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Onchain data encompasses all the information stored on a blockchain. It's a goldmine for quantitative analysts (quants) who need on-chain metrics to develop and backtest algorithmic trading strategies. With access to massive amounts of raw blockchain data, it can be challenging to distinguish which metrics are relevant and impactful to trading strategies.
To help quants and institutional investors filter through data noise, here are key types of blockchain data for generating trading signals:
1. Exchange Flows
Exchange flows show the movement of cryptocurrency assets into and out of decentralized exchanges (DEXs). A substantial inflow of assets to DEX liquidity pools can be a bearish signal, indicating increased pressure to sell. Quants can program trading algorithms to trigger a short position when inflows increase to generate alpha on anticipated price declines.
By contrast, an increase in outflows can mean investors are removing assets from exchanges, indicating a bullish sentiment and potential price increases. Quants might program algorithms to trigger long positions in these cases. However, in DEXs, liquidity removal could point to diminishing confidence. Therefore, investors must consider corresponding on-chain data points, like transaction volumes or token transfers, for a more accurate view of market conditions.
2. Whale Wallet Activity
Since whales can significantly influence prices across exchanges, tracking their wallet activity by monitoring their wallet addresses can help with predicting market trends. Quants can develop algorithms that monitor whale wallet activity and trigger trading signals when large transactions occur.
For example, the algorithm might automatically sell an asset when a whale moves a large amount of the same asset onto an exchange in anticipation of a price decrease. It might also trigger a buy order when a whale removes a large amount of the asset from an exchange to prepare for a price increase.
3. Fundamental Network Metrics
Basic network metrics provide insight into a blockchain's overall health and growth potential. Transaction volume is a fundamental network metric that can help investors determine if price movements are economically real. Algorithms can be programmed to confirm genuine price breakouts or identify “fakeouts” by monitoring crypto asset prices and on-chain transaction volumes.
If the transaction volume surges for a particular asset when its price moves above a resistance level, it indicates a genuine price breakout. Conversely, if transaction volume decreases or doesn't move when an asset's price jumps, a fakeout is likely underway. An algorithm can be designed to trigger a stop-loss order to minimize losses from a fakeout.
Why Trust Amberdata for Actionable Onchain Data?
Amberdata aggregates critical onchain data, like exchange flows and wallet activity from top decentralized exchanges, and delivers clean, actionable insights to drive trading strategies. Financial institutions and quantitative research firms partner with us to develop and backtest advanced algorithmic trading strategies with data they can trust.
Ready to Unlock Crypto Alpha Generation?
Algorithmic trading can automate decision-making and help investors generate significant alpha. However, an algorithm is only as good as the data it's trained on.
Ensure your trading strategies are built on robust, high-quality blockchain data with Amberdata. Request a demo today and discover how our solutions empower investors and quantitative analysts to make decisions confidently.
