It is often said that those who ignore history run the risk of repeating mistakes from the past. This holds true even for financial markets, which is why investors worldwide spend north of $27 billion every year analyzing historical market data. This analysis can provide insight into market behavior which is likely to carry forward into the future. Regardless of whether you are a fundamental or technical trader, past data is of paramount importance in understanding market psychology.
Perks of historical data analysis
Historical data analysis involves studying market behavior over a specified time period, which could span months, years, or decades. This venture focuses on aspects such as price, volatility, and volume data. By examining these aspects, traders can better understand the intricacies of a particular market. This way, they can develop a viable trading methodology based on their intimate understanding of the asset.
Needless to say, this analysis brings with it several useful advantages. For one, it provides one with valuable market insight. This way, one can easily tell what is expected of the market in certain conditions, and what would be an extraordinary market move. This insight can help one generate a viable trading plan, detailing the opportune timing of trades, as well as choosing the appropriate instrument or currency pair to trade.
What’s more, such an intimate understanding of a market’s mechanics can help boost a trader’s confidence in the profitability of their methods. This way, they can easily exercise the required patience in situations that call for it. Traders can also avoid past mistakes that would otherwise prove detrimental.
Data mining
Data mining refers to the analysis of large data sets in search of useful information. This information includes relationships or patterns that may provide insight into future performance. In the past, mining data from the financial markets used to be a tedious and time-consuming process, as this analysis had to be done manually. Nowadays, with the advent of computers, it is now possible to analyze years of data in just a few minutes.
In forex, data miners will typically be concerned with such aspects as prices, volume data, open interest, and volatility shifts. Either of these aspects could be the key to understanding a particular pair’s performance over time and the psychology of the participants behind the pair. However, before one can successfully conduct this analysis, the elements below are required.
- Computer – Using a computer does not only save the time and energy that would have otherwise been spent doing manual analysis, but it greatly reduces human error. To that end, the computer used for this analysis should have minimum specifications of at least 300MHz processing speed, 256MB RAM, and about 100 MB of available storage. These requirements may vary depending on the analytical software one deploys.
- Data set – One should choose an appropriate amount of data to analyze, which should be sufficient to gain a working understanding of that market. Most brokers will provide historical data spanning several years, but for longer periods, one may need to purchase the data.
- Query – It goes without saying that to properly analyze this abundance of data, one needs special questions in the form of algorithms so as to filter out useful information. Otherwise, the analysis would be an exercise in futility.
Evaluating historical pricing
As aforementioned, pricing is one of the most vital aspects of historical data analysis. To that end, two types of data are often considered. The first is end-of-day data (EOD). This is the data reported at the end of a trading session, i.e., a day. Such information is especially useful to long-term traders, swing traders, and day traders.
The second data type is intraday data. This includes price details within the course of the trading day. These may be grouped in time intervals such as every hour or tick-by-tick data. Since this kind of data is more comprehensive than EOD, it tends to be more expensive than the latter.
Regardless of the data type you choose, there are 4 prices that analysts pay attention to. These are the open, close, high, and low prices of the period they choose. These are also the prices that feature in chart construction and analysis.
Backtesting
This is one of the most common forms of historical data analysis. It involves applying a trading strategy to historical data to gauge the performance and, by extension, the profit potential. Therefore, to successfully undertake this venture, one needs to have a detailed trading plan, as well as the relevant software for algorithmic trading. Once the testing is done, the metrics that matter most include the number of opportunities obtained, the strategy’s win rate, and of course, the risk to reward ratio.
Risks of historical data analysis
- Hindsight bias – This is the tendency of analysts to assume that unpredictable events can be foretold before they happen. This bias may lead the analyst to believe that such a result could have been avoided, which tampers with the objectivity and accuracy of their study.
- Human error – Since this analysis deals with multitudes of data points, any omission or data error may significantly impact the results of the study over time. This may reduce the accuracy of the study, leading to distorted insights that cause losses when applied to live trading.
- Software glitches – Any erratic performance by the chosen analytical software may lead to inaccurate results.
- Underestimating randomness – In the financial markets, oftentimes, performance may boil down to sheer chance. Things such as slippage and fluctuations due to natural disasters are impossible to account for during historical analysis. They can, therefore, easily compromise the viability of a seemingly successful trading strategy.
Conclusion
Trade history is of paramount importance to forex traders, as it helps them understand the market insights they need to predict future price moves better. Such traders analyze prices, volatility, and volumes. However, glitches in the software they use and failure to account for randomness may lead to inaccurate results.
