A Claude intelligence layer inside India’s precision trading platform
StockJarvis’s research team went from raw tick-level backtests to polished strategy insight about 60 to 70% faster. Built on Claude and live inside their Strategy Lab, Simulator and Algo modules within one month.
The customer challenge
StockJarvis is India’s precision trading infrastructure platform, purpose-built for NSE, BSE and MCX. It delivers tick-wise historical data, second-wise simulation, multi-leg basket backtesting, and ultra-high-speed algo execution with built-in safeguards such as freak-trade and bid-ask protection. Before the engagement, the research and product teams faced a critical bottleneck: strategy research and documentation remained heavily manual.
Manual review of complex outputs
Analysts spent significant time reviewing complex multi-leg backtest outputs, interpreting Greeks and payoff profiles, writing clear strategy summaries, and translating technical findings into actionable insights for internal use and for users.
Slow iteration cycles
Every strategy waited on a person to read the numbers and write them up, so the time from a backtest to a decision stretched.
Inconsistent documentation
Quality varied from analyst to analyst and strategy to strategy, which made the output harder to trust and compare.
Research output could not scale
In a market where accuracy at every tick and rapid validation of what-if scenarios are decisive, these inefficiencies constrained growth and user confidence.
What Claude does inside the platform
Woodfrog designed and deployed a production-grade, Claude-powered intelligence layer tightly integrated with StockJarvis’s Strategy Lab, Simulator and Algo modules. Implementation followed a structured, time-boxed approach over one full month, with clear phases, deliverables and progressive production readiness.
Secure ingestion from the tick-wise engine
Structured outputs only: performance metrics, multi-leg configurations, risk statistics and second-wise simulation results, without exposing raw market data.
Strategy summaries and risk analyses
Precise, professional strategy summaries, risk analyses and user-facing explanations generated automatically after every run.
Natural-language querying
Teams explore historical performance, drawdowns, win rates and adjustment logic interactively, in plain questions.
Documentation that feeds the workflow
Consistent documentation and insight reports that flow directly into the research-to-execution pipeline.
One month, three phases, in the order it happened
10 August to 10 September 2026. Each phase had its own deliverables and a production-readiness bar before the next began.
- 10 to 19 August 2026
Phase 1: Foundation and secure data layer
A reliable, secure data pipeline ready for higher-level intelligence.
- 20 to 29 August 2026
Phase 2: Core AI agent and analytics intelligence
A working intelligence layer that converts raw tick-level results into clear, actionable strategy insights inside the existing StockJarvis interface.
- 30 August to 10 September 2026
Phase 3: Production hardening, monitoring and full rollout
A fully production-grade Claude solution live inside StockJarvis and actively used as part of the standard research-to-execution pipeline.
Phase 1: Foundation and secure data layer
10 to 19 August 2026, 10 days.
Secure, authenticated data connectors
Extract structured backtest results, multi-leg basket data, Greeks, payoff profiles, win-rate statistics and drawdown metrics without exposing raw market data.
Domain-specific prompt frameworks
Designed and refined so Claude correctly interprets complex trading logic, entry and exit rules, and risk parameters.
Business process automation
The routine extraction and normalisation of backtest reports, which previously needed heavy manual effort, made automatic.
Baseline guardrails
Data privacy, access control and audit logging in place from the start.
Phase 2: Core AI agent and analytics intelligence
20 to 29 August 2026, 10 days.
Specialised research agents
Agents that understand multi-leg basket configurations, interpret second-wise simulation results and generate precise technical summaries, accelerating research and documentation.
Claude inside the Strategy Lab pipeline
After every backtest run the system produces structured analysis: a performance narrative, risk commentary and an explanation of the adjustment logic.
Financial-accuracy and compliance-tone guardrails
Schema validation, factual consistency checks and controlled language suitable for professional traders.
Dashboard automation
Claude maintains and refreshes internal research dashboards and strategy insight cards with the latest validated metrics.
Selective machine learning
Pattern recognition across historical strategy performance, recurring risk signatures and win-rate clusters, to enrich the generated insights.
Phase 3: Production hardening, monitoring and full rollout
30 August to 10 September 2026, 12 days.
Production deployment
Monitoring, alerting and fallback mechanisms so the Claude layer operates stably alongside the ultra-high-speed algo execution path.
End-to-end validation
Across NSE, BSE and MCX strategy types, confirming accuracy under real production loads.
Rollout with training
Internal research and product teams onboarded with structured training and feedback loops.
Ongoing operations
Logging, performance tracking and periodic prompt refinement established as standing processes.
Automation extended to daily research
Automatic generation of strategy documentation, risk summaries and insight reports.
Capabilities applied where they earned their place
Throughout the engagement, complementary capabilities were applied only where they delivered clear value.
AI agent development
Specialised research agents built for the platform’s own strategy structures.
Dashboard automation
Insight cards and research dashboards kept current by the layer itself.
Machine learning, selectively
Pattern detection across historical performance where it sharpened the insights.
Business process automation
End-to-end report and documentation workflows, from backtest to written insight.
Proof of production and adoption
The solution went live in production in early September 2026 and is actively used by the StockJarvis research and product teams as part of daily strategy workflows.
Live backtest results processed
The layer works on real runs, not a demo set.
Documentation generated for live strategies
For strategies running on the platform, every day.
Consistent internal usage
Part of the standard research-to-execution pipeline rather than a side tool.
Positive team feedback
Reduced friction between testing and actionable insight.
Quantified business impact
Figures as reported by the customer after go-live.
Have a research or documentation bottleneck of your own?
Tell our digital representative what is slowing you down, or book the fixed-fee Week 1 audit. Either way you get a written view of what we would build first.