Binary options traded outside the U. They offer a viable alternative when speculating or hedging, but only if the trader fully understands the two potential and opposing outcomes. These types of options are typically found on internet-based trading platforms, not all of which comply with U.

In other words, you test your system using the past as a proxy for the present. MT4 comes with an acceptable tool for backtesting a Forex trading strategy nowadays, there are more professional tools that offer greater functionality.

To start, you setup your timeframes and run your program under a simulation; the tool will simulate each tick knowing that for each unit it should open at certain price, close at a certain price and, reach specified highs and lows. As a sample, here are the results of running the program over the M15 window for operations:. This particular science is known as Parameter Optimization. I did some rough testing to try and infer the significance of the external parameters on the Return Ratio and came up with something like this:.

You may think as I did that you should use the Parameter A. Specifically, note the unpredictability of Parameter A: for small error values, its return changes dramatically. In other words, Parameter A is very likely to over-predict future results since any uncertainty, any shift at all will result in worse performance.

But indeed, the future is uncertain! And so the return of Parameter A is also uncertain. The best choice, in fact, is to rely on unpredictability. Often, a parameter with a lower maximum return but superior predictability less fluctuation will be preferable to a parameter with high return but poor predictability. In turn, you must acknowledge this unpredictability in your Forex predictions.

This does not necessarily mean we should use Parameter B, because even the lower returns of Parameter A performs better than Parameter B; this is just to show you that Optimizing Parameters can result in tests that overstate likely future results, and such thinking is not obvious.

This is a subject that fascinates me. Building your own FX simulation system is an excellent option to learn more about Forex market trading, and the possibilities are endless. The Forex world can be overwhelming at times, but I hope that this write-up has given you some points on how to start on your own Forex trading strategy.

Nowadays, there is a vast pool of tools to build, test, and improve Trading System Automations: Trading Blox for testing, NinjaTrader for trading, OCaml for programming, to name a few. Here are a few write-ups that I recommend for programmers and enthusiastic readers:.

Forex or FX trading is buying and selling via currency pairs e. Forex brokers make money through commissions and fees. Forex traders make or lose money based on their timing: If they're able to sell high enough compared to when they bought, they can turn a profit. Backtesting is the process of testing a particular strategy or system using the events of the past.

Subscription implies consent to our privacy policy. Thank you! Check out your inbox to confirm your invite. Engineering All Blogs Icon Chevron. Filter by. View all results. Author Rogelio Nicolas Mengual. My First Client Around this time, coincidentally, I heard that someone was trying to find a software developer to automate a simple trading system.

MQL5 has since been released. As you might expect, it addresses some of MQL4's issues and comes with more built-in functions, which makes life easier. The significant amount of financial leverage afforded forex traders presents additional risks that must be managed. Leverage provides traders with an opportunity to enhance returns. But leverage and the commensurate financial risk is a double-edged sword that amplifies the downside as much as it adds to potential gains.

The forex market allows traders to leverage their accounts as much as , which can lead to massive trading gains in some cases - and account for crippling losses in others. The market allows traders to use vast amounts of financial risk, but in many cases, it is in a trader's best interest to limit the amount of leverage used.

The amount of leverage available comes from the amount of margin that brokers require for each trade. Margin is simply a good faith deposit that you make to insulate the broker from potential losses on a trade. The bank pools the margin deposits into one very large margin deposit that it uses to make trades with the interbank market. Anyone that has ever had a trade go horribly wrong knows about the dreadful margin call, where brokers demand additional cash deposits; if they don't get them, they will sell the position at a loss to mitigate further losses or recoup their capital.

Many forex brokers require various amounts of margin, which translates into the following popular leverage ratios:. The reason many forex traders fail is that they are undercapitalized in relation to the size of the trades they make. It is either greed or the prospect of controlling vast amounts of money with only a small amount of capital that coerces forex traders to take on such huge and fragile financial risk.

