Market data into spreadsheet
https://www.alphavantage.co/spreadsheets/. data can be shown with flask
GitHub - birdhouses/ai-trader python + open AI
GitHub - birdhouses/trading212: Get insights in your trading212 dividend payouts
Copilot answers APIdog to scrap the data from Tradingview
To use Apidog for generating a schema from TradingView's screener database, you would need to follow these steps. However, note that scraping TradingView's data directly may violate their terms of service, so it's always better to use their official API if available. Here's a general guide for schema generation using Apidog:
1. Understand the Data Source
- TradingView does not provide direct access to its database schema. If you're trying to scrape or analyze screener data, you might need to first identify the structure of the data (e.g., JSON responses from their API or web requests).
- Use tools like browser developer tools (Network tab) to inspect the data format (e.g., JSON or HTML).
2. Use Apidog to Generate Schema
Apidog supports schema generation from JSON or database tables. Here's how you can proceed:
Option 1: From JSON Data
Extract JSON Data:
- If you have access to TradingView's screener data in JSON format (e.g., from API responses or web scraping), save it as a
.json file.
Generate Schema:
- Open Apidog and navigate to the "Generate Schema" section.
- Upload the JSON file or paste the JSON content directly.
- Apidog will analyze the structure and generate a schema automatically.
Option 2: From Database Tables
Connect to Database:
- If you have access to a database containing TradingView-like screener data, connect Apidog to the database.
- Provide the necessary credentials (host, port, username, password).
Generate Schema:
- Apidog will read the table structures and generate schemas for each table.
3. Validate and Customize the Schema
- Review the generated schema to ensure it matches the data structure.
- Customize field names, data types, or relationships as needed.
Important Notes
- Respect Terms of Service: Scraping or unauthorized access to TradingView's data may breach their terms of service. Always prefer using their official API if available.
- Alternative Tools: If Apidog doesn't meet your needs, consider other schema generation tools like Postman or Swagger.
Let me know if you'd like further clarification! 😊
Python Library "TradingView_screener"
https://shner-elmo.github.io/TradingView-Screener/3.0.0/tradingview_screener.html#quickstart
GitHub:
https://github.com/shner-elmo/TradingView-Screener
It is handy to have an online SQL viewer to look at the contents of the database files.
https://beta.sqliteviewer.app/2025-08-17.db/table/stock_data
Copilot suggested python codes to write a data file to Google sheet.
Here’s a concise guide to storing a file's data into a Google Sheet using Python. This involves enabling the Google Sheets API, authenticating, and writing data to the sheet.
Steps Overview
- Enable the Google Sheets API in your Google Cloud Console.
- Download the
credentials.json file for authentication. - Install required Python libraries:
google-auth, google-auth-oauthlib, google-auth-httplib2, and gspread. - Use the following Python code to upload data.
Code Example 1: Writing Data to Google Sheets
import gspread
from google.oauth2.service_account import Credentials
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
creds = Credentials.from_service_account_file("credentials.json", scopes=scope)
client = gspread.authorize(creds)
sheet = client.open("Your Google Sheet Name").sheet1
data = [["Name", "Age", "City"], ["Alice", 30, "San Francisco"], ["Bob", 25, "New York"]]
for row in data:
sheet.append_row(row)
print("Data successfully written to Google Sheet!")
Code Example 2: Reading a File and Storing Its Data
import csv
import gspread
from google.oauth2.service_account import Credentials
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
creds = Credentials.from_service_account_file("credentials.json", scopes=scope)
client = gspread.authorize(creds)
sheet = client.open("Your Google Sheet Name").sheet1
with open("your_file.csv", "r") as file:
reader = csv.reader(file)
for row in reader:
sheet.append_row(row)
print("File data successfully written to Google Sheet!")
Code Example 3: Writing Data from a Python Dictionary
import gspread
from google.oauth2.service_account import Credentials
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
creds = Credentials.from_service_account_file("credentials.json", scopes=scope)
client = gspread.authorize(creds)
sheet = client.open("Your Google Sheet Name").sheet1
data = [{"Name": "Alice", "Age": 30, "City": "San Francisco"},
{"Name": "Bob", "Age": 25, "City": "New York"}]
sheet.append_row(list(data[0].keys()))
for entry in data:
sheet.append_row(list(entry.values()))
print("Dictionary data successfully written to Google Sheet!")
Key Notes
Let me know if you need further clarification or help! 😊
Indian guy stockanalysis
MCP codes only for India market
https://medium.com/@varungangu1/building-a-stock-analysis-mcp-server-with-docker-and-claude-desktop-eae4963dc3a7
his GitHub depository , not much description over there
https://github.com/varun-g-12/StockAnalysisMCP
Python Library "TradingView_scrapper" live example
Saved 3 files:- 52wks high , all time highs, and high gainers in excel formats everyday.
then analyze the patterns, next things I would do is to save the files into my Google sheets.
data scrapped from this TradingView page:
https://www.tradingview.com/markets/stocks-usa/market-movers-52wk-high/
Original
https://medium.com/@alvinmarco8/otomasi-scrapping-data-market-saham-di-tradingview-menggunakan-python-c499a244db40
my codes:
from urllib.request import urlopen
from bs4 import BeautifulSoup
import pandas as pd
from datetime import date
import os
from urllib.error import HTTPError
def tradingviewScrapper():
urls = [
'https://www.tradingview.com/markets/stocks-usa/market-movers-52wk-high/',
'https://www.tradingview.com/markets/stocks-usa/market-movers-ath/',
'https://www.tradingview.com/markets/stocks-usa/market-movers-gainers/'
]
root_path = r'/Users/dl/Documents/codes/tradings/'
print(f"Saving files to: {root_path}")
os.makedirs(root_path, exist_ok=True)
for url in urls:
try:
# Content retrieval
page = urlopen(url)
html = page.read().decode("utf-8")
# Create BeautifulSoup object
soup = BeautifulSoup(html, "html.parser")
rows = soup.find_all('tr')
parts = url.split('market-movers-')
typeurl = parts[1]
today = date.today().strftime('%y-%m-%d')
data = []
# Loop through each line and get data from element <td>
for row in rows:
# Get all <td> elements in a line
cells = row.find_all('td')
# Get the text content from each cell and add it to the data list
row_data = [cell.text.strip() for cell in cells]
data.append(row_data)
th_elements = rows[0].find_all('th')
headers = [th.text.strip() for th in th_elements]
# Create DataFrame from data list
df = pd.DataFrame(data, columns=headers)
filename = f'Data_{str(typeurl)}_{str(today)}.xlsx'
excel_path = os.path.join(root_path, filename)
print(f"Saving Excel to: {excel_path}")
# Ensure the parent directory exists
os.makedirs(os.path.dirname(excel_path), exist_ok=True)
df.to_excel(excel_path, index=False)
print(f'Data {url} successfully discraping.')
except Exception as e:
print(f"Error saving file: {e}")
tradingviewScrapper()
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TradeBot: Stock Trading using Reinforcement Learning — Part1 | by Shivam Akhauri | ETHER Labs | Medium