next-gen-algo-trading-bot

Analyze and evolve the overall architecture of next-gen-algo-trading-bot across ingestion, aggregation, strategy execution, storage, APIs, and UI layers. Use when making cross-module changes, debugging data flow breaks between modules, planning new features, or assessing tradeoffs and risk before refactors.

Implement and maintain broker connectivity for Zerodha in brokers/ including auth, session retry behavior, order/position APIs, and historical data sync helpers. Use when touching login flow, order execution, broker resilience, or market data backfill behavior.

Operate and extend the Typer CLI entrypoints in cli/main.py for engine start/stop and timeframe worker execution. Use when adding commands, wiring new modules into runtime startup, or debugging startup/config/env failures.

Manage and validate runtime configuration schemas and YAML files under config/ plus root config.yaml for the trading engine. Use when adding config fields, fixing env interpolation, or resolving mismatches between configured and supported timeframes/indicators.

Implement and maintain the core orchestration and compute pipeline in core/ including base interfaces, engine actors, aggregation, timeframe generation, and indicator generation. Use when changing runtime data flow, worker behavior, or strategy execution mechanics.

Maintain local operator dashboards in dashboard/ using Streamlit and Dash for candle inspection and indicator visualization. Use when improving manual observability, chart behavior, or live diagnostics from parquet-backed data.

Maintain live market data feed adapters in feeds/ with focus on Zerodha websocket integration and normalized tick payloads. Use when adding new feed providers, normalizing tick schema, or debugging live tick ingestion issues.

Maintain storage abstractions and backends in storage/ including RedisTimeSeries access patterns and parquet persistence. Use when changing candle/signal write semantics, optimizing Redis queries, or evolving data retention behavior.

Implement and refine strategy logic in strategies/ based on BaseStrategy contracts, currently centered on Supertrend + RSI signal generation. Use when adding new strategies, modifying signal criteria, or improving risk controls and dedup behavior.

Maintain and expand test coverage under tests/ with focus on indicator correctness, aggregation behavior, and reliable separation of unit vs integration test paths. Use when adding modules, fixing regressions, or improving confidence in data/strategy pipelines.

Maintain the FastAPI service in tradingview_dashboard/backend for candles, indicators, and latest timestamp endpoints backed by RedisTimeSeries. Use when changing API contracts, performance, indicator response shape, or Redis query behavior for frontend consumers.

Maintain the Vue + lightweight-charts UI in tradingview_dashboard/frontend including multi-timeframe chart rendering, indicator overlays, and API polling. Use when improving chart UX, syncing behavior, performance, or frontend/backend data contracts.