Algorithmic trading, or algo-trading as it is often called, is a method of executing orders using pre-programmed trading instructions accounting for variables such as time, price, and volume. This trading system uses complex formulas combined with mathematical models and human oversight to make decisions. Designed to make trading more systematic and alleviate the burden on traders, Algorithmic trading is specific to individual investor's needs and investment strategies.

Algorithmic Trading possesses unique attributes:

• Efficiency: Algorithmic trading is swift and precise, able to execute orders at a speed far beyond human capability. It assesses multiple market conditions in real-time, making it incredibly efficient.

• Automatic: The central premise of algorithmic trading is automation. Once the system is programmed with a specific investment strategy, the trading process becomes automated, leaving little or no need for human intervention.

• Accuracy: There is a reduced risk of manual errors in order placement as algo-trading systems follow a prescribed set of instructions.

• Minimizing Emotion: By automated trading, algorithmic trading mitigates the influences of human emotions on trading activities.

Algorithmic trading is widely adopted in various sectors for its speed, precision, and ability to minimize emotion and human error from the trading process.

Effective implementation of an Algorithmic Trading system requires methodical planning. It begins with a clear understanding of one's trading strategy, which is in turn programmed into the algorithm. For effective execution, comprehensive backtesting on the designed algorithm is important to ensure the strategy works in various market scenarios.

Additionally, an efficient hardware and software setup to run the system is crucial. Selecting the right broker who allows for algo-trading and provides API support for the trading system to place trades is also essential. Security features should be in place to protect trading data and access.

Maintenance and regular update of the algorithm based on changing market scenarios is a key aspect of using algo-trading systems to stay competitive. Adapting the algorithm as per the changing needs of the trading strategy and monitoring the system regularly ensures successful algorithmic trading.

There are several inherent benefits of Algorithmic Trading including:

• Precision: Algorithmic systems execute orders at the best possible prices. They are precise and instantaneous, which reduces the risk of manual errors in order placement.

• Speed: Algo-trading is significantly faster than manual trading. The system can execute trades in fractions of a second, offering an advantage over manual trading.

• Reduced Costs: Since algo-trading is fully automated, it eliminates the costs associated with human traders.

• Backtesting: Algorithmic trading allows for backtesting, i.e., testing trading strategies using historical and real-time data to ascertain their feasibility. This process helps to fine-tune strategies before application in actual trading scenarios.

• Minimizing Emotion: The involvement of emotion can lead to poor decision-making in trading. Algo-trading limits emotional trading as trades are executed based on pre-set parameters.

• Increased Market Liquidity: With the rapid and frequent trades enabled by algorithmic trading, there is an increase in market liquidity.

However, like any system, Algorithmic Trading is not without its disadvantages:

• System Failure: If the internet connection is disrupted or if there's a glitch in the algorithm, the system might not function as expected, leading to possible losses.

• Over-Optimization: A common pitfall with Algorithmic Trading is the temptation to over-optimize an investment strategy. While backtesting strategies reduces risk, relying solely on past performance does not guarantee future success.

• Limitation with Complexities: Algorithmic Trading can be limited in its handling of complex events such as market crashes or geopolitical events. It may fail to react appropriately to situations that demand human judgment.

• Market Manipulation: Skilled fraudsters can manipulate algorithmic trading systems by creating false trades to change market behaviors.

• Technical Knowledge: Deploying algorithmic trading requires a solid understanding of the markets and programming. Hence, a knowledge gap can limit access and use of algorithmic systems for some market participants.

So, while Algorithmic Trading might seem like an enticing prospect, it comes with its share of challenges. Users must understand these potential drawbacks and set realistic expectations from their algorithmic systems. It is equally crucial to remember that Algorithmic Trading does not eliminate the risks associated with trading but merely manages and mitigates them better. Therefore, it should be used responsibly and as part of a broader risk management strategy.