Xpertoptions @Xpert0ptions

@anujbaid96 @kirubaakaran @DhanHQ The data I used is not from Dhan

This tweet from @kirubaakaran intrigued me enough to push myself towards monetising overnight premium decay. So I gathered data for Nifty and Sensex over the last 2.5 years using @DhanHQ’s API. While studying the data, I realised that simply shorting options overnight wouldn’t be profitable. That led me to build STBT and positional systems designed specifically to capture overnight theta decay. One thing I was very clear about while building this system: irrespective of what the backtest showed, every overnight position would always be hedged. No overnight SLs, only defined risk. After spending a week articulating trade ideas, refining them, and aligning everything with my risk appetite, the consistency of the results genuinely surprised me. As an intraday trader, I was never comfortable carrying risk overnight. But when the data makes sense, the risk is managed, and the numbers back it up, perspectives start to change. The next step is forward testing. I’ll be deploying the system from next month and observing how closely the live results match the backtests. The whole point of this post is that as intraday trading gets tougher and tougher, many of us feel that the edge has disappeared. But in reality, I think the edge has simply shifted elsewhere. And today, with the help of AI, the time, skill, and effort required to test and execute ideas has become simpler than ever. I’m also attaching screenshots of my work. This system requires around ₹20L–30L in capital (hedge benefits not considered). Thank you @kirubaakaran for constantly teaching, inspiring, and pushing a non-coder like me to visualise and build my ideas. I’ve been a long-time follower and will continue to be one. Nandrigal🙏 #Algotrading #Riskmanagement #Nifty #Sensex #AI Disclaimer : For Educational purposes only.

keyboard_arrow_left Previous keyboard_arrow_right Next

Kirubakaran Rajendran @kirubaakaran

a month ago

The Sensex overnight straddle premium drop has exploded in 2026. From Jun to Oct 2025, the average decay from 1 DTE 3:30 to 0 DTE 9:16 was only 5 to 13%. From Feb 2026 onwards it jumped to 26 to 36%

7 6 75 34K 45

Waiting to catch an 8:50pm flight from 8:00pm and now the time is 9:25pm still there is no update from @allianceair .

@_green_fire Dm me

Good research starts with good data. Over the past few days, many of you reached out asking how to collect clean Indian market data for backtesting and systematic research. So we built something simple to solve that. A tool that pulls Options, Futures, Indices, and Equities data directly from the @DhanHQ API and converts it into clean, structured CSV files. No manual downloads. No messy cleaning. No coding required. The goal is simple : reduce time spent on data collection so you can focus more on analysing, testing, and improving your systems. Built for traders who want to make systematic research faster, cleaner, and more accessible. Access it here : xpertdata.lovable.app Drop your thoughts, doubts, suggestions in the comment section👇 #QuantTrading #SystematicTrading #AlgoTrading #MarketData #XpertOptions

@VineetTandon77 @AlgoTest_in These are two different strategies sir. One is nifty and the other is Sensex and no brokerage charges if we use Kotak api

Closed first set of Sensex STBT Positional setup. Margin : 12Lakhs Disclaimer : For Educational purpose only. #Algotrading #STBT #Senex #Xpert0ptions

Xpertoptions @Xpert0ptions

3 weeks ago

Started test running this strategy will be updating once a week or whenever i face any difficulties. Disclaimer : For Educational purposes only. #Algotrading #STBT #Nifty #Xpert0ptions

5 3 20 10K 24

@sni6pe No most of the strategies during the day and few only at the close

@bigbucksnifty ~6.5k

Started test running this strategy will be updating once a week or whenever i face any difficulties. Disclaimer : For Educational purposes only. #Algotrading #STBT #Nifty #Xpert0ptions

Xpertoptions @Xpert0ptions

4 weeks ago

This tweet from @kirubaakaran intrigued me enough to push myself towards monetising overnight premium decay. So I gathered data for Nifty and Sensex over the last 2.5 years using @DhanHQ’s API. While studying the data, I realised that simply shorting options overnight wouldn’t

keyboard_arrow_left Previous keyboard_arrow_right Next

25 9 112 46K 194

@_green_fire x.com/xpert0ptions/s…

Xpertoptions @Xpert0ptions

3 weeks ago

@_green_fire Dhan gives only rolling straddle data you have to convert before testing

0 0 1 216 1

@_green_fire Dhan gives only rolling straddle data you have to convert before testing

@bigbucksnifty Started testing the system from today only

@sni6pe Some legs are like that while some exit based on trailing

@sni6pe If conditions meets till 0dte will keep adding and removing positions till then

Day's PnL : -0.36% June ROI : 0.68% DD : 1.31% Review : System had wild MTM swings from morning. Nifty was too volatile with huge swings on both sides along with cheap premiums made the day go from +0.3% to -0.36%. See my MTM graph in action, powered by @AlgoTest_in robust trading engine. algotest.in/share/mtm-grap… Disclaimer : For Educational purposes only. #Algotrading #Expiry #Nifty #Xpert0ptions

@bigbucksnifty You need dhan api token

A good addition indeed! The reason why It didn’t occur to me is probably because I take an idea and split it across spectrum of parameters for example one of my baskets has no SL but hedged here I add multiple time slots according to the entry conditions, in some where SL is involved I make sure the % also varies in and across entries where core logic stays the same. But all said and done it is an essential parameter which I missed to add.

