Stock Trading Bot: Coding your Trading Algorithm
Many traders strive to be algorithmic but need help correctly coding their robots. These traders will likely find misleading and disorganized information about algorithmic coding and false claims of overnight success online. Lucas Liew is the creator of AlgoTrading101, an online course on algorithmic trading. Since its launch in 2014, the system has attracted over 30,000 students. 1
Liew’s course focuses on presenting algorithmic trading fundamentals in an organized manner. He insists that algorithmic trading “is not a get-rich-quick scheme.” Below are the basic steps to designing, building, and maintaining your algorithmic Trading Robot. (Taken from Liew’s course).
What is a Trading Robot?
The most basic algorithmic trading robot is a computer program that can generate and execute buy-and-sell signals on financial markets. The robot’s main components are the entry rule, which signals when to buy and sell; position size rules that define the quantities to be purchased or sold; and HTML2_ exit rules, which indicate when to close a current position.
MetaTrader 4 (MT4) is an electronic trading platform that uses the MetaQuotes Language 4 (MQL4) to code trading strategies. MetaTrader 4 is an electronic trading platform. It uses MetaQuotes Language 4 to code trading strategies. 2 MT4 has a few significant advantages.
MT4 is a platform that allows you to trade equities and commodities using contracts of difference (CFDs). MT4 is also easier to use than other platforms, has a wide range of FX data available, and is free.
Algorithmic Trading Strategies
To develop an algorithmic trading strategy, it is essential to consider the key traits that all systems must possess. The process must be market-prudent, meaning it should be sound from an economic and market perspective. The mathematical model to develop the strategy should also be based on good statistics.
Determine what information you want your robot to capture. To have an automated trading strategy, your robot must be able to capture persistent inefficiencies. An algorithmic trading strategy follows rigid rules that take advantage of market behavior. More than a single market inefficiency is required to create a plan. If the reason for the market’s inefficiency is known, it will be possible to determine if a strategy’s success or failure was due to luck.
There are several types of strategies that can be used to design your algorithmic trading bot. This includes systems that use the following (or a combination of them):
- Macroeconomic News (e.g., changes in interest rates or non-farm payroll)
- Fundamental Analysis (e.g., using revenue data, earnings release notes, or other sources of information)
- Analysis of statistical data (e.g., correlation or cointegration).
- Technical Analysis (e.g., moving averages)
- Arbitrage or trade infrastructure (e.g., Arbitrage or trade infrastructure
Researching your characteristics is the first step in developing a trading strategy. When creating a trading strategy, it is essential to consider factors such as your personal risk profile, time commitment, and your trading capital. Then you can begin to identify those persistent market inefficiencies that were mentioned earlier. Once you have placed an inefficiency in the market, you can start to code a robot tailored to your characteristics.
Backtesting and optimization
Backtesting is a method of validating your trading robot. This includes checking that the code does what you want and understanding the strategy’s performance over different timeframes, asset classes, or market conditions.
You must first select a good performance measure that captures risk and reward elements, as well as consistency to maximize performance. You must first select a performance measure that combines risk, reward, and consistency (e.g., Sharpe Ratio) to optimize performance.
Overfitting bias is when your robot’s performance is based too heavily on the past. This robot appears to be performing well but could fail because the future will never exactly resemble the past. Overfitting can be prevented by training with more data and removing unnecessary input features.
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