“Algorithm Trading”- A Path of Trading with Technology
Algorithm-based stock trading (Algo-Trading) now become a preferential approach to building substantial wealth for potential traders or investors in the financial market. Trading with technology and automation makes revolutionary transformation in the financial market which gives ample opportunity to get high returns on financial market assets and financial instruments via following accurate approaches. Financial market assets include equities, shares, securities, currency or commodities, etc. Algo trading is a way to generate real cash in a portfolio with zero errors and extensive accuracy. In this ongoing “FIN-TECH” era, everybody looks chance to achieve financial growth and make a sight to accomplish the values of Algo trading strategies on which the entire world can believe.
Retail traders make trading based on technical research and short-term market trends which are thoroughly distinct from fundamental research and long-term trend. Simultaneously high net worth investors always focus on long-term growth and value addition in wealth. Algorithm-based Investing is a strategy to build a healthy investment portfolio with the help of combined techniques defined under technical as well fundamental research which generate higher returns because of its significant research on various short terms as well long-term trading or investing methodologies.
For instance, a trader has a strategy to buy 100 stocks when its 75 days simple moving average price goes above 360 days’ moving average price (A moving average is an average of previous prices for a defined timeline).
Global Market Scale: The global market size of algorithm-based trading was valued at around USD 12.14 billion in 2020 and is expected to reach USD 31.54 billion by 2028 with a Compounded Annual Growth Rate (CAGR) of around 12.67%.
What is Algorithmic Trading
Algorithmic Trading is also termed Algo-based trading or black box trading or automated trading. Via using the supervised learning approaches in computer programming where algorithms have been trained by providing the diversified large size data set of the financial market to determine trends, patterns, and structure of data.
Based on the outcomes derived through the trained data system can recognize relations in various parameters like volume, order size, price, time, frequency, or any ideal model of the data set, build logic, understand the limitation, and focus on giving the best results with least errors. With the help of tremendous research on historical data or on trained data, we can set logic or build a command which helps us to execute the next trade as and when fulfilling the defined criteria or logic.
Algo-based and logic-based trading increase the chances and probability of arising high profit in every trade. It’s impossible for a human being to compete with the speed and accuracy of trade executed with the support of a computer program. The benefit of Algo trading is not restricted to only high-profit opportunities but also renders wider options to execute trade systematically in large market size.
In a simple term, we can say that Algo trading is trading via a pre-defined trading ideology to execute the trade with at most high frequency in the share market. Trading ideology needs to be set in a computer through coding or algorithms to execute the trade as and when the defined instructions meet all the criteria depending on order size, price, time, volume, or any other mathematical strategy.
Illustrative Rule-Based Logic:
- Illustration 1 (Breakout Rule): User has set an algorithm like as and when any share or security breaks 52 weeks low record then automatically order of buy 75 shares at new lowest 52 weeks low price should trigger.
- Illustration 2 (Relative Momentum Rule): The User has set a program if the share price of XYZ Limited changed more than their industry average change (in terms of percentage) then trade should execute.
- Illustration 3 (Moving Average Crossover Rule): Buy 50 Shares of XYZ Limited if its 25-day moving average goes above the mark of the 200-day moving average before 3 PM.
How does Algorithmic Trading differ from Traditional Trading
In a traditional trading approach, traders or investors always have to focus on market trends and wait till their expected outcome which may arise or not to execute a trade deal of specific share or securities at a specific rate and volume. The understanding of market trends is not just limited to understanding macro factors but includes understanding industry, segment, and micro factors. The trader has to place manual orders and wait until the actual level is reached. If the price fails to touch that level, the trader must modify orders manually, necessitating continuous, time-consuming monitoring.
In algorithmic trading, traders or investors are much more competent to trade without human intervention at a pre-defined rate and volume and it takes less than a fraction of second timing to execute an accurate deal. The entire trading process systematically executes entire trades and realized real-time gain (loss). With the support of supervised or unsupervised learning techniques in machine learning1, programmers build logical code modules based on historical data research, cross-correlating multiple quantifiable factors (volume, rate, frequency).
