Enfrentando um erro ao percorrer a lista e salvar os dados

Em relação à sua primeira pergunta, você precisa se certificar de que o valor no campo de entrada esteja limpo antes de inserir o próximo símbolo. Use o código abaixo para fazer isso.

def clear_web_field(element):
    while element.get_attribute("value") != "":
        element.send_keys(Keys.BACKSPACE)

Código completo:

import undetected_chromedriver as uc
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.keys import Keys
from bs4 import BeautifulSoup
import time
import csv
import os

# Step 1: Create a list of stock symbols (for illustration purposes, here are a few)
# You can replace this with a list of 500 stock symbols
stock_symbols = ["INFY", "TCS", "RELIANCE", "HDFCBANK", "ITC"]  # Add all 500 symbols here

# Define the CSV file name
csv_file = "nse_data.csv"

# Check if file already exists (to avoid writing headers again)
file_exists = os.path.isfile(csv_file)

# Step 2: Open browser and go to URL
driver = uc.Chrome()
driver.get("https://www.nseindia.com/report-detail/eq_security")
driver.maximize_window()
wait = WebDriverWait(driver, 10)

def clear_web_field(element):
    while element.get_attribute("value") != "":
        element.send_keys(Keys.BACKSPACE)

# Function to get data for a single stock
def get_stock_data(symbol):
    # Step 1: Clear the symbol input field and enter new symbol
    symbol_input = wait.until(EC.element_to_be_clickable((By.ID, "hsa-symbol")))

    # Clear the input field by sending backspace
    clear_web_field(symbol_input)

    # Wait a moment to ensure the field is cleared before entering the new symbol
    time.sleep(0.5)  # Add a small delay for clarity

    # Enter the new symbol
    symbol_input.send_keys(symbol)  # Enter the new symbol
    print(f"✅ Selected symbol '{symbol}'")
    time.sleep(2)

    # Step 2: Click on the first dropdown option
    wait.until(EC.element_to_be_clickable((By.XPATH, "//div[@id='hsa-symbol_listbox']//div//div[1]"))).click()
    print(f"✅ Clicked on the first option for '{symbol}'")
    time.sleep(5)

    # Step 3: Click on the 1W button
    one_week_button = wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="oneW"]')))
    one_week_button.click()
    print(f"✅ Clicked on the 1W button for {symbol}")
    time.sleep(5)

    # Step 4: Parse the table and extract data for a specific date
    target_date = "11-Apr-2025"
    soup = BeautifulSoup(driver.page_source, "html.parser")

    table = soup.find("table", {"id": "hsaTable"})
    rows = table.find_all("tr")

    # Get headers to map column names to index
    headers = [th.text.strip() for th in rows[0].find_all("th")]

    # Mapping the column names we care about
    columns_of_interest = ["Date", "Total Traded Quantity", "No. of Trades", "Deliverable Qty",
                           "% Dly Qt to Traded Qty"]
    indices = {col: headers.index(col) for col in columns_of_interest}

    # Look for the row with the target date
    for row in rows[1:]:
        cols = [td.text.strip() for td in row.find_all("td")]
        if cols and cols[indices["Date"]] == target_date:
            extracted_data = {
                "Symbol": symbol,
                "Date": cols[indices["Date"]],
                "Total Traded Quantity": cols[indices["Total Traded Quantity"]],
                "No. of Trades": cols[indices["No. of Trades"]],
                "Deliverable Qty": cols[indices["Deliverable Qty"]],
                "% Dly Qt to Traded Qty": cols[indices["% Dly Qt to Traded Qty"]],
            }
            print(f"✅ Data for {symbol} on {target_date}:")
            for key, val in extracted_data.items():
                print(f"{key}: {val}")
            break
    else:
        print(f"❌ No data found for date: {target_date} for {symbol}")
        extracted_data = None

    return extracted_data


# Write data for multiple stocks
for symbol in stock_symbols:
    extracted_data = get_stock_data(symbol)

    if extracted_data:
        # Write to CSV
        with open(csv_file, mode="a", newline="", encoding="utf-8") as f:
            writer = csv.DictWriter(f, fieldnames=extracted_data.keys())

            # Write header only once
            if not file_exists:
                writer.writeheader()

            # Write the row
            writer.writerow(extracted_data)

        print(f"✅ Data for {symbol} written to CSV.")

# Close the browser when all data is collected
driver.quit()

print("✅ All data extraction completed.")

