Development of investment strategies is most of the time based on an analysis of the behavior of the financial instruments on historical data. The main axiom is that we are able to quantify certain behavior patterns that will occur in the future and will be similar.
We can identify behavior patterns using three types of analysis
- technical analysis (historical prices, volume)
- fundamental analysis (macroeconomic indicators, fundamental data)
- psychological analysis (crowd behavior, game theory)
The most commonly used patterns are based on technical analysis. Historical pricing data is very easy to access even in one-minute granularity for all market, and basically it is an analysis of the relative positions of open, high, low and close prices over a certain time period or the value of technical indicators. To search for patterns using technical analysis, you can use the Ta-lib library (https://www.ta-lib.org/ ), which is open-source solutions and contains the well-known technical indicators (Bollinger Bands, Average True Range, Moving Average, RSI, Commodity Channel Index, …) and also includes predefined price patterns (Three Black Crows, Doji, Hanging Man, Marubozu, Shooting Star, …). The use of the price patterns themselves for the analysis of future price developments is a very powerful tool because of the possibility of automated testing on historical data and a direct expression of the price behavior of the financial instruments.
As part of the internal development of PatternLab, the goal is to take advantage of standard, affordable and well-known price patterns and to enrich them with additional input parameters. We do enrichment in two ways. The first method is based on the position of the price pattern against the historical price. In practice, this means that in the case of monitoring the price pattern DOJI we are still interested in whether this pattern occurred, for example, by the maximum of the current week or at the maximum of the last month.
The second way to enrich pricing patterns is to deliver fundamental information such as the date of the dividend, earnings, changes in the company, and so on. This information can be obtained from Quandl (https://www.quandl.com ), which aggregates various sources of data. In practice, this means that the DOJI pattern is monitored only a few days after the dividend or only with surprising earnings results etc.
It is also possible to implement alternative data sources such as social media analysis (Instagram, Facebook, Youtube, Twitter – https://www.quandl.com/databases/SMA1 ) , satellite images (https://www.quandl.com/databases/RSMMS ), railroad traffic for individual commodities (https://www.quandl.com/data/RR1-Railroad-Traffic ).
Benefit for PatternLab users
All of these features mentioned above, you can see in PatternLab application which serve to survey and find demande price patterns. PatternLab users will be able to easily use enriched price patterns and get a tool to more accurately analyze future developments in the price of the monitored financial instruments. Users will be able to get information about enriched price patterns historical performance and will know more information about risks and profit potential.
For more information about the analytics tools, please contact us at firstname.lastname@example.org or +420 538 705 775.