Trading Forex. What is Forex? Where is Forex Traded? Why Do People Trade Currencies? Are Forex Markets Volatile? Are Forex Markets Regulated? How to get started with forex trading. The Bottom Line. Part of.
Part Of. Basic Forex Overview. Key Forex Concepts. Currency Markets. Advanced Forex Trading Strategies and Concepts. Key Takeaways The foreign exchange also known as forex or FX market is a global marketplace for exchanging national currencies. Because of the worldwide reach of trade, commerce, and finance, forex markets tend to be the largest and most liquid asset markets in the world. Currencies trade against each other as exchange rate pairs.
Forex markets exist as spot cash markets as well as derivatives markets, offering forwards, futures, options, and currency swaps. Market participants use forex to hedge against international currency and interest rate risk, to speculate on geopolitical events, and to diversify portfolios, among other reasons. Pros and Cons of Trading Forex Pros Forex markets are the largest in terms of daily trading volume in the world and therefore offer the most liquidity.
Automation of forex markets lends itself well to rapid execution of trading strategies. Cons Even though they are the most liquid markets in the world, forex trades are much more volatile than regular markets.
Extreme amounts of leverage have led to many dealers becoming insolvent unexpectedly. Article Sources. Investopedia requires writers to use primary sources to support their work. These include white papers, government data, original reporting, and interviews with industry experts.
We also reference original research from other reputable publishers where appropriate. You can learn more about the standards we follow in producing accurate, unbiased content in our editorial policy. Compare Accounts. The offers that appear in this table are from partnerships from which Investopedia receives compensation.
This compensation may impact how and where listings appear. Investopedia does not include all offers available in the marketplace. Related Articles. Partner Links. Related Terms. Foreign Exchange Forex The foreign exchange Forex is the conversion of one currency into another currency.
The distribution aspect encompasses a wide array of areas, such as logistical, political, regulatory, legal, social, and economic. When properly managed, commercial activity can quickly enhance the standard of living in a nation and increase its standing in the world. However, when commerce is allowed to run unregulated, large businesses can become too powerful and impose negative externalities on citizens for the benefit of the business owners.
Many nations have established governmental agencies responsible for promoting and managing commerce, such as the Department of Commerce in the United States. Large organizations with hundreds of countries as members also regulate commerce across borders. The rules are meant to facilitate commerce and establish a level playing field for member countries. The idea of commerce has expanded to include electronic commerce in the 21st century.
Electronic commerce, or ecommerce , describes any business or commercial transaction that includes the transfer of financial information over the Internet. Ecommerce, unlike traditional commerce between two agents, allows individual consumers to exchange value for goods and services with little to no barriers.
Ecommerce has changed how economies conduct commerce. In the past, imports and exports conducted by a nation posed many logistical hurdles, both on the part of the buyer and the seller. This created an environment where only larger companies with scale could benefit from export customers.
Now, with the rise of the internet and ecommerce, small business owners have a chance to market to international customers and fulfill international orders. Companies of all shapes and sizes can engage in international commerce. Export management companies help domestic small businesses with the logistics of selling internationally.
Export trading companies help small businesses by identifying international buyers and domestic sourcing companies that can fulfill the demand. Commerce is not interchangeable with business, but is rather a subset of business.
Business includes manufacturing and production, whereas commerce pertains to the distribution side of business, specifically the distribution of goods and services. Electronic Commerce, or Ecommerce, is the process of buying and selling goods or services over the Internet. It can be conducted over computers, tablets, smart phones, smart watches, and other smart devices. Most products and services that exist are available through ecommerce. While ecommerce can be a substitute for brick-and-mortar selling, many companies market their products both online and offline.
Ecommerce operates in a number of major market segments, the largest of which are business to business B2B , or the direct sale of goods and services between businesses; business to consumer B2C , or sales between businesses and customers; consumer to consumer, in which people sell to each other, such as over eBay; and consumer to business, in which individuals sell to businesses.
Your Money. Personal Finance. Your Practice. Popular Courses. Table of Contents Expand. Table of Contents. What Is Commerce? Understanding Commerce. Implementing and Management. The Rise of Ecommerce. Commerce FAQs. Economy Economics. Key Takeaways Commerce has existed from the early days of human civilization when humans bartered goods to the more complex development of trade routes and corporations.
Today, commerce refers to the macroeconomic purchases and sales of goods and services by organizations.
Smaller companies tend to have a more diverse workforce, with young and old, different aspirations, etc. While in many large organizations the workforce can begin to take the form of the company itself. This is the case with many who adopt the culture of the company. Those who think differently can be expelled, leaving an almost standardized and regulated employee.
