LEANWARE TEAM
1 x Senior Full Stack Developer, 3 x Mid Full Stack Developers, 1 x Product Designer, 1 x Product Owner, 1 x Quality Assurance
2Moon Capital
COMPANY
USA
COUNTRY
Managed Team
engagement MODEL
CLIENT OVERVIEW
2Moon was looking for a reliable partner to develop a scalable product that encapsulates their trading strategies, ensuring successful and efficient programming of their operations.
As a trading company, 2Moon required a sophisticated solution to manage and execute trading strategies efficiently, especially given the dynamic and often volatile nature of financial markets.
Leanware's solution for 2Moon resolved the critical need for an automated, reliable, and scalable trading system, enhancing the company's ability to execute and manage complex trading strategies efficiently.
2Moon's initiative focused on creating an efficient trading system that could respond dynamically to market signals and manage multiple trading strategies and accounts effectively.
Python, Flask, Docker, Amazon Web Services, AWS Lambda, AWS SNS, AWS DynamoDB, AWS Step Functions, RDS, Serverless, AWS Glue, AWS SQS, AWS SES, Cognito, NextJS, Django, AWS ECS, AWS ELB, AWS EC2, AWS S3, AWS Quicksight
Tech Stack Involved
Event-Driven Serverless Architecture:
Our team of Data Engineers and Software Architects designed and implemented a serverless architecture that is event-driven. This architecture supports the automation of trading strategies and ensures scalability and reliability.
Workflow Implementation for Trading Strategies:
We developed workflows containing the business logic, forming the core of the automated trading strategies. These strategies are adjustable based on algorithmic results to maximize positive trading outcomes.
Development of Joshua: Joshua, the brain of the bot, is a specialized library containing business logic for selecting optimal contracts and closing conditions, vital for effective trading decisions.
Integration with Trading Platforms & Interactive Brokers:
The bot was designed to interpret signals from Postman and TradingView, executing buy, sell, and close orders, and modifying strategy statuses. These alerts ultimately translate into orders on Interactive Brokers' systems.
Multi-Strategy and Account Management:
The bot can handle multiple trading strategies and operate across various Interactive Brokers accounts, showcasing its versatility and robustness.
Multiple Production Environments:
We maintained three paper trading (simulated) environments and one real money trading environment, ensuring thorough testing and real-world applicability.
SERVICES PROVIDED
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UX & UI DESIGN
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Components designed to scale effortlessly according to client needs.
To ensure a smooth handoff, it's crucial to name wireframes and clearly defined flows.
Before Leanware:
2Moon faced challenges in automating and efficiently executing trading strategies.
The company needed a scalable and reliable solution to optimize trading outcomes.
After Implementing Leanware’s Solution:
Interactive Brokers Integration: This integration played a critical role in operationalizing and optimizing 2Moon's automated trading strategies.
Automated Trading System: The bot, powered by Joshua, effectively automates trading decisions, enhancing the speed and accuracy of operations.
Optimized Trading Strategies: The event-driven architecture allows for the dynamic adjustment of strategies, improving profitability and reducing risks.
Scalability and Flexibility: The serverless architecture ensures the system's scalability, handling multiple strategies and accounts without compromising performance.
Effective Risk Management: The use of paper trading environments alongside a real-money environment enables effective risk management and strategy testing.
Enhanced Trading Outcomes: Overall, the automation and intelligent strategy management have led to more efficient and potentially more profitable trading operations.
Through these developments, 2Moon has successfully transitioned to an advanced, automated trading system, positioning itself strongly in the competitive trading industry.
From Blueprint to Delivery
RESULTS
FAQ
Frequently Asked Questions
What are signs a development company doesn’t understand trading systems?
Red flags include vague answers about market data, promising fixed timelines for strategy performance, not mentioning risk controls, misusing backtesting vs live trading terminology, and lack of experience with broker APIs like IBKR or FIX.
What testing environment costs should I budget for paper trading?
You should expect costs for market data feeds, IBKR or exchange sandbox access where required, logging infrastructure, and compute resources for running strategies at scale during testing.
