Built for ourselves, shared with everyone else. An open-source terminal at the core, with data, SDKs, backtests and infrastructure around it. Our strategy know-how stays inside the company.
Seven parts, one stack.
Each piece started as a tool we needed ourselves. Use any of them alone, or run them together through the terminal.
- 01
Trader SDK
Fast, ready-made APIs for working with exchanges. One normalized entry point instead of one integration per venue.
- 02
Terminal
Profitmaker: a fully customizable, AI-native interface for your bots.
- 03
Portfolio management and rebalancing
Twelve allocation algorithms and automated rebalancing, from HRP to Black-Litterman.
- 04
Warehouses
Realtime data streams plus history on S3, one warehouse per kind of data.
- warehouse.marketmaker.cc Classic historical market data
- listingapis.com Exchange listings: historical and live data
- manipulators.marketmaker.cc Market manipulations we detected soon
- predictions.marketmaker.cc Prediction markets soon
- news.marketmaker.cc News flow soon
- 05
Backtest engines
Replay history against your strategy and see how it would have behaved.
- 06
Bots arena, marketplace, portfolio
A social network for traders: compete, publish and follow bots.
- 07
HFT and MFT infrastructure for rent
Low-latency servers and messaging, operated by us and rented to you.
The core
Everything is managed through Profitmaker.
An open-source, fully customizable, AI-native terminal. Connect your bots, read the warehouses, rebalance portfolios, run backtests and rent infrastructure from one interface. Self-hosted, your keys stay with you.
We share the tooling. The strategies themselves stay inside the company.
Latest from the research desk
All articlesCollections
All collectionsBacktesting Without Fooling Yourself
A step-by-step path from what your backtest really optimizes to proving an edge survives overfitting, multiple testing, and live execution. Read top to bottom - each part builds on the last.
High-Performance Backtest Engines
How to build a backtest engine that runs hundreds of times faster without changing a single PnL number - data layout, caching, adaptive resolution, and architecture, from first speedups to production internals.
Complex Arbitrage in Rust
A six-part build-up of multi-leg crypto arbitrage - from negative-cycle detection to the linear algebra, copulas, and machine learning behind it, ending in low-latency Rust execution.
Order Book & Market Microstructure
How the order book really works - accessing the data, reading queue position, rebuilding bars from order flow, and modeling it with deep learning and Hawkes processes.
Portfolio Construction & Risk
From Markowitz to production HRP + CVaR: how to allocate across crypto assets, model tail dependence with copulas, and size positions without blowing up.
Risk & Position Sizing
Build position sizes from drawdown math, realistic trading costs, and uncertainty in backtest results before applying Kelly.
Statistical Arbitrage & Pairs Trading
Trade the spread between correlated assets - from the distance approach to cointegration and Kalman filters, then dynamically combining mean reversion with momentum.
Deep Learning for Markets
Neural forecasting for crypto - transformers, diffusion models, and foundation models, and how conformal prediction keeps their uncertainty honest.
AI Agents for Trading
The agentic-AI stack for markets - multi-agent frameworks, open-source hedge funds, and LLMs that mine alpha from earnings calls.
QuestDB for Algorithmic Trading
Stand up a time-series stack for trading on QuestDB - from architecture to the SQL that matters, to a production deployment.
Low-Latency Trading Infrastructure
The plumbing under an HFT stack - how components talk (WebSocket, FIX, gRPC, Aeron), messaging on Aeron and Zig, and a C++ FIX/FAST scalper.
Execution & Market Making
From TWAP/VWAP and Almgren-Chriss to TCA, slippage models, and the Avellaneda-Stoikov market maker - how to turn a signal into fills without paying the spread twice.
Onchain & DeFi Quant
Quantitative DeFi from first principles - MEV and sandwich attacks, atomic arbitrage, liquidation cascades on lending protocols, and LP profitability on Uniswap v3.
Causality & Lead-Lag in Markets
Who moves whom - Granger causality, transfer entropy, PCMCI discovery, and the causal ML toolbox (double ML, causal forests, synthetic control) applied to crypto markets.
Volatility Modeling & Regimes
Forecast volatility with the GARCH family - asymmetry and leverage effects, dynamic correlations, vol targeting - then detect market regimes with HMMs and trade adaptively.
Modern ML for Trading
The full gradient-boosting-to-neural-operator toolkit - ensembles, AutoML, probabilistic forecasting, distillation and pruning for latency, plus physics-informed architectures like Neural ODEs and Fourier operators.