Artificial Intelligence for Capital Appreciation and Preservation

Fundface’s artificial intelligence is a sophisticated prediction system powered by deep-learning neural networks. It collects, stores, and analyzes vast volumes of financial data to deliver capital appreciation outcomes for our users on the Fundface Capital Appreciation Platform.

Take a closer look at Fundface’s structure and internal design. Our system integrates diverse data types into a unified format, incorporating factors like chart patterns, technical indicators, fundamentals, correlations, trading activity from other traders, analysts, and hedge fund managers, as well as tweets, news, articles, and other data that may influence asset prices. Fundface’s multi-layer architecture allows us to scale complexity across each layer simultaneously.

1. Data Layer
Raw market data, financial reports, news, and alternative data.

1

Sources:
Bloomberg, SEC Filings, APIs, RSS, Satellite Images, etc

2. Processing Layer
Data is cleaned, normalized, enriched, and prepared.

2

Tools:
NLP, OCR, Time-Series Normalization, Signal Extraction

3. Intelligence Layer
Machine learning models extract actionable insights.

3

Models:
LLMs, Forecasting, Topic Modeling, Clustering

4. Output Layer
Final output is an automated capital appreciation product .

4

Examples:
AI Portfolio Strategies, Small Consistent Gains

Fundface Machine Learning Algorithm

All layers above are powered by Fundface’s proprietary machine learning system that ingests data, applies structured transformations, learns patterns, and outputs capital appreciation actions in real-time.

We gather, process, and analyze massive volumes of financial data from over 60 data sources. Each source—whether a public or private site or API—provides information proven to correlate with asset prices.

Our team continuously seeks new partnerships and integrations to enhance our capital appreciation performance, with plans to add more data sources in the future.

Strategies are the coded logic that interprets data from our sources, deciding whether to buy, sell, or remain neutral when new data arrives. Each strategy generates a prognosis during backtesting, storing details like the command (buy/sell), confidence level, duration, and market conditions in our database. We employ strategies based on trading activity of market players (traders, experts, hedge fund managers), forecasting trade success based on historical performance. Other strategies focus on quote data, applicable across financial instruments and timeframes, while some analyze tweets, news, and articles using syntax analysis tools to gauge sentiment, supplemented by sentiment results from our data sources.

Our simulation layer aggregates estimates from all strategies, calculating weights based on performance. These weights inform a cumulative prediction used to place buy and sell orders in a simulated environment. We run multiple simulations with varied weight distribution logic, leveraging deep-learning neural nets, decision trees, gradient boosting, reinforcement learning, and Markov decision process-based quality learning. We utilize both proprietary and open-source machine learning tools, including:

We’re committed to pushing the complexity of all three layers to boost the accuracy of Fundface’s predictions and enhance our platform’s offerings. The potential is limitless—imagine the results with thousands of strategies in our system!