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Trading Automation

Supertrend Optimizer & Zerodha Backtester

A Streamlit app for parallel Supertrend backtesting and parameter optimization on Indian equities.

Large-scale ATR-by-multiplier grid optimization on Zerodha Kite history, with equity curves, heatmaps, and 3D surfaces.

What makes it serious
  • Parallel ATR-by-multiplier grid optimization via multiprocessing
  • Zerodha Kite historical data on 5m/15m timeframes
  • Trade lists, equity curves, and parameter heatmaps
  • 3D surface analytics to judge robustness

A local Streamlit application for backtesting and optimizing the Supertrend indicator on Indian-market data. It pulls Zerodha Kite historical candles, then runs large-scale parallel parameter optimization across an ATR-length by multiplier grid on 5-minute and 15-minute timeframes, using multiprocessing to make the sweep tractable. Results come back as trade lists, equity curves, a parameter heatmap, and a 3D surface so you can see how robust a setting is rather than cherry-picking one lucky combination.

It is quant tooling for the practical trader: fast, visual, and honest about how sensitive a strategy is to its parameters. Seeing an entire parameter surface at once discourages the common mistake of trusting a single lucky ATR-and-multiplier combination, and the multiprocessing sweep makes exploring that surface fast enough to actually do.

Backtesting tool for research only; past performance does not guarantee future results and nothing here is financial advice.

Stack
PythonStreamlitPandasNumPyMultiprocessingPlotlyZerodha Kite API

Automation is infrastructure, not financial advice. No profit guarantees. Every live system needs staged testing, risk limits, and owner approval.

#supertrend#backtesting#optimization#zerodha#streamlit#quant