How many iterations should I expect before a production-ready trading system?
Usually three to eight iterations: initial prototype, paper trading, real-time simulation, limited-capital live testing, and then full deployment with monitoring.
Fixed price or time & materials for algorithmic trading development?
Time & materials is standard because requirements evolve after real market testing. Fixed price only works for very small, tightly defined MVPs.
Do I need developers with finance background for trading systems?
Yes, for anything beyond rudimentary automation. Developers must understand market microstructure, order types, slippage, risk, and data timing issues to avoid costly mistakes.
What’s the typical failure rate for trading platform development projects?
Industry failure rates are high—many projects stall or get rewritten because teams underestimate market data complexity, latency, edge-case handling, and exchange/broker quirks. Experienced fintech vendors dramatically reduce this risk.
How do I validate a dev shop actually knows trading/Interactive Brokers?
Look for past IBKR implementations, knowledge of TWS/Gateway/API constraints, experience with market data throttling, order routing nuances, paper trading environments, and practical latency handling.
What regulatory compliance is needed for automated trading software?
Depending on geography and use case, you may need to align with SEC/FINRA rules, MiFID II, audit logging requirements, data security controls, and broker-side certifications like IBKR’s API terms.
Should I hire a managed team or staff augmentation for trading platform development?
Managed teams work better for complex trading systems because they bring product discipline, DevOps, QA, and delivery ownership. Staff augmentation works only if you already have internal trading-system leadership.
What’s the ongoing monthly cost to run an algorithmic trading system on AWS?
Most teams spend $300–$2,500 per month depending on real-time data, database scale, logging volume, redundancy requirements, and whether you run multiple strategy instances in parallel.
Can I maintain the trading system myself after development or do I need the vendor forever?
If built cleanly with documentation, you can maintain it with your own developers. A good vendor structures the architecture to be handover-ready, not dependent on them.
What happens to my trading algorithms IP – who owns the code?
Your trading strategies, logic, and custom code should remain yours entirely. A proper contract assigns all IP to you, with the vendor only retaining rights to internal tooling they already owned before the project.
What size development team do I need for an automated trading system?
Most trading systems need a compact team of 3–6 people: a backend engineer with IBKR or FIX experience, a data engineer, a DevOps/cloud specialist, a QA automation engineer, and optionally a technical PM.
How long does it take to develop a trading bot with Interactive Brokers integration?
A basic IBKR-integrated trading bot takes 8–12 weeks, while a fully featured production-ready system with risk controls, dashboards, backtesting, and paper trading takes 4–7 months.
How much does it cost to build an algorithmic trading platform?
A custom algorithmic trading platform typically ranges from $80K to $450K depending on complexity, order types, latency requirements, data integrations, and security needs. Platforms that include multi-exchange routing, risk engines, and automated monitoring land on the higher end.
Red flags include vague answers about market data, promising fixed timelines for strategy performance, not mentioning risk controls, misusing backtesting vs live trading terminology, and lack of experience with broker APIs like IBKR or FIX.
MVP development typically requires a few months. Complex migrations take longer. Timeline depends on scope, integration complexity, and data migration requirements.
Yes, we accommodate various engagement lengths for dedicated developers. Project-based work handles shorter timelines for specific deliverables like migrations or performance optimization.
All code undergoes peer review, includes comprehensive tests, follows TypeScript strict mode, and meets ESLint standards. We implement CI/CD pipelines with automated testing before production deployment.
Yes, we regularly join ongoing projects. Initial assessment reviews architecture, identifies technical debt, and establishes development standards before beginning feature work.
We work with current Supabase platform including latest PostgreSQL versions, Edge Functions, Realtime, Storage API, and Auth. We stay current with platform evolution and beta features.
Daily async updates via Slack, weekly video calls for sprint planning, bi-weekly demos showing progress. Full code visibility through GitHub with detailed pull request documentation.
Yes, we execute NDAs before discovery phase. All code and intellectual property belongs to you. We maintain strict confidentiality and security protocols for proprietary systems.
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