Operational Crypto Automated Trading: A Technical Product Overview - silentYazata/operationalCryptoAI GitHub Wiki
OperationalCryptoAI_PRODUCTOVERVIEW_V101.pdf
<style> </style>Operational Crypto AI – Technical Product Overview Version 1.1
Built, developed, and maintained by Armond A. Ansari. https://armond.io/ https://github.com/silentYazata/operationalCryptoAI
Operational Crypto AI is a modular, AI-enabled cryptocurrency trading platform designed for rapid decision-making and intelligent trade execution. This system empowers product and engineering teams to deliver scalable trading capabilities, optimize for profit, and integrate risk-managed strategies across both decentralized and centralized exchanges.
Platform Capabilities
Operational Crypto AI enables the following technical and business outcomes:
- Market Intelligence through real-time sentiment analysis and token legitimacy validation
- AI & RL Models for trade prediction and auto-adaptive strategy execution
- Portfolio Optimization based on live risk metrics and asset correlation
- Automated Execution across DEX/CEX using smart order routing and arbitrage
- Data-Driven Governance with full trade logging, DAO voting modules, and audit compliance
Primary Use Cases:
- Institutional algorithmic trading
- DeFi token filtering and decision support
- Risk-managed yield farming
- Influencer-based trading signal ingestion
Architecture & Modular Design
System Modules Overview
| Module | Product Function | Owner/Dependency |
|---|---|---|
| trading_bot.py | Central orchestration for trade logic | Depends on predictive_ai, verify_token |
| predictive_ai.py / train_model.py | Model lifecycle for supervised prediction | Requires labeled market data |
| train_rl.py | RL agent training and simulation | Tied to historical market environments |
| smart_order_routing.py | Execution optimization across markets | Integrates DEX/CEX APIs |
| arbitrage_trading.py | Price delta detection and arbitrage logic | Requires multiple order book feeds |
| sentiment_analysis.py | Social listening and sentiment scoring | Feeds into strategy models |
| kol_tweet_scanner.py | Scans KOL Twitter content for token signals | Integrates Twitter API or scraping layer |
| fake_volume_detector.py | Flags volume manipulation indicators | Enhances token validation pipeline |
| verify_token.py | Checks contract safety, authenticity | Required precondition for execution |
| portfolio_management.py | Handles dynamic allocation and rebalancing | Utilizes portfolio DB tables |
| risk_management.py | Calculates volatility, VaR, and applies controls | Integrated with strategy filters |
| telegram_alerts.py / voice_alerts.py | Real-time status reporting | User-configurable channels |
| cloud_deployment.py | Deployment templates and CI/CD hooks | Supports AWS/Azure/GCP deployment |
| pumpfun_gmgn_tracker.py | Monitors meme coin minting and trading activity | Optional alpha discovery tool |
Roadmap & Forward Strategy
- Integrated strategy dashboard for real-time portfolio visibility
- LLM-powered interface to simulate and define strategies via natural language
- Multi-chain routing: Optimism, Base, zkSync, Arbitrum
- LayerZero support for cross-chain signal and liquidity monitoring
- Governance module for DAO proposal creation and execution
Maintained by Armond A. Ansari