AI4Cryptos
White paper
AI4Cryptos AI Trading Automation Platform
Version 1.0 – February 2025
01
Introduction
The cryptocurrency market is one of the most dynamic and volatile trading environments in the world. The abundance of data (price history, trading volume, news, social signals, etc.) makes it both rich in opportunities and complex to analyze. In this context, a trading automation solution based on artificial intelligence (AI) offers a major competitive advantage: it allows you to quickly identify investment opportunities, react in real time to market fluctuations, and execute high-frequency trading strategies without human intervention. This white paper outlines the vision, strategy, and architecture of our AI-powered automated trading platform, AI4C, as well as the major milestones in its development. Our goal is to build a modular and scalable solution, capable of integrating with different markets (cryptocurrencies and potentially other assets), and which will then serve as a technological basis for the creation of a web application, a mobile application, as well as the subsequent implementation of a cryptocurrency dedicated to sharing computing power (in a more advanced phase of the project).
02
Vision and Mission
2.1 Vision
• Accessibility: Making algorithmic trading accessible not only to experts, but also to retail investors and traders of all levels.
• Performance: Leverage deep learning and machine learning technologies to analyze large volumes of data in real time, detect patterns and execute complex strategies.
• Scalability: Design an architecture that allows you to connect new data sources, new algorithms and new exchange platforms without additional complexity.
2.2 Mission
1. Automate crypto trading (and potentially other assets) via AI algorithms.
2. Optimize decision-making by relying on robust prediction models (neural networks, transformers, etc.).
3. Reduce the risk of human error by entrusting the execution of transactions to an automated system monitored in real time.
4. Provide a strategy management infrastructure that allows developers to quickly iterate on new trading approaches.
5. Prepare the next step: a decentralized ecosystem (token, sharing of computing resources for AI), after validating the viability of the trading platform.
03
Market and opportunity
3.1 Crypto Market Volatility
The volatility of the cryptocurrency market presents a high risk, but also numerous opportunities for arbitrage and profit in the short and medium term. Rapid price fluctuations can be exploited through algorithmic trading and market making strategies.
3.2 Growth of Automated Trading
Algorithmic trading is growing rapidly across all financial markets. In the crypto sector, we're seeing the emergence of multiple exchange APIs, decentralized protocols (DeFi), and third-party solutions that facilitate automation. However, it remains complex for a standard user to configure and maintain a high-performance bot. Hence the importance of an integrated, turnkey solution.
3.3 Advancement of AI
AI today offers extremely powerful possibilities:
• Sentiment Analysis (social networks, news, etc.)
• Modeling of Time Series to predict price trends (RNN, LSTM, Transformers, etc.).
•Portfolio Management (automated rebalancing, anomaly detection).
04
Solution architecture
4. Architecture de la Solution La plateforme de trading automatisé est conçue autour de cinq grandes composantes :
1. Collecte et Intégration des Données
2. Module d’Analyse et de Prédiction (IA/ML)
3. Gestionnaire de Stratégies
4. Exécution des Transactions (Trading Bot) 5.Supervision, Backtesting et Reporting
Nous décrirons ci-dessous chaque composant ainsi que leurs interactions.
4.1 Collecte et Intégration des Données
• Sources de Données :
o Prix en temps réel (via APIs des échanges : Binance, Coinbase, Kraken, etc.).
o Historique de prix et volumes (order books, tick data).
o Données on-chain (analyse des flux sur Ethereum, Bitcoin, etc.).
o News, réseaux sociaux, flux RSS, Twitter, etc.
• Infrastructure de Stockage : Une base de données (MongoDB) et/ou un data lake (HDFS, S3) pour historiser de grands volumes de données.
• Pipeline de Traitement : Un système d’ETL (Extract, Transform, Load).
4.2 Module d’Analyse et de Prédiction (IA/ML)
• Modèles de Prévision :
o Réseaux de neurones récurrents (LSTM, GRU) pour les séries temporelles.
o Transformers adaptés aux timeseries (Temporal Fusion Transformers, etc.).
o Modèles de régression/ensembles (XGBoost, Random Forest) pour le feature engineering classique.
