Algo-Trading Robot – many traders, aspire to become algorithmic but have difficulty coding their trading robots correctly. These traders often find promises of overnight prosperity and patchy and misleading algorithmic coding information online. However, a potential source for reliable information it creator of the AlgoTrading101 online algorithmic trading course. The course has attracted more than 30,000 students since its launch in 2014.
Liew’s program focuses on presenting the fundamentals of algo-trading robot in an organized manner. He is adamant that algorithmic trading is “not a get-rich-quick scheme.” The basics required to design.
What is a Trading Robot?
At its most basic level, an algo-trading robot is computer code capable of generating and executing trading signals in financial markets. The main components of such a robot include entry rules that indicate when to trade, exit rules that indicate when to close the current position, and position sizing rules that define quantities to sell.
You will need a computer and internet connection to become an algorithmic trader. After that, a suitable operating system must run MetaTrader (MT4), an electronic trading platform that uses Meta Quotes Language (MQL4) to code trading strategies. While MT4 is not the only software that can use to make a robot, it does have several significant benefits.
One advantage is that while MT4’s main asset class is foreign exchange (FX), the platform can also trade stocks, stock indices, commodities, and Bitcoin using contracts for difference (CFDs). Other benefits of using MT4 (as opposed to other platforms) are that it is easy to learn, has many FX data sources available, and is free.
Algorithmic Trading Strategies
One of the first steps in emerging an algorithmic strategy is to consider some key features that every algorithmic trading strategy should have. The market needs to be prudent, as the design is fundamentally sound regarding the market and the economy. In addition, the mathematical model used in developing the plan must base on sound statistical methods.
Next, determine what information your robot intends to capture. Your bot must capture persistent and identifiable market inefficiencies to take an automated strategy. Algorithmic trading strategies follow a strict set of rules that take advantage of market behaviour, and a single exposure to market inefficiency is not enough to build a system. Furthermore, if the cause of market inefficiency cannot identify, there is no way of knowing whether the success or failure of the strategy is due to chance.
It considering the above, some strategy types will inform the design of your algorithmic trading robot. These include strategies that take advantage of (or any combination of) the following:
- Macroeconomic news (for example, nonfarm payrolls or interest rate changes)
- Fundamental analysis (for example, using earnings data or earnings release notes)
- Statistical analysis (e.g. correlation or cointegration)
- Technical analysis (e.g. moving averages)
- Microstructure of the market (for example, arbitrage or trading infrastructure)
Preliminary research focuses on developing a strategy that fits their characteristics. When creating a plan, it is essential to consider factors such as personal risk profile, time commitment and trading capital. You can then begin to identify the persistent market inefficiencies outlined above. Once you identify a market inefficiency, you can start coding a trading bot that suits your characteristics.