tse-tick 0.3.0

High-performance Nikkei NEEDS tick data processing with Polars

tse_tick

A Python library for parsing, filtering, and querying Nikkei NEEDS tick data from the Tokyo Stock Exchange.

Who it's for: Researchers working with NEEDS tick data who need to convert thousands of zipped CSVs into queryable Parquet stores, filter by ticker or event windows, and handle format changes across historical eras.

What it solves: NEEDS data is delivered as daily ZIP files (1–27 parts per day) with era-dependent schemas — 2016 used fixed-width records for indices, 2017+ switched to CSV, and individual stocks have 95 columns with complex quote-book nesting. This library detects the format automatically, validates for security, parses everything into clean DataFrames, and writes Hive-partitioned Parquet.

Data access required: This tool does NOT provide NEEDS data itself. You must have an institutional subscription (Nikkei NEEDS) and access to the raw TICST120/TICSS110/TICIT110/TICIS110 ZIP files. If your data is shared via Google Drive, see the rclone download guide for mirroring it to local disk.

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Features

• 4 data types — TICST120 (individual stock ticks, 95 cols), TICSS110 (daily stock summary, 82 cols), TICIT110 (index ticks, 10 cols), TICIS110 (daily index summary, 17 cols)
• Multi-era format support — 2016 fixed-width (TICIT010/TICIS010) and 2017-2025 CSV, auto-detected from the ZIP filename
• Polars backend — fast CSV parsing, vectorized cleaning, memory-efficient
• CLI batch ingestion — tse-tick ingest converts entire years/months/date ranges to partitioned Parquet
• Ticker filtering (--tickers) — keep only specific stock codes at read time
• Event-window extraction (--filter-csv) — extract ±N minute windows around corporate events with automatic after-hours reaction-anchor shifting
• Bilingual columns — English and Japanese column names via --language en|jp
• Security guards — ZIP bomb detection (5 GB max decompressed, 100:1 compression ratio cap, max 5 entries), path traversal prevention, query row limits (10M)

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Installation

pip install tse-tick               # from PyPI: core (polars, pyarrow)
pip install "tse-tick[query]"      # + DuckDB-powered Parquet queries

To work from the latest (unreleased) source instead, install in editable mode:

git clone https://github.com/tse-tick/tse_tick.git
cd tse_tick

pip install -e .             # core: polars, pyarrow
pip install -e ".[query]"    # + DuckDB-powered Parquet queries
pip install -e ".[dev]"      # + everything for development (tests, linters, jupyter)

Requires Python ≥3.9. Core dependencies are polars and pyarrow; the query extra adds DuckDB (see pyproject.toml).

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Quick Start

Python API — load a single ZIP

import tse_tick

# Load individual stock tick data (auto-detects data type and year)
df = tse_tick.create_df("path/to/HTICST120.20230104.1.zip", language="en")

# Load with Japanese column names
df_jp = tse_tick.create_df("path/to/HTICST120.20230104.1.zip", language="jp")

# Sample first 1000 rows only
df_sample = tse_tick.create_df("path/to/HTICST120.20230104.1.zip", rows=1000)

# Explicit data type and year (skip auto-detection)
df = tse_tick.create_df(
    "path/to/file.zip",
    auto_detect=False,
    data_type="individual_stock",
    year=2023,
)

CLI — batch ingest to Parquet

# Ingest a date range
tse-tick ingest \
    --data-type individual_stock \
    --period 20240201-20240205 \
    --input-root /path/to/TSE_DATA \
    --output-root /path/to/PARQUET_STORE

# Ingest a full year
tse-tick ingest \
    --data-type individual_stock \
    --period 2024 \
    --input-root /path/to/TSE_DATA \
    --output-root /path/to/PARQUET_STORE

# Ticker-filtered ingest (keep only specified stocks)
tse-tick ingest \
    --data-type individual_stock \
    --period 2024 \
    --input-root /path/to/TSE_DATA \
    --output-root /path/to/PARQUET_STORE \
    --tickers 7203,6758,9984

# Ticker filter from file (one ticker per line)
tse-tick ingest \
    --data-type individual_stock \
    --period 2024 \
    --input-root /path/to/TSE_DATA \
    --output-root /path/to/PARQUET_STORE \
    --tickers @ticker_list.txt

# Event-window filtered ingest (±120 min around each event)
tse-tick ingest \
    --data-type individual_stock \
    --period 20250106-20250131 \
    --input-root /path/to/TSE_DATA \
    --output-root /path/to/PARQUET_STORE \
    --filter-csv event_filter_list.csv \
    --window 120

Query the Parquet store

import tse_tick

# Query specific ticker and date
df = tse_tick.query_ticks(
    "/path/to/PARQUET_STORE",
    data_type="individual_stock",
    ticker=7203,
    date="20240201",
    start_time="09:00:00",
    end_time="11:30:00",
)

