vectorbt-expert

Detailed reference for each topic is in rules/ : Production-ready scripts with realistic fees, NIFTY benchmark, comparison table, and plain-language report: - Python with vectorbt, pandas, numpy, plotly - Data sources: OpenAlgo (Indian markets), DuckDB (direct database), yfinance (US/Global), CCXT (Crypto), custom providers - DuckDB support: supports both custom DuckDB and OpenAlgo Historify format - API keys loaded from single root .env via python-dotenv + find_dotenv() — never hardcode keys - Technical indicators: TA-Lib (ALWAYS - never use VectorBT built-in indicators) - Specialty indicators: openalgo.ta for Supertrend, Donchian, Ichimoku, HMA, KAMA, ALMA, ZLEMA, VWMA - Signal cleaning: openalgo.ta for exrem, crossover, crossunder, flip - Fee model: Indian market standard (STT + statutory charges + Rs 20/order) - Benchmark: NIFTY 50 via OpenAlgo ( NSE_INDEX ) by default - Charts: Plotly with template="plotly_dark" - Environment variables loaded from single .env at project root via find_dotenv() (walks up from script dir) - Scripts go in backtesting/{strategy_name}/ directories (created on-demand, not pre-created) - Never use icons/emojis in code or logger output - ALWAYS use TA-Lib for ALL technical indicators (EMA, SMA, RSI, MACD, BBANDS, ATR, ADX, STDDEV, MOM). NEVER use vbt.MA.run() , vbt.RSI.run() , or any VectorBT built-in indicator. - Use OpenAlgo ta for indicators...