hft-lob 0.2.3

Python wrapper for a high-performance Rust orderbook CLI

hft-lob

Python library for reading NSE binary market feed files and reconstructing a Limit Order Book (LOB).

---

Install

pip install hft-lob

---

Configure (once)

Tell the library where your file is and which token to read:

cat > ~/.hft_lob << 'EOF'
FILE=/nas/50.30/NSE_CM/Feed_CM_StreamID_2_29_12_2025.bin
TOKEN=1333
EOF

For multiple tokens, use comma-separated values:

TOKEN=1333,2885,5900

---

Run

hft-lob get_next

Prints the next LOB message as a CSV row.

hft-lob get_all

Prints all LOB messages, one CSV row per line.

hft-lob eof

Prints True if no messages available, False if messages exist.

---

Python API

from hft_lob.cli import Reader

r = Reader("/path/to/feed.bin", tokens=1333)

r.get_next_message()    # returns one CSV string, or None
r.get_all_messages()    # returns list of all CSV strings
r.is_end_of_file()      # returns True / False
r.header                # CSV column names

Multiple tokens:

r = Reader("/path/to/feed.bin", tokens=[1333, 2885, 5900])
msgs = r.get_all_messages()
print(f"Total: {len(msgs)}")

---

CSV Output Format

23 fields per row:

Field	Description
local_ts	Local timestamp (nanoseconds)
exch_ts	Exchange timestamp (nanoseconds)
mid_price	(best bid + best ask) / 2
bid_price_0..4	Bid price at depth level 0–4
bid_qty_0..4	Bid quantity at depth level 0–4
ask_price_0..4	Ask price at depth level 0–4
ask_qty_0..4	Ask quantity at depth level 0–4

---

Load into pandas

import io, pandas as pd
from hft_lob.cli import Reader

r = Reader("/path/to/feed.bin", tokens=1333)
msgs = r.get_all_messages()
df = pd.read_csv(io.StringIO(r.header + "\n" + "\n".join(msgs)))
print(df.head())

---

Platform

Linux x86_64 only. The Rust binary is bundled — no extra install needed.

---

License

MIT