import yfinance as yf
import pandas as pd
import numpy as np

# Get stock data
ticker = "AAPL"
data = yf.download(ticker, period="1y")

# Calculate 20-day and 50-day moving averages
data["MA20"] = data["Close"].rolling(window=20).mean()
data["MA50"] = data["Close"].rolling(window=50).mean()

# Trend identification
data["Trend"] = "None"
data.loc[data["MA20"] > data["MA50"], "Trend"] = "Uptrend"
data.loc[data["MA20"] < data["MA50"], "Trend"] = "Downtrend"

# Simple signal strength based on distance between MAs
data["SignalStrength"] = abs(data["MA20"] - data["MA50"])

# Buy/Sell signals
data["Signal"] = "None"
data.loc[(data["MA20"] > data["MA50"]) & (data["MA20"].shift(1) < data["MA50"].shift(1)), "Signal"] = "Buy"
data.loc[(data["MA20"] < data["MA50"]) & (data["MA20"].shift(1) > data["MA50"].shift(1)), "Signal"] = "Sell"

# Radius Trend Indicator
# User inputs
step = 0.15
multi = 2
n = 3

# Initialize variables
trend = np.nan
multi1 = 0
multi2 = 0
band = 0

# Indicator calculations
distance = data["High"].rolling(100).apply(lambda x: np.mean(np.abs(x - data["Low"].rolling(100).min()))) * multi
distance1 = data["High"].rolling(100).apply(lambda x: np.mean(np.abs(x - data["Low"].rolling(100).min()))) * 0.2

# Initialize trend and band on the 101st bar
if len(data) > 100:
    trend = True
    # Accessing the first value of band using iloc[0] assuming it's a single value
    band = data["Low"].iloc[100] * 0.8  

# Update trend based on price relation to band
# Using the single value of band for comparison
data["band"] = np.nan  # Initialize 'band' column
data.iloc[100:, data.columns.get_loc("band")] = [band] * len(data.iloc[100:])  # Assign the band value to the 'band' column

# Ensure 'Close' and 'band' columns are aligned
data["Close"] = data["Close"].ffill()
data["band"] = data["band"].ffill()

# Perform comparison on aligned data
aligned_data = data[100:]
aligned_data.loc[aligned_data["Close"] < aligned_data["band"], "Trend"] = False  
aligned_data.loc[aligned_data["Close"] > aligned_data["band"], "Trend"] = True

# Update original DataFrame with aligned data
data[100:] = aligned_data

# Adjust band on trend changes
# Ensure 'band' column exists before assigning values
data.loc[(data["Trend"].shift(1) == False) & (data["Trend"].diff() == 1), "band"] = data["Low"] - distance.values
data.loc[(data["Trend"].shift(1) == True) & (data["Trend"].diff() == -1), "band"] = data["High"] + distance.values

# Apply step angle to trend lines
# Assuming data["Trend"] is boolean
data.loc[data.index % n == 0 & data["Trend"].astype(bool), "band"] += distance1.values * multi2 
data.loc[data.index % n == 0 & ~data["Trend"].astype(bool), "band"] -= distance1.values * multi1

data["Sband"] = data["band"].rolling(n).mean()

# Set color based on trend
data.loc[data["Trend"] == True, "color"] = "#54b6d4"  # Use == True for boolean comparison
data.loc[data["Trend"] == False, "color"] = "#cf2b2b" # Use == False for boolean comparison

# Calculate upper and lower bands
data["band_upper"] = data["Sband"] + distance * 0.5
data["band_lower"] = data["Sband"] - distance * 0.5
data.loc[data["Trend"] == True, "band1"] = data["band_upper"]  # Use == True for boolean comparison
data.loc[data["Trend"] == False, "band1"] = data["band_lower"] # Use == False for boolean comparison

print(data.tail())

...fix this error:
ValueError                                Traceback (most recent call last)
<ipython-input-1-d2ccc30525ce> in <cell line: 59>()
     57 # Perform comparison on aligned data
     58 aligned_data = data[100:]
---> 59 aligned_data.loc[aligned_data["Close"] < aligned_data["band"], "Trend"] = False
     60 aligned_data.loc[aligned_data["Close"] > aligned_data["band"], "Trend"] = True
     61 

3 frames
/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py in _align_for_op(self, other, axis, flex, level)
   8203             if not flex:
   8204                 if not left.axes[axis].equals(right.index):
-> 8205                     raise ValueError(
   8206                         "Operands are not aligned. Do "
   8207                         "`left, right = left.align(right, axis=1, copy=False)` "

ValueError: Operands are not aligned. Do `left, right = left.align(right, axis=1, copy=False)` before operating.