Before risking a single rupee on any trading strategy, ask yourself one question: Can I trust this backtest? Over the years, I’ve realized that most traders ask the wrong questions. They ask: ❌ “What’s the CAGR?” ❌ “How much profit did it make?” ❌ “Can I make ₹1 crore with this?” Instead, they should be asking: “Is this edge real?” To explain how I analyze a backtest, I’ll use one of my own NIFTY options baskets as an example. The basket trades only 2 days a week, executing the same core strategy on both 0DTE and 1DTE options, along with a small allocation to a few option buying strategies. It is deployed with ₹30 lakh capital and has been backtested for over 7 years (June 2019 – June 2026). The backtest includes: ✅ Brokerage ✅ STT ✅ Exchange Charges ✅ GST ✅ Stamp Duty ✅ Slippages Now here’s how I decide whether a strategy deserves my capital. 1. I don’t look at returns first. The first thing I check is: How long has the strategy survived? This basket has lived through: • The COVID crash • The 2020 recovery • The 2021 bull market • The 2022 bear market • Multiple volatility regimes (Low to High Vix scenarios) • Different expiry structures A strategy that survives different market environments deserves far more attention than one tested only on the last 12 months. 2. Sample size matters more than profit. A strategy with 200 trades tells me almost nothing. A strategy with thousands of trades tells a story. This basket has over 28,000 executed option trades across 7+ years of historical data. (first tested on 1min data and then validated with tick level) That’s important because the larger the sample, the harder it becomes for luck alone to explain the results. Large sample sizes don’t guarantee a profitable future, but they significantly increase confidence that the observed edge wasn’t created by a handful of lucky trades. 3. Costs are not optional. If your backtest ignores: • Brokerage • STT • GST • Stamp Duty • Exchange Charges • Slippage you’re not testing a strategy. You’re testing an idea. Many profitable systems disappear the moment realistic costs are included. 4. Winning percentage means very little. I’ve seen strategies with a 90% win rate eventually blow up. I’ve also seen strategies with a 45% win rate quietly compound capital for years. The real questions are: • Is expectancy positive? • Is the drawdown acceptable? • Are average winners larger than average losers? Those matter far more than the win rate. 5. The equity curve tells the truth. Two strategies can both make ₹35 lakh. One grows steadily. The other spends two years underwater before recovering. Most traders never look beyond the final profit number. Professionals do. 6. Every strategy has a reason to exist. This is the question I ask myself before trusting any backtest: Why should this edge exist? If I can’t explain why the market is rewarding the strategy, I assume the edge may disappear. A profitable backtest without a logical explanation deserves skepticism. 7. A long backtest reduces one risk—but not every risk. Testing over 7+ years (June 2019 – June 2026) gives me far more confidence than testing only the last year or two. But it still doesn’t guarantee the future. Markets evolve. Participants change. Execution changes. Even the best systematic strategies need monitoring, periodic validation, and sometimes retirement. A good trader accepts that reality. 8. When should you stop trading? Not after five losing trades. Not after one bad month. Not because someone on social media says the market has changed. You stop when the live performance consistently falls outside the historical behaviour that the backtest prepared you for. If your historical drawdown was 5% and you’re sitting at 10% with no similar precedent, that’s a signal to investigate. If your live expectancy turns negative over a statistically meaningful sample, that’s another. Stopping should be based on evidence—not emotion. 9. Don’t chase the highest CAGR. The best strategy isn’t the one with the biggest returns. It’s the one you can actually keep trading through difficult periods. Consistency beats excitement. 10. Would I invest in this strategy? Assume I manage a quantitative fund. Would I reject this strategy? No. Would I allocate capital? Yes—but progressively. I wouldn’t deploy the entire allocation on Day 1. Instead, I would: • Start with a fraction of the intended capital. • Compare live performance against the backtest over 1–2 months. • Scale up only if live execution continues to match the historical results. That’s how professional systematic trading is done. Backtests earn you the right to start trading. Only live performance earns you the right to scale. Final Thoughts A backtest doesn’t predict the future. It simply answers one question: “Has this idea demonstrated evidence of working under the conditions tested?” Nothing more. Nothing less. The job of a trader isn’t to find a strategy that never fails. It’s to find one with a believable edge, validate it honestly, trade it with discipline, and continuously verify that the edge still exists. That’s how trust in a trading system is earned—not through impressive equity curves, but through rigorous testing, realistic assumptions, and the humility to keep questioning your own strategy long after it starts making money. I’m attaching screenshots of this 7+ year backtest (June 2019 – June 2026) below. Don’t just look at the net profit—use the framework above to judge whether you would trust your strategy with your own capital. Inputs are always welcome🙂 Drop your doubts in the comment section👇 Disclaimer : For Educational purposes only. #Algotrading #AI #Nifty #Xpert0ptions

keyboard_arrow_left Previous keyboard_arrow_right Next

@_green_fire Hello! I used to 1 minute candle data first to validate the strategy and then used tick data for extra confirmation. And yes all changes are accounted for.