Fact Engine

Fact Engine and Data Handling

The idea of a “fact engine” broadens Sylvi’s capabilities by:

1. Ingesting Data from Multiple Sources: Market data, social media feeds, user database records, etc.

2. Filtering and Storing Facts: The system organizes relevant facts or insights in a central store (e.g., PostgreSQL).

3. Real-time Updating: Whenever new data arrives (e.g., a tweet mentioning a particular hashtag or a blockchain event), the fact engine updates the store accordingly.

4. Enabling Targeted Reactions: Agents can examine these facts to decide whether certain workflows or automated tasks need to be triggered.

3. Social Media Analysis

Alongside chart data, Sylvi can integrate social signals, particularly from Twitter, to assess market sentiment. Using a combination of:

1. Keyword Tracking: Monitor tweets for coin mentions or project hashtags.

2. Sentiment Analysis: Gauge tweet tone (positive, negative, neutral) for trending sentiments.

3. Engagement Metrics: Measure how often a coin or topic is retweeted, liked, or replied to.

4. Correlations to Market Movement: Combine tweet volume spikes and sentiment with real-time chart analytics.

Example Steps

1. Data Collection: Sylvi queries Twitter’s API for recent tweets about a coin (e.g., $BTC, $ETH), filtering spam and collecting relevant data from websites or on chain movements.

2. Preprocessing: Cleans text and removes noise, emoticons, or spam.

3. Scoring: Calculates mean sentiment polarity (positive/negative).

4. Integration: Merges sentiment data with real-time price movement. If sentiment is strongly positive but the market remains stagnant, Sylvi can highlight a possible upcoming move.

This approach ensures that decisions made by Sylvi’s agents remain well-grounded in updated and verified information.