How to Filter Stocks In A Large Dataset Using Python?

11 minutes read

To filter stocks in a large dataset using Python, you can follow these steps:

  1. Import the necessary libraries: Begin by importing the required libraries like pandas, numpy, or any other library you may need to manipulate and analyze data.
  2. Load the dataset: Read the dataset containing the stock information into a pandas DataFrame. You can use the pandas' read_csv method or any other appropriate method based on the format of your dataset.
  3. Analyze the dataset: Analyze the dataset to understand its structure, available columns, and the information it provides about the stocks. This step allows you to gain insights into the dataset and decide which columns or criteria you want to filter based on.
  4. Define filter criteria: Determine the criteria you want to filter your stocks by. For example, you might filter stocks based on the stock symbol, industry, minimum or maximum stock price, market capitalization, etc. Define the appropriate filters based on your requirements.
  5. Apply filters: Use the pandas DataFrame methods to apply the defined filters to the dataset. For instance, you can use the boolean indexing technique to filter rows based on certain conditions. You might use methods like loc or query combined with boolean operations like & (and) or | (or) to define complex filters.
  6. Create filtered dataset: Create a new DataFrame that contains only the filtered stocks based on the defined criteria.
  7. Further analysis or export: Once you have applied the filters and obtained the desired results, you can further analyze the filtered dataset or export it to a CSV, Excel, or any other suitable format for further use.


Remember, the specific implementation details may vary depending on the structure of your dataset and the criteria you want to filter by. You can always refer to the documentation of pandas and other related libraries to explore more advanced filtering techniques and functionalities.

Best Stock Day Trading Books of 2024

1
How to Day Trade for a Living: A Beginner’s Guide to Trading Tools and Tactics, Money Management, Discipline and Trading Psychology

Rating is 5 out of 5

How to Day Trade for a Living: A Beginner’s Guide to Trading Tools and Tactics, Money Management, Discipline and Trading Psychology

  • As a day trader, you can live and work anywhere in the world. You can decide when to work and when not to work.
  • You only answer to yourself. That is the life of the successful day trader. Many people aspire to it, but very few succeed. Day trading is not gambling or an online poker game.
  • To be successful at day trading you need the right tools and you need to be motivated, to work hard, and to persevere.
2
How to Day Trade: The Plain Truth

Rating is 4.9 out of 5

How to Day Trade: The Plain Truth

3
Day Trading QuickStart Guide: The Simplified Beginner's Guide to Winning Trade Plans, Conquering the Markets, and Becoming a Successful Day Trader (QuickStart Guides™ - Finance)

Rating is 4.8 out of 5

Day Trading QuickStart Guide: The Simplified Beginner's Guide to Winning Trade Plans, Conquering the Markets, and Becoming a Successful Day Trader (QuickStart Guides™ - Finance)

4
DAY TRADING STRATEGIES: THE COMPLETE GUIDE WITH ALL THE ADVANCED TACTICS FOR STOCK AND OPTIONS TRADING STRATEGIES. FIND HERE THE TOOLS YOU WILL NEED TO INVEST IN THE FOREX MARKET.

Rating is 4.7 out of 5

DAY TRADING STRATEGIES: THE COMPLETE GUIDE WITH ALL THE ADVANCED TACTICS FOR STOCK AND OPTIONS TRADING STRATEGIES. FIND HERE THE TOOLS YOU WILL NEED TO INVEST IN THE FOREX MARKET.

5
The Fibonacci Effect: The 5 Rules of Highly Successful Traders

Rating is 4.6 out of 5

The Fibonacci Effect: The 5 Rules of Highly Successful Traders

6
A Beginner's Guide to Day Trading Online (2nd edition)

Rating is 4.5 out of 5

A Beginner's Guide to Day Trading Online (2nd edition)


How to filter stocks based on their assets under management using Python?

