Platform Architecture Overview
Last updated
Last updated
Our execution models process real-time and historical market data to generate optimal trade signals, execute orders efficiently, and minimize transaction costs. The core architecture follows this structured flow:
By leveraging advanced data ingestion, signal processing, and execution algorithms, our system ensures your orders are executed with minimal market impact while optimizing for liquidity, price improvement, and efficiency.
We aggregate and normalize both market data (quotes, order book updates, transactions) and trade data (historical fills, trade sizes, execution slippage) in real time to provide the most accurate execution decisions.
Data Cleaning & Standardization: We remove anomalies, reconstruct missing values, and align timestamps across multiple venues.
Trend Adjustments & Seasonality Corrections: Using advanced filtering techniques (e.g., Kalman filtering), we adjust for market seasonality.
Order Book Reconstruction: Multi-level order books are reconstructed using resampling techniques:
Time-based resampling (e.g., 100ms intervals for market microstructure studies)
Tick-based resampling (e.g., per N trades to analyze liquidity shifts)
Order Flow Imbalance resampling (to dynamically adjust granularity based on activity levels)
These preprocessing steps allow for cleaner and more actionable data, reducing noise and improving execution precision.
Processing Latency: X μs
Once the data is preprocessed, we compute multiple classes of trading signals to predict short-term price movements and market impact, ensuring optimal execution.
Momentum-Based Indicators:
Short-term price trends derived from volume-weighted momentum metrics.
Adaptive filters to detect shifts in buying/selling pressure.
Correlated Asset Flow Indicators:
Cross-asset flow relationships to detect liquidity imbalances.
Proprietary correlation models trained on historical slippage patterns.
Order Flow Imbalance Indicators:
Real-time monitoring of bid-ask spread pressure.
Tracking hidden liquidity signals across multiple venues.
These indicators dynamically adjust trading parameters within our execution algorithms, ensuring optimal trade timing and placement.
Signal Computation Latency: X μs
When an order is placed via our API, our execution algorithms leverage real-time market data and trading signals to determine the optimal speed, order size, and routing strategy.
We offer two primary execution algorithms:
Optimized Execution Schedule: Determines the best time and size to trade while minimizing impact.
Superior to TWAP/VWAP: Unlike traditional algorithms, it adapts to market conditions rather than following static time-based execution.
Designed for Institutional Blocks: Ideal for large trades that require careful market participation.
Venue & Order Type Selection: Determines the optimal venue and order type (e.g., limit, market, iceberg) based on market depth.
Liquidity-Optimized Routing: Routes trades to the deepest liquidity pools with minimal slippage.
Multi-Asset Execution: Supports execution across Equities, Fixed Income, Options, and Crypto markets.
Clients can select their preferred execution algorithm via our API and customize trading parameters to suit their objectives.
Order Routing Decision Latency: X μs
Once an execution decision is made, our system routes the order through integrated EMS/OMS APIs to ensure seamless placement and confirmations.
Fixed Income: Tradeweb
Crypto: Binance, Kraken, Coinbase
Equities: NYSE, Nasdaq, CBOE
Equity Options: Cboe, BOX, ISE
Futures: CME, ICE, Eurex
All order fills are logged and returned via our FIX API, ensuring clients receive full order confirmations and trade analytics.
Order Placement & Execution Latency: X μs
Our proprietary execution engine ensures that market data ingestion, signal processing, and order execution occur in less than X μs—delivering a speed advantage for latency-sensitive trading strategies.
"From analytics to execution in less than X μs."
