Manual Trading

What Is Manual Trading?

Manual trading is a trading process that involves human decision-making for entering and exiting trades.

This is in contrast to automated trading which employs computer programs that originate trades based on algorithmic or human-instructed criteria.

Understanding Manual Trading

Manual traders often employ computer programs in order to consolidate information. In some cases, they may also set automated indicators to alert them of potential trading opportunities. However, in all cases, human input is required to authorize trades when manual trading.

There is an ongoing debate as to whether automated trading is advisable or not. Some traders believe that manual trading is superior since human judgment is required to gauge market trends and control risk. They feel that the proper place for automation is in monitoring data and consolidating it for human interpretation.

Proponents of automated trading argue that this method is superior since it takes irrational human behavior out of the equation. Automated trading is also based on rules and statistics, whereas manual trading may be based more on emotion. This doesn't always have to be the case though, as a manual trader can base their strategy on sound logic, statistics, and discipline.

Manual vs. Automated Trading

Automated trading systems—also referred to as mechanical trading systems, algorithmic trading, automated trading, or system trading—allow traders to establish specific rules for both trade entries and exits that, once programmed, can be automatically executed via a computer.

Automated systems still need to be built by a human, which means they are still prone to human error, except the errors occur in the programming code and not in the execution of the code. Automated trading typically reduces the number of errors, such as fat finger mistakes which are more prevalent in manual trading, yet errors still occur in programming or implementing an automated system.

Time will tell if computers are superior to humans in allocating capital. In the interim, many investors are more comfortable with a human executing buy and sell orders manually. Flash crashes are a painful reminder that turning over investment decisions to computers is not without risks. The most obvious example is the flash crash of May 2010. In a matter of minutes, popular indexes including S&P 500, Dow Jones Industrial Average, and Nasdaq Composite collapsed 5-6% and rebounded very quickly. During the flash crash, trades for certain individual stocks executed for a penny or less, while others traded as high as $100,000, before prices returned to normal.

In the wake of this episode, traders and regulators alike blamed computer-automated trading systems set up to execute rapid-fire buy and sell orders. Since then, investors and money managers have not forgotten the destabilizing market potential of computer-driven investment strategies.

Manual Trading Strategies

Any strategy that involves a human placing buy and sell orders is a manual trading strategy. Some popular styles of trading involve buy-and-hold. This is when an investor purchases investments they believe will appreciate in value over the long-term. Since trades are infrequent, they are often done manually when an opportunity arises. The investor may sell at a predetermined price, or when a technical indicator or fundamental indicator shifts to indicate it is time to exit.

Swing trading can be manual or automated and involves placing trades that last a few days to a few months. The general idea is to capture the bulk of an expected price move, during a trend or price range, and then get out and move on to the next opportunity.

Day trading can be manual or automated and involves making multiple transactions per day, taking advantage of intraday price movements.

Manual Trading Example

Jim is a trend trader. He looks for opportunities to enter strongly trending stocks around the 100-day moving average (MA), and then also uses the 100-day MA as his exit. This requires manual trading since there is some subjectivity involved when he enters a trade. Subjectivity doesn't translate into an automated system very well.

For example, Jim often likes to see a rising stock drop below the 100-day MA, but only slightly, and then rise back above triggering his long trade. Once he is in the trade, he exits when the price crosses back below the 100-day. The price also can't be moving sideways. It needs to be in an uptrend. This helps avoid the whipsaw scenarios which occur when the price moves back and forth across the MA as it moves sideways.

In 2017, Netflix (NFLX) was rising. It dropped briefly below the 100-day, creating a bit of space below the line, and then moved back above. Jim bought. Toward the end of that year, Jim sold when the price crossed back below the 100-day.

Soon after he sold, the price found support at the 100-day and then started to rise off of it. Jim bought again. This trade lasted most of the year until the price dropped below the 100-day again. Jim sold his position.

Not long after, the price, still in an uptrend, crossed back above the MA and Jim went long. He had to sell a few days later as Netflix stock continued dropping. By this point, the uptrend was in question, and the price was whipsawing the MA.

This is a situation Jim likes to avoid and therefore opted not to trade any of the crossovers that occurred in the rest of 2018 and 2019. This type of subjective decision-making is very hard to program into a computer. Therefore, Jim likes to place all his trades manually.