To filter stocks based on their assets under management using Python, you can perform the following steps:

  1. Import the necessary libraries:
1
2
3
import pandas as pd
import numpy as np
import yfinance as yf


  1. Define a function to calculate the assets under management:
1
2
3
def get_asset_under_management(ticker):
    stock = yf.Ticker(ticker)
    return stock.info['totalAssets']


  1. Define a function to filter stocks based on their assets under management:
1
2
3
4
5
6
7
def filter_stocks_by_aum(stock_list, min_aum, max_aum):
    filtered_stocks = []
    for stock in stock_list:
        aum = get_asset_under_management(stock)
        if aum >= min_aum and aum <= max_aum:
            filtered_stocks.append(stock)
    return filtered_stocks


  1. List the stocks you want to filter:
1
stocks = ['AAPL', 'MSFT', 'GOOGL', 'AMZN']


  1. Define the minimum and maximum value for assets under management:
1
2
min_aum = 1000000000  # Minimum AUM
max_aum = 5000000000  # Maximum AUM


  1. Call the function to filter the stocks and store the filtered result:
1
filtered_stocks = filter_stocks_by_aum(stocks, min_aum, max_aum)


The filtered_stocks list will contain the tickers of the stocks that meet the specified assets under management criteria.


Note: This example uses the yfinance library to retrieve the stock information, so make sure you have it installed (pip install yfinance)


How to filter stocks based on their correlation with a specific index using Python?

To filter stocks based on their correlation with a specific index using Python, you can follow these steps:


Step 1: Import the necessary libraries:

1
2
3
import pandas as pd
import numpy as np
import yfinance as yf


Step 2: Define the index ticker symbol and the list of stock ticker symbols:

1
2
index_ticker = '^GSPC'  # Example index symbol (S&P 500)
stock_tickers = ['AAPL', 'MSFT', 'GOOG']  # Example stock symbols


Step 3: Retrieve historical data for the index and stocks using Yahoo Finance API:

1
2
index_data = yf.download(index_ticker, start='2010-01-01', end='2021-12-31')['Adj Close']
stock_data = yf.download(stock_tickers, start='2010-01-01', end='2021-12-31')['Adj Close']


Step 4: Calculate the correlation between each stock and the index:

1
correlations = stock_data.corrwith(index_data)


Step 5: Filter stocks based on a desired correlation threshold:

1
2
threshold = 0.7  # Example correlation threshold
filtered_stocks = correlations[abs(correlations) > threshold].index


The filtered_stocks variable will contain the ticker symbols of the stocks that have a correlation greater than the specified threshold with the index.


Complete example code:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
import pandas as pd
import numpy as np
import yfinance as yf

index_ticker = '^GSPC'  # Example index symbol (S&P 500)
stock_tickers = ['AAPL', 'MSFT', 'GOOG']  # Example stock symbols

index_data = yf.download(index_ticker, start='2010-01-01', end='2021-12-31')['Adj Close']
stock_data = yf.download(stock_tickers, start='2010-01-01', end='2021-12-31')['Adj Close']

correlations = stock_data.corrwith(index_data)

threshold = 0.7  # Example correlation threshold
filtered_stocks = correlations[abs(correlations) > threshold].index

print(filtered_stocks)


Note: In the example code, the periods for retrieving historical data are set from '2010-01-01' to '2021-12-31'. You can modify these dates according to your requirements.


How to filter stocks based on their return on equity using Python?

To filter stocks based on their Return on Equity (ROE) using Python, you can follow these steps:

  1. Import the necessary libraries:
1
2
import pandas as pd
import yfinance as yf


  1. Define a function to fetch the data:
1
2
3
4
5
def get_stock_data(ticker):
    stock = yf.Ticker(ticker)
    data = stock.history(period='1y')
    data['Ticker'] = ticker
    return data


  1. Define a function to calculate ROE:
1
2
3
def calculate_roe(data):
    data['ROE'] = (data['Dividends'] + data['Stock Splits']) / data['Shareholders Equity']
    return data


  1. Define a function to filter stocks based on ROE:
1
2
3
def filter_stocks(data, min_roe):
    filtered_stocks = data[data['ROE'] > min_roe]
    return filtered_stocks


