lynsa.blogg.se - Python exponentially weighted standard deviation

Note that the value of these columns are calculated the first time you access We allow the user to access the statistics directly with some specified column Of pandas.DataFrame should work the same as before. StockDataFrame is a subclass of pandas.DataFrame. Note that unwrap won't reset the columns and the index. Use unwrap to convert it back to a pandas.DataFrame. Index column name in the wrap or retype function.ĭataFrame loaded from CSV. They are converted to lower caseīy default, the date column is used as the index.

Note these column names are case-insensitive.

volume: the volume of stocks traded during the interval.

high: the highest price of the interval.

date: timestamp of the record, optional.

This package takes for granted that your data is sorted by read_csv ( 'stock.csv' ) stock = StockDataFrame. import pandas as pd from stockstats import StockDataFrame df = pd. Initialize the StockDataFrame with wrap or retype. StockDataFrame works as a wrapper for the pandas.DataFrame. The build checks the compatibility for the last two major release of python3 andīSD-3-Clause License Tutorial Initialization

Supertrend: with the Upper Band and Lower Band.

KAMA: Kaufman's Adaptive Moving Average.

TEMA: Another Triple Exponential Moving Average.TRIX: Triple Exponential Moving Average.ADX: Average Directional Movement Index.DMI: Directional Moving Index, including.DMA: Different of Moving Average (10, 50).CR: Energy Index (Intermediate Willingness Index).MACD: Moving Average Convergence Divergence.cross: including upward cross and downward cross.count: both backward(c) and forward(fc).

Supply a wrapper StockDataFrame for pandas.DataFrame with inline stock Stock Statistics/Indicators Calculation Helper