Delta Scope: Crypto Arbitrage
DeltaScope is a crypto arbitrage platform that tracks spreads in real time, simulates trades under realistic constraints, and builds trust through transparency and control. By monitoring live price feeds across exchanges with full fee and latency awareness, it bridges the gap between idea and revenue. Today it operates as a backtested, demo-ready system; tomorrow it becomes the foundation for a production-grade trading engine.
Introduction
DeltaScope began as a personal initiative to test whether disciplined product design and lean engineering could transform crypto market inefficiencies into a viable trading strategy. Rather than chasing hype or building a “black box bot,” I approached this as a product: define the real constraints (fees, latency, exchange rules), design for trust (transparent logging, clear controls), and build only what matters to the operator. Over six months, this evolved into a full-stack platform with live dashboards, simulation capabilities, and a roadmap toward production-grade execution.
The Challenge
Arbitrage sounds simple in theory—buy low on one exchange, sell high on another—but in practice, the edge is razor-thin. Fees, latency, and transfer delays often erase profits before a trade can settle. Most retail arbitrage tools either ignore these frictions or hide them behind opaque algorithms, making them unreliable at best and dangerous at worst.
The challenge was to design a system that confronted these realities head-on: a platform that could monitor markets in real time, account for every fee and delay, and prove whether true opportunities exist after costs. To succeed, it had to balance technical rigor with user trust, offering transparency, persistent logging, and controls that made the operator confident in each decision.
Key Objectives
Fee-Aware Arbitrage Detection
Build an engine that identifies opportunities only after accounting for trading fees, spreads, and latency, ensuring signals represent real profit potential.
Transparent Simulation & Logging
Design a configurable simulation engine with persistent trade logs to validate strategies before risking capital, building trust through visibility.
Lean Full-Stack Architecture
Deliver a minimal but complete product [FastAPI backend, TimescaleDB storage, and Next.js dashboard] that prioritizes core functionality over unnecessary features.
Approach
The application is built from the ground up with one guiding principle: design for reality, not theory. Arbitrage opportunities vanish quickly when ignored costs or unrealistic assumptions creep in, so the architecture accounts for every friction point from day one.
The build begins with a backend-first approach: a FastAPI application ingests real-time feeds from crypto exchanges, normalizes price data, and applies a fee- and latency-aware spread model. A TimescaleDB hypertable structure stores price and trade data at scale, enabling backtesting and historical analysis without losing fidelity.
On top of this foundation, the Next.js dashboard provides the operator’s perspective: real-time charts, arbitrage tables, and controls to pause, parameterize, or export data. The design goal is not flash, but trust; a lean interface where the trader knows exactly what the system is doing.
The simulation engine extends this loop, parameterized for thresholds, trade sizes, and latency. Every simulated trade is logged persistently, ensuring strategies can be evaluated transparently before advancing toward live execution.
Process
- 1.
Ingest & Normalize Market Data
Connect to real-time feeds from centralized exchanges, normalize the data, and prepare it for consistent analysis across exchanges
- 2.
Model Opportunities with Fee Awareness
Apply trading fee structures, latency assumptions, and thresholds to detect only those spreads that represent real profit potential.
- 3.
Persist & Structure Data
Store price streams and trade logs in TimescaleDB hypertables to enable efficient querying, historical analysis, and backtesting.
- 4.
Design the Operator Dashboard
Deliver a Next.js interface with live charts, arbitrage tables, and start/stop controls, prioritizing trust and usability over visual flash.
- 5.
Simulate Trades & Log Outcomes
Run parameterized simulations that model trade size, slippage, and latency, with every outcome logged for transparent evaluation.
- 6.
Validate Strategies Through Backtesting
Use historical data and persistent logs to test and refine thresholds, filters, and rules before moving toward live execution.
- 7.
Extend to Multiple Exchanges & Assets
Build the abstraction layer that allows DeltaScope to scale across more exchanges and trading pairs, expanding opportunities and proving the model’s versatility.
Solution
The result is a lean but complete arbitrage platform, running end-to-end in real time and designed to operate under real market constraints. The backend ingests and normalizes live feeds from Coinbase and Kraken, models opportunities with fee and latency awareness, and records data to TimescaleDB for both persistence and backtesting.
On the frontend, the Next.js dashboard provides the operator with live charts, arbitrage tables, and feed controls. The interface is designed for trust: every signal can be traced back to logged data, every trade is recorded, and the operator remains firmly in control of when and how strategies run.
The simulation engine closes the loop, turning opportunities into parameterized trade scenarios. By modeling thresholds, trade sizes, and slippage, it gives a realistic view of performance without risking capital. Persistent logging ensures every outcome is transparent, enabling disciplined refinement before live execution.
Together, these elements form a system that is more than a “bot.” DeltaScope is a product: an integrated platform that balances technical rigor, operator trust, and extensibility, a foundation for a production-grade trading engine.
Bitcoin Price
Results
Early results demonstrate the platform’s viability as a lean, product-driven arbitrage system. The system detects fee-positive opportunities in real time, simulates trades with realistic parameters, and logs every outcome for transparent evaluation. Backtesting confirms that profitable spreads persist after costs, validating the core concept and the technical approach.
- Detects realistic, fee-adjusted opportunities across Coinbase and Kraken
- Provides deterministic, transparent trade logging with persistent storage
- Runs a simulation engine that models thresholds, trade sizes, and slippage before risking capital
- Delivers a live operator dashboard with charts, controls, and historical trade views
- Establishes a scalable foundation for multi-exchange, multi-asset expansion
Reflection
Building Delta Scope has been as much about discipline as it has been about architecture and code. Designing for a hostile environment like crypto markets forces me to confront constraints head-on and prioritize only what matters: fee models, latency, transparency, and trust. I’m reminded that product design is about clarity: reducing uncertainty in the system and giving the user confidence.
Technologies
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Team
Solo project