Using AI To Create A Successful Forex Trading Robot

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Forex trading bots have existed for decades since retail Forex trading appeared in the early 2000s. They've helped hundreds of thousands of traders implement automated strategies they might not have been able to trade manually. Until now, developing a trading bot required specialized knowledge and programming skills, plus a lot of time to test it.

Artificial Intelligence is changing that, making the process much faster and enabling anyone to build and test trading bots without specialized knowledge.

Let's take a closer look at the opportunities you have here is a Forex Trading Bot?

A Forex trading bot (“bot” is short for“robot”) is software that automatically trades for the user according to a trading method programmed in the bot. Some users build bots according to a trading method they developed, and others purchase bots containing someone else's method.

A trading method systematically defines when to enter the market and when to exit to either take profits or exit at a loss. A bot's trading method must have all the following components for it to trade an account automatically without human intervention:

Entry rules. These are the criteria the price (and perhaps volume) must meet to place a long or short entry trade. The entry rules are often the most complex part of the trading strategy, as they define the rules by which the program believes the market will move in a particular direction. The entry rules can be simple with only price rules or more complex with multiple indicators. For example, I built a bot with an entry rule consisting of only two candles and no indicators. I've seen examples of other bots with five or six indicators, such as RSIs , MACD , a moving average, etc., requiring each indicator to be at a specified level to enter the market. Some entry rules also specify the times of day to trade, the currency pairs, or the timeframes. For example, some strategies capitalize on the two hours of the London or New York opens, when there is a lot of liquidity , and Forex pairs such as the EUR/USD and GBP/USD tend to move in predictable ways.

Take profit rules. This can be a pip value, e.g. 50 pips, or the method can base the take profit on indicators, e.g., two moving averages crossing to signify the momentum is turning against the trade. Some trading methods scale out of trades by taking some profits at one level and more profits at further levels.

Stop loss rules. Similar to the take profit rules, these can be a pip value or based on indicator settings. Another common strategy is to“trail” the stop loss in the direction of the price move. For example, the first bot I built had the stop-loss move to break even when the price was halfway to the take-profit level.

Money management rules. These rules determine the trade size. They can be a static level, e.g., one standard lot per trade, or more dynamic, e.g., the position size is determined by risking a set percentage of the account on each trade. Dynamic money management rules are great because the trade sizes adjust automatically after a series of wins or losses. This especially helps protect the account after a losing streak by not having trade sizes that are too large Expert Advisor“Bots” (H3)

The most common Forex trading bots are programs called“Expert Advisors” or“EAs” that run on the MetaTrader platform (the world's most popular Forex platform for retail traders). Note: An EA is just a MetaTrader program, but not necessarily a bot-some EAs are additional indicators that do not place trades automatically. For example, my first EA was a simple indicator showing the time left until the current candle closed.

MetaTrader EAs use the proprietary MQL programming language (MQL4 for MetaTrader 4, MQL5 for MetaTrader 5). MQL is similar to C++, making it easier for people with C++ experience to learn it.

Individuals can buy MetaTrader EA-bots created by other traders, and MetaTrader has a site dedicated to buying or renting EAs is an AI Trading Robot?

An AI trading robot is software that automatically places trades without human intervention while using AI to analyze the markets and adjust the underlying trading strategy. AI trading bots can leverage aspects of AI, including:

Machine Learning & Deep Learning. Machine learning (ML) allows the bot to learn from historical and real-time market data to identify patterns and improve decision-making over time. Deep learning is a subset of ML that uses more artificial“neural networks,” making ML more efficient and requiring less human effort to train the software. For example, ChatGPT is a deep-learning AI model.

Natural Language Processing (NLP). This enables AI to scan fundamental data, economic announcements, and news and gauge investor sentiment (i.e., sentiment analysis) from social media to assess market-moving events.

The first AI trading bot I encountered used machine learning to continuously backtest multiple indicators (MACD, RSI, moving averages, etc.), deciding which ones worked in current market conditions. It then applied the most optimal indicators in real-time trading AI Trading Bots Profitable?

AI can help refine a strategy much quicker than traditional methods of building systematic trading models because humans are no match for the amount of data that AI can analyze. However, that does not guarantee that AI bots will be profitable, and in most circumstances, AI cannot make a strategy profitable if it has bad underlying principles. The initial parameters that the trader gives the AI model must be robust. Using an exaggerated example, if a trader asks AI to optimize a trading strategy based on astrological signs, and there's no correlation between star signs and market movements, then AI will not be able to make a profitable strategy out of it. However, it will be able to tell the trader very quickly that there's no correlation between astrology and market movements, and the trader can then move on to look at other ideas.