There is also a strong argument that large companies tend to attract those seeking job security. On the contrary, smaller companies attract those who want to work in different areas, and are looking for growth, change our willingness to take risks. One aspect that few people disagree with is that larger companies tend to have higher salaries. However, this aspect is recognized by smaller employers, and many combats it by adding advantages, eg.
Perhaps one of the most obvious differences between the two types of organization is that of culture. For startups, any decision that is made can be dangerous, so they tend to be less risk-averse than larger established companies. Therefore, large companies avoid risky decisions. Instead, they prefer to be more conservative and enhance what already exists with their existing customers. Many large companies are now trying to find a way to maintain a small business mindset regardless of size.
They recognize that elements such as long-term planning with an element of risk-taking will benefit them in the medium and long term. Check out 50 Best Startup Ideas for including business plan. The United States adheres to different definitions of SMEs and policies, which vary from industry to industry.
The system was developed jointly by the United States, Canada, and Mexico to establish a set of guidelines and standards that allow the collection and analysis of operational statistics in North America. The list is not specifically directed at SMEs, as it mainly refers to smaller companies. This is important, as many small businesses can apply for government contracts and financing, as long as they comply with all required regulations.
The United States also has a specific definition of SMEs based on the industry in which they operate. For example, if a company is part of the manufacturing industry, it can be classified as an SME if it has a maximum of employees, but a company engaged in the wholesale trade can only have There are also differences between branches of industry.
For example, in the mining industry, companies that extract nickel or copper ore can employ up to 1, people, while a silver mining company can only employ a maximum of people to qualify as SMEs. In Canada, Small and Mid-size enterprises are companies that employ less than people. Companies with or more employees are only considered large companies. Industry Canada, an organization that promotes economic and industry growth in Canada, assumes that small businesses have fewer than employees if the business produces goods.
The limit for small businesses that provide services is 49 employees or less. Companies that fall somewhere between these thresholds for the number of employees are considered SMEs. Another organization, Statistics Canada, which is researching and collecting data on businesses and trade in the country, meets the requirement that SMEs have no more than employees.
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Yoyo Chinese. Med School Coach. Gupta Program. Income School. Lexi Ladies Academy. We determined the count of each bin and sorted them in descending order. Then, the maximum difference value of the last bin added was used as the upper bound of the threshold value. As can be seen in Algorithm 1, it has two phases.
In the first phase, which simply corresponds to line 2, the whole data set is processed linearly to determine the distributions of the differences, using a simple histogram construction function. The second phase is depicted in detail, corresponding to the rest of the algorithm. The threshold value should be determined based on entropy. Entropy is related to the distribution of the data.
To get balanced distribution, we calculated the entropy of class distribution in an iterative way for each threshold value up until the maximum difference value. However, we precalculated the threshold of the upper bound value and used it instead of the maximum difference value. Algorithm 2 shows the details of our approach. In Algorithm 2, to find the best threshold, potential threshold values are attempted with increments of 0.
Dropping the maximum threshold value is thus very important in order to reduce the search space. Then, the entropy value for this distribution is calculated. At the end of the while loop, the distribution that gives the best entropy is determined, and that distribution is used to determine the increase, decrease, and no-change classes. In our experiments, we observed that in most cases, the threshold upper bound approach significantly reduced the search space i.
For example, in one case, the maximum difference value was 0. In this case, the optimum threshold value was found to be 0. The purpose of this processing is to determine the final class decision. If the predictions of the two models are different, we choose for the final decision the one whose prediction has higher probability. This is a type of conservative approach to trading; it reduces the number of trades and favors only high-accuracy predictions.
Measuring the accuracy of the decisions made by these models also requires a new approach. If that is the case, then the prediction is correct, and we treat this test case as the correct classification. We introduced a new performance metric to measure the success of our proposed method. We can interpret this metric such that it gives the ratio of the number of profitable transactions over the total number of transactions, defined using Table 2. In the below formula, the following values are used:.
After applying the labeling algorithm, we obtained a balanced distribution of the three classes over the data set. This algorithm calculates different threshold values for each period and forms different sets of class distributions. For predictions of different periods, the thresholds and corresponding number of data points explicitly via training and test sets in each class are calculated, as shown in Table 3.
This table shows that the class distributions of the training and test data have slightly different characteristics. While the class decrease has a higher ratio in the training set and a lower ratio in the test set, the class increase shows opposite behavior. This is because a split is made between the training and test sets without shuffling the data sets to preserve the order of the data points.
We used the first days of this data to train our models and the last days to test them. If one of these is predicted, a transaction is considered to be started on the test day ending on the day of the prediction 1, 3, or 5 days ahead. Otherwise, no transaction is started.
