massive-speedup 0.1.3

Nanobind/C++ parsers for polygon, bulk S3, and websocket market data.

massive-speedup

Native C++/nanobind readers for Polygon/Massive flat-file market data.

See INSTALL.md for installation details and DEVELOPMENT.md for release and PyPI publishing notes.

CSV Gzip Files

Install/build the native extension:

pip3 install -e .

Iterate parsed records directly from a .csv.gz file:

import massive_speedup

for trade in massive_speedup.FlatFiles.Stock.Trade.parse("trades.csv.gz"):
    print(trade.ticker, trade.sip_timestamp, trade.price)

for quote in massive_speedup.FlatFiles.Stock.Quote.parse("quotes.csv.gz"):
    print(quote.ticker, quote.bid_price, quote.ask_price)

for quote in massive_speedup.FlatFiles.currency.Quote.parse("currency_quotes.csv.gz"):
    print(quote.ticker, quote.participant_timestamp)

You can also iterate raw CSV fields as bytes tuples:

for row in massive_speedup.FlatFiles.Stock.Trade.parse_raw("trades.csv.gz"):
    print(row[0], row[8])

Example scripts:

• examples/howto_csv_gzip_daily_vwap.py computes daily stock-trade VWAP using gzip and csv.DictReader.
• examples/howto_database_daily_vwap.py computes the same value from a massive-speedup binary database file using mmap and the native C++ aggregator.

Record Access

Parsed records expose read-only attributes and are iterable in CSV field order:

trade = next(massive_speedup.FlatFiles.Stock.Trade.parse("trades.csv.gz"))

print(trade.ticker)
print(trade.conditions)
print(trade.sip_timestamp)
print(trade.pack())
print(list(trade))

Packed records do not include the ticker. Reconstruct with the ticker from the file name:

packed = trade.pack()
trade2 = massive_speedup.StockTrade.from_packed(packed, trade.ticker)

Window Aggregation

The native aggregators consume iterables of parsed records and yield C++ result objects exposed through nanobind. Result attributes are read-only and lazily converted to Python objects on first access. The aggregation interval and offset are expressed in seconds; the returned window_start is still nanoseconds since epoch.

import massive_speedup

trades = massive_speedup.FlatFiles.Stock.Trade.parse("trades.csv.gz")

for bar in massive_speedup.FlatFiles.Stock.Trade.Aggregator(
    trades,
    interval_seconds=60,
):
    print(
        bar.ticker,
        bar.window_start,
        bar.open,
        bar.close,
        bar.high,
        bar.low,
        bar.avg,
        bar.volume_weighted_avg,
        bar.volume,
        bar.transactions,
        bar.stddev,
    )

Available aggregators:

• massive_speedup.StockTradeAggregator / FlatFiles.Stock.Trade.Aggregator
• massive_speedup.StockQuoteAggregator / FlatFiles.Stock.Quote.Aggregator
• massive_speedup.CurrencyQuoteAggregator / FlatFiles.currency.Quote.Aggregator

Stock trades aggregate price and use size for volume and volume_weighted_avg. Stock quotes aggregate ask and bid prices separately and use ask/bid sizes for ask/bid volume-weighted averages. Currency quotes aggregate ask and bid prices separately and omit volume and volume-weighted averages because the source rows have no size field.

quotes = massive_speedup.StockQuoteDatabase("/data/massive-db", "2026-01-23", "A")

for quote_bar in massive_speedup.StockQuoteAggregator(
    quotes,
    interval_seconds=1,
    offset_seconds=0,
):
    print(quote_bar.ask_open, quote_bar.ask_close, quote_bar.bid_avg)

Aggregators stream consecutive (ticker, window_start) groups. Use input ordered by ticker and timestamp, such as the native database iterators or default Massive/Polygon flat-file order. stddev is population standard deviation.

Build Database Files

Build fixed-length binary database files from one or more input .csv.gz files:

massive-speedup-build-database --database /data/massive-db 2026-01-23.csv.gz

The input type is inferred from the CSV header. Output layout is:

{database}/{stock_trade|stock_quote|currency_quote}/{YYYY-MM-DD}/{ticker}

Existing ticker files are not overwritten by default. The builder keeps reading the input until the next ticker and only writes missing ticker files. Use --force to rebuild existing ticker files, which is useful after a binary record format change:

massive-speedup-build-database --force --database /data/massive-db 2026-01-23.csv.gz

Date-level idempotency uses an .incomplete marker in {database}/{type}/{YYYY-MM-DD}. If the date directory exists without .incomplete, the input file is skipped. If the directory is new, .incomplete is created before processing and removed only after successful completion. Use --force to process a date even when .incomplete is absent.

Use --benchmark to print throughput:

massive-speedup-build-database --benchmark --database /data/massive-db *.csv.gz

Database Files

Open a fixed-length binary file through mmap and iterate records:

records = massive_speedup.StockTradeDatabase(
    "/data/massive-db",
    "2026-01-23",
    "A",
)

for trade in records:
    print(trade.sip_timestamp, trade.price)

Merge stock trades and quotes for one date and ticker in SIP timestamp order:

for trade, quote in massive_speedup.stock_trade_quote_timeline(
    "/data/massive-db",
    "2026-01-23",
    "A",
):
    if trade:
        print("trade", trade.sip_timestamp, trade.price, quote)
    else:
        print("quote", quote.sip_timestamp, quote.bid_price, quote.ask_price)

Quote rows yield (None, current_quote). Trade rows yield (trade, last_quote), where last_quote is None until the first quote has appeared. When a trade and quote have the same SIP timestamp, the quote is yielded first.

Database files support indexing and timestamp search:

first = records[0]
last = records[-1]

index = records.index_before_timestamp(1769161728012983416)
near_open = records.index_before_timestamp(1769161728012983416, galloping=0)
next_index = records.index_after_timestamp(1769161728012983416, galloping=index + 1)

Timestamp arguments are nanoseconds since epoch. Database readers also accept datetime.time values, which are resolved using the reader's date:

import datetime as dt

index = records.index_before_timestamp(dt.time(9, 30))

Find the closest record before or after a participant timestamp:

before = records.find_before_participant_timestamp(
    1769161728012624580,
)
after = records.find_after_participant_timestamp(
    1769161728012624580,
    fuzz=250_000_000,
    galloping=True,
)
strict_before = records.find_before_participant_timestamp(
    1769161728012624580,
    on=False,
)

find_before_participant_timestamp returns the record with the highest participant timestamp less than or equal to the target. find_after_participant_timestamp returns the record with the lowest participant timestamp greater than or equal to the target. Set on=False for strict < or > comparisons. fuzz is a nanosecond scan window around the searched timestamp and defaults to one second (1_000_000_000). Both methods return records, not indexes.

Stock database readers also expose NYSE market session timestamps in nanoseconds:

print(records.market_open)
print(records.market_close)