zerodha-talib 0.2.1

Auto-batched Zerodha historical data with TA-Lib style indicators

zerodha_talib

Zerodha historical data to pandas with TA-Lib indicators.

• GitHub: https://github.com/AKNavin/zerodha_talib

Install

pip install zerodha-talib

For local development:

pip install -e .[dev]

Usage

from kiteconnect import KiteConnect
import pandas as pd
from zerodha_talib import HistoricalClient, add_basics, add_talib

# Display settings
pd.set_option('display.max_rows', 100000)
pd.set_option('display.max_columns', 50)
pd.set_option('display.width', None)

# API credentials
api_key = 'your api key'
access_token = 'your access token'

kite = KiteConnect(api_key=api_key)
kite.set_access_token(access_token)

client = HistoricalClient(kite)

# 1) Fetch historical candles only
df = client.fetch(
    instrument_name='NIFTY 50',
    exchange='NSE',
    from_date='2010-01-01',
    to_date='2026-02-28',
    interval='day',
)

print('Raw OHLCV:')
print(df.tail(5))

# 2) add_basics + add_talib (nubra_talib style)
#df = add_basics(df)
df_ta = add_talib(
    df,
    funcs={
        "RSI": {"timeperiod": 14},
        "EMA": {"timeperiod": 21},
        "CCI": {"timeperiod": 14},
        "MACD": {"fastperiod": 12, "slowperiod": 26, "signalperiod": 9},
    },
)

print('\nWith indicators (add_talib):')
print(df_ta.tail(5))

Example Script

python examples/example.py

Historical Fetch Defaults

• batch_days=2000
• throttle_seconds=0.5
• retries=3
• backoff_seconds=1.0

API

• HistoricalClient(kite)
• HistoricalClient.fetch(...)
• add_basics(df) -> adds SMA_9, EMA_21, RSI_14
• add_talib(df, funcs={...})

Release Checklist

• Update version in pyproject.toml
• Validate imports with pip install -e .
• Run your sample script: python examples/example.py
• Run tests (if using dev deps): pytest
• Build artifacts: python -m build
• Check artifacts in dist/
• Commit + tag release in Git
• Push to GitHub

License

MIT