1. Strategy Generation
A portfolio manager or analyst prompts Kamba’s AI Data Analyst to help build a custom trading strategy. The user provides high-level input such as:
- Risk Tolerance (e.g., conservative, balanced, aggressive)
- Asset Classes or Products (equities, ETFs, options, etc.)
- Technical Preferences (momentum, volatility filters, trend indicators)
The Analyst works interactively to:
- Select relevant datasets
- Interpret the inputs
- Assemble strategy logic with constraints
- Recommend strategy components (entry/exit signals, sizing rules, etc.)
Why This Is Revolutionary
Traditional strategy development can take weeks, involving data scientists, quant developers, and back-and-forth validation. Kamba’s AI Analyst compresses this into a few minutes and unlocks continuous experimentation.
Design and Refine Trading Strategies: Generate strategy prototypes from prompt inputs, then iterate in real time with human feedback.
Suggest Enhancements Based on Context: Analyze connected historical and real-time data to suggest improvements or complementary logic.
Incorporate Learnings From Previous Trades: Understand past performance, recommend course corrections, and evolve strategies.
Set Alerts for Strategy Performance and Signal Deviations: Track live execution and notify users of risks, alpha decay, or new signal opportunities.
Additional Usage: Beyond Strategy Generation
- Run Instant Backtests: Validate strategy performance across historical data without writing a single line of code.
- Evaluate New Datasets: Test the value of third-party data sources before purchasing or integrating.
- Generate Audit-Ready DQRs: Run automated Data Quality Reports to assess vendor reliability and coverage.
- Produce Real-Time Executive Reports: Create NAV summaries, compliance snapshots, and performance reports on demand.
- Enable Team Collaboration: Share strategies, test results, and reports securely with colleagues across functions—all inside Symphony.
2. Instant Backtesting & Dataset Validation
Problem: Backtesting signals or datasets requires coding, slows adoption, and blocks experimentation.
Solution: Analysts prompt the system to run backtests in seconds, validating value across portfolios or market conditions.
- Backtests with a Single Prompt
- Compare Datasets, Periods, or Signal Strength
- Visualize Results in Real-Time
Impact: Streamlines validation, boosts data ROI, and supports fast decision-making.
3. Data Quality Audits & Vendor Due Diligence
Problem: Evaluating data vendors is manual, inconsistent, and burdens procurement and legal teams.
Solution: The AI Analyst generates Data Quality Reports, compares vendors, and automates evaluation workflows.
- On-Demand Data Quality Reports (DQRs)
- Vendor Comparison and Documentation
- Audit-Ready Reports for Legal & Compliance
Impact: Faster onboarding, standardized evaluation, and reduced compliance risk.
4. Executive & Regulatory Reporting
Problem: NAV reports, compliance summaries, and performance views are slow and error-prone when done manually.
Solution: Generate real-time reports using AI and schedule delivery via Symphony messaging or dashboards.
- Smart Report Templates for Executives
- Dynamic Alerts on Risk or Compliance Shifts
- Secure Distribution to Stakeholders
Impact: Accurate, timely reports with no manual assembly or risk of error.
5. Cross-Team Collaboration & Workflow Automation
Problem: Analysts, data teams, and compliance operate in silos—wasting time and increasing risks.
Solution: Kamba enables shared access to data, reports, and workflows inside Symphony with compliant, traceable communication.
- Integrated AI + Collaboration in Symphony
- Unified Workflows Across Teams
- Custom Prompts, Reports & Alerts per Role
Impact: Aligned workflows, real-time collaboration, and accelerated decision-making across departments.