Why Algo Trading is a Great Option for Busy Investors

As a working professional trying to balance a career, personal life, and finances, actively trading stocks can be incredibly taxing. The primary reason is that manual trading requires constant attention to price charts, news updates, and technical indicators. The fact that the markets are so dynamic means there is a high chance of missing the correct entry or exit point if we are not focused on taking the trades, which can ultimately affect the returns. 

The truth is that most people simply don’t have that kind of time. Most of the time, we are in meetings, commuting, or handling family commitments, which makes it nearly impossible to monitor markets all day. Even if we try trading before or after work, mental fatigue often leads to poor decision-making.

Some of the major issues faced by part-time traders are:

• Making impulsive trades without proper analysis
• Holding on to losing positions too long
• Missing out on opportunities due to delayed action
• Getting overwhelmed by the amount of data to track

It is well known that successful investing requires consistency and timing. This is where algorithmic trading offers the perfect solution, where the investors can run automatic strategies without requiring constant presence.

How Algo Trading Solves the Time Problem

Algorithmic trading, or algo trading, offers a perfect solution to investors who want to invest in the market without being tied to a screen all day. Algo trading allows investors to automate their investing strategy. Investors can define a set of rules, such as which stock to buy, when to buy, when to sell, and what risk levels to maintain, and then let the algorithm do the rest. Once the algo is deployed, it executes trades on your behalf based on logic, not emotions.

Algo trading is hailed as a game-changer for busy professionals. Some of the reasons many busy investors are shifting towards algorithmic trading are the following:

• No constant monitoring needed: The investors don’t have to sit in front of charts all day. The algorithm tracks the market 24/7 and automatically executes trades based on market conditions and established rules. 
• Eliminates emotional decision-making: This is extremely helpful. As investors, we are bound to have emotions such as greed and ego, but when we shift to algorithmic trading, there is no emotional decision-making. Because everything runs based on predefined logic, there's no panic-selling or impulsive buying. The investment strategy is followed consistently.
• Efficient execution: Algorithms react instantly to market movements and on different assets. The kind of speed and precision achieved by algorithmic trading is hard to match manually.
• Scalability: The best part about algorithmic trading is that we can run multiple strategies across different markets or instruments. This is almost impossible to do manually.

In short, algorithmic trading bridges the gap between market opportunity and time availability. Whether you're in a client meeting or on vacation, your strategy keeps running.

How Does Algo Trading Work?

Algo trading involves writing a set of rules or instructions that a computer can follow to place trades on your behalf. These are objective rules based on data analysis, and the algorithm can run automatically in the background. Here are the steps required in algorithmic trading:

1. Define Your Strategy
   The first step is to clearly define the investing logic. The logic means that there should be clear and objective rules for the selection of stock, entry rule, exit rule and position sizing. This could be as simple as: “Buy a stock if its 10-day moving average crosses above the 30-day average, and sell if it drops below.” You can make more advanced and complex rules as well. These rules can be based on technical indicators, price movements, volume changes, or even news events.
2. Code the Logic or Use a Platform
   Once the rules are defined, the next step is to code the logic using programming languages like Python. However, if you don't have a coding background, there are also platforms that allow you to create strategies without coding, using drag-and-drop interfaces or pre-built templates.
3. Backtest the Strategy
   Many investors may want to gauge how their strategy has performed in the past. To do this, before risking real money, traders can test the strategy on historical data to check how it would have performed in the past. This step helps filter out weak or risky ideas.
4. Deploy the Algorithm
   Once the strategy has been vetted and we are confident in the strategy, we can deploy it on a live account. The algorithm then watches the market in real-time, placing buy or sell orders the moment the conditions are met.
5. Monitor and Refine
   While algo trading doesn’t need constant supervision, it’s still smart to check performance from time to time. This helps us to adjust the rules if market conditions change. We should also check for fundamental changes or major international events and modify our strategy accordingly. 

In essence, algorithmic trading turns a trading strategy into an automated system that can act instantly and emotionlessly, even when no one is watching the trading screen.

Types of Algo Trading Strategies

Not all algorithmic strategies are created equally. Some require frequent checking, monitoring and active oversight, while others are better suited for those with limited time. If you're a busy investor or working professional, here are some popular algo trading strategies that balance performance with low-maintenance effort:

• Passive index tracking 

Passive index tracking is considered the most beginner-friendly strategy. The idea is to track the market index, such as Nifty 50 or Sensex, and balance the portfolio if there are any changes in the index. This strategy mimics the returns of the index and is ideal for long-term, hands-off investing. 

As a busy investor, this can be a perfect strategy because it requires minimal intervention. Moreover, there is reduced risk of emotional trading, and it usually offers steady, market-linked growth over time.

• Momentum trading 

Momentum investment strategies focus on buying assets that are rising and have good momentum. The basic tenet is that momentum stocks continue to increase, and hence the strategy is to ride the winners. Many times, 52-week high or all-time high stocks are chosen for this strategy. The exit is also rule-based, wherein those stocks are sold that are falling. Again, the idea is that trends tend to persist. The algorithm scans for strong price movements, high volume, or breakout patterns, and enters trades accordingly.

As a working professional, this can be a good strategy because it captures short- to mid-term opportunities automatically. There is also no need to track intraday charts, and it can be run with daily or weekly scans.

• Mean reversion trading 

Mean reversion strategy assumes that prices eventually return to their average or "mean." So, if a stock is significantly above or below its average price, the algorithm will trade expecting a reversal. As an investor, the strategy aims to purchase those stocks which are fundamentally strong but are fairly below the average. 

For busy investors, this can be the go-to strategy because it operates effectively within defined rules and lends itself to backtesting. The strategy also has lower trade frequency than momentum trading, and it is excellent for part-time traders who want fewer but high-probability trades. It is important to note that this strategy has high risk and reward since we are buying those stocks which are currently significantly below their historical average.

• Event-driven trading 

This is a slightly complex strategy, and the algorithm trades based on specific events such as earnings announcements, interest rate decisions, or company news. The plan aims to capture short-term volatility and can be customised according to the type of event. This strategy is typically employed by investors who are focused on specific dates or triggers. This kind of strategy requires higher coding knowledge, but can offer better returns as well. 

• Machine learning and artificial intelligence-based trading 

Finally, in today’s age, tech-savvy investors are also deploying advanced strategies that use algorithms which learn from past data and improve over time. These kinds of algorithms can identify patterns that are too complex for humans to spot and adapt to changing market conditions. While it may be very tough to trade manually, even such complex strategies can be fully automated with self-learning capabilities. The strategy can be run on a massive scale and can manage and adjust multiple positions without supervision. But remember that these are usually suited for tech-savvy professionals who want to scale.