  1. Define a list of tickers you want to analyze:
1
tickers = ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'FB']


  1. Create an empty dataframe to hold the filtered results:
1
filtered_data = pd.DataFrame(columns=['Date', 'Ticker', 'Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits', 'ROE'])


  1. Loop through the tickers, fetch the data, calculate ROE, and filter the stocks:
1
2
3
4
5
for ticker in tickers:
    data = get_stock_data(ticker)
    data = calculate_roe(data)
    filtered_stocks = filter_stocks(data, 0.15)  # Minimum ROE of 15%
    filtered_data = pd.concat([filtered_data, filtered_stocks])


  1. Print the filtered stocks:
1
print(filtered_data)


Note that in this example, we're using the Yahoo Finance library (yfinance) to fetch a year's worth of historical stock data. You can modify the time period or use a different data source based on your requirements.


Also, the filter_stocks function applies a minimum ROE threshold of 0.15 (15%). You can adjust the minimum ROE value to your preference.


Remember to install the necessary libraries by running the command pip install yfinance pandas before executing the code.


How to filter stocks based on their dividend yield using Python?

To filter stocks based on their dividend yield using Python, you can follow these steps:

  1. Import the necessary libraries:
1
2
import pandas as pd
import yfinance as yf


  1. Create a function to retrieve the dividend yield of a stock:
1
2
3
4
def get_dividend_yield(ticker):
    stock = yf.Ticker(ticker)
    dividend_yield = stock.info['dividendYield']
    return dividend_yield


  1. Create a list of stock tickers you want to analyze:
1
tickers = ['AAPL', 'MSFT', 'IBM', 'GOOGL', 'AMZN']


  1. Create an empty dictionary to store the dividend yield of each stock:
1
dividend_yield_dict = {}


  1. Iterate over each ticker and retrieve the dividend yield using the function created in step 2:
1
2
3
for ticker in tickers:
    dividend_yield = get_dividend_yield(ticker)
    dividend_yield_dict[ticker] = dividend_yield


  1. Convert the dictionary into a pandas DataFrame for easier manipulation:
1
df = pd.DataFrame.from_dict(dividend_yield_dict, orient='index', columns=['Dividend Yield'])


  1. Filter the stocks based on the desired dividend yield threshold. For example, to filter stocks with a dividend yield greater than 2%:
1
filtered_stocks = df[df['Dividend Yield'] > 0.02]


  1. Print the filtered stocks:
1
print(filtered_stocks)


Note: The dividend yield data is obtained using the yfinance library, which retrieves data from Yahoo Finance. Make sure to install this library before running the code: pip install yfinance.

Facebook Twitter LinkedIn Whatsapp Pocket

Related Posts:

Volatile stocks are those that experience significant price fluctuations and high trading volume within a short period. Day traders often look for these stocks as they provide opportunities for quick profits. Here are some ways to find volatile stocks for day ...
Selling stocks on the stock market involves a process that allows investors to liquidate their holdings and realize profits or cut losses. Here&#39;s a step-by-step guide on how to sell stocks:Choose a Broker: To sell stocks on the stock market, you need to ha...
Investing in dividend stocks is a popular strategy for generating income and building wealth. Dividend stocks refer to stocks issued by companies that distribute a portion of their profits as dividends to shareholders on a regular basis.To invest in dividend s...
Investing in growth stocks can be an effective strategy to grow your wealth over the long term. These stocks are typically issued by companies that are experiencing rapid growth or have the potential for significant expansion in the future.To invest in growth ...
Using a stock screener for backtesting involves first selecting the criteria that you want to use to filter through stocks. This can include factors such as market cap, P/E ratio, or industry sector. Once you have defined your criteria, you can run the stock s...
To backtest a 52-week high strategy, you first need to select a group of stocks or securities that you want to analyze. This can be done by using a stock screener to filter for stocks that are currently trading at or near their 52-week high.Next, you need to d...