Introduction to Quantitative Finance Part 02: Technical analysis, algorithmic, automated and quant trading


Technical analysis is one of the most popular techniques, used in trading systems design. While it is considered by many a simplified approach to trading on the financial markets, based on identifying trends and looking for patterns on charts, it has evolved to something much more than that.

Back in the days technical analysts relied on simple things such as drawing trendlines or following a simple Moving Average crossover strategy. Today everyone interested in technical analysis has a huge variety of powerful tools to their disposal. Multiple technical indicators, intra-market analysis, machine learning, and artificial intelligence are just a few examples of the tools and methods in their arsenal. Nowadays a trading strategy can rely on from a simple combination of indicators to an advanced neural network to forecast price moves. The financial markets are a complex system so there is more than one way to achieve a given result. In addition, the quantitative approach allows you to backtest different trading systems and estimate their expected risk and reward parameters.

There are some aspects of technical analysis that have become so mainstream that the financial media is using them to provide simple explanations to every major event on the markets. Everyone is constantly bombarded by news and headlines talking about price trends, double tops, and bottoms, low volume, high volume, consolidation, stagnation, etc. For example, the 200-day moving average has become the benchmark for the stock market indices trend direction. Even indicators considered quite exotic not so long ago, such as the Fibonacci retracement levels, find their place in popular daily newsletters and analyses. This publicity relies on simplicity and shows the widespread acceptance of technical analysis. Even those traders who do not rely on technical indicators in their trading strategies, tend to keep them on their charts and watch them along during the day.

Fundamental vs Technical analysis

There has always been an opposing camp of people who are skeptical about the predictive power of the technical analysis. A lot of academic research suggests that price movements are close to a random walk. Many analysts support the thesis that prices are formed by the balance of supply and demand and are driven only by fundamental and economic data. As prices move only in response to the latest available information or news release and it is impossible to predict their move only by looking at a chart.

On the contrary, the supporters of technical analysis and quantitative research, insist that if the random walk theory is correct, it means that many well-defined and proven trading methods based on mathematics and pattern recognition should fail. Their main argument is the existence of successful algorithmic trading strategies. There are plenty of famous hedge funds with proven successful track records spanning for more than 20 or 30 years that have achieved their success by using systematized and automated trading systems. This shouldn’t be taken as an assumption that all automated trading systems are successful, there is plenty of evidence against that, but rather that success depends on following a sound strategy, whether discretionary or systematic. The real cause of doubt in technical analysis methods is that not a lot of people are able to create and implement such reliable and robust strategies.

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Automated trading systems

But there is much more to technical analysis than that which is “visible on the surface”. Automated trading systems based on quantitative methods for data analysis have become a dominant part of the daily turnover in the financial markets in all asset classes.

There are thousands of financial institutions over the world with billions, even trillions of dollars’ worth of assets under management that are using automated trend-following, mean reversion, statistical arbitrage, and other trading systems, based on some form of technical or quantitative analysis. Data shows that more than half of all managed funds rely on algorithmic trading.

What is the difference between algorithmic, automated and quant trading?

Algorithmic trading, automated trading, and quantitative trading are frequently considered one and the same thing. While in a lot of cases the terms are used interchangeably, there are important nuances that distinguish them from one another.

Algorithmic trading

Algorithmic trading (also known as algo-trading) refers to a method of trading using computer programs. In this method, a computer program is created according to a predefined algorithm. The program follows a set of predefined rules and calculates the order parameters. These can be entry and exit price levels, trading volumes, timing, etc. As a result, the program generates signals, which are used for trading. The trades can be executed manually by a trader, or semi/fully automatic.

The algorithm can be based on simple rules and conditions. For example, a moving average cross over, levels breakout, daily open/close above or below a certain level and etc. On the other side of complexity, there are advanced trading algorithms with hundreds of rules and inputs and thousands of lines of code.

Automated trading

Automated trading refers to a fully automated process of signal generation and trade execution. As such we can classify automated trading as an extension of algorithmic trading. This is because the computer program does not only calculate the order input parameters but also has a module that is able to execute the trades automatically, without any human intervention.

The key distinction between algorithmic and automated trading is that in the first case there is no human intervention in the generation of the trading signal, while in the latter there is no human interaction during the whole process – from the trigger of the rule to the execution of the trade on the market.

Quantitative trading

Quantitative trading refers to a method of using advanced mathematical or statistical approaches to produce trading signals. As such quantitative trading is a subset of algorithmic trading. In the case of quantitative trading, the computer program is based on a more complex algorithm which is relying on quantitative analysis of historical data.

The quantitative trading algorithm is relying on a model that describes the different components and events of observation with mathematical concepts and language. The aim of the model is to study the behavior of an observed system and produce predictions based on repetitive patterns.

Quantitative models can be based on various arrays of data. Some quantitative traders prefer to derive their models from fundamental data – monetary policy, interest rates, company financials, stock earnings, etc. Others are focused on technical analysis of historical price data. Such models can rely on technical indicators like Moving Average, RSI, Bollinger Bands, etc. or historical time-series analysis.

Examples of automated trading systems

Here are just a few examples of the wide-spread application technical analysis and quantitative methods for building fully automated trading systems.

Technical analysis trading strategies

The most popular approach for automated systematic trading is developing trading systems that rely on technical analysis and technical indicators for their rules and conditions. The systems are designed to recognize recurring patterns in historical price charts and quantify the most likely outcomes of these patterns. The algorithms consist of well-defined repeatable procedures, which produce unambiguous signals. This means that the trading systems can be successfully backtested on historical data. Therefore, their results can be properly measured, and their performance can be improved with mathematical optimization methods.

Arbitrage strategies

Arbitrage is a trading strategy that aims to take advantage of a price difference between two or more markets. For example, statistical arbitrage (also known as Stat Arb or StatArb) employs mean reversion models applied to portfolios of diversified portfolios of securities, held for short periods of time. Interest rate arbitrage is a major revenue source for banks. Location arbitrage is the process that keeps the price of gold and other commodities in line all over the world. Automated arbitrage trading systems keep the price of the underlying assets diverging from the futures and ETFs like the popular GLD and SPY.

High frequency trading strategies

If you change the time horizon of the holding time of the positions from hours and days to milliseconds, you have high-frequency trading. High-frequency trading is basically exploiting arbitrage opportunities that last just milliseconds. While highly profitable it is also quite capital intensive, as it requires significant investments in IT staff and infrastructure. Competition is so tight that the cost of placing computer equipment in data centers close to the exchange trading infrastructure has skyrocketed. As a result, high-frequency trading has become a huge revenue stream, but only for large financial institutions. High-frequency trading is praised by some for adding liquidity by increasing the trading volume in equity markets. However, on the other side are the ones who blame it for the spectacular, high volatility price moves that lead to not one or two market crashes.

Seasonal trading strategies

The observation of seasonal patterns is also a form of technical analysis. There are trading systems that take advantage of the seasonality patterns that you can find in the airline industry or agricultural commodities such as wheat and soybeans. Such trading systems rely on clear seasonal patterns, but there are periods when other factors (such as weather conditions or disruption in oil supply) overwhelm the seasonal factors.

Market neutral strategies

Then there are the so-called market-neutral strategies (also known as pairs trading), where long and short positions are taken in related trading instruments with the aim to profit from one market rising or falling faster than the other.

This article is a part of a series on quantitative finance developed by “Quantitative Strategies Academy” Foundation according to its mission for the benefit of people who want to know more about quantitative analysis and automated systems.

1) Trading systems and methods, book by Perry J. Kaufman
2) Wikipedia
3) Investopedia

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