Add noise level metric to backtest range

[MDUYN]

For a backtest range we would like to show the noise level for a given symbol.

Where:

• Net Price Change is the absolute difference between the price at the end of the period and the price at the beginning of the period.
• Sum of Absolute Price Changes is the sum of the absolute differences between each consecutive day's prices within the period.

import pandas as pd

# Example DataFrame with OHLCV data
data = {
    'Date': ['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05'],
    'Open': [100, 102, 101, 103, 104],
    'High': [105, 106, 104, 107, 108],
    'Low': [99, 101, 100, 102, 103],
    'Close': [104, 105, 102, 106, 107],
    'Volume': [1000, 1100, 1200, 1300, 1400]
}
df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)

# Calculate daily price changes
df['Daily Change'] = df['Close'].diff()

# Calculate net price change over the period
net_price_change = abs(df['Close'].iloc[-1] - df['Close'].iloc[0])

# Calculate the sum of absolute daily price changes
sum_absolute_changes = df['Daily Change'].abs().sum()

# Calculate Efficiency Ratio
efficiency_ratio = net_price_change / sum_absolute_changes
print(f'Efficiency Ratio: {efficiency_ratio}')

There should be function in the backtest report that check if there is a symbol entry in the algorithm context object. If there is it will check with this symbol if a pandas dataframe can be found with ohlcv data. if there is a efficiency ratio will be add to the backtest range for the specific symbol.