Module marketools.analysis.moving_average
Expand source code
import pandas as pd
import numpy as np
def simple_moving_average(ohlc: pd.DataFrame,
price: str = 'Close',
window: int = 15):
"""
Returns Pandas Series with the Simple Moving Average for the indicated price. NaN will be placed for days
where there is not enough previous data (depends on min_periods)
Parameters
----------
ohlc : pandas.DataFrame
DataFrame with OHLC data
price : str
The price on which SMA will be calculated (Close by default)
window : int
Size of the moving window. This is the number of observations used for calculating the statistic
Returns
-------
pandas.Series
"""
# implement simple moving average
output = ohlc[price].rolling(window=window).mean()
output = output.rename(f'SMA{window}')
return output
def weighted_moving_average(ohlc: pd.DataFrame,
price: str = 'Close',
window: int = 15):
"""
Returns Pandas Series with the Weighted Moving Average for the indicated price. NaN will be placed for days
where there is not enough previous data
Parameters
----------
ohlc : pandas.DataFrame
DataFrame with OHLC data
price : str
The price on which WMA will be calculated (Close by default)
window : int
Size of the moving window. This is the number of observations used for calculating the statistic
Returns
-------
pandas.Series
"""
weights = np.arange(1, window + 1)
# implement weighted moving average
output = ohlc[price].rolling(window=window)\
.apply(lambda prices: np.dot(prices, weights)/weights.sum(), raw=True)
output = output.rename(f'WMA{window}')
return output
def exponential_moving_average(ohlc: pd.DataFrame,
price: str = 'Close',
window: int = 15):
"""
Returns Pandas Series with the Exponential Moving Average for the indicated price.
Parameters
----------
ohlc : pandas.DataFrame
DataFrame with OHLC data
price : str
The price on which EMA will be calculated (Close by default)
window : int
Size of the moving window. (span in EMA, alpha = 2/(span + 1) for span >= 1)
Returns
-------
pandas.Series
"""
# implement exponential moving average
output = ohlc[price].ewm(span=window, adjust=False).mean()
output = output.rename(f'EMA{window}')
return output
if __name__ == '__main__':
passFunctions
def exponential_moving_average(ohlc: pandas.core.frame.DataFrame, price: str = 'Close', window: int = 15)-
Returns Pandas Series with the Exponential Moving Average for the indicated price.
Parameters
ohlc:pandas.DataFrame- DataFrame with OHLC data
price:str- The price on which EMA will be calculated (Close by default)
window:int- Size of the moving window. (span in EMA, alpha = 2/(span + 1) for span >= 1)
Returns
pandas.Series
Expand source code
def exponential_moving_average(ohlc: pd.DataFrame, price: str = 'Close', window: int = 15): """ Returns Pandas Series with the Exponential Moving Average for the indicated price. Parameters ---------- ohlc : pandas.DataFrame DataFrame with OHLC data price : str The price on which EMA will be calculated (Close by default) window : int Size of the moving window. (span in EMA, alpha = 2/(span + 1) for span >= 1) Returns ------- pandas.Series """ # implement exponential moving average output = ohlc[price].ewm(span=window, adjust=False).mean() output = output.rename(f'EMA{window}') return output def simple_moving_average(ohlc: pandas.core.frame.DataFrame, price: str = 'Close', window: int = 15)-
Returns Pandas Series with the Simple Moving Average for the indicated price. NaN will be placed for days where there is not enough previous data (depends on min_periods)
Parameters
ohlc:pandas.DataFrame- DataFrame with OHLC data
price:str- The price on which SMA will be calculated (Close by default)
window:int- Size of the moving window. This is the number of observations used for calculating the statistic
Returns
pandas.Series
Expand source code
def simple_moving_average(ohlc: pd.DataFrame, price: str = 'Close', window: int = 15): """ Returns Pandas Series with the Simple Moving Average for the indicated price. NaN will be placed for days where there is not enough previous data (depends on min_periods) Parameters ---------- ohlc : pandas.DataFrame DataFrame with OHLC data price : str The price on which SMA will be calculated (Close by default) window : int Size of the moving window. This is the number of observations used for calculating the statistic Returns ------- pandas.Series """ # implement simple moving average output = ohlc[price].rolling(window=window).mean() output = output.rename(f'SMA{window}') return output def weighted_moving_average(ohlc: pandas.core.frame.DataFrame, price: str = 'Close', window: int = 15)-
Returns Pandas Series with the Weighted Moving Average for the indicated price. NaN will be placed for days where there is not enough previous data
Parameters
ohlc:pandas.DataFrame- DataFrame with OHLC data
price:str- The price on which WMA will be calculated (Close by default)
window:int- Size of the moving window. This is the number of observations used for calculating the statistic
Returns
pandas.Series
Expand source code
def weighted_moving_average(ohlc: pd.DataFrame, price: str = 'Close', window: int = 15): """ Returns Pandas Series with the Weighted Moving Average for the indicated price. NaN will be placed for days where there is not enough previous data Parameters ---------- ohlc : pandas.DataFrame DataFrame with OHLC data price : str The price on which WMA will be calculated (Close by default) window : int Size of the moving window. This is the number of observations used for calculating the statistic Returns ------- pandas.Series """ weights = np.arange(1, window + 1) # implement weighted moving average output = ohlc[price].rolling(window=window)\ .apply(lambda prices: np.dot(prices, weights)/weights.sum(), raw=True) output = output.rename(f'WMA{window}') return output