Custom Trading Platform Development: 2Moon
Custom trading platform development for 2Moon Capital: an event-driven, rules-based system that runs the firm's strategies through Interactive Brokers, validated in paper-trading environments first.
- Geography
- San Diego, California, United States
- Stage
- Growth-stage fintech
- Team
- 1 senior + 3 mid full-stack engineers, 1 product designer, 1 product owner, 1 QA
The situation
2Moon Capital trades systematically. The firm's edge lives in strategies it has already codified, and the operational question was how to run them reliably at scale without someone watching every execution. That is what brought the firm to custom trading platform development: volatile markets demand orchestration that does not freeze when conditions change, and manual intervention stops scaling past a handful of strategies.
The brief was an automated trading system that encapsulates those strategies as code and runs them across multiple accounts at once. The strategies are deterministic rules, not machine learning, and because real money moves through the execution path, the platform needed paper-trading environments alongside the live one so nothing unproven ever touches capital.
What we built
The build ran under Shipverra's AI product engineering service line with a team sized for the complexity: one senior and three mid-level full-stack engineers, a product designer, a product owner, and a QA engineer.
From signal to order without a human in the loop
The architecture is event-driven and serverless. Trading signals arrive from TradingView, the strategy engine evaluates each one against the active rule set, and order execution routes through Interactive Brokers. The strategy-specific business logic, contract selection, closing conditions, and position sizing live in a proprietary library as explicit code. There is no ML inference in the decision path, which means every order the system places can be traced back to a rule the firm wrote.
Rehearsal environments before real money
Three paper-trading environments run alongside the live-money execution path. A strategy can be validated under real market conditions, against real signals, before a single dollar of capital is committed to it. For a firm whose product is its strategies, that separation is the risk-management backbone of the whole platform.
One dashboard for every strategy and account
The platform manages multiple strategies against multiple accounts from a single operational dashboard, a shape that was part of the brief from the start.
The automated trading software development choices favor managed AWS services throughout: Python and Flask for the strategy engine, Docker for packaging, Lambda for serverless execution, SNS and SQS for messaging, DynamoDB and RDS for data, Step Functions for orchestration, Glue for data movement, and SES for transactional email. Next.js and Django power the operational dashboards on ECS behind ELB, with QuickSight for reporting.
Outcome
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Event-driven serverless trading platform in production, executing rules-based strategies
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Three paper-trading environments running alongside the real-money execution path
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Multi-strategy and multi-account management consolidated into one dashboard
The platform runs in production, executing the firm's rules-based strategies across strategies and accounts from one dashboard, with the three paper-trading environments operational alongside the live path. Trading performance belongs to the client, so this case reports the engineering outcome rather than P&L.
For a firm evaluating algorithmic trading software development, the shape of the result matters more than any single number: strategies that used to depend on manual operation now run as code, with a validation path in front of live capital and an execution path that runs across strategies and accounts without manual intervention.
"We enjoy working with Shipverra’s team."
Engagement line
Engagement FAQ
How much does it cost to build a custom trading platform?
It depends on how much of the pipeline you automate, from signal intake to order execution to reporting. The 2Moon Capital build was staffed with four full-stack engineers, a product designer, a product owner, and a QA engineer, covering the full path from TradingView signals to Interactive Brokers execution plus three paper-trading environments.
Can an automated trading system integrate with Interactive Brokers?
Yes. 2Moon Capital's platform routes all order execution through Interactive Brokers, with trading signals arriving from TradingView and a rules engine deciding what to place. The same event-driven pattern applies to other broker APIs.
How do you test trading strategies before risking real capital?
The platform runs three paper-trading environments alongside the real-money execution path. Strategies are validated under live market conditions, with real signals, before any capital is committed. Promotion to the live environment happens only after a strategy has proven itself on paper.
Is this platform AI or machine-learning driven?
No, and that is deliberate. The strategies are deterministic rules written by the firm, executed as code. When real money is on the line, a system whose every decision can be audited back to an explicit rule is worth more than a model you can only describe statistically.
What tech stack works for automated trading software development?
2Moon Capital's platform uses Python and Flask for the strategy engine, with AWS Lambda, SNS, SQS, Step Functions, DynamoDB, and RDS for event-driven serverless execution, and Next.js and Django dashboards for operations. Serverless suits trading workloads because capacity follows market activity instead of running idle.
Can one platform run multiple trading strategies across multiple accounts?
Yes. The platform was built for multi-strategy, multi-account management from the start, operated from a single dashboard.