What are Technical Indicators?
Technical indicators, in the context of financial markets, are mathematical calculations based on historical price and volume data of a security that are used to analyze and forecast future price movements.
Indicators are made with either price, volume, or both price and volume.
Different indicators have to be used under different circumstances and for different types of strategies, you cannot take the same indicator and use it across everything, you have to have a certain environment for the indicator to work.
So the indicator you choose depends on the strategy and asset you’re trading.
While there are many indicators out there, only a few are truly beneficial for trading. Let’s focus on the select indicators that traders commonly use and that I’ve found most effective.
Here are the top indicators for day trading:
What are Simple Moving Averages(SMA)?
A simple moving average (SMA) is a widely used technical analysis tool that helps traders and investors identify trends and smooth out price data over a specified period.
It is a calculation that provides an average value of a financial asset’s price over a set number of periods, such as days, weeks, or months.
To calculate a simple moving average, you sum up the closing prices of the asset for the desired number of periods and then divide the total by the number of periods.
For example, to calculate a 10-day simple moving average, you would add up the closing prices of the asset for the last 10 days and divide the sum by 10.
Here’s a step-by-step example of calculating a simple moving average:
- Select a time frame, such as 10 days.
- Add up the closing prices of the asset for the last 10 days.
- Divide the sum by 10 to get the average.
- Finally plot the value to the chart continuously, with new data
The resulting value represents the SMA for that particular period. As new price data becomes available, the SMA is recalculated by dropping the oldest price and adding the most recent one.
Traders use SMAs for various purposes:
1. Trend Identification
By plotting SMAs on a price chart, you can visualize the overall direction of the asset’s price.
When the price is above the SMA, it suggests an uptrend, and when the price is below the SMA, it suggests a downtrend.
2. Finding Support and Resistance Levels
SMAs can act as support or resistance levels, where the price may encounter buying or selling pressure.
For example, if the price is in an uptrend and pulls back to touch the SMA, it may find support and bounce back up.
3. Moving Average Crossovers
Traders often pay attention to the crossover of different SMAs.
For example, when a shorter-term SMA (e.g., 50-day) crosses above a longer-term SMA (e.g., 200-day), it is considered a bullish signal, indicating a potential trend reversal or an upward move. Conversely, a crossover of a shorter-term SMA below a longer-term SMA is seen as a bearish signal.
It’s important to note that SMAs are based solely on historical price data and do not take into account other factors such as market conditions, news events, or fundamental analysis.
What are Bollinger Bands?
Let’s start with a basic explanation of Bollinger bands that can be easily understood by everyone, before delving into more technical terminology.
A Bollinger band is a band that is drawn around your chart, that tells you that your price should remain within those bands at a certain probability
Bollinger Bands are particularly suitable for implementing mean reverting strategies.
In the future, we’ll explore mean-reverting strategies more deeply. For now, it’s enough to know that this strategy involves lots of ups and downs, with lows and highs happening frequently and sometimes reaching the same level.
Bollinger Bands are used in financial markets to measure price volatility.
They consist of three lines plotted on a price chart: a middle band (typically a simple moving average), an upper band (calculated by adding a specified number of standard deviations to the middle band), and a lower band (calculated by subtracting the same number of standard deviations from the middle band).
Some of you may be familiar with standard deviation, but for those who aren’t, let me give a brief explanation.
What’s the Standard Deviation?
Standard deviation is a measure of how spread out or dispersed a set of data points is from its average (mean). It provides information about the variability or volatility within a dataset.
Think of a standard deviation as a way to understand how much individual data points differ from the average. If the standard deviation is low, it means that most of the data points are close to the average. On the other hand, if the standard deviation is high, it indicates that the data points are more scattered and farther away from the average.
Imagine you have a dataset representing daily temperatures in a city over a week. If the standard deviation of the temperatures is low, it suggests that the weather was fairly consistent throughout the week, with temperatures mostly clustered around the average. However, if the standard deviation is high, it implies that the temperatures varied significantly from day to day, with some days being much hotter or colder than the average.
In summary, standard deviation helps us understand the dispersion or spread of data points from the average, giving us insights into the overall variability or consistency of a dataset.
The middle band of Bollinger band represents the average price over a specified period, usually 20 days. The upper and lower bands dynamically adjust based on market volatility, expanding or contracting as prices become more or less volatile.
If you have no idea what this is and you just want to use Bollinger bands, let me help you, you can always dive deep later in the future.
A 2 standard deviation Bollinger band indicates that the price is expected to remain within the upper and lower bands with a 95% probability. On the other hand, a 3 standard deviation Bollinger band suggests that the price is even more likely to stay within the bands, with a 99.7% probability.
As we increase the standard deviation, the width of the Bollinger Bands expands. and depending on the volatility of the market Bollinger band going to adjust its bands for the volatility.
When we choose a higher standard deviation it reduces the risk of your trade.
Bollinger Bands narrows when the stocks are not volatile, and widens when the stocks are volatile.
We traders most of the time use 2 Standard deviation with 20 days moving average Bollinger band as default. Bollinger Bands helps identify potential overbought and oversold conditions in the market.
