Optimization of an exponential sliding average (EMA) for better alignment with Yahoo Finance
As a data analyst or a merchant, it is necessary to ensure that your technical indicator, such as EMA, is consistent with the market and reflect the exact values. One common challenge is when the calculated EMA does not match the defined value of Yahoo funding. In this article, we explore why this can happen and give instructions to optimize the EMA calculation.
Why is EMA not in line with Yahoo Finance?
There are several reasons why the calculated EMA may not respond to the defined of Yahoo funding:
- Differences : Information set values may differ from the values of Yahoo funding, such as different factors, such as differences in timestamp or data sampling.
2
- Data Type Conversions : If your data set contains different types of data (eg date vs. numeric values), it may affect the accuracy of the EMA calculation.
EMA calculation optimization
To face the calculated EMA on the Yahoo Finance site, as defined as these steps:
Step 1: Clean and prepare your data
Make sure your data set is not errors or inconsistencies in data formatting. To clean up and preliminate your data in Panda:
`Python
Bring pandas pd
Download the data set to the Panda information frame
df = pd.read_csv (‘your_data.csv’)
Convert Date columns to Datetime format
df [‘date’] = pd.to_datetime (df [‘date’])
Fill in the missing values with the latest value in each column
df.fillna (method = ‘ffill’, inplace = true)
`
Step 2: Set EMA Calculation
Determine your EMA calculation with a panda ‘ta.Mas function that allows you to customize parameters:
`Python
Bring pandas_ta like ta
Create a new column to save the calculated EMA
df [‘EMA48’] = ta.MaMa (df [‘closed’], episode = 48)
`
Step 3: Target with Yahoo Finance Value
Compare the calculated EMA to the value specified in Yahoo Finance by setting the alignment parameter:
`Python
Bring Numphy as NP
Set the alignment parameter to ensure the corresponding
alignment = ‘cross’
Use nan values to indicate mismatch
df [’ema_match’] = pd.isnull (df [‘EMA48’]) | (np.isennan (DF [targeting]) and df [alignment]! = DF [‘EMA48’])
`
Step 4: Visualize the results
Draw EMA and targeting meters to visualize the differences:
`Python
Bring MatplotLib.pplot like plt
Draw your data with the calculated EMA
Plt.plot (df [‘date’], df [‘closed’], etiquette = ‘Close’)
Plt.plot (df [‘date’], df [‘EMA48’], Label = ‘EMA’)
plt.legend ()
Plt.show ()
Highlight mismatch values
Mask = DF [‘EMA_match’]. Values [0]
Plt.Scatter (df [‘date’] [mask], df [‘EMA48’] [mask], Color = ‘red’)
Plt.title (‘mismatch values’)
Plt.show ()
`
By following these steps, you should be able to optimize your EMA calculation and target it to the defined value of Yahoo funding. Be sure to check and update the EMA settings regularly as market conditions change.