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4 min readTo calculate the Stochastic Oscillator in TypeScript, you can follow these steps:Define a function that takes an array of numbers representing the closing prices of a financial asset over a period of time as input. Calculate the highest high and lowest low prices over a specified period within the input array.
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7 min readTo calculate the Commodity Channel Index (CCI) using R, you first need to install the 'quantmod' package, which provides functions for financial quantitative analysis. After installing the package, you can use the 'CCI' function to calculate the CCI for a given dataset of price data. The CCI is typically calculated using a 20-period moving average, along with a constant multiplier.
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8 min readFibonacci retracements are a technical analysis tool used in financial markets to identify potential levels of support and resistance. In JavaScript, you can calculate Fibonacci retracement levels by first identifying a significant peak and trough in the price movement. From there, you can calculate the percentage retracement levels based on the Fibonacci sequence (0, 23.6%, 38.2%, 50%, 61.8%, and 100%).
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6 min readSupport and resistance levels are key concepts in technical analysis that help traders identify potential price levels where a stock may reverse or continue its trend. In R, these levels can be calculated by analyzing historical price data using various techniques such as moving averages, trendlines, and Fibonacci retracement levels. By plotting these support and resistance levels on a chart, traders can make informed decisions about when to buy or sell a particular stock.
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5 min readCommodity Channel Index (CCI) is a technical indicator used to identify overbought or oversold conditions in a financial market. It measures the current price variation from the average price over a specified period of time.In Haskell, we can compute the CCI by first calculating the Typical Price (TP) which is the average of the high, low, and close prices for each period. Then, we calculate the Simple Moving Average (SMA) of the TP over the specified period.
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10 min readMoving Averages (MA) are commonly used in statistical analysis to smooth out fluctuations in data and identify trends over time. In C++, calculating moving averages involves taking the average of a specified number of data points in a series and updating the average as new data points are added.To calculate a simple moving average, you would create an array or vector to store the data points and a variable to store the moving average.
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5 min readTo calculate Chaikin Money Flow (CMF) in Java, you first need to gather historical stock data such as closing price, high price, low price, and volume.Next, you will need to calculate the Money Flow Multiplier by determining whether the closing price is higher or lower than the previous day's closing price. If it is higher, you multiply the Money Flow Multiplier by the volume. If it is lower, you multiply the Money Flow Multiplier by the negative volume.
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4 min readPivot points are a popular technical analysis tool used by traders to identify potential levels of support and resistance. In order to compute pivot points using Lisp, you will need to write a function that takes the high, low, and close prices of a financial instrument as input.
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6 min readIn Rust, the Simple Moving Average (SMA) can be used to calculate the average value of a dataset over a specified period of time. This can be done by taking the sum of the closing prices of the dataset over the specified period and dividing it by the number of data points in that period. This calculation can be done using a loop that iterates over the dataset and updates the moving average at each step.
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7 min readCalculating Pivot Points using Java involves using various formulas to determine key levels of support and resistance for a particular asset or security. These levels can help traders and investors make informed decisions about entry and exit points for trades.One common method for calculating Pivot Points is the Standard Pivot Points formula, which involves identifying the high, low, and closing prices for a specific time period.
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8 min readVolume analysis in Rust is a method used by traders and investors to gauge the strength or weakness of a price movement by analyzing the volume of trading activity. This analysis involves studying the amount of trading activity occurring in a particular stock, commodity, or asset over a specific time period. By looking at trading volume, traders can assess the level of interest and participation in the market, which can help them make more informed decisions.