# Get available dates and tickers
dates = tse_tick.get_available_dates("/path/to/PARQUET_STORE")
tickers = tse_tick.get_available_tickers("/path/to/PARQUET_STORE", date="20240201")

Feature extraction

import tse_tick

df = tse_tick.query_ticks("/store", ticker=7203, date="20220201")

# Bid-ask spread
spread = tse_tick.compute_spread(df)

# Order-book depth (10 levels per side)
depth = tse_tick.compute_depth(df, levels=5, side="both")

# Order flow imbalance over rolling window
ofi = tse_tick.compute_flow_imbalance(df, window="5min")

# All features in one pass
features = tse_tick.compute_all_features(df)

Two access patterns

tse_tick gives you a filtered DataFrame two ways:

1. Two-stage (scale / repeated work) — ingest the raw ZIPs into a Hive-partitioned Parquet store once, then query_ticks it repeatedly. Querying the store prunes by date/ticker and is far faster than re-reading raw files (~694× vs a pandas CSV scan; see Performance).
2. One-shot (quick, targeted exploration) (planned for 0.3.0) — read_ticks(...) reads straight from raw ZIPs to a ticker/time-filtered DataFrame with no store to build first; best for a few tickers over a bounded window. Today, create_df(zip, ticker_filter=...) already covers the single-file, ticker-filtered case.

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Data Types

Code	Internal Name	Output Fields	Description
TICST120	individual_stock	95	Tick-level executions, 10-level bid/ask quotes, volume
TICSS110	stock_summary	82 (83 raw)	Daily OHLC, VWAP, session splits, quote statistics
TICIT110	indices	10 (23 raw, 15 in 2016)	Index tick updates (Nikkei 225, TOPIX, etc.)
TICIS110	indices_summary	17 (83 raw)	Daily index summary prices

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Multi-Era Format Support

The format changed only once, after 2016, and only for the index types (fixed-width to CSV); individual stock and stock summary files were CSV throughout. The library detects the era automatically from the ZIP filename (the year) and applies the correct parser.

Era	Individual Stocks	Stock Summary	Index Ticks	Index Summary
2016	CSV, 95 cols	CSV, 83 cols	Fixed-width (69 bytes)	Fixed-width (hybrid)
2017-2025	CSV, 95 cols	CSV, 83 cols	CSV, 23 cols	CSV, 83 cols

No user action needed — if your ZIP filename contains 2016, the fixed-width parser is used automatically for index data.

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Performance

tse_tick is built on Polars (CSV parsing, vectorized cleaning) and DuckDB over Hive-partitioned Parquet (queries). Measured on one day of HTICST120 (4.78 M rows, 95 columns, 2.16 GB raw CSV) on an Intel Core i5-14400F (10-core / 16-thread) with 32 GB RAM, Python 3.11, Polars 1.40, pandas 2.2.

Comparison	Speedup	Source
Polars (16T) vs pandas (Python engine)	55.5×	benchmarks/results_engine_summary.csv
Polars (16T) vs pandas (C engine, fair baseline)	22.8×	benchmarks/results_engine_summary.csv
Polars (1 thread) vs pandas (C engine)	6.2×	benchmarks/results_engine_summary.csv
DuckDB + Hive Parquet vs pandas CSV scan (single-ticker hour slice)	694.1×	benchmarks/results_query.csv
Parquet (Snappy) storage size vs raw CSV	22× smaller (100 MB vs 2.2 GB)	benchmarks/results_format.csv

The three Polars speedup numbers are deliberately reported together: against the original pandas Python-engine prototype, against a fair C-engine baseline (all-string dtypes, forced column count), and at single-thread parity to isolate the contribution of threading from the engine itself. Polars wins on all three.

tse_tick defaults to Polars because the ingest workload (multi-GB daily CSVs, mostly columnar transformations) hits exactly the case where lazy expression planning and parallel CSV parsing dominate; pandas-on-DataFrame's row-oriented model leaves throughput on the table even with the C engine. For querying, the Parquet store + DuckDB combination converts repeated single-ticker / single-date filters from full file scans into partition pruning, which is the source of the ~700× query speedup.

To reproduce: python benchmarks/run_all.py (see benchmarks/ENVIRONMENT.md).

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Expected Input Layout

The CLI expects NEEDS data organized as delivered by Nikkei:

{input_root}/
  2016/
    201601/
      HTICST120.20160104.1.zip
      HTICST120.20160104.2.zip
      ...
    201602/
    ...
  2017/
    201701/
    ...

---

Parquet Output Layout

Standard ingest produces Hive-partitioned Parquet per ticker per date:

{output_root}/
  individual_stock/
    date=20230104/
      ticker=7203.parquet
      ticker=6758.parquet
      ...

