Posts (page 148)
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8 min readThe Average Directional Index (ADX) is a popular technical analysis indicator used to measure the strength of a trend. It is often used by traders to determine the presence and strength of a trend, as well as to determine whether a trend is gaining or losing strength.In R, the ADX indicator can be easily calculated using the 'ADX' function from the 'TTR' package. To calculate the ADX, you will need to have the high, low, and close prices of the stock or asset you are analyzing.
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6 min readIn Clojure, you can compute the Parabolic SAR (Stop and Reverse) by implementing the necessary equations and algorithms. The Parabolic SAR is a technical indicator used in trading to determine potential reversal points in the price movement of an asset. To calculate the Parabolic SAR, you will need to use the previous SAR value, the acceleration factor (AF), and the extreme price.
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6 min readThe Moving Average Convergence Divergence (MACD) is a popular technical analysis indicator used by traders to identify potential buy or sell signals in the market. In Ruby, the MACD can be calculated by subtracting the 26-day exponential moving average (EMA) from the 12-day EMA, and then taking the 9-day EMA of that difference. This creates a MACD line that is plotted on a chart along with a signal line (usually a 9-day EMA of the MACD line) to indicate potential entry or exit points.
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6 min readParabolic SAR (Stop and Reverse) is a technical analysis indicator used to determine the potential reversal points in the price direction of a security. To calculate Parabolic SAR using Haskell, you first need to define the initial values for the indicator, which include the acceleration factor (AF), the maximum AF, and the initial SAR value.After that, you need to iterate through the price data and update the SAR value for each period.
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5 min readIn this tutorial, we will explore volume analysis in Rust programming language. Volume analysis refers to the study of the number of trades or contracts for a specific asset being traded in a given period of time. This analysis can provide valuable insights into market trends and potential trading opportunities.We will cover the basics of volume analysis, including how to collect and analyze volume data using Rust.
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6 min readThe Stochastic Oscillator is a technical indicator used in trading to identify overbought or oversold conditions in a financial asset. In Kotlin, the Stochastic Oscillator can be implemented by calculating the percentage value between the current closing price and the range of high and low prices over a specified period of time. This calculation helps traders determine the momentum and potential reversal points in the price movement of the asset.
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8 min readTo calculate Moving Averages (MA) using Lua, you can first collect the data points that you want to analyze. Then, you can loop through the data points and calculate the average of a specific number of previous data points. This average value is the moving average for that point in the data series. You can repeat this process for each data point to generate a moving average sequence for the entire data set. This can help in analyzing trends and smoothing out fluctuations in the data.
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7 min readRate of Change (ROC) can be calculated in Swift by measuring the percentage change in a value over a specified period of time. To calculate ROC, you first need to determine the difference between the current value and the value from the previous period. Then, divide this difference by the value from the previous period and multiply by 100 to get the percentage change.
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3 min readMoving averages (MA) can be calculated using Python by first importing the necessary libraries such as numpy and pandas. Next, you can load your data into a pandas DataFrame and use the rolling() function with the mean() method to calculate the moving average. For example, if you want to calculate a 10-day moving average for a column in your DataFrame, you can do so by using df['column'].rolling(window=10).mean(). This will give you a new column with the 10-day moving average values.
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4 min readVolume analysis is a common technique used by traders to understand market activity and predict future price movements. In Python, you can create volume analysis by first gathering historical volume data for a particular asset. This can be done by using APIs provided by financial data providers or by manually collecting and storing volume data.Once you have the historical volume data, you can use Python libraries such as Pandas and Matplotlib to analyze and visualize the data.
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5 min readIn Erlang, to compute the Rate of Change (ROC), you would first need to calculate the change in a particular value over a specific time period. This can be done by subtracting the initial value from the final value and then dividing by the time interval. The formula for ROC is: ROC = (final value - initial value) / time interval. By using this formula, you can determine how quickly a value is increasing or decreasing over time in an Erlang program.
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5 min readTo calculate the volume analysis using Go, one would typically need to define the dimensions of the object or space for which the volume is being calculated. This could include the length, width, and height in the case of a three-dimensional object.One would then use this information to write a function or method in Go that calculates the volume based on the given dimensions. This function would typically involve multiplying the length, width, and height together to get the total volume.