Posts (page 146)
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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.
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8 min readChaikin Money Flow (CMF) is a technical analysis indicator that measures the money flow volume over a specific period of time. It is used to gauge the buying and selling pressure in a particular financial instrument.
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5 min readThe Relative Strength Index (RSI) is a popular technical indicator used in financial markets to measure the magnitude of recent price changes. In Clojure, RSI calculations can be implemented by first calculating the average gain and average loss over a specified period, typically 14 days. The RSI value is then calculated using the formula: 100 - (100 / (1 + RS)), where RS is the average gain divided by the average loss.
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8 min readTo compute the Commodity Channel Index (CCI) using Scala, you can start by calculating the typical price of the asset. The typical price is the average of the high, low, and closing prices for a specific period.Next, you need to calculate the simple moving average (SMA) of the typical price over a designated period. This moving average is used as a baseline for the CCI calculation.Once you have the typical price and SMA values, you can calculate the mean deviation.
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4 min readTo calculate the Fibonacci retracements using C#, you first need to calculate the Fibonacci numbers. These are a sequence of numbers where each number is the sum of the two preceding ones, starting with 0 and 1. Once you have generated the sequence of Fibonacci numbers, you can use them to calculate the retracement levels.The retracement levels are used in technical analysis to predict potential levels of support or resistance in a financial instrument's price movement.
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8 min readUsing the Momentum platform with Erlang involves leveraging the high performance and fault-tolerant capabilities of the Erlang programming language to build scalable and reliable messaging applications. Erlang's concurrency model and built-in support for distributed systems make it well-suited for handling the demands of email delivery and processing in a large-scale environment.
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4 min readTo compute Fibonacci extensions using Java, you can start by creating a Java program that calculates the Fibonacci sequence. This can be done using a loop or a recursive function to generate the Fibonacci numbers. Once you have the Fibonacci sequence, you can use it to calculate the Fibonacci extensions.To calculate Fibonacci extensions, you can use the formula: Extension = (Fibonacci number at index n - Fibonacci number at index n-1) + Fibonacci number at index n.
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5 min readThe Moving Average Convergence Divergence (MACD) is a popular technical analysis indicator used by traders to identify trends in a stock's price movement. In Java, implementing the MACD indicator involves calculating a series of exponential moving averages based on historical price data. The MACD line is calculated by subtracting the 26-day exponential moving average from the 12-day exponential moving average.
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4 min readMoving Averages (MA) are a commonly used technical indicator in the world of finance and trading. In Groovy, a programming language for the Java platform, moving averages can be calculated using various methods such as the Simple Moving Average (SMA) or the Exponential Moving Average (EMA).The Simple Moving Average calculates the average price over a specified number of time periods, giving equal weight to each period.
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6 min readThe Relative Strength Index (RSI) is a popular momentum oscillator used in technical analysis to measure the speed and change of price movements. In R, the RSI can be calculated using the \textit{quantmod} package, which provides tools for financial analysis.To calculate the RSI in R, you will first need to install and load the \textit{quantmod} package. Once the package is loaded, you can use the \textit{RSI} function to calculate the RSI for a given time series data.