SEGUNDA PERGUNTA: Não tenho certeza se é isso que você está procurando. Veja o código abaixo para clicar em3M

# Using ID locator (this is preferred)
three_month_button = wait.until(EC.element_to_be_clickable((By.ID, 'threeM')))
three_month_button.click()

#or using the XPath expression
three_month_button = wait.until(EC.element_to_be_clickable((By.XPATH, '//a[@id="threeM"]')))
three_month_button.click()

ATUALIZAÇÃO - Resposta à segunda pergunta:

import undetected_chromedriver as uc
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.keys import Keys
from bs4 import BeautifulSoup
import time
import csv
import os

# Step 1: Create a list of stock symbols (for illustration purposes, here are a few)
# You can replace this with a list of 500 stock symbols
stock_symbols = ["INFY", "TCS", "RELIANCE", "HDFCBANK", "ITC"]  # Add all 500 symbols here

# Define the CSV file name
csv_file = "nse_data.csv"

# Check if file already exists (to avoid writing headers again)
file_exists = os.path.isfile(csv_file)
header_written = os.path.getsize(csv_file) > 0 if file_exists else False  # Track header writing during script run

# Step 2: Open browser and go to URL
driver = uc.Chrome()
driver.get("https://www.nseindia.com/report-detail/eq_security")
driver.maximize_window()
wait = WebDriverWait(driver, 10)

def clear_web_field(element):
    while element.get_attribute("value") != "":
        element.send_keys(Keys.BACKSPACE)

# Function to get data for a single stock
def get_stock_data(symbol):
    # Step 1: Clear the symbol input field and enter new symbol
    symbol_input = wait.until(EC.element_to_be_clickable((By.ID, "hsa-symbol")))

    # Clear the input field by sending backspace
    clear_web_field(symbol_input)

    # Wait a moment to ensure the field is cleared before entering the new symbol
    time.sleep(0.5)  # Add a small delay for clarity

    # Enter the new symbol
    symbol_input.send_keys(symbol)  # Enter the new symbol
    print(f"✅ Selected symbol '{symbol}'")
    time.sleep(2)

    # Step 2: Click on the first dropdown option
    wait.until(EC.element_to_be_clickable((By.XPATH, "//div[@id='hsa-symbol_listbox']//div//div[1]"))).click()
    print(f"✅ Clicked on the first option for '{symbol}'")
    time.sleep(5)

    # Step 3: Click on the 1W button
    one_week_button = wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="threeM"]')))
    one_week_button.click()

    print(f"✅ Clicked on the 1W button for {symbol}")
    time.sleep(5)

    # Step 4: Parse the table and extract data for a specific date
    target_date = "11-Apr-2025"
    soup = BeautifulSoup(driver.page_source, "html.parser")

    table = soup.find("table", {"id": "hsaTable"})
    rows = table.find_all("tr")

    # Get headers to map column names to index
    headers = [th.text.strip() for th in rows[0].find_all("th")]

    # Mapping the column names we care about
    columns_of_interest = ["Date", "Total Traded Quantity", "No. of Trades", "Deliverable Qty",
                           "% Dly Qt to Traded Qty"]
    indices = {col: headers.index(col) for col in columns_of_interest}

    extracted_data_list = []

    for row in rows[1:]:
        cols = [td.text.strip() for td in row.find_all("td")]
        extracted_data = {
            "Symbol": symbol,
            "Date": cols[indices["Date"]],
            "Total Traded Quantity": cols[indices["Total Traded Quantity"]],
            "No. of Trades": cols[indices["No. of Trades"]],
            "Deliverable Qty": cols[indices["Deliverable Qty"]],
            "% Dly Qt to Traded Qty": cols[indices["% Dly Qt to Traded Qty"]],
        }
        extracted_data_list.append(extracted_data)
        print(f"✅ Data for {symbol} on {target_date}:")
        for key, val in extracted_data.items():
            print(f"{key}: {val}")
    return extracted_data_list


# Write data for multiple stocks
for symbol in stock_symbols:
    extracted_data_list  = get_stock_data(symbol)

    if extracted_data_list:
        # Write to CSV
        with open(csv_file, mode="a", newline="", encoding="utf-8") as f:
            writer = csv.DictWriter(f, fieldnames=extracted_data_list[0].keys())

            # Write header only once
            if not header_written:
                writer.writeheader()
                header_written = True

            # Write the row
            for row_data in extracted_data_list:
                writer.writerow(row_data)

        print(f"✅ Data for {symbol} written to CSV.")

# Close the browser when all data is collected
driver.quit()

print("✅ All data extraction completed.")