And every loss, even the small ones taken by being stopped out of a trade early, only exacerbates the problem by reducing the overall account balance and further increasing the leverage ratio. Not only does leverage magnify losses, but it also increases transaction costs as a percent of the account value. The higher the leverage, the higher the transaction costs as a percentage of the account value, and these costs increase as the account value drops.

While the forex market is expected to be less volatile in the long term than the equity market, it is obvious that the inability to withstand periodic losses and the negative effect of those periodic losses through high leverage levels are a disaster waiting to happen. These issues are compounded by the fact that the forex market contains a significant level of macroeconomic and political risks that can create short-term pricing inefficiencies and play havoc with the value of certain currency pairs.

Many of the factors that cause forex traders to fail are similar to those that plague investors in other asset classes. The simplest way to avoid some of these pitfalls is to build a relationship with other successful forex traders who can teach you the trading disciplines required by the asset class, including the risk and money management rules required to trade the forex market. Only then will you be able to plan appropriately and trade with the return expectations that keep you from taking an excessive risk for the potential benefits.

While understanding the macroeconomic, technical, and fundamental analysis necessary for trading forex is as important as the requisite trading psychology , one of the largest factors that separates success from failure is a trader's ability to manage a trading account. The keys to account management include making sure to be sufficiently capitalized, using appropriate trade sizing, and limiting financial risk by using smart leverage levels.

Your Money. Personal Finance. Your Practice. Popular Courses. Table of Contents Expand. Table of Contents.

This comprises 4. Trading Days:. Latest trade:. Trades per week:. Avg holding time:. See more 5. Watch the video tutorials about trading signals on YouTube. Luigi Vella. To see trades in realtime, please log in or register. Profit Trades:. Loss Trades:. Best trade:. Worst trade:. Gross Profit:. Gross Loss:. Maximum consecutive wins:. Maximal consecutive profit:. Sharpe Ratio:. Trading activity:. Max deposit load:. Recovery Factor:. Long Trades:. Short Trades:. Profit Factor:. Expected Payoff:.

Average Profit:. Average Loss:. Maximum consecutive losses:. Maximal consecutive loss:. Monthly growth:. Annual Forecast:. Algo trading:. Drawdown by balance:. Relative drawdown:. By Balance:. By Equity:.

No data. VantageFXInternational-Live 7. Average rating:. TradingJoe They compared SVM with linear discriminant analysis, quadratic discriminant analysis, and Elman back-propagation neural networks. They also proposed a model that combined SVM with other classifiers. Their direction calculation was based on the first-order difference natural logarithmic transformation, and the directions were either increasing or decreasing.

Kara et al. Ten technical indicators were used as inputs for the model. They found that ANN, with an accuracy of In the first approach, they used 10 technical indicator values as inputs with different parameter settings for classifiers.

Prediction accuracy fell within the range of 0. In the other approach, they represented same 10 technical indicator results as directions up and down , which were used as inputs for the classifiers. Although their experiments concerned short-term prediction, the direction period was not explicitly explained.

Ballings et al. They used different stock market domains in their experiments. According to the median area under curve AUC scores, random forest showed the best performance, followed by SVM, random forest, and kernel factory. Hu et al. Using Google Trends data in addition to the opening, high, low, and closing price, as well as trading volume, in their experiments, they obtained an Gui et al.

That study also compared the result for SVM with BPNN and case-based reasoning models; multiple technical indicators were used as inputs for the models. That study found that SVM outperformed the other models with an accuracy of GA was used to optimize the initial weights and bias of the model.

Two types of input sets were generated using several technical indicators of the daily price of the Nikkei index and fed into the model. They obtained accuracies Zhong and Enke used deep neural networks and ANNs to forecast the daily return direction of the stock market. They performed experiments on both untransformed and PCA-transformed data sets to validate the model.

In addition to classical machine learning methods, researchers have recently started to use deep learning methods to predict future stock market values. LSTM has emerged as a deep learning tool for application to time-series data, such as financial data. Zhang et al. By decomposing the hidden states of memory cells into multiple frequency components, they could learn the trading patterns of those frequencies.

