fscraper 1.0.9

Financial Data Web Scraper

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.