• Infrastructure de Training :
oEn local (GPU, CPU) ou sur le cloud pour les modèles les plus lourds (accès à des clusters GPU).
o Possibilité à terme d’exploiter la puissance de calcul communautaire (phase 2 du projet).
• Enrichissement des Données :
o Features basées sur l’analyse technique (RSI, MACD, bandes de Bollinger, etc.).
o Sentiment (analyse du contenu Twitter, Reddit, etc.).
• Évaluation :
o Mesures classiques (RMSE, MAPE, Sharpe ratio sur le backtest, etc.).
o Métriques spécifiques au trading (profit factor, drawdown max, ratio gain/perte).
4.3 Gestionnaire de Stratégies
• Stratégies Paramétrables :
o Scalping, swing trading, arbitrage, market making, etc.
o Possibilité de mixer des signaux IA avec des indicateurs techniques ou des règles fixes.
• Interface de Configuration :
o Permet de définir et de personnaliser la logique d’entrée/sortie de position.
o Paramétrage du money management (taille de position, stop-loss, take-profit, pyramiding).
• Couche de Test :
o Backtesting : simulation d’une stratégie sur des données historiques.
o Paper Trading : simulation en conditions réelles, mais sans exécuter de transactions réelles.
4.4 Exécution des Transactions (Trading Bot)
• Intégration Multi-Exchanges : Connecteurs pour Binance, Coinbase Pro, Kraken, etc.
• Gestion de la Sécurité :
o Stockage chiffré des clés API.
o Authentification forte (2FA, OAuth2).
•Moteur d’Ordres :
o Capable de passer des ordres au marché (market, limit, stop-limit, etc.) en fonction des signaux reçus.
o Latence minimale pour réagir rapidement aux fluctuations.
• Gestion des Erreurs :
o Vérification du solde disponible.
o Traitement des rejets d’ordres ou des ratés d’API.
o Système d’alertes et de logs pour diagnostiquer les problèmes.
4.5 Supervision, Backtesting et Reporting
• Interface de Suivi :
o Tableau de bord en temps réel : positions ouvertes, PnL (Profit and Loss), historique des transactions, etc.
o Visualisation des signaux IA et de la performance des stratégies.
•Statistiques Avancées :
oDrawdown, leverage, ratio reward/risk, historique du capital, etc. oComparaison de plusieurs stratégies sur une même période.
•Alertes et Notifications :
Envoi de notifications push (mobile), emails ou messages Telegram/Discord en fonction de la configuration (erreurs, drawdown élevé, opportunités particulières).
05
Security and Compliance
5.1 Platform Security
• Encryption: Passwords, API keys and sensitive information are encrypted in the database.
• Restricted Access: Each user can only see their own policies and settings.
• API Key Management: Integration of cyclical management (possibility of automatically renewing API keys with exchanges to limit breaches).
5.2 Risk Management
• Position Limits: Possibility of setting maximum amounts invested per asset.
• Automatic Stop-Loss: As a precautionary measure, implementation of a global stop-loss (total cut-off of positions beyond a defined loss percentage).
• Exhaustive Testing: Before any production of a new model or a new strategy, backtesting and paper trading are mandatory.
5.3 Regulations
• KYC / AML
• Disclaimer: Our platform provides automation tools; each user remains responsible for their capital management and risks.
06
Project roadmap
Le projet se décompose en trois grandes étapes :
6.1 Phase 1 : Plateforme de Trading Automatisé (Focus actuel)
1.MVP (Minimum Viable Product)
o Collecte de données en temps réel (API Binance, Coinbase, etc.).
o Développement d’un premier modèle IA simple (MLP ou LSTM basique).
o Bot d’exécution connecté à un exchange (paper trading).
2.Backtesting & Paper Trading
o Outil de simulation pour évaluer la performance des stratégies sur des jeux de données historiques.
o Module d’évaluation (tableaux de bord statistiques).
3.Version Bêta
o Lancement d’un premier groupe d’utilisateurs testeurs.
o Ajustement des modèles et stratégies en fonction des retours.
