ALGO TRADING PLATFORM DEVELOPMENT
PROPELOO engineers algo trading platforms — strategy execution engine, market data ingestion, order routing, risk controls, position management and backtesting infrastructure. A strategy that earns in backtesting but fails in live execution due to slippage, latency or order rejection is not a trading strategy — it is a liability.
Algorithmic trading platforms fail in production for three reasons: latency in the execution path that eats the edge the strategy was designed around, risk controls that are too slow to prevent runaway positions, and market data pipelines that drop ticks under load. The strategy is the easy part. The infrastructure that executes it reliably, at microsecond precision, with pre-trade risk in the critical path, is what separates a working algo trading platform from a demo. PROPELOO engineers the execution infrastructure — strategy SDK, event-driven execution engine, order router, risk pre-checks, position ledger and real-time P&L — and designs the backtesting environment to match live conditions as closely as possible.
Strategy logic is a small fraction of the total system.
Event-driven strategy framework — strategies subscribe to market data events (tick, bar, order book) and emit order signals. Paper trading mode uses the same code path as live execution. Backtest mode replays historical data through the same engine.
Low-latency order execution: strategy signal → risk check → order construction → exchange API call in under 10ms. Event loop architecture with no blocking I/O in the critical path.
Multi-exchange tick data ingestion, normalisation to a common schema, order book reconstruction, OHLCV bar building and historical data storage with nanosecond timestamps.
Per-strategy position limits, portfolio-level exposure caps, maximum drawdown halt, order rate limiting, kill switch (per-strategy and global). Risk checks are synchronous in the execution path.
Event-driven backtester using the same strategy SDK as live trading. Realistic simulation: latency modelling, partial fills, market impact, slippage. Walk-forward optimisation and out-of-sample validation.
Real-time P&L by strategy and portfolio, drawdown charts, fill quality analysis, strategy comparison, parameter sensitivity visualisation.
The most important decision in algo trading infrastructure is where to put the latency budget — and what to sacrifice to keep the critical path fast.
Every network call, database write, or lock acquisition in the execution path adds latency. The path from market data event to exchange order submission must be entirely in-memory, event-driven, with journal writes happening asynchronously after order submission — not before.
Axiom: ASYNC EVERYTHING OFF THE CRITICAL PATH
A strategy tested on a separate backtesting framework that does not model latency, partial fills, or fee structure will show different results in live trading. The backtesting engine must be the live execution engine replaying historical data — same SDK, same risk checks, same order model.
Axiom: ONE CODEBASE, THREE MODES
A risk control that takes 5ms to evaluate while the strategy executes in 2ms is not a risk control — it is overhead that strategies route around. Pre-trade risk must be in-process, reading from in-memory state, with sub-millisecond evaluation.
Axiom: RISK IN THE CRITICAL PATH
These choices determine execution speed, strategy flexibility and operational cost.
Impact: Event-driven for any strategy where execution speed matters. Request-driven only for end-of-day rebalancing strategies.
Impact: Cloud deployment in the exchange region is sufficient for strategies with edges measured in basis points over seconds. Co-location is only justified for strategies with edges measured in microseconds.
Impact: Start single-exchange unless the strategy requires cross-exchange execution. Smart order routing adds significant complexity — only build it when the strategy P&L justifies it.
Impact: Plugin architecture for most platforms. Process isolation when strategies from different clients share the same platform.
Impact: TimescaleDB for most platforms — SQL familiarity, good compression, and satisfactory query performance for bar data and tick data up to billions of rows.
Full-stack algo trading platform for crypto — multi-exchange connectivity, strategy SDK, risk engine, real-time dashboard.
Algo trading infrastructure for equity or forex — broker API integration, FIX connectivity, strategy engine, compliance reporting.
Quantitative research environment — historical data pipeline, event-driven backtester, walk-forward optimisation, strategy analytics.
Low-latency execution infrastructure — kernel bypass networking, FPGA order entry, co-location setup, nanosecond-level latency measurement.
Platform for multiple strategy providers — strategy submission, paper trading validation, live deployment, profit-sharing settlement.
Selected for latency, reliability and strategy flexibility.
Stack: Go / Rust (execution engine), Event-driven architecture, Lock-free queues, In-memory risk state, Async journal writes
Stack: WebSocket (tick feed), FIX 4.2/4.4, TimescaleDB (historical), Redis (real-time cache), Kafka (fan-out)
Stack: Binance / Bybit / OKX APIs, FIX gateway, REST + WebSocket, Order state machine, Retry + backoff logic
Stack: React dashboard, Recharts / TradingView, P&L attribution, Drawdown monitoring, Strategy comparison
A compromised API key or a runaway strategy can drain an account in minutes.
Exchange API keys stored in secrets manager (AWS Secrets Manager / HashiCorp Vault), never in environment variables or config files. Per-strategy keys with minimum required permissions (trade-only, no withdrawal).
Per-strategy and global kill switch that cancels all open orders and halts new order submission. Must work even when the strategy process is unresponsive — implemented as a separate watchdog process.
Maximum order rate per strategy (orders/second), maximum position size, maximum daily loss. Any breach triggers automatic strategy halt and alert. These limits are enforced by the risk engine, not the strategy code.
Every order, fill, cancel and risk event logged to append-only journal with timestamp. Required for dispute resolution with exchanges and for regulatory reporting.
Execution model, data pipeline, risk engine design, exchange connectivity plan, strategy SDK interface.
Exchange connectors, tick normalisation, order book reconstruction, historical data store.
Strategy SDK, event loop, order router, order state machine, fill handling.
Pre-trade checks, position limits, drawdown halt, kill switch.
Historical replay, latency modelling, slippage simulation, performance metrics.
Real-time P&L, position monitor, strategy comparison, trade journal.
Paper trading validation, co-location/cloud setup, monitoring, incident response.
For cloud-deployed platforms in the same region as the exchange: 2–10ms from strategy signal to exchange order submission. For co-located infrastructure: sub-millisecond. The dominant latency factor is usually the exchange API response time, not the platform itself.
Yes — and we build it using the same execution engine as live trading. Strategies written for live trading run in backtest mode without modification. The backtester models latency, partial fills, exchange fees and slippage based on historical order book data.
Yes. The platform supports concurrent strategy execution with independent position ledgers, risk limits and P&L accounting. Strategies can share market data subscriptions to reduce exchange connection overhead.
Yes. If you have existing FIX or REST connections to exchanges, we integrate the execution engine with those connections. We also build new exchange connectors where needed.
No — we build the infrastructure that executes strategies. We provide the strategy SDK and documentation, and your quant team or strategy providers write the strategy logic. We can advise on strategy SDK design to ensure the interface supports your strategy requirements.
Our institutional FIX engine provides automated logon/logout sequence synchronization, heartbeat monitoring, and in-flight message gap recovery. We also ingest broker drop-copy sessions in real time to reconcile external fills against internal strategy orders.
We enforce multi-tiered pre-trade risk controls: maximum order quantity per second, position concentration limits, short-sale restrictions, and circuit-breaker triggers that immediately pause strategy execution and cancel open orders if anomalous fill rates or drawdown thresholds occur.
Yes. We build high-performance C++/Rust execution cores paired with Python SDK bindings (PyO3 / Cython). Quant researchers can write predictive models using PyTorch, NumPy, or pandas while the underlying engine handles microsecond trade routing.