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BUSTED BY COSTS

₹25 momentum option buying with a strategy-level loss cap

Option buying · NIFTY weekly · tested 2019-02 to 2026-10

The claim

About ₹94.5k from Jan to mid-Jul 2025, max drawdown about ₹20k.

Our test, after costs

₹39,000

2019-02 to 2026-10 · 5 lots per leg, as in the video

With fills exactly at the trigger price we beat the claim, but with realistic fills and costs the same window made about ₹24k and the rules lose in 2024–26.

29WEAK

Algrow Reality Score

8/40
claim
11/30
profit
10/30
steady
Net, full test
₹39,000
Worst drawdown
−₹1.36 L
Trades
—
Win rate
—

Size: 5 lots per leg, as in the video.

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How our test compares with the video

Filling at the exact trigger price with no costs we got ₹1.17 L vs their ₹94.5k for Jan–Jul 2025; with next-minute fills, broker charges and slippage it fell to ₹23.7k. Entries are on momentum spikes, so buying exactly at the trigger price is not realistic.

Worth knowing: Full history 2019–26 made about ₹39k in total with a ₹1.36 L drawdown; 2024 about −₹14k, 2025 about +₹7k, 2026 about −₹55k.

The rules we tested

Every day at 09:16 pick the call and put whose premium is closest to ₹25 (5 lots each). Each leg is bought if it rises 10% from that price, with a 30% stop that trails ₹2 for every ₹5 gained, no re-entry. Whole strategy: ₹1,000 max loss, ₹10,000 max profit, lock ₹1,000 once it is reached. Exit 14:30.

Method and Reality Score

Full method: how we test

1-minute option data, stops checked on each minute's close and filled at the next open, broker charges incl. STT and taxes, slippage per order, lot size of each day.

  • 1-minute historical NSE/BSE option and futures data; each video's rules coded exactly as taught, with stated assumptions where the video left a gap.
  • Signals on a candle's close are filled at the next minute's open (unless the rules say otherwise); stops and targets are checked against each minute's high and low.
  • Real broker charges, STT, exchange fees, stamp duty and GST deducted on every order; lot size as it was on each trade date.
  • Results are per lot unless stated. No leverage, compounding or position sizing beyond the video's own rules.

Reality Score (0–100) = claim match (up to 40: matches 40, partly 25, fails after costs 8, not reproduced 0) + after-cost profit (up to 30: zero if it loses; more when total profit is large versus the worst drawdown) + steadiness (up to 30: share of profitable years, plus 5 if the latest year made money).

Questions people ask

Does the ₹25 momentum option buying with a strategy-level loss cap strategy work?

Not as taught, once real charges are included. With fills exactly at the trigger price we beat the claim, but with realistic fills and costs the same window made about ₹24k and the rules lose in 2024–26. Tested on NIFTY weekly, 2019-02 to 2026-10, after real costs. Reality Score 29/100.

What is the win rate of the ₹25 momentum option buying with a strategy-level loss cap strategy after costs?

We did not count a per-trade win rate for this one; the net result after real charges was ₹39,000 with a worst drawdown of −₹1.36 L.

How much did it make or lose in the backtest?

Our test: ₹39,000 after real costs. Over 2019-02 to 2026-10 (5 lots per leg, as in the video), the net after charges was ₹39,000 and the worst fall from a peak was −₹1.36 L.

What exact rules were tested?

Every day at 09:16 pick the call and put whose premium is closest to ₹25 (5 lots each). Each leg is bought if it rises 10% from that price, with a 30% stop that trails ₹2 for every ₹5 gained, no re-entry. Whole strategy: ₹1,000 max loss, ₹10,000 max profit, lock ₹1,000 once it is reached. Exit 14:30.

How was the ₹25 momentum option buying with a strategy-level loss cap backtest done?

On 1-minute exchange data since 2017, signals filled at the next minute's open, with real broker charges, STT, exchange fees, SEBI fee, stamp duty and GST, and the lot size of each date. Rules exactly as taught; gaps we had to fill are stated. Full method on our How we test page.

Run it yourself — coming soon

These rules use features our public backtester can't express exactly yet, so we won't pre-fill an approximation. Build your own idea in the meantime.

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