The U. Its tick size is 0. On Oct. The test collected data, including the profit margins of market makers in these securities. As part of the test, the SEC separated a sample of small-cap securities into one control group and two test groups. According to the SEC, each test group included about securities, with the remainder placed in the control group. The foreign exchange forex market uses a four-decimal quoting convention utilizing pips for the tick size.
Some forex brokers also offer fractional pip pricing, which is to the fifth decimal place. For example, the above quote could be further specified as 1. The value of a pip varies based on the currency pair being traded. Soft Commodities Trading. Metals Trading. Company News. Trading Basic Education.
Your Money. Personal Finance. Your Practice. Popular Courses. Trading Skills Trading Basic Education. What Is Tick Size? Key Takeaways Tick size is the minimum price increment change of a trading instrument. Tick sizes were once quoted in fractions e. Compare Accounts. By design, there will only ever be 21 million Bitcoins created. The closer Bitcoin gets to its limit, the higher its price will be, as long as demand remains the same or increases. Bitcoins are created by mining software and hardware at a specified rate.
This rate splits in half every four years , slowing down the number of coins created. Following the laws of supply and demand, Bitcoin's price should continue to rise as its supply may not be able to meet its demand—as long as it continues to grow in popularity.
However, if popularity wanes and demand falls, there will be more supply than demand, and Bitcoin's price should drop unless it maintains its value for other reasons. Another factor that affects Bitcoin's price falls in line with supply and demand; Bitcoin became an instrument that investors and financial institutions used to store value and generate returns. Derivatives have been created and traded by investors, further influencing Bitcoin's price.
Speculation, investment product hype, irrational exuberance, or investor panic and fear can also be expected to affect Bitcoin's price because demand will rise and fall with investors' sentiments. Other cryptocurrencies may also affect Bitcoin's price. There are several cryptocurrencies, and the number continues to rise as regulators, institutions, and merchants address concerns and adopt them as acceptable forms of payment and currency.
Lastly, if consumers and investors believe that other coins will prove to be more valuable than Bitcoin, demand will fall, taking prices with it—or demand will rise, along with prices, if sentiments change in the opposite direction.
The rate of difficulty changes. Mining depends on the software and hardware used as well as available energy resources, but the average time to find a block is about ten minutes. Bitcoin is a cryptocurrency designed to be used as a payment method.
Investors and traders began using it as an investment, but its price is very volatile—this creates a significant amount of financial risk. It is best to talk to a professional financial advisor about your circumstances and goals before buying Bitcoin as an investment. Since each individual's situation is unique, a qualified professional should always be consulted before making any financial decisions.
Investopedia makes no representations or warranties as to the accuracy or timeliness of the information contained herein. The Coinbase Blog. Use mouse wheel to zoom chart in to view Nov. Your Money. Personal Finance. Your Practice. Popular Courses. Table of Contents Expand. Table of Contents. Bitcoin Price History.
Factors Influencing Bitcoin Price. Bitcoin FAQs. Cryptocurrency Bitcoin. Part of. Guide to Bitcoin. Part Of. Bitcoin Basics. Bitcoin Mining. How to Store Bitcoin. Bitcoin Exchanges. Bitcoin Advantages and Disadvantages. Bitcoin vs. Other Cryptocurrencies. Bitcoin Value and Price. Key Takeaways Since it was first introduced, Bitcoin has had a choppy and volatile trading history.
Bitcoin's price has risen and fallen sharply over its short history. As an asset class, Bitcoin continues to evolve along with the factors that influence its prices.
Spread is the difference between the ask and bid prices. A lower spread means the trader can profit from small price changes. Spread value is dependent on market volatility and liquidity. Stop loss is an order to sell a currency when it reaches a specified price.
This order is used to prevent larger losses for the trader. Take profit is an order by the trader to close the open position transaction for a gain when the price reaches a predefined value. This order guarantees profit for the trader without having to worry about changes in the market price.
Market order is an order that is performed instantly at the current price. Swap is a simultaneous buy and sell action for the currency at the same amount at a forward exchange rate. This protects traders from fluctuations in the interest rates of the base and quote currencies.
If the base currency has a higher interest rate and the quote currency has a lower interest rate, then a positive swap will occur; in the reverse case, a negative swap will occur. Fundamental analysis and technical analysis are the two techniques commonly used for predicting future prices in Forex.
While the first is based on economic factors, the latter is related to price actions Archer Fundamental analysis focuses on the economic, social, and political factors that can cause prices to move higher, move lower, or stay the same Archer ; Murphy These factors are also called macroeconomic factors. Technical analysis uses only the price to predict future price movements Kritzer and Service This approach studies the effect of price movement.
Technical analysis mainly uses open, high, low, close, and volume data to predict market direction or generate sell and buy signals Archer It is based on the following three assumptions Murphy :. Chart analysis and price analysis using technical indicators are the two main approaches in technical analysis.