Event-window filtered ingest writes per-date files:

{output_root}/
  year=2025/
    month=01/
      20250106.parquet
      20250107.parquet
      ...

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CLI Reference

Flag	Description
--data-type (required)	individual_stock, stock_summary, indices, or indices_summary
--input-root (required)	Root directory with NEEDS ZIPs in {year}/{yearmonth}/ layout
--output-root (required)	Root directory for Parquet output
--period	Date range: YYYY, YYYYMM-YYYYMM, or YYYYMMDD-YYYYMMDD
--language	Column name language: en (default) or jp
--parallel	Number of parallel workers (default 1, max 8)
--no-resume	Disable resume (reprocess dates even if output exists)
--tickers	Comma-separated codes or @file.txt with one per line. Keeps only these stocks.
--filter-csv	Path to event filter CSV. Enables event-window mode. Overrides --tickers.
--window	Window minutes around each event's reaction anchor (default 120). Only with --filter-csv.
--flat	Treat input-root as a flat directory (no year/month subdirectories)
--years / --year	Legacy flags for specifying year(s) directly

Event Filter CSV Format

When using --filter-csv, the file must have these columns:

Column	Description
ticker	4-digit stock code (string)
event_date	Original event date YYYY-MM-DD
event_time	Original event time HH:MM (JST)
event_type	Category (earnings, buyback, dividend, etc.)
session_type	intraday or after_hours
reaction_anchor_dt	Datetime to center the window on YYYY-MM-DD HH:MM (JST)
zip_date	TICST120 date YYYYMMDD whose ZIP contains the relevant ticks

For after-hours events, reaction_anchor_dt shifts to the next trading day's 09:00 open, and zip_date points to that next day's ZIP file. This is critical: centering on the event time (e.g., 15:30) would produce empty windows because the market is closed.

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Python API Reference

create_df(folder_path, language="en", rows=None, auto_detect=True, data_type=None, year=None, ticker_filter=None)

Load and clean tick data from a ZIP file or directory of ZIP files.

• folder_path — path to a .zip file or directory of .zip files
• language — "en" or "jp" for column names
• rows — max rows to return
• auto_detect — if True, detect data type and year from path. If False, must provide data_type and year
• data_type — "individual_stock", "stock_summary", "indices", or "indices_summary"
• year — data year (e.g., 2023)
• ticker_filter — optional set of 4-digit stock codes to pre-filter at line level

Returns a Polars DataFrame with English or Japanese column names.

export_to_csv(folder_path, output_path=None, language="en", rows=None)

Load and export to CSV. If output_path is None, generates a filename.

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Security

Built-in protections for local data processing:

Guard	Value
ZIP bomb detection (max decompressed)	5 GB
ZIP compression ratio cap	100:1
Max ZIP entries	5
Max parallel workers	8
Query row limit	10,000,000
Path traversal prevention	Resolved path validation
SQL injection prevention	Identifier/date/time format validation

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Roadmap (0.3.0)

The public API names are stable — no renames (an earlier proposal to rename functions to yfinance/Polygon/ccxt conventions was reversed). Planned additions are purely additive:

• read_ticks one-shot reader — raw ZIPs → ticker/time-filtered DataFrame without building a Parquet store (see Two access patterns).
• translate() name mapping — a static lookup from yfinance / Polygon / ccxt names to tse_tick's, so users of those libraries can find the equivalent call without us coupling to their (changing) APIs.
• DataType / Language enums — for autocomplete and to avoid magic strings.

See PYPI_RELEASE_PLAN.md for the full plan.

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Contributing

Contributions are welcome. Please open an issue or submit a pull request.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add your feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

Development setup:

pip install -e ".[dev]"
pytest tests/ -v

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Testing

pytest tests/ -v

The suite collects 181 tests. Without a local NEEDS store, 133 pass and 48 skip; with a complete NEEDS store, all 181 pass. Stage-1 (ingestion) and Stage-2 (query, order-book features, and event-window-from-Parquet) both run with no proprietary data — a session-scoped pytest fixture builds a tiny Hive-partitioned Parquet store at test time by feeding synthetic, obviously-fake individual_stock (TICST120) ZIPs through the real ingest pipeline (tests/synthetic_data.py, tests/conftest.py).

The 48 skips load real NEEDS files from local paths (test_real_data.py and the real-ZIP cases in test_ingest.py), plus a handful of fixtures outside the synthetic store's scope. They run automatically once a local NEEDS store is present.

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Citation

If you use this software in your research, please cite it using the CITATION.cff file in the repository. A technical paper describing the library is in preparation.

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License

MIT

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Authors

• Kazumi Li — Schema definitions, package architecture, current maintainer
• Masataka Hayashi — Initial pandas-based prototype
• Peter Romero — Original concept and initial project design

Developed at Keio University, Nakatsuma Seminar.