Market context
Combined pool metadata and oracle prices, with volatility and technical-indicator calculations for studying changing conditions.
Liquidity Position Monitoring & Strategy Prototypes
A family of research tools for exploring pool conditions, position ranges and fee estimates without presenting simulated actions as live trades.
My role
Implemented market-data collection, strategy calculations, monitoring interfaces and execution experiments across the related prototypes.
The problem
Liquidity providers need to understand changing market conditions and position health across fragmented pool data, price feeds and strategy parameters.
The approach
Developed separate data, strategy and monitoring prototypes that combine pool information with price signals, make range and threshold decisions inspectable, and present position context through dashboards and alerts.
What it enables
Created concrete monitoring and research workflows for examining liquidity ranges, volatility and estimated fees. The work separates external market data from demonstration positions and simulated execution; no trading returns are claimed.
The work,
and its boundaries.
Inspectable strategies
Expressed position thresholds, price ranges and budget conversions as configurable research behavior.
Position visibility
Built dashboard views for tracked ranges and fee estimates, alongside an on-chain position-monitoring experiment with health and range alerts.
Operator feedback
Implemented notification paths and status information so relevant position changes can be surfaced outside the analysis loop.
Scope
Separate prototypes, not one integrated live trading platform. Several execution paths are mocked or unimplemented, and fee/PnL displays include estimates or demonstration state. No profit, backtest-performance or custody-security claims.
Technology
- Python
- TypeScript
- Solana
- Orca
- Pyth
- pandas
- React
- Rich
- Telegram
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