Predictive Strength
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Predictive Strength
Measures interest in the asset by tracking the number of mentions on Telegram.
With Telegram poised to onboard the next billion crypto users through its mini-app ecosystem, mention volume spikes correlate with emerging retail interest. The platform's 30% lower user acquisition cost vs competitors creates a first-mover advantage in detecting new market entrants' activity.
While Twitter sentiment analysis requires filtering ~40% bot accounts (per CoinGecko research), Telegram's invitation-only groups and channel moderation substantially lower synthetic noise. This improves signal quality.
YouTube mentions data is tracking public channels and chats of cryptocurrency-related groups, with counts normalized against historical averages to measure relative interest levels.
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To understand a predictive factors predictive power, we create a simple long/short strategy and simulate its past performance (with daily rebalancing):
The strategy is rebalanced daily, on a continuous basis. There are 0.5% transaction costs applied on each position adjustment.
Get started by validating the historical performance of the strategy with our transparent code snippets.
Copy and paste the code snippets below into your Python environment or download the files below.