What Is Drawdown & Why It Matters in Algo Trading

Most traders judge a strategy by how much it can make, yet what really decides whether they stick with it is how much it can lose along the way. That number has a name, the drawdown, and understanding it is one of the most useful things you can do before you let an algorithm trade on your behalf.
 

At ArihantPlus, we believe good automation begins with planning for the tough stretches, because every strategy, however well built, will go through them at some point.

A drawdown is the fall in the value of your account or strategy from its highest point, called the peak, to its lowest point before it climbs back, called the trough. It is usually expressed as a percentage of the peak, so it tells you how much ground you gave up before things turned around.
 

Say you start with ₹1,00,000 and a strategy grows it to ₹1,20,000. The market then turns, and your capital slips to ₹90,000 before it begins to recover. Your drawdown here is ₹30,000, or 25% of the ₹1,20,000 peak, even though you are only 10% below the amount you started with.
 

The largest such fall over a given period is called the maximum drawdown. It is the figure most worth knowing about any strategy, because it shows the worst stretch you would have had to sit through to stay invested.

Drawdowns matter because recovering from a loss always takes a bigger gain than the loss itself. If your capital falls by 25%, you need a gain of about 33% just to get back to where you were, and a 50% fall needs a full 100% gain to recover.
 

An algorithm follows its rules consistently, through winning streaks and losing streaks alike, which is exactly what makes it disciplined. That also means the time to decide how much downside you can handle is before the strategy goes live, while you are thinking calmly, rather than in the middle of a difficult week. 
 

This is why at ArihantPlus we encourage every trader to look at a strategy's drawdown before they look at anything else.
 

• Right sizing: Understanding how deep a strategy can fall helps you decide how much capital to give it, so that a bad phase stays within what you can comfortably afford.

   

• Realistic expectations: A backtest shows the deepest fall in past data, but live markets can produce deeper ones, so it is wise to leave a margin of safety.

   

• Clear limits: Deciding in advance the level at which you would pause and review a strategy means your plan makes the call, rather than fear or frustration.

ArihantPlus Algo is built on the belief that risk management comes first and everything else follows. Before you deploy a strategy, you can test it on historical data to understand how deep its falls have been, so you know what a tough phase might look like before you commit real capital.
 

Once a strategy is live, you stay in charge of it. You decide how much capital it uses and the limits it works within, and you can pause or stop it whenever you choose. Every algo order also runs through exchange approved systems with a unique Algo ID and the broker's risk checks, as SEBI's retail algo framework requires, so your automation stays transparent and within the guardrails you have set.

Returns show what a strategy might achieve in its good phases, while drawdown shows what it will ask of you in its difficult ones. Understanding it before you automate helps you choose strategies you can genuinely stick with, size your capital sensibly and trade with a clear head, and that disciplined, risk-first approach is exactly what ArihantPlus Algo is designed to support.
 

Investments in the securities market are subject to market risks. Read all related documents carefully before investing. The figures in this article are illustrative examples only and do not represent the past or expected performance of any strategy.