AI Trading. A New Level

    A platform where AI creates, tests, and optimizes your trading strategies

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    Problem

    Modern trading requires processing huge amounts of data and rapid adaptation to changing market conditions. Traders and investors face several key problems:

    Information Overload

    Numerous trading strategies are scattered across different resources without a unified system for evaluating their effectiveness.

    Testing Complexity

    Lack of a universal tool for testing strategies in various market conditions.

    High Entry Barrier

    Creating effective trading strategies requires specialized knowledge and skills.

    Suboptimal Portfolio Management

    Traditional methods fail to adapt to rapidly changing market conditions.

    Solution: MarketMaker.cc

    MarketMaker.cc is an innovative platform that combines artificial intelligence, crowdsourcing of trading strategies, and advanced backtesting technologies to create a revolutionary algorithmic trading ecosystem.

    Key AI Components:

    AI-Powered Strategy Aggregation

    • Intelligent search and collection of open trading strategies from GitHub, specialized forums, and other online resources.
    • Automatic classification and categorization of strategies by market types, instruments, and methodologies.
    • Continuous database updates with new strategies.

    Visual Strategy Constructor

    • AI assistant for decomposing complex strategies into functional blocks and automatically creating new strategies based on them.
    • Intuitive drag-and-drop interface for creating and modifying strategies.
    • Ability to combine elements from different strategies without programming.

    Advanced Backtester

    • High-speed strategy testing on historical data.
    • Detailed performance analytics with key metrics.
    • Stress testing in various market conditions.

    AI Agents for Portfolio Management

    • Autonomous AI agents optimizing trading strategies in real time.
    • Competition system among agents to identify the most effective approaches.
    • Reward mechanism for successful agents with allocation of additional resources.

    Market Opportunities

    The global algorithmic trading market is growing rapidly:

    AI Tools for Finance

    By 2028, 80% of the total financial planning and investment management market will be reached.

    Agent AI

    33% of enterprise software applications will include agent AI by 2028 (less than 1% in 2024).

    Autonomous Solutions

    15% of daily business decisions are already made autonomously by AI agents.

    Competitive Advantages

    Innovative Strategy Aggregation Approach

    Unlike competitors offering a limited set of pre-installed strategies, MarketMaker.cc uses AI to continuously search for and integrate new strategies from open sources.

    Unique Visual Constructor

    Our AI automatically transforms complex code into visual blocks, making strategy creation accessible to users without programming skills.

    Ecosystem of Competing AI Agents

    A system where AI agents compete for resources, ensuring constant improvement of strategies and adaptation to changing market conditions.

    Comprehensive Solution

    Combining all stages of working with strategies on a single platform: from discovery and creation to testing and real-world application.

    Platform Features

    Trading Terminal

    Real-time data providers, unified interface for all exchanges, and advanced order management

    Portfolio Management

    Portfolio tree management, rebalancing, and virtual portfolios with tokens

    Historical Data

    Ready-to-use API providers and custom data collection in Clickhouse/DuckDB

    Strategy Builder

    Visual bot constructor, 100+ strategies, and integration with TradingView

    Strategy Testing

    Comprehensive testing on historical data, virtual portfolios, and real accounts

    Analytics

    Advanced market analysis, signals, and automated trading solutions

    Development Roadmap

    PRE-SEED STAGE 1

    Trading Terminal

    • Real-time data providers
    • Trading order management
    • Unified exchange interface
    • Payment processing
    PRE-SEED STAGE 2

    Portfolio Management

    • Portfolio tree management
    • Portfolio rebalancing
    • Accounting visualization
    • Virtual portfolios and tokens
    • First sales MVP
    SEED STAGE 1

    Strategy Builder

    • Visual bot constructor
    • Building block creation
    • 100+ strategy implementation
    • TradingView integration
    • GitHub strategy collection
    SEED STAGE 2

    Strategy Testing

    • Comprehensive testing on historical data
    • Virtual portfolio testing
    • Real account testing
    • Strategy performance analysis
    • Parameter optimization
    SEED STAGE 3

    Marketplace

    • Marketplace for strategies and bots
    • Mobile application
    • Global launch

    Our Team

    @suenot

    Chief Executive Officer

    Fullstack, DevOps, AI Engineer

    @ibnteo

    Chief Technology Officer

    Fullstack

    @aliex_z

    Chief Financial Officer

    Fullstack

    @timax

    Head of Quantitative Research

    Fullstack, AI Engineer

    @thatmean

    Risk Manager

    Fullstack, AI Engineer

    @markinmatrix

    Business Development Manager

    Fullstack, AI Engineer

    @alexlog9

    Product owner

    Quant Analyst/Researcher

    @your_name

    Be Part of Our Team

    Join Us

    Technology Stack

    C++
    Golang
    Rust
    Python
    Pytorch
    TypeScript
    Elixir
    ClickHouse
    QuestDB
    DuckDB
    PostgreSQL
    Hasura
    GraphQL
    gRPC
    Websocket
    OpenAPI

