technical-indicator

问题 I was defining a function Heiken Ashi which is one of the popular chart type in Technical Analysis. I was writing a function on it using Pandas but finding little difficulty. This is how Heiken Ashi [HA] looks like- Heikin-Ashi Candle Calculations HA_Close = (Open + High + Low + Close) / 4 HA_Open = (previous HA_Open + previous HA_Close) / 2 HA_Low = minimum of Low, HA_Open, and HA_Close HA_High = maximum of High, HA_Open, and HA_Close Heikin-Ashi Calculations on First Run HA_Close = (Open +

问题 I was defining a function Heiken Ashi which is one of the popular chart type in Technical Analysis. I was writing a function on it using Pandas but finding little difficulty. This is how Heiken Ashi [HA] looks like- Heikin-Ashi Candle Calculations HA_Close = (Open + High + Low + Close) / 4 HA_Open = (previous HA_Open + previous HA_Close) / 2 HA_Low = minimum of Low, HA_Open, and HA_Close HA_High = maximum of High, HA_Open, and HA_Close Heikin-Ashi Calculations on First Run HA_Close = (Open +

问题 I was defining a function Heiken Ashi which is one of the popular chart type in Technical Analysis. I was writing a function on it using Pandas but finding little difficulty. This is how Heiken Ashi [HA] looks like- Heikin-Ashi Candle Calculations HA_Close = (Open + High + Low + Close) / 4 HA_Open = (previous HA_Open + previous HA_Close) / 2 HA_Low = minimum of Low, HA_Open, and HA_Close HA_High = maximum of High, HA_Open, and HA_Close Heikin-Ashi Calculations on First Run HA_Close = (Open +

问题 I have a timeseries data. Generating data date_rng = pd.date_range('2019-01-01', freq='s', periods=400) df = pd.DataFrame(np.random.lognormal(.005, .5,size=(len(date_rng), 3)), columns=['data1', 'data2', 'data3'], index= date_rng) s = df['data1'] I want to create a zig-zag line connecting between the local maxima and local minima, that satisfies the condition that on the y-axis, |highest - lowest value| of each zig-zag line must exceed a percentage (say 20%) of the distance of the previous

问题 I have a timeseries data. Generating data date_rng = pd.date_range('2019-01-01', freq='s', periods=400) df = pd.DataFrame(np.random.lognormal(.005, .5,size=(len(date_rng), 3)), columns=['data1', 'data2', 'data3'], index= date_rng) s = df['data1'] I want to create a zig-zag line connecting between the local maxima and local minima, that satisfies the condition that on the y-axis, |highest - lowest value| of each zig-zag line must exceed a percentage (say 20%) of the distance of the previous

问题 I have a timeseries data. Generating data date_rng = pd.date_range('2019-01-01', freq='s', periods=400) df = pd.DataFrame(np.random.lognormal(.005, .5,size=(len(date_rng), 3)), columns=['data1', 'data2', 'data3'], index= date_rng) s = df['data1'] I want to create a zig-zag line connecting between the local maxima and local minima, that satisfies the condition that on the y-axis, |highest - lowest value| of each zig-zag line must exceed a percentage (say 20%) of the distance of the previous

问题 I am having trouble getting a smoothed RSI. The below picture is from freestockcharts.com. The calculation uses this code. public static double CalculateRsi(IEnumerable<double> closePrices) { var prices = closePrices as double[] ?? closePrices.ToArray(); double sumGain = 0; double sumLoss = 0; for (int i = 1; i < prices.Length; i++) { var difference = prices[i] - prices[i - 1]; if (difference >= 0) { sumGain += difference; } else { sumLoss -= difference; } } if (sumGain == 0) return 0; if

问题 I am having trouble getting a smoothed RSI. The below picture is from freestockcharts.com. The calculation uses this code. public static double CalculateRsi(IEnumerable<double> closePrices) { var prices = closePrices as double[] ?? closePrices.ToArray(); double sumGain = 0; double sumLoss = 0; for (int i = 1; i < prices.Length; i++) { var difference = prices[i] - prices[i - 1]; if (difference >= 0) { sumGain += difference; } else { sumLoss -= difference; } } if (sumGain == 0) return 0; if

问题 I'm currently converting a ThinkScript indicator to C#, however, I've run into this CompoundValue function and I'm unsure how to covert it. The documents reads : Calculates a compound value according to following rule: if a bar number is greater than length then the visible data value is returned, otherwise the historical data value is returned. This function is used to initialize studies with recursion. Example Use: declare lower; def x = CompoundValue(2, x[1] + x[2], 1); plot

问题 I've a pandas dataset with open, high, low, close and key column. Now I want to group the dataset by key and calculate pivot with the formula - (high + low + close) / 3. Upto this I'm able to do. But the requirement is to shift the calculated data to next group which I'm unable to code. I'm able to group the dataset by key column and able to calculate pivot data. import pandas as pd data = pd.DataFrame([[110, 115, 105, 111, 1],[11, 16, 6, 12, 1],[12, 17, 7, 13, 1],[12, 16, 6, 11, 2],[9, 13, 4