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The Concept of Algorithmic Trading

Concept of Algotrading

Algorithmic trading, also known as automated trading, black-box trading, or algo-trading, employs a computer programme that executes a set of instructions known as an algorithm to place a deal. In theory, the deal can create profits at a rate and frequency that a human trader cannot match. 

Timing, pricing, quantity, or any mathematical model are used to define sets of instructions. Aside from profit prospects, algo-trading makes markets more liquid and trading more methodical by removing the influence of human emotions on trading activity. 

Trading Using Algorithms in Action 

So let’s say that the trader follows the following simple criteria: 

●A stock’s 50-day moving average rises above its 200-day moving average, and you should buy 50 shares. It smoothens out day-to-day price volatility and finds trends. 

●Stocks are sold when their 50-day moving average falls below their 200-day moving average. 

Automated computer software will watch the stock price (and the moving average indicators) and place buy and sell orders when the required criteria are met, using these two basic instructions. No longer does the trader need to keep an eye on live prices and graphs, or manually enter the orders. Traders use algorithmic trading systems to locate trade opportunities. 

The Features of Algorithmic Trading 

Algorithmic trading has many benefits. Automated trading has the following advantages: 

●Execution of trades occurs at the best price achievable. 

●The placement of trade orders is instantaneous and accurate (there is a high chance of execution at the desired levels). 

●They are executed at the right time so that major price changes are minimized or even prevented altogether. 

●Costs associated with transactions are reduced. 

●Automated checks of multiple market conditions at the same time. 

●When placing trades, the possibility of human error has been reduced. 

●In order to determine if an algorithmic trading strategy is practical, it can be backtested utilizing historical and real-time data available at the time. 

●Trades made by humans based on emotional and psychological considerations are less likely to be mistaken. 

Algorithmic trading today is dominated by high-frequency trading (HFT), which tries to capitalize on placing a large number of orders at rapid rates across numerous markets with multiple decision parameters based on preprogrammed instructions. 

There are various types of trading and financial operations that use algorithmic trading. 

Mid-to-long-term investors: (pension funds, mutual funds, insurance companies) who don’t wish to impact stock prices with discrete enormous-volume investments employ algo-trading to purchase stocks in large quantities. 

Short-term trader: In addition, algo-trading creates sufficient liquidity for market sellers by executing trades on behalf of short-term traders and sell-side participants, including market makers (such as brokerage firms), speculators, and arbitrageurs. 

Systematic Traders: Traders that follow a systematic trading strategy—trend followers, hedge funds, or pairs traders—find it far more efficient to programme their trading rules and let the software trade automatically. 

A more systematic approach to active trading is provided by algorithmic trading, as opposed to trading strategies relying on trader intuition. 

Algorithmic Trading Strategies 

For any algorithmic trading approach to be successful, it must be possible to identify a profitable opportunity in terms of increased earnings or cost reduction. Common trading tactics in algo-trading include the following. 

Arbitrage Opportunities: As a result, the price gap between the two markets can be used as a source of risk-free profit, or arbitrage. If you’d like to compare stocks to futures instruments, 

you may do the same thing, as there are price differences from time to time. Algorithms can be used to find pricing differentials and place orders efficiently. 

Index Fund Rebalancing: There are predetermined periods of time during which index funds must rebalance their holdings to put them in line with the benchmark indices. In this way, algorithmic traders can profit from projected trades that give 20 to 80 basis points in earnings, depending on the number of stocks in the index fund right before index fund rebalancing. Initiated using algorithmic trading systems, these trades are executed at the best possible price. 


Disclaimer: There are potential risks relating to trading and investing and you should not trade with money that you cannot afford to lose however, for those that educate themselves and adopt appropriate risk management strategies, the potential update can be significant. 

Please note that all opinions, research, analysis, and other information are provided as general market commentary and not as specific investment advice. 

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