With the age of the Internet, technology such as big data and AI is completely revolutionising the way the stock market works. From momentum trading to algorithmic trading, everything is changing. This includes machine learning and practising using computer algorithms to find patterns, assigning an automated trade to make predictions, and executing trading at rapid speeds and frequencies.
The stock trader monitors the stock trends in real-time, incorporates possible low prices, and becomes a handy tool to trade and make smart decisions with fewer errors and to make smart decisions and reduce wrong influences or emotional decisions on trading. The amalgamation of big data and algorithmic trading provided highly optimised insights for traders to make maximum returns from their investments.
Table of Contents
What is Momentum Trading?
The ‘buy high and sell high’ principle is referred to as momentum trading or momentum trading strategies. This style of trading takes advantage of differences in stock prices, and you can see that a large portion of trading takes place where asset volatility is high. To understand the concept of momentum-based trading strategies, consider how quickly stock prices move. The fundamental condition is that if the price of a security rises, it will continue to rise, or vice versa.
Momentum Indicators
Momentum indicators are widely used in trading to assess the strength or weakness of a price movement. They help traders identify potential trend reversals, overbought or oversold conditions, and the overall momentum of a security. Here are some popular momentum indicators:
Relative Strength Index (RSI)
RSI measures the speed and change of price movements. It ranges from 0 to 100 and is typically used to identify overbought or oversold conditions. A reading above 70 suggests overbought, while a reading below 30 suggests oversold.
Moving Average Convergence Divergence (MACD)
MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. It consists of a MACD line, a signal line, and a histogram. Traders look for crossovers and divergences between the MACD and a signal line for potential buy or sell signals.
Stochastic Oscillator
The Stochastic Oscillator compares a security’s closing price to its price range over a specific period. It consists of two lines: %K and %D. Traders often use crossovers, overbought/oversold conditions, and divergences to identify potential reversals.
Average Directional Index (ADX)
ADX is used to quantify the strength of a trend. A rising ADX indicates a strong trend, while a falling ADX suggests a weakening trend. Traders often use ADX in conjunction with other indicators to determine the strength of a trend.
Momentum (MOM)
Momentum simply measures the rate of price change over a specified time period. It compares the current closing price to a previous closing price, and a positive value indicates upward momentum, while a negative value indicates downward momentum.
Commodity Channel Index (CCI)
CCI measures the statistical variation from the average. It’s often used to identify overbought or oversold conditions and potential trend reversals. Readings above +100 may indicate overbought conditions, while readings below -100 may indicate oversold conditions.
Williams %R
Williams %R is similar to the Stochastic Oscillator and is used to identify overbought or oversold conditions. It is plotted on a negative scale from -100 to 0, with readings above -20 considered overbought and readings below -80 considered oversold.
These indicators can be used individually or in combination to form a comprehensive analysis of a security’s momentum and potential trading opportunities. It’s essential to note that no single indicator is foolproof, and traders often use a combination of indicators and other technical analysis tools for more accurate decision-making.
Technical Indicators
Moving Averages
Moving Averages help smooth out the ups and downs in price to show the overall trend. It’s like a line that follows the average price over a certain period.
Bollinger Bands
Bollinger Bands show if prices are high or low compared to their recent average. It’s like a set of boundaries around the price that can signal if it might go up or down.
Relative Strength Index (RSI)
RSI helps figure out if a stock is getting too popular (overbought) or overlooked (oversold). It gives a number between 0 and 100.
Moving Average Convergence Divergence (MACD)
MACD helps see if a trend is getting stronger or weaker. It’s shown as lines that move above or below each other.
Stochastic Oscillator
The Stochastic Oscillator helps find out if a stock is likely to go up or down based on recent price movements. It gives a percentage.
Average True Range (ATR)
ATR measures how much a stock’s price can change. It helps set a sensible stop-loss level to manage risk.
Volume
Volume indicators show how many shares are being traded. High volume often means strong market interest.
Remember, these are just tools, and it’s essential to use them alongside other information for better decision-making in trading.
ML Algorithms
Some of the algorithm models that can be used in trading are as follows:
K Nearest Neighbour
In this ML model, the algorithm works with supervised learning techniques. This KL-NN algorithm finds similarities between the new case data and the existing case and adds a new case to the category as a result. It is the simplest machine learning algorithm.
A Decision Tree
A decision tree is a tree-structured classifier where the internal nodes represent the rules, and each leaf node shows the outcome of the algorithm. In this decision tree, there are two nodes—the decision node and the leaf node. The decision nodes, which are used to make any decision, have multiple branches, whereas the leaf nodes are the outputs of the following decisions and do not have any branches.
Random Forest
A random forest is a supervised ML algorithm. The forest is built with multiple decision trees, which are trained with the bagging method. It is the combination of learning models that increases the overall result. It merges several decision trees and uses them to make an accurate and stable prediction.
Conclusion
As algo-trading is booming, so is technical reading. Financial experts are using multiple trading strategies to empower their trades in a better way.
The risk of human error or delay is minimised with the help of an algo-trading software. Depending upon the risk and entry and exit points, trades are pre-set in the trading system. However, once you’ve decided and the stock is at the trend’s price point, you can create a proper framework for your trade or seek expert advice, such as on the Share India platform, where you can get complete guidance to do algorithmic trading.
