Skip to main content

Financial Data Web Scraper

Project description

FSCRAPER

Financial Data Scraper

Introduction

The project contains a collection of functions used to scrape financial data, together with financial indicators calculator such as RSI, beta, MACD, etc. Web scraping is implemented using BeautifulSoup and requests for the site that provided RESTful API endpoint.

Getting Started

Installation

pip install fscraper

Financial Data

import fscraper as fs

# Yahoo Finance
yfs = fs.YahooFinanceScraper('7203.T')
df = yfs.get_stock_price(period='10y', interval='1d')
df = yfs.get_stock_price2(start='2010-01-01', end='2020-12-12')

df = yfs.get_statistics()

# Reuters(Japan)
rs = fs.ReutersScraper('7203.T')
df = rs.get_income_statement(period='annual')
df = rs.get_income_statement(period='interim')
df = rs.get_balance_sheet(period='annual')
df = rs.get_balance_sheet(period='interim')
df = rs.get_cash_flow(period='annual')
df = rs.get_cash_flow(period='interim')
news = rs.get_news(keyword='7203.T', size=5)

# Kabuyoho
ks = fs.KabuyohoScraper('7203.T')
df = ks.get_report_top()
df = ks.get_report_target()
df = ks.get_target_price()

# Kabutan
kbs = fs.KabutanScraper('7203.T')
df = kbs.get_stock_price_by_minutes()

# Minkabu
ms = fs.MinkabuScraper('7203.T')
df = ms.get_analysis()
queries = ms.query_news()
news_list = ms.get_news_list(queries)

Indicator

# RSI
df['rsi'] = fs.calculate_rsi(df['close'])
df['rsi'] = fs.calculate_rsi(df['close'], periods=14)

# Stochastic Oscillator Index
df['%K'], df['%D'] = fs.calculate_stochastic_oscillator(df['high'], df['low'], df['close'])
df['%K'], df['%D'] = fs.calculate_stochastic_oscillator(df['high'], df['low'], df['close'], k_period=14, d_period=3)

# Bollinger Band
df['top'], df['bottom'] = fs.calculate_bollinger_bands(df['close'])
df['top'], df['bottom'] = fs.calculate_bollinger_bands(df['close'], smooth_period=20, standard_deviation=2)

# MACD(Moving Average Convergence/Divergence)
df['macd'], df['macd_signal'], df['macd_histogram'] = fs.calculate_macd(df['close'])
df['macd'], df['macd_signal'], df['macd_histogram'] = fs.calculate_macd(df['close'], short_periods=12, long_periods=26, signal_periods=9)

# Pearson Correlation
cor = fs.calculate_pearson_correlation(df1['close'], df2['close'])

# beta with Nikkei 225
beta = fs.calculate_beta(code='6753.T', market='^N225', period='1y')

# 100 days min&max price
df['100-high'], df['100-low'] = fs.set_x_days_high_low(df['high'], df['low'], window=100)

# On Balance Volume (OBV)
df['OBV'] = fs.calculate_obv(df['close'], df['volume'])

Contribution

Any suggestions for improvement or contribution to this project are appreciated.

Disclaimer

The project is for informational and educational purposes only. The author assumes no responsibility or liability for any errors in the content of this project.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fscraper-1.0.9.tar.gz (14.0 kB view details)

Uploaded Source

Built Distribution

fscraper-1.0.9-py3-none-any.whl (16.0 kB view details)

Uploaded Python 3

File details

Details for the file fscraper-1.0.9.tar.gz.

File metadata

  • Download URL: fscraper-1.0.9.tar.gz
  • Upload date:
  • Size: 14.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for fscraper-1.0.9.tar.gz
Algorithm Hash digest
SHA256 662eb207126a162286e946823b29d6bfad42fba6c6ddf253341e76fe1053ea37
MD5 c3c78224dad47e8d2c55eca26860f966
BLAKE2b-256 468f9bce4e69861effa6f92d62f58e8331d23da33662bf3b501613e9a730f815

See more details on using hashes here.

File details

Details for the file fscraper-1.0.9-py3-none-any.whl.

File metadata

  • Download URL: fscraper-1.0.9-py3-none-any.whl
  • Upload date:
  • Size: 16.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.18

File hashes

Hashes for fscraper-1.0.9-py3-none-any.whl
Algorithm Hash digest
SHA256 4fc4cae9d87dd03d344a209b6719313d21eae5df3a55452eea76f9cca5c00285
MD5 cf5915004f2c791cbbe4da2911bca4a7
BLAKE2b-256 8169c349810d7a726376586a41b54a57c51ef5f3196838b93cd8f4fce7c5dab3

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page