Remember, AI is a broad term. Asking whether AI trading bots are profitable is like asking whether technical analysis is profitable-it can be profitable, but only in the right hands. AI bots are incredibly varied in their complexity and trading methods. Some bots rely on a fixed set of indicators and use AI to adjust the settings, and other bots can pick and choose between indicators or trading concepts.

Many vendors are also already selling AI trading bots. Most rely on either testimonials or self-published results to claim profitability, i.e., without third-party verification to back their claims. That does not mean they are not profitable, but there is no way to verify the claims without trialling or paying for the product and testing them in real time.

It is also difficult to verify their claims that their bots use AI. AI is a hugely broad term, and some bots may use AI to a small degree, making them not very different from non-AI trading bots and Cons of Trading with an AI Bot

Pros:

Trading bots can be“always on,” eliminating the need to be at the screen to execute trades.

They can monitor more markets at once.

Trading bots eliminate human emotions, i.e. they are one hundred per cent disciplined.

They can trade more rapidly than humans, which is especially useful for short-term trading.

AI can continuously analyze and adapt strategies faster than human traders, finding patterns or profitable indicator settings that humans might miss.

Cons:

If purchasing a trading bot, it's hard to know if it's profitable in advance because most sellers don't publish verifiable results, only testimonials.

MetaTrader EAs allow for only limited AI usage because of the limits of its programming language, MQL.

AI cannot turn a poor strategy into a profitable one to Create Your AI Trading Bot (Step-by-Step Guide)

Define the trading strategy . The heart of any trading bot is the trading strategy, which should be the starting point. This is where some trading experience is necessary. Most traders wanting to build an AI-powered bot already have a trading strategy and want AI to help them code or improve it. Remember, a trading strategy should consist of entry rules where you believe the market will likely move in a specific direction and take-profit and stop-loss rules. For example, are you trading indicators with support and resistance? Is it a strategy that requires the price to break out from prior candles' ranges and also above or below a moving average level? AI can code and further improve these strategies if they have shown a profit.

Next, code the trading strategy and execution parameters in the programming language of your choice. Popular languages for bots are JavaScript, Python and C++. Many AI coding tools can help code the strategy in your chosen programming language. Remember, coding the bot must include instructions on fetching data in real time and executing orders on the broker's API.

Download price historical data to train the bot. Nearly all exchanges and brokers offer data to download, either free or paid. Spot Forex is an OTC market without standardized data, so a quality source such as a large broker can be used for historical data.

Test the strategy. Most traders start by backtesting, and this is a good thing. But sometimes, positive results from backtesting do not work in real-time because the strategy is overoptimized for historical data that won't repeat itself exactly (this is known as“curve fitting”). To combat this, forward test in real-time. Some traders take data samples from other periods, such as five years ago, to see if the strategy worked then and not just in the recent past. However, there's a balance between wanting the strategy to work in today's market conditions versus many years ago. This step is where implementing AI machine learning algorithms can help test strategy variations, e.g., adjusting stop loss levels or indicator settings's & Don'ts in AI Trading

Do's:

Start with a strategy that you have experience trading and use AI to help code or improve it.

Use quality data to test or train the bot.

Continue to work on the bot's AI aspects. More AI tools become available almost daily, such as machine learning tools to analyze data and help traders get the most out of the market.

Don't:

Rush into an AI-powered bot with real money without testing it first Take

Trading bots have existed for decades, and they offer phenomenal advantages: they automate trading, remove human emotions, and can execute your trades without needing you in front of a screen. AI can improve trading bots and make them easier to create. However, AI is a broad field, and the quality of AI-powered bots will vary.

Creators of AI trading bots should begin with a robust underlying trading strategy, making it necessary to have some trading experience. AI can then help test and refine trading strategies much faster than humans. Another use for AI in this area is using AI coding tools to code trading strategies into bots, even if the final bot does not have AI features.

There are many AI-powered trading bots available to buy or rent. However, few have independently verified performance data, and it's also hard to authenticate whether they genuinely use AI machine learning to adapt trading strategies in real time.

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