PNSEA - Python NSE API (v1.0.0)

PNSEA is a high-performance, stealthy Python library for fetching data from the National Stock Exchange of India (NSE). Powered by Stealthkit to bypass rate limits and blocks.

🛠 Usage Reference

Initialize

Python

from pnsea import NSE
nse = NSE()

🔍 Discovery & Debugging

Python

# Autocomplete search for symbols
print(nse.autocomplete("info"))

# Endpoint Tester (Debug any NSE API URL directly through the stealth session)
url = "https://www.nseindia.com/api/option-chain-indices?symbol=NIFTY"
print(nse.endpoint_tester(url).json())

📈 Equity Data

Python

# Get CMP (Current Market Price)
print(nse.equity.info("SBIN")['priceInfo']['lastPrice'])

# Get Full Info or specific blocks
print(nse.equity.info("SBIN"))
print(nse.equity.info("SBIN")['info'])

# All Stocks Snapshot & Market Status
print(nse.equity.all_stocks_data())
print(nse.equity.market_status())

# Historical Data (Returns DataFrame)
print(nse.equity.history("ESCORTS", "01-02-2025", "14-02-2025"))

# --- Institutional Tracking (Price & Delivery) ---

# 1. Default: Fetch Price & Delivery (Most common for accumulation analysis)
df_delivery = nse.equity.delivery_history("RELIANCE", "13-12-2025", "13-01-2026")

# 2. Advanced: Specific Data Types or Series
# Options for type: "priceVolumeDeliverable", "priceVolume", "deliverable"
df_only_deliv = nse.equity.delivery_history("RELIANCE", "13-12-2025", "13-01-2026", type="deliverable", series="ALL")

# 3. Example: Filter for high institutional accumulation (> 60% delivery)
# This uses the clean mapped column names
high_delivery = df_delivery[df_delivery['Delivery_Pct'] > 60]
print(high_delivery[['Date', 'Close', 'Delivery_Pct']])

🏢 Insider Trading & Corporate Actions

Python

# Insider Trading (All or Filtered)
print(nse.insider.insider_data())
print(nse.insider.insider_data("SBIN"))
print(nse.insider.insider_data("INFY", from_date="11-12-2023", to_date="14-02-2025"))

# Pledged Data
print(nse.insider.getPledgedData("ESCORTS"))

# SAST Data
print(nse.insider.getSastData("ESCORTS"))
print(nse.insider.getSastData("INFY", from_date="01-01-2024", to_date="01-02-2025"))

📉 Indices Options (NIFTY, BANKNIFTY, etc.)

Python

# Returns: [0] DataFrame, [1] Expiries List, [2] Underlying Value
print(nse.options.option_chain("NIFTY")[0])

# Filtered Calls
print(nse.options.option_chain("NIFTY", expiry_date="06-Mar-2025")[0])
print(nse.options.option_chain("NIFTY", strike_price=22000)[0])

# Helpers
print(nse.options.expiry_dates("NIFTY"))
print(nse.options.get_indices())

🏎 Equity Options (Stock FnO)

Python

# Get list of all FNO Stocks
print(nse.equityOptions.fno_stocks_list())

# Option Chain for Stocks
print(nse.equityOptions.option_chain("SBIN")[0])
print(nse.equityOptions.option_chain("SBIN", expiry_date="27-Mar-2025", strike_price=800)[0])

# Helpers
print(nse.equityOptions.expiry_dates("SBIN"))

💰 Mutual Fund Insider Data

Python

# Filter by date, ISIN, or Symbol
print(nse.mf.mf_insider_data(from_date="01-02-2025", to_date="02-02-2025"))
print(nse.mf.mf_insider_data(isin="INF879O01027"))
print(nse.mf.mf_insider_data(symbol="PPFAS Mutual Fund"))

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🛡 Why PNSEA?

1. Human-like Fingerprinting: Uses stealthkit to rotate TLS and headers, preventing 403 Forbidden errors.

2. Analysis Ready: Complex nested JSON is automatically flattened into Pandas DataFrames.

3. v3 API Support: Uses the most modern NSE endpoints for speed and reliability.