An algorithm is a specific set of clearly defined instructions aimed to carry out a task or process. And there are instances when a human trader isn’t able to handle enormous numbers of trading, and that’s when you need intervention of an intelligent algorithm.
Algorithms have gained popularity in the trading landscape and many big clients demand it. These mathematical algorithms analyse every quote and trade in the stock market, identify liquidity opportunities, and turn the information into intelligent trading decisions. Algorithmic trading, or computer-directed trading, cuts down transaction costs, and allows investment managers to take control of their own trading processes. Algorithm innovation continues to offer returns for firms with the scale to absorb the costs and to reap the benefits.
Algorithmic trading (automated trading, black-box trading, or simply algo-trading) is the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader. Any strategy for algorithmic trading requires an identified opportunity, which is profitable in terms of improved earnings or cost reduction. The algorithmic trading strategies follow defined sets of rules, and are based on timing, price, quantity or any mathematical model. Apart from profit opportunities for the trader, algorithmic-trading makes markets more liquid and makes trading more systematic by ruling out emotional human impacts on trading activities.
Suppose a trader follows these simple trade criteria:
Buy 100 shares of a stock, when its 100-day moving average goes above the 200-day moving average
Sell shares of the stock, when its 100-day moving average goes below the 200-day moving average
Using this set of two simple instructions, a computer program can be written that will automatically monitor the stock price (and the moving average indicators) and place the buy and sell orders when the defined conditions are met. There is no manual intervention required here. The trader no longer has to monitor the live prices and graphs, or place orders himself. This algorithm does his work for him every efficiently.
Algorithmic-trading has many benefits.
1. Trades are executed at the best possible prices
2. Instant and accurate trade order placement
3. Trades timed correctly and instantly. This avoids significant price changes
4. Reduced transaction costs due to lack of human intervention
5. Simultaneous automated checks on multiple market conditions
6. Reduced risk of manual errors in placing the trades
7. Reduced possibility of mistakes by human traders based on emotional and psychological factors
8. The greatest portion of present day algorithmic-trading is high frequency trading (HFT). This trading method attempts to capitalize on placing a large number of orders at very fast speeds, across multiple markets, and multiple decision parameters, based on per-programmed instructions.
Algorithmic-trading can be applied in many forms of trading and investment activities:
Mid to long term investors or buy side firms (pension funds, mutual funds, insurance companies) who purchase stocks in large quantities but do not want to influence stocks prices with discrete, large-volume investments.
Short term traders and sell side participants (market makers, speculators, and arbitrageurs)benefit from automated trade execution; in addition, algorithmic-trading aids in creating sufficient liquidity for sellers in the market.
Systematic traders (trend followers, pairs traders, hedge funds, etc.) find it much more efficient to program their trading rules and let the program trade automatically.
Automated trading provides a more systematic approach to active trading than methods based on a human trader's intuition or instinct.
To get into algorithmic trading the following requirements have to be met.
A computer program that can read current market prices
Price feeds from both LSE and AEX
A forex rate feed for exchange rate
Order placing capability which can route the order to the correct exchange
Back-testing capability on historical price feeds
However, as a smart investor, we need to understand risks and challenges. For example, system failure risks, network connectivity errors, time-lags between trade orders and execution, and, most important of all, imperfect algorithms. Remember, if you can place an algo-generated trade, so can the other market participants. Consequently, prices fluctuate in milli- and even microseconds. The more complex an algorithm, the more stringent back testing is needed before it is put into action.
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