The Forex market has a daily turnover exceeding $7 trillion; data suggests it could exceed $10 trillion by the end of this decade. Algorithmic trading solutions execute over 80%+ trades and trading volume daily, ranging from simple trading bots to advanced AI-powered trading solutions. Most traders with profitable trading strategies will eventually get them coded into trading bots, also known as expert advisors or EAs, on the MT4/MT5 trading platforms.
MT4 is the leading algorithmic Forex trading platform but has its own coding language, MQL4. Before traders worry about learning the MQL4 coding language, they can still code their trading bot using Python and connect it via an API, which most brokers offer for their MT4/MT5 trading platforms.
How do you make a Forex trading bot in Python? Below is a quick guide on coding your trading strategy into a Python-based trading bot that can run on any trading platform, including the industry-leading MT4.
How Do Trading Bots Work?
Trading bots are computer programs that execute trading strategies based on algorithms. Simple trading bots can only use technical analysis and indicator-based algorithms, while advanced machine learning and AI-powered alternatives include fundamental data. They can read economic reports, adjust algorithms, and provide an almost human-like approach, but they can be expensive to code and maintain.
Python emerged as a leading choice for machine learning trading bots due to its high-level, general-purpose programming language and comprehensive standard library. However, it still requires human intervention to code, and before coding can start, the trading strategy being coded must be developed.
Any trading bot is only as good as the trading strategy and ability of the coder. Many traders love the idea of having a trading bot generate profits on autopilot, but most trading bots available for sale lose money in the long term. The MT4/MT5 libraries have thousands of EAs, which are strongly hyped but usually fail to deliver. People who developed their own profitable trading bots never sell them and guard the code to maintain their competitive advantage.
Why would anyone sell a profitable trading bot? Usually, the answer is simple: the bot only makes money from sales to retail traders hoping for a profitable trading bot! Therefore, the only reliable way to have a profitable trading bot is to code your own strategy, and Python offers one way to do it.
The Most Visible Benefits of Using Trading Bots
- The ability to analyze all assets in seconds or faster, depending on the underlying infrastructure.
- Emotionless decision-making, trade execution, and trade management.
- High-speed trading
- 24/5 Forex trading and 24/7 cryptocurrency trading.
Financial Data for Trading Bots
A trading bot relies on financial data, and the quality and speed of retrieving the data will have an exponential impact on the ability of the trading bot to perform. Some companies pay millions annually for accurate and fast data streams, but for most retail traders, their brokers provide the necessary financial data for free.
For example, MT4/MT5 brokers who fully support algorithmic trading offer APIs, allowing traders to run trading bots with financial data from the trading platform. It can result in low-latency data transfers if traders deploy their trading bots on cutting-edge infrastructure.
Creating a Python Trading Bot
How do you make a Forex trading bot in Python? Below is a rough walk-through on how to code their trading strategy into a trading bot using Python.
Having a Profitable Trading Strategy
Before coding a trading bot in Python, traders must have a profitable trading strategy worth coding. Most traders fail at this step, which can take years of development. Many profitable traders often pair with skilled coders to create a trading bot, bringing together professionals who excel in their respective fields.
Setting Up the Development Environment
Installing Python and a code editor of choice form the core of the development environment. Importing the necessary libraries, like Pandas for data analysis and NumPy for numerical calculations, is equally important. Traders must also install a platform that supports algorithmic trading via APIs, such as MT4, MT5, cTrader, or Ninja Trader.
Connecting to the Trading Platform
A trading platform offers access to the Forex market, and brokers provide them free of charge. MT4 is the leading algorithmic trading platform, followed by MT5, and most Forex brokers offer them. After installing the preferred trading platform, traders must also obtain the API, which allows a Python-coded trading bot to connect and interact with the platform. Before traders can use MT4/MT5 with third-party trading bots, they must enable "AutoTrading” and allow “DLL imports,” which remain disabled by default.
Retrieving Market Data
MT4/MT5 supports traders with historical and real-time data, which the Python-coded trading bot can retrieve via API. How the trading bot uses the data depends on what traders want it to do. One possibility is to store the historical data in Pandas DataFrame for further analysis or to conduct in-depth analytics on real-time data.
Building Trading Strategies
Trading strategies tell the trading bot when to buy and sell assets. There are no limits, and since Python supports advanced machine learning algorithms, you can use a combination of technical and fundamental analysis to create trading strategies if they can access quality data streams. One benefit Python coders enjoy is the TA-Lib, featuring many technical indicators. Alternatively, the MQL4 guide provides details on every technical indicator, which Python coders can replicate with the data received from the trading platform. The same applies to any other source that shows the math behind a technical indicator.
Implementing Trade Execution
Trade execution consists of opening and closing trades based on the trading strategy. It also includes risk management settings, including how and where to place stop loss and take profit levels, if they are static or trailing based on other conditions, and how to manage positions.
Backtesting and Optimization
Backtesting is one of the most important tasks before deploying any trading bot. Python offers excellent backtesting libraries like Backtrader and PyAlgoTrade, ensuring coders can backtest trading bots in an efficient environment. Alternatively, traders can backtest and bug-fix the trading bot in MT4/MT5 demo accounts, free and without time restrictions. You can also backtest with competitive brokers that embrace algorithmic traders.
All backtesting uses historical data, and once traders have sufficient trade data of at least 25,000+ trades, they can optimize and fine-tune their trading bot.
Deploying the Trading Bot
After backtesting and optimizing the trading bot, consider forward-testing it in a live account with a small deposit. Backtesting is ideal for bug-fixing, but past performance does not guarantee future success.
You can increase the portfolio size once the trading bot delivers the desired results in a smaller portfolio. For algorithmic trading, you should have a stable, high-speed internet connection, and high-end computers can ensure faster processing of real-time analysis.
Bottom Line
Making a Forex trading bot in Python is simple for skilled coders, but they must either hold or work with someone possessing a deep understanding of Forex trading. Python is an excellent coding language that emerged as a leading choice for machine learning solutions, which can power AI-assisted trading bots. Python also features many useful libraries for creating trading bots.
You should avoid ready-made trading bots, as they usually fail to deliver but cash in on the algorithmic trading hype, benefiting the trading bot seller.
