What is algorithmic trading and how is it different from manual trading?

What is algorithmic trading and how is it different from manual trading?

What is Manual Trading?

Manual trading is a trading process that involves human decision-making for entering and exiting trades. Computer software is used by manual traders to unify information. In many cases, traders may also set indicators to alert them automatically of potential trading opportunities. Such trading signal generating software solutions are popular in the market globally.

However, when it comes to manual trading, the need for human input to execute the trade is almost always the case.

Manual Trading Strategies

Any strategy where a human in involved in placing buy and sell orders is a manual trading strategy. Buy-and-hold is a popular strategy where an investor buys a company's stock believing that over the long-term it will appreciate in value. In such cases, there is only a small number of trades happening. Since not very frequent, they are most often than not done manually by the trader themselves whenever they feel it is the right opportunity to do so. Here the investor could sell based when the stock value hits a predetermined price. Some investors would  technical indicator or fundamental indicator shifts to indicate it is time to exit.

Traders are required to possess all of these in-order to succeed

What is Algorithmic Trading?

Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. The trade, in theory, can generate profits at a speed and frequency that is impossible for a human trader.

Also called as algo-trading, black-box trading, or automated trading, a computer program that follows a defined set of instructions (an algorithm) to place a trade is used. Hence, no human input is needed to execute on the trade, unlike manual trading.

How algorithmic trading works?

To make algorithmic trading work, computer systems are put in place where various strategies are tested and evaluated and one checks the impact of those on profitability. Systems are then configured to deploy multiple strategies one any given point in time ensuring that without any human intervention and manual inputs from time-to-time, quick and accurate trades can happen automatically.

Why use algorithmic trading?

If you are new to the stock market, being a trader can be overwhelming. It is a long learning process where you need to invest a lot of time in research and continuous follow up with market conditions, and the various stocks that are impacted and in-between all this take decision to execute a trade.

Executing a trade would involve picking a particular stock, entering the quantity you want and the hitting the Buy / Sell button. This requires speed and accuracy and is not something that can be delayed as in a few seconds the variation is price could be considerable and may eventually make the difference between potential profit / loss.

Much has been written about the fat finger error. It is a human error caused by pressing the wrong key when using a computer to input data and is one of the most common errors traders make while manually trading.

Majority of active retail investors end up losing money due to various human biases that come up knowingly or unknowingly in one's decision making.

Some of the most popular human biases while manually trading

Algorithmic trading removes common human errors like fat finger error and the various human biases that may occur. Instead, it is fully system driven with zero human emotions or manual processes happening during the trading process.

Benefits of Algorithmic trading compared to Manual trading

Comparing Benefits of Algorithmic trading vs Manual trading

What are algorithmic trading strategies?

An identified opportunity that is profitable in terms of improved earnings or cost reduction is required for any algorithmic trading strategy.

Some of the common trading strategies used in algo-trading are the following:

Trend-following Strategies

The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements, and related technical indicators. These are the easiest and simplest strategies to implement through algorithmic trading because these strategies do not involve making any predictions or price forecasts. Based on the occurrence of desirable trends, the trades are initiated. These are easy and straightforward to implement through algorithms as it does not involve the complexities of predictive analysis. One of the popular trend-following strategy is using 50-day and 200-day moving averages.

Index Fund Rebalancing

Index funds have defined periods of rebalancing to bring their holdings to par with their respective benchmark indices. Depending on the number of stocks in the index fund just before index fund rebalancing, this creates profitable opportunities for algo-traders who capitalise on expected trades that offer 20 - 80 basis points profits. Algorithmic trading systems initiates such trades for timely execution and the best prices.

Arbitrage Opportunities

Arbitrage (risk-free profit) is  simply the act of buying a product in one market and simultaneously selling it in another for a higher price at some later time. One is profiting from the temporary difference in prices. Since price differentials exists from time to time, this can be replicated for stocks vs. futures instruments. Thus, implementing an algorithm to identify such price differentials and placing the orders efficiently allows profitable opportunities.

Mathematical Model-based Strategies

Proven mathematical models, like the delta-neutral trading strategy, allow trading on a combination of options and the underlying security.
Delta neutral is a portfolio strategy consisting of multiple positions with offsetting positive and negative deltas so that the overall delta of the assets in question totals zero.

Trading Range (Mean Reversion)

Mean reversion strategy is based on the concept that the high and low prices of an asset are a temporary phenomenon that revert to their mean value (average value) periodically. Identifying and defining a price range and implementing an algorithm based on it allows trades to be placed automatically when the price of an asset breaks in and out of its defined range.

Time Weighted Average Price (TWAP)

Breaking up a large order and releasing dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time is called the time-weighted average price strategy. The aim is to execute the order close to the average price between the start and end times thereby minimising market impact.

Volume-weighted Average Price (VWAP)

Breaking up a large order and releasing dynamically determined smaller chunks of the order to the market using stock-specific historical volume profiles is called the volume-weighted average price strategy. The aim of the strategy is to execute the order close to the volume-weighted average price (VWAP).

Percentage of Volume (POV)

Until the trade order is fully filled, this algorithm continues sending partial orders according to the defined participation ratio and according to the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate when the stock price reaches user-defined levels.

Implementation Shortfall

Minimising the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution is called the implementation shortfall strategy. The strategy will increase the targeted participation rate when the stock price moves favourably and decrease it when the stock price moves adversely.

Beyond the Usual Trading Algorithms

Additionally there are few algorithms that consider various other external factors and parameters including happenings globally, evaluating impact of the capital markets and buying or selling stocks accordingly.

Summary on Algorithmic trading in India

While retail participation using algo-trading in India is still in infancy related to some more mature markets. Data suggests that between April, 2020 and January, 2021 about ~11 million new demat accounts were opened during this period. The initial excitement of investing in the stock market was possible due to the work from home option available to the work force in the country.

However, once the market corrects itself and work from home rules eases and people are back to working from offices, generating profits and creating wealth through manual trading and investing can become challenging during market hours.

Thus, there is huge opportunity for bringing algorithmic trading and automation for the retail investors in India. It has it's own pros and cons and it is upto the investor to pick hat works best for them. It is a great time to be an investor in the Indian stock market with various innovative products and offerings available to them.

At TRDR, we believe that life is too short for trading terminals.

If you want to learn more about how TRDR provides the investor with an Automated Investing Platform, write to us at care@trdr.money or WhatsApp us: +91 93410 60007.

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