On-chain analytics has become a crucial tool for investors and traders looking to manage risk and make informed decisions. By analyzing data from blockchain networks, investors can gain valuable insights into market trends and sentiment. In this tutorial, we will explore how to set up dashboards with key metrics such as exchange inflows, SOPR (Spent Output Profit Ratio)MVRV (Market Value to Realized Value) and funding rates.
These metrics provide a unique perspective on market activity and can be used to identify potential buying and selling opportunities. Exchange inflows for example, can indicate increased selling pressure, while SOPR can help identify periods of profit-taking. MVRV provides insight into market valuation, and funding rates can indicate market sentiment.
Setting up a dashboard
To get started, investors will need to set up a dashboard with the relevant metrics. This can be done using a variety of tools and platforms, such as Python notebooks and open data sources. By combining these metrics, investors can create a comprehensive view of market activity and make more informed decisions.
Turning on-chain signals into entry and exit rules
Once the dashboard is set up, investors can begin to analyze the data and identify potential entry and exit points. This can be done by creating backtests using historical data and evaluating the performance of different strategies. By using on-chain signals in combination with other forms of analysis, investors can create a robust and effective investment strategy.
Step-by-step examples
To illustrate the process, let’s consider a step-by-step example using Python notebooks and open data. First, investors will need to import the relevant libraries and load the data. Next, they can create a dashboard with the key metrics and begin to analyze the data. By using if-then statements and looping functions investors can create a set of rules for entering and exiting positions based on the on-chain signals.
Backtesting and evaluation
Once the strategy is created, investors can begin to backtest it using historical data. This involves evaluating the performance of the strategy over time and making adjustments as needed. By using backtesting and evaluation techniques, investors can refine their strategy and improve its effectiveness.