They used state-frequency components to predict future price values through nonlinear regression. They used stock prices from several sectors and performed experiments to make forecasts for 1, 3, and 5 days. They obtained errors of 5. Fulfillment et al. He aimed to predict the next 3 h using hourly historical stock data. The accuracy results ranged from That study also built a stock trading simulator to test the model on real-world stock trading activity.

With that simulator, he managed to make profit in all six stock domains with an average of 6. Nelson et al. They used technical indicators i. They compared their model with a baseline consisting of multilayer perceptron, random forest, and pseudo-random models.

The accuracy of LSTM for different stocks ranged from 53 to They concluded that LSTM performed significantly better than the baseline models, according to the Kruskal—Wallis test. They investigated many different aspects of the stock market and found that LSTM was very successful for predicting future prices for that type of time-series data. They also compared LSTM with more traditional machine learning tools to show its superior performance.

Similarly, Di Persio and Honchar applied LSTM and two other traditional neural network based machine learning tools to future price prediction. They also analyzed ensemble-based solutions by combining results obtained using different tools. In addition to traditional exchanges, many studies have also investigated Forex. Some studies of Forex based on traditional machine learning tools are discussed below.

Galeshchuk and Mukherjee investigated the performance of a convolutional neural network CNN for predicting the direction of change in Forex. That work used basic technical indicators as inputs. Ghazali et al. To predict exchange rates, Majhi et al. They demonstrated that those new networks were more robust and had lower computational costs compared to an MLP trained with back-propagation. In what is commonly called a mark-to-market approach, market prices are increasingly being used to calibrate models to quantify risk in several sectors.

The net present value of a financial institution, for example, is an important input for estimating both bankruptcy risk e. In such a context, stock price crashes not only dramatically damage the capital market but also have medium-term adverse effects on the financial sector as a whole Wen et al. Credit risk is a major factor in financial shocks. Therefore, a realistic appraisal of solvency needs to be an objective for banks. At the level of the individual borrower, credit scoring is a field in which machine learning methods have been used for a long time e.

In one recent work, Shen et al. They were able to show that deep learning approaches outperformed traditional methods. Even though LSTM is starting to be used in financial markets, using it in Forex for direction forecasting between two currencies, as proposed in the present work, is a novel approach. Forex has characteristics that are quite different from those of other financial markets Archer ; Ozorhan et al. To explain Forex, we start by describing how a trade is made.

If the ratio of the currency pair increases and the trader goes long, or the currency pair ratio decreases and the trader goes short, the trader will profit from that transaction when it is closed. Otherwise, the trader not profit.

When the position closes i. When the position closes with a ratio of 1. Furthermore, these calculations are based on no leverage. If the trader uses a leverage value such as 10, both the loss and the gain are multiplied by Here, we explain only the most important ones. Base currency, which is also called the transaction currency, is the first currency in the currency pair while quote currency is the second one in the pair.

Being long or going long means buying the base currency or selling the quote currency in the currency pair. Being short or going short means selling the base currency or buying the quote currency in the currency pair. In general, pip corresponds to the fourth decimal point i. Pipette is the fractional pip, which corresponds to the fifth decimal point i. In other words, 1 pip equals 10 pipettes. Leverage corresponds to the use of borrowed money when making transactions.

A leverage of indicates that if one opens a position with a volume of 1, the actual transaction volume will be After using leverage, one can either gain or lose times the amount of that volume. Margin refers to money borrowed by a trader that is supplied by a broker to make investments using leverage.

Bid price is the price at which the trader can sell the base currency. Ask price is the price at which the trader can buy the base currency. 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.

Learn the strategies and techniques forex traders around the world use to speculate in the largest market in the world. Becoming a successful forex trader means achieving a few big wins while suffering many smaller losses. Experiencing many consecutive losses is difficult to. With so many options available, you're probably asking yourself – which currencies should I trade? A good rule of thumb for traders new to the market is to.