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Exponential Moving Average(EMA) Calculation with Python

Updated: Apr 29

Exponential Moving Average Python

Here's an example Python code that calculates the exponential moving average (EMA) for a given stock using the pandas library:

import pandas as pd

# Read stock data from a CSV file
df = pd.read_csv('stock_data.csv', index_col=0)

# Set the smoothing factor
smoothing_factor = 0.5

# Calculate the initial SMA
sma = df['Close'].rolling(window=5).mean()

# Calculate the EMA using the first value of the SMA as the starting point
ema = [sma[0]]

for i in range(1, len(df)):
    close_price = df['Close'][i]
    ema_value = (close_price * smoothing_factor) + (ema[i-1] * (1 - smoothing_factor))

# Add the EMA values as a new column to the dataframe
df['EMA'] = ema

# Print the final dataframe with the EMA column

In this example, we first read the stock data from a CSV file using the pandas library. We then set the smoothing factor to 0.5 and calculate the initial SMA using the rolling() function. We then use a loop to calculate the EMA for each data point using the formula we discussed earlier. Finally, we add the EMA values as a new column to the dataframe and print the final dataframe with the EMA column.

Note that you will need to have a CSV file with stock data in order to run this code, and you may need to modify the code depending on the format and structure of your data.

Learn More : What is Exponential Moving Average(EMA) | What is Moving Average Formula | Mastering Moving Averages: A Guide to Analyzing Time-Series Data and Identifying Trends

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