~~~ Introduction to Algorithmic Trading ~~~
Any algorithmic trading system has 3 components: A trading algorithm or trading strategy that determines the buy and sell rules; a trading software that connects to online exchanges, brokers, or data sources for receiving price data and placing the orders; and a backtesting software for determining the expected profit of the algorithm (or whether it is profitable at all). Normally the trading algorithm is realized by a software program or 'script'. But a computer is not always involved. The famous 'Turtle Trading' system of the 1980s was a manually executed algorithm, and backtesting was done with pen and paper. However, a simple backtest also shows that this system would not work anymore today. Since the markets have changed and large hedge funds have switched to algo trading, increasingly complex algorithms are needed for achieving continuous profit with algorithmic trading. But this does not mean that private traders are excluded. Advanced software tools such as Zorro allow algo trading with the same methods and algorithms that large hedge funds apply. 
Risk premium strategiesThey accept a certain amount of risk in exchange for a certain amount of profit. Normally they use algorithms that accumulate small frequent profits with a low, or ideally negligible, risk of a high loss. A trivial example is entering arbitrary trades with a very tight profit target and a very distant stop loss - as often seen in 'live trading' webinars. More serious, classical risk premium examples are options selling systems and mean-variance optimization. Algo trading systems that exploit volatility, such as grid traders, can also fall in that category. Model based strategiesThese strategies take advantage of market inefficiencies. Behavior patterns of market participants can be described in a market model and produce particular price curve anomalies - deviations of the price curve from pure randomness. A correct model of a market inefficiency allows limited price prediction and thus profitable trading. Model based strategies that exploit anomalies in price curves can be based on detecting market regimes, market cycles, price borders or channels, price differences of correlated assets (statistical arbitrage), or short-lived price differences of similar assets at different exchanges (HFT arbitrage). More examples and details about building model based strategies can be found in an article series on the Financial Hacker blog. Data mining strategiesThey have nothing to do with mining bitcoin, but evaluate the market with a machine learning algorithm for predicting short-term price trends. For this they use signals that are usually derived from the order book or the price curve, but sometimes also from fundamental data, like earnings reports or the Commitment Of Traders (COT) report. Even exotic data sources such as blockchain parameters or twitter keywords are used for data mining algorithms. Just as model based system, data mining also exploits market inefficiencies and would not work on a totally random market. But the details of those inefficiencies and the derived trading rules are unknown to the developer - the system is a 'black box'. More information and an example of a deep learning forex system can be found in this Data Mining article. Indicator soupsThese algo trading systems are based on 'technical analysis' - the belief that technical indicators, geometric price curve properties, or price patterns can predict future prices. Indicator soups are often found on trader forums or in trading books. They do not always use indicators, but can also derive trade signals from traditional candle patterns or with exotic methods such as Elliott Waves or Harmonic Patterns. Although many studies have revealed that technical analysis is mostly useless for trading, complex indicator soups can indeed be profitable in certain market situations - no one really knows why. If you like playing the lottery, you will probably also like algorithmic trading with technical analysis and a soup of indicators.
|