This is because each market has individual characteristics which quant traders try to take advantage of. One of the simplest forms of quantitative trading out there is seasonal trading. This is where traders would look at the monthly performance of a financial instrument over many different years to find the average probability of the instrument closing higher or lower by the end of the month.
It is very similar to the well-known seasonal effects such as: 'Sell in May and go away' or the 'Santa Claus rally. Let's take the price of gold as an example. Below is the percentage of months in which the price of gold closed higher than where it opened at the start of the month from to A customised bar chart showing the percentage of months the price of gold closed higher than where it opened at the start of the month from to In the above image, it shows that between the beginning of to the beginning of , January tends to be one of the best performing months.
This is the simplest type of quantitative analysis - crunching the numbers to find patterns which could provide a profitable edge in trading the financial markets.
Strategy Backtesting
Quant traders would then take this edge and crunch the numbers even further, using lots of different inputs. They may further refine by analysing the best performing days in the best performing months, or analyse weather patterns during that time, or output figures from commodity mines, or what the US dollar was doing during that time.
There are many different possible ideas to further build a possible quantitative strategy. This is time consuming work for most retail traders to do which is why quant traders are well versed in computer programming language so they can build a program to perform this quantitative analysis automatically and at great speed. In the monthly chart below of gold, each dotted vertical line represents the month of January. While a quantitative analysis may be simple enough over measuring several historical bars, or data points, quant traders do this on different times with a variety of different inputs, thereby producing thousands to millions of data points for analysis.
Please note: Past performance is not a reliable indicator of future results.
Quantitative Trading
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Admiral Markets is a multi-award winning, globally regulated Forex and CFD broker, offering trading on over 8, financial instruments via the world's most popular trading platforms: MetaTrader 4 and MetaTrader 5. Start trading today! This material does not contain and should not be construed as containing investment advice, investment recommendations, an offer of or recommendation for any transactions in financial instruments. Please note that such trading analysis is not a reliable indicator for any current or future performance, as circumstances may change over time.
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While the infrastructure to support quantitative and algorithmic trading is quite robust, the key to finding success is in identifying the right opportunities and building a solid trading strategy. Quants traders make use of programming tools such as R, Python, and Matlab to build and backtest their trading strategies before deploying them for real trade execution. Apart from these, there are also other forms of trading strategies such as factor-based, scalping, and mean reversion, among others. So, how does one go about building their own quantitative strategy or trading system? The first step of the process is to identify a trading strategy.
The quants trader will typically start with an idea or a hypothesis that is based on some economic condition, a market trend or some market anomaly. Based on this hypothesis, we can then say that the null hypothesis is that there is no mean-reverting behavior and the price spread follows a random pattern. The idea or hypothesis is then supported by market research. Using research and quantitative analysis, the quants trader can then prove that the null hypothesis is false and that the spread does exhibit a mean-reverting behavior. This involves identifying factors for our model, identifying and collecting data required to build these factors, analyzing these factors and then building a model of a trading strategy.
The model is a mathematical representation of the trading strategy. Everything from research, factor selection, and the model building should be driven by your original idea or hypothesis. To become an expert at identifying profitable trading opportunities and building strategies around them will take time and practice. To begin with, you should look for academic research papers, read quant blogs as well as trade journals. But most of all, it is important to build a sound conceptual understanding of the quantitative methods underlying it.
Quantitative Trading Definition
Once your strategy is ready, the next step is to backtest the strategy. The goal of backtesting is to make sure that the strategy built is indeed profitable when applied to historical data. This provides some proof and confidence that the strategy actually works, however, backtesting, is in no way a guarantee of success, as the real-markets can behave differently for various reasons. The traders must develop their trading strategies in good faith and avoid any biases such as survivorship bias, lookahead bias, etc while conducting backtesting. This is important to achieve dependable backtesting results.
It is also important to use clean historical data, include all transaction costs, and use a solid backtesting platform. Only if backtesting provides satisfactory results, a trader should implement the strategy in reality.