Algorithmic Trading (Technology-Driven)
- Real-time automated monitoring and trade execution
- Sub-second execution speed (fraction of a second)
- 100% execution accuracy (zero human error)
- Zero emotional bias or fatigue
- Cost-effective and scalable across multiple asset classes
Traditional Trading (Manual)
- Heavy reliance on continuous manual screen monitoring
- Slow, prone to execution delays and missed price levels
- High risk of manual errors and erroneous inputs
- Vulnerable to emotional obstruction (fear/greed)
- High recurring time and opportunity costs
Process Flow Comparison: Algorithmic vs Traditional Trading
| Stage | Algorithmic Trading Route (Technology) | Traditional Trading Route (Manual) |
|---|---|---|
| Initial Phase | Define Trading Strategy ➔ Programmer Formulates Algorithm ➔ Coding & Logic Configuration. | Understand Financial Markets ➔ Analyze Market & Stock/Share Trends Manually. |
| Order Placement | Automated order routing with pre-set volume, limit rates, and trigger conditions. | Manually place Buy/Sell order with specified volume & rate. |
| Trading Execution | Conditions fulfilled? ➔ Instant Automatic Trade Execution in milliseconds. | Wait & review till expected level touched. If reached ➔ Manually execute trade; If not ➔ Manual order modification. |
| Result & Impact | Realized Profit & Loss with systematic execution, cost reduction, and zero emotional bias. | Realized Profit & Loss burdened by high execution risk, time consumption, and human intervention limits. |
Strategies for Algorithmic Trading
Algorithmic trading requires patience, calmness, discipline, and emotional detachment to perform flawlessly. It’s necessary not to obstruct strategies when under execution. Time availability is also critical. Below are the five prominent algorithmic trading strategies:
1. Arbitrage Trading Strategy
Trades or invests in two different markets to exploit real-time price discrepancies. For instance, if a security trades simultaneously on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE), the algorithm buys where the price is lower and sells where higher in microseconds, capitalizing on minute price spreads multiplied by large trading volumes.
2. Momentum & Trend-Based Strategy
The simplest and most popular strategy, following mathematical moving average crossovers. E.g., system buys 50 shares of XYZ Ltd if its 45-day moving average crosses above its 200-day simple moving average, or sells 100 shares of ABC Ltd when its 60-day moving average drops below the 200-day moving average.
3. Statistical Arbitrage Strategy
An intensive short-term quantitative approach identifying price inexpediencies and misquotations between highly correlated peers in the same sector. E.g., between TCS and Infosys: if TCS rallies with the IT sector but Infosys lags, the algorithm purchases Infosys expecting a mean-reversion catch-up rally, exiting once parity is restored.
4. Options Trading Strategy
Aims to capture bid-ask spread variations and swap differences. Extensively utilized in Foreign Exchange (Forex) risk hedging, where algorithms continuously monitor real-time forward rate premiums to execute, roll over, or cancel forward covers at points of highest profit, lowest risk, or optimal breakeven before maturity.
5. Valuation Strategy
Scans all listed securities on exchanges using key valuation multiples (Price-to-Book / P/B, Price-to-Earnings / P/E, Forward P/E) to pinpoint mispriced assets. For example, if a company with a Book Value of ₹160 trades at ₹95, the algorithm identifies the discount and executes purchases, selling once market valuation converges to book value.
Advantages of Algorithmic Trading
Algorithmic trading is an entirely technology-enabled process absolutely free from human emotions, delivering speed, discipline, and accuracy:
- Technology Enabler & Machine Learning: Logically back-tested across technical and fundamental data. Immense historical data research empowers algorithms to apply sophisticated analytical skills.
- Artificial Neural Networks: Under Deep Learning, Artificial Neural Networks (ANN)2 model the human brain’s neural processing to analyze complex data sets and generate optimal trading decisions.
- Uncompromising Accuracy: Eliminates human error (fat-finger inputs, latency errors, emotional hesitations).
- Permanent Cost & Time Reduction: Significantly curtails per-transaction overheads and eliminates hours spent in manual chart watching.
- Multi-Asset Exposure: Traders can participate across diverse sectors, geographies, and instruments without in-depth individual asset expertise, leveraging quantitative developer models.
- Superior Returns (Alpha Generation): Generates high returns with minimized risk, delivering excess returns above market expectations known as Alpha.
Core Pillars of Algorithmic Trading
Disadvantages and Risks of Algorithmic Trading
Uncontrollable Black-Swan Factors: No algorithm can achieve 100% forecasting precision. Macro shocks—such as inflation spikes, sudden Foreign Institutional Investor (FII) disinvestments, global federal interest rate hikes, unexpected tax law modifications, and geopolitical governance uncertainties—cannot be fully anticipated or coded into algorithms.
Overfitting & Execution Vulnerability: Algorithmic rules built upon historical market datasets may perform disastrously in unprecedented or hostile market environments.
High Infrastructure Costs: Successful deployment requires significant capital investments in programmer fees, quantitative research costs, specialized data feeds, robust hardware, and ongoing system maintenance.
Conclusive Opinion: “Trading with Technology”
Over the past many years, we have seen extreme revolutionary reforms and decisions taken in favor to amplify ample opportunities in the Fin-Tech era whose preliminary objective is to enhance the capability to make high profits and build an effective portfolio with the support of technology enablers.
Trading or investing with an algorithm is in huge demand nowadays and rapidly increasing every day. Algorithm Trading can give the opportunity to get high returns and high performance provided the trader or investor has to keep patience and follows the adequate approach.