SuperTrend Weekly Options Strategy
Budget: ₹1,500 – ₹12,500 INR
I actively trade NSE and BSE weekly expiry options and now want to formalise a clear, rules-based system that uses the SuperTrend indicator as the sole trigger for my buy-and-sell decisions. My own experience is advanced, so I already understand option Greeks, IV behaviour, and position management; what I need is rigorous strategy development rather than generic coaching.
Here is what I want to achieve:
• A complete set of entry, exit and re-entry rules that translate SuperTrend signals into directional option trades, taking into account intraday margin changes and the unique Thursday-expiry dynamics of India’s weekly contracts.
• Robust risk-management logic covering position sizing, stop-loss placement, and adjustment criteria for rapid moves.
• A defensible back-test over at least three years of historical data on NIFTY, BANKNIFTY and the most liquid stock weeklies, summarising key metrics such as win rate, expectancy, drawdown and capital utilisation.
• Clean, well-commented code (Python, Pine Script or another mutually agreed environment) so I can reproduce the results on my own machine. A concise write-up that explains parameters, assumptions and data sources must accompany the code.
I will consider the strategy delivered when it replicates your stated performance on my independent data set and the trade logs align for a randomly chosen month.
Here is what I want to achieve:
• A complete set of entry, exit and re-entry rules that translate SuperTrend signals into directional option trades, taking into account intraday margin changes and the unique Thursday-expiry dynamics of India’s weekly contracts.
• Robust risk-management logic covering position sizing, stop-loss placement, and adjustment criteria for rapid moves.
• A defensible back-test over at least three years of historical data on NIFTY, BANKNIFTY and the most liquid stock weeklies, summarising key metrics such as win rate, expectancy, drawdown and capital utilisation.
• Clean, well-commented code (Python, Pine Script or another mutually agreed environment) so I can reproduce the results on my own machine. A concise write-up that explains parameters, assumptions and data sources must accompany the code.
I will consider the strategy delivered when it replicates your stated performance on my independent data set and the trade logs align for a randomly chosen month.