Aidena

— AI STACK RECOMMENDATION

AI Algorithmic Trading System with Real-Time Analysis

Scalable stack for building AI-powered trading agents that analyze market data in real-time, execute trades, and adapt strategies using LLMs with low-latency inference and persistent memory.

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AI Algorithmic Trading System with Real-Time Analysis

Scalable stack for building AI-powered trading agents that analyze market data in real-time, execute trades, and adapt strategies using LLMs with low-latency inference and persistent memory.

high confidence

Core Stack ℹ︎

Cerebras Inference

Primary

Extreme token throughput (2000+ tokens/sec) essential for real-time market analysis at scale. Processes streaming price data and generates trading signals with sub-100ms latency.

$0.50-$2/hour

Bee Agent Framework

Primary

IBM's framework designed for agentic workflows at scale. ReAct-based agents with memory modules and tool integration perfect for autonomous trading decision-making and strategy adaptation.

$0/month

Airbyte

Primary

300+ connectors enable real-time ingestion of market data from exchanges, brokers, and financial APIs. Centralizes price feeds, order books, and news for RAG-based analysis.

$0-$500/month

Complete the Stack ℹ︎

Cognee

Alternative

Builds persistent knowledge graph from market events, price patterns, and news. Agents query this graph for contextual trading decisions and pattern recognition across historical data.

$0/month

AgentOps

Alternative

Session replay and cost tracking for trading agents. Monitor LLM call latency, token usage per trade decision, and P&L correlation with agent actions for optimization.

$0-$500/month

Cloudflare Workers

Alternative

Edge serverless platform with Workers AI for low-latency inference at global edge locations. Durable Objects enable stateful trading agents with sub-100ms response times.

$0-$200/month

Getting started

  1. 1Set up Airbyte pipelines to ingest real-time market data from your broker/exchange APIs (e.g., Alpaca, Interactive Brokers, Binance).
  2. 2Deploy Bee Agent Framework agents on Cerebras Inference for ultra-fast LLM-based market analysis and signal generation.
  3. 3Integrate Cognee to build a knowledge graph of market patterns, news sentiment, and price correlations from ingested data.
  4. 4Use AgentOps to monitor agent performance, track latency per trade decision, and correlate LLM costs with P&L.
  5. 5Deploy edge inference via Cloudflare Workers for sub-100ms latency on market data preprocessing and signal validation.
  6. 6Implement circuit breakers and risk limits in agent tool definitions to prevent catastrophic losses.
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