Business Intelligence Part 2 - Historical Data Analysis

Business Intelligence Part 2 – Historical Data Analysis

-George Fassett

Date :- August 4, 2022

The exploration of economic trends over a certain period of time is called historical data analysis. For instance, “market behavior” refers to how various market components coexist with one other. It is possible to quantify and analyze market-related indicators such as inflationary constraints, price, and volume over a certain amount of time. Portfolio managers may obtain insight into a market’s internal structure by studying its prior behavior. You may be able to utilize the data you collect to create a new investment portfolio or refine an old one.

There are numerous ways in which historical data related to a particular asset or market might be helpful:

Helps in providing market insight: Empirical investigation on previous behavior of an investment tool or marketplace may give entrepreneurs an understanding of which aspects of the investment tool or market are regular and those that are out of the norm.

Boosts consistency: Confidence may be gained by selecting deals that have a predetermined result. It is possible to reduce the risk of unexpected outcomes by studying the performance of a particular trade in the past.

Development of system: The foundation of a successful trading strategy is a clear understanding of when, what, and how to trade a in certain market. The parametric “edge” for active trading may be found and built by analyzing historical data.

Backtesting

Today, backtesting is still the most widely used type of historical data analysis, and for good reason. Backtesting is the process of applying a business technique or strategy to a historical data collection that has been chosen. Backtesting studies, automated trading systems, algorithmic trading, and more conventional trading methodologies all depend on statistical data that has been gathered via lengthy statistical forecasting studies. Backtesting can only be done if the trader has access to the appropriate data. Following this, the technique is applied as an overlay over the data and a simulation of the approach’s efficacy is carried out. Simplicity or complexity of backtesting studies is mostly determined by the trading strategy’s level of intricacy.

After the testing, achievement measurements may be added to the data and utilized to assess the technique’s feasibility. A thorough backtesting investigation yields the following important statistics:

Performance rate: It is possible to determine if a strategy is feasible for a certain product or market by looking at its win/loss percentage or chance of accomplishment. Additionally, it might provide insight about the best time and goods to partake in business.

Reward versus risks involved: Using a backtesting research, you can figure out how much money you’ll need to get started trading a certain market or commodity. Diagnosing the underlying volatility of a market may be important in determining the level of risk associated with a trading strategy or the amount of reward that might be anticipated.

In the past decades, Backtesting was a tedious and time-consuming process that required the use of a pencil and paper. As a benefit to modern-day vendors, digitization has greatly expedited the process. Backtesting of complex strategies may be performed using trading platforms’ software capabilities.

Although historical data analysis may be an effective instrument for both system creation and strategic fine-tuning, there are several drawbacks that must be considered:

Omission of data and errors: The backtesting evaluation relies heavily on the historical data set’s physical discourse. Over time, even a small number of data mistakes may have a significant influence on the findings of a research. In examining an intraday data, this element must be considered. It’s difficult to collect price data precisely when working with short time frames or tick-by-tick intervals. An accurate back test depends on a high-quality historical data source; even minor errors might skew the findings.

Unreliable software: It is possible for a software “glitch” to undermine the trustworthiness of test findings. The strategy testing software serves as a filter through which market data is sorted and analyzed. If there is any mismatch between the intended function of the program and the actual function of the software, the findings of the back test will be erroneous. Software faults may be incredibly difficult to detect and correct. In order to ensure accuracy, both manual inspections and automatic diagnostics are required.

Historical data analysis is a frequent way to understand the “irrational” behavior of markets. By doing a thorough assessment of the past, traders and investors equally may prevent numerous errors whilst preserving subsequent prospects. However, it is critical to be aware of the quality, sources, and dependability of the historical market data itself while using historical market data. Despite the fact that mistakes may often be inevitable, practices such as financial data mining and backtesting can offer traders with critical insights if they are conducted with the correct due diligence.

Using historical data analysis in conjunction with other analytical techniques and risk-management concepts may help a vendor achieve success in the long run just as it does with several other areas of trading.

Stay tuned for the next part in this 6 part series breaking down each of our Business Intelligence Topics. Please post any feedback on the Mebuis LinkedIn page where we share daily thought leadership, business solutions, and discuss the latest in business and technology.

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