Trend Line Analysis

import pandas as pd
import numpy as np
from scipy import stats

def find_trend_line(df: pd.DataFrame, lookback: int = 20, line_type: str = 'support'):
    """Calculate trend line slope and intercept using linear regression."""
    if line_type == 'support':
        # Find swing lows
        lows = df['low'].rolling(5, center=True).min()
        points = df[df['low'] == lows][['low']].dropna().tail(lookback)
    else:
        # Find swing highs
        highs = df['high'].rolling(5, center=True).max()
        points = df[df['high'] == highs][['high']].dropna().tail(lookback)
    
    if len(points) < 3:
        return None
    
    x = np.arange(len(points))
    y = points.iloc[:, 0].values
    slope, intercept, r_value, _, _ = stats.linregress(x, y)
    
    return {'slope': slope, 'intercept': intercept, 'r_squared': r_value**2}

//+------------------------------------------------------------------+
//| Draw trend line between two swing points                          |
//+------------------------------------------------------------------+
void DrawTrendLine(string name, datetime t1, double p1, datetime t2, double p2, color clr)
{
    if(ObjectFind(0, name) >= 0)
        ObjectDelete(0, name);
    
    ObjectCreate(0, name, OBJ_TREND, 0, t1, p1, t2, p2);
    ObjectSetInteger(0, name, OBJPROP_COLOR, clr);
    ObjectSetInteger(0, name, OBJPROP_WIDTH, 2);
    ObjectSetInteger(0, name, OBJPROP_RAY_RIGHT, true);
}

// Find and connect swing lows for uptrend line
int FindSwingLows(double &prices[], datetime &times[], int count = 5)
{
    int found = 0;
    for(int i = 2; i < Bars - 2 && found < count; i++) {
        if(Low[i] < Low[i-1] && Low[i] < Low[i-2] && 
           Low[i] < Low[i+1] && Low[i] < Low[i+2]) {
            prices[found] = Low[i];
            times[found] = Time[i];
            found++;
        }
    }
    return found;
}