We build trading infrastructure.

Crypto trading bot development is the engineering of software that automatically places and manages orders in crypto markets based on a strategy. Categories range from retail tools like grid bots and copy-trading to professional systems like execution algos, market-making and arbitrage. Real production systems involve exchange connectivity across multiple venues, deterministic order-state tracking, risk engines that enforce limits, reconciliation against exchange balances, and monitoring that catches failures in seconds.

Cost is driven by latency tier, venue coverage, asset-class breadth, strategy count, user-facing surface and operating scope — not by strategy logic, which is usually the smallest piece. A retail DCA bot is one build. A multi-venue low-latency arbitrage engine with cross-margin perpetuals coverage is an order of magnitude heavier, and HFT market-making with colocation is another order beyond that.

Exchange connectivity across your target venues, redundant market-data feeds, an OMS with deterministic state tracking, a risk engine with pre- and post-trade limits, execution algos, reconciliation, monitoring and runbooks for known failure modes. The strategy alpha itself stays yours — we implement it. Exchange agreements and MM contracts get integrated technically; you sign the commercial deals, and anything legal stays with you and your counsel while we build to what they specify.

It depends on latency tier and team skill. Latency-critical paths like HFT and aggressive market-making benefit from Rust or C++, where microseconds matter. Throughput-heavy infrastructure often lands well in Go. Strategy research and less-latency-sensitive execution runs comfortably in Python with optimized hot paths. Most serious systems mix languages across the stack rather than picking one.

As a first-class infrastructure concern, not a retry loop. Each venue has specific rate limits across order placement, cancellation, market data and account queries. Dedicated rate-limit tracking per venue per endpoint, queued orders as limits approach, and prioritized cancellations over placements mean getting rate-limited during a volatility event is a known risk, not a surprise.

It depends on what you're operating — proprietary trading for your own account differs from running a broker or a retail product. Proprietading your own book is the lightest case. Serving external clients is heavier, and retail-facing automated platforms add consumer-protection requirements. Your counsel works out which apply. We architect the system to match what they tell us.

Yes — DEX execution runs alongside CEX execution in the same system. DEX execution has different characteristics: no rate limits but gas costs, MEV exposure requiring private-mempool solutions, and slippage modeling against AMM curves. We build DEX execution into the same OMS and risk framework as CEX trading — from a strategy's perspective, venue type is configuration, not a different system.

Custom earns its place when the strategy, the latency or the integration is the thing that makes you competitive. Unusual instruments, specific execution behaviour, deep integration with an OMS you already run, or throughput no vendor product will give you — those genuinely need building. Where standard tooling already executes what you run, using it gets you trading sooner and you keep the option to build later. We map your requirements against both honestly, and the answer is usually obvious once the list exists.