AI:python code for scanner or stocks screener which can identify a basket of stock which are generally consolidating at pre-market or more specifically consolidating above a support level or consolidating

Below is a Python code outline for creating a simple stock screener that identifies stocks consolidating above a support level:

python
import yfinance as yf
import pandas as pd

# Define function to check if stock is consolidating
def is_consolidating(data, percentage=2):
    recent_candlesticks = data[-15:]
    support_level = min(recent_candlesticks['Low'])
    resistance_level = max(recent_candlesticks['High'])
    range = resistance_level - support_level
    if abs((resistance_level - data['Close'].iloc[-1]) / range * 100) < percentage and abs((data['Close'].iloc[-1] - support_level) / range * 100) < percentage:
        return True
    return False

# Define function to screen stocks
def stock_screener(tickers):
    consolidating_stocks = []
    for ticker in tickers:
        try:
            data = yf.download(ticker, period="1d", interval="1d")
            if is_consolidating(data):
                consolidating_stocks.append(ticker)
        except Exception as e:
            print(f"Error processing {ticker}: {e}")
    return consolidating_stocks

# Example usage
tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'FB']
consolidating_stocks = stock_screener(tickers)
print("Consolidating stocks:", consolidating_stocks)

This code uses the Yahoo Finance API (yfinance) to fetch stock data and pandas for data manipulation. It defines a function is_consolidating to check if a stock is consolidating and another function stock_screener to screen a list of tickers for consolidating stocks. The is_consolidating function determines if the stock is consolidating by checking if the recent candlesticks are within a certain percentage range of

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 Here's Python code for a stock screener that identifies stocks consolidating in pre-market or above a support level using technical analysis indicators:

Python

Explanation:

1. The code uses yfinance to download pre-market and historical stock data.
2. The get_premarket_data function retrieves the latest pre-market data point (if available) using yfinance.
3. The is_consolidating function calculates the Average True Range (ATR) to estimate volatility and checks if the recent price movement falls within a certain threshold, indicating consolidation.
4. The identify_consolidating_stocks function iterates through a list of tickers, downloads data, and checks for consolidation in pre-market or above a support level (if provided).
5. The example usage demonstrates how to call the function with a list of tickers and an optional

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Here's Python code for a stock screener that identifies stocks consolidating in pre-market or above a support level:

Python

Explanation:

1. The code defines functions for:

   • Downloading pre-market data using yfinance.
   • Calculating support levels (replace the placeholder with your logic or technical analysis libraries).
   • Identifying consolidating stocks based on pre-market price range and/or price above a support level (adjust the criteria as needed).

2. The main section defines a list of symbols to scan, downloads pre-market data, calculates support levels, and identifies consolidating stocks.

Improvements:

• Support Level Calculation: Replace the placeholder function with your strategy for calculating support levels based on historical data (e.g., moving averages, Bollinger Bands).
• Consolidation Criteria: Adjust the consolidation criteria (pre-market price range or support level) based on your trading strategy.
• Technical Analysis Libraries: Consider using libraries like TA-Lib or pyTA for more advanced technical analysis calculations.
• Data Filtering and Sorting: You can further filter and sort the results based on additional factors like average daily trading volume or market capitalization.

Disclaimer: This is for educational purposes only. Always do your own research before making any investment decisions.

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Here's Python code for a stock screener that identifies stocks consolidating in pre-market or above a support level:

Python

import yfinance as yf
import pandas as pd

def is_consolidating(symbol, premarket_data=False, support_level=None):
  """
  Checks if a stock is consolidating based on pre-market data or above a support level.

  Args:
      symbol (str): The stock ticker symbol.
      premarket_data (bool, optional): If True, uses pre-market data (if available). Defaults to False.
      support_level (float, optional): The support level for consolidation. Defaults to None.

  Returns:
      bool: True if the stock is consolidating, False otherwise.
  """

  # Download historical data (replace with your preferred time period)
  data = yf.download(symbol, period="1d", premarket=premarket_data)

  # Check if pre-market data is available
  if premarket_data and not data.empty:
    # Calculate pre-market high-low range
    premarket_range = data["High"] - data["Low"]
  else:
    # Use regular market data
    premarket_range = None

  # Check for consolidation based on pre-market data
  if premarket_range is not None:
    # Replace 0.05 with your desired percentage threshold for consolidation range
    consolidated_premarket = premarket_range <= data["Close"] * 0.05
    if consolidated_premarket:
      return True

  # Check for consolidation above support level (if provided)
  if support_level is not None:
    consolidated_above_support = data["Low"] >= support_level
    if consolidated_above_support:
      return True

  # Stock is not consolidating
  return False

def screen_stocks(symbols, premarket_data=False, support_level=None):
  """
  Screens a list of stocks for consolidation.

  Args:
      symbols (list): List of stock ticker symbols.
      premarket_data (bool, optional): If True, uses pre-market data (if available). Defaults to False.
      support_level (float, optional): The support level for consolidation. Defaults to None.

  Returns:
      list: List of symbols that are consolidating.
  """

  consolidated_stocks = []
  for symbol in symbols:
    if is_consolidating(symbol, premarket_data, support_level):
      consolidated_stocks.append(symbol)
  return consolidated_stocks

# Example usage
symbols = ["AAPL", "GOOG", "TSLA"]  # Replace with your list of symbols
premarket_data = True  # Set to True to use pre-market data (if available)
support_level = 100  # Optional support level for consolidation

consolidated_list = screen_stocks(symbols, premarket_data, support_level)

if consolidated_list:
  print("Consolidating stocks:")
  for stock in consolidated_list:
    print(stock)
else:
  print("No consolidating stocks found.")

Use code with caution.

Explanation:

1. The is_consolidating function checks for consolidation based on two criteria:
   • Pre-market data (if available): It calculates the pre-market high-low range and compares it to a percentage threshold of the previous day's closing price (adjustable).
   • Support level (if provided): It checks if the stock's intraday low is above the specified support level.
2. The screen_stocks function iterates through a list of symbols and calls is_consolidating for each one. It returns a list of symbols that meet the consolidation criteria.
3. The example usage demonstrates screening a list of symbols and printing the results.

Additional Notes:

• You can replace yfinance with other stock data providers (adjust code accordingly).
• Adjust the consolidation criteria (pre-market range threshold, support level) based on your strategy.
• This code provides a basic framework. You can extend it to incorporate additional technical indicators or filtering criteria.
• Consider using a scheduled task to run the screener periodically and update your stock watchlist.