Removing Noise from Crypto Volume & Liquidity Data (GDA Level I — Level III filters and indicators framework)
This article is a brief introduction to GDA’s data insights and what we do with market data before we use it to trade.
Data is the backbone of any trading strategy, and is what differentiates trading from betting. There is an insance abundance of data; whether it is historical data, market cycle data, sentiment/speculative data, or some other category. However, it can be difficult to know what to do with it. Here at GDA we trade largely based on market microstructure data which includes liquidity clusters and volume data points.
Of course, this alone is not special. Anyone with an internet connection can access this data and then trade with it. What many people don’t understand is the inherent flaws in this type of data, and much of the data in crypto. Large market participants know that this data is extremely useful for trading to predict trends and price movements, which is why they manipulate this data to abuse and rip off traders that rely on it. For example, practices such as wash trading can indicate significant trading volume and liquidity clusters around a certain price, which a trader interprets as a point of support and relies on it within their trade. In reality, such a support does not in fact exist causing the trade to be less profitable, or more often, will result in a loss for the trader. For this reason, we cannot rely on this raw data alone.
To overcome this issue, GDA has developed proprietary indicators, data filters and formulas to cut the noise from the raw data and extract the true information from within.
The image above is a great illustration of how some of our indicators work. First, we collect raw market microstructure data. Using a simple filter, this data can be transformed into a liquidity cluster chart. This type of data is similar to that which is available from sites like ExoCharts. At this stage it is still suceptible to manipulation. By applying additional technical analysis and a level 2 formula, the false, or fake, data is cut away from the dataset, leaving a true representation of liquidity clusters. This can be further processed with a level 3 formula to produce actionable values, or indicators. It is this level of data that GDA will trade with confidence because we know that it is not only superior to raw data available through data providers, but it is also extremely accurate.
At the moment, we are unable to release more detail on our indicators or the modules used to generate them, but we are hoping to do so soon. Please stay tuned to our channel for an extended version of the framework.