    MarketMaker.cc Technologies

    Machine Learning Technologies

    Financial Data Structures
    Advanced systems for transforming unstructured financial datasets into organized bar formats, including traditional tick, volume, and dollar bars alongside innovative information-driven bar structures.
    Labelling Techniques
    Comprehensive suite of data labeling methodologies including Triple-Barrier, Meta-Labeling, Trend Scanning, Tail Sets, and Matrix Flags for precise classification of financial patterns.
    Feature Engineering
    Sophisticated processes that transform raw financial data into informative model features using domain knowledge, including techniques from market microstructure analysis and fractionally differentiated features.

    Portfolio Optimization

    Critical Line Algorithm
    Advanced portfolio optimization technique that overcomes limitations of traditional Mean-Variance approaches by allowing precise upper and lower boundaries on asset allocation weights.
    Mean-Variance Optimization
    Collection of classic portfolio construction methodologies including Inverse Variance, Minimum Volatility, and Maximum Sharpe portfolios with customizable objectives and constraints.
    Entropy Pooling
    Sophisticated methodology that enables specification of non-linear market views to generate posterior distributions, extending beyond traditional return-focused models.
    Shrinkage Methods
    Specialized techniques for reducing noise in covariance matrices, creating more robust foundations for portfolio optimization applications.
    Hierarchical Risk Parity
    Modern optimization algorithm leveraging unsupervised machine learning through hierarchical tree clustering to group assets by risk characteristics.
    Black-Litterman Model
    Sophisticated allocation framework combining Capital Asset Pricing Theory with Bayesian statistics to generate efficient portfolio weight estimates.
    Robust Bayesian Allocation
    Advanced algorithm that formulates assumptions about prior market parameters and generates robust portfolios along the Bayesian Efficient Frontier.
    De-noising and De-toning
    Advanced matrix refinement methods that efficiently remove noise from covariance structures without information loss.

    Arbitrage Strategies

    Distance Approach
    Widely cited pairs trading strategy valued for its simplicity and transparency, making it ideal for large-scale empirical research applications.
    Cointegration Approach
    Established methodology that identifies pairs with econometrically reliable equilibrium relationships for statistical arbitrage trading.
    Time Series Approach
    Enhanced trading rule framework utilizing time series modeling of mean-reverting processes beyond traditional cointegration methods.
    Stochastic Control Approach
    Advanced methodology using stochastic processes to determine optimal trading rules without requiring spread forecasting or formation periods.
    Machine Learning Approach
    Integrated framework combining various statistical arbitrage techniques with machine learning algorithms to enhance strategy creation.

    MM / USDT Liquidity Pool

    Provide liquidity to our MM/USDT pool on STON.fi and earn rewards.

    MMUSDTT
    View Pool on STON.fi

    Investment Opportunity

    Valuation: $10M USDT

    Pre-seed round: 5% for $500k USDT

    (Valuation as of January 2025)

    Contact for Investment

    MM Token: Usage and Business Model

    MM is the utility token of the MarketMaker.cc platform, used to pay for all key services and incentivize ecosystem participants.

    1. Payment for Platform Services

    • Strategy Aggregation: access to advanced search and automatic addition of new trading strategies from open sources.
    • Visual Strategy Constructor: use of the drag-and-drop interface for creating and modifying strategies.
    • Backtesting: running strategy tests on historical data, including stress tests and analytics.
    • Launching and Managing AI Agents: activation and support of autonomous AI agents for portfolio management.
    • Access to Premium Analytics: receiving extended reports, market signals, and individual recommendations.

    2. Strategy Marketplace

    • Buying and Selling Strategies: payment for acquiring ready-made strategies from other users or selling your own solutions.
    • In-platform Commissions: marketplace transaction fees are charged in MM.

    3. Rewards and Staking

    • Rewards for Top AI Agents: top agents receive MM for successful results in competitions and portfolio management.
    • Staking for Access to Exclusive Features: locking MM to access closed services, early releases, and voting.

    4. Governance and Voting

    • Platform Development Voting: MM holders can participate in decision-making for ecosystem development (DAO mechanics).
    In short: MM is a universal settlement and incentive tool for the platform. All key actions, services, and participant motivation are tied to the use of the MM token, which is freely traded on DEX.