6.2 Phase 2 : Applications Web & Mobile
1.Application Web oInterface de supervision en temps réel (positions, PnL, logs).
o Configuration et déploiement de nouvelles stratégies.
o Gestion fine des permissions (inviter un développeur à régler une stratégie, etc.).
2.Application Mobile (Android / iOS)
o Consultation des performances et des positions ouvertes.
o Réception de notifications push (alerts drawdown, exécutions, etc.).
o Possibilité d’ajuster certains paramètres à la volée (stop-loss, take-profit).
3.Ouverture API & Marketplace
o Mise à disposition d’API publiques pour développer des connecteurs ou des stratégies externes.
o Marketplace de stratégies (permet aux utilisateurs de partager ou de vendre leurs algorithmes).
6.3 Phase 3 : Extension vers le Partage de Puissance de Calcul (Crypto)
1.Tokenisation du Projet
o Lancement d’un jeton ERC-20 (ou équivalent) pour encourager la participation des utilisateurs.
o Mécanismes d’incitation : staking, gouvernance, accès à des fonctionnalités premium.
2.Partage de Ressources de Calcul
o Mise à disposition d’un réseau décentralisé de GPU/CPU pour l’entraînement des modèles.
o Les utilisateurs peuvent « prêter » leur puissance de calcul en échange de tokens.
3.Gouvernance & DAO
o Mise en place d’une organisation autonome décentralisée (DAO) pour les décisions stratégiques (ajout de nouveaux marchés, révision du tokenomics, etc.).
07
System Economics (Projection)
Although the first step is to develop the automated trading platform, it is useful to anticipate the introduction of a token and the associated economic logic:
• Usefulness of the Token:
1. Payment of Platform Fees: Users will pay performance or subscription fees via the token.
2. Rewards: Holders or providers of services (computing power, winning strategies, etc.) will be compensated in tokens.
3. Governance: Token holders will participate in decision-making (adding features, partnerships, etc.).
• Distribution :
o Initial sale (ICO/IDO) or progressive distribution (mining, airdrop), depending on the launch strategy.
o Reserves for development, marketing and partnerships.
08
Trading Strategies: Overview
The platform plans to integrate and test several approaches to address varied risk profiles:
1. Scalping / High Frequency:
o Very short trades based on micro-movements in the market.
o Requires fast infrastructure and low latency.
2. Arbitration:
o Exploitation of price differences between multiple exchanges.
o “Safer” approach but often with low margins (therefore need for large volume).
3.Marketing:
o Provision of liquidity on certain order books by placing limit orders (bid/ask).
o Harvesting spread and potentially rewards (liquidity mining on some DeFi platforms).
4. AI-based Long/Short:
o Predictive models seeking to anticipate trends (bullish or bearish).
o Long position (buy) or short position (short). The system will allow you to launch one or more strategies in parallel, with separate capital allocation.
09
Competitive Advantage
• Continuously Trained AI : Our module continuously monitors the market state and retrains its models (if necessary) to adjust to new conditions.
• Modularity : Advanced users can plug in their own Python models or scripts, while novices can choose “out-of-the-box” strategies.
• Security & Transparency : Risk control protocols (loss limit, audits of subsequent smart contracts, etc.) ensure the reliability of the system.
• Scalability : Microservices architecture, integration of new exchange APIs, possibility of extending to other markets (Forex, tokenized stocks, etc.).
10
Conclusions and Perspectives
The creation of an AI-powered automated trading platform addresses an urgent need: to enable traders of all backgrounds to exploit the volatility of the crypto market without devoting considerable time and energy.
Our solution is based on:
1. A solid AI foundation for prediction and analysis of market data.
2. A fast execution engine interfacing with multiple exchanges.
3. Monitoring tools (backtesting, reporting) to help users refine their strategies.
This first step will lay the technical and financial foundations necessary for the deployment of future features, including the web application and the mobile application, before moving on to the tokenization phase and decentralized sharing of computing power.
We believe that the future of algorithmic trading, especially in the crypto ecosystem, will involve a convergence of AI, decentralization, and large-scale automation.
Our project is positioning itself as a major player in this transformation. Join us now to shape the future of AI-powered automated trading!