Deep Learning Models

Neural Network Architecture

Under the hood, Agent Arc's AI is driven by a proprietary deep neural network that ingests a wide range of market data including price action and transaction volume, and correlations with other assets.

• The architecture is designed to identify patterns at multiple timescales simultaneously, from tick-level to longer term trends and regime shifts.

• Compared to traditional bots with fixed parameters, Agent Arc's model is able to dynamically adapt its strategies as market conditions evolve.

• The training process incorporates both supervised learning on historical data and learning as the agent trades in real-time. Techniques such as meta-learning and transfer learning enable faster adaptation and generalization to new assets and market conditions.

We are committed to staying on the cutting edge of AI research, continually enhancing the underlying model to improve performance, efficiency and robustness. Our approach combines the latest breakthroughs from both the AI and quantitative finance domains to create a uniquely powerful trading engine.

Core Technology

Our deep neural network model has been trained on the entire crypto market and incorporates nine sophisticated financial indicators, each carefully selected and optimized for cryptocurrency markets. Combined with data from alpha signals, profitable trading history from trading houses and professional traders, we establish our foundational data systems.

Market Understanding Layer

At the heart of our system lies a sophisticated market understanding layer that processes raw market data through multiple specialized neural networks. Traditional technical analysis typically examines price action in isolation, using predetermined indicators and timeframes. Our system, by contrast, maintains a holistic view of market conditions across multiple timeframes simultaneously, from one-minute charts to weekly trends. This multi-temporal analysis allows our agents to identify opportunities that would be invisible to traditional trading systems.

These indicators, along with raw price and volume data, are used as inputs to the neural network. The model architecture is designed to identify profitable trading opportunities across different timeframes and market conditions.

Prediction Engine

The neural network processes market data to make predictions about which assets to buy and sell from the entire universe of cryptocurrencies. It assigns higher prediction values to assets that are expected to outperform, and lower values to underperforming assets. The agents then execute long positions on the assets with the highest prediction values and short positions on those with the lowest values.

The deep learning architecture allows the model to uncover complex, non-linear patterns and relationships in the data that traditional trading strategies might miss. By training on a large historical dataset covering various market regimes (bull, bear, and sideways), the model learns to adapt its predictions to changing conditions.

During the testing period in 2024, the AI demonstrated strong outperformance compared to the overall crypto market and Bitcoin across different market environments. This suggests that the neural network is able to generate robust alpha rather than simply tracking broader market movements.

Enhanced Data Integration

In addition to the market data and technical indicators mentioned before, Agent Arc's deep neural network also leverages trades data and sentiment analysis to inform its predictions.

• Professional Trading Data The model incorporates data from actual executed trades, including those made by professional human traders and trading firms. This data provides valuable insights into market dynamics and the behavior of informed market participants. By learning from the patterns and decisions of successful traders, the AI can identify high-conviction opportunities and adapt its own strategies accordingly.

• Sentiment Analysis Agent Arc's neural network goes beyond just numerical data by also processing sentiment information from various sources. This includes social media posts, news articles, and other textual data that can provide important context about market sentiment and potential catalysts for price movements. The AI uses advanced natural language processing (NLP) techniques to analyze the sentiment expressed in these sources. It can detect shifts in market sentiment, identify key topics and themes driving investor attention, and gauge the overall bullishness or bearishness around specific assets.

By incorporating sentiment data, the model can better anticipate market reactions to news events, detect changes in investor sentiment that may precede price movements, and adjust its positions accordingly. This gives Agent Arc an edge in staying ahead of market narratives and positioning itself to capitalize on sentiment-driven opportunities.

The fusion of market data, technical indicators, trades data, and sentiment analysis creates a comprehensive view of the market environment for the deep neural network to learn from. The architecture is designed to find complex relationships and patterns across these diverse data sources, allowing it to make more informed and adaptive predictions.