While the former is used to detect patterns in price charts, the latter is used to predict future price actions Ozorhan et al. LSTM is a recurrent neural network architecture that was designed to overcome the vanishing gradient problem found in conventional recurrent neural networks RNNs Biehl Errors between layers tend to vanish or blow up, which causes oscillating weights or unacceptably long convergence times.
In this way, the architecture ensures constant error flow between the self-connected units Hochreiter and Schmidhuber The memory cell of the initial LSTM structure consists of an input gate and an output gate. While the input gate decides which information should be kept or updated in the memory cell, the output gate controls which information should be output.
This standard LSTM was extended with the introduction of a new feature called the forget gate Gers et al. The forget gate is responsible for resetting a memory state that contains outdated information. LSTM offers an effective and scalable model for learning problems that includes sequential data Greff et al. It has been used in many different fields, including handwriting recognition Graves et al.
In the forward pass, the calculation moves forward by updating the weights Greff et al. The weights of LSTM can be categorized as follows:. The other main operation is back-propagation. Calculation of the deltas is performed as follows:.
Then, the calculation of the gradient of the weights is performed. The calculations are as follows:. Using Eqs. 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 These formulas generally use the close, open, high, low, and volume data.
Technical indicators can be applied to anything that can be traded in an open market e. They are empirical assistants that are widely used in practice to identify future price trends and measure volatility Ozorhan et al. By analyzing historical data, they can help forecast the future prices. According to their functionalities, technical indicators can be grouped into three categories: lagging, leading, and volatility. Lagging indicators, also referred to as trend indicators, follow the past price action.
Leading indicators, also known as momentum-based indicators, aim to predict future price trend directions and show rates of change in the price. Volatility-based indicators measure volatility levels in the price. BB is the most widely used volatility-based indicator.
Moving average MA is a trend-following or lagging indicator that smooths prices by averaging them in a specified period. In this way, MA can help filter out noise. MA can not only identify the trend direction but also determine potential support and resistance levels TIO It is a trend-following indicator that uses the short and long term exponential moving averages of prices Appel MACD uses the short-term moving average to identify price changes quickly and the long-term moving average to emphasize trends Ozorhan et al.
Rate of change ROC is a momentum oscillator that defines the velocity of the price. This indicator measures the percentage of the direction by calculating the ratio between the current closing price and the closing price of the specified previous time Ozorhan et al. Momentum measures the amount of change in the price during a specified period Colby It is a leading indicator that either shows rises and falls in the price or remains stable when the current trend continues.
Momentum is calculated based on the differences in prices for a set time interval Murphy The relative strength index RSI is a momentum indicator developed by J. Welles Wilder in RSI is based on the ratio between the average gain and average loss, which is called the relative strength RS Ozorhan et al.
RSI is an oscillator, which means its values change between 0 and It determines overbought and oversold levels in the prices. Bollinger bands BB refers to a volatility-based indicator developed by John Bollinger in the s. It has three bands that provide relative definitions of high and low according to the base Bollinger While the middle band is the moving average in a specific period, the upper and lower bands are calculated by the standard deviations in the price, which are placed above and below the middle band.
The distance between the bands depends on the volatility of the price Bollinger ; Ozturk et al. CCI is based on the principle that current prices should be examined based on recent past prices, not those in the distant past, to avoid confusing present patterns Lambert This indicator can be used to highlight a new trend or warn against extreme conditions. Interest and inflation rates are two fundamental indicators of the strength of an economy. In the case of low interest rates, individuals tend to buy investment tools that strengthen the economy.
In the opposite case, the economy becomes fragile. If supply does not meet demand, inflation occurs, and interest rates also increase IRD In such economies, the stock markets have strong relationships with their currencies. The data set was created with values from the period January —January This 5-year period contains data points in which the markets were open.
Table 1 presents explanations for each field in the data set. Monthly inflation rates were collected from the websites of central banks, and they were repeated for all days of the corresponding month to fill the fields in our daily records.
The main structure of the hybrid model, as shown in Fig. These technical indicators are listed below:. Our proposed model does not combine the features of the two baseline LSTMs into a single model. The training phase was carried out with different numbers of iterations 50, , and Our data points were labeled based on a histogram analysis and the entropy approach.
At the end of these operations, we divided the data points into three classes by using a threshold value:. Otherwise, we treated the next data point as unaltered. This new class enabled us to eliminate some data points for generating risky trade orders. This helped us improve our results compared to the binary classification results. In addition to the decrease and increase classes, we needed to determine the threshold we could use to generate a third class—namely, a no-action class—corresponding to insignificant changes in the data.
Algorithm 1 was used to determine the upper bound of this threshold value. The aim was to prevent exploring all of the possible difference values and narrow the search space. 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. Appel G Technical analysis: power tools for active investors. Financial Times Prentice Hall, p Wiley, London, p Google Scholar. Bahrammirzaee A A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems.