A transaction is successful and the traders profit if the prediction of the direction is correct. For time-series data, LSTM is typically used to forecast the value for the next time point. It can also forecast the values for further time points by replacing the output value with not the next time point value but the value for the chosen number of data points ahead. This way, during the test phase, the model predicts the value for that many time points ahead. However, as expected, the accuracy of the forecast usually diminishes as the distance becomes longer.
They defined it as an n-step prediction as follows:. They performed experiments for 1, 3, and 5 days ahead. In their experiments, the accuracy of the prediction decreased as n became larger. We also present the number of total transactions made on test data for each experiment. Accuracy results are obtained for transactions that are made. For each experiment, we performed 50, , , and iterations in the training phases to properly compare different models.
The execution times of the experiments were almost linear with the number of iterations. For our data set, using a typical high-end laptop MacBook Pro, 2. As seen in Table 4 , this model shows huge variance in the number of transactions. Additionally, the average predicted transaction number is For this LSTM model, the average predicted transaction number is The results for this model are shown in Table 6.
The average predicted transaction number is One major difference of this model is that it is for iterations. For this test case, the accuracy significantly increased, but the number of transactions dropped even more significantly. In some experiments, the number of transactions is quite low. Basically, the total number of decrease and increase predictions are in the range of [8, ], with an overall average of When we analyze the results for one-day-ahead predictions, we observe that although the baseline models made more transactions Table 8 presents the results of these experiments.
One significant observation concerns the huge drop in the number of transactions for iterations without any increase in accuracy. Furthermore, the variance in the number of transactions is also smaller; the average predicted transaction number is There is a drop in the number of transactions for iterations but not as much as with the macroeconomic LSTM.
The results for this model are presented in Table However, the case with iterations is quite different from the others, with only 10 transactions out of a possible generating a very high profit accuracy. On average, this value is However, all of these cases produced a very small number of transactions. When we compare the results, similar to the one-day-ahead cases, we observe that the baseline models produced more transactions more than The results of these experiments are shown in Table Table 13 shows the results of these experiments.
Again, the case of iterations shows huge differences from the other cases, generating less than half the number of the lowest number of transactions generated by the others. Table 14 shows the results of these experiments. Meanwhile, the average predicted transaction number is However, the case of iterations is not an exception, and there is huge variance among the cases. From the five-days-ahead prediction experiments, we observe that, similar to the one-day- and three-days-ahead experiments, the baseline models produced more transactions more than This extended data set has data points, which contain increases and decreases overall.
Applying our labeling algorithm, we formed a data set with a balanced distribution of three classes. Table 16 presents the statistics of the extended data set. Below, we report one-day-, three-days-, and five-days-ahead prediction results for our hybrid model based on the extended data. The average the number of predictions is The total number of generated transactions is in the range of [2, 83]. Some cases with iterations produced a very small number of transactions. The average number of transactions is Table 19 shows the results for the five-days-ahead prediction experiments.
Interestingly, the total numbers predictions are much closer to each other in all of the cases compared to the one-day- and three-days-ahead predictions. These numbers are in the range of [59, 84]. On average, the number of transactions is Table 20 summarizes the overall results of the experiments.
However, they produced 3. In these experiments, there were huge differences in terms of the number of transactions generated by the two different LSTMs. As in the above case, this higher accuracy was obtained by reducing the number of transactions to Moreover, the hybrid model showed an exceptional accuracy performance of Also, both were higher than the five-days-ahead predictions, by 5.
The number of transactions became higher with further forecasting, for It is difficult to form a simple interpretation of these results, but, in general, we can say that with macroeconomic indicators, more transactions are generated. The number of transactions was less in the five-days-ahead predictions than in the one-day and three-day predictions. The transaction number ratio over the test data varied and was around These results also show that a simple combination of two sets of indicators did not produce better results than those obtained individually from the two sets.
Hybrid model : Our proposed model, as expected, generated much higher accuracy results than the other three models. Moreover, in all cases, it generated the smallest number of transactions compared to the other models The main motivation for our hybrid model solution was to avoid the drawbacks of the two different LSTMs i. Some of these transactions were generated with not very good signals and thus had lower accuracy results.
Although the two individual baseline LSTMs used completely different data sets, their results seemed to be very similar. Even though LSTMs are, in general, quite successful in time-series predictions, even for applications such as stock price prediction, when it comes to predicting price direction, they fail if used directly.
Moreover, combining two data sets into one seemed to improve accuracy only slightly. For that reason, we developed a hybrid model that takes the results of two individual LSTMs separately and merges them using smart decision logic. That is why incorrect directional predictions made by LSTMs correspond to a very small amount of errors. This causes LSTMs to produce models making many such predictions with incorrect directions. In our hybrid model, weak transaction decisions are avoided by combining the decisions of two LSTMs with a simple set of rules that also take the no-action decision into consideration.
