A new report has been updated. Recent price movements are bringing correlations between Altcoins to nearly 1.
Prices are moved by sentiment, especially for the smaller Altcoins. Therefore I created an individual setup analysing individual altcoins compared to the benchmark. Below an example of Golem. Below a short summary on what the report shows. In case of request for specific Altcoin analysis, let me know!
- Risk summary
- Standard deviation of coin
- Downside deviation
- Correlation with BTC
- Regression statistics wrt BTC
- Twitter popularity and sentiment analysis
- Mentions over time
- Mentions compared other coins
- Sentiment of Tweets over time
The latest has been uploaded. Subscribe to newsletter to receive it asap!
Lately I’ve been setting up a Twitter related database in order to see if this data adds value in valuing Crypto currencies. What can be seen from the data is that #Bitcoin and #Ethereum have far greater volume in Twitter mentions (respectively approximately 1000 and 500 hourly mentions) relative to smaller CryptoCurrencies, which is no surprise. However from that I thought it would be interesting to plot the Twitter mentions of smaller Crypto’s versus their respective market capitalizations. This results in the following graph (data can be found in Data section, mentions of last week have been used):
- Yellow bars – Hourly mentions over last week
- Dark bullets – Market capitalizations (mio $)
- Grey bullets – Count of significant positive mentions (score >0.5), this does not imply that the remainder is negative
- Legend x-axis – Polarity (from -1 (negative) to +1 (positive))
Interesting observation is that Steem gets relative a lot of mentions relative to it’s market cap, while Ripple shows the opposite. This data will be monitored over time in the Report (so subscribe if you are interested).
A new report has been updated. Correlations between various Altcoins are rising. New updates wrt the report are in the works.
The report has been added with textual analysis of Twitter data. Using the TextBlob library in Python tweets can be analysed for sentiment (positive or negative). This has resulted in two new metrics;
- Average polarity per hour – Tweets are analysed on polarity (-1 to +1, negative to positive). This results in a line chart that shows average sentiment per hour
- Count of positive and negative sentiment – Tweets are analysed on outspoken negative or positive tweets (<-0,5 or >0,5). This result in a area graph which shows the volume of negative tweets versus positive Tweets.
Historic data has to be build up over time, but most recent data has been added in the data section. Latest reports including the new metrics has also been uploaded. Subscribe to the newsletter to receive new reports first!
The twitter hourly frequency graph has been updated, along with a new report. #Bitcoin data is showin a gap in the query, this is being looked in to.
Twitter analysis is now added to the crypto reports. Analysis includes:
- Number of tweets per hour with certaing hashtags (eg, number of “#Bitcoin” tweets per hour)
- Connected hashtags of most recent 50.000 tweets (eg, which hashtags appear in relation to tweets with #Bitcoin)
Raw data can be downloaded as .csv in the data section.
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The recent downturn in crypto value was preceded by a decrease in correlations. Upticks of correlations will be monitored for predictive value.