Neural Comput Appl — Article Google Scholar. Expert Syst Appl — Biehl M Supervised sequence labelling with recurrent neural neural networks. The forex market is open 24 hours a day, five days a week, in major financial centers across the globe. This means that you can buy or sell currencies at virtually any hour. In the past, forex trading was largely limited to governments, large companies, and hedge funds. Now, anyone can trade on forex. Many investment firms, banks, and retail brokers allow individuals to open accounts and trade currencies.
When trading in the forex market, you're buying or selling the currency of a particular country, relative to another currency. But there's no physical exchange of money from one party to another as at a foreign exchange kiosk. In the world of electronic markets, traders are usually taking a position in a specific currency with the hope that there will be some upward movement and strength in the currency they're buying or weakness if they're selling so that they can make a profit.
A currency is always traded relative to another currency. If you sell a currency, you are buying another, and if you buy a currency you are selling another. The profit is made on the difference between your transaction prices. A spot market deal is for immediate delivery, which is defined as two business days for most currency pairs. The business day excludes Saturdays, Sundays, and legal holidays in either currency of the traded pair. During the Christmas and Easter season, some spot trades can take as long as six days to settle.
Funds are exchanged on the settlement date , not the transaction date. The U. The euro is the most actively traded counter currency , followed by the Japanese yen, British pound, and Swiss franc. Market moves are driven by a combination of speculation , economic strength and growth, and interest rate differentials.
Retail traders don't typically want to take delivery of the currencies they buy. They are only interested in profiting on the difference between their transaction prices. Because of this, most retail brokers will automatically " roll over " their currency positions at 5 p. EST each day. The broker basically resets the positions and provides either a credit or debit for the interest rate differential between the two currencies in the pairs being held.
The trade carries on and the trader doesn't need to deliver or settle the transaction. When the trade is closed the trader realizes a profit or loss based on the original transaction price and the price at which the trade was closed. The rollover credits or debits could either add to this gain or detract from it. Since the forex market is closed on Saturday and Sunday, the interest rate credit or debit from these days is applied on Wednesday.
Therefore, holding a position at 5 p. Any forex transaction that settles for a date later than spot is considered a forward. The price is calculated by adjusting the spot rate to account for the difference in interest rates between the two currencies. The amount of adjustment is called "forward points. The forward points reflect only the interest rate differential between two markets. They are not a forecast of how the spot market will trade at a date in the future.
A forward is a tailor-made contract. It can be for any amount of money and can settle on any date that's not a weekend or holiday. As in a spot transaction, funds are exchanged on the settlement date. A forex or currency futures contract is an agreement between two parties to deliver a set amount of currency at a set date, called the expiry, in the future.
Futures contracts are traded on an exchange for set values of currency and with set expiry dates. Unlike a forward, the terms of a futures contract are non-negotiable. A profit is made on the difference between the prices the contract was bought and sold at. Instead, speculators buy and sell the contracts prior to expiration, realizing their profits or losses on their transactions.
There are some major differences between the way the forex operates and other markets such as the U. This means investors aren't held to as strict standards or regulations as those in the stock, futures or options markets. There are no clearinghouses and no central bodies that oversee the entire forex market. You can short-sell at any time because in forex you aren't ever actually shorting; if you sell one currency you are buying another.
Since the market is unregulated, fees and commissions vary widely among brokers. Most forex brokers make money by marking up the spread on currency pairs. Others make money by charging a commission, which fluctuates based on the amount of currency traded.
Some brokers use both. There's no cut-off as to when you can and cannot trade. Because the market is open 24 hours a day, you can trade at any time of day. The exception is weekends, or when no global financial center is open due to a holiday.
The forex market allows for leverage up to in the U. Leverage is a double-edged sword; it magnifies both profits and losses. Later that day the price has increased to 1. If the price dropped to 1. Currency prices move constantly, so the trader may decide to hold the position overnight. The broker will rollover the position, resulting in a credit or debit based on the interest rate differential between the Eurozone and the U.
Therefore, at rollover, the trader should receive a small credit. Rollover can affect a trading decision, especially if the trade could be held for the long term. Large differences in interest rates can result in significant credits or debits each day, which can greatly enhance or erode profits or increase or reduce losses of the trade. Most brokers provide leverage. Many U. Let's assume our trader uses leverage on this transaction.
That shows the power of leverage. The flip side is that the trader could lose the capital just as quickly. Your Money.
Price action is the movement of a security's price over time, which forms the basis for a securities price chart and makes technical analysis possible. A more valuable foreign currency does not necessarily mean that the foreign economy is larger or stronger. Exchange rates are set by a variety of factors. A store of value is essentially an asset, commodity, or currency that can be saved, retrieved, and exchanged in the future without deteriorating in value.