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King Login – The Forex (foreign exchange) market is the most liquid market in the world. In this trading tutorial, learn from the first-hand experience of the software developers who created algorithmic forex trading strategies and more.

Authors are selected experts in their fields and write on topics with proven expertise. All of our content is peer-reviewed and verified by experts in the same field.

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Rogelio is a full-stack engineer with over 13 years of hands-on experience across many languages, frameworks and platforms.

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Editor’s note: This article was updated by our editorial team on July 21, 2022. Revised to current editorial standards, including updated sources.

As you know, the Forex (forex or forex) market is used to trade between currency pairs. But what you may not know is that it is the most liquid market in the world. (Yes, even compared to cryptocurrencies, forex is generally considered safe and profitable).

A few years ago, driven by curiosity, I introduced myself to the world of algorithmic forex trading by creating a demo account and running a simulation (using fake money) on the Meta Trader 4 trading platform. You have taken the first step.

After a week of “trading” my funds almost doubled. Inspired by the success of algorithmic trading, I dug deeper and ended up signing up on several forex forums. Pretty soon, I spent hours reading about Forex algorithmic trading systems (the set of rules that determine whether to buy or sell), custom indicators, market moods, and more.

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Around this time, I heard that someone was looking for a software developer to build a simple automated forex trading system. This goes back to my college days, learning about concurrent programming in Java (threads, semaphores, etc.). I figured this automated forex trading system couldn’t be much more complicated than what I had been doing in my advanced data science course, so I asked about the job and decided to join.

The client wanted algorithmic trading software built with MQL4, the functional programming language that the Meta Trader 4 platform uses to perform stock-related actions.

After that, MQL5 was released. As you can imagine, it addresses some of the problems of MQL4 and includes more built-in features to make your work easier.

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The role of the trading platform (in this case Meta Trader 4) is to provide a connection to the forex broker. Brokers then provide the platform with real-time market information and execute buy and sell orders. For readers unfamiliar with Forex algorithmic trading, here is the information provided by the data source.

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Through Meta Trader 4, you can access all these data through internal functions and in different time periods: M1 (every minute), M5 (every 5 minutes), M15, M30, H1 (every hour), H4, D1 ( daily), W1 (weekly) and MN (monthly).

The current price movement is called a tick. A tick is a change in the buying or selling price of a currency pair. An active market can have many ticks per second. During periods of market slowdown, minutes can go by without a brand appearing. A tick is the heartbeat of a forex market robot.

When you place an order through these platforms, you buy or sell a certain amount of a certain currency. We also set stop loss and take profit limits. The stop loss limit is the maximum amount of pips (price change) you can lose before giving up on the trade. Take Profit Limit is the amount of pips you can accumulate profitably before cashing out.

BabyPips is a great resource if you want to learn more about the basics of trading (pips, order types, spreads, slippage, market orders, etc.).

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The client’s algorithmic trading specification was simple, a forex robot based on two indicators. As a background, indicators are based on historical data (eg, historical highs, etc.), which can be very useful in defining market conditions and making trading decisions.

Day by day). Many are built into Meta Trader 4. However, the indicator my client was interested in was from a custom forex trading system.

A client wanted to trade whenever two of these custom indicators crossed only at a certain angle.

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The start function is critical to any MQL4 program. This is executed every time the market moves (ie the function is executed once per tick). This is true regardless of the specific time period. For example, the start function can be executed thousands of times per hour, even if it is operating in the H1 period (1 hour).

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Once I built my algorithmic trading system, I wanted to know if it was working properly and if the forex trading strategy I used was the right one.

Backtesting is the process of testing a particular system (automated or not) based on past events. In other words, use the past tense instead of the present tense to test your system.

MT4 comes with acceptable tools to test forex trading strategies (now there are more professional tools that offer better functionality). First, set the deadline and run the program under simulation. The tool simulates each tick, knowing that for each unit it must reach the opening at a certain price, the closing at a certain price, and the specified high and low.

Comparing the program’s behavior with past prices gives a good idea of ​​whether the program is working properly.

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Through backtesting, we check the profitability of FX robots at random time intervals. I knew that the trading strategies my clients were using would not make them rich. The chosen metrics and decision-making logic were not profitable. Here are the results of running the program 164 times on an M15 window.

A warning. It is not always accurate to say that a system is “profitable” or “unprofitable”. In many cases, the system may not be profitable for a period of time depending on the “mood” of the market. Market “mood” follows different chart patterns, such as:

After backtesting, I was skeptical about the usefulness of this forex robot, but when I started experimenting with external parameters and noticed a big difference in overall returns, I was intrigued. this is known as

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After some rough tests to infer the importance of external parameters on the rate of return, we come to the following conclusions:

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Like me, you might think that parameter A should be used. But the decision is not as easy as it seems. In particular, note the unpredictability of the parameter A. If the error value is small, the return value will change dramatically. In other words, parameter A is very likely to overestimate future results. This is because any uncertainty makes performance worse, even if there are changes.

But in fact, the future is uncertain! Therefore, the return of parameter A is also uncertain. In fact, the best option is to trust the unpredictability. A parameter with low peak performance but high predictability (low variability) is often preferred over a parameter with high performance but low predictability.

The only thing we know for sure is that the future of the market is unknown. It’s a mistake to think you know how the market will behave based on historical data. Therefore, foreign exchange forecasting should take this unpredictability into account.

It’s a mistake to think you know what the market will be based on historical data.

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This does not necessarily mean that parameter B should be used. Although the return of parameter A is smaller, it performs better than the return of parameter B. Optimizing parameters can result in an exaggeration of possible future results, and this thought is not trivial.

Since my first experience with algorithmic forex trading, I have built several automated trading systems for my clients, but I can say that there is always room to explore and do more forex analysis. For example, I recently built a system based on the discovery of the so-called “big fish” movements, that is, large pip fluctuations in very small time units. This is a topic that fascinates me.

Building your own forex simulation system is a great way to learn more about trading the Forex market, and the possibilities are endless. For example, you can decipher the probability distribution of price changes based on the volatility of a market (eg EUR/USD) or create a Monte Carlo simulation model using distributions by volatility state regardless of precision. I like it more I will leave this as an exercise

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The world of Forex can be overwhelming at times, but we encourage you to explore your own strategies for algorithmic Forex trading.

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There are now a wide variety of tools for building, testing and improving the automation of forex trading systems. Trading Blox for testing, NinjaTrader for trading, OCaml for programming to name a few.

I have read many books about the mysterious world of forex markets. Here are some resources for developers and avid readers. Explore 7Cric, India’s leading betting trading platform. We offer a wide range of sports, a friendly interface and interesting promotions.

Betting exchanges offer a unique take on sports betting, betting with other players rather than competing with betting platforms. this

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