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| from bs4 import BeautifulSoup | |
| import csv | |
| import requests | |
| import numpy as np | |
| import pandas as pd | |
| import os | |
| import datetime | |
| from tensorflow import keras | |
| import subprocess | |
| import streamlit as st | |
| import jpype | |
| def getdata(): | |
| #------------------------GETTING HTML TABLE DATA--------------------------- | |
| url = f'https://ltp.investingdaddy.com/detailed-options-chain.php' | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| html_source = response.text | |
| #print(html_source) | |
| else: | |
| print(f"Failed to retrieve the webpage. Status code: {response.status_code}") | |
| #------------------------FILTERING TABLE DATA------------------------------- | |
| soup = BeautifulSoup(html_source, 'html.parser') | |
| tables = soup.find_all('table') | |
| ##price = soup.find('label', {'id':'future_val'}) | |
| ##price = price.text | |
| ##prices.append(price) | |
| ##return | |
| if len(tables) >= 2: | |
| second_table = tables[1] | |
| else: | |
| print("There are not enough tables in the HTML source.") | |
| #-----------------------CONVERTING HTML TABLE DATA TO CSV-------------------- | |
| html_content = "<html>"+str(second_table)+"</html>" | |
| soup = BeautifulSoup(html_content, 'html.parser') | |
| table = soup.find('table', {'id': 'tech-companies-1'}) | |
| table_data = [] | |
| rows = table.find_all('tr') | |
| for row in rows: | |
| row_data = [] | |
| cols = row.find_all(['th', 'td']) | |
| for col in cols: | |
| row_data.append(col.get_text(strip=True)) | |
| table_data.append(row_data) | |
| csv_file = 'sample.csv' | |
| r=False | |
| with open(csv_file, 'w', newline='') as csvfile: | |
| csv_writer = csv.writer(csvfile) | |
| for row_data in table_data: | |
| if r: | |
| csv_writer.writerow(row_data) | |
| else: | |
| r=True | |
| print(f'Table data has been successfully written to {csv_file}.') | |
| def get_his_data(m,d,h,min): | |
| #------------------------GETTING HTML TABLE DATA--------------------------- | |
| url = f'https://ltp.investingdaddy.com/historical-option-chain.php?symbol=NIFTY&expiry=2023-09-07&filterdate1=2023-09-04&filtertime=09%3A15&filterdate=2023-{m}-{d}T{h}%3A{min}' | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| html_source = response.text | |
| #print(html_source) | |
| else: | |
| print(f"Failed to retrieve the webpage. Status code: {response.status_code}") | |
| #------------------------FILTERING TABLE DATA------------------------------- | |
| soup = BeautifulSoup(html_source, 'html.parser') | |
| tables = soup.find_all('table') | |
| ##price = soup.find('label', {'id':'future_val'}) | |
| ##price = price.text | |
| ##prices.append(price) | |
| ##return | |
| if len(tables) >= 2: | |
| second_table = tables[1] | |
| else: | |
| print("There are not enough tables in the HTML source.") | |
| #-----------------------CONVERTING HTML TABLE DATA TO CSV-------------------- | |
| html_content = "<html>"+str(second_table)+"</html>" | |
| soup = BeautifulSoup(html_content, 'html.parser') | |
| table = soup.find('table', {'id': 'tech-companies-1'}) | |
| table_data = [] | |
| rows = table.find_all('tr') | |
| for row in rows: | |
| row_data = [] | |
| cols = row.find_all(['th', 'td']) | |
| for col in cols: | |
| row_data.append(col.get_text(strip=True)) | |
| table_data.append(row_data) | |
| csv_file = 'sample.csv' | |
| r=False | |
| with open(csv_file, 'w', newline='') as csvfile: | |
| csv_writer = csv.writer(csvfile) | |
| for row_data in table_data: | |
| if r: | |
| csv_writer.writerow(row_data) | |
| else: | |
| r=True | |
| print(f'historical Table data has been successfully written to {csv_file}.') | |
| def changedatashape(): | |
| # Load your CSV data into a Pandas DataFrame | |
| df = pd.read_csv('sample.csv') # Replace 'your_data.csv' with your actual file path | |
| # Check the shape of the original DataFrame (it should be 20x20) | |
| #print("Original DataFrame Shape:", df.shape) | |
| # Convert the DataFrame into a NumPy array | |
| data = df.to_numpy() | |
| # Reshape the data into a 1D array and transpose it | |
| horizontal_data = data.flatten().reshape(1, -1) | |
| # Check the shape of the reshaped data (it should be 1x400) | |
| #print("Horizontal Data Shape:", horizontal_data.shape) | |
| # If you want to save this reshaped data to a new CSV file: | |
| horizontal_df = pd.DataFrame(horizontal_data) | |
| # Save it to a new CSV file without row or column labels | |
| horizontal_df.to_csv('sample2.csv', index=False, header=False) | |
| os.remove('sample.csv') | |
| #jpype.startJVM(jpype.getDefaultJVMPath()) | |
| def generate_result(): | |
| data = pd.read_csv("sample2.csv") | |
| st.write(data) | |
| # Define your Java command as a string | |
| #java_command = "java -version" | |
| #os.system("docker build -t my-java-app") | |
| # Run the Java command in a subprocess | |
| #jpype.runScript(java_command) | |
| def predict(m,historical=True): | |
| if historical: | |
| date = input('date:') | |
| hour = input('hour:') | |
| min = input('min') | |
| get_his_data(m,date,hour,min) | |
| else: | |
| getdata() | |
| changedatashape() | |
| generate_result() | |
| # Specify the path to your CSV file | |
| csv_file_path = 'sample2.csv' | |
| # Read the existing data to determine the number of columns and rows | |
| with open(csv_file_path, 'r') as csv_file: | |
| reader = csv.reader(csv_file) | |
| data = list(reader) | |
| num_columns = len(data[0]) if data else 0 | |
| num_rows = len(data) | |
| # Generate the header row for the new column | |
| new_column_header = 'sample names' | |
| # Generate the values for the new column | |
| new_column_values = ['sample{}'.format(i-1) for i in range(1, num_rows + 1)] | |
| # Insert the new column into the data | |
| for i in range(num_rows): | |
| data[i].insert(0, new_column_values[i]) | |
| # Write the modified data back to the file | |
| with open(csv_file_path, 'w', newline='') as csv_file: | |
| writer = csv.writer(csv_file) | |
| writer.writerow([new_column_header] + ['feature{}'.format(i+1) for i in range(1, num_columns + 1)]) | |
| writer.writerows(data) | |
| if st.button("Predict result"): | |
| predict(9,historical=False) | |
| if 1==2: | |
| predi = pd.read_csv('predictions.csv') | |
| if 0.5<predi.iloc[0,1]<0.63: | |
| st.write("increase") | |
| else: | |
| st.write("decrease") | |
| # Shutdown the JVM when you're done | |
| #jpype.shutdownJVM() |