nsefast 0.2.2

Fast, robots.txt-respecting NSE India market data collector for swing trading, quant research, and backtesting

nsefast

Fast NSE India data collector for swing trading, quant research, AI training, backtesting, and market intelligence.

⚠️ Ethics & Compliance: nsefast only uses publicly downloadable NSE reports and pages allowed by NSE's robots.txt. It does not bypass logins, captchas, Cloudflare, anti-bot systems, or rate limits. Add appropriate delays and use responsibly. You are responsible for complying with NSE's terms of service.

Features

• Polite, retrying HTTP client with robots.txt checks
• Modular collectors for equity, derivatives, corporate, deals, indices, surveillance, calendar, and master data
• Polars for fast dataframe processing
• Parquet primary storage, partitioned by dataset/date
• DuckDB local analytics layer
• Optional PostgreSQL storage
• Optional Rust core (rust-core/) for hashing / dedup / large parsing
• Typer-based CLI

Install

pip install nsefast

Optional extras:

pip install "nsefast[pandas]"      # pandas export helpers
pip install "nsefast[postgres]"    # PostgreSQL sink
pip install "nsefast[api]"         # FastAPI server scaffold
pip install "nsefast[dev]"         # pytest, ruff, build, twine

For development:

git clone https://github.com/nikhilshinde/nsefast
cd nsefast
pip install -e ".[dev]"
pytest -q

Quick start

# Discover all downloadable report links from NSE public pages
nsefast collect-reports

# Run the full scaffold
nsefast collect-all

# Equity bhavcopy for a date
nsefast collect equity-bhavcopy --date 2026-05-07

# Corporate announcements range
nsefast collect corporate-announcements --start 2026-05-01 --end 2026-05-07

# Build swing-trading features
nsefast features swing --date 2026-05-07

# Export a dataset to Parquet
nsefast export parquet --dataset daily_bhavcopy

In Python:

from nsefast.collectors.report_links import collect_report_links
from nsefast.storage.parquet_store import save_parquet

df = collect_report_links()  # polars DataFrame
save_parquet(df, dataset="report_links")

Project layout

nsefast/
├── pyproject.toml
├── requirements.txt
├── main.py
├── README.md
│
├── nsefast/
│   ├── config.py          # URLs, headers, paths
│   ├── http_client.py     # session + retries
│   ├── robots.py          # robots.txt checker
│   ├── collectors/        # one module per data domain
│   ├── processing/        # normalize, features, technicals
│   ├── storage/           # parquet, duckdb, postgres
│   └── cli.py             # Typer CLI
│
└── rust-core/             # optional pyo3 module
    ├── Cargo.toml
    └── src/lib.rs

Storage zones

• data/raw/ — raw downloads exactly as fetched
• data/clean/ — normalized intermediate files
• data/parquet/ — partitioned Parquet, the canonical store

Rust core (optional)

The rust-core/ crate exposes a nsefast_core Python module via PyO3 for CPU-bound work (SHA-256 hashing, dedup, fast CSV normalization). HTTP scraping stays in Python — it's I/O bound.

Build with maturin:

cd rust-core
maturin develop --release

Verify your install

pip install nsefast
nsefast verify              # offline checks: imports, parquet, duckdb
nsefast verify --network    # also pings NSE warm-up + robots.txt
nsefast version

Cache, logging, partitioning

# Cache (5-min TTL by default; collectors opt in via cached_get())
nsefast cache stats
nsefast cache clear

# Structured JSON logs (for production / log shippers)
NSEFAST_LOG_FORMAT=json NSEFAST_LOG_LEVEL=INFO nsefast collect bulk-deals --start 2026-04-01 --end 2026-05-07

# Hive-partitioned parquet writes
from nsefast.storage.parquet_store import (
    save_parquet_partitioned, read_parquet_partitioned, derive_date_partitions,
)
df = derive_date_partitions(df, "trade_date", parts=("year", "month"))
save_parquet_partitioned(df, dataset="daily_bhavcopy", by=["year", "month"])
# -> data/parquet/daily_bhavcopy/year=2026/month=05/*.parquet

q1 = read_parquet_partitioned("daily_bhavcopy",
                              filters=[("year","==",2026), ("month",">=",4)])

# DuckDB analytics
from nsefast.storage.duckdb_store import (
    connect, register_all, top_gainers, sector_leaderboard,
)
con = connect()
register_all(con)
top_gainers(con, dataset="all_indices", n=10)
sector_leaderboard(con, dataset="sector_strength")

Swing-trading research (nsefast.swing)

from nsefast.collectors.equity   import daily_bhavcopy, delivery_data
from nsefast.collectors.indices  import sector_strength
from nsefast.collectors.deals    import bulk_deals
from nsefast.collectors.corporate import corporate_announcements
from nsefast.processing.features import add_volume_breakout
from nsefast.swing import (
    top_upside, top_downside, avoid_list, sector_leaders,
    delivery_breakout, volume_breakout,
    bulk_block_watchlist, corporate_announcement_watchlist, combined_watchlist,
)

bhav = daily_bhavcopy("2026-05-07")
bhav = add_volume_breakout(bhav)            # adds avg_volume_20

# Long candidates (filtered, scored, ranked)
top_upside(bhav, n=20, min_turnover=1e7)

# Weakest names (short candidates)
top_downside(bhav, n=20)

# What to skip (surveillance + extreme-move list)
avoid_list(bhav, max_volatility_pct=15.0)

# Sector rotation
sector_leaders(sector_strength(), n=5)

# Sticky-money & spike scans
delivery_breakout(delivery_data("2026-05-07"), min_delivery_pct=70)
volume_breakout(bhav, min_ratio=2.0)

# Smart-money & event watchlists
bulk_block_watchlist(bulk_deals("2026-04-01", "2026-05-07"), min_qty=10_000)
corporate_announcement_watchlist(corporate_announcements("2026-04-01", "2026-05-07"))
combined_watchlist(deals_df=..., ann_df=...)

# Position sizing & ATR stops
from nsefast.swing.risk import (
    position_size, add_atr, add_atr_stop, add_position_size,
)
qty = position_size(capital=500_000, entry=120, stop=115, risk_pct=1.0)
sized = (bhav.pipe(add_atr).pipe(add_atr_stop, mult=2.0)
         .pipe(add_position_size, capital=500_000, risk_pct=1.0))

# Minimal walk-forward backtest (full engine + ML lands in v0.3.0)
from nsefast.swing.backtest import run_backtest, summary_stats
trades = run_backtest(history_df, signal_fn=lambda d: d["close"] > d["close"].shift(20),
                      holding_days=5)
summary_stats(trades)

Documentation

• docs/USAGE.md — full Python + CLI usage, canonical schemas, polite-use rules
• docs/PUBLISHING.md — how to release new versions to PyPI
• CHANGELOG.md — version history

Failure semantics

Every public collector returns a Polars DataFrame with its canonical schema on any failure (invalid input, network error, malformed payload, polars error, robots block). Collectors never raise — your pipelines stay crash-proof.

Tests

pytest -q     # 77 unit tests, no network calls

License

MIT — see LICENSE