This extension significantly reduced the number of transactions, by mostly preventing risky ones. As can be seen in Table 20 , which summarizes all of the results, the new approach predicted fewer transactions than the other models. Moreover, the accuracy of the proposed transactions of the hybrid approach is much higher than that of the other models.
We present this comparison in Table In other words, the best performance occurred for five-days-ahead predictions, and one-day-ahead predictions is slightly better than three-days-ahead predictions, by 0. Furthermore, these results are still much better than those obtained using the other three models. We can also conclude that as the number of transactions increased, it reduced the accuracy of the model.
This was an expected result, and it was observed in all of the experiments. Depending on the data set, the number of transactions generated by our model could vary. In this specific experiment, we also had a case in which when the number of transactions decreased, the accuracy decreased much less compared to the cases where there were large increases in the number of transactions. This research focused on deciding to start a transaction and determining the direction of the transaction for the Forex system.
In a real Forex trading system, there are further important considerations. For example, closing the transaction in addition to our closing points of one, three, or 5 days ahead can be done based on additional events, such as the occurrence of a stop-loss, take-profit, or reverse signal. Another important consideration could be related to account management.
The amount of the account to be invested at each transaction could vary. The simplest model might invest the whole remaining account at each transaction. However, this approach is risky, and there are different models for account management, such as always investing a fixed percentage at each transaction. Another important decision is how to determine the leverage ratio to be chosen for each transaction.
Simple models use fixed ratios for all transactions. Our predictions included periods of one day, three days, and 5 days ahead. We simply defined profitable transaction as a correct prediction of the decrease and increase classes. Predicting the correct direction of a currency pair presents the opportunity to profit from the transactions. This was the main objective of our study. We used a balanced data set with almost the same number of increases and decreases. Thus, our results were not biased.
Two baseline models were implemented, using only macroeconomic or technical indicator data. However, the difference was very small and insignificant. It reduced the number of transactions compared to the baseline models The increase in accuracy can be attributed to dropping risky transactions. The proposed hybrid model was also tested using a recent data set. Macroeconomic and technical indicators can both be used to train LSTMs, separately or together, to predict the directional movement of currency pairs in Forex.
We showed that rather than combining these parameters into a single LSTM, processing them separately with different LSTMs and combining their results using smart decision logic improved prediction accuracy significantly. Rather than trying to determine whether the currency pair rate will increase or decrease, a third class was introduced—a no-change class—corresponding to small changes between the prices of two consecutive days. This, too, improved the accuracy of direction prediction.
We described a novel way to determine the most appropriate threshold value for defining the no-change class. We used this feature to predict three days and 5 days ahead, with some decreases in accuracy values. Typically, the accuracy of LSTMs can be improved by increasing the number of iterations during training.
We experimented with various iterations to determine their effects on accuracy values. The results showed that more iterations increased accuracy while decreasing the number of transactions i. Additionally, a trading simulator could be developed to further validate the model. Such a simulator could be useful for observing the real-time behavior of our model. However, for such a simulator to be meaningful, several issues related to real trading e.
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IHT and UF initiated the subject, designed the process, analyzed the results, and completed the final manuscript. All authors read and approved the final manuscript. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. In Eq. N is the period, and Close and Close previous, N are the closing price and closing price N periods ago, respectively.
In Eqs. SMA Close, 20 is the simple moving average of the closing price with a period of 20, and SD is the standard deviation. Typical price is the typical price of the currency pair. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.
If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Reprints and Permissions. Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators. Financ Innov 7, 1 Download citation. Received : 09 October Accepted : 11 December Published : 04 January Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all SpringerOpen articles Search. Download PDF. Abstract Forex foreign exchange is a special financial market that entails both high risks and high profit opportunities for traders. Introduction The foreign exchange market, known as Forex or FX, is a financial market where currencies are bought and sold simultaneously.
The contributions of this study are as follows: A popular deep learning tool called LSTM, which is frequently used to forecast values in time-series data, is adopted to predict direction in Forex data. Both macroeconomic and technical indicators are used as features to make predictions. Related work Various forecasting methods have been considered in the finance domain, including machine learning approaches e. Forex preliminaries Forex has characteristics that are quite different from those of other financial markets Archer ; Ozorhan et al.
It is based on the following three assumptions Murphy : Market action discounts everything. Price moves in trends. History repeats itself. Full size image. Technical indicators A technical indicator is a time series that is obtained from mathematical formula s applied to another time series, which is typically a price TIO The technical indicators used in this study are described below. Moving average MA Moving average MA is a trend-following or lagging indicator that smooths prices by averaging them in a specified period.
Momentum Momentum measures the amount of change in the price during a specified period Colby The data set Interest and inflation rates are two fundamental indicators of the strength of an economy.