We have built automated trading systems for clients operating across equity, forex, and cryptocurrency markets — ranging from simple rules-based execution tools to sophisticated multi-strategy platforms with ML-driven decision logic. This is not an area where we write theoretical frameworks; these are production systems that have executed real trades on live markets with real capital at stake.
Every automated trading system we build is designed around the principle that automation amplifies both performance and risk. A strategy that loses 0.1% per day manually loses far more than 0.1% per day when running at machine speed without oversight mechanisms. Risk management is not a feature we add to a working trading system — it is an architectural foundation we build from the start.
Automated trading systems we deliver
Algorithmic execution systems
Execution algorithms that break large orders into smaller child orders and execute them with minimal market impact — VWAP (Volume-Weighted Average Price), TWAP (Time-Weighted Average Price), and Implementation Shortfall algorithms that dynamically balance urgency against impact. These are the tools that allow large institutional orders to be executed without moving the market against the trade, and they are relevant to any organisation executing at sufficient size to experience meaningful market impact.
We build these as modular execution services that receive order parameters (size, direction, asset, urgency level, constraints) and manage execution autonomously, feeding back execution progress and performance attribution to the order management system.
Strategy automation platforms
Full automation of quantitative trading strategies: signal ingestion from the signal generation layer, position sizing based on signal strength and risk parameters, order generation and routing, position monitoring, and automated risk management actions (stop-losses, position limit enforcement, drawdown-triggered de-risking). We have built these for mean-reversion, momentum, statistical arbitrage, and macro strategies across equity and forex markets.
Cryptocurrency trading bots
Automated trading systems for digital asset markets present a distinct set of technical requirements: 24/7 operation without the market close and open that traditional markets provide for batch processing; API rate limits and reliability issues that require robust retry and failover logic; the need to operate simultaneously across multiple exchanges for arbitrage and best-execution; and the higher volatility of crypto markets that requires tighter risk controls relative to expected move sizes.
We have built crypto trading bots operating on Binance, Coinbase, and Kraken using the CCXT library for exchange connectivity, with Redis-based position state management for consistency across exchange API failures, and multi-exchange arbitrage logic that handles the latency and execution risk of simultaneous cross-exchange orders.
Backtesting and simulation
A trading strategy that looks profitable in backtest is not necessarily profitable in live trading — the gap is often caused by look-ahead bias (using future information in historical simulation), overfitting (finding patterns in historical noise that do not persist), optimistic transaction cost assumptions, and survivor bias in the universe of instruments used. We build backtesting infrastructure that addresses these problems explicitly: point-in-time data construction that eliminates look-ahead bias; out-of-sample test periods with strict separation from strategy development; realistic market impact and slippage modelling based on instrument liquidity; and Monte Carlo stress testing against extreme market scenarios.
Risk and compliance infrastructure
Every automated trading system we build includes: configurable position and notional limits enforced in the order generation layer before orders reach the market; intraday and cumulative P&L loss limits that trigger automated de-risking or shutdown; kill switches operable by authorised users with sub-second effect; anomaly detection that flags unexpected position concentrations, abnormal order patterns, or unusual P&L attribution; and comprehensive order and execution audit trails for regulatory compliance and post-trade analysis.
- Exchange connectivity: CCXT for crypto, FIX and REST for traditional markets
- Real-time state: Redis for position and risk state with microsecond read latency
- Strategy logic: Python with NumPy vectorisation for high-frequency computation
- Storage: PostgreSQL for trade records and audit trails, time-series databases for market data
- Infrastructure: AWS with low-latency co-location options for latency-sensitive strategies
