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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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| Benchmark (Ethereum Classic) | Strategy | |
|---|---|---|
100% | 100% | |
100% | 100% | |
1.00 | 1.00 | |
100% | 100% | |
1.00 | 1.00 | |
1.00 | 1.00 |
Predictive factors are designed to be translated into simple long-only strategy, with simulated past performance:
The strategy is rebalanced daily, on a continuous basis. There are 0.05% transaction costs applied on each position adjustment.
Get started by replicating the historical performance with our code snippets.
from api import get_normalized_series, get_price_series from backtest import vectorized_backtest from plotting import plot_backtest_results UNRAVEL_API_KEY = "YOUR-API-KEY" risk_factor = "mentions_telegram" ticker= "ETC" start_date = "2022-01-01" end_date = "2024-06-01" smoothing = 7 risk_factor_signal = get_normalized_series(ticker, risk_factor, start_date, end_date, smoothing, UNRAVEL_API_KEY) price = get_price_series(ticker, start_date, end_date, UNRAVEL_API_KEY) price = price[risk_factor_signal.index] results = vectorized_backtest(price, risk_factor_signal) plot_backtest_results(results, ticker, risk_factor, smoothing)