In finance, a moving average (MA) is a stock indicator commonly used in technical analysis. The reason for calculating the moving average of a stock is to help smooth out the price data by creating a constantly updated average price. By calculating the moving average, the impacts of random, short-term fluctuations on the price of a stock over a specified time frame are mitigated. Simple moving averages (SMAs) use a simple arithmetic average of prices over some timespan, while exponential moving averages (EMAs) place greater weight on more recent prices than older ones over the time period.

1. MOVING AVERAGES

2. • A moving average is a statistic that captures the
average change in a data series over time.
• In finance, moving averages are often used by technical
analysts to keep track of price trends for specific securities.

3. 1. Simple Moving Average (SMA)
Finance: SMA is used to smooth out short-term
fluctuations and highlight longer-term trends in stock prices.
For instance, a 30-day SMA of a stock price series is the
average of the stock prices over the last 30 days.
Weather Analysis: SMA can be used to smooth
temperature readings over a period, like a 7-day SMA of daily
temperatures to understand weekly trends.

4. 2. Exponential Moving Average (EMA)
Stock Market Analysis: EMA gives more weight to recent
prices, making it more responsive to new information. Traders
use it to detect price trends and potential reversals.
Sales Forecasting: EMA can be applied to sales data to
quickly respond to changes in sales trends, such as a sudden
increase in demand.

5. 3. Cumulative Moving Average (CMA)
Quality Control: CMA is used to track the average quality
measurement of products over time, where each new
measurement is averaged with all previous measurements.
Sensor Data Analysis: In environmental monitoring, CMA
helps to keep an ongoing average of pollutant levels or
temperature readings.

6. 4. Weighted Moving Average (WMA)
Inventory Management: WMA can be used to forecast
inventory requirements by giving more importance to recent
demand patterns.
Energy Consumption: WMA is useful for smoothing daily
energy consumption data, giving more weight to recent days
to better predict short-term consumption

7. 5. Moving Median
Noise Reduction: In signal processing, the moving median
can reduce noise more effectively than moving averages in
certain types of data, such as in audio signal processing.
Outlier Detection: Moving median is used in outlier
detection algorithms where the median is more robust to
outliers compared to the mean.

8. Program:
import pandas as pd
data = {'Date': pd.date_range(start='2023-01-01',periods=10,freq='D'),
'Value': [10, 12, 14, 16, 18, 20, 22, 24, 26, 28]}
df = pd.DataFrame(data)
df['SMA_3'] = df['Value'].rolling(window=3).mean()
print(df)

9. • OUTPUT:

10. THANK YOU