In the world of positional trading, conventional wisdom dictates that a stop loss is mandatory to protect capital. At QuantTau Research, we have developed a systematic approach that challenges this methodology. Our defined rule framework, powered by the Guha™ Algorithm, operates on a different mathematical and structural paradigm. Through rigorous forward testing since 2021, we have demonstrated that our specific trading strategy does not require a traditional stop loss to achieve consistent profitability.
The Foundation: Stock Selection
The foundation of our approach lies in the stock selection process. The Guha™ Algorithm is engineered to identify momentum stocks already in established, strong uptrends. The core philosophy is straightforward: when a stock is in a major upward move, any downward price action is typically a minor pullback rather than a trend reversal. Because we buy into inherent strength, the structural integrity of the trend acts as the primary defense. This high-probability selection process is the primary reason the model achieves a success rate of over 90% in hitting profitable targets.
Entry and capital allocation
When entering a trade, strict capital allocation and clear exit rules are critical. We begin by allocating exactly 25% of the intended capital for any stock selected by the algorithm. For profit booking, we target the next Pivot or Fibonacci resistance level, typically aiming for a maximum gain of 3% to 6%. A key observation from our testing: never use round numbers for exit orders. We always place exit orders just a few points below the exact resistance level to ensure fills. Traders also maintain discretion to exit early and secure profits if broader market conditions warrant it.
Averaging down: the three-step framework
Rather than a static stop loss that crystallises a loss during a routine pullback, our framework uses a structured averaging-down strategy. This scale-in approach takes advantage of temporary dips through three strict rules.
Step 1
4% decline from entry — deploy another 25%
If the stock drops 4% from the initial purchase price, or reaches the nearest support level around that fall, deploy another 25% of capital. Based on historical testing, there is less than a 50% chance that a selected momentum stock will drop to this level. The original target remains unchanged.
Step 2
6% decline from entry — deploy remaining 50%
If the stock declines 6% from the initial entry price, deploy the remaining 50% of allocated capital. The probability of selected stocks dropping this far is less than 25%. At this stage the trailing target rule activates — the original target is abandoned and a new target is set based on the next immediate resistance level from the new average price.
Step 3 — Contingency
12% decline from entry — maximum capital deployment
This is the absolute worst-case scenario, occurring less than 5% of the time. At a 12% decline from initial entry, maximum capital is allocated to the trade. The trailing target rule applies. Our forward testing shows that averaging at this level has consistently produced a profitable exit on the subsequent price bounce.
One important caveat: news events affecting the stock, its sector, or the broader index are not controllable. Their impact on a position once purchased cannot be managed by any framework, and this is acknowledged as an inherent limitation.
A worked example
The following illustrates how the framework operates across each step.
| Event | Price | Action | Shares held | Avg. price | Target |
|---|---|---|---|---|---|
| Initial entry | ₹100 | Buy 5 shares (25% capital) | 5 | ₹100 | ₹107 |
| 4% drop | ₹96 | Buy 5 more shares (25% capital) | 10 | ₹98 | ₹107 (unchanged) |
| 6% drop from entry | ₹94 | Buy 10 more shares (50% capital) | 20 | ₹96 | Trailing target from ₹96 average |
| 12% drop from entry (rare) | ₹88 | Maximum quantity purchase | Maximised | Reduced | Trailing target from new average |
Why it works
By buying established momentum and systematically lowering average cost during low-probability pullbacks, market volatility becomes an advantage rather than a threat.
The Guha™ Algorithm framework relies on the synergy between superior stock selection and mathematical scaling. Structured patience and precise capital management allow the strategy to thrive without a stop loss — not through ignoring risk, but through selecting stocks where the probability structure of the trade works in the investor's favour before a single share is purchased.
Quick reference: the complete rule set
This article describes QuantTau Research's proprietary trading framework for informational purposes only. It does not constitute investment advice. Past performance and forward-testing results do not guarantee future outcomes. All trading decisions remain the full responsibility of the individual.