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 for Traders, Analysts, and PMs
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: Backtesting the Strategy
- Select relevant historical data
- Run backtests on defined portfolios
- Visualize outcomes (return, Sharpe, drawdown, win rate)
- Store results in a transparent and auditable format