When prices touch or exceed the upper band, it may indicate an overbought situation, suggesting a possible price reversal or correction. Conversely, when prices touch or fall below the lower band, it may suggest an oversold condition, indicating a potential buying opportunity.
Overall, Bollinger Bands provide a visual representation of price volatility and serve as a helpful tool for traders to assess potential price reversals, and identify overbought or oversold conditions.
What is the Relative Strength Index(RSI)?
RSI is one of the extremely popular and useful indicators out there. It’s what we use to predict if the stock is overbought or oversold.
Overbought and oversold are terms used in financial markets to describe situations where the price of an asset or security may have deviated from its perceived fair value, potentially indicating a reversal or correction in price.
Overbought refers to a condition where the price of an asset has risen sharply and quickly, potentially reaching levels that are considered too high or overextended. It suggests that buying pressure has pushed the price to an excessive level, and a price correction or a downward movement may be imminent.
Conversely, oversold refers to a condition where the price of an asset has fallen sharply and quickly, potentially reaching levels that are considered too low or undervalued. It suggests that selling pressure has driven the price to an excessively low level, and a price rebound or an upward movement may be likely.
The RSI is typically displayed as a line graph oscillating between 0 and 100. The indicator compares the magnitude of recent price gains to recent price losses over a specified period, usually 14 days, and generates a relative strength value accordingly.
When the RSI value is above 70, it suggests that the asset is overbought, meaning the price has experienced a significant upward movement and may be due for a potential correction or reversal. This is an indication that selling pressure could increase, leading to a possible decrease in price.
On the other hand, when the RSI value is below 30, it indicates that the asset is oversold, meaning the price has declined significantly and may be due to a potential rebound or upward movement. This suggests that buying pressure could increase, potentially leading to a price increase.
The Relative Strength Index (RSI) is calculated using the following steps:
- Determine the period for RSI calculation: Typically, a 14-day period is used, but this can be adjusted depending on the desired time frame.
- Calculate the average gain and average loss: For each day within the chosen period, calculate the price difference (gain or loss) between the current day’s closing price and the previous day’s closing price. If the price increased (gain), take the positive value; if it decreased (loss), take the negative value.
- Calculate the average gain and average loss over the chosen period: Sum up all the gains and losses within the selected period, then divide each sum by the chosen period. This will give you the average gain and average loss values.
- Calculate the relative strength (RS): Divide the average gain by the average loss to get the relative strength (RS). RS = Average Gain / Average Loss.
- Calculate the RSI: The RSI is calculated using the following formula: RSI = 100 – (100 / (1 + RS))
The RSI value obtained from this calculation will be a number between 0 and 100. Higher RSI values indicate overbought conditions, while lower RSI values indicate oversold conditions.
It’s important to note that some variations in RSI calculation may exist, such as using different time periods or smoothing techniques. Traders and analysts may also apply different adjustments based on their preferences and trading strategies.
If you don’t understand these terms then let me explain to you in simple
The RSI function analyzes the previous 14 periods of price data and examines the size of the upward price movements (green candles) and downward price movements (red candles). By comparing these sizes, it generates a numerical output. and it’s going to range between 0 – 100. If this output is higher than 70, it indicates that the asset is overbought. Conversely, if the output is lower than 30, it signifies that the asset is oversold.
By continuously plotting the calculated value on a chart, we obtain the RSI indicator.
The RSI indicator tends to be more effective when used in mean-reversion strategies.
What is the Average True Range(ATR)?
Average True Range (ATR) is used to measure the volatility or price fluctuation of a financial instrument over a specific period.
The ATR calculation involves taking the average of the true ranges of a series of price bars or periods. The true range is the greatest value among the following three calculations:
- The difference between the high and low prices of the current period.
- The absolute value of the difference between the high of the current period and the close of the previous period.
- The absolute value of the difference between the low of the current period and the close of the previous period.
To calculate the ATR, you typically take the average of the true ranges over a specified number of periods, commonly 14 days.
However, the number of periods can be adjusted based on the trader’s preference and the time frame being analyzed.
The ATR value represents the average range in which the price of an asset has moved over the selected period. Higher ATR values indicate greater volatility, while lower values suggest less volatility.
So if you put it simply an ATR tells how much a stock going to move in a specific time period in dollar value. For example, if a stock has an ATR value of $2, it suggests that, on average, the price has moved to $2 per day over the specified period.
To provide another example, suppose you are considering trading a stock with an ATR of $5. If the stock opened at $15 and has now reached $17, so it moved $2, the ATR suggests that the stock may potentially move an additional $3.
Conclusion
As mentioned earlier, each indicator requires a specific environment to function effectively. These environments are determined through strategies. When discussing strategies, I will explain how to use the indicators appropriately. For now, it is important to grasp an understanding of what each indicator represents.
I have a few more indicators that I would like to share with you, but each serves a distinct purpose. To explain these purposes properly, I will need to do so in a separate post in the future.
Now head over to TradingView and play with these indicators on your charts and get to know them.