Loved it!! Thanks for sharing Boris. This may never get read. But I most certainly agree with most of this article. Mainly because it was an easier practice of mine.

50-Pips a Day Forex Strategy

But later the realization set in that, even your stated strategy is too time intensive. Why check to see if the market is going the other direction? Why not just set 50 trades at RR and let it ride. Do it 10 more times to be sure it work. It will. The add leverage. I let randomness work and pay me for letting it so.

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• Picking the Best Forex Strategy for You in 2021?
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So simple its litterally stupid. Lastly, i also appreciate the guru la-la world comment lol. Good luck with everything! Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Remember Me. Lost your password? Username or E-mail:. Log in. Random Trading is the Best by admin One of the greatest books on trading contains no practical advice about the markets whatsoever but still describes price action better than a thousand hedge fund managers ever could.

Boris Schlossberg. However, machines cannot replace human intelligence or human critical aspect. In addition, there could also be problems with the calibration of the trading system, which would give incorrect timing of the buying and selling of an asset.

In the last decades, the growth of global trading markets made the foreign exchange market the largest and most lucrative of the financial markets. This monetary market is characterized by high liquidity, large volume of trade, and continuous transactions.

Forex market is a volatile market with great uncertainty. However, foreign exchange investors are exposed to currency risk, which can seriously jeopardize international trade flows [ 9 , 10 ]. These investors need to be aware of the uncertainty of this market and the major impact on their investment decisions. However, accurate forecasting of exchange rates could reduce this uncertainty and would be beneficial for both international trade flows and investor profits. As a result, exchange rate movements and predictability have been studied extensively in recent decades [ 11 ].

Among the most modern practical methods for predicting currency movements, using fundamental and technical analysis is of paramount significance. There are multiple studies that have applied fundamental analysis to forecast currency exchange rate. Among these researches, we can quote, e. In this paper, we concentrate our study mainly on technical analysis using data mining algorithms and technical indicators to predict future exchange rate values.

Poole and Dooley and Schafer were the pioneers to describe technical analysis [ 18 , 19 ]. Poole indicated that the application of trading rules generates important benefits. Thanks to these rules, we can fix the buy or sell orders if the exchange rate increases or falls compared to the percentage already fixed. Dooley and Schafer also applied seven different filter rules on nine currencies. They concluded that using simple trading strategy based on information about past exchange rate fluctuations generated significant returns.

Recently, numerous advanced techniques have been widely applied to predict exchange rate fluctuations [ 20 ].

These techniques exploit the technological progress of computer tools. This progress permitted to manage the big data and to study the complex, nonlinear, and dynamic characteristics of the financial markets. To improve accuracy, Booth et al. It shows that a regency-weighted ensemble of random forests produces superior results when analyzed on a large sample of stocks from the DAX in terms of both profitability and prediction accuracy compared with other ensemble techniques [ 27 ]. In addition, Sorensen et al. Wang et al. Some researchers have focused on neural networks to train algorithms.

According to Shaoo et al. Actually, some researchers suggest applying ensemble methods in order to improve the regression and classification performance. In [ 32 ], He and Shen have used a bootstrap method based on neural networks to construct multiple learning models and combined the output of these models to predict currency exchange rates. Nowadays, few counted studies use Random Forests and Probit regression to predict exchange rate.

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According to Lv and Zhang [ 33 ], the RF algorithm showed its performance against the SVM method and the multiple linear regression method to accurately predict the Chinese Yuan. In this paper, we will use a Random Forest classification algorithm and Probit regression. We combined this two algorithms to forecast currency exchange rate.

Trading strategy is an important financial method. It can be defined as a set of instructions to make a profit and generate a positive return on its investment. Some trading strategies are not always outright profitable as standalone strategies. Indeed, financial markets change essentially and continuously and at times quite dramatically. One of the consequences of this transiency is that trading strategies that may have worked well for some time may die, sometimes quite abruptly. There are many factors that affect the trading strategy results and thus no universal model can predict everything well for all problems or even be a single best trading method for all situations.

However, technological advances gave rise to new types of trading such as the trading strategies based on data mining and machine learning. This strategy is based on algorithm trading and shows how it can execute complex analyses in real time and take the required decisions based on the strategy defined without human intervention and send the trade for execution automatically from the computer to the exchange. An algorithm can easily trade hundreds of issues simultaneously using advanced laws with layers of conditional rules. Algorithm trading seeks to identify typically quite ephemeral signals or trends by analyzing large volumes of diverse types of data.

random entry trading systems making 1,000 % a year

Many of these trading signals are so faint that they cannot be traded on their own. Thus, one combines a lot of such signals with nontrivial weights to amplify and enhance the overall signal and it becomes tradable on its own and profitable after trading costs. Currently, foreign exchange market is the biggest and most liquid market in the world. However, a trading strategy using algorithmic trading has become an absolute must for survival both for the buy and sell sides. Due to the chaotic, noisy, and nonstationary nature of the data, major trader has had to migrate to the use of automated algorithmic trading in order to stay competitive.

To make profit from each strategy, the majority of the research has focused on daily, weekly, or even monthly prediction.

random entry trading systems making 1, % a year | Forex Forum - EarnForex

Neural Networks are a key topic in several papers in order germane to trading systems. Matas et al. Enam [ 35 ] experimented with the predictability of ANN on weekly FX data and concluded that, among other issues, one of the most critical issues to encounter when introducing such models is the structure of the data. Kamruzzaman [ 36 ] compared different ANN models, feeding them with technical indicators based on past Forex data, and concluded that a Scaled Conjugate Gradient based model achieved closer prediction compared to the other algorithms.

Evans et al. They used dataset for their research comprising 70 weeks of past currency rates of the 3 most traded currency pairs: , , and.