Predictive Strength

Negligible
Cardano historically had -6.82% 30 days returns when Telegram Sentiment was▆ Very Low (0 - 0.2). It indicates negative expected returns.
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ADA Price with Telegram Sentiment

Factor Plot

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▆ Very Low▆ Low▆ Moderate▆ High▆ Very High

Predictive Strength

Negligible

Measures the ratio of positive to negative mentions on Telegram.

Potential Edge

First-Mover Signal Capture

Telegram groups exhibit faster reaction times to market-moving events compared to traditional sentiment sources. Crypto OGs and professional traders often coordinate in private Telegram channels before news becomes mainstream, creating a 6-12 hour lead time window. The platform's encrypted nature allows discreet position-building that later manifests in public market moves.

Concentrated Insider Activity

Top crypto Telegram groups represent a self-selected pool of committed market participants. Unlike Twitter's broad audience, these channels contain:

  • Whale wallet activity discussions
  • Miner/MVN coordination signals
  • Protocol upgrade debates among core developers

This density of high-conviction players creates a "smart money" signal filter.

Reflexive Validation Cycles

Positive sentiment → Price increases → More bullish messages → New entrants joining groups creates a self-fulfilling prophecy loop. The platform's group discovery algorithms surface trending channels to new users, creating a natural momentum amplifier. This reflexivity is particularly potent in low-float altcoins where Telegram communities directly influence liquidity conditions.

Data Collection Methodology

Telegram sentiment data is sourced through exported chat logs of specific cryptocurrency-related groups, followed by preprocessing and analysis via machine learning models. The raw sentiment scores are then normalized against historical averages to create a relative index.

Read more about our methodology

Track this predictive factor on your dashboard

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Scatter plot - Telegram Sentiment and ADA 30 and 90 Day Average Returns

Backtest - Strategy Performance

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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):

  • 100% Long when the predictive factor is close to 1, with a position size equivalent to the predictive factor value.
  • Flat when the predictive factor is close to 0, with a position size equivalent to the predictive factor value.

The strategy is rebalanced daily, on a continuous basis. There are 0.5% transaction costs applied on each position adjustment.

API

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.

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Our Methodology