Getting Started
Installation
Or with uv:
Python API
Fetch Rankings
import pynisa
# Get default ranking (JP stocks, purchase amount)
result = pynisa.ranking("rakuten")
result.data # pandas DataFrame
result.period # "2026年2月1日～2026年2月28日"
result.updated # "2026年3月2日更新"
# US stocks from SBI
result = pynisa.ranking("sbi", "us_stock_buy", count=5)
# Specific category
result = pynisa.ranking("sbi", "fund_buy")
List Sources and Categories
pynisa.sources()
# ['rakuten', 'sbi']
pynisa.categories("rakuten")
# ['jp_stock_buy', 'jp_stock_balance', 'jp_etf_buy', ...]
pynisa.categories("sbi")
# ['stock_buy', 'stock_count', ..., 'us_stock_buy', 'cn_stock_buy', ...]
RankingResult
pynisa.ranking() returns a RankingResult with:
data— pandas DataFrame with columns:rank,change,ticker,name,source,categoryperiod— ranking period (e.g."2026年2月1日～2026年2月28日")updated— last update date (if available)
Some categories include extra columns (sector for US stocks, market for foreign ETFs).
CLI
Asset Type Commands
# JP stock rankings from all brokerages (side by side)
nisa
# US stock rankings from all brokerages
nisa us
# Chinese/HK stocks
nisa cn
# Investment trusts
nisa fund
# Filter to a specific brokerage
nisa us rakuten
nisa us sbi -n 5
Per-Brokerage Commands
nisa rakuten # Default (JP stocks)
nisa sbi -c fund_buy -n 5 # Specific category
nisa rakuten -f csv # Output as CSV
nisa sbi -f json # Output as JSON