Predictive Strength

Negligible
Binance Coin historically had -6.82% 30 days returns when Reddit Mentions was▆ Very Low (0 - 0.2). It indicates negative expected returns.
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BNB Price with Reddit Mentions

Factor Plot

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

Predictive Strength

Negligible

Reddit Mentions tracking the daily count posts, comments, and upvotes related to a digital asset on popular subreddits dedicated to cryptocurrency, providing insights into retail interest.

Potential Edge

Event-Driven Sentiment Amplification

Reddit communities rapidly concentrate attention around market-moving events like exchange listings, protocol upgrades, or regulatory actions. During these periods, sentiment analysis of forum discussions (particularly using VADER's social media-optimized lexicon) captures shifts in crowd psychology before traditional metrics reflect them. For example, Dogecoin's 2021 rally preceeded with a 600% increase in Reddit mentions weeks prior to price spikes.

Data Collection Methodology

Reddit mentions data is tracking posts, comments, and upvotes across cryptocurrency subreddits, with counts normalized against historical averages to measure relative interest levels.

Read more about our methodology

Track this predictive factor on your dashboard

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Scatter plot - Reddit Mentions and BNB 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