Currency Pair Strength List Based On Dataframe Row Values Mới Update

To create a currency pair strength list based on a dataframe row values, you would first need to define what you mean by “currency pair strength”. One common approach is to calculate the percentage change in the exchange rate for each currency pair over a specified time period, and then rank the pairs by the magnitude of this change.

Assuming you have a dataframe with columns representing different currency pairs and rows representing different time periods, you can calculate the percentage change in exchange rates using the following steps:

1. Calculate the exchange rate for each currency pair at the beginning and end of the time period.

2. Calculate the percentage change in exchange rate using the following formula:

   sql
   percentage change = (end exchange rate / start exchange rate - 1) * 100%
   

3. Rank the currency pairs by the magnitude of their percentage change.

Here’s some sample code that shows how to do this using the Pandas library in Python:

python
import pandas as pd

# create sample dataframe with exchange rates
df = pd.DataFrame({'USDJPY': [110.5, 109.0, 107.5],
                   'EURUSD': [1.15, 1.18, 1.20],
                   'GBPUSD': [1.30, 1.35, 1.40]})

# calculate percentage change for each currency pair
start_rates = df.iloc[0]
end_rates = df.iloc[-1]
pct_change = (end_rates / start_rates - 1) * 100

# rank currency pairs by percentage change
strength_list = pct_change.sort_values(ascending=False).index.tolist()

print(strength_list)

This code would output a list of currency pairs ranked in order of percentage change, from highest to lowest.