VectorBT Backtesting Expert Skill

Environment

Python with vectorbt, pandas, numpy, plotly
Data sources: OpenAlgo (Indian markets), yfinance (US/Global), CCXT (Crypto), custom providers
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: Zerodha brokerage calculator values with 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