How to store High or Low values (trading)

Question

I would like to develop a code which add a Series to my DataFrame; the Series should store the lowest value of the Close until a new low is reached. When a new low is reached a new value should appear in the Series. The starting code is:

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

ticker = 'EURUSD=X'
df = yf.download(ticker, start='2020-1-1')

df['fixed_low'] = ...?

So, for example, if the most recent low of EURUSD is 1.1000, in the column 'fixed_low' that value should stay until a new low is reached (let's say 1.0999). Then, if the asset is still going down, the column should store new low values until a new low will last for some time, and so on. I hope I was clear. Thank you

Answer 1

import yfinance as yf
import numpy as np


ticker = 'EURUSD=X'
df = yf.download(ticker, start='2021-2-1', end= '2021-3-1')
minimum = np.min(df['Close'])#you saved the minimum
print('minimum', minimum)

df1 = yf.download(ticker, start='2021-3-2', end= '2022-5-1')
for i in df1['Close'].values:
    if i < minimum:
        minimum = i

print('update minimum', minimum)

Two dataframes are created. In the first we find the minimum, in the second we update the minimum value.

ticker = 'EURUSD=X'
df = yf.download(ticker, start='2020-1-1')

df['fixed_low'] = np.nan

low = np.inf
for i in range(0, len(df)):
    if df.loc[df.index[i], 'Low'] < low:
        low = round(df.loc[df.index[i], 'Low'], 6)
    df.loc[df.index[i], 'fixed_low'] = low

Output df

                Open      High       Low  ...  Adj Close  Volume  fixed_low
Date                                      ...                              
2019-12-31  1.120448  1.124101  1.120072  ...   1.120230       0   1.120072
2020-01-01  1.122083  1.122838  1.115947  ...   1.122083       0   1.115947
2020-01-02  1.121894  1.122712  1.116682  ...   1.122083       0   1.115947
2020-01-03  1.117081  1.118068  1.112570  ...   1.117144       0   1.112570
2020-01-06  1.116246  1.120825  1.115810  ...   1.116196       0   1.112570
...              ...       ...       ...  ...        ...     ...        ...
2022-05-06  1.053974  1.059839  1.048537  ...   1.053974       0   1.047285

Before the loop, I set the value of the low variable to the largest. If the current minimum is less than low, then an update occurs. df['fixed_low'] set all values to nan first.